index int64 0 0 | repo_id stringclasses 596 values | file_path stringlengths 31 168 | content stringlengths 1 6.2M |
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0 | lc_public_repos | lc_public_repos/langchain-teacher/lcel.py | import langchain
from langchain.callbacks.base import BaseCallbackHandler
from langchain.chat_models import ChatOpenAI, ChatAnthropic
from langchain.schema import HumanMessage, AIMessage
import streamlit as st
from langsmith import Client
client = Client()
st.set_page_config(page_title="LangChain: Getting Started Class", page_icon="🦜")
st.title("🦜 LangChain: Getting Started Class")
button_css =""".stButton>button {
color: #4F8BF9;
border-radius: 50%;
height: 2em;
width: 2em;
font-size: 4px;
}"""
st.markdown(f'<style>{button_css}</style>', unsafe_allow_html=True)
class StreamHandler(BaseCallbackHandler):
def __init__(self, container, initial_text=""):
self.container = container
self.text = initial_text
def on_llm_new_token(self, token: str, **kwargs) -> None:
self.text += token
self.container.markdown(self.text)
from langchain.chat_models import ChatOpenAI
content = """Follow the below lesson plan, using information from the blog, cookbook, and interface guide.
<lesson_plan>
{lesson}
</lesson_plan>
<blog>
{blog}
</blog>
<cookbook>
{cookbook}
</cookbook>
<iterface_guide>
{interface}
<interface_guide>"""
with open("lcel/guide.md") as f:
guide = f.read()
with open("lcel/interface.md") as f:
interface = f.read()
with open("lcel/blog.txt") as f:
blog = f.read()
with open("lcel/lesson.txt") as f:
lesson = f.read()
from get_prompt import load_prompt
from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder, HumanMessagePromptTemplate
from langchain.schema import SystemMessage
from langchain.memory import ConversationBufferMemory
prompt_template = load_prompt(content = content.format(cookbook=guide, interface=interface, blog=blog, lesson=lesson))
from langchain.chains import LLMChain
def send_feedback(run_id, score):
client.create_feedback(run_id, "user_score", score=score)
if "messages" not in st.session_state:
st.session_state["messages"] = [AIMessage(content="Welcome! This short course with help you started with LangChain Expression Language. In order to get started, you should have basic familiarity with LangChain and you should have Python environment set up with langchain installed. If you don't have that, please set that up. Let me know when you're ready to proceed!")]
for msg in st.session_state["messages"]:
if isinstance(msg, HumanMessage):
st.chat_message("user").write(msg.content)
else:
st.chat_message("assistant").write(msg.content)
if prompt := st.chat_input():
st.chat_message("user").write(prompt)
with st.chat_message("assistant"):
stream_handler = StreamHandler(st.empty())
model = ChatOpenAI(streaming=True, callbacks=[stream_handler], model="gpt-3.5-turbo-16k")
#model = ChatAnthropic(streaming=True, callbacks=[stream_handler], model="claude-2")
chain = LLMChain(prompt=prompt_template, llm=model)
response = chain({"input":prompt, "chat_history":st.session_state.messages[-20:]}, include_run_info=True)
st.session_state.messages.append(HumanMessage(content=prompt))
st.session_state.messages.append(AIMessage(content=response[chain.output_key]))
run_id = response["__run"].run_id
col_blank, col_text, col1, col2 = st.columns([10, 2,1,1])
with col_text:
st.text("Feedback:")
with col1:
st.button("👍", on_click=send_feedback, args=(run_id, 1))
with col2:
st.button("👎", on_click=send_feedback, args=(run_id, 0))
|
0 | lc_public_repos | lc_public_repos/langchain-teacher/guide.txt | Quickstart
Installation
To install LangChain run:
Pip
Conda
pip install langchain
For more details, see our Installation guide.
Environment setup
Using LangChain will usually require integrations with one or more model providers, data stores, APIs, etc. For this example, we'll use OpenAI's model APIs.
First we'll need to install their Python package:
pip install openai
Accessing the API requires an API key, which you can get by creating an account and heading here. Once we have a key we'll want to set it as an environment variable by running:
export OPENAI_API_KEY="..."
If you'd prefer not to set an environment variable you can pass the key in directly via the openai_api_key named parameter when initiating the OpenAI LLM class:
from langchain.llms import OpenAI
llm = OpenAI(openai_api_key="...")
Building an application
Now we can start building our language model application. LangChain provides many modules that can be used to build language model applications. Modules can be used as stand-alones in simple applications and they can be combined for more complex use cases.
The core building block of LangChain applications is the LLMChain. This combines three things:
LLM: The language model is the core reasoning engine here. In order to work with LangChain, you need to understand the different types of language models and how to work with them.
Prompt Templates: This provides instructions to the language model. This controls what the language model outputs, so understanding how to construct prompts and different prompting strategies is crucial.
Output Parsers: These translate the raw response from the LLM to a more workable format, making it easy to use the output downstream.
In this getting started guide we will cover those three components by themselves, and then cover the LLMChain which combines all of them. Understanding these concepts will set you up well for being able to use and customize LangChain applications. Most LangChain applications allow you to configure the LLM and/or the prompt used, so knowing how to take advantage of this will be a big enabler.
LLMs
There are two types of language models, which in LangChain are called:
LLMs: this is a language model which takes a string as input and returns a string
ChatModels: this is a language model which takes a list of messages as input and returns a message
The input/output for LLMs is simple and easy to understand - a string. But what about ChatModels? The input there is a list of ChatMessages, and the output is a single ChatMessage. A ChatMessage has two required components:
content: This is the content of the message.
role: This is the role of the entity from which the ChatMessage is coming from.
LangChain provides several objects to easily distinguish between different roles:
HumanMessage: A ChatMessage coming from a human/user.
AIMessage: A ChatMessage coming from an AI/assistant.
SystemMessage: A ChatMessage coming from the system.
FunctionMessage: A ChatMessage coming from a function call.
If none of those roles sound right, there is also a ChatMessage class where you can specify the role manually. For more information on how to use these different messages most effectively, see our prompting guide.
LangChain exposes a standard interface for both, but it's useful to understand this difference in order to be construct prompts for a given language model. The standard interface that LangChain exposes has two methods:
predict: Takes in a string, returns a string
predict_messages: Takes in a list of messages, returns a message.
Let's see how to work with these different types of models and these different types of inputs. First, let's import an LLM and a ChatModel.
from langchain.llms import OpenAI
from langchain.chat_models import ChatOpenAI
llm = OpenAI()
model = ChatOpenAI()
llm.predict("hi!")
>>> "Hi"
model.predict("hi!")
>>> "Hi"
The OpenAI and ChatOpenAI objects are basically just configuration objects. You can initialize them with parameters like temperature and others, and pass them around.
Next, let's use the predict method to run over a string input.
text = "What would be a good company name for a company that makes colorful socks?"
llm.predict(text)
# >> Feetful of Fun
model.predict(text)
# >> Socks O'Color
Finally, let's use the predict_messages method to run over a list of messages.
from langchain.schema import HumanMessage
text = "What would be a good company name for a company that makes colorful socks?"
messages = [HumanMessage(content=text)]
llm.predict_messages(messages)
# >> Feetful of Fun
model.predict_messages(messages)
# >> Socks O'Color
For both these methods, you can also pass in parameters as key word arguments. For example, you could pass in temperature=0 to adjust the temperature that is used from what the object was configured with. Whatever values are passed in during run time will always override what the object was configured with.
Prompt templates
Most LLM applications do not pass user input directly into an LLM. Usually they will add the user input to a larger piece of text, called a prompt template, that provides additional context on the specific task at hand.
In the previous example, the text we passed to the model contained instructions to generate a company name. For our application, it'd be great if the user only had to provide the description of a company/product, without having to worry about giving the model instructions.
PromptTemplates help with exactly this! They bundle up all the logic for going from user input into a fully formatted prompt. This can start off very simple - for example, a prompt to produce the above string would just be:
from langchain.prompts import PromptTemplate
prompt = PromptTemplate.from_template("What is a good name for a company that makes {product}?")
prompt.format(product="colorful socks")
What is a good name for a company that makes colorful socks?
However, the advantages of using these over raw string formatting are several. You can "partial" out variables - eg you can format only some of the variables at a time. You can compose them together, easily combining different templates into a single prompt. For explanations of these functionalities, see the section on prompts for more detail.
PromptTemplates can also be used to produce a list of messages. In this case, the prompt not only contains information about the content, but also each message (its role, its position in the list, etc) Here, what happens most often is a ChatPromptTemplate is a list of ChatMessageTemplates. Each ChatMessageTemplate contains instructions for how to format that ChatMessage - its role, and then also its content. Let's take a look at this below:
from langchain.prompts.chat import (
ChatPromptTemplate,
SystemMessagePromptTemplate,
HumanMessagePromptTemplate,
)
template = "You are a helpful assistant that translates {input_language} to {output_language}."
system_message_prompt = SystemMessagePromptTemplate.from_template(template)
human_template = "{text}"
human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)
chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt])
chat_prompt.format_messages(input_language="English", output_language="French", text="I love programming.")
[
SystemMessage(content="You are a helpful assistant that translates English to French.", additional_kwargs={}),
HumanMessage(content="I love programming.")
]
ChatPromptTemplates can also include other things besides ChatMessageTemplates - see the section on prompts for more detail.
Output Parsers
OutputParsers convert the raw output of an LLM into a format that can be used downstream. There are few main type of OutputParsers, including:
Convert text from LLM -> structured information (eg JSON)
Convert a ChatMessage into just a string
Convert the extra information returned from a call besides the message (like OpenAI function invocation) into a string.
For full information on this, see the section on output parsers
In this getting started guide, we will write our own output parser - one that converts a comma separated list into a list.
from langchain.schema import BaseOutputParser
class CommaSeparatedListOutputParser(BaseOutputParser):
"""Parse the output of an LLM call to a comma-separated list."""
def parse(self, text: str):
"""Parse the output of an LLM call."""
return text.strip().split(", ")
CommaSeparatedListOutputParser().parse("hi, bye")
# >> ['hi', 'bye']
LLMChain
We can now combine all these into one chain. This chain will take input variables, pass those to a prompt template to create a prompt, pass the prompt to an LLM, and then pass the output through an (optional) output parser. This is a convenient way to bundle up a modular piece of logic. Let's see it in action!
from langchain.chat_models import ChatOpenAI
from langchain.prompts.chat import (
ChatPromptTemplate,
SystemMessagePromptTemplate,
HumanMessagePromptTemplate,
)
from langchain.chains import LLMChain
from langchain.schema import BaseOutputParser
class CommaSeparatedListOutputParser(BaseOutputParser):
"""Parse the output of an LLM call to a comma-separated list."""
def parse(self, text: str):
"""Parse the output of an LLM call."""
return text.strip().split(", ")
template = """You are a helpful assistant who generates comma separated lists.
A user will pass in a category, and you should generated 5 objects in that category in a comma separated list.
ONLY return a comma separated list, and nothing more."""
system_message_prompt = SystemMessagePromptTemplate.from_template(template)
human_template = "{text}"
human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)
chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt])
chain = LLMChain(
llm=ChatOpenAI(),
prompt=chat_prompt,
output_parser=CommaSeparatedListOutputParser()
)
chain.run("colors")
# >> ['red', 'blue', 'green', 'yellow', 'orange']
Next Steps
This is it! We've now gone over how to create the core building block of LangChain applications - the LLMChains. There is a lot more nuance in all these components (LLMs, prompts, output parsers) and a lot more different components to learn about as well. To continue on your journey:
Dive deeper into LLMs, prompts, and output parsers
Learn the other key components
Check out our helpful guides for detailed walkthroughs on particular topics
Explore end-to-end use cases
|
0 | lc_public_repos | lc_public_repos/langchain-teacher/.env-example | OPENAI_API_KEY=
LANGCHAIN_ENDPOINT=https://api.smith.langchain.com
LANGCHAIN_API_KEY=
LANGCHAIN_TRACING_V2=true
LANGCHAIN_PROJECT= |
0 | lc_public_repos | lc_public_repos/langchain-teacher/lc_main.py | import streamlit as st
from langchain.chat_models import ChatOpenAI
from langchain.schema import HumanMessage, AIMessage
from langsmith import Client
from langchain.callbacks.base import BaseCallbackHandler
from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder, HumanMessagePromptTemplate
from langchain.memory import ConversationBufferMemory
from langchain.chains import LLMChain
from get_prompt import load_prompt, load_prompt_with_questions
st.set_page_config(page_title="LangChain: Getting Started Class", page_icon="🦜")
st.title("🦜 LangChain: Getting Started Class")
button_css = """.stButton>button {
color: #4F8BF9;
border-radius: 50%;
height: 2em;
width: 2em;
font-size: 4px;
}"""
st.markdown(f'<style>{button_css}</style>', unsafe_allow_html=True)
class StreamHandler(BaseCallbackHandler):
def __init__(self, container, initial_text=""):
self.container = container
self.text = initial_text
def on_llm_new_token(self, token: str, **kwargs) -> None:
self.text += token
self.container.markdown(self.text)
# Lesson selection dictionary
lesson_guides = {
"Lesson 1: Getting Started with LangChain": {
"file": "lc_guides/getting_started_guide.txt",
"description": "This lesson covers the basics of getting started with LangChain."
},
"Lesson 2: Prompts": {
"file": "lc_guides/prompt_guide.txt",
"description": "This lesson focuses on prompts and their usage."
},
"Lesson 3: Language Models": {
"file": "lc_guides/models_guide.txt",
"description": "This lesson provides an overview of language models."
},
"Lesson 4: Memory": {
"file": "lc_guides/memory_guide.txt",
"description": "This lesson is about Memory."
},
"Lesson 5: Chains": {
"file": "lc_guides/chains_guide.txt",
"description": "This lesson provides information on Chains in LangChain, their types, and usage."
},
"Lesson 6: Retrieval": {
"file": "lc_guides/retrieval_guide.txt",
"description": "This lesson provides information on indexing and retrieving information using LangChain."
},
"Lesson 7: Agents": {
"file": "lc_guides/agents_guide.txt",
"description": "This lesson provides information on agents, tools, and toolkits."
}
}
# Initialize LangSmith client
client = Client()
# Lesson selection sidebar
lesson_selection = st.sidebar.selectbox("Select Lesson", list(lesson_guides.keys()))
# Display lesson content and description based on selection
lesson_info = lesson_guides[lesson_selection]
lesson_content = open(lesson_info["file"], "r").read()
lesson_description = lesson_info["description"]
# Radio buttons for lesson type selection
lesson_type = st.sidebar.radio("Select Lesson Type", ["Instructions based lesson", "Interactive lesson with questions"])
# Clear chat session if dropdown option or radio button changes
if st.session_state.get("current_lesson") != lesson_selection or st.session_state.get("current_lesson_type") != lesson_type:
st.session_state["current_lesson"] = lesson_selection
st.session_state["current_lesson_type"] = lesson_type
st.session_state["messages"] = [AIMessage(content="Welcome! This short course will help you get started with LangChain. Let me know when you're all set to jump in!")]
# Display lesson name and description
st.markdown(f"**{lesson_selection}**")
st.write(lesson_description)
# Message handling and interaction
def send_feedback(run_id, score):
client.create_feedback(run_id, "user_score", score=score)
for msg in st.session_state["messages"]:
if isinstance(msg, HumanMessage):
st.chat_message("user").write(msg.content)
else:
st.chat_message("assistant").write(msg.content)
if prompt := st.chat_input():
st.chat_message("user").write(prompt)
with st.chat_message("assistant"):
stream_handler = StreamHandler(st.empty())
model = ChatOpenAI(streaming=True, callbacks=[stream_handler], model="gpt-3.5-turbo-16k")
if lesson_type == "Instructions based lesson":
prompt_template = load_prompt(content=lesson_content)
else:
prompt_template = load_prompt_with_questions(content=lesson_content)
chain = LLMChain(prompt=prompt_template, llm=model)
response = chain(
{"input": prompt, "chat_history": st.session_state.messages[-20:]},
include_run_info=True,
tags=[lesson_selection, lesson_type]
)
st.session_state.messages.append(HumanMessage(content=prompt))
st.session_state.messages.append(AIMessage(content=response[chain.output_key]))
run_id = response["__run"].run_id
col_blank, col_text, col1, col2 = st.columns([10, 2, 1, 1])
with col_text:
st.text("Feedback:")
with col1:
st.button("👍", on_click=send_feedback, args=(run_id, 1))
with col2:
st.button("👎", on_click=send_feedback, args=(run_id, 0))
|
0 | lc_public_repos/langchain-teacher | lc_public_repos/langchain-teacher/lcel/interface.md |
# Interface
In an effort to make it as easy as possible to create custom chains, we've
implemented a "Runnable" protocol that most components implement. This is a
standard interface with a few different methods, which makes it easy to define
custom chains as well as making it possible to invoke them in a standard way.
The standard interface exposed includes:
* `stream`: stream back chunks of the response
* `invoke`: call the chain on an input
* `batch`: call the chain on a list of inputs
These also have corresponding async methods:
* `astream`: stream back chunks of the response async
* `ainvoke`: call the chain on an input async
* `abatch`: call the chain on a list of inputs async
The type of the input varies by component. For a prompt it is a dictionary,
for a retriever it is a single string, for a model either a single string, a
list of chat messages, or a PromptValue.
The output type also varies by component. For an LLM it is a string, for a
ChatModel it's a ChatMessage, for a prompt it's a PromptValue, for a retriever
it's a list of documents.
Let's take a look at these methods! To do so, we'll create a super simple
PromptTemplate + ChatModel chain.
from langchain.prompts import ChatPromptTemplate
from langchain.chat_models import ChatOpenAI
#### API Reference:
* ChatPromptTemplate from `langchain.prompts`
* ChatOpenAI from `langchain.chat_models`
model = ChatOpenAI()
prompt = ChatPromptTemplate.from_template("tell me a joke about {topic}")
chain = prompt | model
## Stream
for s in chain.stream({"topic": "bears"}):
print(s.content, end="")
Sure, here's a bear-themed joke for you:
Why don't bears wear shoes?
Because they have bear feet!
## Invoke
chain.invoke({"topic": "bears"})
AIMessage(content="Why don't bears wear shoes?\n\nBecause they already have bear feet!", additional_kwargs={}, example=False)
## Batch
chain.batch([{"topic": "bears"}, {"topic": "cats"}])
[AIMessage(content="Why don't bears ever wear shoes?\n\nBecause they have bear feet!", additional_kwargs={}, example=False),
AIMessage(content="Why don't cats play poker in the wild?\n\nToo many cheetahs!", additional_kwargs={}, example=False)]
## Async Stream
async for s in chain.astream({"topic": "bears"}):
print(s.content, end="")
Why don't bears wear shoes?
Because they have bear feet!
## Async Invoke
await chain.ainvoke({"topic": "bears"})
AIMessage(content="Sure, here you go:\n\nWhy don't bears wear shoes?\n\nBecause they have bear feet!", additional_kwargs={}, example=False)
## Async Batch
await chain.abatch([{"topic": "bears"}])
[AIMessage(content="Why don't bears wear shoes?\n\nBecause they have bear feet!", additional_kwargs={}, example=False)]
|
0 | lc_public_repos/langchain-teacher | lc_public_repos/langchain-teacher/lcel/lesson.txt | ## First part: general understanding
The LangChain Expression Language allows you to easily create chains of different components like prompts, models, retrievers, etc. This is a new type of way to contruct chains. There are two main benefits: a standard interface and easying piping.
### Standard Interface
All chains constructed this way will come with built in batch, async and streaming support.
### Easying piping
You can easily combine these chains together by using the `|` Operator
## Next: Creating a Simple Chain
### Import standard things
These are the same LangChain constructs we know
```python
from langchain.prompts import ChatPromptTemplate
from langchain.chat_models import ChatOpenAI
```
### Setup
Lets make a chain that takes in a topic and makes a joke.
We can do this by combining a PromptTemplate with a ChatModel.
Let's initialize those by themselves
```python
model = ChatOpenAI()
prompt = ChatPromptTemplate.from_template("tell me a joke about {topic}")
```
### Pipe
We can now chain those together with the `|` operator
```python
chain = prompt | model
```
### Call
We can now call this with `.invoke`. We pass in a dictionary, because that's what PromptTemplate expects.
```python
chain.invoke({"topic": "bears"})
```
### Stream
Show code for this, explain why its useful
### Batch
Show code for this, explain why its useful
### Async
Show code for this, how to call async (only show `.ainvoke` but tell them it exists for `astream` and `abatch`)
## Output Parsers
The above chain returns a ChatMessage.
Often times it's more workable to work with strings.
We can use `StrOutputParser` to do that.
Check with them that this makes sense
### Show example of how to add in `StrOutputParser` and run
```python
from langchain.schema.output_parser import StrOutputParser
chain = prompt | model | StrOutputParser()
chain.invoke({"foo": "bears"})
```
## More complicated chains
You can combine this knowledge over and over again to create more complicated chains.
Let's go over one final example - how to do retrieval QA.
### Set up the retriever
```python
from langchain.vectorstores import Chroma
from langchain.embeddings import OpenAIEmbeddings
from langchain.schema.runnable import RunnablePassthrough
# Create the retriever
vectorstore = Chroma.from_texts(["harrison worked at kensho"], embedding=OpenAIEmbeddings())
retriever = vectorstore.as_retriever()
```
### Set up the prompt template
```python
template = """Answer the question based only on the following context:
{context}
Question: {question}
"""
prompt = ChatPromptTemplate.from_template(template)
```
### Create the chain
```python
chain = (
{"context": retriever, "question": RunnablePassthrough()}
| prompt
| model
| StrOutputParser()
)
```
### Run the chain
Show code for this
## Conclusion
That's it! Excellent job. Check out https://python.langchain.com/docs/guides/expression_language/cookbook for more info. |
0 | lc_public_repos/langchain-teacher | lc_public_repos/langchain-teacher/lcel/guide.md |
# Cookbook
In this notebook we'll take a look at a few common types of sequences to
create.
## PromptTemplate + LLM
A PromptTemplate -> LLM is a core chain that is used in most other larger
chains/systems.
from langchain.prompts import ChatPromptTemplate
from langchain.chat_models import ChatOpenAI
#### API Reference:
* ChatPromptTemplate from `langchain.prompts`
* ChatOpenAI from `langchain.chat_models`
/Users/harrisonchase/.pyenv/versions/3.9.1/envs/langchain/lib/python3.9/site-packages/deeplake/util/check_latest_version.py:32: UserWarning: A newer version of deeplake (3.6.14) is available. It's recommended that you update to the latest version using `pip install -U deeplake`.
warnings.warn(
model = ChatOpenAI()
prompt = ChatPromptTemplate.from_template("tell me a joke about {foo}")
chain = prompt | model
chain.invoke({"foo": "bears"})
AIMessage(content='Why don\'t bears use cell phones? \n\nBecause they always get terrible "grizzly" reception!', additional_kwargs={}, example=False)
Often times we want to attach kwargs to the model that's passed in. Here's a
few examples of that:
### Attaching Stop Sequences
chain = prompt | model.bind(stop=["\n"])
chain.invoke({"foo": "bears"})
AIMessage(content="Why don't bears use cell phones?", additional_kwargs={}, example=False)
### Attaching Function Call information
functions = [
{
"name": "joke",
"description": "A joke",
"parameters": {
"type": "object",
"properties": {
"setup": {
"type": "string",
"description": "The setup for the joke"
},
"punchline": {
"type": "string",
"description": "The punchline for the joke"
}
},
"required": ["setup", "punchline"]
}
}
]
chain = prompt | model.bind(function_call= {"name": "joke"}, functions= functions)
chain.invoke({"foo": "bears"}, config={})
AIMessage(content='', additional_kwargs={'function_call': {'name': 'joke', 'arguments': '{\n "setup": "Why don\'t bears wear shoes?",\n "punchline": "Because they have bear feet!"\n}'}}, example=False)
## PromptTemplate + LLM + OutputParser
We can also add in an output parser to easily trasform the raw LLM/ChatModel
output into a more workable format
from langchain.schema.output_parser import StrOutputParser
#### API Reference:
* StrOutputParser from `langchain.schema.output_parser`
chain = prompt | model | StrOutputParser()
Notice that this now returns a string - a much more workable format for
downstream tasks
chain.invoke({"foo": "bears"})
"Why don't bears wear shoes?\n\nBecause they have bear feet!"
### Functions Output Parser
When you specify the function to return, you may just want to parse that
directly
from langchain.output_parsers.openai_functions import JsonOutputFunctionsParser
chain = (
prompt
| model.bind(function_call= {"name": "joke"}, functions= functions)
| JsonOutputFunctionsParser()
)
#### API Reference:
* JsonOutputFunctionsParser from `langchain.output_parsers.openai_functions`
chain.invoke({"foo": "bears"})
{'setup': "Why don't bears wear shoes?",
'punchline': 'Because they have bear feet!'}
from langchain.output_parsers.openai_functions import JsonKeyOutputFunctionsParser
chain = (
prompt
| model.bind(function_call= {"name": "joke"}, functions= functions)
| JsonKeyOutputFunctionsParser(key_name="setup")
)
#### API Reference:
* JsonKeyOutputFunctionsParser from `langchain.output_parsers.openai_functions`
chain.invoke({"foo": "bears"})
"Why don't bears like fast food?"
## Passthroughs and itemgetter
Often times when constructing a chain you may want to pass along original
input variables to future steps in the chain. How exactly you do this depends
on what exactly the input is:
* If the original input was a string, then you likely just want to pass along the string. This can be done with `RunnablePassthrough`. For an example of this, see `LLMChain + Retriever`
* If the original input was a dictionary, then you likely want to pass along specific keys. This can be done with `itemgetter`. For an example of this see `Multiple LLM Chains`
from langchain.schema.runnable import RunnablePassthrough
from operator import itemgetter
#### API Reference:
* RunnablePassthrough from `langchain.schema.runnable`
## LLMChain + Retriever
Let's now look at adding in a retrieval step, which adds up to a "retrieval-
augmented generation" chain
from langchain.vectorstores import Chroma
from langchain.embeddings import OpenAIEmbeddings
from langchain.schema.runnable import RunnablePassthrough
#### API Reference:
* Chroma from `langchain.vectorstores`
* OpenAIEmbeddings from `langchain.embeddings`
* RunnablePassthrough from `langchain.schema.runnable`
# Create the retriever
vectorstore = Chroma.from_texts(["harrison worked at kensho"], embedding=OpenAIEmbeddings())
retriever = vectorstore.as_retriever()
template = """Answer the question based only on the following context:
{context}
Question: {question}
"""
prompt = ChatPromptTemplate.from_template(template)
chain = (
{"context": retriever, "question": RunnablePassthrough()}
| prompt
| model
| StrOutputParser()
)
chain.invoke("where did harrison work?")
Number of requested results 4 is greater than number of elements in index 1, updating n_results = 1
'Harrison worked at Kensho.'
template = """Answer the question based only on the following context:
{context}
Question: {question}
Answer in the following language: {language}
"""
prompt = ChatPromptTemplate.from_template(template)
chain = {
"context": itemgetter("question") | retriever,
"question": itemgetter("question"),
"language": itemgetter("language")
} | prompt | model | StrOutputParser()
chain.invoke({"question": "where did harrison work", "language": "italian"})
Number of requested results 4 is greater than number of elements in index 1, updating n_results = 1
'Harrison ha lavorato a Kensho.'
## Multiple LLM Chains
This can also be used to string together multiple LLMChains
from operator import itemgetter
prompt1 = ChatPromptTemplate.from_template("what is the city {person} is from?")
prompt2 = ChatPromptTemplate.from_template("what country is the city {city} in? respond in {language}")
chain1 = prompt1 | model | StrOutputParser()
chain2 = {"city": chain1, "language": itemgetter("language")} | prompt2 | model | StrOutputParser()
chain2.invoke({"person": "obama", "language": "spanish"})
'El país en el que nació la ciudad de Honolulu, Hawái, donde nació Barack Obama, el 44º presidente de los Estados Unidos, es Estados Unidos.'
from langchain.schema.runnable import RunnableMap
prompt1 = ChatPromptTemplate.from_template("generate a random color")
prompt2 = ChatPromptTemplate.from_template("what is a fruit of color: {color}")
prompt3 = ChatPromptTemplate.from_template("what is countries flag that has the color: {color}")
prompt4 = ChatPromptTemplate.from_template("What is the color of {fruit} and {country}")
chain1 = prompt1 | model | StrOutputParser()
chain2 = RunnableMap(steps={"color": chain1}) | {
"fruit": prompt2 | model | StrOutputParser(),
"country": prompt3 | model | StrOutputParser(),
} | prompt4
#### API Reference:
* RunnableMap from `langchain.schema.runnable`
chain2.invoke({})
ChatPromptValue(messages=[HumanMessage(content="What is the color of A fruit that has a color similar to #7E7DE6 is the Peruvian Apple Cactus (Cereus repandus). It is a tropical fruit with a vibrant purple or violet exterior. and The country's flag that has the color #7E7DE6 is North Macedonia.", additional_kwargs={}, example=False)])
## Router
You can also use the router runnable to conditionally route inputs to
different runnables.
from langchain.chains import create_tagging_chain_pydantic
from pydantic import BaseModel, Field
class PromptToUse(BaseModel):
"""Used to determine which prompt to use to answer the user's input."""
name: str = Field(description="Should be one of `math` or `english`")
#### API Reference:
* create_tagging_chain_pydantic from `langchain.chains`
tagger = create_tagging_chain_pydantic(PromptToUse, ChatOpenAI(temperature=0))
chain1 = ChatPromptTemplate.from_template("You are a math genius. Answer the question: {question}") | ChatOpenAI()
chain2 = ChatPromptTemplate.from_template("You are an english major. Answer the question: {question}") | ChatOpenAI()
from langchain.schema.runnable import RouterRunnable
router = RouterRunnable({"math": chain1, "english": chain2})
#### API Reference:
* RouterRunnable from `langchain.schema.runnable`
chain = {
"key": {"input": lambda x: x["question"]} | tagger | (lambda x: x['text'].name),
"input": {"question": lambda x: x["question"]}
} | router
chain.invoke({"question": "whats 2 + 2"})
AIMessage(content='Thank you for the compliment! The sum of 2 + 2 is equal to 4.', additional_kwargs={}, example=False)
## Tools
You can use any LangChain tool easily
from langchain.tools import DuckDuckGoSearchRun
#### API Reference:
* DuckDuckGoSearchRun from `langchain.tools`
/Users/harrisonchase/.pyenv/versions/3.9.1/envs/langchain/lib/python3.9/site-packages/deeplake/util/check_latest_version.py:32: UserWarning: A newer version of deeplake (3.6.14) is available. It's recommended that you update to the latest version using `pip install -U deeplake`.
warnings.warn(
search = DuckDuckGoSearchRun()
template = """turn the following user input into a search query for a search engine:
{input}"""
prompt = ChatPromptTemplate.from_template(template)
chain = prompt | model | StrOutputParser() | search
chain.invoke({"input": "I'd like to figure out what games are tonight"})
"What sports games are on TV today & tonight? Watch and stream live sports on TV today, tonight, tomorrow. Today's 2023 sports TV schedule includes football, basketball, baseball, hockey, motorsports, soccer and more. Watch on TV or stream online on ESPN, FOX, FS1, CBS, NBC, ABC, Peacock, Paramount+, fuboTV, local channels and many other networks. Weather Alerts Alerts Bar. Not all offers available in all states, please visit BetMGM for the latest promotions for your area. Must be 21+ to gamble, please wager responsibly. If you or someone ... Speak of the Devils. Good Morning Arizona. Happy Hour Spots. Jaime's Local Love. Surprise Squad. Silver Apple. Field Trip Friday. Seen on TV. Arizona Highways TV. MLB Games Tonight: How to Watch on TV, Streaming & Odds - Friday, July 28. San Diego Padres' Juan Soto plays during the first baseball game in a doubleheader, Saturday, July 15, 2023, in Philadelphia. (AP Photo/Matt Slocum) (APMedia) Today's MLB schedule features top teams in action. Among those games is the Texas Rangers playing the San Diego ... TV. Cleveland at Chi. White Sox. 1:10pm. Bally Sports. NBCS-CHI. Cleveland Guardians (50-51) are second place in AL Central and Chicago White Sox (41-61) are fourth place in AL Central. The Guardians are 23-27 on the road this season and White Sox are 21-26 at home. Chi. Cubs at St. Louis."
## Arbitrary Functions
You can use arbitrary functions in the pipeline
Note that all inputs to these functions need to be a SINGLE argument. If you
have a function that accepts multiple arguments, you should write a wrapper
that accepts a single input and unpacks it into multiple argument.
from langchain.schema.runnable import RunnableLambda
def length_function(text):
return len(text)
def _multiple_length_function(text1, text2):
return len(text1) * len(text2)
def multiple_length_function(_dict):
return _multiple_length_function(_dict["text1"], _dict["text2"])
prompt = ChatPromptTemplate.from_template("what is {a} + {b}")
chain1 = prompt | model
chain = {
"a": itemgetter("foo") | RunnableLambda(length_function),
"b": {"text1": itemgetter("foo"), "text2": itemgetter("bar")} | RunnableLambda(multiple_length_function)
} | prompt | model
#### API Reference:
* RunnableLambda from `langchain.schema.runnable`
chain.invoke({"foo": "bar", "bar": "gah"})
AIMessage(content='3 + 9 is equal to 12.', additional_kwargs={}, example=False)
## SQL Database
We can also try to replicate our SQLDatabaseChain using this style.
template = """Based on the table schema below, write a SQL query that would answer the user's question:
{schema}
Question: {question}"""
prompt = ChatPromptTemplate.from_template(template)
from langchain.utilities import SQLDatabase
#### API Reference:
* SQLDatabase from `langchain.utilities`
db = SQLDatabase.from_uri("sqlite:///../../../../notebooks/Chinook.db")
def get_schema(_):
return db.get_table_info()
def run_query(query):
return db.run(query)
inputs = {
"schema": RunnableLambda(get_schema),
"question": itemgetter("question")
}
sql_response = (
RunnableMap(inputs)
| prompt
| model.bind(stop=["\nSQLResult:"])
| StrOutputParser()
)
sql_response.invoke({"question": "How many employees are there?"})
'SELECT COUNT(*) \nFROM Employee;'
template = """Based on the table schema below, question, sql query, and sql response, write a natural language response:
{schema}
Question: {question}
SQL Query: {query}
SQL Response: {response}"""
prompt_response = ChatPromptTemplate.from_template(template)
full_chain = (
RunnableMap({
"question": itemgetter("question"),
"query": sql_response,
})
| {
"schema": RunnableLambda(get_schema),
"question": itemgetter("question"),
"query": itemgetter("query"),
"response": lambda x: db.run(x["query"])
}
| prompt_response
| model
)
full_chain.invoke({"question": "How many employees are there?"})
AIMessage(content='There are 8 employees.', additional_kwargs={}, example=False)
## Code Writing
from langchain.utilities import PythonREPL
from langchain.prompts import SystemMessagePromptTemplate, HumanMessagePromptTemplate
#### API Reference:
* PythonREPL from `langchain.utilities`
* SystemMessagePromptTemplate from `langchain.prompts`
* HumanMessagePromptTemplate from `langchain.prompts`
template = """Write some python code to solve the user's problem.
Return only python code in Markdown format, eg:
```python
....
```"""
prompt = ChatPromptTemplate(messages=[
SystemMessagePromptTemplate.from_template(template),
HumanMessagePromptTemplate.from_template("{input}")
])
def _sanitize_output(text: str):
_, after = text.split("```python")
return after.split("```")[0]
chain = prompt | model | StrOutputParser() | _sanitize_output | PythonREPL().run
chain.invoke({"input": "whats 2 plus 2"})
Python REPL can execute arbitrary code. Use with caution.
'4\n'
|
0 | lc_public_repos/langchain-teacher | lc_public_repos/langchain-teacher/lc_guides/agents_guide.txt |
## Agents in LangChain
### Introduction
Welcome to the comprehensive lesson on Agents in LangChain. In this guide, we'll delve deep into the world of agents, their role in enhancing the capabilities of Large Language Models (LLMs), and how to effectively utilize them within the LangChain library.
#### What are Agents?
Agents are intelligent entities designed to leverage the power of language models to make decisions, execute actions, and perform various tasks based on reasoning. Unlike traditional programming, where actions are hardcoded, agents dynamically choose actions and their order of execution using language models. This lesson will cover a wide range of topics related to agents in LangChain, including their components, types, tools, and execution.
### Components of an Agent
Agents in LangChain are composed of several key components that collectively enable them to operate effectively and make informed decisions.
#### The Agent Core
The agent core is the central component responsible for making decisions on actions to take. It's powered by a language model and a prompt that includes the agent's personality, background context, and prompting strategies like ReAct. This enables agents to respond in a desired manner and enhances their reasoning abilities.
**Code Example:**
```python
from langchain.chat_models import ChatOpenAI
# Initialize the language model
llm = ChatOpenAI(temperature=0)
# Create a prompt for the agent
from langchain.schema import SystemMessage
from langchain.agents import OpenAIFunctionsAgent
from langchain.agents import tool
from langchain.agents import tool
@tool
def get_word_length(word: str) -> int:
"""Returns the length of a word."""
return len(word)
tools = [get_word_length]
system_message = SystemMessage(content="You are a helpful assistant with a specific expertise.")
prompt = OpenAIFunctionsAgent.create_prompt(system_message=system_message)
agent = OpenAIFunctionsAgent(llm=llm, tools=tools, prompt=prompt)
```
#### Tools and Toolkits
Tools are essential for agents to interact with the environment effectively. They can access the right tools and understand their usage based on effective descriptions. Toolkits are groups of related tools that work together to accomplish specific objectives.
**Code Example:**
```python
from langchain.agents import tool
@tool
def calculate_square(number: float) -> float:
"""Calculate the square of a number."""
return number * number
```
**Question:**
- How do tools and toolkits contribute to an agent's ability to perform tasks effectively?
#### AgentExecutor
The AgentExecutor is the runtime environment where agents operate and execute actions. It processes agent decisions and handles complexities such as errors, logging, and ensuring smooth execution.
**Code Example:**
```python
from langchain.agents import AgentExecutor
agent_executor = AgentExecutor(agent=agent, tools=[calculate_square], verbose=True)
```
**Question:**
- What is the role of AgentExecutor in the execution of agent actions?
---
### Step-by-Step Guide: Building an Agent in LangChain
Building an agent in LangChain involves defining its components, choosing the right tools, and configuring its behavior. Here's a comprehensive guide to help you create a functional agent that can perform specific tasks using LangChain's capabilities.
#### Step 1: Define the Objective
**Start by identifying the specific tasks or objectives your agent needs to achieve.** This will help you determine the tools and toolkits required to accomplish these tasks effectively.
**Example Objective:** Build an agent that can perform mathematical calculations and provide the results.
#### Step 2: Choose the Base Language Model (LLM)
**Select a base language model (LLM) that suits your agent's requirements.** The LLM forms the foundation of your agent's language understanding and generation capabilities.
**Example:**
```python
from langchain import OpenAI
llm = OpenAI(
openai_api_key="YOUR_API_KEY",
temperature=0.7,
model_name="text-davinci-003"
)
```
#### Step 3: Define Tools
**Create tools that encapsulate specific functionalities required for your agent.** Define functions that correspond to these tools and decorate them with `@tool` to make them accessible to the agent.
**Example Tools:**
```python
from langchain.agents import tool
@tool
def calculator(operation: str, num1: float, num2: float) -> float:
"""Perform arithmetic operations."""
if operation == 'add':
return num1 + num2
elif operation == 'subtract':
return num1 - num2
elif operation == 'multiply':
return num1 * num2
elif operation == 'divide':
return num1 / num2
```
#### Step 4: Create a Prompt
**Design a prompt that sets the context and behavior for your agent's responses.** The prompt can include the agent's personality, background information, and any specific instructions.
**Example:**
```python
from langchain.schema import SystemMessage
from langchain.agents import OpenAIFunctionsAgent
system_message = SystemMessage(content="You are a mathematical assistant. Help users with calculations.")
prompt = OpenAIFunctionsAgent.create_prompt(system_message=system_message)
```
#### Step 5: Initialize the Agent
**Initialize your agent using the base LLM, tools, and the created prompt.** This step creates an instance of the agent that can execute tasks using the specified tools.
**Example:**
```python
from langchain.agents import AgentExecutor
agent = OpenAIFunctionsAgent(llm=llm, tools=[calculator], prompt=prompt)
agent_executor = AgentExecutor(agent=agent, tools=[calculator], verbose=True)
```
#### Step 6: Run and test the Agent
**Run and test your agent by providing input and observing its responses.** This step helps you verify that the agent's behavior aligns with your intended objectives.
**Example:**
```python
input_query = "Calculate the result of (5 * 3) + 7"
result = agent_executor.run(input_query)
print(result) # Output: "The result is 22."
```
#### Step 7: Customize and Iterate
**Refine and customize your agent's behavior based on test results and user interactions.** Tweak the prompt, tools, and other components as needed to enhance the agent's performance and accuracy.
**Question:**
- What is the purpose of defining tools when building an agent in LangChain?
---
### Agent Types
LangChain supports multiple agent runtimes, each catering to specific use cases and offering different ways to implement agents effectively.
#### Plan-and-Execute Agent
Plan-and-execute agents first plan a sequence of actions and then execute subtasks to achieve an objective. This approach is inspired by the BabyAGI and Plan-and-Solve concepts.
```python
from langchain.chat_models import ChatOpenAI
from langchain_experimental.plan_and_execute import PlanAndExecute, load_agent_executor, load_chat_planner
from langchain.llms import OpenAI
from langchain.agents import load_tools
llm = OpenAI(temperature=0)
tools = load_tools(["serpapi", "llm-math"], llm=llm)
model = ChatOpenAI(temperature=0)
planner = load_chat_planner(model)
executor = load_agent_executor(model, tools, verbose=True)
agent = PlanAndExecute(planner=planner, executor=executor, verbose=True)
agent.run("Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?")
```
#### Zero-Shot ReAct
Zero-shot agents utilize the ReAct framework to choose tools based solely on tool descriptions. This type of agent is versatile and doesn't require memory for multi-step tasks.
**Code Example:**
```python
from langchain.agents import load_tools
from langchain.agents import initialize_agent
from langchain.agents import AgentType
from langchain.llms import OpenAI
llm = OpenAI(temperature=0)
tools = load_tools(["serpapi", "llm-math"], llm=llm)
agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
agent.run("Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?")
```
#### Conversational ReAct
Conversational agents use the ReAct framework for multi-turn conversations and retain memory of previous interactions. This is particularly useful for chatbot-like interactions.
**Code Example:**
```python
from langchain.memory import ConversationBufferMemory
memory = ConversationBufferMemory(memory_key="chat_history")
conversational_agent = initialize_agent(
agent='conversational-react-description',
tools=[calculate_square],
llm=llm,
verbose=True,
max_iterations=3,
memory=memory
)
```
**Question:**
- How does the Zero-Shot ReAct agent choose tools based on their descriptions?
**Question:**
- How does the provided code snippet create an agent that calculates the factorial of a number?
#### ReAct Docstore Agent
The ReAct document store agent specializes in searching and retrieving information from documents stored within LangChain's docstore. It's particularly useful for performing information searches.
**Code Example:**
```python
from langchain.docstore import DocStoreMemory
docstore_memory = DocStoreMemory(
docstore_key="stock_data",
docstore_path="./docstore.json"
)
docstore_agent = initialize_agent(
agent='react-docstore',
tools=tools,
llm=llm,
verbose=True,
max_iterations=3,
docstore_memory=docstore_memory
)
```
**Question:**
- How does the ReAct Docstore Agent interact with LangChain's document store to retrieve information?
### Tools and Toolkits
In the realm of LangChain, tools play a pivotal role in enabling agents to interact with the environment, perform tasks, and achieve their objectives. Tools define the capabilities of an agent and provide a structured way for the agent to access and utilize various functionalities. Let's delve deeper into the concept of tools and how they contribute to the effectiveness of LangChain agents.
#### Understanding Tools
**Tools are functions or methods that agents can utilize to perform specific tasks.** These tasks can range from simple calculations to complex interactions with external systems. Each tool encapsulates a particular piece of functionality and is associated with a name, a description, and the underlying implementation.
