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https://api.github.com/repos/langchain-ai/langchain/issues/13458
|
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| 1,996,761,493
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I_kwDOIPDwls53BCmV
| 13,458
|
Output being rephrased
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| 2023-11-16T12:34:29
| 2023-11-16T12:49:07
| null |
NONE
| null |
@dosu-bot
1 more question for my code thats below.
This is my code:
loader = PyPDFLoader(file_name)
documents = loader.load()
llm = ChatOpenAI(temperature = 0, model_name='gpt-3.5-turbo', callbacks=[StreamingStdOutCallbackHandler()], streaming = True)
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=300,
chunk_overlap=50,
)
chunks = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
persist_directory = "C:\Users\Asus\OneDrive\Documents\Vendolista"
knowledge_base = Chroma.from_documents(chunks, embeddings, persist_directory = persist_directory)
#save to disk
knowledge_base.persist()
#To delete the DB we created at first so that we can be sure that we will load from disk as fresh db
knowledge_base = None
new_knowledge_base = Chroma(persist_directory = persist_directory, embedding_function = embeddings)
knowledge_base = Chroma.from_documents(chunks, embeddings, persist_directory=persist_directory)
knowledge_base.persist()
prompt_template = """
Text: {context}
Question: {question}
Answer :
"""
PROMPT = PromptTemplate(
template=prompt_template, input_variables=["context", "question"]
)
memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)
conversation = ConversationalRetrievalChain.from_llm(
llm=llm,
memory = memory,
retriever=knowledge_base.as_retriever(search_type = "similarity", search_kwargs = {"k":2}),
chain_type="stuff",
verbose=False,
combine_docs_chain_kwargs={"prompt":PROMPT}
)
def main():
chat_history = []
while True:
query = input("Ask me anything about the files (type 'exit' to quit): ")
if query.lower() in ["exit"] and len(query) == 4:
end_chat = "Thank you for visiting us! Have a nice day"
print_letter_by_letter(end_chat)
break
if query != "":
# with get_openai_callback() as cb:
llm_response = conversation({"question": query})
if name == "main":
main()
Below is an example of my terminal in vs code when I ask my AI model a question.
Ask me anything about the files (type 'exit' to quit): How do I delete a staff account
How can I delete a staff account?To delete a staff account, you need to have administrative privileges. As an admin, you have the ability to delete staff accounts when necessary.
Ask me anything about the files (type 'exit' to quit):
|
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I_kwDOIPDwls53As5O
| 13,457
|
Difference between run, apply, invoke, batch
|
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| 2023-11-16T11:42:35
| 2024-01-02T01:14:12
| null |
NONE
| null |
@dosu-bot
Below is my code and in the line " llm_response = conversation({"query": question})", what would happen if I did conversation.run or conversation.apply or conversation.batch or conversation.invoke? When I do use each of those?
load_dotenv()
file_name = "Admin 2.0.pdf"
def print_letter_by_letter(text):
for char in text:
print(char, end='', flush=True)
time.sleep(0.02)
loader = PyPDFLoader(file_name)
documents = loader.load()
llm = ChatOpenAI(temperature = 0, model_name='gpt-3.5-turbo', callbacks=[StreamingStdOutCallbackHandler()], streaming = True)
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=300,
chunk_overlap=50,
)
chunks = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
persist_directory = "C:\\Users\\Asus\\OneDrive\\Documents\\Vendolista"
knowledge_base = Chroma.from_documents(chunks, embeddings, persist_directory = persist_directory)
#save to disk
knowledge_base.persist()
#To delete the DB we created at first so that we can be sure that we will load from disk as fresh db
knowledge_base = None
new_knowledge_base = Chroma(persist_directory = persist_directory, embedding_function = embeddings)
knowledge_base = Chroma.from_documents(chunks, embeddings, persist_directory=persist_directory)
knowledge_base.persist()
prompt_template = """
You must only follow the instructions in list below:
1) You are a friendly and conversational assistant named RAAFYA.