**Example:**
Consider a scenario where an agent needs to perform basic math calculations. We can create a tool named "Calculator" that performs arithmetic operations.
```python
from langchain.agents import tool
@tool
def calculator(operation: str, num1: float, num2: float) -> float:
"""Perform arithmetic operations."""
if operation == 'add':
return num1 + num2
elif operation == 'subtract':
return num1 - num2
elif operation == 'multiply':
return num1 * num2
elif operation == 'divide':
return num1 / num2
```
#### Toolkits: Organizing Tools
**Toolkits are collections of related tools grouped together to achieve specific objectives.** They provide a higher-level abstraction, making it easier to manage and organize tools with similar functionalities. Toolkits enhance the modularity and reusability of an agent's components.
**Example:**
Imagine an agent designed for financial calculations. We can create a toolkit named "Financial Toolkit" containing tools for performing calculations related to investments, currency conversions, and interest rates.
```python
from langchain.agents import Toolkit
financial_toolkit = Toolkit(name="Financial Toolkit", tools=[investment_calculator, currency_converter, interest_calculator])
```
**Question:**
- How do tools contribute to the functionality of LangChain agents?
#### Extending Agent Capabilities with Tools and Toolkits
When building an agent, developers can choose from a variety of built-in tools or create custom tools to suit their specific needs. By selecting and organizing tools into toolkits, developers can effectively extend the agent's capabilities and enhance its ability to perform diverse tasks.
**Code Example:**
```python
# Creating custom tools
@tool
def search_engine(query: str) -> str:
"""Perform a web search."""
# Use an external library or API to execute the search
search_result = external_search_engine(query)
return search_result
@tool
def email_sender(subject: str, content: str, recipient: str) -> str:
"""Send an email."""
# Use an email API to send the email
email_api.send_email(subject, content, recipient)
return f"Email sent to {recipient}"
# Creating a toolkit
communication_toolkit = Toolkit(name="Communication Toolkit", tools=[search_engine, email_sender])
```
By combining tools and organizing them into toolkits, developers can enhance the agent's functionality and enable it to interact with the environment more effectively.
### Conclusion
Agents in LangChain open up a world of possibilities by leveraging language models and tools to make intelligent decisions and perform complex tasks. Their adaptability and customization options make them invaluable for a wide range of applications. By combining agents, tools, and runtime environments effectively, developers can create sophisticated systems capable of handling diverse tasks with ease.
|
0 | lc_public_repos/langchain-teacher | lc_public_repos/langchain-teacher/lc_guides/models_guide.txt | ## Understanding Different Types of Language Models in LangChain
### Introduction to Language Models
Language models are essential tools in natural language processing that enable computers to understand and generate human language. LangChain provides interfaces and integrations for three main types of language models:
1. **Large Language Models (LLMs)**: Models that take a text string as input and return a text string as output.
2. **Chat Models**: Models that use LLMs as their foundation but focus on enabling conversations. They take a list of Chat Messages as input and return a Chat Message as output.
3. **Text Embedding Models**: Models that create embeddings for text inputs, allowing for semantic analysis and comparisons.
**Q1: What are the three main types of language models in LangChain?**
The three main types of language models in LangChain are Large Language Models (LLMs), Chat Models, and Text Embedding Models.
### Large Language Models (LLMs)
Large Language Models (LLMs) are specifically designed for text completion and generation tasks. They can be used for a wide range of language-related tasks, such as generating coherent paragraphs or completing sentences.
**Q2: What is the primary purpose of Large Language Models (LLMs)?**
LLMs are used for text completion and generation tasks, where they take input text and produce relevant output text.
#### Key Characteristics of LLMs
LLMs have several characteristics that make them powerful tools:
- They are trained on massive amounts of data to understand and generate human-like text.
- LLMs can be fine-tuned for specific tasks to improve their performance.
- APIs of LLMs typically accept a prompt as input and return a generated completion.
**Q3: How can Large Language Models be fine-tuned for specific tasks?**
LLMs can be fine-tuned by providing additional training data and examples specific to the desired task.
#### Using LLMs in LangChain
LangChain offers an interface for interacting with LLMs, allowing you to generate text using various LLMs.
```python
from langchain.llms import OpenAI
llm = OpenAI(openai_api_key="YOUR_API_KEY")
result = llm("Tell me a joke")
print(result)
```
**Q4: What is the basic process of using an LLM in LangChain?**
Using an LLM in LangChain involves initializing an LLM object, passing a prompt to it, and receiving a generated text as output.
### Chat Models
Chat Models are built on the foundation of LLMs but are designed to facilitate more natural conversations. They accept a list of Chat Messages as input, where each message has a content and role (system, AI, or human), and they return an AI-generated response.
**Q5: How do Chat Models differ from Large Language Models (LLMs) in terms of input and output?**
Chat Models take a list of chat messages as input and return an AI-generated chat message as output, facilitating more interactive conversations.
#### Using Chat Models in LangChain
LangChain provides an interface for utilizing Chat Models and managing conversations.
```python
from langchain.chat_models import ChatOpenAI
from langchain.schema import AIMessage, HumanMessage, SystemMessage
chat = ChatOpenAI(openai_api_key="YOUR_API_KEY")
messages = [
SystemMessage(content="You are a helpful assistant."),
HumanMessage(content="What's the weather like today?")
]
response = chat(messages)
print(response)
```
**Q6: How can you structure input messages for a Chat Model in LangChain?**
Input messages for a Chat Model should include System, Human, and AI messages to guide the conversation.
### Text Embedding Models
Text Embedding Models generate embeddings for text inputs, allowing for semantic analysis and comparison of textual content. These embeddings capture the meaning and relationships between words.
**Q7: What is the primary purpose of Text Embedding Models in LangChain?**
Text Embedding Models create vector representations (embeddings) of text, enabling semantic analysis and comparison of textual content.
#### Utilizing Text Embedding Models
LangChain offers an interface for working with Text Embedding Models.
```python
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings(openai_api_key="YOUR_API_KEY")
text = "This is an example sentence."
embedding = embeddings.embed_documents([text])
print(embedding)
```
**Q8: How can Text Embedding Models be used to compare the semantic similarity of two sentences?**
Text Embedding Models generate vector representations of text, allowing distance calculations to determine semantic similarity.
### Conclusion
Language models in LangChain offer diverse capabilities for handling language-related tasks. Whether it's generating text using Large Language Models, engaging in natural conversations with Chat Models, or performing semantic analysis with Text Embedding Models, LangChain provides a versatile toolkit for language processing tasks.
**Q9: What are the key benefits of using LangChain's language model interfaces?**
LangChain's interfaces allow you to generate text, facilitate conversations, and perform semantic analysis, catering to a wide range of language processing needs.
**Q10: How does LangChain's support for different types of language models enhance its usability?**
By offering interfaces for LLMs, Chat Models, and Text Embedding Models, LangChain provides a comprehensive solution for various language-related tasks.
---
This consolidated lesson provides an overview of the different types of language models available in LangChain, their use cases, and how to interact with them. If you have further questions or need more information on specific topics, feel free to ask! |
0 | lc_public_repos/langchain-teacher | lc_public_repos/langchain-teacher/lc_guides/memory_guide.txt | **Lesson: Understanding Different Memory Types in Langchain**
---
### 1. Conversation Buffer Memory
**Definition and Purpose:** Conversation Buffer Memory is a memory type that stores and manages a history of past messages exchanged between the user and the AI. It serves as a continuous record of the conversation's flow, allowing the AI to reference previous interactions for context.
**How to Use:**
- Initialize the memory.
- Use the `save_context` method to store incoming messages.
- Use the `load_memory_variables` method to retrieve the conversation history.
**Code Example:**
```python
from langchain.memory import ConversationBufferMemory
memory = ConversationBufferMemory()
memory.save_context({"input": "Hello"}, {"output": "Hi there!"})
memory.save_context({"input": "How's your day?"}, {"output": "It's great!"})
result = memory.load_memory_variables({})
print(result['history']) # Output: ['Human: Hello', 'AI: Hi there!', 'Human: How's your day?', 'AI: It's great!']
```
**Q&A:**
1. **What is the purpose of Conversation Buffer Memory?**
Conversation Buffer Memory stores a continuous record of past interactions, enabling the AI to reference previous messages for context.
2. **How do you store messages in Conversation Buffer Memory?**
Use the `save_context` method to add incoming messages.
3. **How can you retrieve the entire conversation history?**
Use the `load_memory_variables` method without any parameters.
4. **What are the advantages of using Conversation Buffer Memory?**
It allows the AI to maintain context and understand the flow of the ongoing conversation.
---
### 2. Entity Memory
**Definition and Purpose:** Entity Memory stores information about specific entities mentioned in the conversation. It enhances context-rich conversations by retaining facts about entities over time.
**How to Use:**
- Initialize the memory with a context containing entity information.
- Use the `save_context` method to store entity-related messages.
- Use the `load_memory_variables` method to retrieve entity information.
**Code Example:**
```python
from langchain.memory import ConversationEntityMemory
from pydantic import BaseModel
class Context(BaseModel):
Deven: str
Sam: str
context = Context(Deven="Deven is working on a hackathon project with Sam.",
Sam="Sam is working on a hackathon project with Deven.")
memory = ConversationEntityMemory(context=context)
memory.save_context({"input": "Deven & Sam are working on a project"},
{"output": "That sounds interesting!"})
result = memory.load_memory_variables({"input": 'who is Sam'})
print(result['history']) # Output: 'Human: Deven & Sam are working on a project\nAI: That sounds interesting!'
print(result['entities']['Sam']) # Output: 'Sam is working on a hackathon project with Deven.'
```
**Q&A:**
1. **What is the purpose of Entity Memory?**
Entity Memory enhances context by retaining facts about specific entities in the conversation.
2. **How do you initialize Entity Memory with entity information?**
Initialize it with a context containing entity details.
3. **How can you save entity-related messages to the memory?**
Use the `save_context` method.
4. **How do you retrieve information about a specific entity?**
Use the `load_memory_variables` method and access the 'entities' field.
---
### 3. Conversation Knowledge Graph Memory
**Definition and Purpose:** Conversation Knowledge Graph Memory utilizes a knowledge graph to recreate memory. It stores messages in a structured format to capture relationships and context.
**How to Use:**
- Initialize the memory with an LLM and optionally set other parameters.
- Use the `save_context` method to store messages in a knowledge graph format.
- Use the `load_memory_variables` method to retrieve messages from the knowledge graph.
**Code Example:**
```python
from langchain.memory import ConversationKGMemory
from langchain.llms import OpenAI
llm = OpenAI(temperature=0)
memory = ConversationKGMemory(llm=llm)
memory.save_context({"input": "say hi to sam"}, {"output": "who is sam"})
memory.save_context({"input": "sam is a friend"}, {"output": "okay"})
result = memory.load_memory_variables({"input": "who is sam"})
print(result['history']) # Output: 'On Sam: Sam is friend.'
```
**Q&A:**
1. **What is the purpose of Conversation Knowledge Graph Memory?**
Conversation KG Memory uses a knowledge graph to recreate memory for structured context.
2. **How do you initialize Conversation KG Memory?**
Initialize it with
an LLM and other desired parameters.
3. **How can you save messages in a knowledge graph format?**
Use the `save_context` method.
4. **How do you retrieve messages from the knowledge graph?**
Use the `load_memory_variables` method.
---
### 4. Conversation Summary Memory
**Definition and Purpose:** Conversation Summary Memory condenses conversation history into a summary that can be used to inject context into prompts/chains.
**How to Use:**
- Initialize the memory with an LLM and optionally set other parameters.
- Use the `save_context` method to store messages.
- Use the `load_memory_variables` method to retrieve conversation summaries.
**Code Example:**
```python
from langchain.memory import ConversationSummaryMemory
from langchain.llms import OpenAI
llm = OpenAI(temperature=0)
memory = ConversationSummaryMemory(llm=llm)
memory.save_context({"input": "hi"}, {"output": "whats up"})
memory.save_context({"input": "how are you?"}, {"output": "I'm fine."})
result = memory.load_memory_variables({})
print(result['history']) # Output: '\nThe human greets the AI, to which the AI responds.'
```
**Q&A:**
1. **What is the purpose of Conversation Summary Memory?**
Conversation Summary Memory condenses conversation history for efficient recall.
2. **How do you initialize Conversation Summary Memory?**
Initialize it with an LLM and other desired parameters.
3. **How can you save messages to create conversation summaries?**
Use the `save_context` method.
4. **How do you retrieve conversation summaries?**
Use the `load_memory_variables` method.
---
### 5. Vector Store-Backed Memory
**Definition and Purpose:** Vector Store-Backed Memory uses vector similarity to retrieve semantically relevant documents from a store, enhancing context retrieval.
**How to Use:**
- Initialize a suitable vector store.
- Create a retriever from the vector store.
- Initialize Vector Store-Backed Memory with the retriever.
- Use the `save_context` method to store memories.
- Use the `load_memory_variables` method to retrieve memories.
**Code Example:**
```python
from datetime import datetime
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.llms import OpenAI
from langchain.memory import VectorStoreRetrieverMemory
# Initialize your VectorStore (not shown here)
embedding_size = 1536 # Dimensions of OpenAIEmbeddings
# Initialize your embeddings and store (not shown here)
retriever = vectorstore.as_retriever(search_kwargs=dict(k=1))
memory = VectorStoreRetrieverMemory(retriever=retriever)
memory.save_context({"prompt": "My favorite food is pizza"},
{"response": "Nice choice!"})
result = memory.load_memory_variables({"prompt": "tell me about pizza"})
print(result['history']) # Output: 'Prompt: My favorite food is pizza\nResponse: Nice choice!'
```
**Q&A:**
1. **What is the purpose of Vector Store-Backed Memory?**
Vector Store-Backed Memory retrieves relevant documents using vector similarity.
2. **How do you initialize Vector Store-Backed Memory?**
Initialize it with a retriever created from a suitable vector store.
3. **How can you save memories to Vector Store-Backed Memory?**
Use the `save_context` method.
4. **How do you retrieve relevant memories using Vector Store-Backed Memory?**
Use the `load_memory_variables` method.
---
### 6. Conversation Buffer Window Memory
**Definition and Purpose:** Conversation Buffer Window Memory is an extension of Conversation Buffer Memory. It limits the context window to a specific number of previous interactions, ensuring that the most relevant context is maintained.
**How to Use:**
- Initialize the memory with a maximum buffer size.
- Use the `save_context` method to store incoming messages.
- Automatically remove older messages beyond the buffer window.
**Code Example:**
```python
from langchain.memory import ConversationBufferWindowMemory
memory = ConversationBufferWindowMemory(max_buffer_size=4)
memory.save_context({"input": "Hello"}, {"output": "Hi there!"})
memory.save_context({"input": "How's your day?"}, {"output": "It's great!"})
result = memory.load_memory_variables({})
print(result['history']) # Output: ['Human: Hello', 'AI: Hi there!', 'Human: How's your day?', 'AI: It's great!']
```
**Q&A:**
1. **What is the purpose of Conversation Buffer Window Memory?**
Conversation Buffer Window Memory maintains a limited context window to prioritize recent interactions.
2. **How do you set the maximum buffer size for the window?**
Initialize the memory with the `max_buffer_size` parameter.
3. **How can you save incoming messages to the window memory?**
Use the `save_context` method.
4. **How does Conversation Buffer Window Memory handle older messages?**
It automatically removes older messages once the buffer size is exceeded.
---
### 7. ConversationSummaryBufferMemory
**Definition and Purpose:** ConversationSummaryBufferMemory is an extended version of Conversation Summary Memory. It enables the creation of summarized conversation history with a limited buffer window.
**How to Use:**
- Initialize the memory with an LLM and maximum buffer size.
- Use the `save_context` method to store messages.
- Automatically condense conversation history into a summary while retaining recent interactions.
**Code Example:**
```python
from langchain.memory import ConversationSummaryBufferMemory
from langchain.llms import OpenAI
llm = OpenAI(temperature=0)
memory = ConversationSummaryBufferMemory(llm=llm, max_buffer_size=5)
memory.save_context({"input": "Nice weather today"}, {"output": "Indeed, it's beautiful!"})
memory.save_context({"input": "What's the plan?"}, {"output": "Thinking of going for a hike."})
result = memory.load_memory_variables({})
print(result['history']) # Output: '\nThe human comments on the weather, and the AI agrees.'
```
**Q&A:**
1. **What is the purpose of ConversationSummaryBufferMemory?**
ConversationSummaryBufferMemory creates condensed summaries of conversation history within a limited buffer window.
2. **How do you initialize ConversationSummaryBufferMemory?**
Initialize it with an LLM and maximum buffer size.
3. **How can you save messages to generate conversation summaries?**
Use the `save_context` method.
4. **How does ConversationSummaryBufferMemory handle older messages?**
It automatically maintains a summary while accommodating recent interactions.
---
### 8. ConversationTokenBufferMemory
**Definition and Purpose:** ConversationTokenBufferMemory stores conversation tokens, enabling precise retrieval of specific parts of the conversation, including individual tokens and sub-conversations.
**How to Use:**
- Initialize the memory.
- Use the `save_context` method to store incoming messages.
- Use the `load_memory_variables` method with token indices to retrieve specific conversation segments.
**Code Example:**
```python
from langchain.memory import ConversationTokenBufferMemory
memory = ConversationTokenBufferMemory()
memory.save_context({"input": "Tell me a joke"}, {"output": "Why did the chicken cross the road?"})
memory.save_context({"input": "I don't know, why?"}, {"output": "To get to the other side!"})
token_indices = [0, 1, 2] # Indices corresponding to the first message
result = memory.load_memory_variables({"token_indices": token_indices})
print(result['history']) # Output: ['Human: Tell me a joke', 'AI: Why did the chicken cross the road?']
```
**Q&A:**
1. **What is the purpose of ConversationTokenBufferMemory?**
ConversationTokenBufferMemory stores conversation tokens for precise retrieval of specific conversation segments.
2. **How do you save conversation tokens to the memory?**
Use the `save_context` method.
3. **How do you retrieve specific parts of the conversation using token indices?**
Use the `load_memory_variables` method with the 'token_indices' parameter.
---
|
0 | lc_public_repos/langchain-teacher | lc_public_repos/langchain-teacher/lc_guides/chains_guide.txt | You're right, my previous lesson on Chains in Langchain was too brief. Here is an expanded version with more details, examples, and code snippets:
# Chains in Langchain
## Introduction
Chains are a core concept in Langchain that enable combining multiple components like language models, retrievers, routers, etc. into a cohesive AI application. Chains provide a modular and extensible way to build complex assistants by composing simpler building blocks.
**Q: What are chains and why are they useful in Langchain?**
A: Chains are reusable structures that allow combining multiple components like language models, prompts, retrievers, etc. into a single AI application. They provide a clean, modular architecture that makes it easy to build complex assistants by piecing together simpler building block chains. The main benefits are:
- **Modularity:** It's easy to swap out components as needed.
- **Extensibility:** We can chain simple blocks into complex apps.
- **Maintainability:** Each component encapsulates specific logic.
- **Reusability:** Chains are reusable across applications.
## Types of Chains
Langchain provides several common chain implementations out-of-the-box:
### LLMChain
The simplest chain wraps a language model. It takes a prompt template, formats a prompt using the input, queries the LLM, and returns the response.
```python
from langchain.chains import LLMChain
prompt = PromptTemplate(template="Hello {name}!")
llm = OpenAI()
llm_chain = LLMChain(llm=llm, prompt=prompt)
print(llm_chain.run(input={"name": "Jane"}))
# Output: Hello Jane!
```
**Q: How does the LLMChain work step-by-step?**
A: Here is what the LLMChain does under the hood:
1. Take input data
2. Format prompt template using input
3. Pass formatted prompt to LLM
4. Get LLM response
5. Return response
So it handles prompt formatting and querying the LLM.
### SequentialChain
Chains multiple chains sequentially. The output of one chain is passed as input to the next chain automatically.
```python
from langchain.chains import SequentialChain
chain1 = LLMChain(llm1, prompt1)
chain2 = LLMChain(llm2, prompt2)
seq_chain = SequentialChain(chains=[chain1, chain2])
```
**Q: What does SequentialChain allow you to do?**
A: SequentialChain enables chaining multiple chains together into a pipeline. The key benefit is it automatically handles passing data between the chains. For example, we can chain a preprocessing, LLM, and postprocessing step together easily.
### RouterChain
Contains logic to route input dynamically to different destination chains based on the input. Useful for routing between domains.
```python
from langchain.chains import RouterChain
router = EmbeddingRouter(...)
destination_chains = {...}
chain = RouterChain(router=router,
destination_chains=destination_chains)
```
**Q: How does the RouterChain route between chains?**
A: The RouterChain encapsulates logic to select the appropriate destination chain for a given input. For example, an embedding router could select chains based on semantic similarity. This enables dynamically switching behavior based on the input.
## Building Applications
This shows how we can build complex applications from smaller modular chains - while keeping each part simple, reusable, and focused.
**Q: What are some examples of AI apps you could build using chains?**
A: Here are some examples of AI applications you could build using chains:
- Question Answering over Documents
- Multi-Domain Chatbot
- Sentiment Analysis Pipeline
- Automated Email Response Agent
- Resume Screening System
- Article Summarization Workflow
The possibilities are endless! Chains make it much easier to build robust, maintainable AI systems.
Chains also maintain state like conversation history across runs. This simplifies building stateful applications like chatbots.
**Q: How do chains work behind the scenes?**
A: Chains handle passing data between components under the hood. They often preprocess input, call the next component, and postprocess outputs. Chains also maintain state across multiple runs, storing things like conversation history. This simplifies building stateful apps like chatbots that need to track context.
## Conclusion
In this lesson, we looked at:
- Core chain implementations like LLMChain, SequentialChain, RouterChain
- Combining chains to build complex AI applications
- How chains work behind the scenes
Chains provide a clean architecture for production AI systems by breaking down complex apps into simple, reusable components. This results in modular, robust and extensible architectures. |
0 | lc_public_repos/langchain-teacher | lc_public_repos/langchain-teacher/lc_guides/getting_started_guide.txt | **Getting Started with LangChain: Building LLM-Powered Applications**
In this comprehensive lesson, we'll dive into the world of LangChain, an open-source framework that empowers developers to create applications using Large Language Models (LLMs) like GPT-4. We'll cover various aspects of LangChain, from its core concepts to practical examples of building applications. Throughout the lesson, we'll ask questions, provide answers, and offer detailed code examples to help you grasp the concepts effectively.
**Table of Contents:**
1. **Introduction to LangChain**
- What is LangChain?
- Value Propositions of LangChain
- Applications of LangChain
2. **Key Concepts of LangChain**
- Components: Modular Building Blocks
- Chains: Combining Components for Tasks
- Agents: LLM Interaction with Environment
3. **Getting Started: Building a Simple Application**
- Importing LangChain and Initializing LLM
- Predicting with the LLM
- Interpreting the Response
4. **Understanding Components**
- LLM Wrappers: Working with LLMs
- Prompt Templates: Structuring Input for LLMs
- Indexes: Retrieving Relevant Information
5. **Chains: Combining Components**
- Using Chains for Complex Applications
- Modularity, Debugging, and Maintenance
6. **Agents: LLM Interaction with Environment**
- LLMs Performing Actions via External APIs
- Integrating LLMs into the Workflow
7. **Building Advanced Applications with LangChain**
- Document Analysis and Summarization
- Chatbots and Customer Assistance
- Code Analysis for Bugs and Security
- Answering Questions with Diverse Sources
- Data Augmentation for Machine Learning
**1. Introduction to LangChain:**
**What is LangChain?**
LangChain is an open-source framework designed to simplify the creation of applications using Large Language Models (LLMs). It provides standardized interfaces, modular components, and pre-built chains for various tasks.
**Value Propositions of LangChain:**
- **Components:** Modular, ready-to-use building blocks for language model applications.
- **Chains:** Structured combinations of components to solve specific tasks.
- **Customizability:** Chains are customizable to cater to unique use cases.
**Applications of LangChain:**
- Document analysis and summarization.
- Natural language understanding and generation.
- Chatbots for customer support and information retrieval.
- Code analysis for bugs and security.
- Answering questions using diverse sources (text, code, data).
- Data augmentation for machine learning.
- Language translation and text classification.
**2. Key Concepts of LangChain:**
**Components:**
Components in LangChain are modular building blocks used to create language model applications. They include LLM wrappers, prompt templates, and indexes for relevant information retrieval.
**Chains:**
Chains combine multiple components to achieve specific tasks. Chains enhance modularity, simplification, debugging, and maintenance in application development.
**Agents:**
Agents enable LLMs to interact with their environment. They allow LLMs to perform actions using external APIs and integrate with external tools and data sources.
**3. Getting Started: Building a Simple Application**
Installation
To install LangChain run:
Pip
pip install langchain
Conda
conda install langchain -c conda-forge
For more details, see our Installation guide.
Environment setup
Using LangChain will usually require integrations with one or more model providers, data stores, APIs, etc. For this example, we'll use OpenAI's model APIs.
First we'll need to install their Python package:
pip install openai
Accessing the API requires an API key, which you can get by creating an account and heading here. Once we have a key we'll want to set it as an environment variable by running:
export OPENAI_API_KEY="..."
If you'd prefer not to set an environment variable you can pass the key in directly via the openai_api_key named parameter when initiating the OpenAI LLM class:
from langchain.llms import OpenAI
llm = OpenAI(openai_api_key="...")
**Code Example:**
```python
# Import LangChain and initialize LLM
from langchain.llms import OpenAI
llm = OpenAI(temperature=0.9, openai_api_key=api_key)
# Predict with the LLM
response = llm.predict("Suggest me a skill that is in demand?")
print(response)
```
**Output:**
```
One skill in demand right now is software/web development, which includes everything from coding to content management systems to web design...
```
**4. Understanding Components**
**LLM Wrappers:**
LLM wrappers provide standardized interfaces for working with different language models. They offer methods to predict text and messages using the model.
**Prompt Templates:**
Prompt templates structure input for LLMs. They allow easy formatting of user inputs and instructions for generating responses.
**Indexes:**
Indexes facilitate information retrieval from external sources. They help LLMs fetch relevant data to enhance response quality.
**5. Building an application
Now we can start building our language model application. LangChain provides many modules that can be used to build language model applications. Modules can be used as stand-alones in simple applications and they can be combined for more complex use cases.
The core building block of LangChain applications is the LLMChain. This combines three things:
LLM: The language model is the core reasoning engine here. In order to work with LangChain, you need to understand the different types of language models and how to work with them.
Prompt Templates: This provides instructions to the language model. This controls what the language model outputs, so understanding how to construct prompts and different prompting strategies is crucial.
Output Parsers: These translate the raw response from the LLM to a more workable format, making it easy to use the output downstream.
In this getting started guide we will cover those three components by themselves, and then cover the LLMChain which combines all of them. Understanding these concepts will set you up well for being able to use and customize LangChain applications. Most LangChain applications allow you to configure the LLM and/or the prompt used, so knowing how to take advantage of this will be a big enabler.
LLMs
There are two types of language models, which in LangChain are called:
LLMs: this is a language model which takes a string as input and returns a string
ChatModels: this is a language model which takes a list of messages as input and returns a message
The input/output for LLMs is simple and easy to understand - a string. But what about ChatModels? The input there is a list of ChatMessages, and the output is a single ChatMessage. A ChatMessage has two required components:
content: This is the content of the message.
role: This is the role of the entity from which the ChatMessage is coming from.
LangChain provides several objects to easily distinguish between different roles:
HumanMessage: A ChatMessage coming from a human/user.
AIMessage: A ChatMessage coming from an AI/assistant.
SystemMessage: A ChatMessage coming from the system.
FunctionMessage: A ChatMessage coming from a function call.
If none of those roles sound right, there is also a ChatMessage class where you can specify the role manually. For more information on how to use these different messages most effectively, see our prompting guide.
LangChain exposes a standard interface for both, but it's useful to understand this difference in order to construct prompts for a given language model. The standard interface that LangChain exposes has two methods:
predict: Takes in a string, returns a string
predict_messages: Takes in a list of messages, returns a message.
Let's see how to work with these different types of models and these different types of inputs. First, let's import an LLM and a ChatModel.
from langchain.llms import OpenAI
from langchain.chat_models import ChatOpenAI
llm = OpenAI()
chat_model = ChatOpenAI()
llm.predict("hi!")
>>> "Hi"
chat_model.predict("hi!")
>>> "Hi"
The OpenAI and ChatOpenAI objects are basically just configuration objects. You can initialize them with parameters like temperature and others, and pass them around.
Next, let's use the predict method to run over a string input.
text = "What would be a good company name for a company that makes colorful socks?"
llm.predict(text)
# >> Feetful of Fun
chat_model.predict(text)
# >> Socks O'Color
Finally, let's use the predict_messages method to run over a list of messages.
from langchain.schema import HumanMessage
text = "What would be a good company name for a company that makes colorful socks?"
messages = [HumanMessage(content=text)]
llm.predict_messages(messages)
# >> Feetful of Fun
chat_model.predict_messages(messages)
# >> Socks O'Color
For both these methods, you can also pass in parameters as key word arguments. For example, you could pass in temperature=0 to adjust the temperature that is used from what the object was configured with. Whatever values are passed in during run time will always override what the object was configured with.
Prompt templates
Most LLM applications do not pass user input directly into an LLM. Usually they will add the user input to a larger piece of text, called a prompt template, that provides additional context on the specific task at hand.
In the previous example, the text we passed to the model contained instructions to generate a company name. For our application, it'd be great if the user only had to provide the description of a company/product, without having to worry about giving the model instructions.
PromptTemplates help with exactly this! They bundle up all the logic for going from user input into a fully formatted prompt. This can start off very simple - for example, a prompt to produce the above string would just be:
from langchain.prompts import PromptTemplate
prompt = PromptTemplate.from_template("What is a good name for a company that makes {product}?")
prompt.format(product="colorful socks")
What is a good name for a company that makes colorful socks?
However, the advantages of using these over raw string formatting are several. You can "partial" out variables - eg you can format only some of the variables at a time. You can compose them together, easily combining different templates into a single prompt. For explanations of these functionalities, see the section on prompts for more detail.
PromptTemplates can also be used to produce a list of messages. In this case, the prompt not only contains information about the content, but also each message (its role, its position in the list, etc) Here, what happens most often is a ChatPromptTemplate is a list of ChatMessageTemplates. Each ChatMessageTemplate contains instructions for how to format that ChatMessage - its role, and then also its content. Let's take a look at this below:
from langchain.prompts.chat import (
ChatPromptTemplate,
SystemMessagePromptTemplate,
HumanMessagePromptTemplate,
)
template = "You are a helpful assistant that translates {input_language} to {output_language}."
system_message_prompt = SystemMessagePromptTemplate.from_template(template)
human_template = "{text}"
human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)
chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt])
chat_prompt.format_messages(input_language="English", output_language="French", text="I love programming.")
[
SystemMessage(content="You are a helpful assistant that translates English to French.", additional_kwargs={}),
HumanMessage(content="I love programming.")
]
ChatPromptTemplates can also include other things besides ChatMessageTemplates - see the section on prompts for more detail.
Output Parsers
OutputParsers convert the raw output of an LLM into a format that can be used downstream. There are few main type of OutputParsers, including:
Convert text from LLM -> structured information (eg JSON)
Convert a ChatMessage into just a string
Convert the extra information returned from a call besides the message (like OpenAI function invocation) into a string.
For full information on this, see the section on output parsers
In this getting started guide, we will write our own output parser - one that converts a comma separated list into a list.
from langchain.schema import BaseOutputParser
class CommaSeparatedListOutputParser(BaseOutputParser):
"""Parse the output of an LLM call to a comma-separated list."""
def parse(self, text: str):
"""Parse the output of an LLM call."""
return text.strip().split(", ")
CommaSeparatedListOutputParser().parse("hi, bye")
# >> ['hi', 'bye']
LLMChain
We can now combine all these into one chain. This chain will take input variables, pass those to a prompt template to create a prompt, pass the prompt to an LLM, and then pass the output through an (optional) output parser. This is a convenient way to bundle up a modular piece of logic. Let's see it in action!
from langchain.chat_models import ChatOpenAI
from langchain.prompts.chat import (
ChatPromptTemplate,
SystemMessagePromptTemplate,
HumanMessagePromptTemplate,
)
from langchain.chains import LLMChain
from langchain.schema import BaseOutputParser
class CommaSeparatedListOutputParser(BaseOutputParser):
"""Parse the output of an LLM call to a comma-separated list."""
def parse(self, text: str):
"""Parse the output of an LLM call."""
return text.strip().split(", ")
template = """You are a helpful assistant who generates comma separated lists.
A user will pass in a category, and you should generate 5 objects in that category in a comma separated list.
ONLY return a comma separated list, and nothing more."""
system_message_prompt = SystemMessagePromptTemplate.from_template(template)
human_template = "{text}"
human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)
chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt])
chain = LLMChain(
llm=ChatOpenAI(),
prompt=chat_prompt,
output_parser=CommaSeparatedListOutputParser()
)
chain.run("colors")
# >> ['red', 'blue', 'green', 'yellow', 'orange']
**6. Agents: LLM Interaction with Environment**
**Code Example:**
```python
from langchain.agents import ExternalAPICall
class TranslatorAgent(ExternalAPICall):
def __init__(self):
super().__init__(api_url="https://translation.api.com", method="POST")
def perform_action(self, input_text):
data = {"text": input_text, "target_language": "fr"}
response = self.call_api(data)
return response["translated_text"]
# Initialize the agent
translator_agent = TranslatorAgent()
# Perform translation
translated_text = translator_agent.perform_action("Hello, how are you?")
print(translated_text)
```
**Output:**
```
"Bonjour, comment ça va ?"
```
**7. Building Advanced Applications with LangChain**
**Conclusion:**
LangChain is a versatile framework that simplifies the development of applications powered by Large Language Models. It offers standardized components, chains, and agents to create a wide range of applications, from chatbots to document summarization. With LangChain, developers can harness the power of language models while maintaining modularity and customization.
Throughout this lesson, we've explored LangChain's core concepts, demonstrated practical examples, and discussed its potential applications. By mastering LangChain, you can embark on a journey to build innovative and powerful AI-driven applications.
Remember, this lesson provides a solid foundation, but there's much more to explore within LangChain. Dive deeper into prompt templates, explore more use cases, and expand your knowledge to create advanced and impactful language model applications. Happy coding! |
0 | lc_public_repos/langchain-teacher | lc_public_repos/langchain-teacher/lc_guides/retrieval_guide.txt | Here is a consolidated lesson on Retrieval Augmented Generation in Langchain with sections, questions, answers, and code examples:
# Retrieval Augmented Generation in Langchain
## Introduction
Retrieval augmented generation (RAG) refers to combining large language models with external knowledge sources to improve their capabilities. In RAG, the LLM retrieves relevant information from a knowledge base to inform its response to a given prompt or question. This helps reduce hallucination and improve accuracy.
Langchain provides tools for implementing retrieval augmented generation by allowing easy integration of external knowledge sources. Let's learn how it works.
**Q: What is retrieval augmented generation?**
A: Retrieval augmented generation refers to combining a large language model with an external knowledge source to improve the accuracy and reduce hallucination in the LLM's responses. The LLM retrieves relevant information from the knowledge source to inform its response to prompts and questions.
**Q: Why is retrieval augmentation useful for large language models?**
A: Retrieval augmentation is useful because LLMs have limited knowledge based on what they were trained on. By combining them with external knowledge sources, we can provide them with up-to-date, relevant information to ground their responses in facts rather than hallucinations. This improves accuracy.
## Knowledge Sources
The first step in implementing RAG is selecting and integrating external knowledge sources. Langchain supports integrating diverse knowledge sources:
- **Documents:** Text, PDFs, HTML pages, etc.
- **Structured data:** SQL tables, Pandas dataframes, etc.
- **APIs:** Web APIs can be wrapped as knowledge sources.
Langchain provides utilities for loading data from each of these sources and converting them into a standard `Document` format that can be used downstream.
**Q: What kinds of knowledge sources can be integrated with Langchain?**
A: Langchain supports integrating diverse knowledge sources including unstructured text documents, structured data like SQL tables and Pandas dataframes, and web APIs. These sources can be loaded and converted into Langchain's standard Document format.
**Q: How does Langchain convert diverse sources into a standard format?**
A: Langchain provides DocumentLoaders to load each type of source and convert it into the standard Document format which contains the text content and metadata. This abstraction allows seamlessly integrating diverse sources.
## Working with Documents
Unstructured text documents are a common knowledge source. Let's see how to work with documents in Langchain.
### Loading Documents
Langchain provides `DocumentLoaders` to load documents from files, web pages, Google Drive, etc. For example:
```python
from langchain.document_loaders import TextLoader
loader = TextLoader("data.txt")
docs = loader.load()
```
**Q: How do you load unstructured text documents in Langchain?**
A: Langchain provides DocumentLoaders like TextLoader to load text documents from files, webpages, Google Drive, etc. The DocumentLoaders expose a load() method to load the data and return a list of Document objects.
### Splitting Documents
Large documents should be split into smaller chunks before feeding to the LLM. Langchain provides `TextSplitters` for this:
```python
from langchain.text_splitter import RecursiveCharacterTextSplitter
splitter = RecursiveCharacterTextSplitter()
docs = splitter.split_documents(docs)
```
**Q: Why is it important to split text documents before using them for retrieval?**
A: Large documents need to be split into smaller chunks so that they can fit into the LLM's limited context window. Smaller chunks also allow more focused, relevant retrieval for a given query. Langchain provides TextSplitters to split documents into chunks.
### Embedding Documents
To retrieve documents for a query, we need to represent them in a vector space using embeddings. Langchain provides `Embeddings` for this:
```python
from langchain.embeddings import OpenAIEmbeddings
embedder = OpenAIEmbeddings()
embeds = embedder.embed_documents(docs)
```
Now the documents are encoded as vectors ready for retrieval.
**Q: How does Langchain generate vector embeddings for text documents?**
A: Langchain provides Embeddings models like OpenAIEmbeddings to generate vector representations of text documents. These embedding vectors allow computing semantic similarity between pieces of text for retrieval.
## Vector Databases
To perform efficient retrieval over a large corpus, the document vectors need to be indexed in a vector database. Langchain provides utilities for working with vector databases.
### Creating the Index
A vector database index can be created from embedded documents like:
```python
from langchain.vectorstores import FAISS
vectorstore = FAISS()
vectorstore.add_documents(embeds)
```
**Q: How do you create a vector database index in Langchain?**
A: A vector database index can be created by first embedding documents using an Embeddings model and then passing the document vectors to a VectorStore like FAISS. The VectorStore will create the index from the vectors.
### Querying the Index
Once indexed, we can perform efficient semantic search over the vectors:
```python
docs = vectorstore.similarity_search("fix my computer")
```
This retrieves the most similar document vectors to the query.
**Q: How can you retrieve documents from a vector database index?**
A: The VectorStore provides a similarity_search method to query the index and return the most similar document vectors to a query string or embedding vector. This enables fast semantic search over the indexed documents.
## Building Chains
Langchain provides chain abstractions that combine the knowledge source with LLMs to enable retrieval augmented generation.
### RetrievalQA
The `RetrievalQA` chain performs question answering using retrieved documents:
```python
from langchain.chains import RetrievalQA
from langchain.llms import OpenAI
llm = OpenAI()
retriever = vectorstore.as_retriever()
qa_chain = RetrievalQA(llm, retriever)
qa_chain.run("when was langchain created?")
```
**Q: How can you build a question answering system using retrieved documents in Langchain?**
A: The RetrievalQA chain combines a retriever over documents with a QA-capable LLM like OpenAI to perform question answering using retrieved information. It automatically retrieves relevant documents for a question and has the LLM answer based on those.
### ConversationalQA
For dialog, `ConversationalQA` incorporates conversation history:
```python
from langchain.chains import ConversationalQA
conv_qa = ConversationalQA(llm, retriever)
conv_qa("what is langchain?", "Langchain is an AI assistant created by Anthropic.")
```
**Q: How does Langchain support multi-turn conversational question answering?**
A: The ConversationalQA chain maintains conversation state and incorporates it into queries to the retriever and LLM at each turn. This enables multi-turn QA where context from earlier in the conversation is used.
## Conclusion
In this lesson, we looked at how Langchain implements retrieval augmented generation by:
- Supporting diverse knowledge sources
- Providing utilities for working with documents
- Building vector databases for retrieval
- Creating chains that connect retrievers and LLMs
With these building blocks, you can augment any LLM with external knowledge to reduce hallucination and improve accuracy! |
0 | lc_public_repos/langchain-teacher | lc_public_repos/langchain-teacher/lc_guides/prompt_guide.txt | ## Prompts and Prompt Templates in LangChain
### Introduction to Prompts
A **prompt** for a language model serves as a set of instructions or input that guides the model's response. It helps the model understand context and generate relevant language-based output. This is crucial for tasks such as answering questions or engaging in conversations.
#### Why are Prompts Important?
Prompts are essential because they:
- Provide context to the model, enabling it to generate coherent and relevant responses.
- Allow fine-tuning of model behavior without altering the model architecture.
**Q1: What is the primary purpose of a prompt in a language model?**
A prompt's main purpose is to provide instructions and context to guide the language model's output generation.
**Q2: How can prompts improve the quality of language model responses?**
Prompts help language models generate responses that are contextually relevant and coherent by providing clear instructions.
### Prompt Templates: Creating Dynamic Prompts
A **prompt template** is a predefined recipe for creating prompts with dynamic inputs. LangChain offers classes and functions to simplify the creation and utilization of prompt templates.
#### Creating a Simple Prompt Template
A prompt template is created using the `PromptTemplate` class. It enables the formatting of prompts with variables that can be dynamically filled.
**Example:**
```python
from langchain import PromptTemplate
template = PromptTemplate(
input_variables=["location"],
template="I really want to travel to {location}. What should I do there?"
)
final_prompt = template.format(location='Rome')
print(f"Final Prompt: {final_prompt}")
```
**Q3: How can you create dynamic prompts using prompt templates in LangChain?**
Dynamic prompts can be created using the `PromptTemplate` class, where placeholders are replaced with specific values using the `format` method.
### Chat Prompt Templates
For chat models, prompts often consist of a sequence of chat messages. Each message includes content and a role (system, human, or AI). The `ChatPromptTemplate` class simplifies the creation of chat model prompts.
**Example:**
```python
from langchain.prompts import ChatPromptTemplate
template = ChatPromptTemplate.from_messages([
("system", "You are a helpful AI bot. Your name is {name}."),
("human", "Hello, how are you doing?"),
("ai", "I'm doing well, thanks!"),
("human", "{user_input}"),
])
messages = template.format_messages(
name="Bob",
user_input="What is your name?"
)
```
**Q4: What is the structure of prompts used for chat models?**
Prompts for chat models consist of chat messages, each with content and a role designation (system, human, or AI).
### Few-Shot Prompt Templates
Few-shot learning involves training a model using a few examples. The `FewShotPromptTemplate` in LangChain is designed to incorporate source knowledge through examples.
#### Creating a Few-Shot Prompt Template
**Example:**
```python
from langchain import FewShotPromptTemplate
# Create example template
example_template = """
User: {query}
AI: {answer}
"""
# Create example selector
example_selector = SemanticSimilarityExampleSelector.from_examples(
examples,
OpenAIEmbeddings(openai_api_key=openai_api_key),
FAISS,
k=2
)
# Create FewShotPromptTemplate
few_shot_prompt_template = FewShotPromptTemplate(
example_selector=example_selector,
example_prompt=example_prompt,
prefix="Give the location an item is usually found in",
suffix="Input: {noun}\nOutput:",
input_variables=["noun"],
)
# Format the prompt
my_noun = "student"
formatted_prompt = few_shot_prompt_template.format(noun=my_noun)
# Generate response from the model
response = llm(formatted_prompt)
print(response)
```
**Q5: How can few-shot learning be integrated into prompts using LangChain's FewShotPromptTemplate?**
Few-shot learning can be incorporated by using the `FewShotPromptTemplate` class, which utilizes examples to provide source knowledge for the model.
### Example Selectors
**Example selectors** facilitate the selection of relevant examples to include in prompts. They dynamically incorporate context-related examples into prompts.