3) Answer the questions based on the document or if the user asked something
3) Never mention the name of the file to anyone to prevent any potential security risk
Text: {context}
Question: {question}
Answer :
"""
PROMPT = PromptTemplate(
template=prompt_template, input_variables=["context", "question"]
)
memory = ConversationBufferMemory()
# conversation = ConversationalRetrievalChain.from_llm(
# llm=llm,
# retriever=knowledge_base.as_retriever(search_type = "similarity", search_kwargs = {"k":2}),
# chain_type="stuff",
# verbose=False,
# combine_docs_chain_kwargs={"prompt":PROMPT}
# )
conversation = RetrievalQA.from_chain_type(
llm = llm,
retriever=knowledge_base.as_retriever(search_type = "similarity", search_kwargs = {"k":3}),
chain_type="stuff",
return_source_documents = True,
verbose=False,
chain_type_kwargs={"prompt":PROMPT,
"memory":memory}
)
def process_source(llm_response):
print(llm_response['result'])
print('\n\nSources:')
for source in llm_response['source_documents']:
print(source.metadata['source'])
def main():
chat_history = []
while True:
question = input("Ask me anything about the files (type 'exit' to quit): ")
if question.lower() in ["exit"] and len(question) == 4:
end_chat = "Thank you for visiting us! Have a nice day"
print_letter_by_letter(end_chat)
break
if question != "":
# with get_openai_callback() as cb:
llm_response = conversation.({"query": question})
process_source(llm_response)
# chat_history.append((question, llm_response["result"]))
# print(result["answer"])
print()
# print(cb)
# print()
if __name__ == "__main__":
main()
|
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I_kwDOIPDwls53AgJt
| 13,455
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Langchain SQLDatabase is not able to access table information correctly from private schema of PostgreSQL database
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open
| false
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[] | null | 3
| 2023-11-16T11:12:24
| 2023-12-10T00:07:32
| null |
NONE
| null |
### System Info
Langchain Version: 0.0.336
OS: Windows
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Using "SQLDatabase.from_uri" I am not able to access table information from private schema of PostgreSQL database. But using the same I am able to access table information from public schema of PostgreSQL DB. How can I access table information from all the schema's ? Please find the code below. Can someone help me?
db = SQLDatabase.from_uri(f"postgresql+psycopg2://{username}:{password}@{host}:{port}/{database}",sample_rows_in_table_info=5)
print(db.get_table_names())
### Expected behavior
I expected it give information of tables from all the schemas.
|
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| 1,996,569,188
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I_kwDOIPDwls53ATpk
| 13,454
|
LangChain(OpenAI) not returning JSON format output
|
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open
| false
| null |
[] | null | 2
| 2023-11-16T10:42:38
| 2023-11-16T10:56:37
| null |
NONE
| null |
### System Info
**System Information:**
- Python: 3.10.13
- Conda: 23.3.1
- Openai: 0.28.1
- LangChain: 0.0.330
- System: Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz
- Platform: AWS SageMaker Notebook
**Issue:**
- Few hours ago everything was working fine but now all of a sudden OpenAI isn't generating JSON format results
- I can't upgrade OpenAI or LangChain because another issue related to ChatCompletion due to OpenAI's recent update comes in which LangChain hasn't resolved
- Screenshot of Prompt and the output is below
**Screenshot:**
### Who can help?
@hwchase17 @agola11
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [X] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
**Just ask the OpenAI to generate a JSON format output:**
prompt = """
You're analyzing an agent's calls performance with his customers.
Please generate an expressive agent analysis report by using the executive summary, metadata provided to you and by
comparing the agent emotions against the top agent's emotions. Don't make the report about numbers only.
Make it look like an expressive and qualitative summary but keep it to ten lines only. Also generate only
five training guidelines for the agent in concise bullet points.
The output should be in the following JSON format:
{
"Agent Analysis Report" : "..."
"Training Guidelines" : "..."
}
"""
### Expected behavior
**Output should be like this:**
Output = {
"Agent Performance Report": "Agent did not perform well....",
"Training Guidelines": "Agent should work on empathy..."
}
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OpenAI Assitant
|
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[] | null | 7
| 2023-11-16T10:40:22
| 2024-01-15T04:12:31
| 2023-12-16T21:56:46
|
NONE
| null |
### Feature request
Can you allow the usage of existing Open ai assistant instead of creating a new one every time when using OpenAI Runnable
### Motivation
I dont want to clutter my assistant list with a bunch of clones
### Your contribution
The developer should only provide the assistant ID in the Constructor for OpenAIRunnable
|
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I_kwDOIPDwls53AIE4
| 13,452
|
Can't load docx file with directory in content.