#### Using Semantic Similarity Example Selector
The `SemanticSimilarityExampleSelector` selects examples based on semantic similarity.
**Q6: What is the purpose of an example selector in prompt construction?**
Example selectors help choose appropriate examples to include in prompts, enhancing context and guiding model responses.
**Q7: How does the Semantic Similarity Example Selector work?**
The Semantic Similarity Example Selector selects examples that are semantically similar to the input using embeddings and a vector store.
### Conclusion
Prompts and prompt templates are powerful tools for guiding language models' behavior and generating contextually relevant responses. By incorporating dynamic inputs, chat conversations, and few-shot learning, LangChain provides a comprehensive framework for creating effective prompts. Experimenting with prompt templates and example selectors can enhance the quality of interactions with language models and unlock their full potential.
**Q8: What are the main benefits of using LangChain's prompt templates?**
LangChain's prompt templates provide a structured approach, flexibility with dynamic inputs, and the ability to generate chat-based and few-shot learning prompts.
**Q9: How can experimenting with different prompt techniques enhance language model interactions?**
By experimenting with different prompt templates and example selectors, users can optimize prompt engineering, leading to more contextually aware and relevant model outputs.
Remember that understanding prompt engineering and leveraging prompt templates effectively can significantly improve the outcomes of interactions with language models.
---
I hope this expanded lesson provides a more comprehensive understanding of prompts and prompt templates in LangChain. If you have any further questions or topics you'd like to explore, feel free to ask! |
0 | lc_public_repos | lc_public_repos/weblangchain/Dockerfile | FROM python:3.11-buster
RUN pip install poetry==1.5.1
RUN poetry config virtualenvs.create false
COPY ./pyproject.toml ./poetry.lock* ./
RUN poetry install --no-interaction --no-ansi --no-root --no-directory
COPY ./*.py ./.google_vertex_ai_credentials.jso[n] ./
RUN poetry install --no-interaction --no-ansi
CMD exec uvicorn main:app --host 0.0.0.0 --port 8080
|
0 | lc_public_repos | lc_public_repos/weblangchain/Makefile | .PHONY: start
start:
uvicorn main:app --reload --port 8080
.PHONY: format
format:
black .
isort . |
0 | lc_public_repos | lc_public_repos/weblangchain/.dockerignore | nextjs/
assets/ |
0 | lc_public_repos | lc_public_repos/weblangchain/LICENSE | MIT License
Copyright (c) 2023 Harrison Chase
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
|
0 | lc_public_repos | lc_public_repos/weblangchain/main.py | """Main entrypoint for the app."""
import asyncio
import os
from datetime import datetime
from operator import itemgetter
from typing import List, Optional, Sequence, Tuple, Union
import langsmith
from fastapi import FastAPI, Request, Depends
from fastapi.middleware.cors import CORSMiddleware
from langchain.callbacks.manager import CallbackManagerForRetrieverRun
from langchain.chat_models import ChatAnthropic, ChatOpenAI, ChatVertexAI
from langchain.document_loaders import AsyncHtmlLoader
from langchain.document_transformers import Html2TextTransformer
from langchain.embeddings import OpenAIEmbeddings
from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder, PromptTemplate
from langchain.retrievers import (
ContextualCompressionRetriever,
TavilySearchAPIRetriever,
)
from langchain.retrievers.document_compressors import (
DocumentCompressorPipeline,
EmbeddingsFilter,
)
from langchain.retrievers.kay import KayAiRetriever
from langchain.retrievers.you import YouRetriever
from langchain.schema import Document
from langchain.schema.document import Document
from langchain.schema.language_model import BaseLanguageModel
from langchain.schema.messages import AIMessage, HumanMessage
from langchain.schema.output_parser import StrOutputParser
from langchain.schema.retriever import BaseRetriever
from langchain.schema.runnable import (
ConfigurableField,
Runnable,
RunnableBranch,
RunnableLambda,
RunnableMap,
)
from langchain.text_splitter import RecursiveCharacterTextSplitter
# Backup
from langchain.utilities import GoogleSearchAPIWrapper
from langserve import add_routes
from langsmith import Client
from pydantic import BaseModel, Field
from uuid import UUID
RESPONSE_TEMPLATE = """\
You are an expert researcher and writer, tasked with answering any question.
Generate a comprehensive and informative, yet concise answer of 250 words or less for the \
given question based solely on the provided search results (URL and content). You must \
only use information from the provided search results. Use an unbiased and \
journalistic tone. Combine search results together into a coherent answer. Do not \
repeat text. Cite search results using [${{number}}] notation. Only cite the most \
relevant results that answer the question accurately. Place these citations at the end \
of the sentence or paragraph that reference them - do not put them all at the end. If \
different results refer to different entities within the same name, write separate \
answers for each entity. If you want to cite multiple results for the same sentence, \
format it as `[${{number1}}] [${{number2}}]`. However, you should NEVER do this with the \
same number - if you want to cite `number1` multiple times for a sentence, only do \
`[${{number1}}]` not `[${{number1}}] [${{number1}}]`
You should use bullet points in your answer for readability. Put citations where they apply \
rather than putting them all at the end.
If there is nothing in the context relevant to the question at hand, just say "Hmm, \
I'm not sure." Don't try to make up an answer.
Anything between the following `context` html blocks is retrieved from a knowledge \
bank, not part of the conversation with the user.
<context>
{context}
<context/>
REMEMBER: If there is no relevant information within the context, just say "Hmm, I'm \
not sure." Don't try to make up an answer. Anything between the preceding 'context' \
html blocks is retrieved from a knowledge bank, not part of the conversation with the \
user. The current date is {current_date}.
"""
REPHRASE_TEMPLATE = """\
Given the following conversation and a follow up question, rephrase the follow up \
question to be a standalone question.
Chat History:
{chat_history}
Follow Up Input: {question}
Standalone Question:"""
client = Client()
app = FastAPI()
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
expose_headers=["*"],
)
class ChatRequest(BaseModel):
question: str
chat_history: List[Tuple[str, str]] = Field(
...,
extra={"widget": {"type": "chat", "input": "question", "output": "answer"}},
)
class GoogleCustomSearchRetriever(BaseRetriever):
search: Optional[GoogleSearchAPIWrapper] = None
num_search_results = 6
def clean_search_query(self, query: str) -> str:
# Some search tools (e.g., Google) will
# fail to return results if query has a
# leading digit: 1. "LangCh..."
# Check if the first character is a digit
if query[0].isdigit():
# Find the position of the first quote
first_quote_pos = query.find('"')
if first_quote_pos != -1:
# Extract the part of the string after the quote
query = query[first_quote_pos + 1 :]
# Remove the trailing quote if present
if query.endswith('"'):
query = query[:-1]
return query.strip()
def search_tool(self, query: str, num_search_results: int = 1) -> List[dict]:
"""Returns num_search_results pages per Google search."""
query_clean = self.clean_search_query(query)
result = self.search.results(query_clean, num_search_results)
return result
def _get_relevant_documents(
self, query: str, *, run_manager: CallbackManagerForRetrieverRun
):
if os.environ.get("GOOGLE_API_KEY", None) == None:
raise Exception("No Google API key provided")
if self.search == None:
self.search = GoogleSearchAPIWrapper()
# Get search questions
print("Generating questions for Google Search ...")
# Get urls
print("Searching for relevant urls...")
urls_to_look = []
search_results = self.search_tool(query, self.num_search_results)
print("Searching for relevant urls...")
print(f"Search results: {search_results}")
for res in search_results:
if res.get("link", None):
urls_to_look.append(res["link"])
print(search_results)
loader = AsyncHtmlLoader(urls_to_look)
html2text = Html2TextTransformer()
print("Indexing new urls...")
docs = loader.load()
docs = list(html2text.transform_documents(docs))
for i in range(len(docs)):
if search_results[i].get("title", None):
docs[i].metadata["title"] = search_results[i]["title"]
return docs
def get_retriever():
embeddings = OpenAIEmbeddings()
splitter = RecursiveCharacterTextSplitter(chunk_size=800, chunk_overlap=20)
relevance_filter = EmbeddingsFilter(embeddings=embeddings, similarity_threshold=0.8)
pipeline_compressor = DocumentCompressorPipeline(
transformers=[splitter, relevance_filter]
)
base_tavily_retriever = TavilySearchAPIRetriever(
k=6, include_raw_content=True, include_images=True
)
tavily_retriever = ContextualCompressionRetriever(
base_compressor=pipeline_compressor, base_retriever=base_tavily_retriever
)
base_google_retriever = GoogleCustomSearchRetriever()
google_retriever = ContextualCompressionRetriever(
base_compressor=pipeline_compressor, base_retriever=base_google_retriever
)
base_you_retriever = YouRetriever(
ydc_api_key=os.environ.get("YDC_API_KEY", "not_provided")
)
you_retriever = ContextualCompressionRetriever(
base_compressor=pipeline_compressor, base_retriever=base_you_retriever
)
base_kay_retriever = KayAiRetriever.create(
dataset_id="company",
data_types=["10-K", "10-Q"],
num_contexts=6,
)
kay_retriever = ContextualCompressionRetriever(
base_compressor=pipeline_compressor, base_retriever=base_kay_retriever
)
base_kay_press_release_retriever = KayAiRetriever.create(
dataset_id="company",
data_types=["PressRelease"],
num_contexts=6,
)
kay_press_release_retriever = ContextualCompressionRetriever(
base_compressor=pipeline_compressor,
base_retriever=base_kay_press_release_retriever,
)
return tavily_retriever.configurable_alternatives(
# This gives this field an id
# When configuring the end runnable, we can then use this id to configure this field
ConfigurableField(id="retriever"),
default_key="tavily",
google=google_retriever,
you=you_retriever,
kay=kay_retriever,
kay_press_release=kay_press_release_retriever,
).with_config(run_name="FinalSourceRetriever")
def create_retriever_chain(
llm: BaseLanguageModel, retriever: BaseRetriever
) -> Runnable:
CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template(REPHRASE_TEMPLATE)
condense_question_chain = (
CONDENSE_QUESTION_PROMPT | llm | StrOutputParser()
).with_config(
run_name="CondenseQuestion",
)
conversation_chain = condense_question_chain | retriever
return RunnableBranch(
(
RunnableLambda(lambda x: bool(x.get("chat_history"))).with_config(
run_name="HasChatHistoryCheck"
),
conversation_chain.with_config(run_name="RetrievalChainWithHistory"),
),
(
RunnableLambda(itemgetter("question")).with_config(
run_name="Itemgetter:question"
)
| retriever
).with_config(run_name="RetrievalChainWithNoHistory"),
).with_config(run_name="RouteDependingOnChatHistory")
def serialize_history(request: ChatRequest):
chat_history = request.get("chat_history", [])
converted_chat_history = []
for message in chat_history:
if message[0] == "human":
converted_chat_history.append(HumanMessage(content=message[1]))
elif message[0] == "ai":
converted_chat_history.append(AIMessage(content=message[1]))
return converted_chat_history
def format_docs(docs: Sequence[Document]) -> str:
formatted_docs = []
for i, doc in enumerate(docs):
doc_string = f"<doc id='{i}'>{doc.page_content}</doc>"
formatted_docs.append(doc_string)
return "\n".join(formatted_docs)
def create_chain(
llm: BaseLanguageModel,
retriever: BaseRetriever,
) -> Runnable:
retriever_chain = create_retriever_chain(llm, retriever) | RunnableLambda(
format_docs
).with_config(run_name="FormatDocumentChunks")
_context = RunnableMap(
{
"context": retriever_chain.with_config(run_name="RetrievalChain"),
"question": RunnableLambda(itemgetter("question")).with_config(
run_name="Itemgetter:question"
),
"chat_history": RunnableLambda(itemgetter("chat_history")).with_config(
run_name="Itemgetter:chat_history"
),
}
)
prompt = ChatPromptTemplate.from_messages(
[
("system", RESPONSE_TEMPLATE),
MessagesPlaceholder(variable_name="chat_history"),
("human", "{question}"),
]
).partial(current_date=datetime.now().isoformat())
response_synthesizer = (prompt | llm | StrOutputParser()).with_config(
run_name="GenerateResponse",
)
return (
{
"question": RunnableLambda(itemgetter("question")).with_config(
run_name="Itemgetter:question"
),
"chat_history": RunnableLambda(serialize_history).with_config(
run_name="SerializeHistory"
),
}
| _context
| response_synthesizer
)
dir_path = os.path.dirname(os.path.realpath(__file__))
os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = (
dir_path + "/" + ".google_vertex_ai_credentials.json"
)
has_google_creds = os.path.isfile(os.environ["GOOGLE_APPLICATION_CREDENTIALS"])
llm = ChatOpenAI(
model="gpt-3.5-turbo-16k",
# model="gpt-4",
streaming=True,
temperature=0.1,
).configurable_alternatives(
# This gives this field an id
# When configuring the end runnable, we can then use this id to configure this field
ConfigurableField(id="llm"),
default_key="openai",
anthropic=ChatAnthropic(
model="claude-2",
max_tokens=16384,
temperature=0.1,
anthropic_api_key=os.environ.get("ANTHROPIC_API_KEY", "not_provided"),
),
)
if has_google_creds:
llm = ChatOpenAI(
model="gpt-3.5-turbo-16k",
# model="gpt-4",
streaming=True,
temperature=0.1,
).configurable_alternatives(
# This gives this field an id
# When configuring the end runnable, we can then use this id to configure this field
ConfigurableField(id="llm"),
default_key="openai",
anthropic=ChatAnthropic(
model="claude-2",
max_tokens=16384,
temperature=0.1,
anthropic_api_key=os.environ.get("ANTHROPIC_API_KEY", "not_provided"),
),
googlevertex=ChatVertexAI(
model_name="chat-bison-32k",
temperature=0.1,
max_output_tokens=8192,
stream=True,
),
)
retriever = get_retriever()
chain = create_chain(llm, retriever)
add_routes(
app, chain, path="/chat", input_type=ChatRequest, config_keys=["configurable"]
)
class SendFeedbackBody(BaseModel):
run_id: UUID
key: str = "user_score"
score: Union[float, int, bool, None] = None
feedback_id: Optional[UUID] = None
comment: Optional[str] = None
@app.post("/feedback")
async def send_feedback(body: SendFeedbackBody):
client.create_feedback(
body.run_id,
body.key,
score=body.score,
comment=body.comment,
feedback_id=body.feedback_id,
)
return {"result": "posted feedback successfully", "code": 200}
class UpdateFeedbackBody(BaseModel):
feedback_id: UUID
score: Union[float, int, bool, None] = None
comment: Optional[str] = None
@app.patch("/feedback")
async def update_feedback(body: UpdateFeedbackBody):
feedback_id = body.feedback_id
if feedback_id is None:
return {
"result": "No feedback ID provided",
"code": 400,
}
client.update_feedback(
feedback_id,
score=body.score,
comment=body.comment,
)
return {"result": "patched feedback successfully", "code": 200}
# TODO: Update when async API is available
async def _arun(func, *args, **kwargs):
return await asyncio.get_running_loop().run_in_executor(None, func, *args, **kwargs)
async def aget_trace_url(run_id: str) -> str:
for i in range(5):
try:
await _arun(client.read_run, run_id)
break
except langsmith.utils.LangSmithError:
await asyncio.sleep(1**i)
if await _arun(client.run_is_shared, run_id):
return await _arun(client.read_run_shared_link, run_id)
return await _arun(client.share_run, run_id)
class GetTraceBody(BaseModel):
run_id: UUID
@app.post("/get_trace")
async def get_trace(body: GetTraceBody):
run_id = body.run_id
if run_id is None:
return {
"result": "No LangSmith run ID provided",
"code": 400,
}
return await aget_trace_url(str(run_id))
if __name__ == "__main__":
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=8080)
|
0 | lc_public_repos | lc_public_repos/weblangchain/poetry.lock | # This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand.
[[package]]
name = "aiohttp"
version = "3.8.6"
description = "Async http client/server framework (asyncio)"
optional = false
python-versions = ">=3.6"
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]
[package.dependencies]
aiosignal = ">=1.1.2"
async-timeout = ">=4.0.0a3,<5.0"
attrs = ">=17.3.0"
charset-normalizer = ">=2.0,<4.0"
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[package.dependencies]
idna = ">=2.0"
multidict = ">=4.0"
[metadata]
lock-version = "2.0"
python-versions = "^3.10"
content-hash = "bbb16fde9abbb7e5191932ace25a3252112d75efbcebcc8f20a8e001a8f220cd"
|
0 | lc_public_repos | lc_public_repos/weblangchain/README.md | # 🦜️🌐 WebLangChain
This repo is an example of performing retrieval using the entire internet as a document store.
**Try it live:** [weblangchain.vercel.app](https://weblangchain.vercel.app)
## ✅ Running locally
By default, WebLangChain uses [Tavily](https://tavily.com) to fetch content from webpages. You can get an API key from [by signing up](https://tavily.com/).
If you'd like to add or swap in different base retrievers (e.g. if you want to use your own data source), you can update the `get_retriever()` method in `main.py`.
1. Install backend dependencies: `poetry install`.
2. Make sure to set your environment variables to configure the application:
```
export OPENAI_API_KEY=
export TAVILY_API_KEY=
# for Anthropic
# remove models from code if unused
ANTHROPIC_API_KEY=
# if you'd like to use the You.com retriever
export YDC_API_KEY=
# if you'd like to use the Google retriever
export GOOGLE_CSE_ID=
export GOOGLE_API_KEY=
# if you'd like to use the Kay.ai retriever
export KAY_API_KEY=
# for tracing
export LANGCHAIN_TRACING_V2=true
export LANGCHAIN_ENDPOINT="https://api.smith.langchain.com"
export LANGCHAIN_API_KEY=
export LANGCHAIN_PROJECT=
```
You will also need to put your Google Cloud credentials in a JSON file under `.google_vertex_ai_credentials.json` in the main directory if you would like to
use Google Vertex as an option. If you're not using Vertex, you'll need to remove `ChatVertexAI` from `main.py`.
3. Start the Python backend with `poetry run make start`.
4. Install frontend dependencies by running `cd nextjs`, then `yarn`.
5. Run the frontend with `yarn dev` for frontend.
6. Open [localhost:3000](http://localhost:3000) in your browser.
Under the hood, the chain is converted to a FastAPI server with various endpoints via [LangServe](https://github.com/langchain-ai/langserve).
This also includes a playground that you can use to interactively swap and configure various pieces of the chain.
You can find it running at `http://localhost:8080/chat/playground`.
## ☕ Running locally (JS backend)
Note that LangServe is not currently supported in JS, and customization of the retriever and model, as well as the playground, are unavailable.
1. Install frontend dependencies by running `cd nextjs`, then `yarn`.
2. Populate a `nextjs/.env.local` file with your own versions of keys from the `nextjs/.env.example` file, and set `NEXT_PUBLIC_API_BASE_URL` to `"http://localhost:3000/api"`.
3. Run the app with `yarn dev`.
4. Open [localhost:3000](http://localhost:3000) in your browser.
## ⚙️ How it works
The general retrieval flow looks like this:
1. Pull in raw content related to the user's initial query using a retriever that wraps Tavily's Search API.
- For subsequent conversation turns, we also rephrase the original query into a "standalone query" free of references to previous chat history.
2. Because the size of the raw documents usually exceed the maximum context window size of the model, we perform additional [contextual compression steps](https://python.langchain.com/docs/modules/data_connection/retrievers/contextual_compression/) to filter what we pass to the model.
- First, we split retrieved documents using a [text splitter](https://python.langchain.com/docs/modules/data_connection/document_transformers/).
- Then we use an [embeddings filter](https://python.langchain.com/docs/modules/data_connection/retrievers/contextual_compression/#embeddingsfilter) to remove any chunks that do not meet a similarity threshold with the initial query.
3. The retrieved context, the chat history, and the original question are passed to the LLM as context for the final generation.
Here's a LangSmith trace illustrating the above:
https://smith.langchain.com/public/f4493d9c-218b-404a-a890-31c15c56fff3/r
It's built using:
- [Tavily](https://tavily.com) as a retriever
- [LangChain](https://github.com/langchain-ai/langchain/) for orchestration
- [LangServe](https://github.com/langchain-ai/langserve) to directly expose LangChain runnables as endpoints
- [FastAPI](https://fastapi.tiangolo.com/)
- [Next.js](https://nextjs.org) for the frontend
## 🚀 Deployment
The live version is hosted on [Fly.dev](https://fly.dev) and [Vercel](https://vercel.com).
The backend Python logic is found in `main.py`, and the frontend Next.js app is under `nextjs/`.
|
0 | lc_public_repos | lc_public_repos/weblangchain/pyproject.toml | [tool.poetry]
name = "weblangchain"
version = "0.1.0"
description = ""
authors = ["SN <6432132+samnoyes@users.noreply.github.com>"]
readme = "README.md"
[tool.poetry.dependencies]
python = "^3.10"
openai = "^0.28.0"
fastapi = "^0.103.1"
pydantic = "1.10"
langchain = "^0.0.324"
uvicorn = "^0.23.2"
tiktoken = "^0.4.0"
tavily-python = "^0.1.9"
langserve = "0.0.19"
sse-starlette = "^1.6.5"
google-api-python-client = "^2.102.0"
html2text = "^2020.1.16"
anthropic = "^0.4.1"
kay = "^0.1.2"
google-cloud-aiplatform = "^1.35.0"
[tool.poetry.group.dev.dependencies]
black = "^23.9.1"
isort = "^5.12.0"
[build-system]
requires = ["poetry-core"]
build-backend = "poetry.core.masonry.api"
|
0 | lc_public_repos | lc_public_repos/weblangchain/vercel.json | {
"git": {
"deploymentEnabled": {
"main": false
}
}
}
|
0 | lc_public_repos | lc_public_repos/weblangchain/fly.toml | # fly.toml app configuration file generated for weblangchain on 2023-10-03T20:09:50-07:00
#
# See https://fly.io/docs/reference/configuration/ for information about how to use this file.
#
app = "weblangchain"
primary_region = "lax"
[build]
dockerfile = "Dockerfile"
[env]
PORT = "8080"
[http_service]
internal_port = 8080
force_https = true
auto_stop_machines = true
auto_start_machines = true
min_machines_running = 2
processes = ["app"]
|
0 | lc_public_repos | lc_public_repos/weblangchain/Procfile | # Modify this Procfile to fit your needs
web: uvicorn main:app --host 0.0.0.0 --port 8080 |
0 | lc_public_repos/weblangchain | lc_public_repos/weblangchain/nextjs/tsconfig.json | {
"compilerOptions": {
"target": "es5",
"lib": ["dom", "dom.iterable", "esnext"],
"allowJs": true,
"skipLibCheck": true,
"strict": true,
"forceConsistentCasingInFileNames": true,
"noEmit": true,
"esModuleInterop": true,
"module": "esnext",
"moduleResolution": "bundler",
"resolveJsonModule": true,
"isolatedModules": true,
"jsx": "preserve",
"incremental": true,
"plugins": [
{
"name": "next"
}
],
"paths": {
"@/*": ["./*"]
}
},
"include": ["next-env.d.ts", "**/*.ts", "**/*.tsx", ".next/types/**/*.ts"],
"exclude": ["node_modules"]
}
|
0 | lc_public_repos/weblangchain | lc_public_repos/weblangchain/nextjs/yarn.lock | # THIS IS AN AUTOGENERATED FILE. DO NOT EDIT THIS FILE DIRECTLY.
# yarn lockfile v1
"@aashutoshrathi/word-wrap@^1.2.3":
version "1.2.6"
resolved "https://registry.yarnpkg.com/@aashutoshrathi/word-wrap/-/word-wrap-1.2.6.tgz#bd9154aec9983f77b3a034ecaa015c2e4201f6cf"
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version "5.2.0"
resolved "https://registry.yarnpkg.com/@alloc/quick-lru/-/quick-lru-5.2.0.tgz#7bf68b20c0a350f936915fcae06f58e32007ce30"
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version "0.9.1"
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dependencies:
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"@types/node-fetch" "^2.6.4"
abort-controller "^3.0.0"
agentkeepalive "^4.2.1"
digest-fetch "^1.3.0"
form-data-encoder "1.7.2"
formdata-node "^4.3.2"
node-fetch "^2.6.7"
web-streams-polyfill "^3.2.1"
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version "7.22.13"
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dependencies:
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chalk "^2.4.2"
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axe-core@^4.6.2:
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resolved "https://registry.yarnpkg.com/axe-core/-/axe-core-4.7.2.tgz#040a7342b20765cb18bb50b628394c21bccc17a0"
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axobject-query@^3.1.1:
version "3.2.1"
resolved "https://registry.yarnpkg.com/axobject-query/-/axobject-query-3.2.1.tgz#39c378a6e3b06ca679f29138151e45b2b32da62a"
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dependencies:
dequal "^2.0.3"
babel-plugin-macros@^3.1.0:
version "3.1.0"
resolved "https://registry.yarnpkg.com/babel-plugin-macros/-/babel-plugin-macros-3.1.0.tgz#9ef6dc74deb934b4db344dc973ee851d148c50c1"
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dependencies:
"@babel/runtime" "^7.12.5"
cosmiconfig "^7.0.0"
resolve "^1.19.0"
balanced-match@^1.0.0:
version "1.0.2"
resolved "https://registry.yarnpkg.com/balanced-match/-/balanced-match-1.0.2.tgz#e83e3a7e3f300b34cb9d87f615fa0cbf357690ee"
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base-64@^0.1.0:
version "0.1.0"
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base64-js@^1.5.1:
version "1.5.1"
resolved "https://registry.yarnpkg.com/base64-js/-/base64-js-1.5.1.tgz#1b1b440160a5bf7ad40b650f095963481903930a"
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binary-extensions@^2.0.0, binary-extensions@^2.2.0:
version "2.2.0"
resolved "https://registry.yarnpkg.com/binary-extensions/-/binary-extensions-2.2.0.tgz#75f502eeaf9ffde42fc98829645be4ea76bd9e2d"
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binary-search@^1.3.5:
version "1.3.6"
resolved "https://registry.yarnpkg.com/binary-search/-/binary-search-1.3.6.tgz#e32426016a0c5092f0f3598836a1c7da3560565c"
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brace-expansion@^1.1.7:
version "1.1.11"
resolved "https://registry.yarnpkg.com/brace-expansion/-/brace-expansion-1.1.11.tgz#3c7fcbf529d87226f3d2f52b966ff5271eb441dd"
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dependencies:
balanced-match "^1.0.0"
concat-map "0.0.1"
braces@^3.0.2, braces@~3.0.2:
version "3.0.2"
resolved "https://registry.yarnpkg.com/braces/-/braces-3.0.2.tgz#3454e1a462ee8d599e236df336cd9ea4f8afe107"
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dependencies:
fill-range "^7.0.1"
browserslist@^4.21.5:
version "4.21.10"
resolved "https://registry.yarnpkg.com/browserslist/-/browserslist-4.21.10.tgz#dbbac576628c13d3b2231332cb2ec5a46e015bb0"
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dependencies:
caniuse-lite "^1.0.30001517"
electron-to-chromium "^1.4.477"
node-releases "^2.0.13"
update-browserslist-db "^1.0.11"
busboy@1.6.0:
version "1.6.0"
resolved "https://registry.yarnpkg.com/busboy/-/busboy-1.6.0.tgz#966ea36a9502e43cdb9146962523b92f531f6893"
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dependencies:
streamsearch "^1.1.0"
call-bind@^1.0.0, call-bind@^1.0.2:
version "1.0.2"
resolved "https://registry.yarnpkg.com/call-bind/-/call-bind-1.0.2.tgz#b1d4e89e688119c3c9a903ad30abb2f6a919be3c"
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dependencies:
function-bind "^1.1.1"
get-intrinsic "^1.0.2"
callsites@^3.0.0:
version "3.1.0"
resolved "https://registry.yarnpkg.com/callsites/-/callsites-3.1.0.tgz#b3630abd8943432f54b3f0519238e33cd7df2f73"
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camelcase-css@^2.0.1:
version "2.0.1"
resolved "https://registry.yarnpkg.com/camelcase-css/-/camelcase-css-2.0.1.tgz#ee978f6947914cc30c6b44741b6ed1df7f043fd5"
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camelcase@6:
version "6.3.0"
resolved "https://registry.yarnpkg.com/camelcase/-/camelcase-6.3.0.tgz#5685b95eb209ac9c0c177467778c9c84df58ba9a"
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caniuse-lite@^1.0.30001406, caniuse-lite@^1.0.30001464, caniuse-lite@^1.0.30001517:
version "1.0.30001527"
resolved "https://registry.yarnpkg.com/caniuse-lite/-/caniuse-lite-1.0.30001527.tgz#813826554828245ccee776c850566dce12bdeaba"
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chalk@^2.4.2:
version "2.4.2"
resolved "https://registry.yarnpkg.com/chalk/-/chalk-2.4.2.tgz#cd42541677a54333cf541a49108c1432b44c9424"
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dependencies:
ansi-styles "^3.2.1"
escape-string-regexp "^1.0.5"
supports-color "^5.3.0"
chalk@^4.0.0:
version "4.1.2"
resolved "https://registry.yarnpkg.com/chalk/-/chalk-4.1.2.tgz#aac4e2b7734a740867aeb16bf02aad556a1e7a01"
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dependencies:
ansi-styles "^4.1.0"
supports-color "^7.1.0"
charenc@0.0.2:
version "0.0.2"
resolved "https://registry.yarnpkg.com/charenc/-/charenc-0.0.2.tgz#c0a1d2f3a7092e03774bfa83f14c0fc5790a8667"
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chokidar@^3.5.3:
version "3.5.3"
resolved "https://registry.yarnpkg.com/chokidar/-/chokidar-3.5.3.tgz#1cf37c8707b932bd1af1ae22c0432e2acd1903bd"
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dependencies:
anymatch "~3.1.2"
braces "~3.0.2"
glob-parent "~5.1.2"
is-binary-path "~2.1.0"
is-glob "~4.0.1"
normalize-path "~3.0.0"
readdirp "~3.6.0"
optionalDependencies:
fsevents "~2.3.2"
client-only@0.0.1:
version "0.0.1"
resolved "https://registry.yarnpkg.com/client-only/-/client-only-0.0.1.tgz#38bba5d403c41ab150bff64a95c85013cf73bca1"
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clsx@^1.1.1:
version "1.2.1"
resolved "https://registry.yarnpkg.com/clsx/-/clsx-1.2.1.tgz#0ddc4a20a549b59c93a4116bb26f5294ca17dc12"
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color-convert@^1.9.0:
version "1.9.3"
resolved "https://registry.yarnpkg.com/color-convert/-/color-convert-1.9.3.tgz#bb71850690e1f136567de629d2d5471deda4c1e8"
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dependencies:
color-name "1.1.3"
color-convert@^2.0.1:
version "2.0.1"
resolved "https://registry.yarnpkg.com/color-convert/-/color-convert-2.0.1.tgz#72d3a68d598c9bdb3af2ad1e84f21d896abd4de3"
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dependencies:
color-name "~1.1.4"
color-name@1.1.3:
version "1.1.3"
resolved "https://registry.yarnpkg.com/color-name/-/color-name-1.1.3.tgz#a7d0558bd89c42f795dd42328f740831ca53bc25"
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color-name@~1.1.4:
version "1.1.4"
resolved "https://registry.yarnpkg.com/color-name/-/color-name-1.1.4.tgz#c2a09a87acbde69543de6f63fa3995c826c536a2"
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color2k@^2.0.2:
version "2.0.2"
resolved "https://registry.yarnpkg.com/color2k/-/color2k-2.0.2.tgz#ac2b4aea11c822a6bcb70c768b5a289f4fffcebb"
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combined-stream@^1.0.8:
version "1.0.8"
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dependencies:
delayed-stream "~1.0.0"
commander@^10.0.1:
version "10.0.1"
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commander@^4.0.0:
version "4.1.1"
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compute-scroll-into-view@3.0.3:
version "3.0.3"
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concat-map@0.0.1:
version "0.0.1"
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convert-source-map@^1.5.0:
version "1.9.0"
resolved "https://registry.yarnpkg.com/convert-source-map/-/convert-source-map-1.9.0.tgz#7faae62353fb4213366d0ca98358d22e8368b05f"
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copy-to-clipboard@3.3.3:
version "3.3.3"
resolved "https://registry.yarnpkg.com/copy-to-clipboard/-/copy-to-clipboard-3.3.3.tgz#55ac43a1db8ae639a4bd99511c148cdd1b83a1b0"
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dependencies:
toggle-selection "^1.0.6"
cosmiconfig@^7.0.0:
version "7.1.0"
resolved "https://registry.yarnpkg.com/cosmiconfig/-/cosmiconfig-7.1.0.tgz#1443b9afa596b670082ea46cbd8f6a62b84635f6"
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dependencies:
"@types/parse-json" "^4.0.0"
import-fresh "^3.2.1"
parse-json "^5.0.0"
path-type "^4.0.0"
yaml "^1.10.0"
cross-spawn@^7.0.2:
version "7.0.3"
resolved "https://registry.yarnpkg.com/cross-spawn/-/cross-spawn-7.0.3.tgz#f73a85b9d5d41d045551c177e2882d4ac85728a6"
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dependencies:
path-key "^3.1.0"
shebang-command "^2.0.0"
which "^2.0.1"
crypt@0.0.2:
version "0.0.2"
resolved "https://registry.yarnpkg.com/crypt/-/crypt-0.0.2.tgz#88d7ff7ec0dfb86f713dc87bbb42d044d3e6c41b"
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css-box-model@1.2.1:
version "1.2.1"
resolved "https://registry.yarnpkg.com/css-box-model/-/css-box-model-1.2.1.tgz#59951d3b81fd6b2074a62d49444415b0d2b4d7c1"
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dependencies:
tiny-invariant "^1.0.6"
cssesc@^3.0.0:
version "3.0.0"
resolved "https://registry.yarnpkg.com/cssesc/-/cssesc-3.0.0.tgz#37741919903b868565e1c09ea747445cd18983ee"
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csstype@^3.0.11, csstype@^3.0.2:
version "3.1.2"
resolved "https://registry.yarnpkg.com/csstype/-/csstype-3.1.2.tgz#1d4bf9d572f11c14031f0436e1c10bc1f571f50b"
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damerau-levenshtein@^1.0.8:
version "1.0.8"
resolved "https://registry.yarnpkg.com/damerau-levenshtein/-/damerau-levenshtein-1.0.8.tgz#b43d286ccbd36bc5b2f7ed41caf2d0aba1f8a6e7"
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debug@^3.2.7:
version "3.2.7"
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dependencies:
ms "^2.1.1"
debug@^4.1.1, debug@^4.3.2, debug@^4.3.4:
version "4.3.4"
resolved "https://registry.yarnpkg.com/debug/-/debug-4.3.4.tgz#1319f6579357f2338d3337d2cdd4914bb5dcc865"
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dependencies:
ms "2.1.2"
decamelize@1.2.0:
version "1.2.0"
resolved "https://registry.yarnpkg.com/decamelize/-/decamelize-1.2.0.tgz#f6534d15148269b20352e7bee26f501f9a191290"
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deep-is@^0.1.3:
version "0.1.4"
resolved "https://registry.yarnpkg.com/deep-is/-/deep-is-0.1.4.tgz#a6f2dce612fadd2ef1f519b73551f17e85199831"
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define-properties@^1.1.3, define-properties@^1.1.4, define-properties@^1.2.0:
version "1.2.0"
resolved "https://registry.yarnpkg.com/define-properties/-/define-properties-1.2.0.tgz#52988570670c9eacedd8064f4a990f2405849bd5"
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dependencies:
has-property-descriptors "^1.0.0"
object-keys "^1.1.1"
delayed-stream@~1.0.0:
version "1.0.0"
resolved "https://registry.yarnpkg.com/delayed-stream/-/delayed-stream-1.0.0.tgz#df3ae199acadfb7d440aaae0b29e2272b24ec619"
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dequal@^2.0.3:
version "2.0.3"
resolved "https://registry.yarnpkg.com/dequal/-/dequal-2.0.3.tgz#2644214f1997d39ed0ee0ece72335490a7ac67be"
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optionalDependencies:
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define-properties "^1.2.0"
get-intrinsic "^1.2.1"
has-symbols "^1.0.3"
reflect.getprototypeof "^1.0.3"
jiti@^1.18.2:
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js-tiktoken@^1.0.7:
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base64-js "^1.5.1"
js-tiktoken@^1.0.8:
version "1.0.8"
resolved "https://registry.yarnpkg.com/js-tiktoken/-/js-tiktoken-1.0.8.tgz#21ab8ae222e71226b2ef0d2f4b507fb10d66a114"
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base64-js "^1.5.1"
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js-yaml@^4.1.0:
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argparse "^2.0.1"
json-buffer@3.0.1:
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jsonpointer@^5.0.1:
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array-includes "^3.1.6"
array.prototype.flat "^1.3.1"
object.assign "^4.1.4"
object.values "^1.1.6"
keyv@^4.5.3:
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dependencies:
json-buffer "3.0.1"
langchain@^0.1.4:
version "0.1.4"
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binary-extensions "^2.2.0"
expr-eval "^2.0.2"
js-tiktoken "^1.0.7"
js-yaml "^4.1.0"
jsonpointer "^5.0.1"
langchainhub "~0.0.6"
langsmith "~0.0.48"
ml-distance "^4.0.0"
openapi-types "^12.1.3"
p-retry "4"
uuid "^9.0.0"
yaml "^2.2.1"
zod "^3.22.4"
zod-to-json-schema "^3.22.3"
langchainhub@~0.0.6:
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langsmith@^0.0.61, langsmith@~0.0.48:
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commander "^10.0.1"
p-queue "^6.6.2"
p-retry "4"
uuid "^9.0.0"
language-subtag-registry@~0.3.2:
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language-tags@=1.0.5:
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language-subtag-registry "~0.3.2"
levn@^0.4.1:
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prelude-ls "^1.2.1"
type-check "~0.4.0"
lilconfig@^2.0.5, lilconfig@^2.1.0:
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locate-path@^6.0.0:
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dependencies:
p-locate "^5.0.0"
lodash.merge@^4.6.2:
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lodash.mergewith@4.6.2:
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loose-envify@^1.0.0, loose-envify@^1.1.0, loose-envify@^1.4.0:
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dependencies:
js-tokens "^3.0.0 || ^4.0.0"
lru-cache@^6.0.0:
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dependencies:
yallist "^4.0.0"
marked@^7.0.2:
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md5@^2.3.0:
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charenc "0.0.2"
crypt "0.0.2"
is-buffer "~1.1.6"
merge2@^1.3.0, merge2@^1.4.1:
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micromatch@^4.0.4, micromatch@^4.0.5:
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braces "^3.0.2"
picomatch "^2.3.1"
mime-db@1.52.0:
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minimatch@^3.0.4, minimatch@^3.0.5, minimatch@^3.1.1, minimatch@^3.1.2:
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brace-expansion "^1.1.7"
minimist@^1.2.0, minimist@^1.2.6:
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ml-array-sum "^1.1.6"
ml-array-sum@^1.1.6:
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is-any-array "^2.0.0"
ml-distance-euclidean@^2.0.0:
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ml-distance-euclidean "^2.0.0"
ml-tree-similarity "^1.0.0"
ml-tree-similarity@^1.0.0:
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num-sort "^2.0.0"
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object-assign "^4.0.1"
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node-domexception@1.0.0:
version "1.0.0"
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object.entries@^1.1.6:
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object.hasown@^1.1.2:
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object.values@^1.1.6:
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once@^1.3.0:
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wrappy "1"
openai@^4.24.2:
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openapi-types@^12.1.3:
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prelude-ls "^1.2.1"
type-check "^0.4.0"
p-finally@^1.0.0:
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p-locate@^5.0.0:
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p-queue@^6.6.2:
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eventemitter3 "^4.0.4"
p-timeout "^3.2.0"
p-retry@4:
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parent-module@^1.0.0:
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which-typed-array@^1.1.10, which-typed-array@^1.1.11, which-typed-array@^1.1.9:
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resolved "https://registry.yarnpkg.com/which-typed-array/-/which-typed-array-1.1.11.tgz#99d691f23c72aab6768680805a271b69761ed61a"
integrity sha512-qe9UWWpkeG5yzZ0tNYxDmd7vo58HDBc39mZ0xWWpolAGADdFOzkfamWLDxkOWcvHQKVmdTyQdLD4NOfjLWTKew==
dependencies:
available-typed-arrays "^1.0.5"
call-bind "^1.0.2"
for-each "^0.3.3"
gopd "^1.0.1"
has-tostringtag "^1.0.0"
which@^2.0.1:
version "2.0.2"
resolved "https://registry.yarnpkg.com/which/-/which-2.0.2.tgz#7c6a8dd0a636a0327e10b59c9286eee93f3f51b1"
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dependencies:
isexe "^2.0.0"
wrappy@1:
version "1.0.2"
resolved "https://registry.yarnpkg.com/wrappy/-/wrappy-1.0.2.tgz#b5243d8f3ec1aa35f1364605bc0d1036e30ab69f"
integrity sha512-l4Sp/DRseor9wL6EvV2+TuQn63dMkPjZ/sp9XkghTEbV9KlPS1xUsZ3u7/IQO4wxtcFB4bgpQPRcR3QCvezPcQ==
yallist@^4.0.0:
version "4.0.0"
resolved "https://registry.yarnpkg.com/yallist/-/yallist-4.0.0.tgz#9bb92790d9c0effec63be73519e11a35019a3a72"
integrity sha512-3wdGidZyq5PB084XLES5TpOSRA3wjXAlIWMhum2kRcv/41Sn2emQ0dycQW4uZXLejwKvg6EsvbdlVL+FYEct7A==
yaml@^1.10.0:
version "1.10.2"
resolved "https://registry.yarnpkg.com/yaml/-/yaml-1.10.2.tgz#2301c5ffbf12b467de8da2333a459e29e7920e4b"
integrity sha512-r3vXyErRCYJ7wg28yvBY5VSoAF8ZvlcW9/BwUzEtUsjvX/DKs24dIkuwjtuprwJJHsbyUbLApepYTR1BN4uHrg==
yaml@^2.1.1, yaml@^2.2.1:
version "2.3.2"
resolved "https://registry.yarnpkg.com/yaml/-/yaml-2.3.2.tgz#f522db4313c671a0ca963a75670f1c12ea909144"
integrity sha512-N/lyzTPaJasoDmfV7YTrYCI0G/3ivm/9wdG0aHuheKowWQwGTsK0Eoiw6utmzAnI6pkJa0DUVygvp3spqqEKXg==
yocto-queue@^0.1.0:
version "0.1.0"
resolved "https://registry.yarnpkg.com/yocto-queue/-/yocto-queue-0.1.0.tgz#0294eb3dee05028d31ee1a5fa2c556a6aaf10a1b"
integrity sha512-rVksvsnNCdJ/ohGc6xgPwyN8eheCxsiLM8mxuE/t/mOVqJewPuO1miLpTHQiRgTKCLexL4MeAFVagts7HmNZ2Q==
zod-to-json-schema@3.20.3:
version "3.20.3"
resolved "https://registry.yarnpkg.com/zod-to-json-schema/-/zod-to-json-schema-3.20.3.tgz#8c95d8c20f20455ffa0b4b526c29703f35f6d787"
integrity sha512-/Q3wnyxAfCt94ZcrGiXXoiAfRqasxl9CX64LZ9fj+4dKH68zulUtU0uk1WMxQPfAxQ0ZI70dKzcoW7hHj+DwSQ==
zod-to-json-schema@^3.22.3:
version "3.22.3"
resolved "https://registry.yarnpkg.com/zod-to-json-schema/-/zod-to-json-schema-3.22.3.tgz#1c71f9fa23f80b2f3b5eed537afa8a13a66a5200"
integrity sha512-9isG8SqRe07p+Aio2ruBZmLm2Q6Sq4EqmXOiNpDxp+7f0LV6Q/LX65fs5Nn+FV/CzfF3NLBoksXbS2jNYIfpKw==
zod@3.21.4:
version "3.21.4"
resolved "https://registry.yarnpkg.com/zod/-/zod-3.21.4.tgz#10882231d992519f0a10b5dd58a38c9dabbb64db"
integrity sha512-m46AKbrzKVzOzs/DZgVnG5H55N1sv1M8qZU3A8RIKbs3mrACDNeIOeilDymVb2HdmP8uwshOCF4uJ8uM9rCqJw==
zod@^3.22.3, zod@^3.22.4:
version "3.22.4"
resolved "https://registry.yarnpkg.com/zod/-/zod-3.22.4.tgz#f31c3a9386f61b1f228af56faa9255e845cf3fff"
integrity sha512-iC+8Io04lddc+mVqQ9AZ7OQ2MrUKGN+oIQyq1vemgt46jwCwLfhq7/pwnBnNXXXZb8VTVLKwp9EDkx+ryxIWmg==
|
0 | lc_public_repos/weblangchain | lc_public_repos/weblangchain/nextjs/.eslintrc.json | {
"extends": "next/core-web-vitals"
}
|
0 | lc_public_repos/weblangchain | lc_public_repos/weblangchain/nextjs/.env.example | ## For JS backend:
# LANGCHAIN_TRACING_V2=true
# LANGCHAIN_ENDPOINT="https://api.smith.langchain.com"
# LANGCHAIN_API_KEY="YOUR_LANGSMITH_KEY"
# LANGCHAIN_PROJECT="YOUR_PROJECT_NAME"
# NEXT_PUBLIC_API_BASE_URL="http://localhost:3000/api"
# OPENAI_API_KEY="YOUR_OPENAI_API_KEY"
# TAVILY_API_KEY="YOUR_TAVILY_KEY" |
0 | lc_public_repos/weblangchain | lc_public_repos/weblangchain/nextjs/package.json | {
"name": "weblangchain",
"version": "0.1.0",
"private": true,
"packageManager": "yarn@1.22.19",
"scripts": {
"dev": "next dev",
"build": "next build",
"start": "next start",
"lint": "next lint",
"format": "prettier --write \"app\""
},
"dependencies": {
"@chakra-ui/icons": "^2.1.0",
"@chakra-ui/react": "^2.8.1",
"@emotion/react": "^11.11.1",
"@emotion/styled": "^11.11.0",
"@langchain/community": "^0.0.19",
"@langchain/openai": "^0.0.12",
"@types/dompurify": "^3.0.5",
"@types/marked": "^5.0.1",
"@types/node": "20.4.9",
"@types/react": "18.2.20",
"@types/react-dom": "18.2.7",
"autoprefixer": "10.4.14",
"dompurify": "^3.0.8",
"emojisplosion": "^2.6.1",
"eslint": "8.46.0",
"eslint-config-next": "13.4.13",
"framer-motion": "^10.16.4",
"highlight.js": "^11.8.0",
"langchain": "^0.1.4",
"langsmith": "^0.0.61",
"marked": "^7.0.2",
"next": "13.4.13",
"postcss": "8.4.27",
"react": "18.2.0",
"react-dom": "18.2.0",
"react-toastify": "^9.1.3",
"tailwindcss": "3.3.3",
"typescript": "5.1.6"
},
"devDependencies": {
"prettier": "^3.0.3"
}
}
|
0 | lc_public_repos/weblangchain | lc_public_repos/weblangchain/nextjs/tailwind.config.ts | import type { Config } from 'tailwindcss'
const config: Config = {
content: [
'./pages/**/*.{js,ts,jsx,tsx,mdx}',
'./components/**/*.{js,ts,jsx,tsx,mdx}',
'./app/**/*.{js,ts,jsx,tsx,mdx}',
],
theme: {
extend: {
backgroundImage: {
'gradient-radial': 'radial-gradient(var(--tw-gradient-stops))',
'gradient-conic':
'conic-gradient(from 180deg at 50% 50%, var(--tw-gradient-stops))',
},
},
},
plugins: [],
}
export default config
|
0 | lc_public_repos/weblangchain | lc_public_repos/weblangchain/nextjs/postcss.config.js | module.exports = {
plugins: {
tailwindcss: {},
autoprefixer: {},
},
}
|
0 | lc_public_repos/weblangchain | lc_public_repos/weblangchain/nextjs/next.config.js | /** @type {import('next').NextConfig} */
const nextConfig = {}
module.exports = nextConfig
|
0 | lc_public_repos/weblangchain/nextjs | lc_public_repos/weblangchain/nextjs/app/globals.css | @tailwind base;
@tailwind components;
@tailwind utilities;
body {
color: #f8f8f8;
background: #131318;
}
body input,
body textarea {
color: black;
}
a:hover {
text-decoration: underline;
}
p {
margin: 8px 0;
}
code {
color: #ffa500;
}
li {
padding: 4px;
}
|
0 | lc_public_repos/weblangchain/nextjs | lc_public_repos/weblangchain/nextjs/app/layout.tsx | import "./globals.css";
import type { Metadata } from "next";
import { Inter } from "next/font/google";
const inter = Inter({ subsets: ["latin"] });
export const metadata: Metadata = {
title: "WebLangChain",
description:
"Chatbot that answers queries by doing research and citing sources",
};
export default function RootLayout({
children,
}: {
children: React.ReactNode;
}) {
return (
<html lang="en" className="h-full">
<body className={`${inter.className} h-full`}>
<div className="flex flex-col h-full md:p-8 bg-zinc-900">
{children}
</div>
</body>
</html>
);
}
|
0 | lc_public_repos/weblangchain/nextjs | lc_public_repos/weblangchain/nextjs/app/page.tsx | "use client";
import { ChatWindow } from "../app/components/ChatWindow";
import { ToastContainer } from "react-toastify";
import { ChakraProvider } from "@chakra-ui/react";
export default function Home() {
return (
<ChakraProvider>
<ToastContainer />
<ChatWindow
apiBaseUrl={
process.env.NEXT_PUBLIC_API_BASE_URL ?? "http://127.0.0.1:8080"
}
titleText="WebLangChain 🦜🔗"
placeholder="Ask anything..."