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[] | null | 3
| 2023-11-16T10:17:19
| 2023-11-16T10:59:05
| null |
NONE
| null |
### System Info
Error info:
<CT_SectPr '<w:sectPr>' at 0x2c9fa6c50> is not in list
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. create a new word ( .docx ) file,
2. wirte some content inside, then insert a directory in some where and save file.
3. upload .docx file.
4. click Button 'add file to Knowledge base'
5. check the file added.
6. you could see "DocumentLoader, spliter is None" and Document count is 0.
### Expected behavior
load docx file successful.
|
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PR_kwDOIPDwls5fnAKd
| 13,451
|
VertexAI LLM count_tokens method requires list of prompts
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[] | null | 5
| 2023-11-16T09:54:28
| 2023-11-20T17:40:49
| 2023-11-20T17:40:49
|
CONTRIBUTOR
| null |
I encountered this during summarization with VertexAI. I was receiving an INVALID_ARGUMENT error, as it was trying to send a list of about 17000 single characters.
The [count_tokens method](https://github.com/googleapis/python-aiplatform/blob/main/vertexai/language_models/_language_models.py#L658) made available by Google takes in a list of prompts. It does not fail for small texts, but it does for longer documents because the argument list will be exceeding Googles allowed limit. Enforcing the list type makes it work successfully.
This change will cast the input text to count to a list of that single text so that the input format is always correct.
[Twitter](https://www.x.com/stijn_tratsaert)
|
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PR_kwDOIPDwls5fm3mh
| 13,449
|
Astra DB: minor improvements to docstrings and demo notebook
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[] | null | 2
| 2023-11-16T09:32:09
| 2023-11-16T20:48:40
| 2023-11-16T20:48:32
|
CONTRIBUTOR
| null |
This PR brings a few minor improvements to the docs, namely class/method docstrings and the demo notebook.
- A note on how to control concurrency levels to tune performance in bulk inserts, both in the class docstring and the demo notebook;
- Slightly increased concurrency defaults after careful experimentation (still on the conservative side even for clients running on less-than-typical network/hardware specs)
- renamed the DB token variable to the standardized `ASTRA_DB_APPLICATION_TOKEN` name (used elsewhere, e.g. in the Astra DB docs)
- added a note and a reference (add_text docstring, demo notebook) on allowed metadata field names.
Thank you!
|
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PR_kwDOIPDwls5fmjPZ
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Feature cognitive health
|
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| 2023-11-20T06:28:48
| 2023-11-20T02:44:01
|
CONTRIBUTOR
| null |
- **Description:** This change adds an agent to the Azure Cognitive Services toolkit for identifying healthcare entities
- **Dependencies:** azure-ai-textanalytics (Optional)
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I_kwDOIPDwls52_PjQ
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|
Issue: How to return retrieval texts?
|
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| 2023-11-16T08:39:04
| null |
NONE
| null |
### Issue you'd like to raise.
Friends, how can I return the retrieval texts(context)? My script is as follows:
```python
from langchain.document_loaders import PyPDFDirectoryLoader
from langchain.embeddings import OpenAIEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import FAISS
from langchain.schema import Document
from langchain.chat_models.openai import ChatOpenAI
from langchain.prompts import ChatPromptTemplate
from langchain.schema.output_parser import StrOutputParser
from langchain.schema.runnable import RunnablePassthrough
pdf_path = '/home/data/pdf'
pdf_loader = PyPDFDirectoryLoader(pdf_path)
docs = pdf_loader.load()
splitter = RecursiveCharacterTextSplitter(chunk_size=256,chunk_overlap=0)
split_docs = splitter.split_documents(docs)
docsearch = FAISS.from_documents(split_docs,OpenAIEmbeddings())
retriever = docsearch.as_retriever()
template = """Answer the question based only on the following context:
{context}
Question: {question}
"""
prompt = ChatPromptTemplate.from_template(template)
model = ChatOpenAI(temperature=0.2)
# RAG pipeline
chain = (
{"context": retriever, "question": RunnablePassthrough()}
| prompt
| model
| StrOutputParser()
)
print(chain.invoke("How to maintain the seat belts in the Jingke model?"))
```
### Suggestion:
_No response_
|
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| 1,996,188,135
|
I_kwDOIPDwls52-2nn
| 13,444
|
create_csv_agent UnicodeDecodeError('utf-8' )
|
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open
| false
| null |
[] | null | 3
| 2023-11-16T06:55:28
| 2023-11-16T11:09:56
| null |
NONE
| null |
### System Info
python
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
from langchain import OpenAI
from langchain.chains import AnalyzeDocumentChain
from langchain.chains.summarize import load_summarize_chain
from langchain_experimental.agents.agent_toolkits import create_csv_agent
from langchain.chains.question_answering import load_qa_chain
from langchain.chat_models import ChatOpenAI
from langchain.agents import AgentType
model = ChatOpenAI(model="gpt-4") # gpt-3.5-turbo, gpt-4
agent = create_csv_agent(model,"APT.csv",verbose=True)
agent.run("how many rows are there?")
### Expected behavior
UnicodeDecodeError Traceback (most recent call last)
[<ipython-input-25-0a5f8e8f5933>](https://localhost:8080/#) in <cell line: 11>()
9 model = ChatOpenAI(model="gpt-4") # gpt-3.5-turbo, gpt-4
10
---> 11 agent = create_csv_agent(model,"APT.csv",verbose=True)
12
13 agent.run("how many rows are there?")
11 frames
/usr/local/lib/python3.10/dist-packages/pandas/_libs/parsers.pyx in pandas._libs.parsers.raise_parser_error()
UnicodeDecodeError: 'utf-8' codec can't decode byte 0xb1 in position 0: invalid start byte
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https://github.com/langchain-ai/langchain/pull/13443
| 1,996,181,351
|
PR_kwDOIPDwls5fl_Ic
| 13,443
|
Use List instead of list
|
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| 2023-11-16T06:49:54
| 2023-11-17T10:24:02
| 2023-11-16T21:15:59
|
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
Unify List usages in libs/langchain/langchain/text_splitter.py, only one place it's `list`, all other ocurrences are `List`
|
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https://github.com/langchain-ai/langchain/issues/13442
| 1,996,065,154
|
I_kwDOIPDwls52-YmC
| 13,442
|
openai tools don't work with streaming=True
|
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[] | null | 6
| 2023-11-16T05:02:17
| 2023-12-16T07:55:17
| 2023-12-16T07:55:16
|
NONE
| null |
### System Info
langchain: latest (0.0.336)
### Who can help?
@hwchase17 (from git blame and from #13110)
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Code to reproduce (based on [code from docs](https://python.langchain.com/docs/modules/agents/agent_types/openai_tools))
```python
from langchain.agents import AgentExecutor
from langchain.agents.format_scratchpad.openai_tools import (
format_to_openai_tool_messages,
)
from langchain.agents.output_parsers.openai_tools import OpenAIToolsAgentOutputParser
from langchain.chat_models import ChatOpenAI
from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain.tools import BearlyInterpreterTool, DuckDuckGoSearchRun
from langchain.tools.render import format_tool_to_openai_tool
lc_tools = [DuckDuckGoSearchRun()]
oai_tools = [format_tool_to_openai_tool(tool) for tool in lc_tools]
prompt = ChatPromptTemplate.from_messages(
[
("system", "You are a helpful assistant"),
("user", "{input}"),
MessagesPlaceholder(variable_name="agent_scratchpad"),
]
)
llm = ChatOpenAI(temperature=0, model="gpt-3.5-turbo-1106",
streaming=True)
agent = (
{
"input": lambda x: x["input"],
"agent_scratchpad": lambda x: format_to_openai_tool_messages(
x["intermediate_steps"]
),
}
| prompt
| llm.bind(tools=oai_tools)
| OpenAIToolsAgentOutputParser()
)
agent_executor = AgentExecutor(agent=agent, tools=lc_tools, verbose=True)
agent_executor.invoke(
{"input": "What's the average of the temperatures in LA, NYC, and SF today?"}
)
```
Logs:
```
> Entering new AgentExecutor chain...