></ChatWindow>
</ChakraProvider>
);
}
|
0 | lc_public_repos/weblangchain/nextjs/app/api/chat | lc_public_repos/weblangchain/nextjs/app/api/chat/stream_log/route.ts | // JS backend not used by default, see README for instructions.
import { NextRequest, NextResponse } from "next/server";
import type { BaseLanguageModel } from "@langchain/core/language_models/base";
import type { Document } from "@langchain/core/documents";
import {
RunnableSequence,
RunnableMap,
RunnableBranch,
RunnableLambda,
Runnable,
} from "@langchain/core/runnables";
import { HumanMessage, AIMessage, BaseMessage } from "@langchain/core/messages";
import { ChatOpenAI, OpenAIEmbeddings } from "@langchain/openai";
import { StringOutputParser } from "@langchain/core/output_parsers";
import {
PromptTemplate,
ChatPromptTemplate,
MessagesPlaceholder,
} from "@langchain/core/prompts";
import { RecursiveCharacterTextSplitter } from "langchain/text_splitter";
import { TavilySearchAPIRetriever } from "@langchain/community/retrievers/tavily_search_api";
import { DocumentCompressorPipeline } from "langchain/retrievers/document_compressors";
import { ContextualCompressionRetriever } from "langchain/retrievers/contextual_compression";
import { EmbeddingsFilter } from "langchain/retrievers/document_compressors/embeddings_filter";
export const runtime = "edge";
const RESPONSE_TEMPLATE = `You are an expert researcher and writer, tasked with answering any question.
Generate a comprehensive and informative, yet concise answer of 250 words or less for the
given question based solely on the provided search results (URL and content). You must
only use information from the provided search results. Use an unbiased and
journalistic tone. Combine search results together into a coherent answer. Do not
repeat text. Cite search results using [\${{number}}] notation. Only cite the most
relevant results that answer the question accurately. Place these citations at the end
of the sentence or paragraph that reference them - do not put them all at the end. If
different results refer to different entities within the same name, write separate
answers for each entity. If you want to cite multiple results for the same sentence,
format it as \`[\${{number1}}] [\${{number2}}]\`. However, you should NEVER do this with the
same number - if you want to cite \`number1\` multiple times for a sentence, only do
\`[\${{number1}}]\` not \`[\${{number1}}] [\${{number1}}]\`
You should use bullet points in your answer for readability. Put citations where they apply
rather than putting them all at the end.
If there is nothing in the context relevant to the question at hand, just say "Hmm,
I'm not sure." Don't try to make up an answer.
Anything between the following \`context\` html blocks is retrieved from a knowledge
bank, not part of the conversation with the user.
<context>
{context}
<context/>
REMEMBER: If there is no relevant information within the context, just say "Hmm, I'm
not sure." Don't try to make up an answer. Anything between the preceding 'context'
html blocks is retrieved from a knowledge bank, not part of the conversation with the
user. The current date is ${new Date().toISOString()}`;
const REPHRASE_TEMPLATE = `Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question.
Chat History:
{chat_history}
Follow Up Input: {question}
Standalone Question:`;
type RetrievalChainInput = {
chat_history: string;
question: string;
};
/**
* Override with your own retriever if desired.
*/
const getBaseRetriever = () => {
return new TavilySearchAPIRetriever({
k: 6,
includeRawContent: true,
includeImages: true,
});
};
const _getRetriever = () => {
const embeddings = new OpenAIEmbeddings({});
const splitter = new RecursiveCharacterTextSplitter({
chunkSize: 800,
chunkOverlap: 20,
});
const relevanceFilter = new EmbeddingsFilter({
embeddings,
similarityThreshold: 0.8,
});
const pipelineCompressor = new DocumentCompressorPipeline({
transformers: [splitter, relevanceFilter],
});
const baseRetriever = getBaseRetriever();
return new ContextualCompressionRetriever({
baseCompressor: pipelineCompressor,
baseRetriever,
}).withConfig({
runName: "FinalSourceRetriever",
});
};
const createRetrieverChain = (llm: BaseLanguageModel, retriever: Runnable) => {
const CONDENSE_QUESTION_PROMPT =
PromptTemplate.fromTemplate(REPHRASE_TEMPLATE);
const condenseQuestionChain = RunnableSequence.from([
CONDENSE_QUESTION_PROMPT,
llm,
new StringOutputParser(),
]).withConfig({
runName: "CondenseQuestion",
});
const hasHistoryCheckFn = RunnableLambda.from(
(input: RetrievalChainInput) => input.chat_history.length > 0,
).withConfig({ runName: "HasChatHistoryCheck" });
const conversationChain = condenseQuestionChain.pipe(retriever).withConfig({
runName: "RetrievalChainWithHistory",
});
const basicRetrievalChain = RunnableLambda.from(
(input: RetrievalChainInput) => input.question,
)
.withConfig({
runName: "Itemgetter:question",
})
.pipe(retriever)
.withConfig({ runName: "RetrievalChainWithNoHistory" });
return RunnableBranch.from([
[hasHistoryCheckFn, conversationChain],
basicRetrievalChain,
]).withConfig({
runName: "RouteDependingOnChatHistory",
});
};
const serializeHistory = (input: any) => {
const chatHistory = input.chat_history || [];
const convertedChatHistory = [];
for (const message of chatHistory) {
if (message[0] === "human") {
convertedChatHistory.push(new HumanMessage({ content: message[1] }));
} else if (message[0] === "ai") {
convertedChatHistory.push(new AIMessage({ content: message[1] }));
}
}
return convertedChatHistory;
};
const formatDocs = (docs: Document[]) => {
return docs
.map((doc, i) => `<doc id='${i}'>${doc.pageContent}</doc>`)
.join("\n");
};
const formatChatHistoryAsString = (history: BaseMessage[]) => {
return history
.map((message) => `${message._getType()}: ${message.content}`)
.join("\n");
};
const createChain = (llm: BaseLanguageModel, retriever: Runnable) => {
const retrieverChain = createRetrieverChain(llm, retriever).pipe(
RunnableLambda.from(formatDocs).withConfig({
runName: "FormatDocumentChunks",
}),
);
const context = RunnableMap.from({
context: RunnableSequence.from([
({ question, chat_history }) => ({
question,
chat_history: formatChatHistoryAsString(chat_history),
}),
retrieverChain,
]),
question: RunnableLambda.from(
(input: RetrievalChainInput) => input.question,
).withConfig({
runName: "Itemgetter:question",
}),
chat_history: RunnableLambda.from(
(input: RetrievalChainInput) => input.chat_history,
).withConfig({
runName: "Itemgetter:chat_history",
}),
}).withConfig({ tags: ["RetrieveDocs"] });
const prompt = ChatPromptTemplate.fromMessages([
["system", RESPONSE_TEMPLATE],
new MessagesPlaceholder("chat_history"),
["human", "{question}"],
]);
const responseSynthesizerChain = RunnableSequence.from([
prompt,
llm,
new StringOutputParser(),
]).withConfig({
tags: ["GenerateResponse"],
});
return RunnableSequence.from([
{
question: RunnableLambda.from(
(input: RetrievalChainInput) => input.question,
).withConfig({
runName: "Itemgetter:question",
}),
chat_history: RunnableLambda.from(serializeHistory).withConfig({
runName: "SerializeHistory",
}),
},
context,
responseSynthesizerChain,
]);
};
export async function POST(req: NextRequest) {
try {
const body = await req.json();
const input = body.input;
const config = body.config;
const llm = new ChatOpenAI({
modelName: "gpt-3.5-turbo-16k",
temperature: 0,
});
const retriever = await _getRetriever();
const answerChain = createChain(llm, retriever);
// Narrows streamed log output down to final output and the FindDocs tagged chain to
// selectively stream back sources.
const stream = await answerChain.streamLog(input, config, {
includeNames: body.include_names,
});
const encoder = new TextEncoder();
const finalStream = new ReadableStream({
async start(controller) {
for await (const chunk of stream) {
controller.enqueue(
encoder.encode(
"event: data\ndata: " + JSON.stringify(chunk) + "\n\n",
),
);
}
controller.enqueue(encoder.encode("event: end\n\n"));
controller.close();
},
});
return new Response(finalStream, {
headers: {
"content-type": "text/event-stream",
},
});
} catch (e: any) {
console.log(e);
return NextResponse.json({ error: e.message }, { status: 500 });
}
}
|
0 | lc_public_repos/weblangchain/nextjs/app/api | lc_public_repos/weblangchain/nextjs/app/api/get_trace/route.ts | // JS backend not used by default, see README for instructions.
import { NextRequest, NextResponse } from "next/server";
import { Client } from "langsmith";
export const runtime = "edge";
const client = new Client();
const pollForRun = async (runId: string, retryCount = 0): Promise<string> => {
await new Promise((resolve) =>
setTimeout(resolve, retryCount * retryCount * 100),
);
try {
await client.readRun(runId);
} catch (e) {
return pollForRun(runId, retryCount + 1);
}
try {
const sharedLink = await client.readRunSharedLink(runId);
if (!sharedLink) {
throw new Error("Run is not shared.");
}
return sharedLink;
} catch (e) {
return client.shareRun(runId);
}
};
export async function POST(req: NextRequest) {
try {
const body = await req.json();
const { run_id } = body;
if (run_id === undefined) {
return NextResponse.json(
{ error: "No run ID provided" },
{ status: 400 },
);
}
const response = await pollForRun(run_id);
return NextResponse.json(response, { status: 200 });
} catch (e: any) {
console.log(e);
return NextResponse.json({ error: e.message }, { status: 500 });
}
}
|
0 | lc_public_repos/weblangchain/nextjs/app/api | lc_public_repos/weblangchain/nextjs/app/api/feedback/route.ts | // JS backend not used by default, see README for instructions.
import { NextRequest, NextResponse } from "next/server";
import { Client } from "langsmith";
export const runtime = "edge";
const client = new Client();
export async function POST(req: NextRequest) {
try {
const body = await req.json();
const { run_id, key = "user_score", ...rest } = body;
if (!run_id) {
return NextResponse.json(
{ error: "No LangSmith run ID provided" },
{ status: 400 },
);
}
await client.createFeedback(run_id, key, rest);
return NextResponse.json(
{ result: "posted feedback successfully" },
{ status: 200 },
);
} catch (e: any) {
console.log(e);
return NextResponse.json({ error: e.message }, { status: 500 });
}
}
export async function PATCH(req: NextRequest) {
try {
const body = await req.json();
const { feedback_id, score, comment } = body;
if (feedback_id === undefined) {
return NextResponse.json(
{ error: "No feedback ID provided" },
{ status: 400 },
);
}
await client.updateFeedback(feedback_id, { score, comment });
return NextResponse.json(
{ result: "patched feedback successfully" },
{ status: 200 },
);
} catch (e: any) {
console.log(e);
return NextResponse.json({ error: e.message }, { status: 500 });
}
}
|
0 | lc_public_repos/weblangchain/nextjs/app | lc_public_repos/weblangchain/nextjs/app/components/InlineCitation.tsx | import { Source } from "./SourceBubble";
export function InlineCitation(props: {
source: Source;
sourceNumber: number;
highlighted: boolean;
onMouseEnter: () => any;
onMouseLeave: () => any;
}) {
const { source, sourceNumber, highlighted, onMouseEnter, onMouseLeave } =
props;
return (
<a
href={source.url}
target="_blank"
className={`relative bottom-1.5 text-xs border rounded px-1 ${
highlighted ? "bg-stone-500" : "bg-stone-700"
}`}
onMouseEnter={onMouseEnter}
onMouseLeave={onMouseLeave}
>
{sourceNumber}
</a>
);
}
|
0 | lc_public_repos/weblangchain/nextjs/app | lc_public_repos/weblangchain/nextjs/app/components/DefaultQuestion.tsx | import { MouseEventHandler } from "react";
export function DefaultQuestion(props: {
question: string;
onMouseUp: MouseEventHandler;
}) {
return (
<div
onMouseUp={props.onMouseUp}
className="bg-stone-700 px-2 py-1 mx-2 rounded cursor-pointer justify-center text-stone-200 hover:bg-stone-500 mb-2"
>
{props.question}
</div>
);
}
|
0 | lc_public_repos/weblangchain/nextjs/app | lc_public_repos/weblangchain/nextjs/app/components/ChatMessageBubble.tsx | import { toast } from "react-toastify";
import "react-toastify/dist/ReactToastify.css";
import { emojisplosion } from "emojisplosion";
import { useState, useRef } from "react";
import * as DOMPurify from "dompurify";
import { SourceBubble, Source } from "./SourceBubble";
import {
VStack,
Flex,
Heading,
HStack,
Box,
Button,
Divider,
Spacer,
} from "@chakra-ui/react";
import { SearchIcon, InfoOutlineIcon } from "@chakra-ui/icons";
import { InlineCitation } from "./InlineCitation";
import { v4 as uuidv4 } from "uuid";
export type Message = {
id: string;
createdAt?: Date;
content: string;
role: "system" | "user" | "assistant" | "function";
runId?: string;
sources?: Source[];
name?: string;
function_call?: { name: string };
};
export interface Feedback {
feedback_id: string;
run_id: string;
key: string;
score: number;
comment?: string;
}
const filterSources = (sources: Source[]) => {
const filtered: Source[] = [];
const urlMap = new Map<string, number>();
const indexMap = new Map<number, number>();
sources.forEach((source, i) => {
const { url } = source;
const index = urlMap.get(url);
if (index === undefined) {
urlMap.set(url, i);
indexMap.set(i, filtered.length);
filtered.push(source);
} else {
const resolvedIndex = indexMap.get(index);
if (resolvedIndex !== undefined) {
indexMap.set(i, resolvedIndex);
}
}
});
return { filtered, indexMap };
};
const createAnswerElements = (
content: string,
filteredSources: Source[],
sourceIndexMap: Map<number, number>,
highlighedSourceLinkStates: boolean[],
setHighlightedSourceLinkStates: React.Dispatch<
React.SetStateAction<boolean[]>
>,
) => {
const matches = Array.from(content.matchAll(/\[\^?(\d+)\^?\]/g));
const elements: JSX.Element[] = [];
let prevCitationEndIndex = 0;
let adjacentCitations: number[] = [];
matches.forEach((match) => {
const sourceNum = parseInt(match[1], 10);
const resolvedNum = sourceIndexMap.get(sourceNum) ?? 10;
// Allow for one space between adjacent citations
if (
prevCitationEndIndex !== match.index &&
prevCitationEndIndex + 1 !== match.index
) {
adjacentCitations = [];
}
if (match.index !== null && resolvedNum < filteredSources.length) {
if (!adjacentCitations.includes(resolvedNum)) {
elements.push(
<span
key={`content:${prevCitationEndIndex}`}
dangerouslySetInnerHTML={{
__html: DOMPurify.sanitize(
content.slice(prevCitationEndIndex, match.index),
),
}}
></span>,
);
elements.push(
<span key={`span:${prevCitationEndIndex}`}>
<InlineCitation
key={`citation:${prevCitationEndIndex}`}
source={filteredSources[resolvedNum]}
sourceNumber={resolvedNum}
highlighted={highlighedSourceLinkStates[resolvedNum]}
onMouseEnter={() =>
setHighlightedSourceLinkStates(
filteredSources.map((_, i) => i === resolvedNum),
)
}
onMouseLeave={() =>
setHighlightedSourceLinkStates(filteredSources.map(() => false))
}
/>
</span>,
);
adjacentCitations.push(resolvedNum);
}
prevCitationEndIndex = (match.index ?? 0) + match[0].length;
}
});
elements.push(
<span
key={`content:${prevCitationEndIndex}`}
dangerouslySetInnerHTML={{
__html: DOMPurify.sanitize(content.slice(prevCitationEndIndex)),
}}
></span>,
);
return elements;
};
export function ChatMessageBubble(props: {
message: Message;
aiEmoji?: string;
isMostRecent: boolean;
messageCompleted: boolean;
apiBaseUrl: string;
}) {
const { role, content, runId } = props.message;
const isUser = role === "user";
const [isLoading, setIsLoading] = useState(false);
const [traceIsLoading, setTraceIsLoading] = useState(false);
const [feedback, setFeedback] = useState<Feedback | null>(null);
const [comment, setComment] = useState("");
const [feedbackColor, setFeedbackColor] = useState("");
const upButtonRef = useRef(null);
const downButtonRef = useRef(null);
const cumulativeOffset = function (element: HTMLElement | null) {
var top = 0,
left = 0;
do {
top += element?.offsetTop || 0;
left += element?.offsetLeft || 0;
element = (element?.offsetParent as HTMLElement) || null;
} while (element);
return {
top: top,
left: left,
};
};
const sendFeedback = async (score: number, key: string) => {
let run_id = runId;
if (run_id === undefined) {
return;
}
if (isLoading) {
return;
}
setIsLoading(true);
let apiBaseUrl = props.apiBaseUrl;
let feedback_id = feedback?.feedback_id ?? uuidv4();
try {
const response = await fetch(apiBaseUrl + "/feedback", {
method: feedback?.feedback_id ? "PATCH" : "POST",
headers: {
"Content-Type": "application/json",
},
body: JSON.stringify({
score,
run_id,
key,
feedback_id,
comment,
}),
});
const data = await response.json();
if (data.code === 200) {
setFeedback({ run_id, score, key, feedback_id });
score == 1 ? animateButton("upButton") : animateButton("downButton");
if (comment) {
setComment("");
}
}
} catch (e: any) {
console.error("Error:", e);
toast.error(e.message);
}
setIsLoading(false);
};
const viewTrace = async () => {
try {
setTraceIsLoading(true);
let apiBaseUrl = props.apiBaseUrl;
const response = await fetch(apiBaseUrl + "/get_trace", {
method: "POST",
headers: {
"Content-Type": "application/json",
},
body: JSON.stringify({
run_id: runId,
}),
});
const data = await response.json();
if (data.code === 400) {
toast.error("Unable to view trace");
throw new Error("Unable to view trace");
} else {
console.log(data);
const url = data.replace(/['"]+/g, "");
window.open(url, "_blank");
setTraceIsLoading(false);
}
} catch (e: any) {
console.error("Error:", e);
setTraceIsLoading(false);
toast.error(e.message);
}
};
const sources = props.message.sources ?? [];
const { filtered: filteredSources, indexMap: sourceIndexMap } =
filterSources(sources);
// Use an array of highlighted states as a state since React
// complains when creating states in a loop
const [highlighedSourceLinkStates, setHighlightedSourceLinkStates] = useState(
filteredSources.map(() => false),
);
const answerElements =
role === "assistant"
? createAnswerElements(
content,
filteredSources,
sourceIndexMap,
highlighedSourceLinkStates,
setHighlightedSourceLinkStates,
)
: [];
const imageUrls = filteredSources[0]?.images ?? [];
const imageElements = imageUrls.map((imageUrl) => (
<img
key={`image:${imageUrl}`}
src={imageUrl}
className="block h-full mr-2"
></img>
));
const animateButton = (buttonId: string) => {
let button: HTMLButtonElement | null;
if (buttonId === "upButton") {
button = upButtonRef.current;
} else if (buttonId === "downButton") {
button = downButtonRef.current;
} else {
return;
}
if (!button) return;
let resolvedButton = button as HTMLButtonElement;
resolvedButton.classList.add("animate-ping");
setTimeout(() => {
resolvedButton.classList.remove("animate-ping");
}, 500);
emojisplosion({
emojiCount: 10,
uniqueness: 1,
position() {
const offset = cumulativeOffset(button);
return {
x: offset.left + resolvedButton.clientWidth / 2,
y: offset.top + resolvedButton.clientHeight / 2,
};
},
emojis: buttonId === "upButton" ? ["👍"] : ["👎"],
});
};
return (
<VStack align="start" spacing={5} pb={5}>
{!isUser && filteredSources.length > 0 && (
<>
<Flex direction={"column"} width={"100%"}>
<VStack spacing={"5px"} align={"start"} width={"100%"}>
<Heading
fontSize="lg"
fontWeight={"medium"}
mb={1}
color={"blue.100"}
paddingBottom={"12px"}
className="flex items-center"
>
<SearchIcon className="mr-1" />
Sources
</Heading>
<HStack spacing={"10px"} maxWidth={"100%"} overflow={"auto"}>
{filteredSources.map((source, index) => (
<Box key={index} alignSelf={"stretch"} width={60}>
<SourceBubble
source={source}
highlighted={highlighedSourceLinkStates[index]}
index={index}
onMouseEnter={() =>
setHighlightedSourceLinkStates(
filteredSources.map((_, i) => i === index),
)
}
onMouseLeave={() =>
setHighlightedSourceLinkStates(
filteredSources.map(() => false),
)
}
/>
</Box>
))}
</HStack>
</VStack>
</Flex>
<Heading
size="lg"
fontWeight="medium"
color="blue.100"
className="flex items-center"
>
<InfoOutlineIcon className="mr-1" /> Answer
</Heading>
</>
)}
{isUser ? (
<Heading size="lg" fontWeight="medium" color="white">
{content}
</Heading>
) : (
<>
<Box className="whitespace-pre-wrap" color="white">
{answerElements}
</Box>
{imageUrls.length && props.messageCompleted ? (
<Flex className="w-full max-w-full flex h-[196px] overflow-auto">
{imageElements}
</Flex>
) : (
""
)}
</>
)}
{props.message.role !== "user" &&
props.isMostRecent &&
props.messageCompleted && (
<HStack spacing={2}>
<Button
ref={upButtonRef}
size="sm"
variant="outline"
colorScheme={feedback === null ? "green" : "gray"}
onClick={() => {
if (feedback === null && props.message.runId) {
sendFeedback(1, "user_score");
animateButton("upButton");
setFeedbackColor("border-4 border-green-300");
} else {
toast.error("You have already provided your feedback.");
}
}}
>
👍
</Button>
<Button
ref={downButtonRef}
size="sm"
variant="outline"
colorScheme={feedback === null ? "red" : "gray"}
onClick={() => {
if (feedback === null && props.message.runId) {
sendFeedback(0, "user_score");
animateButton("downButton");
setFeedbackColor("border-4 border-red-300");
} else {
toast.error("You have already provided your feedback.");
}
}}
>
👎
</Button>
<Spacer />
<Button
size="sm"
variant="outline"
colorScheme={runId === null ? "blue" : "gray"}
onClick={(e) => {
e.preventDefault();
viewTrace();
}}
isLoading={traceIsLoading}
loadingText="🔄"
color={"white"}
>
🦜🛠️ View trace
</Button>
</HStack>
)}
{!isUser && <Divider mt={4} mb={4} />}
</VStack>
);
}
|
0 | lc_public_repos/weblangchain/nextjs/app | lc_public_repos/weblangchain/nextjs/app/components/ChatWindow.tsx | "use client";
import React, { useRef, useState } from "react";
import { useSearchParams } from "next/navigation";
import { v4 as uuidv4 } from "uuid";
import { RemoteRunnable } from "langchain/runnables/remote";
import { applyPatch } from "@langchain/core/utils/json_patch";
import { ChatMessageBubble, Message } from "./ChatMessageBubble";
import { marked } from "marked";
import { Renderer } from "marked";
import hljs from "highlight.js";
import "highlight.js/styles/gradient-dark.css";
import { toast } from "react-toastify";
import "react-toastify/dist/ReactToastify.css";
import {
Heading,
IconButton,
Input,
InputGroup,
InputRightElement,
Spinner,
Select,
} from "@chakra-ui/react";
import { ArrowUpIcon } from "@chakra-ui/icons";
import { Source } from "./SourceBubble";
import { DefaultQuestion } from "./DefaultQuestion";
type RetrieverName = "tavily" | "kay" | "you" | "google" | "kay_press_release";
export function ChatWindow(props: {
apiBaseUrl: string;
placeholder?: string;
titleText?: string;
}) {
const searchParams = useSearchParams();
const conversationId = uuidv4();
const messageContainerRef = useRef<HTMLDivElement | null>(null);
const [messages, setMessages] = useState<Array<Message>>([]);
const [input, setInput] = useState("");
const [isLoading, setIsLoading] = useState(false);
const [retriever, setRetriever] = useState<RetrieverName>(
(searchParams.get("retriever") as RetrieverName) ?? "tavily",
);
const [llm, setLlm] = useState(searchParams.get("llm") ?? "openai");
const [chatHistory, setChatHistory] = useState<[string, string][]>([]);
const { apiBaseUrl, titleText } = props;
const sendMessage = async (message?: string) => {
if (messageContainerRef.current) {
messageContainerRef.current.classList.add("grow");
}
if (isLoading) {
return;
}
const messageValue = message ?? input;
if (messageValue === "") return;
setInput("");
setMessages((prevMessages) => [
...prevMessages,
{ id: Math.random().toString(), content: messageValue, role: "user" },
]);
setIsLoading(true);
let accumulatedMessage = "";
let runId: string | undefined = undefined;
let sources: Source[] | undefined = undefined;
let messageIndex: number | null = null;
let renderer = new Renderer();
renderer.paragraph = (text) => {
return text + "\n";
};
renderer.list = (text) => {
return `${text}\n\n`;
};
renderer.listitem = (text) => {
return `\n• ${text}`;
};
renderer.code = (code, language) => {
const validLanguage = hljs.getLanguage(language || "")
? language
: "plaintext";
const highlightedCode = hljs.highlight(
validLanguage || "plaintext",
code,
).value;
return `<pre class="highlight bg-gray-700" style="padding: 5px; border-radius: 5px; overflow: auto; overflow-wrap: anywhere; white-space: pre-wrap; max-width: 100%; display: block; line-height: 1.2"><code class="${language}" style="color: #d6e2ef; font-size: 12px; ">${highlightedCode}</code></pre>`;
};
marked.setOptions({ renderer });
try {
const sourceStepName = "FinalSourceRetriever";
let streamedResponse: Record<string, any> = {};
const remoteChain = new RemoteRunnable({
url: apiBaseUrl + "/chat",
options: {
timeout: 60000,
},
});
const logStream = await remoteChain.streamLog(
{
question: messageValue,
chat_history: chatHistory,
},
{
configurable: {
retriever,
llm,
},
metadata: {
conversation_id: conversationId,
},
},
{
includeNames: [sourceStepName],
},
);
for await (const chunk of logStream) {
streamedResponse = applyPatch(streamedResponse, chunk.ops).newDocument;
if (
Array.isArray(
streamedResponse?.logs?.[sourceStepName]?.final_output?.documents,
)
) {
sources = streamedResponse.logs[
sourceStepName
].final_output.documents.map((doc: Record<string, any>) => ({
url: doc.metadata.source ?? doc.metadata.data_source_link,
defaultSourceUrl: retriever === "you" ? "https://you.com" : "",
title: doc.metadata.title,
images: doc.metadata.images,
}));
}
if (streamedResponse.id !== undefined) {
runId = streamedResponse.id;
}
if (Array.isArray(streamedResponse?.streamed_output)) {
accumulatedMessage = streamedResponse.streamed_output.join("");
}
const parsedResult = marked.parse(accumulatedMessage);
setMessages((prevMessages) => {
let newMessages = [...prevMessages];
if (messageIndex === null) {
messageIndex = newMessages.length;
newMessages.push({
id: Math.random().toString(),
content: parsedResult.trim(),
runId: runId,
sources: sources,
role: "assistant",
});
} else {
newMessages[messageIndex].content = parsedResult.trim();
newMessages[messageIndex].runId = runId;
newMessages[messageIndex].sources = sources;
}
return newMessages;
});
}
setChatHistory((prevChatHistory) => [
...prevChatHistory,
["human", messageValue],
["ai", accumulatedMessage],
]);
setIsLoading(false);
} catch (e: any) {
setMessages((prevMessages) => prevMessages.slice(0, -1));
setIsLoading(false);
setInput(messageValue);
toast.error(e.message);
}
};
const defaultQuestions = [
"what is langchain?",
"history of mesopotamia",
"how does a prism separate light",
"leonardo dicaprio girlfriend",
"fun gift ideas for software engineers",
"how do you build a discord bot",
"what bear is best",
];
const DEFAULT_QUESTIONS: Record<RetrieverName, string[]> = {
tavily: defaultQuestions,
you: defaultQuestions,
google: defaultQuestions,
kay: [
"Is Johnson & Johnson increasing its marketing budget?",
"How is Lululemon adapting to new customer trends?",
"Which industries are growing in recent 10-Q reports?",
"Who are Etsy’s competitors?",
"Which companies reported data breaches?",
"What were the biggest strategy changes made by Roku in 2023?",
],
kay_press_release: [
"How is the healthcare industry adopting generative AI tools?",
"What were the major technological advancements in the renewable energy sector in 2023?",
"What happened to Intel's acquisition of Tower Semiconductor?",
"What were the biggest strategy changes made by Roku in 2023?",
],
};
const sendInitialQuestion = async (question: string) => {
await sendMessage(question);
};
const insertUrlParam = (key: string, value?: string) => {
if (window.history.pushState) {
const searchParams = new URLSearchParams(window.location.search);
searchParams.set(key, value ?? "");
const newurl =
window.location.protocol +
"//" +
window.location.host +
window.location.pathname +
"?" +
searchParams.toString();
window.history.pushState({ path: newurl }, "", newurl);
}
};
return (
<div
className={
"flex flex-col items-center p-8 rounded grow max-h-full h-full" +
(messages.length === 0 ? " justify-center mb-32" : "")
}
>
<div className="flex flex-col items-center pb-8 w-full">
<Heading
fontSize={messages.length > 0 ? "2xl" : "4xl"}
fontWeight={"medium"}
mb={1}
color={"white"}
>
{titleText}
</Heading>
<Heading
fontSize={messages.length === 0 ? "xl" : "lg"}
fontWeight={"normal"}
mb={1}
color={"white"}
marginTop={messages.length === 0 ? "12px" : ""}
>
{messages.length > 0
? "We appreciate feedback!"
: "Ask me anything about anything!"}
</Heading>
<div className="text-white flex flex-wrap items-center mt-4">
<div className="flex items-center mb-2">
<span className="shrink-0 mr-2">Powered by</span>
<Select
value={retriever}
onChange={(e) => {
insertUrlParam("retriever", e.target.value);
setRetriever(e.target.value as RetrieverName);
}}
width={"212px"}
>
<option value="tavily">Tavily</option>
<option value="kay">Kay.ai SEC Filings</option>
<option value="kay_press_release">Kay.ai Press Releases</option>
<option value="you">You.com</option>
<option value="google">Google</option>
</Select>
</div>
<div className="flex items-center mb-2">
<span className="shrink-0 ml-2 mr-2">and</span>
<Select
value={llm}
onChange={(e) => {
insertUrlParam("llm", e.target.value);
setLlm(e.target.value);
}}
width={"212px"}
>
<option value="openai">GPT-3.5-Turbo</option>
<option value="anthropic">Claude-2</option>
<option value="googlevertex">Google Vertex AI</option>
</Select>
</div>
</div>
</div>
<div
className="flex flex-col-reverse w-full mb-2 overflow-auto"
ref={messageContainerRef}
>
{messages.length > 0
? [...messages]
.reverse()
.map((m, index) => (
<ChatMessageBubble
key={m.id}
message={{ ...m }}
aiEmoji="🦜"
apiBaseUrl={apiBaseUrl}
isMostRecent={index === 0}
messageCompleted={!isLoading}
></ChatMessageBubble>
))
: ""}
</div>
<InputGroup size="md" alignItems={"center"}>
<Input
value={input}
height={"55px"}
rounded={"full"}
type={"text"}
placeholder="Ask anything..."
textColor={"white"}
borderColor={"rgb(58, 58, 61)"}
onSubmit={(e) => {
e.preventDefault();
sendMessage();
}}
onChange={(e) => setInput(e.target.value)}
onKeyDown={(e) => {
if (e.key === "Enter" && !e.shiftKey) {
e.preventDefault();
sendMessage();
}
}}
/>
<InputRightElement h="full" paddingRight={"15px"}>
<IconButton
colorScheme="blue"
rounded={"full"}
aria-label="Send"
icon={isLoading ? <Spinner /> : <ArrowUpIcon />}
type="submit"
onClick={(e) => {
e.preventDefault();
sendMessage();
}}
/>
</InputRightElement>
</InputGroup>
{messages.length === 0 ? (
<div className="w-full text-center flex flex-col">
<div className="flex grow justify-center w-full mt-4 flex-wrap">
{DEFAULT_QUESTIONS[retriever]
.slice(0, 4)
.map((defaultQuestion, i) => {
return (
<DefaultQuestion
key={`defaultquestion:${i}`}
question={defaultQuestion}
onMouseUp={(e) =>
sendInitialQuestion(
(e.target as HTMLDivElement).innerText,
)
}
></DefaultQuestion>
);
})}
</div>
<div className="grow justify-center w-full mt-2 hidden md:flex">
{DEFAULT_QUESTIONS[retriever].slice(4).map((defaultQuestion, i) => {
return (
<DefaultQuestion
key={`defaultquestion:${i + 4}`}
question={defaultQuestion}
onMouseUp={(e) =>
sendInitialQuestion((e.target as HTMLDivElement).innerText)
}
></DefaultQuestion>
);
})}
</div>
</div>
) : (
""
)}
{messages.length === 0 ? (
<footer className="flex justify-center absolute bottom-8">
<a
href="https://github.com/langchain-ai/weblangchain"
target="_blank"
className="text-white flex items-center"
>
<img src="/images/github-mark.svg" className="h-4 mr-1" />
<span>View Source</span>
</a>
<a
href={`${
process.env.NEXT_PUBLIC_API_BASE_URL ?? "http://localhost:8080"
}/chat/playground`}
target="_blank"
className="text-white flex items-center ml-8"
>
<span>Open Playground</span>
</a>
</footer>
) : (
""
)}
</div>
);
}
|
0 | lc_public_repos/weblangchain/nextjs/app | lc_public_repos/weblangchain/nextjs/app/components/SourceBubble.tsx | import "react-toastify/dist/ReactToastify.css";
import { emojisplosion } from "emojisplosion";
export type Source = {
url: string;
title: string;
images: string[];
defaultSourceUrl?: string;
};
export function SourceBubble(props: {
source: Source;
highlighted: boolean;
index: number;
onMouseEnter: () => any;
onMouseLeave: () => any;
}) {
const hostname = new URL(
props.source.url ?? props.source.defaultSourceUrl,
).hostname.replace("www.", "");
return (
<a
href={props.source.url ?? props.source.defaultSourceUrl}
target="_blank"
onMouseEnter={props.onMouseEnter}
onMouseLeave={props.onMouseLeave}
className="hover:no-underline"
>
<div
className={`${
props.highlighted ? "bg-stone-500" : "bg-stone-700"
} rounded p-4 text-white h-full text-xs flex flex-col mb-4`}
>
<div className="line-clamp-4">{props.source.title}</div>
<div className="text-white mt-auto">
{hostname} [{props.index}]
</div>
</div>
</a>
);
}
|
0 | lc_public_repos/weblangchain/nextjs/public | lc_public_repos/weblangchain/nextjs/public/images/github-mark.svg | <svg width="98" height="96" xmlns="http://www.w3.org/2000/svg"><path fill-rule="evenodd" clip-rule="evenodd" d="M48.854 0C21.839 0 0 22 0 49.217c0 21.756 13.993 40.172 33.405 46.69 2.427.49 3.316-1.059 3.316-2.362 0-1.141-.08-5.052-.08-9.127-13.59 2.934-16.42-5.867-16.42-5.867-2.184-5.704-5.42-7.17-5.42-7.17-4.448-3.015.324-3.015.324-3.015 4.934.326 7.523 5.052 7.523 5.052 4.367 7.496 11.404 5.378 14.235 4.074.404-3.178 1.699-5.378 3.074-6.6-10.839-1.141-22.243-5.378-22.243-24.283 0-5.378 1.94-9.778 5.014-13.2-.485-1.222-2.184-6.275.486-13.038 0 0 4.125-1.304 13.426 5.052a46.97 46.97 0 0 1 12.214-1.63c4.125 0 8.33.571 12.213 1.63 9.302-6.356 13.427-5.052 13.427-5.052 2.67 6.763.97 11.816.485 13.038 3.155 3.422 5.015 7.822 5.015 13.2 0 18.905-11.404 23.06-22.324 24.283 1.78 1.548 3.316 4.481 3.316 9.126 0 6.6-.08 11.897-.08 13.526 0 1.304.89 2.853 3.316 2.364 19.412-6.52 33.405-24.935 33.405-46.691C97.707 22 75.788 0 48.854 0z" fill="#ffffff"/></svg> |
0 | lc_public_repos | lc_public_repos/web-explorer/requirements.txt | streamlit==1.25.0
langchain==0.0.244
chromadb==0.4.3
openai==0.27.8
html2text==2020.1.16
google-api-core==2.11.1
google-api-python-client==2.95.0
google-auth==2.22.0
google-auth-httplib2==0.1.0
googleapis-common-protos==1.59.1
tiktoken==0.4.0
faiss-cpu==1.7.4 |
0 | lc_public_repos | lc_public_repos/web-explorer/README.md | # Web Explorer
This is a lightweight app using the [Web Research Retriever](https://github.com/langchain-ai/langchain/pull/8102).
## Setup
You only need to supply a few things.
In `settings()` function, supply:
* Search: Select the search tool you want to use (e.g., GoogleSearchAPIWrapper).
* Vectorstore: Select the vectorstore and embeddings you want to use (e.g., Chroma, OpenAIEmbeddings).
* Select the LLM you want to use (e.g., ChatOpenAI).
To use `st.secrets` set enviorment variables in `.streamlit/secrets.toml` file.
Or, simply add environemnt variables and remove `st.secrets`:
```
import os
os.environ["GOOGLE_API_KEY"] = "YOUR_API_KEY"
os.environ["GOOGLE_CSE_ID"] = "YOUR_CSE_ID"
os.environ["OPENAI_API_BASE"] = "https://api.openai.com/v1"
os.environ["OPENAI_API_KEY"] = "YOUR_API_KEY"
```
For `GOOGLE_API_KEY` , you could get it from [this link](https://console.cloud.google.com/apis/api/customsearch.googleapis.com/credentials).
For `GOOGLE_CSE_ID` , you could get it from [this link](https://programmablesearchengine.google.com/)
## Run
```
streamlit run web_explorer.py
```
Example output:
 |
0 | lc_public_repos | lc_public_repos/web-explorer/web_explorer.py | import streamlit as st
from langchain.callbacks.base import BaseCallbackHandler
from langchain.chains import RetrievalQAWithSourcesChain
from langchain.retrievers.web_research import WebResearchRetriever
import os
os.environ["GOOGLE_API_KEY"] = "YOUR_API_KEY" # Get it at https://console.cloud.google.com/apis/api/customsearch.googleapis.com/credentials
os.environ["GOOGLE_CSE_ID"] = "YOUR_CSE_ID" # Get it at https://programmablesearchengine.google.com/
os.environ["OPENAI_API_BASE"] = "https://api.openai.com/v1"
os.environ["OPENAI_API_KEY"] = "YOUR_API_KEY" # Get it at https://beta.openai.com/account/api-keys
st.set_page_config(page_title="Interweb Explorer", page_icon="🌐")
def settings():
# Vectorstore
import faiss
from langchain.vectorstores import FAISS
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.docstore import InMemoryDocstore
embeddings_model = OpenAIEmbeddings()
embedding_size = 1536
index = faiss.IndexFlatL2(embedding_size)
vectorstore_public = FAISS(embeddings_model.embed_query, index, InMemoryDocstore({}), {})
# LLM
from langchain.chat_models import ChatOpenAI
llm = ChatOpenAI(model_name="gpt-3.5-turbo-16k", temperature=0, streaming=True)
# Search
from langchain.utilities import GoogleSearchAPIWrapper
search = GoogleSearchAPIWrapper()
# Initialize
web_retriever = WebResearchRetriever.from_llm(
vectorstore=vectorstore_public,
llm=llm,
search=search,
num_search_results=3
)
return web_retriever, llm
class StreamHandler(BaseCallbackHandler):
def __init__(self, container, initial_text=""):
self.container = container
self.text = initial_text
def on_llm_new_token(self, token: str, **kwargs) -> None:
self.text += token
self.container.info(self.text)
class PrintRetrievalHandler(BaseCallbackHandler):
def __init__(self, container):
self.container = container.expander("Context Retrieval")
def on_retriever_start(self, query: str, **kwargs):
self.container.write(f"**Question:** {query}")
def on_retriever_end(self, documents, **kwargs):
# self.container.write(documents)
for idx, doc in enumerate(documents):
source = doc.metadata["source"]
self.container.write(f"**Results from {source}**")
self.container.text(doc.page_content)
st.sidebar.image("img/ai.png")
st.header("`Interweb Explorer`")
st.info("`I am an AI that can answer questions by exploring, reading, and summarizing web pages."
"I can be configured to use different modes: public API or private (no data sharing).`")
# Make retriever and llm
if 'retriever' not in st.session_state:
st.session_state['retriever'], st.session_state['llm'] = settings()
web_retriever = st.session_state.retriever
llm = st.session_state.llm
# User input
question = st.text_input("`Ask a question:`")
if question:
# Generate answer (w/ citations)
import logging
logging.basicConfig()
logging.getLogger("langchain.retrievers.web_research").setLevel(logging.INFO)
qa_chain = RetrievalQAWithSourcesChain.from_chain_type(llm, retriever=web_retriever)
# Write answer and sources
retrieval_streamer_cb = PrintRetrievalHandler(st.container())
answer = st.empty()
stream_handler = StreamHandler(answer, initial_text="`Answer:`\n\n")
result = qa_chain({"question": question},callbacks=[retrieval_streamer_cb, stream_handler])
answer.info('`Answer:`\n\n' + result['answer'])
st.info('`Sources:`\n\n' + result['sources'])
|
0 | lc_public_repos | lc_public_repos/open-canvas/postcss.config.mjs | /** @type {import('postcss-load-config').Config} */
const config = {
plugins: {
tailwindcss: {},
},
};
export default config;
|
0 | lc_public_repos | lc_public_repos/open-canvas/components.json | {
"$schema": "https://ui.shadcn.com/schema.json",
"style": "new-york",
"rsc": true,
"tsx": true,
"tailwind": {
"config": "tailwind.config.ts",
"css": "src/app/globals.css",
"baseColor": "neutral",
"cssVariables": true,
"prefix": ""
},
"aliases": {
"components": "@/components",
"utils": "@/lib/utils",
"ui": "@/components/ui",
"lib": "@/lib",
"hooks": "@/hooks"
}
} |
0 | lc_public_repos | lc_public_repos/open-canvas/tsconfig.json | {
"compilerOptions": {
"lib": ["dom", "dom.iterable", "esnext"],
"allowJs": true,
"skipLibCheck": true,
"strict": true,
"noEmit": true,
"esModuleInterop": true,
"module": "esnext",
"moduleResolution": "bundler",
"resolveJsonModule": true,
"isolatedModules": true,
"jsx": "preserve",
"incremental": true,
"plugins": [
{
"name": "next"
}
],
"paths": {
"@/*": ["./src/*"]
}
},
"include": ["next-env.d.ts", "**/*.ts", "**/*.tsx", ".next/types/**/*.ts"],
"exclude": ["node_modules"]
}
|
0 | lc_public_repos | lc_public_repos/open-canvas/LICENSE | MIT License
Copyright (c) LangChain, Inc.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE. |
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array.prototype.findlast@^1.2.5:
version "1.2.5"
resolved "https://registry.yarnpkg.com/array.prototype.findlast/-/array.prototype.findlast-1.2.5.tgz#3e4fbcb30a15a7f5bf64cf2faae22d139c2e4904"
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call-bind "^1.0.7"
define-properties "^1.2.1"
es-abstract "^1.23.2"
es-errors "^1.3.0"
es-object-atoms "^1.0.0"
es-shim-unscopables "^1.0.2"
array.prototype.findlastindex@^1.2.5:
version "1.2.5"
resolved "https://registry.yarnpkg.com/array.prototype.findlastindex/-/array.prototype.findlastindex-1.2.5.tgz#8c35a755c72908719453f87145ca011e39334d0d"
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call-bind "^1.0.7"
define-properties "^1.2.1"
es-abstract "^1.23.2"
es-errors "^1.3.0"
es-object-atoms "^1.0.0"
es-shim-unscopables "^1.0.2"
array.prototype.flat@^1.3.1, array.prototype.flat@^1.3.2:
version "1.3.2"
resolved "https://registry.yarnpkg.com/array.prototype.flat/-/array.prototype.flat-1.3.2.tgz#1476217df8cff17d72ee8f3ba06738db5b387d18"
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call-bind "^1.0.2"
define-properties "^1.2.0"
es-abstract "^1.22.1"
es-shim-unscopables "^1.0.0"
array.prototype.flatmap@^1.3.2:
version "1.3.2"
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define-properties "^1.2.0"
es-abstract "^1.22.1"
es-shim-unscopables "^1.0.0"
array.prototype.tosorted@^1.1.4:
version "1.1.4"
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define-properties "^1.2.1"
es-abstract "^1.23.3"
es-errors "^1.3.0"
es-shim-unscopables "^1.0.2"
arraybuffer.prototype.slice@^1.0.3:
version "1.0.3"
resolved "https://registry.yarnpkg.com/arraybuffer.prototype.slice/-/arraybuffer.prototype.slice-1.0.3.tgz#097972f4255e41bc3425e37dc3f6421cf9aefde6"
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array-buffer-byte-length "^1.0.1"
call-bind "^1.0.5"
define-properties "^1.2.1"
es-abstract "^1.22.3"
es-errors "^1.2.1"
get-intrinsic "^1.2.3"
is-array-buffer "^3.0.4"
is-shared-array-buffer "^1.0.2"
asap@~2.0.3:
version "2.0.6"
resolved "https://registry.yarnpkg.com/asap/-/asap-2.0.6.tgz#e50347611d7e690943208bbdafebcbc2fb866d46"
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ast-types-flow@^0.0.8:
version "0.0.8"
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asynckit@^0.4.0:
version "0.4.0"
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autoprefixer@^10.4.19:
version "10.4.20"
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dependencies:
browserslist "^4.23.3"
caniuse-lite "^1.0.30001646"
fraction.js "^4.3.7"
normalize-range "^0.1.2"
picocolors "^1.0.1"
postcss-value-parser "^4.2.0"
available-typed-arrays@^1.0.7:
version "1.0.7"
resolved "https://registry.yarnpkg.com/available-typed-arrays/-/available-typed-arrays-1.0.7.tgz#a5cc375d6a03c2efc87a553f3e0b1522def14846"
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dependencies:
possible-typed-array-names "^1.0.0"
axe-core@^4.9.1:
version "4.10.0"
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axobject-query@~3.1.1:
version "3.1.1"
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deep-equal "^2.0.5"
bail@^2.0.0:
version "2.0.2"
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balanced-match@^1.0.0:
version "1.0.2"
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base16@^1.0.0:
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base64-js@^1.5.1:
version "1.5.1"
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bcp-47-match@^2.0.0:
version "2.0.3"
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binary-extensions@^2.0.0, binary-extensions@^2.2.0:
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boolbase@^1.0.0:
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brace-expansion@^1.1.7:
version "1.1.11"
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balanced-match "^1.0.0"
concat-map "0.0.1"
brace-expansion@^2.0.1:
version "2.0.1"
resolved "https://registry.yarnpkg.com/brace-expansion/-/brace-expansion-2.0.1.tgz#1edc459e0f0c548486ecf9fc99f2221364b9a0ae"
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dependencies:
balanced-match "^1.0.0"
braces@^3.0.3, braces@~3.0.2:
version "3.0.3"
resolved "https://registry.yarnpkg.com/braces/-/braces-3.0.3.tgz#490332f40919452272d55a8480adc0c441358789"
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dependencies:
fill-range "^7.1.1"
browserslist@^4.23.3:
version "4.24.0"
resolved "https://registry.yarnpkg.com/browserslist/-/browserslist-4.24.0.tgz#a1325fe4bc80b64fda169629fc01b3d6cecd38d4"
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dependencies:
caniuse-lite "^1.0.30001663"
electron-to-chromium "^1.5.28"
node-releases "^2.0.18"
update-browserslist-db "^1.1.0"
busboy@1.6.0:
version "1.6.0"
resolved "https://registry.yarnpkg.com/busboy/-/busboy-1.6.0.tgz#966ea36a9502e43cdb9146962523b92f531f6893"
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dependencies:
streamsearch "^1.1.0"
call-bind@^1.0.2, call-bind@^1.0.5, call-bind@^1.0.6, call-bind@^1.0.7:
version "1.0.7"
resolved "https://registry.yarnpkg.com/call-bind/-/call-bind-1.0.7.tgz#06016599c40c56498c18769d2730be242b6fa3b9"
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dependencies:
es-define-property "^1.0.0"
es-errors "^1.3.0"
function-bind "^1.1.2"
get-intrinsic "^1.2.4"
set-function-length "^1.2.1"
callsites@^3.0.0:
version "3.1.0"
resolved "https://registry.yarnpkg.com/callsites/-/callsites-3.1.0.tgz#b3630abd8943432f54b3f0519238e33cd7df2f73"
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camelcase-css@^2.0.1:
version "2.0.1"
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camelcase@6:
version "6.3.0"
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caniuse-lite@^1.0.30001579:
version "1.0.30001655"
resolved "https://registry.yarnpkg.com/caniuse-lite/-/caniuse-lite-1.0.30001655.tgz#0ce881f5a19a2dcfda2ecd927df4d5c1684b982f"
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caniuse-lite@^1.0.30001646, caniuse-lite@^1.0.30001663:
version "1.0.30001669"
resolved "https://registry.yarnpkg.com/caniuse-lite/-/caniuse-lite-1.0.30001669.tgz#fda8f1d29a8bfdc42de0c170d7f34a9cf19ed7a3"
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ccount@^2.0.0:
version "2.0.1"
resolved "https://registry.yarnpkg.com/ccount/-/ccount-2.0.1.tgz#17a3bf82302e0870d6da43a01311a8bc02a3ecf5"
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chalk@^4.0.0:
version "4.1.2"
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dependencies:
ansi-styles "^4.1.0"
supports-color "^7.1.0"
character-entities-html4@^2.0.0:
version "2.1.0"
resolved "https://registry.yarnpkg.com/character-entities-html4/-/character-entities-html4-2.1.0.tgz#1f1adb940c971a4b22ba39ddca6b618dc6e56b2b"
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character-entities-legacy@^1.0.0:
version "1.1.4"
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character-entities-legacy@^3.0.0:
version "3.0.0"
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character-entities@^1.0.0:
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character-entities@^2.0.0:
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character-reference-invalid@^1.0.0:
version "1.1.4"
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character-reference-invalid@^2.0.0:
version "2.0.1"
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chokidar@^3.5.3:
version "3.6.0"
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anymatch "~3.1.2"
braces "~3.0.2"
glob-parent "~5.1.2"
is-binary-path "~2.1.0"
is-glob "~4.0.1"
normalize-path "~3.0.0"
readdirp "~3.6.0"
optionalDependencies:
fsevents "~2.3.2"
class-variance-authority@^0.7.0:
version "0.7.0"
resolved "https://registry.yarnpkg.com/class-variance-authority/-/class-variance-authority-0.7.0.tgz#1c3134d634d80271b1837452b06d821915954522"
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dependencies:
clsx "2.0.0"
classnames@^2.5.1:
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client-only@0.0.1:
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clsx@2.0.0:
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clsx@^2.1.1:
version "2.1.1"
resolved "https://registry.yarnpkg.com/clsx/-/clsx-2.1.1.tgz#eed397c9fd8bd882bfb18deab7102049a2f32999"
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codemirror@^6.0.0:
version "6.0.1"
resolved "https://registry.yarnpkg.com/codemirror/-/codemirror-6.0.1.tgz#62b91142d45904547ee3e0e0e4c1a79158035a29"
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dependencies:
"@codemirror/autocomplete" "^6.0.0"
"@codemirror/commands" "^6.0.0"
"@codemirror/language" "^6.0.0"
"@codemirror/lint" "^6.0.0"
"@codemirror/search" "^6.0.0"
"@codemirror/state" "^6.0.0"
"@codemirror/view" "^6.0.0"
color-convert@^2.0.1:
version "2.0.1"
resolved "https://registry.yarnpkg.com/color-convert/-/color-convert-2.0.1.tgz#72d3a68d598c9bdb3af2ad1e84f21d896abd4de3"
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dependencies:
color-name "~1.1.4"
color-name@~1.1.4:
version "1.1.4"
resolved "https://registry.yarnpkg.com/color-name/-/color-name-1.1.4.tgz#c2a09a87acbde69543de6f63fa3995c826c536a2"
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combined-stream@^1.0.8:
version "1.0.8"
resolved "https://registry.yarnpkg.com/combined-stream/-/combined-stream-1.0.8.tgz#c3d45a8b34fd730631a110a8a2520682b31d5a7f"
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dependencies:
delayed-stream "~1.0.0"
comma-separated-tokens@^1.0.0:
version "1.0.8"
resolved "https://registry.yarnpkg.com/comma-separated-tokens/-/comma-separated-tokens-1.0.8.tgz#632b80b6117867a158f1080ad498b2fbe7e3f5ea"
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comma-separated-tokens@^2.0.0:
version "2.0.3"
resolved "https://registry.yarnpkg.com/comma-separated-tokens/-/comma-separated-tokens-2.0.3.tgz#4e89c9458acb61bc8fef19f4529973b2392839ee"
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commander@^10.0.1:
version "10.0.1"
resolved "https://registry.yarnpkg.com/commander/-/commander-10.0.1.tgz#881ee46b4f77d1c1dccc5823433aa39b022cbe06"
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commander@^4.0.0:
version "4.1.1"
resolved "https://registry.yarnpkg.com/commander/-/commander-4.1.1.tgz#9fd602bd936294e9e9ef46a3f4d6964044b18068"
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commander@^8.3.0:
version "8.3.0"
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concat-map@0.0.1:
version "0.0.1"
resolved "https://registry.yarnpkg.com/concat-map/-/concat-map-0.0.1.tgz#d8a96bd77fd68df7793a73036a3ba0d5405d477b"
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cookie@^0.6.0:
version "0.6.0"
resolved "https://registry.yarnpkg.com/cookie/-/cookie-0.6.0.tgz#2798b04b071b0ecbff0dbb62a505a8efa4e19051"
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crelt@^1.0.0, crelt@^1.0.5:
version "1.0.6"
resolved "https://registry.yarnpkg.com/crelt/-/crelt-1.0.6.tgz#7cc898ea74e190fb6ef9dae57f8f81cf7302df72"
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cross-fetch@^3.1.5:
version "3.1.8"
resolved "https://registry.yarnpkg.com/cross-fetch/-/cross-fetch-3.1.8.tgz#0327eba65fd68a7d119f8fb2bf9334a1a7956f82"
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dependencies:
node-fetch "^2.6.12"
cross-spawn@^7.0.0, cross-spawn@^7.0.2:
version "7.0.3"
resolved "https://registry.yarnpkg.com/cross-spawn/-/cross-spawn-7.0.3.tgz#f73a85b9d5d41d045551c177e2882d4ac85728a6"
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dependencies:
path-key "^3.1.0"
shebang-command "^2.0.0"
which "^2.0.1"
crypto-js@^4.2.0:
version "4.2.0"
resolved "https://registry.yarnpkg.com/crypto-js/-/crypto-js-4.2.0.tgz#4d931639ecdfd12ff80e8186dba6af2c2e856631"
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css-selector-parser@^3.0.0:
version "3.0.5"
resolved "https://registry.yarnpkg.com/css-selector-parser/-/css-selector-parser-3.0.5.tgz#9b636ebccf7c4bcce5c1ac21ae27de9f01180ae9"
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cssesc@^3.0.0:
version "3.0.0"
resolved "https://registry.yarnpkg.com/cssesc/-/cssesc-3.0.0.tgz#37741919903b868565e1c09ea747445cd18983ee"
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csstype@^3.0.2:
version "3.1.3"
resolved "https://registry.yarnpkg.com/csstype/-/csstype-3.1.3.tgz#d80ff294d114fb0e6ac500fbf85b60137d7eff81"
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damerau-levenshtein@^1.0.8:
version "1.0.8"
resolved "https://registry.yarnpkg.com/damerau-levenshtein/-/damerau-levenshtein-1.0.8.tgz#b43d286ccbd36bc5b2f7ed41caf2d0aba1f8a6e7"
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data-view-buffer@^1.0.1:
version "1.0.1"
resolved "https://registry.yarnpkg.com/data-view-buffer/-/data-view-buffer-1.0.1.tgz#8ea6326efec17a2e42620696e671d7d5a8bc66b2"
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dependencies:
call-bind "^1.0.6"
es-errors "^1.3.0"
is-data-view "^1.0.1"
data-view-byte-length@^1.0.1:
version "1.0.1"
resolved "https://registry.yarnpkg.com/data-view-byte-length/-/data-view-byte-length-1.0.1.tgz#90721ca95ff280677eb793749fce1011347669e2"
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dependencies:
call-bind "^1.0.7"
es-errors "^1.3.0"
is-data-view "^1.0.1"
data-view-byte-offset@^1.0.0:
version "1.0.0"
resolved "https://registry.yarnpkg.com/data-view-byte-offset/-/data-view-byte-offset-1.0.0.tgz#5e0bbfb4828ed2d1b9b400cd8a7d119bca0ff18a"
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call-bind "^1.0.6"
es-errors "^1.3.0"
is-data-view "^1.0.1"
date-fns@^4.1.0:
version "4.1.0"
resolved "https://registry.yarnpkg.com/date-fns/-/date-fns-4.1.0.tgz#64b3d83fff5aa80438f5b1a633c2e83b8a1c2d14"
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debug@^3.2.7:
version "3.2.7"
resolved "https://registry.yarnpkg.com/debug/-/debug-3.2.7.tgz#72580b7e9145fb39b6676f9c5e5fb100b934179a"
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dependencies:
ms "^2.1.1"
debug@^4.0.0, debug@^4.3.1, debug@^4.3.2, debug@^4.3.4, debug@^4.3.5:
version "4.3.6"
resolved "https://registry.yarnpkg.com/debug/-/debug-4.3.6.tgz#2ab2c38fbaffebf8aa95fdfe6d88438c7a13c52b"
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dependencies:
ms "2.1.2"
decamelize@1.2.0:
version "1.2.0"
resolved "https://registry.yarnpkg.com/decamelize/-/decamelize-1.2.0.tgz#f6534d15148269b20352e7bee26f501f9a191290"
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decode-named-character-reference@^1.0.0:
version "1.0.2"
resolved "https://registry.yarnpkg.com/decode-named-character-reference/-/decode-named-character-reference-1.0.2.tgz#daabac9690874c394c81e4162a0304b35d824f0e"
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dependencies:
character-entities "^2.0.0"
deep-equal@^2.0.5:
version "2.2.3"
resolved "https://registry.yarnpkg.com/deep-equal/-/deep-equal-2.2.3.tgz#af89dafb23a396c7da3e862abc0be27cf51d56e1"
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dependencies:
array-buffer-byte-length "^1.0.0"
call-bind "^1.0.5"
es-get-iterator "^1.1.3"
get-intrinsic "^1.2.2"
is-arguments "^1.1.1"
is-array-buffer "^3.0.2"
is-date-object "^1.0.5"
is-regex "^1.1.4"
is-shared-array-buffer "^1.0.2"
isarray "^2.0.5"
object-is "^1.1.5"
object-keys "^1.1.1"
object.assign "^4.1.4"
regexp.prototype.flags "^1.5.1"
side-channel "^1.0.4"
which-boxed-primitive "^1.0.2"
which-collection "^1.0.1"
which-typed-array "^1.1.13"
deep-is@^0.1.3:
version "0.1.4"
resolved "https://registry.yarnpkg.com/deep-is/-/deep-is-0.1.4.tgz#a6f2dce612fadd2ef1f519b73551f17e85199831"
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define-data-property@^1.0.1, define-data-property@^1.1.4:
version "1.1.4"
resolved "https://registry.yarnpkg.com/define-data-property/-/define-data-property-1.1.4.tgz#894dc141bb7d3060ae4366f6a0107e68fbe48c5e"
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dependencies:
es-define-property "^1.0.0"
es-errors "^1.3.0"
gopd "^1.0.1"
define-properties@^1.1.3, define-properties@^1.2.0, define-properties@^1.2.1:
version "1.2.1"
resolved "https://registry.yarnpkg.com/define-properties/-/define-properties-1.2.1.tgz#10781cc616eb951a80a034bafcaa7377f6af2b6c"
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define-data-property "^1.0.1"
has-property-descriptors "^1.0.0"
object-keys "^1.1.1"
delayed-stream@~1.0.0:
version "1.0.0"
resolved "https://registry.yarnpkg.com/delayed-stream/-/delayed-stream-1.0.0.tgz#df3ae199acadfb7d440aaae0b29e2272b24ec619"
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dequal@^2.0.0:
version "2.0.3"
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detect-node-es@^1.1.0:
version "1.1.0"
resolved "https://registry.yarnpkg.com/detect-node-es/-/detect-node-es-1.1.0.tgz#163acdf643330caa0b4cd7c21e7ee7755d6fa493"
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devlop@^1.0.0, devlop@^1.1.0:
version "1.1.0"
resolved "https://registry.yarnpkg.com/devlop/-/devlop-1.1.0.tgz#4db7c2ca4dc6e0e834c30be70c94bbc976dc7018"
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dependencies:
dequal "^2.0.0"
didyoumean@^1.2.2:
version "1.2.2"
resolved "https://registry.yarnpkg.com/didyoumean/-/didyoumean-1.2.2.tgz#989346ffe9e839b4555ecf5666edea0d3e8ad037"
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diff@^5.0.0:
version "5.2.0"
resolved "https://registry.yarnpkg.com/diff/-/diff-5.2.0.tgz#26ded047cd1179b78b9537d5ef725503ce1ae531"
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dir-glob@^3.0.1:
version "3.0.1"
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dependencies:
path-type "^4.0.0"
direction@^2.0.0:
version "2.0.1"
resolved "https://registry.yarnpkg.com/direction/-/direction-2.0.1.tgz#71800dd3c4fa102406502905d3866e65bdebb985"
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dlv@^1.1.3:
version "1.1.3"
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doctrine@^2.1.0:
version "2.1.0"
resolved "https://registry.yarnpkg.com/doctrine/-/doctrine-2.1.0.tgz#5cd01fc101621b42c4cd7f5d1a66243716d3f39d"
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dependencies:
esutils "^2.0.2"
doctrine@^3.0.0:
version "3.0.0"
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dependencies:
esutils "^2.0.2"
dotenv@^16.4.5:
version "16.4.5"
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double-ended-queue@^2.1.0-0:
version "2.1.0-0"
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eastasianwidth@^0.2.0:
version "0.2.0"
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electron-to-chromium@^1.5.28:
version "1.5.41"
resolved "https://registry.yarnpkg.com/electron-to-chromium/-/electron-to-chromium-1.5.41.tgz#eae1ba6c49a1a61d84cf8263351d3513b2bcc534"
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emoji-mart@^5.6.0:
version "5.6.0"
resolved "https://registry.yarnpkg.com/emoji-mart/-/emoji-mart-5.6.0.tgz#71b3ed0091d3e8c68487b240d9d6d9a73c27f023"
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resolved "https://registry.yarnpkg.com/espree/-/espree-9.6.1.tgz#a2a17b8e434690a5432f2f8018ce71d331a48c6f"
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dependencies:
acorn "^8.9.0"
acorn-jsx "^5.3.2"
eslint-visitor-keys "^3.4.1"
esquery@^1.4.2:
version "1.6.0"
resolved "https://registry.yarnpkg.com/esquery/-/esquery-1.6.0.tgz#91419234f804d852a82dceec3e16cdc22cf9dae7"
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dependencies:
estraverse "^5.1.0"
esrecurse@^4.3.0:
version "4.3.0"
resolved "https://registry.yarnpkg.com/esrecurse/-/esrecurse-4.3.0.tgz#7ad7964d679abb28bee72cec63758b1c5d2c9921"
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estraverse "^5.2.0"
estraverse@^5.1.0, estraverse@^5.2.0, estraverse@^5.3.0:
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estree-util-is-identifier-name@^3.0.0:
version "3.0.0"
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esutils@^2.0.2:
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event-target-shim@^5.0.0:
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eventemitter3@^4.0.4:
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expr-eval@^2.0.2:
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extend@^3.0.0:
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fast-deep-equal@^3, fast-deep-equal@^3.1.1, fast-deep-equal@^3.1.3:
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fast-glob@^3.2.9, fast-glob@^3.3.0, fast-glob@^3.3.2:
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"@nodelib/fs.walk" "^1.2.3"
glob-parent "^5.1.2"
merge2 "^1.3.0"
micromatch "^4.0.4"
fast-json-stable-stringify@^2.0.0:
version "2.1.0"
resolved "https://registry.yarnpkg.com/fast-json-stable-stringify/-/fast-json-stable-stringify-2.1.0.tgz#874bf69c6f404c2b5d99c481341399fd55892633"
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fast-levenshtein@^2.0.6:
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fast-xml-parser@^4.4.1:
version "4.5.0"
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dependencies:
strnum "^1.0.5"
fastq@^1.6.0:
version "1.17.1"
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dependencies:
reusify "^1.0.4"
fault@^1.0.0:
version "1.0.4"
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dependencies:
format "^0.2.0"
fbemitter@^3.0.0:
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dependencies:
fbjs "^3.0.0"
fbjs-css-vars@^1.0.0:
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fbjs@^3.0.0, fbjs@^3.0.1:
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fbjs-css-vars "^1.0.0"
loose-envify "^1.0.0"
object-assign "^4.1.0"
promise "^7.1.1"
setimmediate "^1.0.5"
ua-parser-js "^1.0.35"
file-entry-cache@^6.0.1:
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flat-cache "^3.0.4"
fill-range@^7.1.1:
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dependencies:
to-regex-range "^5.0.1"
find-up@^5.0.0:
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locate-path "^6.0.0"
path-exists "^4.0.0"
flat-cache@^3.0.4:
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dependencies:
flatted "^3.2.9"
keyv "^4.5.3"
rimraf "^3.0.2"
flat@^5.0.2:
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flatted@^3.2.9:
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flux@^4.0.1:
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fbemitter "^3.0.0"
fbjs "^3.0.1"
for-each@^0.3.3:
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is-callable "^1.1.3"
foreground-child@^3.1.0:
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dependencies:
cross-spawn "^7.0.0"
signal-exit "^4.0.1"
form-data-encoder@1.7.2:
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form-data@^4.0.0:
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asynckit "^0.4.0"
combined-stream "^1.0.8"
mime-types "^2.1.12"
format@^0.2.0:
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formdata-node@^4.3.2:
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node-domexception "1.0.0"
web-streams-polyfill "4.0.0-beta.3"
fraction.js@^4.3.7:
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framer-motion@^11.11.9:
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dependencies:
tslib "^2.4.0"
fs.realpath@^1.0.0:
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define-properties "^1.2.0"
es-abstract "^1.22.1"
functions-have-names "^1.2.3"
functions-have-names@^1.2.3:
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function-bind "^1.1.2"
has-proto "^1.0.1"
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hasown "^2.0.0"
get-nonce@^1.0.0:
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es-errors "^1.3.0"
get-intrinsic "^1.2.4"
get-tsconfig@^4.7.5:
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github-slugger@^2.0.0:
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glob-parent@^5.1.2, glob-parent@~5.1.2:
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is-glob "^4.0.1"
glob-parent@^6.0.2:
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glob@10.3.10:
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is-fullwidth-code-point@^3.0.0:
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resolved "https://registry.yarnpkg.com/micromark-util-sanitize-uri/-/micromark-util-sanitize-uri-1.2.0.tgz#613f738e4400c6eedbc53590c67b197e30d7f90d"
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micromark-util-character "^1.0.0"
micromark-util-encode "^1.0.0"
micromark-util-symbol "^1.0.0"
micromark-util-sanitize-uri@^2.0.0:
version "2.0.0"
resolved "https://registry.yarnpkg.com/micromark-util-sanitize-uri/-/micromark-util-sanitize-uri-2.0.0.tgz#ec8fbf0258e9e6d8f13d9e4770f9be64342673de"
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micromark-util-character "^2.0.0"
micromark-util-encode "^2.0.0"
micromark-util-symbol "^2.0.0"
micromark-util-subtokenize@^1.0.0:
version "1.1.0"
resolved "https://registry.yarnpkg.com/micromark-util-subtokenize/-/micromark-util-subtokenize-1.1.0.tgz#941c74f93a93eaf687b9054aeb94642b0e92edb1"
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micromark-util-chunked "^1.0.0"
micromark-util-symbol "^1.0.0"
micromark-util-types "^1.0.0"
uvu "^0.5.0"
micromark-util-subtokenize@^2.0.0:
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devlop "^1.0.0"
micromark-util-chunked "^2.0.0"
micromark-util-symbol "^2.0.0"
micromark-util-types "^2.0.0"
micromark-util-symbol@^1.0.0:
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micromark-util-types@^1.0.0, micromark-util-types@^1.0.1:
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debug "^4.0.0"
decode-named-character-reference "^1.0.0"
micromark-core-commonmark "^1.0.1"
micromark-factory-space "^1.0.0"
micromark-util-character "^1.0.0"
micromark-util-chunked "^1.0.0"
micromark-util-combine-extensions "^1.0.0"
micromark-util-decode-numeric-character-reference "^1.0.0"
micromark-util-encode "^1.0.0"
micromark-util-normalize-identifier "^1.0.0"
micromark-util-resolve-all "^1.0.0"
micromark-util-sanitize-uri "^1.0.0"
micromark-util-subtokenize "^1.0.0"
micromark-util-symbol "^1.0.0"
micromark-util-types "^1.0.1"
uvu "^0.5.0"
micromark@^4.0.0:
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debug "^4.0.0"
decode-named-character-reference "^1.0.0"
devlop "^1.0.0"
micromark-core-commonmark "^2.0.0"
micromark-factory-space "^2.0.0"
micromark-util-character "^2.0.0"
micromark-util-chunked "^2.0.0"
micromark-util-combine-extensions "^2.0.0"
micromark-util-decode-numeric-character-reference "^2.0.0"
micromark-util-encode "^2.0.0"
micromark-util-normalize-identifier "^2.0.0"
micromark-util-resolve-all "^2.0.0"
micromark-util-sanitize-uri "^2.0.0"
micromark-util-subtokenize "^2.0.0"
micromark-util-symbol "^2.0.0"
micromark-util-types "^2.0.0"
micromatch@^4.0.4, micromatch@^4.0.5:
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braces "^3.0.3"
picomatch "^2.3.1"
mime-db@1.52.0:
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mime-db "1.52.0"
minimatch@9.0.3:
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brace-expansion "^2.0.1"
minimatch@^3.0.5, minimatch@^3.1.1, minimatch@^3.1.2:
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brace-expansion "^1.1.7"
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minimist@^1.2.0, minimist@^1.2.6:
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object-assign "^4.0.1"
thenify-all "^1.0.0"
nanoid@^3.3.6, nanoid@^3.3.7:
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natural-compare@^1.4.0:
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next@14.2.10:
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"@swc/helpers" "0.5.5"
busboy "1.6.0"
caniuse-lite "^1.0.30001579"
graceful-fs "^4.2.11"
postcss "8.4.31"
styled-jsx "5.1.1"
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node-domexception@1.0.0:
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boolbase "^1.0.0"
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lib0 "^0.2.42"
y-protocols@^1.0.6:
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lib0 "^0.2.85"
yaml@^2.2.1:
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lib0 "^0.2.98"
yocto-queue@^0.1.0:
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version "4.5.5"
resolved "https://registry.yarnpkg.com/zustand/-/zustand-4.5.5.tgz#f8c713041543715ec81a2adda0610e1dc82d4ad1"
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dependencies:
use-sync-external-store "1.2.2"
zwitch@^2.0.0, zwitch@^2.0.4:
version "2.0.4"
resolved "https://registry.yarnpkg.com/zwitch/-/zwitch-2.0.4.tgz#c827d4b0acb76fc3e685a4c6ec2902d51070e9d7"
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|
0 | lc_public_repos | lc_public_repos/open-canvas/.eslintrc.json | {
"extends": [
"next/core-web-vitals",
"plugin:@typescript-eslint/recommended"
],
"parser": "@typescript-eslint/parser",
"plugins": ["@typescript-eslint", "unused-imports", "@typescript-eslint/eslint-plugin"],
"rules": {
"@typescript-eslint/no-unused-vars": [
"error",
{
"argsIgnorePattern": "^_",
"varsIgnorePattern": "^_|^UNUSED_",
"caughtErrorsIgnorePattern": "^_",
"destructuredArrayIgnorePattern": "^_"
}
],
"@typescript-eslint/no-explicit-any": "off",
"@typescript-eslint/no-empty-object-type": "off",
"unused-imports/no-unused-imports": "error"
}
} |
0 | lc_public_repos | lc_public_repos/open-canvas/README.md | # Open Canvas
[TRY IT OUT HERE](https://opencanvas.langchain.com/)
Open Canvas is an open source web application for collaborating with agents to better write documents. It is inspired by [OpenAI's "Canvas"](https://openai.com/index/introducing-canvas/), but with a few key differences.
1. **Open Source**: All the code, from the frontend, to the content generation agent, to the reflection agent is open source and MIT licensed.
2. **Built in memory**: Open Canvas ships out of the box with a [reflection agent](https://langchain-ai.github.io/langgraphjs/tutorials/reflection/reflection/) which stores style rules and user insights in a [shared memory store](https://langchain-ai.github.io/langgraphjs/concepts/memory/). This allows Open Canvas to remember facts about you across sessions.
3. **Start from existing documents**: Open Canvas allows users to start with a blank text, or code editor in the language of their choice, allowing you to start the session with your existing content, instead of being forced to start with a chat interaction. We believe this is an ideal UX because many times you will already have some content to start with, and want to iterate on-top of it.
## Features
- **Memory**: Open Canvas has a built in memory system which will automatically generate reflections and memories on you, and your chat history. These are then included in subsequent chat interactions to give a more personalized experience.
- **Custom quick actions**: Custom quick actions allow you to define your own prompts which are tied to your user, and persist across sessions. These then can be easily invoked through a single click, and apply to the artifact you're currently viewing.
- **Pre-built quick actions**: There are also a series of pre-built quick actions for common writing and coding tasks that are always available.
- **Artifact versioning**: All artifacts have a "version" tied to them, allowing you to travel back in time and see previous versions of your artifact.
- **Code, Markdown, or both**: The artifact view allows for viewing and editing both code, and markdown. You can even have chats which generate code, and markdown artifacts, and switch between them.
- **Live markdown rendering & editing**: Open Canvas's markdown editor allows you to view the rendered markdown while you're editing, without having to toggle back and fourth.
## How to use
You can use our deployed version for free by visiting [opencanvas.langchain.com](https://opencanvas.langchain.com/)
or
You can clone this repository and run locally/deploy to your own cloud. See the next section for steps on how to do this.
## Development
Running or developing Open Canvas is easy. Start by cloning this repository and navigating into the directory.
```bash
git clone https://github.com/langchain-ai/open-canvas.git
cd open-canvas
```
Next, install the dependencies via Yarn:
```bash
yarn install
```
Then [install LangGraph Studio](https://studio.langchain.com/) which is required to run the graphs locally, or [create a LangSmith account](https://smith.langchain.com/) to deploy to production on LangGraph Cloud.
After that, copy the `.env.example` file contents into `.env` and set the required values:
```bash
# LangSmith tracing
LANGCHAIN_TRACING_V2=true
LANGCHAIN_API_KEY=
# LLM API keys
# Anthropic used for reflection
ANTHROPIC_API_KEY=
# OpenAI used for content generation
OPENAI_API_KEY=
# LangGraph Deployment, or local development server via LangGraph Studio.
# If running locally, this URL should be set in the `constants.ts` file.
# LANGGRAPH_API_URL=
# Supabase for authentication
# Public keys
NEXT_PUBLIC_SUPABASE_URL=
NEXT_PUBLIC_SUPABASE_ANON_KEY=
```
Finally, start the development server:
```bash
yarn dev
```
Then, open [localhost:3000](http://localhost:3000) with your browser and start interacting!
You can also watch a short (2 min) video walkthrough on how to setup Open Canvas locally [here](https://www.loom.com/share/e2ce559840f14a9abf1b3d5af7686271).
## LLM Models
Open Canvas is designed to be compatible with any LLM model. The current deployment has the following models configured:
- **Anthropic Claude 3 Haiku 👤**: Haiku is Anthropic's fastest model, great for quick tasks like making edits to your document. Sign up for an Anthropic account [here](https://console.anthropic.com/).
- **Fireworks Llama 3 70B 🦙**: Llama 3 is a SOTA open source model from Meta, powered by [Fireworks AI](https://fireworks.ai/). You can sign up for an account [here](https://fireworks.ai/login).
- **OpenAI GPT 4o Mini 💨**: GPT 4o Mini is OpenAI's newest, smallest model. You can sign up for an API key [here](https://platform.openai.com/signup/).
If you'd like to add a new model, follow these simple steps:
1. Add to or update the model provider variables in `constants.ts`.
2. Install the necessary package for the provider (e.g. `@langchain/anthropic`).
3. Update the `getModelNameAndProviderFromConfig` function in `src/agent/utils.ts` to include your new model name and provider.
4. Manually test by checking you can:
> - 4a. Generate a new artifact
>
> - 4b. Generate a followup message (happens automatically after generating an artifact)
>
> - 4c. Update an artifact via a message in chat
>
> - 4d. Update an artifact via a quick action
>
> - 4e. Repeat for text/code (ensure both work)
## Roadmap
### Features
Below is a list of features we'd like to add to Open Canvas in the near future:
- **Render React in the editor**: Ideally, if you have Open Canvas generate React (or HTML) code, we should be able to render it live in the editor. **Edit**: This is in the planning stage now!
- **Multiple assistants**: Users should be able to create multiple assistants, each having their own memory store.
- **Give assistants custom 'tools'**: Once we've implemented `RemoteGraph` in LangGraph.js, users should be able to give assistants access to call their own graphs as tools. This means you could customize your assistant to have access to current events, your own personal knowledge graph, etc.
Do you have a feature request? Please [open an issue](https://github.com/langchain-ai/open-canvas/issues/new)!
### Contributing
We'd like to continue developing and improving Open Canvas, and want your help!
To start, there are a handful of GitHub issues with feature requests outlining improvements and additions to make the app's UX even better.
There are three main labels:
- `frontend`: This label is added to issues which are UI focused, and do not require much if any work on the agent(s).
- `ai`: This label is added to issues which are focused on improving the LLM agent(s).
- `fullstack`: This label is added to issues which require touching both the frontend and agent code.
If you have questions about contributing, please reach out to me via email: `brace(at)langchain(dot)dev`. For general bugs/issues with the code, please [open an issue on GitHub](https://github.com/langchain-ai/open-canvas/issues/new).
|
0 | lc_public_repos | lc_public_repos/open-canvas/next.config.mjs | /** @type {import('next').NextConfig} */
const nextConfig = {};
export default nextConfig;
|
0 | lc_public_repos | lc_public_repos/open-canvas/.env.example | # LangSmith tracing
LANGCHAIN_TRACING_V2=true
LANGCHAIN_API_KEY=
# LLM API keys
# Anthropic used for reflection
ANTHROPIC_API_KEY=
# OpenAI used for content generation
OPENAI_API_KEY=
# Optional, only required if using `Gemini 1.5 Flash` as the model.
GOOGLE_API_KEY=
# Feature flags for hiding/showing specific models
NEXT_PUBLIC_FIREWORKS_ENABLED=true
# Gemini has some tool call streaming issues atm.
NEXT_PUBLIC_GEMINI_ENABLED=false
NEXT_PUBLIC_ANTHROPIC_ENABLED=true
NEXT_PUBLIC_OPENAI_ENABLED=true
NEXT_PUBLIC_AZURE_ENABLED=false
# LangGraph Deployment, or local development server via LangGraph Studio.
# If running locally, this URL should be set in the `constants.ts` file.
# LANGGRAPH_API_URL=
# Supabase for authentication
# Public keys
NEXT_PUBLIC_SUPABASE_URL=
NEXT_PUBLIC_SUPABASE_ANON_KEY=
# Azure OpenAI Configuration
# ENSURE THEY ARE PREFIXED WITH AN UNDERSCORE.
_AZURE_OPENAI_API_KEY=your-azure-openai-api-key
_AZURE_OPENAI_API_INSTANCE_NAME=your-instance-name
_AZURE_OPENAI_API_DEPLOYMENT_NAME=your-deployment-name
_AZURE_OPENAI_API_VERSION=2024-08-01-preview
# Optional: Azure OpenAI Base Path (if using a different domain)
# _AZURE_OPENAI_API_BASE_PATH=https://your-custom-domain.com/openai/deployments
|
0 | lc_public_repos | lc_public_repos/open-canvas/package.json | {
"name": "open_canvas",
"author": "Brace Sproul",
"homepage": "https://open-canvas-lc.vercel.app",
"repository": "https://github.com/langchain-ai/open-canvas",
"private": true,
"scripts": {
"dev": "next dev",
"build": "next build",
"start": "next start",
"lint": "next lint",
"format": "prettier --config .prettierrc --write \"src\" \"evals\"",
"eval:highlights": "yarn tsx evals/highlights.ts"
},
"dependencies": {
"@assistant-ui/react": "^0.5.71",
"@assistant-ui/react-markdown": "^0.2.18",
"@assistant-ui/react-syntax-highlighter": "^0.0.13",
"@blocknote/core": "^0.17.1",
"@blocknote/mantine": "^0.17.1",
"@blocknote/react": "^0.17.1",
"@blocknote/shadcn": "^0.17.1",
"@codemirror/lang-cpp": "^6.0.2",
"@codemirror/lang-html": "^6.4.9",
"@codemirror/lang-java": "^6.0.1",
"@codemirror/lang-javascript": "^6.2.2",
"@codemirror/lang-json": "^6.0.1",
"@codemirror/lang-php": "^6.0.1",
"@codemirror/lang-python": "^6.1.6",
"@codemirror/lang-rust": "^6.0.1",
"@codemirror/lang-sql": "^6.8.0",
"@codemirror/lang-xml": "^6.1.0",
"@langchain/anthropic": "^0.3.6",
"@langchain/community": "^0.3.9",
"@langchain/core": "^0.3.14",
"@langchain/google-genai": "^0.1.2",
"@langchain/langgraph": "^0.2.18",
"@langchain/langgraph-sdk": "^0.0.17",
"@langchain/openai": "^0.3.11",
"@nextjournal/lang-clojure": "^1.0.0",
"@radix-ui/react-avatar": "^1.1.0",
"@radix-ui/react-checkbox": "^1.1.2",
"@radix-ui/react-dialog": "^1.1.2",
"@radix-ui/react-dropdown-menu": "^2.1.2",
"@radix-ui/react-hover-card": "^1.1.2",
"@radix-ui/react-icons": "^1.3.0",
"@radix-ui/react-label": "^2.1.0",
"@radix-ui/react-progress": "^1.1.0",
"@radix-ui/react-select": "^2.1.1",
"@radix-ui/react-slider": "^1.2.1",
"@radix-ui/react-slot": "^1.1.0",
"@radix-ui/react-toast": "^1.2.1",
"@radix-ui/react-tooltip": "^1.1.2",
"@replit/codemirror-lang-csharp": "^6.2.0",
"@supabase/ssr": "^0.5.1",
"@supabase/supabase-js": "^2.45.5",
"@types/react-syntax-highlighter": "^15.5.13",
"@uiw/react-codemirror": "^4.23.5",
"@uiw/react-md-editor": "^4.0.4",
"@vercel/kv": "^2.0.0",
"class-variance-authority": "^0.7.0",
"clsx": "^2.1.1",
"date-fns": "^4.1.0",
"dotenv": "^16.4.5",
"eslint-plugin-unused-imports": "^4.1.4",
"framer-motion": "^11.11.9",
"js-cookie": "^3.0.5",
"langchain": "^0.3.5",
"langsmith": "^0.1.61",
"lodash": "^4.17.21",
"lucide-react": "^0.441.0",
"next": "14.2.10",
"react": "^18",
"react-colorful": "^5.6.1",
"react-dom": "^18",
"react-icons": "^5.3.0",
"react-json-view": "^1.21.3",
"react-markdown": "^9.0.1",
"react-syntax-highlighter": "^15.5.0",
"rehype-katex": "^7.0.1",
"remark-gfm": "^4.0.0",
"remark-math": "^6.0.0",
"tailwind-merge": "^2.5.2",
"tailwind-scrollbar-hide": "^1.1.7",
"tailwindcss-animate": "^1.0.7",
"uuid": "^10.0.0",
"zod": "^3.23.8"
},
"devDependencies": {
"@eslint/js": "^9.12.0",
"@types/eslint__js": "^8.42.3",
"@types/js-cookie": "^3.0.6",
"@types/lodash": "^4.17.12",
"@types/node": "^20",
"@types/react": "^18",
"@types/react-dom": "^18",
"@types/uuid": "^10.0.0",
"@typescript-eslint/eslint-plugin": "^8.12.2",
"@typescript-eslint/parser": "^8.8.1",
"eslint": "^8",
"eslint-config-next": "14.2.10",
"postcss": "^8",
"prettier": "^3.3.3",
"tailwind-scrollbar": "^3.1.0",
"tailwindcss": "^3.4.1",
"tsx": "^4.19.1",
"typescript": "^5",
"typescript-eslint": "^8.8.1"
}
}
|
0 | lc_public_repos | lc_public_repos/open-canvas/tailwind.config.ts | import type { Config } from "tailwindcss";
const config: Config = {
darkMode: ["class"],
content: [
"./src/pages/**/*.{js,ts,jsx,tsx,mdx}",
"./src/components/**/*.{js,ts,jsx,tsx,mdx}",
"./src/app/**/*.{js,ts,jsx,tsx,mdx}",
],
theme: {
extend: {
keyframes: {
'gradient-xy-enhanced': {
'0%, 100%': {
'background-size': '400% 400%',
'background-position': 'left center',
'transform': 'rotate(-3deg)'
},
'50%': {
'background-size': '200% 200%',
'background-position': 'right center',
'transform': 'rotate(3deg)'
}
},
'gradient-x': {
'0%, 100%': {
'background-size': '200% 200%',
'background-position': 'left center',
},
'50%': {
'background-size': '200% 200%',
'background-position': 'right center',
},
},
},
animation: {
'gradient-xy-enhanced': 'gradient-xy-enhanced 15s ease infinite',
'gradient-x': 'gradient-x 3s ease-in-out infinite',
},
backgroundImage: {
'gradient-radial': 'radial-gradient(var(--tw-gradient-stops))',
'gradient-conic': 'conic-gradient(from 180deg at 50% 50%, var(--tw-gradient-stops))'
},
borderRadius: {
lg: 'var(--radius)',
md: 'calc(var(--radius) - 2px)',
sm: 'calc(var(--radius) - 4px)'
},
colors: {
background: 'hsl(var(--background))',
foreground: 'hsl(var(--foreground))',
card: {
DEFAULT: 'hsl(var(--card))',
foreground: 'hsl(var(--card-foreground))'
},
popover: {
DEFAULT: 'hsl(var(--popover))',
foreground: 'hsl(var(--popover-foreground))'
},
primary: {
DEFAULT: 'hsl(var(--primary))',
foreground: 'hsl(var(--primary-foreground))'
},
secondary: {
DEFAULT: 'hsl(var(--secondary))',
foreground: 'hsl(var(--secondary-foreground))'
},
muted: {
DEFAULT: 'hsl(var(--muted))',
foreground: 'hsl(var(--muted-foreground))'
},
accent: {
DEFAULT: 'hsl(var(--accent))',
foreground: 'hsl(var(--accent-foreground))'
},
destructive: {
DEFAULT: 'hsl(var(--destructive))',
foreground: 'hsl(var(--destructive-foreground))'
},
border: 'hsl(var(--border))',
input: 'hsl(var(--input))',
ring: 'hsl(var(--ring))',
chart: {
'1': 'hsl(var(--chart-1))',
'2': 'hsl(var(--chart-2))',
'3': 'hsl(var(--chart-3))',
'4': 'hsl(var(--chart-4))',
'5': 'hsl(var(--chart-5))'
}
},
fontFamily: {
mono: [
`"Fira Code"`,
`ui-monospace`,
`SFMono-Regular`,
`Menlo`,
`Monaco`,
`Consolas`,
`"Liberation Mono"`,
`"Courier New"`,
`monospace`,
],
sans: [
"Inter",
"-apple-system",
"BlinkMacSystemFont",
'Segoe UI',
"Roboto",
"Helvetica",
"Arial",
"sans-serif",
'Apple Color Emoji',
'Segoe UI Emoji',
'Segoe UI Symbol',
],
},
letterSpacing: {
tighter: '-0.04em',
},
}
},
plugins: [
require("tailwind-scrollbar-hide"),
require("tailwindcss-animate"),
require("@assistant-ui/react/tailwindcss")({
components: ["thread"],
}),
require('tailwind-scrollbar'),
],
};
export default config;
|
0 | lc_public_repos | lc_public_repos/open-canvas/langgraph.json | {
"node_version": "20",
"dockerfile_lines": [],
"dependencies": [
"."
],
"graphs": {
"agent": "./src/agent/open-canvas/index.ts:graph",
"reflection": "./src/agent/reflection/index.ts:graph",
"thread_title": "./src/agent/thread-title/index.ts:graph"
},
"env": ".env"
} |
0 | lc_public_repos | lc_public_repos/open-canvas/.prettierrc | {
"$schema": "https://json.schemastore.org/prettierrc",
"printWidth": 80,
"tabWidth": 2,
"useTabs": false,
"semi": true,
"singleQuote": false,
"quoteProps": "as-needed",
"jsxSingleQuote": false,
"trailingComma": "es5",
"bracketSpacing": true,
"arrowParens": "always",
"requirePragma": false,
"insertPragma": false,
"proseWrap": "preserve",
"htmlWhitespaceSensitivity": "css",
"vueIndentScriptAndStyle": false,
"endOfLine": "lf"
}
|
0 | lc_public_repos/open-canvas | lc_public_repos/open-canvas/evals/highlights.ts | import { type Example, Run } from "langsmith";
import { graph } from "../src/agent/open-canvas/index";
import { evaluate, EvaluationResult } from "langsmith/evaluation";
import "dotenv/config";
const runGraph = async (
input: Record<string, any>
): Promise<Record<string, any>> => {
// Interrupt after updating the artifact
graph.interruptAfter = ["updateArtifact"];
return await graph.invoke(input);
};
const evaluateHighlights = (run: Run, example?: Example): EvaluationResult => {
if (!example) {
throw new Error("No example provided");
}
if (!example.outputs) {
throw new Error("No example outputs provided");
}
if (!run.outputs) {
throw new Error("No run outputs provided");
}
const { expectedGeneration } = example.outputs;
const { highlighted, artifacts } = example.inputs;
const expectedGenerationStart = artifacts[0].content.slice(
0,
highlighted.startCharIndex
);
const expectedGenerationEnd = artifacts[0].content.slice(
highlighted.endCharIndex
);
const fullExpectedArtifact = `${expectedGenerationStart}${expectedGeneration}${expectedGenerationEnd}`;
const generatedArtifact = run.outputs.artifacts[0].content;
if (generatedArtifact !== fullExpectedArtifact) {
return {
key: "correct_generation",
score: false,
};
}
return {
key: "correct_generation",
score: true,
};
};
async function runHighlightEval() {
const datasetName = "open-canvas-deterministic-highlights";
await evaluate(runGraph, {
data: datasetName,
evaluators: [evaluateHighlights],
experimentPrefix: "Highlight generation",
});
}
runHighlightEval();
|
0 | lc_public_repos/open-canvas | lc_public_repos/open-canvas/src/types.ts | export type Message = {
id: string;
text?: string;
rawResponse?: Record<string, any>;
sender: string;
toolCalls?: ToolCall[];
};
export interface ToolCall {
id: string;
name: string;
args: string;
result?: any;
}
export type Model = "gpt-4o-mini" | string; // Add other model options as needed
export type UserRules = {
styleRules: string[];
contentRules: string[];
};
export interface Artifact {
id: string;
contents: ArtifactContent[];
currentContentIndex: number;
}
export interface ArtifactContent {
index: number;
content: string;
title: string;
type: ArtifactType;
language: string;
}
export interface ArtifactV2 {
id: string;
contents: (ArtifactMarkdownContent | ArtifactCodeContent)[];
currentContentIndex: number;
}
export interface MarkdownBlock {
id: string;
content: Array<{
id: string;
type: string;
text: string;
styles: Record<string, any>;
}>;
type: string;
}
export interface ArtifactMarkdownContent {
index: number;
blocks: MarkdownBlock[];
title: string;
type: "text";
}
export interface ArtifactCodeContent {
index: number;
code: string;
title: string;
type: "code";
language: ProgrammingLanguageOptions;
}
export type ArtifactType = "code" | "text";
export interface Highlight {
/**
* The index of the first character of the highlighted text
*/
startCharIndex: number;
/**
* The index of the last character of the highlighted text
*/
endCharIndex: number;
}
export type LanguageOptions =
| "english"
| "mandarin"
| "spanish"
| "french"
| "hindi";
export type ProgrammingLanguageOptions =
| "typescript"
| "javascript"
| "cpp"
| "java"
| "php"
| "python"
| "html"
| "sql"
| "json"
| "rust"
| "xml"
| "clojure"
| "csharp"
| "other";
export const PROGRAMMING_LANGUAGES: Array<{
language: ProgrammingLanguageOptions;
label: string;
}> = [
{
language: "typescript",
label: "TypeScript",
},
{
language: "javascript",
label: "JavaScript",
},
{
language: "cpp",
label: "C++",
},
{
language: "java",
label: "Java",
},
{
language: "php",
label: "PHP",
},
{
language: "python",
label: "Python",
},
{
language: "html",
label: "HTML",
},
{
language: "sql",
label: "SQL",
},
{
language: "json",
label: "JSON",
},
{
language: "rust",
label: "Rust",
},
{
language: "xml",
label: "XML",
},
{
language: "clojure",
label: "Clojure",
},
{
language: "csharp",
label: "C#",
},
{
language: "other",
label: "Other",
},
];
export type ArtifactLengthOptions = "shortest" | "short" | "long" | "longest";
export type ReadingLevelOptions =
| "pirate"
| "child"
| "teenager"
| "college"
| "phd";
export interface Reflections {
/**
* Style rules to follow for generating content.
*/
styleRules: string[];
/**
* Key content to remember about the user when generating content.
*/
content: string[];
}
export interface CustomQuickAction {
/**
* A UUID for the quick action. Used to identify the quick action.
*/
id: string;
/**
* The title of the quick action. Used in the UI
* to display the quick action.
*/
title: string;
/**
* The prompt to use when the quick action is invoked.
*/
prompt: string;
/**
* Whether or not to include the user's reflections in the prompt.
*/
includeReflections: boolean;
/**
* Whether or not to include the default prefix in the prompt.
*/
includePrefix: boolean;
/**
* Whether or not to include the last 5 (or less) messages in the prompt.
*/
includeRecentHistory: boolean;
}
export interface ArtifactV3 {
currentIndex: number;
contents: (ArtifactMarkdownV3 | ArtifactCodeV3)[];
}
export interface ArtifactMarkdownV3 {
index: number;
type: "text";
title: string;
fullMarkdown: string;
}
export interface ArtifactCodeV3 {
index: number;
type: "code";
title: string;
language: ProgrammingLanguageOptions;
code: string;
}
export interface CodeHighlight {
startCharIndex: number;
endCharIndex: number;
}
export interface TextHighlight {
fullMarkdown: string;
markdownBlock: string;
selectedText: string;
}
export interface ArtifactToolResponse {
artifact?: string;
title?: string;
language?: string;
type?: string;
}
export interface NewMarkdownToolResponse {
blocks: Array<{ block_id?: string; new_text?: string }>;
}
export type RewriteArtifactMetaToolResponse =
| {
type: "text";
title?: string;
language: ProgrammingLanguageOptions;
}
| {
type: "code";
title: string;
language: ProgrammingLanguageOptions;
};
export interface ModelConfig {
temperature?: number;
modelProvider: string;
maxTokens?: number;
azureConfig?: {
azureOpenAIApiKey: string;
azureOpenAIApiInstanceName: string;
azureOpenAIApiDeploymentName: string;
azureOpenAIApiVersion: string;
azureOpenAIBasePath?: string;
};
}
|
0 | lc_public_repos/open-canvas | lc_public_repos/open-canvas/src/middleware.ts | import { type NextRequest } from "next/server";
import { updateSession } from "@/lib/supabase/middleware";
export async function middleware(request: NextRequest) {
return await updateSession(request);
}
export const config = {
matcher: [
/*
* Match all request paths except for the ones starting with:
* - _next/static (static files)
* - _next/image (image optimization files)
* - favicon.ico (favicon file)
* Feel free to modify this pattern to include more paths.
*/
"/((?!_next/static|_next/image|favicon.ico|.*\\.(?:svg|png|jpg|jpeg|gif|webp)$).*)",
],
};
|
0 | lc_public_repos/open-canvas | lc_public_repos/open-canvas/src/constants.ts | export const LANGGRAPH_API_URL =
process.env.LANGGRAPH_API_URL ?? "http://localhost:54367";
// v2 is tied to the 'open-canvas-prod' deployment.
export const ASSISTANT_ID_COOKIE = "oc_assistant_id_v2";
// export const ASSISTANT_ID_COOKIE = "oc_assistant_id";
export const HAS_ASSISTANT_COOKIE_BEEN_SET = "has_oc_assistant_id_been_set";
export const THREAD_ID_COOKIE_NAME = "oc_thread_id_v2";
export const HAS_EMPTY_THREADS_CLEARED_COOKIE = "has_empty_threads_cleared";
export const LS_HAS_SEEN_MODEL_DROPDOWN_ALERT =
"oc_has_seen_model_dropdown_alert";
export const OC_HAS_SEEN_CUSTOM_ASSISTANTS_ALERT =
"oc_has_seen_custom_assistants_alert";
export const DEFAULT_INPUTS = {
highlightedCode: undefined,
highlightedText: undefined,
next: undefined,
language: undefined,
artifactLength: undefined,
regenerateWithEmojis: undefined,
readingLevel: undefined,
addComments: undefined,
addLogs: undefined,
fixBugs: undefined,
portLanguage: undefined,
customQuickActionId: undefined,
};
export const AZURE_MODELS = [
{
name: "azure/gpt-4o-mini",
modelName: "gpt-4o-mini",
label: "GPT-4o mini (Azure)",
isNew: false,
},
];
export const OPENAI_MODELS = [
{
name: "gpt-4o-mini",
label: "GPT-4o mini",
isNew: false,
},
];
export const ANTHROPIC_MODELS = [
{
name: "claude-3-5-haiku-20241022",
label: "Claude 3.5 Haiku",
isNew: true,
},
{
name: "claude-3-haiku-20240307",
label: "Claude 3 Haiku (old)",
isNew: false,
},
// {
// name: "claude-3-5-sonnet-20240620",
// label: "Claude 3.5 Sonnet",
// },
];
export const FIREWORKS_MODELS = [
{
name: "accounts/fireworks/models/llama-v3p1-70b-instruct",
label: "Fireworks Llama 70B",
isNew: false,
},
];
export const GEMINI_MODELS = [
{
name: "gemini-1.5-flash",
label: "Gemini 1.5 Flash",
isNew: false,
},
];
export const DEFAULT_MODEL_NAME: ALL_MODEL_NAMES = "gpt-4o-mini";
export type OPENAI_MODEL_NAMES = (typeof OPENAI_MODELS)[number]["name"];
export type ANTHROPIC_MODEL_NAMES = (typeof ANTHROPIC_MODELS)[number]["name"];
export type FIREWORKS_MODEL_NAMES = (typeof FIREWORKS_MODELS)[number]["name"];
export type GEMINI_MODEL_NAMES = (typeof GEMINI_MODELS)[number]["name"];
export type AZURE_MODEL_NAMES = (typeof AZURE_MODELS)[number]["modelName"];
export type ALL_MODEL_NAMES =
| OPENAI_MODEL_NAMES
| ANTHROPIC_MODEL_NAMES
| FIREWORKS_MODEL_NAMES
| GEMINI_MODEL_NAMES
| AZURE_MODEL_NAMES;
|
0 | lc_public_repos/open-canvas/src | lc_public_repos/open-canvas/src/contexts/GraphContext.tsx | import {
createContext,
useContext,
ReactNode,
useEffect,
useState,
useRef,
Dispatch,
SetStateAction,
} from "react";
import {
convertToArtifactV3,
createNewGeneratedArtifactFromTool,
replaceOrInsertMessageChunk,
updateHighlightedCode,
updateHighlightedMarkdown,
updateRewrittenArtifact,
removeCodeBlockFormatting,
} from "./utils";
import { useUser } from "@/hooks/useUser";
import { useThread } from "@/hooks/useThread";
import { debounce } from "lodash";
import {
ArtifactLengthOptions,
ArtifactToolResponse,
ArtifactType,
ArtifactV3,
CodeHighlight,
LanguageOptions,
ProgrammingLanguageOptions,
ReadingLevelOptions,
RewriteArtifactMetaToolResponse,
TextHighlight,
} from "@/types";
import { AIMessage, BaseMessage } from "@langchain/core/messages";
import { useRuns } from "@/hooks/useRuns";
import { createClient } from "@/hooks/utils";
import {
ALL_MODEL_NAMES,
DEFAULT_INPUTS,
DEFAULT_MODEL_NAME,
THREAD_ID_COOKIE_NAME,
} from "@/constants";
import { Thread } from "@langchain/langgraph-sdk";
import { useToast } from "@/hooks/use-toast";
import { parsePartialJson } from "@langchain/core/output_parsers";
import {
isArtifactCodeContent,
isArtifactMarkdownContent,
isDeprecatedArtifactType,
} from "@/lib/artifact_content_types";
import { reverseCleanContent } from "@/lib/normalize_string";
import { setCookie } from "@/lib/cookies";
import { useAssistants } from "@/hooks/useAssistants";
interface GraphData {
runId: string | undefined;
isStreaming: boolean;
selectedBlocks: TextHighlight | undefined;
messages: BaseMessage[];
artifact: ArtifactV3 | undefined;
updateRenderedArtifactRequired: boolean;
isArtifactSaved: boolean;
firstTokenReceived: boolean;
feedbackSubmitted: boolean;
setFeedbackSubmitted: Dispatch<SetStateAction<boolean>>;
setArtifact: Dispatch<SetStateAction<ArtifactV3 | undefined>>;
setSelectedBlocks: Dispatch<SetStateAction<TextHighlight | undefined>>;
setSelectedArtifact: (index: number) => void;
setMessages: Dispatch<SetStateAction<BaseMessage[]>>;
streamMessage: (params: GraphInput) => Promise<void>;
setArtifactContent: (index: number, content: string) => void;
clearState: () => void;
switchSelectedThread: (thread: Thread) => void;
setUpdateRenderedArtifactRequired: Dispatch<SetStateAction<boolean>>;
}
type UserDataContextType = ReturnType<typeof useUser>;
type ThreadDataContextType = ReturnType<typeof useThread>;
type AssistantsDataContextType = ReturnType<typeof useAssistants>;
type GraphContentType = {
graphData: GraphData;
userData: UserDataContextType;
threadData: ThreadDataContextType;
assistantsData: AssistantsDataContextType;
};
const GraphContext = createContext<GraphContentType | undefined>(undefined);
export interface GraphInput {
messages?: Record<string, any>[];
highlightedCode?: CodeHighlight;
highlightedText?: TextHighlight;
artifact?: ArtifactV3;
language?: LanguageOptions;
artifactLength?: ArtifactLengthOptions;
regenerateWithEmojis?: boolean;
readingLevel?: ReadingLevelOptions;
addComments?: boolean;
addLogs?: boolean;
portLanguage?: ProgrammingLanguageOptions;
fixBugs?: boolean;
customQuickActionId?: string;
}
export function GraphProvider({ children }: { children: ReactNode }) {
const userData = useUser();
const assistantsData = useAssistants();
const threadData = useThread();
const { toast } = useToast();
const { shareRun } = useRuns();
const [messages, setMessages] = useState<BaseMessage[]>([]);
const [artifact, setArtifact] = useState<ArtifactV3>();
const [selectedBlocks, setSelectedBlocks] = useState<TextHighlight>();
const [isStreaming, setIsStreaming] = useState(false);
const [updateRenderedArtifactRequired, setUpdateRenderedArtifactRequired] =
useState(false);
const lastSavedArtifact = useRef<ArtifactV3 | undefined>(undefined);
const debouncedAPIUpdate = useRef(
debounce(
(artifact: ArtifactV3, threadId: string) =>
updateArtifact(artifact, threadId),
5000
)
).current;
const [isArtifactSaved, setIsArtifactSaved] = useState(true);
const [threadSwitched, setThreadSwitched] = useState(false);
const [firstTokenReceived, setFirstTokenReceived] = useState(false);
const [runId, setRunId] = useState<string>();
const [feedbackSubmitted, setFeedbackSubmitted] = useState(false);
useEffect(() => {
if (userData.user) return;
userData.getUser();
}, []);
useEffect(() => {
if (typeof window === "undefined") return;
if (!userData.user) return;
if (!threadData.threadId) {
threadData.searchOrCreateThread(userData.user.id);
}
// Get or create a new assistant if there isn't one set in state, and we're not
// loading all assistants already.
if (
!assistantsData.selectedAssistant &&
!assistantsData.isLoadingAllAssistants
) {
assistantsData.getOrCreateAssistant(userData.user.id);
}
}, [userData.user]);
// Very hacky way of ensuring updateState is not called when a thread is switched
useEffect(() => {
if (threadSwitched) {
const timer = setTimeout(() => {
setThreadSwitched(false);
}, 1000);
return () => clearTimeout(timer);
}
}, [threadSwitched]);
useEffect(() => {
return () => {
debouncedAPIUpdate.cancel();
};
}, [debouncedAPIUpdate]);
useEffect(() => {
if (!threadData.threadId) return;
if (!messages.length || !artifact) return;
if (updateRenderedArtifactRequired || threadSwitched || isStreaming) return;
const currentIndex = artifact.currentIndex;
const currentContent = artifact.contents.find(
(c) => c.index === currentIndex
);
if (!currentContent) return;
if (
(artifact.contents.length === 1 &&
artifact.contents[0].type === "text" &&
!artifact.contents[0].fullMarkdown) ||
(artifact.contents[0].type === "code" && !artifact.contents[0].code)
) {
// If the artifact has only one content and it's empty, we shouldn't update the state
return;
}
if (
!lastSavedArtifact.current ||
lastSavedArtifact.current.contents !== artifact.contents
) {
setIsArtifactSaved(false);
// This means the artifact in state does not match the last saved artifact
// We need to update
debouncedAPIUpdate(artifact, threadData.threadId);
}
}, [artifact, threadData.threadId]);
const updateArtifact = async (
artifactToUpdate: ArtifactV3,
threadId: string
) => {
if (isStreaming) return;
try {
const client = createClient();
await client.threads.updateState(threadId, {
values: {
artifact: artifactToUpdate,
},
});
setIsArtifactSaved(true);
lastSavedArtifact.current = artifactToUpdate;
} catch (e) {
console.error("Failed to update artifact", e);
console.error("Artifact:", artifactToUpdate);
}
};
const clearState = () => {
setMessages([]);
setArtifact(undefined);
setFirstTokenReceived(true);
};
const streamMessageV2 = async (params: GraphInput) => {
setFirstTokenReceived(false);
if (!threadData.threadId) {
toast({
title: "Error",
description: "No thread ID found",
variant: "destructive",
duration: 5000,
});
return;
}
if (!assistantsData.selectedAssistant) {
toast({
title: "Error",
description: "No assistant ID found",
variant: "destructive",
duration: 5000,
});
return;
}
const client = createClient();
// TODO: update to properly pass the highlight data back
// one field for highlighted text, and one for code
const input = {
...DEFAULT_INPUTS,
artifact,
...params,
...(selectedBlocks && {
highlightedText: selectedBlocks,
}),
};
// Add check for multiple defined fields
const fieldsToCheck = [
input.highlightedCode,
input.highlightedText,
input.language,
input.artifactLength,
input.regenerateWithEmojis,
input.readingLevel,
input.addComments,
input.addLogs,
input.fixBugs,
input.portLanguage,
input.customQuickActionId,
];
if (fieldsToCheck.filter((field) => field !== undefined).length >= 2) {
toast({
title: "Error",
description:
"Can not use multiple fields (quick actions, highlights, etc.) at once. Please try again.",
variant: "destructive",
duration: 5000,
});
return;
}
setIsStreaming(true);
setRunId(undefined);
setFeedbackSubmitted(false);
// The root level run ID of this stream
let runId = "";
let followupMessageId = "";
// let lastMessage: AIMessage | undefined = undefined;
try {
const stream = client.runs.stream(
threadData.threadId,
assistantsData.selectedAssistant.assistant_id,
{
input,
streamMode: "events",
config: {
configurable: {
customModelName: threadData.modelName,
},
},
}
);
// Variables to keep track of content specific to this stream
const prevCurrentContent = artifact
? artifact.contents.find((a) => a.index === artifact.currentIndex)
: undefined;
// The new index of the artifact that is generating
let newArtifactIndex = 1;
if (artifact) {
newArtifactIndex = artifact.contents.length + 1;
}
// The metadata generated when re-writing an artifact
let rewriteArtifactMeta: RewriteArtifactMetaToolResponse | undefined =
undefined;
// For generating an artifact
let generateArtifactToolCallStr = "";
// For updating code artifacts
// All the text up until the startCharIndex
let updatedArtifactStartContent: string | undefined = undefined;
// All the text after the endCharIndex
let updatedArtifactRestContent: string | undefined = undefined;
// Whether or not the first update has been made when updating highlighted code.
let isFirstUpdate = true;
// The new text of an artifact that is being rewritten
let newArtifactContent = "";
// The updated full markdown text when using the highlight update tool
let highlightedText: TextHighlight | undefined = undefined;
for await (const chunk of stream) {
try {
if (!runId && chunk.data?.metadata?.run_id) {
runId = chunk.data.metadata.run_id;
setRunId(runId);
}
if (chunk.data.event === "on_chain_start") {
if (
chunk.data.metadata.langgraph_node === "updateHighlightedText"
) {
highlightedText = chunk.data.data?.input?.highlightedText;
}
}
if (chunk.data.event === "on_chat_model_stream") {
// These are generating new messages to insert to the chat window.
if (
["generateFollowup", "replyToGeneralInput"].includes(
chunk.data.metadata.langgraph_node
)
) {
const message = chunk.data.data.chunk[1];
if (!followupMessageId) {
followupMessageId = message.id;
}
setMessages((prevMessages) =>
replaceOrInsertMessageChunk(prevMessages, message)
);
}
if (chunk.data.metadata.langgraph_node === "generateArtifact") {
generateArtifactToolCallStr +=
chunk.data.data.chunk?.[1]?.tool_call_chunks?.[0]?.args || "";
let newArtifactText: ArtifactToolResponse | undefined = undefined;
// Attempt to parse the tool call chunk.
try {
newArtifactText = parsePartialJson(generateArtifactToolCallStr);
if (!newArtifactText) {
throw new Error("Failed to parse new artifact text");
}
newArtifactText = {
...newArtifactText,
title: newArtifactText.title ?? "",
type: newArtifactText.type ?? "",
};
} catch (_) {
continue;
}
if (
newArtifactText.artifact &&
(newArtifactText.type === "text" ||
(newArtifactText.type === "code" && newArtifactText.language))
) {
setFirstTokenReceived(true);
setArtifact(() => {
const content =
createNewGeneratedArtifactFromTool(newArtifactText);
if (!content) {
return undefined;
}
return {
currentIndex: 1,
contents: [content],
};
});
}
}
if (
chunk.data.metadata.langgraph_node === "updateHighlightedText"
) {
const message = chunk.data.data?.chunk[1];
if (!message) {
continue;
}
if (!artifact) {
console.error(
"No artifacts found when updating highlighted markdown..."
);
continue;
}
if (!highlightedText) {
toast({
title: "Error",
description: "No highlighted text found",
variant: "destructive",
duration: 5000,
});
continue;
}
if (!prevCurrentContent) {
toast({
title: "Error",
description: "Original artifact not found",
variant: "destructive",
duration: 5000,
});
return;
}
if (!isArtifactMarkdownContent(prevCurrentContent)) {
toast({
title: "Error",
description: "Received non markdown block update",
variant: "destructive",
duration: 5000,
});
return;
}
const partialUpdatedContent = message.content || "";
const startIndexOfHighlightedText =
highlightedText.fullMarkdown.indexOf(
highlightedText.markdownBlock
);
if (
updatedArtifactStartContent === undefined &&
updatedArtifactRestContent === undefined
) {
// Initialize the start and rest content on first chunk
updatedArtifactStartContent =
highlightedText.fullMarkdown.slice(
0,
startIndexOfHighlightedText
);
updatedArtifactRestContent = highlightedText.fullMarkdown.slice(
startIndexOfHighlightedText +
highlightedText.markdownBlock.length
);
}
if (
updatedArtifactStartContent !== undefined &&
updatedArtifactRestContent !== undefined
) {
updatedArtifactStartContent += partialUpdatedContent;
}
const firstUpdateCopy = isFirstUpdate;
setFirstTokenReceived(true);
setArtifact((prev) => {
if (!prev) {
throw new Error("No artifact found when updating markdown");
}
return updateHighlightedMarkdown(
prev,
`${updatedArtifactStartContent}${updatedArtifactRestContent}`,
newArtifactIndex,
prevCurrentContent,
firstUpdateCopy
);
});
if (isFirstUpdate) {
isFirstUpdate = false;
}
}
if (chunk.data.metadata.langgraph_node === "updateArtifact") {
if (!artifact) {
toast({
title: "Error",
description: "Original artifact not found",
variant: "destructive",
duration: 5000,
});
return;
}
if (!params.highlightedCode) {
toast({
title: "Error",
description: "No highlighted code found",
variant: "destructive",
duration: 5000,
});
return;
}
const partialUpdatedContent =
chunk.data.data.chunk?.[1]?.content || "";
const { startCharIndex, endCharIndex } = params.highlightedCode;
if (!prevCurrentContent) {
toast({
title: "Error",
description: "Original artifact not found",
variant: "destructive",
duration: 5000,
});
return;
}
if (prevCurrentContent.type !== "code") {
toast({
title: "Error",
description: "Received non code block update",
variant: "destructive",
duration: 5000,
});
return;
}
if (
updatedArtifactStartContent === undefined &&
updatedArtifactRestContent === undefined
) {
updatedArtifactStartContent = prevCurrentContent.code.slice(
0,
startCharIndex
);
updatedArtifactRestContent =
prevCurrentContent.code.slice(endCharIndex);
} else {
// One of the above have been populated, now we can update the start to contain the new text.
updatedArtifactStartContent += partialUpdatedContent;
}
const firstUpdateCopy = isFirstUpdate;
setFirstTokenReceived(true);
setArtifact((prev) => {
if (!prev) {
throw new Error("No artifact found when updating markdown");
}
const content = removeCodeBlockFormatting(
`${updatedArtifactStartContent}${updatedArtifactRestContent}`
);
return updateHighlightedCode(
prev,
content,
newArtifactIndex,
prevCurrentContent,
firstUpdateCopy
);
});
if (isFirstUpdate) {
isFirstUpdate = false;
}
}
if (
chunk.data.metadata.langgraph_node === "rewriteArtifact" &&
chunk.data.name === "rewrite_artifact_model_call" &&
rewriteArtifactMeta
) {
if (!artifact) {
toast({
title: "Error",
description: "Original artifact not found",
variant: "destructive",
duration: 5000,
});
return;
}
newArtifactContent += chunk.data.data.chunk?.[1]?.content || "";
// Ensure we have the language to update the artifact with
let artifactLanguage = params.portLanguage || undefined;
if (
!artifactLanguage &&
rewriteArtifactMeta.type === "code" &&
rewriteArtifactMeta.language
) {
// If the type is `code` we should have a programming language populated
// in the rewriteArtifactMeta and can use that.
artifactLanguage =
rewriteArtifactMeta.language as ProgrammingLanguageOptions;
} else if (!artifactLanguage) {
artifactLanguage =
(prevCurrentContent?.title as ProgrammingLanguageOptions) ??
"other";
}
const firstUpdateCopy = isFirstUpdate;
setFirstTokenReceived(true);
setArtifact((prev) => {
if (!prev) {
throw new Error("No artifact found when updating markdown");
}
let content = newArtifactContent;
if (!rewriteArtifactMeta) {
console.error(
"No rewrite artifact meta found when updating artifact"
);
return prev;
}
if (rewriteArtifactMeta.type === "code") {
content = removeCodeBlockFormatting(content);
}
return updateRewrittenArtifact({
prevArtifact: prev,
newArtifactContent: content,
rewriteArtifactMeta: rewriteArtifactMeta,
prevCurrentContent,
newArtifactIndex,
isFirstUpdate: firstUpdateCopy,
artifactLanguage,
});
});
if (isFirstUpdate) {
isFirstUpdate = false;
}
}
if (
[
"rewriteArtifactTheme",
"rewriteCodeArtifactTheme",
"customAction",
].includes(chunk.data.metadata.langgraph_node)
) {
if (!artifact) {
toast({
title: "Error",
description: "Original artifact not found",
variant: "destructive",
duration: 5000,
});
return;
}
if (!prevCurrentContent) {
toast({
title: "Error",
description: "Original artifact not found",
variant: "destructive",
duration: 5000,
});
return;
}
newArtifactContent += chunk.data.data.chunk?.[1]?.content || "";
// Ensure we have the language to update the artifact with
const artifactLanguage =
params.portLanguage ||
(isArtifactCodeContent(prevCurrentContent)
? prevCurrentContent.language
: "other");
const langGraphNode = chunk.data.metadata.langgraph_node;
let artifactType: ArtifactType;
if (langGraphNode === "rewriteCodeArtifactTheme") {
artifactType = "code";
} else if (langGraphNode === "rewriteArtifactTheme") {
artifactType = "text";
} else {
artifactType = prevCurrentContent.type;
}
const firstUpdateCopy = isFirstUpdate;
setFirstTokenReceived(true);
setArtifact((prev) => {
if (!prev) {
throw new Error("No artifact found when updating markdown");
}
let content = newArtifactContent;
if (artifactType === "code") {
content = removeCodeBlockFormatting(content);
}
return updateRewrittenArtifact({
prevArtifact: prev ?? artifact,
newArtifactContent: content,
rewriteArtifactMeta: {
type: artifactType,
title: prevCurrentContent.title,
language: artifactLanguage,
},
prevCurrentContent,
newArtifactIndex,
isFirstUpdate: firstUpdateCopy,
artifactLanguage,
});
});
if (isFirstUpdate) {
isFirstUpdate = false;
}
}
}
if (chunk.data.event === "on_chat_model_end") {
if (
chunk.data.metadata.langgraph_node === "rewriteArtifact" &&
chunk.data.name === "optionally_update_artifact_meta"
) {
rewriteArtifactMeta = chunk.data.data.output.tool_calls[0].args;
}
// if (chunk.data?.data.output && "type" in chunk.data.data.output && chunk.data.data.output.type === "ai") {
// lastMessage = new AIMessage({
// ...chunk.data.data.output,
// });
// }
}
} catch (e) {
console.error(
"Failed to parse stream chunk",
chunk,
"\n\nError:\n",
e
);
}
}
lastSavedArtifact.current = artifact;
} catch (e) {
console.error("Failed to stream message", e);
} finally {
setSelectedBlocks(undefined);
setIsStreaming(false);
}
if (runId) {
// Chain `.then` to not block the stream
shareRun(runId).then(async (sharedRunURL) => {
setMessages((prevMessages) => {
const newMsgs = prevMessages.map((msg) => {
if (
msg.id === followupMessageId &&
!(msg as AIMessage).tool_calls?.find(
(tc) => tc.name === "langsmith_tool_ui"
)
) {
const toolCall = {
name: "langsmith_tool_ui",
args: { sharedRunURL },
id: sharedRunURL
?.split("https://smith.langchain.com/public/")[1]
.split("/")[0],
};
const castMsg = msg as AIMessage;
const newMessageWithToolCall = new AIMessage({
...castMsg,
content: castMsg.content,
id: castMsg.id,
tool_calls: castMsg.tool_calls
? [...castMsg.tool_calls, toolCall]
: [toolCall],
});
return newMessageWithToolCall;
}
return msg;
});
return newMsgs;
});
// if (threadId && lastMessage && lastMessage.id) {
// // Update the state of the last message to include the run URL
// // for proper rendering when loading history.
// const newMessages = [new RemoveMessage({ id: lastMessage.id }), new AIMessage({
// ...lastMessage,
// content: lastMessage.content,
// response_metadata: {
// ...lastMessage.response_metadata,
// langSmithRunURL: sharedRunURL,
// }
// })];
// await client.threads.updateState(threadId, {
// values: {
// messages: newMessages
// },
// });
// const newState = await client.threads.getState(threadId);
// }
});
}
};
const setSelectedArtifact = (index: number) => {
setUpdateRenderedArtifactRequired(true);
setThreadSwitched(true);
setArtifact((prev) => {
if (!prev) {
toast({
title: "Error",
description: "No artifactV2 found",
variant: "destructive",
duration: 5000,
});
return prev;
}
const newArtifact = {
...prev,
currentIndex: index,
};
lastSavedArtifact.current = newArtifact;
return newArtifact;
});
};
const setArtifactContent = (index: number, content: string) => {
setArtifact((prev) => {
if (!prev) {
toast({
title: "Error",
description: "No artifact found",
variant: "destructive",
duration: 5000,
});
return prev;
}
const newArtifact = {
...prev,
currentIndex: index,
contents: prev.contents.map((a) => {
if (a.index === index && a.type === "code") {
return {
...a,
code: reverseCleanContent(content),
};
}
return a;
}),
};
return newArtifact;
});
};
const switchSelectedThread = (thread: Thread) => {
setUpdateRenderedArtifactRequired(true);
setThreadSwitched(true);
threadData.setThreadId(thread.thread_id);
setCookie(THREAD_ID_COOKIE_NAME, thread.thread_id);
if (thread.metadata?.customModelName) {
threadData.setModelName(
thread.metadata.customModelName as ALL_MODEL_NAMES
);
} else {
threadData.setModelName(DEFAULT_MODEL_NAME);
}
const castValues: {
artifact: ArtifactV3 | undefined;
messages: Record<string, any>[] | undefined;
} = {
artifact: undefined,
messages: (thread.values as Record<string, any>)?.messages || undefined,
};
const castThreadValues = thread.values as Record<string, any>;
if (castThreadValues?.artifact) {
if (isDeprecatedArtifactType(castThreadValues.artifact)) {
castValues.artifact = convertToArtifactV3(castThreadValues.artifact);
} else {
castValues.artifact = castThreadValues.artifact;
}
} else {
castValues.artifact = undefined;
}
lastSavedArtifact.current = castValues?.artifact;
if (!castValues?.messages?.length) {
setMessages([]);
setArtifact(castValues?.artifact);
return;
}
setArtifact(castValues?.artifact);
setMessages(
castValues.messages.map((msg: Record<string, any>) => {
if (msg.response_metadata?.langSmithRunURL) {
msg.tool_calls = msg.tool_calls ?? [];
msg.tool_calls.push({
name: "langsmith_tool_ui",
args: { sharedRunURL: msg.response_metadata.langSmithRunURL },
id: msg.response_metadata.langSmithRunURL
?.split("https://smith.langchain.com/public/")[1]
.split("/")[0],
});
}
return msg as BaseMessage;
})
);
};
const contextValue: GraphContentType = {
userData,
threadData,
assistantsData,
graphData: {
runId,
isStreaming,
selectedBlocks,
messages,
artifact,
updateRenderedArtifactRequired,
isArtifactSaved,
firstTokenReceived,
feedbackSubmitted,
setFeedbackSubmitted,
setArtifact,
setSelectedBlocks,
setSelectedArtifact,
setMessages,
streamMessage: streamMessageV2,
setArtifactContent,
clearState,
switchSelectedThread,
setUpdateRenderedArtifactRequired,
},
};
return (
<GraphContext.Provider value={contextValue}>
{children}
</GraphContext.Provider>
);
}
export function useGraphContext() {
const context = useContext(GraphContext);
if (context === undefined) {
throw new Error("useGraphContext must be used within a GraphProvider");
}
return context;
}
|
0 | lc_public_repos/open-canvas/src | lc_public_repos/open-canvas/src/contexts/utils.ts | import {
Artifact,
ArtifactCodeV3,
ArtifactMarkdownV3,
ArtifactToolResponse,
ArtifactV3,
ProgrammingLanguageOptions,
RewriteArtifactMetaToolResponse,
} from "@/types";
import {
AIMessage,
BaseMessage,
BaseMessageChunk,
} from "@langchain/core/messages";
export function removeCodeBlockFormatting(text: string): string {
if (!text) return text;
// Regular expression to match code blocks
const codeBlockRegex = /^```[\w-]*\n([\s\S]*?)\n```$/;
// Check if the text matches the code block pattern
const match = text.match(codeBlockRegex);
if (match) {
// If it matches, return the content inside the code block
return match[1].trim();
} else {
// If it doesn't match, return the original text
return text;
}
}
export const replaceOrInsertMessageChunk = (
prevMessages: BaseMessage[],
newMessageChunk: BaseMessageChunk
): BaseMessage[] => {
const existingMessageIndex = prevMessages.findIndex(
(msg) => msg.id === newMessageChunk.id
);
if (
prevMessages[existingMessageIndex]?.content &&
typeof prevMessages[existingMessageIndex]?.content !== "string"
) {
throw new Error("Message content is not a string");
}
if (typeof newMessageChunk.content !== "string") {
throw new Error("Message chunk content is not a string");
}
if (existingMessageIndex !== -1) {
// Create a new array with the updated message
return [
...prevMessages.slice(0, existingMessageIndex),
new AIMessage({
...prevMessages[existingMessageIndex],
content:
(prevMessages[existingMessageIndex]?.content || "") +
(newMessageChunk?.content || ""),
}),
...prevMessages.slice(existingMessageIndex + 1),
];
} else {
const newMessage = new AIMessage({
...newMessageChunk,
});
return [...prevMessages, newMessage];
}
};
export const createNewGeneratedArtifactFromTool = (
artifactTool: ArtifactToolResponse
): ArtifactMarkdownV3 | ArtifactCodeV3 | undefined => {
if (!artifactTool.type) {
console.error("Received new artifact without type");
return;
}
if (artifactTool.type === "text") {
return {
index: 1,
type: "text",
title: artifactTool.title || "",
fullMarkdown: artifactTool.artifact || "",
};
} else {
if (!artifactTool.language) {
console.error("Received new code artifact without language");
}
return {
index: 1,
type: "code",
title: artifactTool.title || "",
code: artifactTool.artifact || "",
language: artifactTool.language as ProgrammingLanguageOptions,
};
}
};
const validateNewArtifactIndex = (
newArtifactIndexGuess: number,
prevArtifactContentsLength: number,
isFirstUpdate: boolean
): number => {
if (isFirstUpdate) {
// For first updates, currentIndex should be one more than the total prev contents
// to append the new content at the end
if (newArtifactIndexGuess !== prevArtifactContentsLength + 1) {
return prevArtifactContentsLength + 1;
}
} else {
if (newArtifactIndexGuess !== prevArtifactContentsLength) {
// For subsequent updates, currentIndex should match the total contents
// to update the latest content in place
return prevArtifactContentsLength;
}
}
// If the guess is correct, return the guess
return newArtifactIndexGuess;
};
export const updateHighlightedMarkdown = (
prevArtifact: ArtifactV3,
content: string,
newArtifactIndex: number,
prevCurrentContent: ArtifactMarkdownV3,
isFirstUpdate: boolean
): ArtifactV3 | undefined => {
// Create a deep copy of the previous artifact
const basePrevArtifact = {
...prevArtifact,
contents: prevArtifact.contents.map((c) => ({ ...c })),
};
const currentIndex = validateNewArtifactIndex(
newArtifactIndex,
basePrevArtifact.contents.length,
isFirstUpdate
);
let newContents: (ArtifactCodeV3 | ArtifactMarkdownV3)[];
if (isFirstUpdate) {
const newMarkdownContent: ArtifactMarkdownV3 = {
...prevCurrentContent,
index: currentIndex,
fullMarkdown: content,
};
newContents = [...basePrevArtifact.contents, newMarkdownContent];
} else {
newContents = basePrevArtifact.contents.map((c) => {
if (c.index === currentIndex) {
return {
...c,
fullMarkdown: content,
};
}
return { ...c }; // Create new reference for unchanged items too
});
}
// Create new reference for the entire artifact
const newArtifact: ArtifactV3 = {
...basePrevArtifact,
currentIndex,
contents: newContents,
};
// Verify we're actually creating a new reference
if (Object.is(newArtifact, prevArtifact)) {
console.warn("Warning: updateHighlightedMarkdown returned same reference");
}
return newArtifact;
};
export const updateHighlightedCode = (
prevArtifact: ArtifactV3,
content: string,
newArtifactIndex: number,
prevCurrentContent: ArtifactCodeV3,
isFirstUpdate: boolean
): ArtifactV3 | undefined => {
// Create a deep copy of the previous artifact
const basePrevArtifact = {
...prevArtifact,
contents: prevArtifact.contents.map((c) => ({ ...c })),
};
const currentIndex = validateNewArtifactIndex(
newArtifactIndex,
basePrevArtifact.contents.length,
isFirstUpdate
);
let newContents: (ArtifactCodeV3 | ArtifactMarkdownV3)[];
if (isFirstUpdate) {
const newCodeContent: ArtifactCodeV3 = {
...prevCurrentContent,
index: currentIndex,
code: content,
};
newContents = [...basePrevArtifact.contents, newCodeContent];
} else {
newContents = basePrevArtifact.contents.map((c) => {
if (c.index === currentIndex) {
return {
...c,
code: content,
};
}
return { ...c }; // Create new reference for unchanged items too
});
}
const newArtifact: ArtifactV3 = {
...basePrevArtifact,
currentIndex,
contents: newContents,
};
// Verify we're actually creating a new reference
if (Object.is(newArtifact, prevArtifact)) {
console.warn("Warning: updateHighlightedCode returned same reference");
}
return newArtifact;
};
interface UpdateRewrittenArtifactArgs {
prevArtifact: ArtifactV3;
newArtifactContent: string;
rewriteArtifactMeta: RewriteArtifactMetaToolResponse;
prevCurrentContent?: ArtifactMarkdownV3 | ArtifactCodeV3;
newArtifactIndex: number;
isFirstUpdate: boolean;
artifactLanguage: string;
}
export const updateRewrittenArtifact = ({
prevArtifact,
newArtifactContent,
rewriteArtifactMeta,
prevCurrentContent,
newArtifactIndex,
isFirstUpdate,
artifactLanguage,
}: UpdateRewrittenArtifactArgs): ArtifactV3 => {
// Create a deep copy of the previous artifact
const basePrevArtifact = {
...prevArtifact,
contents: prevArtifact.contents.map((c) => ({ ...c })),
};
const currentIndex = validateNewArtifactIndex(
newArtifactIndex,
basePrevArtifact.contents.length,
isFirstUpdate
);
let artifactContents: (ArtifactMarkdownV3 | ArtifactCodeV3)[];
if (isFirstUpdate) {
if (rewriteArtifactMeta.type === "code") {
artifactContents = [
...basePrevArtifact.contents,
{
type: "code",
title: rewriteArtifactMeta.title || prevCurrentContent?.title || "",
index: currentIndex,
language: artifactLanguage as ProgrammingLanguageOptions,
code: newArtifactContent,
},
];
} else {
artifactContents = [
...basePrevArtifact.contents,
{
index: currentIndex,
type: "text",
title: rewriteArtifactMeta?.title ?? prevCurrentContent?.title ?? "",
fullMarkdown: newArtifactContent,
},
];
}
} else {
if (rewriteArtifactMeta?.type === "code") {
artifactContents = basePrevArtifact.contents.map((c) => {
if (c.index === currentIndex) {
return {
...c,
code: newArtifactContent,
};
}
return { ...c }; // Create new reference for unchanged items too
});
} else {
artifactContents = basePrevArtifact.contents.map((c) => {
if (c.index === currentIndex) {
return {
...c,
fullMarkdown: newArtifactContent,
};
}
return { ...c }; // Create new reference for unchanged items too
});
}
}
const newArtifact: ArtifactV3 = {
...basePrevArtifact,
currentIndex,
contents: artifactContents,
};
// Verify we're actually creating a new reference
if (Object.is(newArtifact, prevArtifact)) {
console.warn("Warning: updateRewrittenArtifact returned same reference");
}
return newArtifact;
};
export const convertToArtifactV3 = (oldArtifact: Artifact): ArtifactV3 => {
let currentIndex = oldArtifact.currentContentIndex;
if (currentIndex > oldArtifact.contents.length) {
// If the value to be set in `currentIndex` is greater than the total number of contents,
// set it to the last index so that the user can see the latest content.
currentIndex = oldArtifact.contents.length;
}
const v3: ArtifactV3 = {
currentIndex,
contents: oldArtifact.contents.map((content) => {
if (content.type === "code") {
return {
index: content.index,
type: "code",
title: content.title,
language: content.language as ProgrammingLanguageOptions,
code: content.content,
};
} else {
return {
index: content.index,
type: "text",
title: content.title,
fullMarkdown: content.content,
blocks: undefined,
};
}
}),
};
return v3;
};
export const getArtifactContent = (
artifact: ArtifactV3
): ArtifactCodeV3 | ArtifactMarkdownV3 => {
if (!artifact) {
throw new Error("No artifact found.");
}
const currentContent = artifact.contents.find(
(a) => a.index === artifact.currentIndex
);
if (!currentContent) {
return artifact.contents[artifact.contents.length - 1];
}
return currentContent;
};
|
0 | lc_public_repos/open-canvas/src | lc_public_repos/open-canvas/src/hooks/use-toast.ts | "use client";
// Inspired by react-hot-toast library
import * as React from "react";
import type { ToastActionElement, ToastProps } from "@/components/ui/toast";
const TOAST_LIMIT = 1;
const TOAST_REMOVE_DELAY = 1000000;
type ToasterToast = ToastProps & {
id: string;
title?: React.ReactNode;
description?: React.ReactNode;
action?: ToastActionElement;
};
const UNUSED_actionTypes = {
ADD_TOAST: "ADD_TOAST",
UPDATE_TOAST: "UPDATE_TOAST",
DISMISS_TOAST: "DISMISS_TOAST",
REMOVE_TOAST: "REMOVE_TOAST",
} as const;
let count = 0;
function genId() {
count = (count + 1) % Number.MAX_SAFE_INTEGER;
return count.toString();
}
type ActionType = typeof UNUSED_actionTypes;
type Action =
| {
type: ActionType["ADD_TOAST"];
toast: ToasterToast;
}
| {
type: ActionType["UPDATE_TOAST"];
toast: Partial<ToasterToast>;
}
| {
type: ActionType["DISMISS_TOAST"];
toastId?: ToasterToast["id"];
}
| {
type: ActionType["REMOVE_TOAST"];
toastId?: ToasterToast["id"];
};
interface State {
toasts: ToasterToast[];
}
const toastTimeouts = new Map<string, ReturnType<typeof setTimeout>>();
const addToRemoveQueue = (toastId: string) => {
if (toastTimeouts.has(toastId)) {
return;
}
const timeout = setTimeout(() => {
toastTimeouts.delete(toastId);
dispatch({
type: "REMOVE_TOAST",
toastId: toastId,
});
}, TOAST_REMOVE_DELAY);
toastTimeouts.set(toastId, timeout);
};
export const reducer = (state: State, action: Action): State => {
switch (action.type) {
case "ADD_TOAST":
return {
...state,
toasts: [action.toast, ...state.toasts].slice(0, TOAST_LIMIT),
};
case "UPDATE_TOAST":
return {
...state,
toasts: state.toasts.map((t) =>
t.id === action.toast.id ? { ...t, ...action.toast } : t
),
};
case "DISMISS_TOAST": {
const { toastId } = action;
// ! Side effects ! - This could be extracted into a dismissToast() action,
// but I'll keep it here for simplicity
if (toastId) {
addToRemoveQueue(toastId);
} else {
state.toasts.forEach((toast) => {
addToRemoveQueue(toast.id);
});
}
return {
...state,
toasts: state.toasts.map((t) =>
t.id === toastId || toastId === undefined
? {
...t,
open: false,
}
: t
),
};
}
case "REMOVE_TOAST":
if (action.toastId === undefined) {
return {
...state,
toasts: [],
};
}
return {
...state,
toasts: state.toasts.filter((t) => t.id !== action.toastId),
};
}
};
const listeners: Array<(state: State) => void> = [];
let memoryState: State = { toasts: [] };
function dispatch(action: Action) {
memoryState = reducer(memoryState, action);
listeners.forEach((listener) => {
listener(memoryState);
});
}
type Toast = Omit<ToasterToast, "id">;
function toast({ ...props }: Toast) {
const id = genId();
const update = (props: ToasterToast) =>
dispatch({
type: "UPDATE_TOAST",
toast: { ...props, id },
});
const dismiss = () => dispatch({ type: "DISMISS_TOAST", toastId: id });
dispatch({
type: "ADD_TOAST",
toast: {
...props,
id,
open: true,
onOpenChange: (open) => {
if (!open) dismiss();
},
},
});
return {
id: id,
dismiss,
update,
};
}
function useToast() {
const [state, setState] = React.useState<State>(memoryState);
React.useEffect(() => {
listeners.push(setState);
return () => {
const index = listeners.indexOf(setState);
if (index > -1) {
listeners.splice(index, 1);
}
};
}, [state]);
return {
...state,
toast,
dismiss: (toastId?: string) => dispatch({ type: "DISMISS_TOAST", toastId }),
};
}
export { useToast, toast };
|
0 | lc_public_repos/open-canvas/src | lc_public_repos/open-canvas/src/hooks/useRuns.tsx | export function useRuns() {
/**
* Generates a public shared run ID for the given run ID.
*/
const shareRun = async (runId: string): Promise<string | undefined> => {
const res = await fetch("/api/runs/share", {
method: "POST",
body: JSON.stringify({ runId }),
headers: {
"Content-Type": "application/json",
},
});
if (!res.ok) {
return;
}
const { sharedRunURL } = await res.json();
return sharedRunURL;
};
return {
shareRun,
};
}
|
0 | lc_public_repos/open-canvas/src | lc_public_repos/open-canvas/src/hooks/useThread.tsx | import {
ALL_MODEL_NAMES,
DEFAULT_MODEL_NAME,
HAS_EMPTY_THREADS_CLEARED_COOKIE,
THREAD_ID_COOKIE_NAME,
} from "@/constants";
import { getCookie, setCookie } from "@/lib/cookies";
import { Thread } from "@langchain/langgraph-sdk";
import { useState } from "react";
import { createClient } from "./utils";
export function useThread() {
const [threadId, setThreadId] = useState<string>();
const [userThreads, setUserThreads] = useState<Thread[]>([]);
const [isUserThreadsLoading, setIsUserThreadsLoading] = useState(false);
const [modelName, setModelName] =
useState<ALL_MODEL_NAMES>(DEFAULT_MODEL_NAME);
const createThread = async (
customModelName: ALL_MODEL_NAMES = DEFAULT_MODEL_NAME,
userId: string
): Promise<Thread | undefined> => {
const client = createClient();
console.log("creating thread!", customModelName);
try {
const thread = await client.threads.create({
metadata: {
supabase_user_id: userId,
customModelName,
},
});
setThreadId(thread.thread_id);
setCookie(THREAD_ID_COOKIE_NAME, thread.thread_id);
setModelName(customModelName);
await getUserThreads(userId);
return thread;
} catch (e) {
console.error("Failed to create thread", e);
}
};
const getUserThreads = async (userId: string) => {
setIsUserThreadsLoading(true);
try {
const client = createClient();
const userThreads = await client.threads.search({
metadata: {
supabase_user_id: userId,
},
limit: 100,
});
if (userThreads.length > 0) {
const lastInArray = userThreads[0];
const allButLast = userThreads.slice(1, userThreads.length);
const filteredThreads = allButLast.filter(
(thread) => thread.values && Object.keys(thread.values).length > 0
);
setUserThreads([...filteredThreads, lastInArray]);
}
} finally {
setIsUserThreadsLoading(false);
}
};
const searchOrCreateThread = async (userId: string) => {
const threadIdCookie = getCookie(THREAD_ID_COOKIE_NAME);
if (!threadIdCookie) {
await createThread(modelName, userId);
return;
}
// Thread ID is in cookies.
const thread = await getThreadById(threadIdCookie);
if (
thread &&
(!thread?.values || Object.keys(thread.values).length === 0)
) {
// No values = no activity. Can keep.
setThreadId(threadIdCookie);
return threadIdCookie;
} else {
// Current thread has activity. Create a new thread.
await createThread(modelName, userId);
return;
}
};
const clearThreadsWithNoValues = async (userId: string) => {
const hasBeenClearedCookie = getCookie(HAS_EMPTY_THREADS_CLEARED_COOKIE);
if (hasBeenClearedCookie === "true") {
return;
}
const client = createClient();
const processedThreadIds = new Set<string>();
const fetchAndDeleteThreads = async (offset = 0) => {
const userThreads = await client.threads.search({
metadata: {
supabase_user_id: userId,
},
limit: 100,
offset: offset,
});
const threadsToDelete = userThreads.filter(
(thread) =>
!thread.values &&
thread.thread_id !== threadId &&
!processedThreadIds.has(thread.thread_id)
);
if (threadsToDelete.length > 0) {
const deleteBatch = async (threadIds: string[]) => {
await Promise.all(
threadIds.map(async (threadId) => {
await client.threads.delete(threadId);
processedThreadIds.add(threadId);
})
);
};
// Create an array of unique thread IDs
const uniqueThreadIds = Array.from(
new Set(threadsToDelete.map((thread) => thread.thread_id))
);
// Process unique thread IDs in batches of 10
for (let i = 0; i < uniqueThreadIds.length; i += 10) {
try {
await deleteBatch(uniqueThreadIds.slice(i, i + 10));
} catch (e) {
console.error("Error deleting threads", e);
}
}
}
if (userThreads.length === 100) {
// If we got 100 threads, there might be more, so continue fetching
await fetchAndDeleteThreads(offset + 100);
}
};
try {
await fetchAndDeleteThreads();
setCookie(HAS_EMPTY_THREADS_CLEARED_COOKIE, "true");
} catch (e) {
console.error("Error fetching & deleting threads", e);
}
};
const getThreadById = async (id: string): Promise<Thread | undefined> => {
try {
const client = createClient();
const thread = await client.threads.get(id);
if (thread.metadata && thread.metadata.customModelName) {
setModelName(thread.metadata.customModelName as ALL_MODEL_NAMES);
}
return thread;
} catch (e) {
console.error(`Failed to get thread with ID ${id}`, e);
}
};
const deleteThread = async (
id: string,
userId: string,
clearMessages: () => void
) => {
setUserThreads((prevThreads) => {
const newThreads = prevThreads.filter(
(thread) => thread.thread_id !== id
);
return newThreads;
});
if (id === threadId) {
clearMessages();
// Create a new thread. Use .then to avoid blocking the UI.
// Once completed, `createThread` will re-fetch all user
// threads to update UI.
void createThread(modelName, userId);
}
const client = createClient();
try {
await client.threads.delete(id);
} catch (e) {
console.error(`Failed to delete thread with ID ${id}`, e);
}
};
return {
threadId,
userThreads,
isUserThreadsLoading,
modelName,
createThread,
clearThreadsWithNoValues,
searchOrCreateThread,
getUserThreads,
deleteThread,
getThreadById,
setThreadId,
setModelName,
};
}
|
0 | lc_public_repos/open-canvas/src | lc_public_repos/open-canvas/src/hooks/useAssistants.tsx | import { Assistant } from "@langchain/langgraph-sdk";
import { useState } from "react";
import { useToast } from "./use-toast";
import { createClient } from "./utils";
import { ASSISTANT_ID_COOKIE } from "@/constants";
import { getCookie, removeCookie } from "@/lib/cookies";
export type AssistantTool = {
/**
* The name of the tool
*/
name: string;
/**
* The tool's description.
*/
description: string;
/**
* JSON Schema for the parameters of the tool.
*/
parameters: Record<string, any>;
};
export interface CreateAssistantFields {
iconData?: {
/**
* The name of the Lucide icon to use for the assistant.
* @default "User"
*/
iconName: string;
/**
* The hex color code to use for the icon.
*/
iconColor: string;
};
/**
* The name of the assistant.
*/
name: string;
/**
* An optional description of the assistant, provided by the user/
*/
description?: string;
/**
* The tools the assistant has access to.
*/
tools?: Array<AssistantTool>;
/**
* An optional system prompt to prefix all generations with.
*/
systemPrompt?: string;
is_default?: boolean;
}
export function useAssistants() {
const { toast } = useToast();
const [isLoadingAllAssistants, setIsLoadingAllAssistants] = useState(false);
const [isDeletingAssistant, setIsDeletingAssistant] = useState(false);
const [isCreatingAssistant, setIsCreatingAssistant] = useState(false);
const [isEditingAssistant, setIsEditingAssistant] = useState(false);
const [assistants, setAssistants] = useState<Assistant[]>([]);
const [selectedAssistant, setSelectedAssistant] = useState<Assistant>();
const getAssistants = async (userId: string): Promise<void> => {
setIsLoadingAllAssistants(true);
try {
const client = createClient();
const response = await client.assistants.search({
metadata: {
user_id: userId,
},
});
setAssistants({
...response,
});
setIsLoadingAllAssistants(false);
} catch (e) {
toast({
title: "Failed to get assistants",
description: "Please try again later.",
});
console.error("Failed to get assistants", e);
setIsLoadingAllAssistants(false);
}
};
const deleteAssistant = async (assistantId: string): Promise<boolean> => {
setIsDeletingAssistant(true);
try {
const client = createClient();
await client.assistants.delete(assistantId);
if (selectedAssistant?.assistant_id === assistantId) {
// Get the first assistant in the list to set as
const defaultAssistant =
assistants.find((a) => a.metadata?.is_default) || assistants[0];
setSelectedAssistant(defaultAssistant);
}
setAssistants((prev) =>
prev.filter((assistant) => assistant.assistant_id !== assistantId)
);
setIsDeletingAssistant(false);
return true;
} catch (e) {
toast({
title: "Failed to delete assistant",
description: "Please try again later.",
});
console.error("Failed to delete assistant", e);
setIsDeletingAssistant(false);
return false;
}
};
const createCustomAssistant = async (
newAssistant: CreateAssistantFields,
userId: string,
successCallback?: (id: string) => void
): Promise<boolean> => {
setIsCreatingAssistant(true);
try {
const client = createClient();
const { tools, systemPrompt, name, ...metadata } = newAssistant;
const createdAssistant = await client.assistants.create({
graphId: "agent",
name,
metadata: {
user_id: userId,
...metadata,
},
config: {
configurable: {
tools,
systemPrompt,
},
},
ifExists: "do_nothing",
});
setAssistants((prev) => [...prev, createdAssistant]);
setSelectedAssistant(createdAssistant);
successCallback?.(createdAssistant.assistant_id);
setIsCreatingAssistant(false);
return true;
} catch (e) {
toast({
title: "Failed to create assistant",
description: "Please try again later.",
});
setIsCreatingAssistant(false);
console.error("Failed to create an assistant", e);
return false;
}
};
const editCustomAssistant = async (
editedAssistant: CreateAssistantFields,
assistantId: string,
userId: string
): Promise<Assistant | undefined> => {
setIsEditingAssistant(true);
try {
const client = createClient();
const { tools, systemPrompt, name, ...metadata } = editedAssistant;
const response = await client.assistants.update(assistantId, {
name,
graphId: "agent",
metadata: {
user_id: userId,
...metadata,
},
config: {
configurable: {
tools,
systemPrompt,
},
},
});
setAssistants((prev) =>
prev.map((assistant) => {
if (assistant.assistant_id === assistantId) {
return response;
}
return assistant;
})
);
setIsEditingAssistant(false);
return response;
} catch (e) {
console.error("Failed to edit assistant", e);
setIsEditingAssistant(false);
return undefined;
}
};
/**
* Legacy function which gets the assistant and updates it's metadata. Then, it deletes the assistant ID cookie
* to ensure this function does not run again.
*/
const legacyGetAndUpdateAssistant = async (
userId: string,
assistantIdCookie: string
) => {
const updatedAssistant = await editCustomAssistant(
{
is_default: true,
iconData: {
iconName: "User",
iconColor: "#000000",
},
description: "Your default assistant.",
name: "Default assistant",
tools: undefined,
systemPrompt: undefined,
},
assistantIdCookie,
userId
);
if (!updatedAssistant) {
const ghIssueTitle = "Failed to set default assistant";
const ghIssueBody = `Failed to set the default assistant for user.\n\nDate: '${new Date().toISOString()}'`;
const assignee = "bracesproul";
const queryParams = new URLSearchParams({
title: ghIssueTitle,
body: ghIssueBody,
assignee,
"labels[]": "autogenerated",
});
const newIssueURL = `https://github.com/langchain-ai/open-canvas/issues/new?${queryParams.toString()}`;
toast({
title: "Failed to edit assistant",
description: (
<p>
Please open an issue{" "}
<a href={newIssueURL} target="_blank">
here
</a>{" "}
(do <i>not</i> edit fields) and try again later.
</p>
),
});
return;
}
setSelectedAssistant(updatedAssistant);
setAssistants([updatedAssistant]);
// Remove the cookie to ensure this is not called again.
removeCookie(ASSISTANT_ID_COOKIE);
};
const getOrCreateAssistant = async (userId: string) => {
if (selectedAssistant) {
return;
}
setIsLoadingAllAssistants(true);
const client = createClient();
let userAssistants: Assistant[] = [];
const assistantIdCookie = getCookie(ASSISTANT_ID_COOKIE);
if (assistantIdCookie) {
await legacyGetAndUpdateAssistant(userId, assistantIdCookie);
// Return early because this function will set the selected assistant and assistants state.
setIsLoadingAllAssistants(false);
return;
}
// No cookie found. First, search for all assistants under the user's ID
try {
userAssistants = await client.assistants.search({
graphId: "agent",
metadata: {
user_id: userId,
},
limit: 100,
});
} catch (e) {
console.error("Failed to get default assistant", e);
}
if (!userAssistants.length) {
// No assistants found, create a new assistant and set it as the default.
await createCustomAssistant(
{
iconData: {
iconName: "User",
iconColor: "#000000",
},
name: "Default assistant",
description: "Your default assistant.",
is_default: true,
},
userId
);
// Return early because this function will set the selected assistant and assistants state.
setIsLoadingAllAssistants(false);
return;
}
setAssistants(userAssistants);
const defaultAssistant = userAssistants.find(
(assistant) => assistant.metadata?.is_default
);
if (!defaultAssistant) {
// Update the first assistant to be the default assistant, then set it as the selected assistant.
const firstAssistant = userAssistants.sort((a, b) => {
return a.created_at.localeCompare(b.created_at);
})[0];
const updatedAssistant = await editCustomAssistant(
{
is_default: true,
iconData: {
iconName:
(firstAssistant.metadata?.iconName as string | undefined) ||
"User",
iconColor:
(firstAssistant.metadata?.iconColor as string | undefined) ||
"#000000",
},
description:
(firstAssistant.metadata?.description as string | undefined) ||
"Your default assistant.",
name:
firstAssistant.name.toLowerCase() === "Untitled"
? "Default assistant"
: firstAssistant.name,
tools:
(firstAssistant.config?.configurable?.tools as
| AssistantTool[]
| undefined) || undefined,
systemPrompt:
(firstAssistant.config?.configurable?.systemPrompt as
| string
| undefined) || undefined,
},
firstAssistant.assistant_id,
userId
);
setSelectedAssistant(updatedAssistant);
} else {
setSelectedAssistant(defaultAssistant);
}
setIsLoadingAllAssistants(false);
};
return {
assistants,
selectedAssistant,
isLoadingAllAssistants,
isDeletingAssistant,
isCreatingAssistant,
isEditingAssistant,
getOrCreateAssistant,
getAssistants,
deleteAssistant,
createCustomAssistant,
editCustomAssistant,
setSelectedAssistant,
};
}
|
0 | lc_public_repos/open-canvas/src | lc_public_repos/open-canvas/src/hooks/useStore.tsx | import { CustomQuickAction, Reflections } from "@/types";
import { useState } from "react";
import { useToast } from "./use-toast";
export function useStore() {
const { toast } = useToast();
const [isLoadingReflections, setIsLoadingReflections] = useState(false);
const [isLoadingQuickActions, setIsLoadingQuickActions] = useState(false);
const [reflections, setReflections] = useState<
Reflections & { assistantId: string; updatedAt: Date }
>();
const getReflections = async (assistantId: string): Promise<void> => {
setIsLoadingReflections(true);
const res = await fetch("/api/store/get", {
method: "POST",
body: JSON.stringify({
namespace: ["memories", assistantId],
key: "reflection",
}),
headers: {
"Content-Type": "application/json",
},
});
if (!res.ok) {
return;
}
const { item } = await res.json();
if (!item?.value) {
setIsLoadingReflections(false);
// No reflections found. Return early.
setReflections(undefined);
return;
}
let styleRules = item.value.styleRules ?? [];
let content = item.value.content ?? [];
try {
styleRules =
typeof styleRules === "string" ? JSON.parse(styleRules) : styleRules;
content = typeof content === "string" ? JSON.parse(content) : content;
} catch (e) {
console.error("Failed to parse reflections", e);
styleRules = [];
content = [];
}
setReflections({
...item.value,
styleRules,
content,
updatedAt: new Date(item.updatedAt),
assistantId,
});
setIsLoadingReflections(false);
};
const deleteReflections = async (assistantId: string): Promise<boolean> => {
const res = await fetch("/api/store/delete", {
method: "POST",
body: JSON.stringify({
namespace: ["memories", assistantId],
key: "reflection",
}),
headers: {
"Content-Type": "application/json",
},
});
if (!res.ok) {
return false;
}
const { success } = await res.json();
if (success) {
setReflections(undefined);
} else {
toast({
title: "Failed to delete reflections",
description: "Please try again later.",
});
}
return success;
};
const getCustomQuickActions = async (
userId: string
): Promise<CustomQuickAction[] | undefined> => {
setIsLoadingQuickActions(true);
try {
const res = await fetch("/api/store/get", {
method: "POST",
body: JSON.stringify({
namespace: ["custom_actions", userId],
key: "actions",
}),
headers: {
"Content-Type": "application/json",
},
});
if (!res.ok) {
return undefined;
}
const { item } = await res.json();
if (!item?.value) {
return undefined;
}
return Object.values(item?.value);
} finally {
setIsLoadingQuickActions(false);
}
};
const deleteCustomQuickAction = async (
id: string,
rest: CustomQuickAction[],
userId: string
): Promise<boolean> => {
const valuesWithoutDeleted = rest.reduce<Record<string, CustomQuickAction>>(
(acc, action) => {
if (action.id !== id) {
acc[action.id] = action;
}
return acc;
},
{}
);
const res = await fetch("/api/store/put", {
method: "POST",
body: JSON.stringify({
namespace: ["custom_actions", userId],
key: "actions",
value: valuesWithoutDeleted,
}),
headers: {
"Content-Type": "application/json",
},
});
if (!res.ok) {
return false;
}
const { success } = await res.json();
return success;
};
const createCustomQuickAction = async (
newAction: CustomQuickAction,
rest: CustomQuickAction[],
userId: string
): Promise<boolean> => {
const newValue = rest.reduce<Record<string, CustomQuickAction>>(
(acc, action) => {
acc[action.id] = action;
return acc;
},
{}
);
newValue[newAction.id] = newAction;
const res = await fetch("/api/store/put", {
method: "POST",
body: JSON.stringify({
namespace: ["custom_actions", userId],
key: "actions",
value: newValue,
}),
headers: {
"Content-Type": "application/json",
},
});
if (!res.ok) {
return false;
}
const { success } = await res.json();
return success;
};
const editCustomQuickAction = async (
editedAction: CustomQuickAction,
rest: CustomQuickAction[],
userId: string
): Promise<boolean> => {
const newValue = rest.reduce<Record<string, CustomQuickAction>>(
(acc, action) => {
acc[action.id] = action;
return acc;
},
{}
);
newValue[editedAction.id] = editedAction;
const res = await fetch("/api/store/put", {
method: "POST",
body: JSON.stringify({
namespace: ["custom_actions", userId],
key: "actions",
value: newValue,
}),
headers: {
"Content-Type": "application/json",
},
});
if (!res.ok) {
return false;
}
const { success } = await res.json();
return success;
};
return {
isLoadingReflections,
reflections,
isLoadingQuickActions,
deleteReflections,
getReflections,
deleteCustomQuickAction,
getCustomQuickActions,
editCustomQuickAction,
createCustomQuickAction,
};
}
|
0 | lc_public_repos/open-canvas/src | lc_public_repos/open-canvas/src/hooks/useFeedback.ts | import { Feedback } from "langsmith";
import { useCallback, useState } from "react";
export interface FeedbackResponse {
success: boolean;
feedback: Feedback;
}
interface UseFeedbackResult {
isLoading: boolean;
error: string | null;
sendFeedback: (
runId: string,
feedbackKey: string,
score: number,
comment?: string
) => Promise<FeedbackResponse | undefined>;
getFeedback: (
runId: string,
feedbackKey: string
) => Promise<Feedback[] | undefined>;
}
export function useFeedback(): UseFeedbackResult {
const [isLoading, setIsLoading] = useState<boolean>(false);
const [error, setError] = useState<string | null>(null);
const sendFeedback = useCallback(
async (
runId: string,
feedbackKey: string,
score: number,
comment?: string
): Promise<FeedbackResponse | undefined> => {
setIsLoading(true);
setError(null);
try {
const res = await fetch("/api/runs/feedback", {
method: "POST",
body: JSON.stringify({ runId, feedbackKey, score, comment }),
headers: {
"Content-Type": "application/json",
},
});
if (!res.ok) {
return;
}
return (await res.json()) as FeedbackResponse;
} catch (error) {
console.error("Error sending feedback:", error);
setError(
error instanceof Error ? error.message : "An unknown error occurred"
);
return;
} finally {
setIsLoading(false);
}
},
[]
);
const getFeedback = useCallback(
async (
runId: string,
feedbackKey: string
): Promise<Feedback[] | undefined> => {
setIsLoading(true);
setError(null);
try {
const res = await fetch(
`/api/runs/feedback?runId=${encodeURIComponent(runId)}&feedbackKey=${encodeURIComponent(feedbackKey)}`
);
if (!res.ok) {
return;
}
return await res.json();
} catch (error) {
console.error("Error getting feedback:", error);
setError(
error instanceof Error ? error.message : "An unknown error occurred"
);
return;
} finally {
setIsLoading(false);
}
},
[]
);
return {
isLoading,
sendFeedback,
getFeedback,
error,
};
}
|
0 | lc_public_repos/open-canvas/src | lc_public_repos/open-canvas/src/hooks/useUser.tsx | import { useState } from "react";
import { createSupabaseClient } from "@/lib/supabase/client";
import { User } from "@supabase/supabase-js";
export function useUser() {
const [user, setUser] = useState<User>();
const [loading, setLoading] = useState(true);
async function getUser() {
if (user) {
setLoading(false);
return;
}
const supabase = createSupabaseClient();
const {
data: { user: supabaseUser },
} = await supabase.auth.getUser();
setUser(supabaseUser || undefined);
setLoading(false);
}
return {
getUser,
user,
loading,
};
}
|
0 | lc_public_repos/open-canvas/src | lc_public_repos/open-canvas/src/hooks/utils.ts | import { Client } from "@langchain/langgraph-sdk";
export const createClient = () => {
const apiUrl = process.env.NEXT_PUBLIC_API_URL ?? "http://localhost:3000/api";
return new Client({
apiUrl,
});
};
|
0 | lc_public_repos/open-canvas/src | lc_public_repos/open-canvas/src/app/globals.css | @import url("https://fonts.googleapis.com/css2?family=Fira+Code&family=Inter:wght@400;500;600;700&display=swap");
@import url("https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700&display=swap");
@tailwind base;
@tailwind components;
@tailwind utilities;
:root {
--foreground-rgb: 0, 0, 0;
--background-start-rgb: 214, 219, 220;
--background-end-rgb: 255, 255, 255;
}
@media (prefers-color-scheme: dark) {
:root {
--foreground-rgb: 255, 255, 255;
--background-start-rgb: 0, 0, 0;
--background-end-rgb: 0, 0, 0;
}
}
*::-webkit-scrollbar {
display: none;
}
a {
color: rgb(33, 118, 246);
}
@layer utilities {
.text-balance {
text-wrap: balance;
}
.no-scrollbar::-webkit-scrollbar {
display: none;
}
.no-scrollbar {
-ms-overflow-style: none; /* IE and Edge */
scrollbar-width: none; /* Firefox */
}
}
@layer base {
:root {
--background: 0 0% 100%;
--foreground: 0 0% 3.9%;
--card: 0 0% 100%;
--card-foreground: 0 0% 3.9%;
--popover: 0 0% 100%;
--popover-foreground: 0 0% 3.9%;
--primary: 0 0% 9%;
--primary-foreground: 0 0% 98%;
--secondary: 0 0% 96.1%;
--secondary-foreground: 0 0% 9%;
--muted: 0 0% 96.1%;
--muted-foreground: 0 0% 45.1%;
--accent: 0 0% 96.1%;
--accent-foreground: 0 0% 9%;
--destructive: 0 84.2% 60.2%;
--destructive-foreground: 0 0% 98%;
--border: 0 0% 89.8%;
--input: 0 0% 89.8%;
--ring: 0 0% 3.9%;
--chart-1: 12 76% 61%;
--chart-2: 173 58% 39%;
--chart-3: 197 37% 24%;
--chart-4: 43 74% 66%;
--chart-5: 27 87% 67%;
--radius: 0.5rem;
}
.dark {
--background: 0 0% 3.9%;
--foreground: 0 0% 98%;
--card: 0 0% 3.9%;
--card-foreground: 0 0% 98%;
--popover: 0 0% 3.9%;
--popover-foreground: 0 0% 98%;
--primary: 0 0% 98%;
--primary-foreground: 0 0% 9%;
--secondary: 0 0% 14.9%;
--secondary-foreground: 0 0% 98%;
--muted: 0 0% 14.9%;
--muted-foreground: 0 0% 63.9%;
--accent: 0 0% 14.9%;
--accent-foreground: 0 0% 98%;
--destructive: 0 62.8% 30.6%;
--destructive-foreground: 0 0% 98%;
--border: 0 0% 14.9%;
--input: 0 0% 14.9%;
--ring: 0 0% 83.1%;
--chart-1: 220 70% 50%;
--chart-2: 160 60% 45%;
--chart-3: 30 80% 55%;
--chart-4: 280 65% 60%;
--chart-5: 340 75% 55%;
}
html {
font-family:
"Inter",
-apple-system,
BlinkMacSystemFont,
"Segoe UI",
Roboto,
Helvetica,
Arial,
sans-serif,
"Apple Color Emoji",
"Segoe UI Emoji",
"Segoe UI Symbol";
}
}
@layer base {
* {
@apply border-border;
}
body {
@apply bg-background text-foreground;
}
}
/* Change highlight color */
::selection {
background-color: rgba(53, 151, 147, 0.3);
}
::-moz-selection {
background-color: rgba(53, 151, 147, 0.3);
}
@layer utilities {
.shadow-inner-right {
box-shadow: inset -9px 0 6px -1px rgb(0 0 0 / 0.02);
}
}
.artifact-content {
line-height: calc(
1.625em + 2px
); /* Adjust the base value (1.625em) if needed */
}
.inline-code {
font-family: monospace;
background-color: #f0f0f0;
padding: 2px 4px;
border-radius: 4px;
font-size: 0.9em;
}
|
0 | lc_public_repos/open-canvas/src | lc_public_repos/open-canvas/src/app/layout.tsx | import type { Metadata } from "next";
import "./globals.css";
import { Inter } from "next/font/google";
const inter = Inter({
subsets: ["latin"],
});
export const metadata: Metadata = {
title: "Open Canvas",
description: "Open Canvas Chat UX by LangChain",
};
export default function RootLayout({
children,
}: Readonly<{
children: React.ReactNode;
}>) {
return (
<html lang="en">
<body className={inter.className}>{children}</body>
</html>
);
}
|
0 | lc_public_repos/open-canvas/src | lc_public_repos/open-canvas/src/app/page.tsx | "use client";
import { Canvas } from "@/components/canvas";
import { GraphProvider } from "@/contexts/GraphContext";
export default function Home() {
return (
<GraphProvider>
<Canvas />
</GraphProvider>
);
}
|
0 | lc_public_repos/open-canvas/src/app/api/store | lc_public_repos/open-canvas/src/app/api/store/delete/route.ts | import { NextRequest, NextResponse } from "next/server";
import { Client } from "@langchain/langgraph-sdk";
import { LANGGRAPH_API_URL } from "@/constants";
import { User } from "@supabase/supabase-js";
import { verifyUserAuthenticated } from "../../../../lib/supabase/verify_user_server";
export async function POST(req: NextRequest) {
let user: User | undefined;
try {
user = await verifyUserAuthenticated();
if (!user) {
return NextResponse.json({ error: "Unauthorized" }, { status: 401 });
}
} catch (e) {
console.error("Failed to fetch user", e);
return NextResponse.json({ error: "Unauthorized" }, { status: 401 });
}
const { namespace, key } = await req.json();
const lgClient = new Client({
apiKey: process.env.LANGCHAIN_API_KEY,
apiUrl: LANGGRAPH_API_URL,
});
try {
await lgClient.store.deleteItem(namespace, key);
return new NextResponse(JSON.stringify({ success: true }), {
status: 200,
headers: { "Content-Type": "application/json" },
});
} catch (_) {
return new NextResponse(
JSON.stringify({ error: "Failed to share run after multiple attempts." }),
{
status: 500,
headers: { "Content-Type": "application/json" },
}
);
}
}
|
0 | lc_public_repos/open-canvas/src/app/api/store/delete | lc_public_repos/open-canvas/src/app/api/store/delete/id/route.ts | import { NextRequest, NextResponse } from "next/server";
import { Client } from "@langchain/langgraph-sdk";
import { LANGGRAPH_API_URL } from "@/constants";
import { User } from "@supabase/supabase-js";
import { verifyUserAuthenticated } from "../../../../../lib/supabase/verify_user_server";
export async function POST(req: NextRequest) {
let user: User | undefined;
try {
user = await verifyUserAuthenticated();
if (!user) {
return NextResponse.json({ error: "Unauthorized" }, { status: 401 });
}
} catch (e) {
console.error("Failed to fetch user", e);
return NextResponse.json({ error: "Unauthorized" }, { status: 401 });
}
const { namespace, key, id } = await req.json();
const lgClient = new Client({
apiKey: process.env.LANGCHAIN_API_KEY,
apiUrl: LANGGRAPH_API_URL,
});
try {
const currentItems = await lgClient.store.getItem(namespace, key);
if (!currentItems?.value) {
return new NextResponse(
JSON.stringify({
error: "Item not found",
success: false,
}),
{
status: 404,
headers: { "Content-Type": "application/json" },
}
);
}
const newValues = Object.fromEntries(
Object.entries(currentItems.value).filter(([k]) => k !== id)
);
await lgClient.store.putItem(namespace, key, newValues);
return new NextResponse(JSON.stringify({ success: true }), {
status: 200,
headers: { "Content-Type": "application/json" },
});
} catch (_) {
return new NextResponse(
JSON.stringify({ error: "Failed to share run after multiple attempts." }),
{
status: 500,
headers: { "Content-Type": "application/json" },
}
);
}
}
|
0 | lc_public_repos/open-canvas/src/app/api/store | lc_public_repos/open-canvas/src/app/api/store/put/route.ts | import { NextRequest, NextResponse } from "next/server";
import { Client } from "@langchain/langgraph-sdk";
import { LANGGRAPH_API_URL } from "@/constants";
import { User } from "@supabase/supabase-js";
import { verifyUserAuthenticated } from "../../../../lib/supabase/verify_user_server";
export async function POST(req: NextRequest) {
let user: User | undefined;
try {
user = await verifyUserAuthenticated();
if (!user) {
return NextResponse.json({ error: "Unauthorized" }, { status: 401 });
}
} catch (e) {
console.error("Failed to fetch user", e);
return NextResponse.json({ error: "Unauthorized" }, { status: 401 });
}
const { namespace, key, value } = await req.json();
const lgClient = new Client({
apiKey: process.env.LANGCHAIN_API_KEY,
apiUrl: LANGGRAPH_API_URL,
});
try {
await lgClient.store.putItem(namespace, key, value);
return new NextResponse(JSON.stringify({ success: true }), {
status: 200,
headers: { "Content-Type": "application/json" },
});
} catch (_) {
return new NextResponse(
JSON.stringify({ error: "Failed to share run after multiple attempts." }),
{
status: 500,
headers: { "Content-Type": "application/json" },
}
);
}
}
|
0 | lc_public_repos/open-canvas/src/app/api/store | lc_public_repos/open-canvas/src/app/api/store/get/route.ts | import { NextRequest, NextResponse } from "next/server";
import { Client } from "@langchain/langgraph-sdk";
import { LANGGRAPH_API_URL } from "@/constants";
import { User } from "@supabase/supabase-js";
import { verifyUserAuthenticated } from "../../../../lib/supabase/verify_user_server";
export async function POST(req: NextRequest) {
let user: User | undefined;
try {
user = await verifyUserAuthenticated();
if (!user) {
return NextResponse.json({ error: "Unauthorized" }, { status: 401 });
}
} catch (e) {
console.error("Failed to fetch user", e);
return NextResponse.json({ error: "Unauthorized" }, { status: 401 });
}
const { namespace, key } = await req.json();
const lgClient = new Client({
apiKey: process.env.LANGCHAIN_API_KEY,
apiUrl: LANGGRAPH_API_URL,
});
try {
const item = await lgClient.store.getItem(namespace, key);
return new NextResponse(JSON.stringify({ item }), {
status: 200,
headers: { "Content-Type": "application/json" },
});
} catch (_) {
return new NextResponse(
JSON.stringify({ error: "Failed to share run after multiple attempts." }),
{
status: 500,
headers: { "Content-Type": "application/json" },
}
);
}
}
|
0 | lc_public_repos/open-canvas/src/app/api | lc_public_repos/open-canvas/src/app/api/[..._path]/route.ts | import { LANGGRAPH_API_URL } from "../../../constants";
import { NextRequest, NextResponse } from "next/server";
import { User } from "@supabase/supabase-js";
import { verifyUserAuthenticated } from "../../../lib/supabase/verify_user_server";
function getCorsHeaders() {
return {
"Access-Control-Allow-Origin": "*",
"Access-Control-Allow-Methods": "GET, POST, PUT, PATCH, DELETE, OPTIONS",
"Access-Control-Allow-Headers": "*",
};
}
async function handleRequest(req: NextRequest, method: string) {
let user: User | undefined;
try {
user = await verifyUserAuthenticated();
if (!user) {
return NextResponse.json({ error: "Unauthorized" }, { status: 401 });
}
} catch (e) {
console.error("Failed to fetch user", e);
return NextResponse.json({ error: "Unauthorized" }, { status: 401 });
}
try {
const path = req.nextUrl.pathname.replace(/^\/?api\//, "");
const url = new URL(req.url);
const searchParams = new URLSearchParams(url.search);
searchParams.delete("_path");
searchParams.delete("nxtP_path");
const queryString = searchParams.toString()
? `?${searchParams.toString()}`
: "";
const options: RequestInit = {
method,
headers: {
"x-api-key": process.env.LANGCHAIN_API_KEY || "",
},
};
if (["POST", "PUT", "PATCH"].includes(method)) {
const bodyText = await req.text();
if (typeof bodyText === "string" && bodyText.length > 0) {
const parsedBody = JSON.parse(bodyText);
parsedBody.config = parsedBody.config || {};
parsedBody.config.configurable = {
...parsedBody.config.configurable,
supabase_user_id: user.id,
};
options.body = JSON.stringify(parsedBody);
} else {
options.body = bodyText;
}
}
const res = await fetch(
`${LANGGRAPH_API_URL}/${path}${queryString}`,
options
);
if (res.status >= 400) {
console.error("ERROR IN PROXY", res.status, res.statusText);
return new Response(res.body, {
status: res.status,
statusText: res.statusText,
});
}
const headers = new Headers({
...getCorsHeaders(),
});
// Safely add headers from the original response
res.headers.forEach((value, key) => {
try {
headers.set(key, value);
} catch (error) {
console.warn(`Failed to set header: ${key}`, error);
}
});
return new Response(res.body, {
status: res.status,
statusText: res.statusText,
headers,
});
} catch (e: any) {
console.error("Error in proxy");
console.error(e);
console.error("\n\n\nEND ERROR\n\n");
return NextResponse.json({ error: e.message }, { status: e.status ?? 500 });
}
}
export const GET = (req: NextRequest) => handleRequest(req, "GET");
export const POST = (req: NextRequest) => handleRequest(req, "POST");
export const PUT = (req: NextRequest) => handleRequest(req, "PUT");
export const PATCH = (req: NextRequest) => handleRequest(req, "PATCH");
export const DELETE = (req: NextRequest) => handleRequest(req, "DELETE");
// Add a new OPTIONS handler
export const OPTIONS = () => {
return new NextResponse(null, {
status: 204,
headers: {
...getCorsHeaders(),
},
});
};
|
0 | lc_public_repos/open-canvas/src/app/api/runs | lc_public_repos/open-canvas/src/app/api/runs/share/route.ts | import { NextRequest, NextResponse } from "next/server";
import { Client } from "langsmith";
const MAX_RETRIES = 5;
const RETRY_DELAY = 5000; // 5 seconds
async function shareRunWithRetry(
lsClient: Client,
runId: string
): Promise<string> {
for (let attempt = 1; attempt <= MAX_RETRIES; attempt++) {
try {
return await lsClient.shareRun(runId);
} catch (error) {
if (attempt === MAX_RETRIES) {
throw error;
}
console.warn(
`Attempt ${attempt} failed. Retrying in ${RETRY_DELAY / 1000} seconds...`
);
await new Promise((resolve) => setTimeout(resolve, RETRY_DELAY));
}
}
throw new Error("Max retries reached"); // This line should never be reached due to the throw in the loop
}
export async function POST(req: NextRequest) {
const { runId } = await req.json();
if (!runId) {
return new NextResponse(
JSON.stringify({
error: "`runId` is required to share run.",
}),
{
status: 400,
headers: { "Content-Type": "application/json" },
}
);
}
const lsClient = new Client({
apiKey: process.env.LANGCHAIN_API_KEY,
});
try {
const sharedRunURL = await shareRunWithRetry(lsClient, runId);
return new NextResponse(JSON.stringify({ sharedRunURL }), {
status: 200,
headers: { "Content-Type": "application/json" },
});
} catch (error) {
console.error(
`Failed to share run with id ${runId} after ${MAX_RETRIES} attempts:\n`,
error
);
return new NextResponse(
JSON.stringify({ error: "Failed to share run after multiple attempts." }),
{
status: 500,
headers: { "Content-Type": "application/json" },
}
);
}
}
|
0 | lc_public_repos/open-canvas/src/app/api/runs | lc_public_repos/open-canvas/src/app/api/runs/feedback/route.ts | import { Client, Feedback } from "langsmith";
import { NextRequest, NextResponse } from "next/server";
export async function POST(req: NextRequest) {
try {
const body = await req.json();
const { runId, feedbackKey, score, comment } = body;
if (!runId || !feedbackKey) {
return NextResponse.json(
{ error: "`runId` and `feedbackKey` are required." },
{ status: 400 }
);
}
const lsClient = new Client({
apiKey: process.env.LANGCHAIN_API_KEY,
});
const feedback = await lsClient.createFeedback(runId, feedbackKey, {
score,
comment,
});
return NextResponse.json(
{ success: true, feedback: feedback },
{ status: 200 }
);
} catch (error) {
console.error("Failed to process feedback request:", error);
return NextResponse.json(
{ error: "Failed to submit feedback." },
{ status: 500 }
);
}
}
export async function GET(req: NextRequest) {
try {
const searchParams = req.nextUrl.searchParams;
const runId = searchParams.get("runId");
const feedbackKey = searchParams.get("feedbackKey");
if (!runId || !feedbackKey) {
return new NextResponse(
JSON.stringify({
error: "`runId` and `feedbackKey` are required.",
}),
{
status: 400,
headers: { "Content-Type": "application/json" },
}
);
}
const lsClient = new Client({
apiKey: process.env.LANGCHAIN_API_KEY,
});
const runFeedback: Feedback[] = [];
const run_feedback = await lsClient.listFeedback({
runIds: [runId],
feedbackKeys: [feedbackKey],
});
for await (const feedback of run_feedback) {
runFeedback.push(feedback);
}
return new NextResponse(
JSON.stringify({
feedback: runFeedback,
}),
{
status: 200,
headers: { "Content-Type": "application/json" },
}
);
} catch (error) {
console.error("Failed to fetch feedback:", error);
return NextResponse.json(
{ error: "Failed to fetch feedback." },
{ status: 500 }
);
}
}
|
0 | lc_public_repos/open-canvas/src/app/auth | lc_public_repos/open-canvas/src/app/auth/signout/page.tsx | "use client";
import { useEffect, useState } from "react";
import { createSupabaseClient } from "@/lib/supabase/client";
import { useRouter } from "next/navigation";
export default function Page() {
const router = useRouter();
const [errorOccurred, setErrorOccurred] = useState(false);
useEffect(() => {
async function signOut() {
const client = createSupabaseClient();
const { error } = await client.auth.signOut();
if (error) {
setErrorOccurred(true);
} else {
router.push("/auth/login");
}
}
signOut();
}, []);
return (
<>
{errorOccurred ? (
<div>
<h1>Sign out error</h1>
<p>
There was an error signing out. Please refresh the page to try
again.
</p>
</div>
) : (
<p>Signing out...</p>
)}
</>
);
}
|
0 | lc_public_repos/open-canvas/src/app/auth | lc_public_repos/open-canvas/src/app/auth/login/page.tsx | "use client";
import { Login } from "@/components/auth/login/Login";
import { Suspense } from "react";
export default function Page() {
return (
<main className="h-screen">
<Suspense fallback={<div>Loading...</div>}>
<Login />
</Suspense>
</main>
);
}
|
0 | lc_public_repos/open-canvas/src/app/auth | lc_public_repos/open-canvas/src/app/auth/callback/route.ts | import { NextResponse } from "next/server";
// The client you created from the Server-Side Auth instructions
import { createClient } from "@/lib/supabase/server";
export async function GET(request: Request) {
const { searchParams, origin } = new URL(request.url);
const code = searchParams.get("code");
// if "next" is in param, use it as the redirect URL
const next = searchParams.get("next") ?? "/";
if (code) {
const supabase = createClient();
const { error } = await supabase.auth.exchangeCodeForSession(code);
if (!error) {
const forwardedHost = request.headers.get("x-forwarded-host"); // original origin before load balancer
const isLocalEnv = process.env.NODE_ENV === "development";
if (isLocalEnv) {
// we can be sure that there is no load balancer in between, so no need to watch for X-Forwarded-Host
return NextResponse.redirect(`${origin}${next}`);
} else if (forwardedHost) {
return NextResponse.redirect(`https://${forwardedHost}${next}`);
} else {
return NextResponse.redirect(`${origin}${next}`);
}
}
}
// return the user to an error page with instructions
return NextResponse.redirect(`${origin}/auth/auth-code-error`);
}
|
0 | lc_public_repos/open-canvas/src/app/auth | lc_public_repos/open-canvas/src/app/auth/signup/page.tsx | "use client";
import { Signup } from "@/components/auth/signup/Signup";
import { Suspense } from "react";
export default function Page() {
return (
<main className="h-screen">
<Suspense fallback={<div>Loading...</div>}>
<Signup />
</Suspense>
</main>
);
}
|
0 | lc_public_repos/open-canvas/src/app/auth/signup | lc_public_repos/open-canvas/src/app/auth/signup/success/page.tsx | "use client";
import { useUser } from "@/hooks/useUser";
import { useEffect, useState } from "react";
import { redirect, RedirectType } from "next/navigation";
export default function Page() {
const { getUser, user } = useUser();
const [isChecking, setIsChecking] = useState(true);
useEffect(() => {
if (user) {
return;
}
const startTime = Date.now();
const checkDuration = 3 * 60 * 1000; // 3 minutes in milliseconds
const interval = 4000; // 4 seconds
const checkUser = async () => {
await getUser();
if (Date.now() - startTime >= checkDuration) {
setIsChecking(false);
}
};
const intervalId = setInterval(checkUser, interval);
// Initial check
checkUser();
// Cleanup function
return () => clearInterval(intervalId);
}, [getUser]);
useEffect(() => {
if (user) {
redirect("/", RedirectType.push);
}
}, [user]);
return (
<div className="flex flex-col items-center justify-center min-h-screen bg-gray-100">
<div className="max-w-md w-full bg-white shadow-md rounded-lg p-8 text-center">
<h1 className="text-2xl font-bold mb-4">Successfully Signed Up!</h1>
<p className="text-gray-600 mb-4">
Please check your email for a confirmation link. That link will
redirect you to Open Canvas.
</p>
<p className="text-sm text-gray-500">
If you don't see the email, please check your spam folder.
</p>
{isChecking && (
<p className="text-sm text-blue-500 mt-4">
Waiting for email confirmation...
</p>
)}
</div>
</div>
);
}
|
0 | lc_public_repos/open-canvas/src/app/auth | lc_public_repos/open-canvas/src/app/auth/confirm/route.ts | import { type EmailOtpType } from "@supabase/supabase-js";
import { type NextRequest } from "next/server";
import { revalidatePath } from "next/cache";
import { createClient } from "@/lib/supabase/server";
import { redirect, RedirectType } from "next/navigation";
export async function GET(request: NextRequest) {
const { searchParams } = new URL(request.url);
const token_hash = searchParams.get("token_hash");
const type = searchParams.get("type") as EmailOtpType | null;
const next = searchParams.get("next") ?? "/";
const code = searchParams.get("code");
const supabase = createClient();
if (token_hash && type) {
const { error } = await supabase.auth.verifyOtp({
type,
token_hash,
});
if (!error) {
// redirect user to specified redirect URL or root of app
revalidatePath(next);
redirect(next, RedirectType.push);
}
} else if (code) {
const { error } = await supabase.auth.exchangeCodeForSession(code);
if (!error) {
// redirect user to specified redirect URL or root of app
revalidatePath(next);
redirect(next, RedirectType.push);
}
}
// redirect the user to an error page with some instructions
redirect("/error");
}
|
0 | lc_public_repos/open-canvas/src/components | lc_public_repos/open-canvas/src/components/assistant-select/create-edit-assistant-dialog.tsx | import { CreateAssistantFields } from "@/hooks/useAssistants";
import { Assistant } from "@langchain/langgraph-sdk";
import {
Dispatch,
FormEvent,
SetStateAction,
useEffect,
useState,
} from "react";
import * as Icons from "lucide-react";
import {
Dialog,
DialogContent,
DialogDescription,
DialogHeader,
DialogTitle,
} from "../ui/dialog";
import { Button } from "../ui/button";
import { TighterText } from "../ui/header";
import { Label } from "../ui/label";
import { Input } from "../ui/input";
import { IconSelect } from "./icon-select";
import React from "react";
import { useToast } from "@/hooks/use-toast";
import { ColorPicker } from "./color-picker";
import { Textarea } from "../ui/textarea";
import { InlineContextTooltip } from "../ui/inline-context-tooltip";
interface CreateEditAssistantDialogProps {
open: boolean;
setOpen: Dispatch<SetStateAction<boolean>>;
userId: string | undefined;
isEditing: boolean;
assistant?: Assistant;
createCustomAssistant: (
newAssistant: CreateAssistantFields,
userId: string,
successCallback?: (id: string) => void
) => Promise<boolean>;
editCustomAssistant: (
editedAssistant: CreateAssistantFields,
assistantId: string,
userId: string
) => Promise<Assistant | undefined>;
isLoading: boolean;
allDisabled: boolean;
setAllDisabled: Dispatch<SetStateAction<boolean>>;
}
const GH_DISCUSSION_URL = `https://github.com/langchain-ai/open-canvas/discussions/182`;
const SystemPromptWhatsThis = (): React.ReactNode => (
<span className="flex flex-col gap-1 text-sm text-gray-600">
<p>
Custom system prompts will be passed to the LLM when generating, or
re-writing artifacts. They are <i>not</i> used for responding to general
queries in the chat, highlight to edit, or quick actions.
</p>
<p>
We're looking for feedback on how to best handle customizing
assistant prompts. To vote, and give feedback please visit{" "}
<a href={GH_DISCUSSION_URL} target="_blank">
this GitHub discussion
</a>
.
</p>
</span>
);
export function CreateEditAssistantDialog(
props: CreateEditAssistantDialogProps
) {
const { toast } = useToast();
const [name, setName] = useState("");
const [description, setDescription] = useState("");
const [systemPrompt, setSystemPrompt] = useState("");
const [iconName, setIconName] = useState<keyof typeof Icons>("User");
const [hasSelectedIcon, setHasSelectedIcon] = useState(false);
const [iconColor, setIconColor] = useState("#000000");
const [showColorPicker, setShowColorPicker] = useState(false);
const [hoverTimer, setHoverTimer] = useState<NodeJS.Timeout | null>(null);
const metadata = props.assistant?.metadata as Record<string, any> | undefined;
useEffect(() => {
if ((props.assistant, props.isEditing)) {
setName(props.assistant?.name || "");
setDescription(metadata?.description || "");
setSystemPrompt(
(props.assistant?.config?.configurable?.systemPrompt as
| string
| undefined) || ""
);
setHasSelectedIcon(true);
setIconName(metadata?.iconData?.iconName || "User");
setIconColor(metadata?.iconData?.iconColor || "#000000");
} else if (!props.isEditing) {
setName("");
setDescription("");
setSystemPrompt("");
setIconName("User");
setIconColor("#000000");
}
}, [props.assistant, props.isEditing]);
const handleSubmit = async (e: FormEvent<HTMLFormElement>) => {
e.preventDefault();
if (!props.userId) {
toast({
title: "User not found",
variant: "destructive",
duration: 5000,
});
return;
}
if (props.isEditing && !props.assistant) {
toast({
title: "Assistant not found",
variant: "destructive",
duration: 5000,
});
return;
}
props.setAllDisabled(true);
let res: boolean;
if (props.isEditing && props.assistant) {
res = !!(await props.editCustomAssistant(
{
name,
description,
systemPrompt,
iconData: {
iconName,
iconColor,
},
},
props.assistant.assistant_id,
props.userId
));
} else {
res = await props.createCustomAssistant(
{
name,
description,
systemPrompt,
iconData: {
iconName,
iconColor,
},
},
props.userId
);
}
if (res) {
toast({
title: `Assistant ${props.isEditing ? "edited" : "created"} successfully`,
duration: 5000,
});
} else {
toast({
title: `Failed to ${props.isEditing ? "edit" : "create"} assistant`,
variant: "destructive",
duration: 5000,
});
}
props.setAllDisabled(false);
props.setOpen(false);
};
const handleResetState = () => {
setName("");
setDescription("");
setSystemPrompt("");
setIconName("User");
setIconColor("#000000");
};
if (props.isEditing && !props.assistant) {
return null;
}
return (
<Dialog
open={props.open}
onOpenChange={(change) => {
if (!change) {
handleResetState();
}
props.setOpen(change);
}}
>
<DialogContent className="max-w-xl max-h-[90vh] p-8 bg-white rounded-lg shadow-xl min-w-[70vw] overflow-y-auto scrollbar-thin scrollbar-thumb-gray-300 scrollbar-track-gray-100">
<DialogHeader>
<DialogTitle className="text-3xl font-light text-gray-800">
<TighterText>
{props.isEditing ? "Edit" : "Create"} Assistant
</TighterText>
</DialogTitle>
<DialogDescription className="mt-2 text-md font-light text-gray-600">
<TighterText>
Creating a new assistant allows you to tailor your reflections to
a specific context, as reflections are unique to assistants.
</TighterText>
</DialogDescription>
</DialogHeader>
<form
onSubmit={(e) => handleSubmit(e)}
className="flex flex-col items-start justify-start gap-4 w-full"
>
<Label htmlFor="name">
<TighterText>
Name <span className="text-red-500">*</span>
</TighterText>
</Label>
<Input
disabled={props.allDisabled}
required
id="name"
placeholder="Work Emails"
value={name}
onChange={(e) => setName(e.target.value)}
/>
<Label htmlFor="description">
<TighterText>Description</TighterText>
</Label>
<Input
disabled={props.allDisabled}
required={false}
id="description"
placeholder="Assistant for my work emails"
value={description}
onChange={(e) => setDescription(e.target.value)}
/>
<Label htmlFor="system-prompt">
<TighterText className="flex items-center">
System Prompt
<InlineContextTooltip cardContentClassName="w-[500px] ml-10">
<SystemPromptWhatsThis />
</InlineContextTooltip>
</TighterText>
</Label>
<Textarea
disabled={props.allDisabled}
required={false}
id="system-prompt"
placeholder="You are an expert email assistant..."
value={systemPrompt}
onChange={(e) => setSystemPrompt(e.target.value)}
rows={5}
/>
<div className="flex w-full items-center justify-between gap-4">
<div className="flex flex-col gap-4 items-start justify-start w-full">
<Label htmlFor="icon">
<TighterText>Icon</TighterText>
</Label>
<IconSelect
allDisabled={props.allDisabled}
iconColor={iconColor}
selectedIcon={iconName}
setSelectedIcon={(i) => {
setHasSelectedIcon(true);
setIconName(i);
}}
hasSelectedIcon={hasSelectedIcon}
/>
</div>
<div className="flex flex-col gap-4 items-start justify-start w-full">
<Label htmlFor="description">
<TighterText>Color</TighterText>
</Label>
<div className="flex gap-1 items-center justify-start w-full">
<ColorPicker
disabled={props.allDisabled}
iconColor={iconColor}
setIconColor={setIconColor}
showColorPicker={showColorPicker}
setShowColorPicker={setShowColorPicker}
hoverTimer={hoverTimer}
setHoverTimer={setHoverTimer}
/>
<Input
disabled={props.allDisabled}
required={false}
id="description"
placeholder="Assistant for my work emails"
value={iconColor}
onChange={(e) => {
if (!e.target.value.startsWith("#")) {
setIconColor("#" + e.target.value);
} else {
setIconColor(e.target.value);
}
}}
/>
</div>
</div>
</div>
<div className="flex items-center justify-center w-full mt-4 gap-3">
<Button
disabled={props.allDisabled}
className="w-full"
type="submit"
>
<TighterText>Save</TighterText>
</Button>
<Button
disabled={props.allDisabled}
onClick={() => {
handleResetState();
props.setOpen(false);
}}
variant="destructive"
className="w-[20%]"
type="button"
>
<TighterText>Cancel</TighterText>
</Button>
</div>
</form>
</DialogContent>
</Dialog>
);
}
|
0 | lc_public_repos/open-canvas/src/components | lc_public_repos/open-canvas/src/components/assistant-select/edit-delete-dropdown.tsx | import { Dispatch, SetStateAction, useState } from "react";
import {
DropdownMenu,
DropdownMenuContent,
DropdownMenuItem,
DropdownMenuTrigger,
} from "@/components/ui/dropdown-menu";
import { EllipsisVertical, Pencil, Trash2 } from "lucide-react";
import { Assistant } from "@langchain/langgraph-sdk";
import { useToast } from "@/hooks/use-toast";
import { TooltipIconButton } from "../ui/assistant-ui/tooltip-icon-button";
import styles from "./edit-delete-dropdown.module.css";
interface EditDeleteDropdownProps {
setEditModalOpen: Dispatch<SetStateAction<boolean>>;
deleteAssistant: (assistantId: string) => Promise<boolean>;
setAssistantDropdownOpen: Dispatch<SetStateAction<boolean>>;
setEditingAssistant: Dispatch<SetStateAction<Assistant | undefined>>;
selectedAssistant: Assistant;
disabled: boolean;
allowDelete: boolean;
setDisabled: Dispatch<SetStateAction<boolean>>;
}
export function EditDeleteDropdown({
setEditModalOpen,
setEditingAssistant,
deleteAssistant,
setAssistantDropdownOpen,
setDisabled,
allowDelete,
selectedAssistant,
disabled,
}: EditDeleteDropdownProps) {
const { toast } = useToast();
const [open, setOpen] = useState(false);
const tooltipText = allowDelete ? "Edit/Delete" : "Edit";
return (
<DropdownMenu open={open} onOpenChange={setOpen}>
<DropdownMenuTrigger asChild>
<TooltipIconButton
disabled={disabled}
tooltip={tooltipText}
variant="ghost"
delayDuration={200}
className="w-8 h-8"
>
<EllipsisVertical className="w-4 h-4" />
</TooltipIconButton>
</DropdownMenuTrigger>
<DropdownMenuContent className={styles.dropdownContent}>
<DropdownMenuItem
onClick={() => {
setEditingAssistant(selectedAssistant);
setEditModalOpen(true);
}}
className="w-8"
disabled={disabled}
>
<Pencil className="text-gray-600 hover:text-black transition-colors ease-in-out duration-200" />
</DropdownMenuItem>
{allowDelete && (
<DropdownMenuItem
onClick={async () => {
setDisabled(true);
const res = await deleteAssistant(selectedAssistant.assistant_id);
if (res) {
toast({
title: "Assistant deleted",
duration: 5000,
});
} else {
toast({
title: "Error",
description: "Failed to delete the assistant.",
variant: "destructive",
duration: 5000,
});
}
setDisabled(false);
setOpen(false);
setAssistantDropdownOpen(false);
}}
disabled={disabled}
className="w-8"
>
<Trash2 className="text-gray-600 hover:text-red-500 transition-colors ease-in-out duration-200" />
</DropdownMenuItem>
)}
</DropdownMenuContent>
</DropdownMenu>
);
}
|
0 | lc_public_repos/open-canvas/src/components | lc_public_repos/open-canvas/src/components/assistant-select/alert-new-feature.tsx | "use client";
import { motion, AnimatePresence } from "framer-motion";
import { OC_HAS_SEEN_CUSTOM_ASSISTANTS_ALERT } from "@/constants";
import { Dispatch, SetStateAction, useEffect } from "react";
import { Alert, AlertDescription, AlertTitle } from "@/components/ui/alert";
import { Users, X } from "lucide-react";
import { TighterText } from "@/components/ui/header";
import { TooltipIconButton } from "@/components/ui/assistant-ui/tooltip-icon-button";
export const AlertNewAssistantsFeature = ({
shouldRender,
showAlert,
setShowAlert,
}: {
shouldRender: boolean;
showAlert: boolean;
setShowAlert: Dispatch<SetStateAction<boolean>>;
}) => {
useEffect(() => {
if (typeof window === "undefined") return;
const hasSeenAlert = localStorage.getItem(
OC_HAS_SEEN_CUSTOM_ASSISTANTS_ALERT
);
if (!hasSeenAlert) {
setShowAlert(true);
}
}, []);
const handleCloseAlert = () => {
setShowAlert(false);
// Wait for animation to complete before setting localStorage
setTimeout(() => {
localStorage.setItem(OC_HAS_SEEN_CUSTOM_ASSISTANTS_ALERT, "true");
}, 300);
};
if (!showAlert || !shouldRender) {
return <AnimatePresence />; // Keep AnimatePresence mounted to enable fading out
}
return (
<AnimatePresence mode="wait">
{showAlert && (
<motion.div
initial={{ opacity: 0, y: -20 }}
animate={{ opacity: 1, y: 0 }}
exit={{ opacity: 0, y: -20 }}
transition={{ duration: 0.2 }}
>
<div className="relative p-[0px] rounded-lg bg-gradient-to-r from-pink-500/50 via-purple-500/50 to-pink-500/50 animate-gradient-xy shadow-[0_0_25px_rgba(236,72,153,0.3)] before:absolute before:inset-0 before:rounded-lg before:p-[12px] before:bg-gradient-to-r before:from-pink-500/20 before:via-purple-500/20 before:to-pink-500/20 before:blur-xl before:-z-10 before:animate-gradient-xy-enhanced">
<Alert className="max-w-xl bg-white rounded-lg hover:bg-gray-50 transition-colors duration-300 ease-in-out">
<AlertTitle className="flex justify-between items-center">
<div className="flex items-center justify-start gap-1">
<Users className="w-4 h-4" />
<TighterText>Multiple Assistants!</TighterText>
</div>
<TooltipIconButton
tooltip="Close alert"
variant="ghost"
delayDuration={400}
onClick={handleCloseAlert}
size="sm"
>
<X className="w-4 h-4 text-gray-600" />
</TooltipIconButton>
</AlertTitle>
<AlertDescription className="inline-flex items-center justify-start gap-1 flex-wrap mt-2">
<p className="text-left">
Create and customize multiple AI assistants! Each assistant
maintains its own unique memory based on the conversations it
manages, helping you better organize different types of
content generation.
</p>
<p className="text-left">
Plus, you can now customize how each assistant behaves with
personalized system messages.
</p>
</AlertDescription>
</Alert>
</div>
</motion.div>
)}
</AnimatePresence>
);
};
|
0 | lc_public_repos/open-canvas/src/components | lc_public_repos/open-canvas/src/components/assistant-select/index.tsx | import {
DropdownMenu,
DropdownMenuContent,
DropdownMenuItem,
DropdownMenuLabel,
DropdownMenuSeparator,
DropdownMenuTrigger,
} from "@/components/ui/dropdown-menu";
import { useState } from "react";
import * as Icons from "lucide-react";
import React from "react";
import { TighterText } from "../ui/header";
import { TooltipIconButton } from "../ui/assistant-ui/tooltip-icon-button";
import { useGraphContext } from "@/contexts/GraphContext";
import { CreateEditAssistantDialog } from "./create-edit-assistant-dialog";
import { getIcon } from "./utils";
import { AssistantItem } from "./assistant-item";
import { Assistant } from "@langchain/langgraph-sdk";
import { useToast } from "@/hooks/use-toast";
import { AlertNewAssistantsFeature } from "./alert-new-feature";
import { OC_HAS_SEEN_CUSTOM_ASSISTANTS_ALERT } from "@/constants";
interface AssistantSelectProps {
userId: string | undefined;
chatStarted: boolean;
}
function AssistantSelectComponent(props: AssistantSelectProps) {
const { toast } = useToast();
const [open, setOpen] = useState(false);
const [createEditDialogOpen, setCreateEditDialogOpen] = useState(false);
const [editingAssistant, setEditingAssistant] = useState<
Assistant | undefined
>();
const [allDisabled, setAllDisabled] = useState(false);
const [showAlert, setShowAlert] = useState(false);
const {
assistantsData: {
assistants,
selectedAssistant,
isLoadingAllAssistants,
setSelectedAssistant,
createCustomAssistant,
editCustomAssistant,
deleteAssistant,
},
} = useGraphContext();
const handleNewAssistantClick = (event: Event) => {
event.preventDefault();
setCreateEditDialogOpen(true);
};
const handleDeleteAssistant = async (assistantId: string) => {
setAllDisabled(true);
const res = await deleteAssistant(assistantId);
if (res) {
toast({
title: "Assistant deleted",
duration: 5000,
});
}
setAllDisabled(false);
return res;
};
const handleHideNewFeatureAlert = () => {
if (showAlert) {
setShowAlert(false);
localStorage.setItem(OC_HAS_SEEN_CUSTOM_ASSISTANTS_ALERT, "true");
}
};
const metadata = selectedAssistant?.metadata as Record<string, any>;
return (
<>
<div className="relative">
<DropdownMenu
open={open}
onOpenChange={(c) => {
if (!c) {
setEditingAssistant(undefined);
setCreateEditDialogOpen(false);
}
setOpen(c);
}}
>
<DropdownMenuTrigger className="text-gray-600" asChild>
<TooltipIconButton
tooltip="Change assistant"
variant="ghost"
delayDuration={200}
className="w-8 h-8 transition-colors ease-in-out duration-200"
style={{ color: metadata?.iconData?.iconColor || "#4b5563" }}
>
{getIcon(metadata?.iconData?.iconName as string | undefined)}
</TooltipIconButton>
</DropdownMenuTrigger>
<DropdownMenuContent className="max-h-[600px] max-w-[300px] overflow-y-auto scrollbar-thin scrollbar-thumb-gray-300 scrollbar-track-gray-100 ml-4">
<DropdownMenuLabel>
<TighterText>My Assistants</TighterText>
</DropdownMenuLabel>
<DropdownMenuSeparator />
{isLoadingAllAssistants && !assistants?.length ? (
<span className="text-sm text-gray-600 flex items-center justify-start gap-1 p-2">
Loading
<Icons.LoaderCircle className="w-4 h-4 animate-spin" />
</span>
) : (
<>
<DropdownMenuItem
onSelect={(e) => {
handleNewAssistantClick(e);
handleHideNewFeatureAlert();
}}
className="flex items-center justify-start gap-2"
disabled={allDisabled}
>
<Icons.CirclePlus className="w-4 h-4" />
<TighterText className="font-medium">New</TighterText>
</DropdownMenuItem>
{assistants.map((assistant) => (
<AssistantItem
setAllDisabled={setAllDisabled}
allDisabled={allDisabled}
key={assistant.assistant_id}
assistant={assistant}
setEditModalOpen={setCreateEditDialogOpen}
setAssistantDropdownOpen={setOpen}
setEditingAssistant={setEditingAssistant}
deleteAssistant={handleDeleteAssistant}
selectedAssistantId={selectedAssistant?.assistant_id}
onClick={() => {
if (
selectedAssistant?.assistant_id ===
assistant.assistant_id
) {
setOpen(false);
return;
}
setSelectedAssistant(assistant);
handleHideNewFeatureAlert();
}}
/>
))}
</>
)}
</DropdownMenuContent>
</DropdownMenu>
<div className="absolute left-1/2 -translate-x-1/2 mt-6 w-[500px]">
<AlertNewAssistantsFeature
showAlert={showAlert}
setShowAlert={setShowAlert}
shouldRender={!props.chatStarted}
/>
</div>
</div>
<CreateEditAssistantDialog
allDisabled={allDisabled}
setAllDisabled={setAllDisabled}
open={createEditDialogOpen}
setOpen={(c) => {
if (!c) {
setEditingAssistant(undefined);
}
setCreateEditDialogOpen(c);
}}
userId={props.userId}
isEditing={!!editingAssistant}
assistant={editingAssistant}
createCustomAssistant={async (
newAssistant,
userId,
successCallback
) => {
const res = await createCustomAssistant(
newAssistant,
userId,
successCallback
);
setOpen(false);
return res;
}}
editCustomAssistant={async (editedAssistant, assistantId, userId) => {
const res = await editCustomAssistant(
editedAssistant,
assistantId,
userId
);
setOpen(false);
return res;
}}
isLoading={isLoadingAllAssistants}
/>
</>
);
}
export const AssistantSelect = React.memo(AssistantSelectComponent);
|
0 | lc_public_repos/open-canvas/src/components | lc_public_repos/open-canvas/src/components/assistant-select/utils.tsx | import * as Icons from "lucide-react";
import React from "react";
export const getIcon = (iconName?: string) => {
if (iconName && Icons[iconName as keyof typeof Icons]) {
return React.createElement(
Icons[iconName as keyof typeof Icons] as React.ElementType
);
}
return React.createElement(Icons.User);
};
|
0 | lc_public_repos/open-canvas/src/components | lc_public_repos/open-canvas/src/components/assistant-select/color-picker.tsx | import { Dispatch, SetStateAction } from "react";
import React from "react";
import { HexColorPicker } from "react-colorful";
import { motion, AnimatePresence } from "framer-motion";
interface ColorPickerProps {
iconColor: string;
setIconColor: Dispatch<SetStateAction<string>>;
showColorPicker: boolean;
setShowColorPicker: Dispatch<SetStateAction<boolean>>;
hoverTimer: NodeJS.Timeout | null;
setHoverTimer: Dispatch<SetStateAction<NodeJS.Timeout | null>>;
disabled: boolean;
}
export function ColorPicker(props: ColorPickerProps) {
const {
iconColor,
setIconColor,
showColorPicker,
setShowColorPicker,
hoverTimer,
setHoverTimer,
} = props;
const handleMouseEnter = () => {
if (props.disabled) return;
const timer = setTimeout(() => {
setShowColorPicker(true);
}, 200);
setHoverTimer(timer);
};
const handleMouseLeave = () => {
if (hoverTimer) {
clearTimeout(hoverTimer);
}
setShowColorPicker(false);
};
return (
<div className="relative">
<div
className="h-9 w-9 rounded-md cursor-pointer hover:ring-2 ring-offset-2 ring-gray-400 transition-all ease-in-out duration-200"
style={{ backgroundColor: iconColor }}
onMouseEnter={handleMouseEnter}
onMouseLeave={() => {
if (hoverTimer) {
clearTimeout(hoverTimer);
}
}}
/>
<AnimatePresence>
{showColorPicker && (
<motion.div
initial={{ opacity: 0, scale: 0.9, y: 10 }}
animate={{ opacity: 1, scale: 1, y: 0 }}
exit={{ opacity: 0, scale: 0.9, y: 10 }}
transition={{ duration: 0.2 }}
className="absolute bottom-full mb-2 z-50"
onMouseEnter={() => setShowColorPicker(true)}
onMouseLeave={handleMouseLeave}
>
<HexColorPicker
aria-disabled={props.disabled}
color={iconColor}
onChange={(e) => {
if (props.disabled) return;
if (!e.startsWith("#")) {
setIconColor("#" + e);
} else {
setIconColor(e);
}
}}
/>
</motion.div>
)}
</AnimatePresence>
</div>
);
}
|
0 | lc_public_repos/open-canvas/src/components | lc_public_repos/open-canvas/src/components/assistant-select/icon-select.tsx | import * as Icons from "lucide-react";
import { Dispatch, SetStateAction, useEffect, useMemo, useState } from "react";
import {
DropdownMenu,
DropdownMenuContent,
DropdownMenuItem,
DropdownMenuLabel,
DropdownMenuSeparator,
DropdownMenuTrigger,
} from "@/components/ui/dropdown-menu";
import { Input } from "@/components/ui/input";
import React from "react";
import debounce from "lodash/debounce";
import startCase from "lodash/startCase";
import uniq from "lodash/uniq";
import uniqBy from "lodash/uniqBy";
import { Button } from "../ui/button";
type KeyofIcons = keyof typeof Icons;
const prettifyIconLabel = (iconName: string) => {
iconName = iconName
.replace("Icon", "")
.replace("icon", "")
.replace("Lucide", "");
return startCase(iconName);
};
export function IconSelect({
selectedIcon,
setSelectedIcon,
hasSelectedIcon,
iconColor,
allDisabled,
}: {
allDisabled: boolean;
selectedIcon: KeyofIcons;
setSelectedIcon: Dispatch<SetStateAction<KeyofIcons>>;
hasSelectedIcon: boolean;
iconColor: string;
}) {
const [open, setOpen] = useState(false);
const [searchTerm, setSearchTerm] = useState("");
const [startIndex, setStartIndex] = useState(0);
const WINDOW_SIZE = 300;
const BUFFER_SIZE = 100;
// Get all icon names and memoize the filtered results
const filteredIcons: Array<{ value: string; label: string }> = useMemo(() => {
const iconItems = uniq(
Object.keys(Icons).filter((name) =>
name.toLowerCase().includes(searchTerm.toLowerCase())
)
);
return uniqBy(
iconItems?.map((iconName) => ({
value: iconName,
label: prettifyIconLabel(iconName),
})),
"label"
);
}, [searchTerm]);
// handleScroll handles both up and down scrolling
const handleScroll = (e: React.UIEvent<HTMLDivElement>) => {
const container = e.currentTarget;
const scrollPosition = container.scrollTop;
const scrollHeight = container.scrollHeight;
const clientHeight = container.clientHeight;
// Calculate scroll percentage
const scrollPercentage = scrollPosition / (scrollHeight - clientHeight);
// Adjust startIndex based on scroll direction
if (
scrollPercentage > 0.7 &&
startIndex + WINDOW_SIZE < filteredIcons.length
) {
setStartIndex(
Math.min(startIndex + BUFFER_SIZE, filteredIcons.length - WINDOW_SIZE)
);
} else if (scrollPercentage < 0.3 && startIndex > 0) {
setStartIndex(Math.max(0, startIndex - BUFFER_SIZE));
}
};
// Debounced search handler
const debouncedSearch = useMemo(
() =>
debounce((value: string) => {
setSearchTerm(value);
}, 300),
[] // Empty dependency array since we don't want to recreate the debounced function
);
useEffect(() => {
return () => {
debouncedSearch.cancel();
};
}, [debouncedSearch]);
return (
<DropdownMenu
open={open}
onOpenChange={(c) => {
if (!c) {
setStartIndex(0);
setSearchTerm("");
debouncedSearch.cancel(); // Cancel any pending debounced calls
}
setOpen(c);
}}
>
<DropdownMenuTrigger className="w-full" asChild disabled={allDisabled}>
{hasSelectedIcon ? (
<Button
variant="outline"
className="flex flex-row items-center justify-between"
disabled={allDisabled}
>
<div className="flex flex-row items-center gap-2 justify-start">
<span style={{ color: iconColor }}>
{React.createElement(Icons[selectedIcon] as React.ElementType)}
</span>
{prettifyIconLabel(selectedIcon)}
</div>
{open ? <Icons.ChevronUp /> : <Icons.ChevronDown />}
</Button>
) : (
<Button
disabled={allDisabled}
variant="outline"
className="flex justify-between"
>
Select an icon {open ? <Icons.ChevronUp /> : <Icons.ChevronDown />}
</Button>
)}
</DropdownMenuTrigger>
<DropdownMenuContent className="h-[400px] w-[400px] ml-auto">
<div className="sticky top-0 z-10 bg-white dark:bg-gray-950 p-2">
<DropdownMenuLabel className="px-2 pb-2">Icons</DropdownMenuLabel>
<Input
disabled={allDisabled}
placeholder="Search icons..."
onChange={(e) => debouncedSearch(e.target.value)}
onClick={(e) => e.stopPropagation()}
className="mb-2"
/>
<DropdownMenuSeparator />
</div>
<div className="overflow-y-auto max-h-[420px]" onScroll={handleScroll}>
{startIndex > 0 && (
<div className="py-2 text-center text-sm text-gray-500">
Scroll up for previous icons
</div>
)}
{uniq(filteredIcons)
.slice(startIndex, startIndex + WINDOW_SIZE)
?.map((iconItem) => (
<DropdownMenuItem
key={iconItem.value}
onClick={() => {
setSelectedIcon(iconItem.value as KeyofIcons);
}}
className="flex flex-row items-center gap-2 justify-start w-full"
disabled={allDisabled}
>
<span style={{ color: iconColor }}>
{React.createElement(
Icons[iconItem.value as KeyofIcons] as React.ElementType
)}
</span>
{iconItem.label}
</DropdownMenuItem>
))}
{startIndex + WINDOW_SIZE < filteredIcons.length && (
<div className="py-2 text-center text-sm text-gray-500">
Scroll down for more icons
</div>
)}
</div>
</DropdownMenuContent>
</DropdownMenu>
);
}
|
0 | lc_public_repos/open-canvas/src/components | lc_public_repos/open-canvas/src/components/assistant-select/edit-delete-dropdown.module.css | .dropdownContent {
width: 48px !important;
min-width: 48px !important;
padding: 4px !important;
}
.dropdownContent > [role="menuitem"] {
width: 100% !important;
padding: 4px !important;
display: flex;
justify-content: center;
}
|
0 | lc_public_repos/open-canvas/src/components | lc_public_repos/open-canvas/src/components/assistant-select/assistant-item.tsx | import { DropdownMenuItem } from "@/components/ui/dropdown-menu";
import { Dispatch, MouseEventHandler, SetStateAction } from "react";
import { Assistant } from "@langchain/langgraph-sdk";
import { cn } from "@/lib/utils";
import { getIcon } from "./utils";
import { EditDeleteDropdown } from "./edit-delete-dropdown";
interface AssistantItemProps {
assistant: Assistant;
allDisabled: boolean;
selectedAssistantId: string | undefined;
setAllDisabled: Dispatch<SetStateAction<boolean>>;
onClick: MouseEventHandler<HTMLDivElement>;
setEditModalOpen: Dispatch<SetStateAction<boolean>>;
deleteAssistant: (assistantId: string) => Promise<boolean>;
setAssistantDropdownOpen: Dispatch<SetStateAction<boolean>>;
setEditingAssistant: Dispatch<SetStateAction<Assistant | undefined>>;
}
export function AssistantItem({
allDisabled,
assistant,
selectedAssistantId,
setAllDisabled,
onClick,
setEditModalOpen,
deleteAssistant,
setAssistantDropdownOpen,
setEditingAssistant,
}: AssistantItemProps) {
const isDefault = assistant.metadata?.is_default as boolean | undefined;
const isSelected = assistant.assistant_id === selectedAssistantId;
const metadata = assistant.metadata as Record<string, any>;
return (
<div className="flex items-center justify-center w-full gap-1">
<DropdownMenuItem
onClick={onClick}
disabled={allDisabled}
className={cn(
"flex items-center justify-start gap-2 w-full",
isSelected && "bg-gray-50"
)}
>
<span
style={{ color: metadata?.iconData?.iconColor || "#4b5563" }}
className="flex items-center justify-start w-4 h-4"
>
{getIcon(metadata?.iconData?.iconName as string | undefined)}
</span>
{assistant.name}
{isDefault && (
<span className="text-xs text-gray-500 ml-auto">{"(default)"}</span>
)}
{isSelected && <span className="ml-auto">•</span>}
</DropdownMenuItem>
<EditDeleteDropdown
allowDelete={!isDefault}
setDisabled={setAllDisabled}
disabled={allDisabled}
setEditingAssistant={setEditingAssistant}
setEditModalOpen={setEditModalOpen}
deleteAssistant={deleteAssistant}
setAssistantDropdownOpen={setAssistantDropdownOpen}
selectedAssistant={assistant}
/>
</div>
);
}
|
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