Traceback (most recent call last):
File "./test-functions.py", line 42, in <module>
agent_executor.invoke(
File "./venv/lib/python3.11/site-packages/langchain/chains/base.py", line 87, in invoke
return self(
^^^^^
File "./venv/lib/python3.11/site-packages/langchain/chains/base.py", line 310, in __call__
raise e
File "./venv/lib/python3.11/site-packages/langchain/chains/base.py", line 304, in __call__
self._call(inputs, run_manager=run_manager)
File "./venv/lib/python3.11/site-packages/langchain/agents/agent.py", line 1245, in _call
next_step_output = self._take_next_step(
^^^^^^^^^^^^^^^^^^^^^
File "./venv/lib/python3.11/site-packages/langchain/agents/agent.py", line 1032, in _take_next_step
output = self.agent.plan(
^^^^^^^^^^^^^^^^
File "./venv/lib/python3.11/site-packages/langchain/agents/agent.py", line 461, in plan
output = self.runnable.invoke(inputs, config={"callbacks": callbacks})
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "./venv/lib/python3.11/site-packages/langchain/schema/runnable/base.py", line 1427, in invoke
input = step.invoke(
^^^^^^^^^^^^
File "./venv/lib/python3.11/site-packages/langchain/schema/runnable/base.py", line 2787, in invoke
return self.bound.invoke(
^^^^^^^^^^^^^^^^^^
File "./venv/lib/python3.11/site-packages/langchain/chat_models/base.py", line 142, in invoke
self.generate_prompt(
File "./venv/lib/python3.11/site-packages/langchain/chat_models/base.py", line 459, in generate_prompt
return self.generate(prompt_messages, stop=stop, callbacks=callbacks, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "./venv/lib/python3.11/site-packages/langchain/chat_models/base.py", line 349, in generate
raise e
File "./venv/lib/python3.11/site-packages/langchain/chat_models/base.py", line 339, in generate
self._generate_with_cache(
File "./venv/lib/python3.11/site-packages/langchain/chat_models/base.py", line 492, in _generate_with_cache
return self._generate(
^^^^^^^^^^^^^^^
File "./venv/lib/python3.11/site-packages/langchain/chat_models/openai.py", line 422, in _generate
return _generate_from_stream(stream_iter)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "./venv/lib/python3.11/site-packages/langchain/chat_models/base.py", line 61, in _generate_from_stream
generation += chunk
File "./venv/lib/python3.11/site-packages/langchain/schema/output.py", line 94, in __add__
message=self.message + other.message,
~~~~~~~~~~~~~^~~~~~~~~~~~~~~
File "./venv/lib/python3.11/site-packages/langchain/schema/messages.py", line 225, in __add__
additional_kwargs=self._merge_kwargs_dict(
^^^^^^^^^^^^^^^^^^^^^^^^
File "./venv/lib/python3.11/site-packages/langchain/schema/messages.py", line 138, in _merge_kwargs_dict
raise ValueError(
ValueError: Additional kwargs key tool_calls already exists in this message.
```
`left` and `right` from inside`_merge_kwargs_dict`:
```python
left = {'tool_calls': [{'index': 0, 'id': 'call_xhpbRSsUKkzsvtFgnkTXEFtAtHc', 'function': {'arguments': '', 'name': 'duckduckgo_search'}, 'type': 'function'}]}
right = {'tool_calls': [{'index': 0, 'id': None, 'function': {'arguments': '{"qu', 'name': None}, 'type': None}]}
```
### Expected behavior
No errors and same result as without `streaming=True`.
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| 1,996,037,409
|
I_kwDOIPDwls52-R0h
| 13,441
|
langchain.vectorstores.Chroma support for EmbeddingFunction.__call__ update of ChromaDB
|
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open
| false
| null |
[] | null | 6
| 2023-11-16T04:32:31
| 2023-12-04T14:40:26
| null |
NONE
| null |
### System Info
This was working fine for my previous configuration,
langchain v0.0.225
chromadb v0.4.7
But now neither this is working, nor the latest version of both
langchain v0.0.336
chromadb v0.4.17
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I have the packages installed
Running these pieces of code
```
from langchain.document_loaders import TextLoader
from langchain.indexes import VectorstoreIndexCreator
loader = TextLoader(file_path)
index = VectorstoreIndexCreator().from_loaders([loader]) # this is where I am getting the error
```
OR
```
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import Chroma
from langchain.embeddings.openai import OpenAIEmbeddings
text_splitter = RecursiveCharacterTextSplitter()
splits = text_splitter.split_documents(docs)
embedding = OpenAIEmbeddings()
vectordb = Chroma.from_documents( # this is where I am getting the error
documents=splits,
embedding=embedding,
)
```
Here is the error
```
Expected EmbeddingFunction.__call__ to have the following signature: odict_keys(['self', 'input']), got odict_keys(['self', 'args', 'kwargs'])\nPlease see https://docs.trychroma.com/embeddings for details of the EmbeddingFunction interface.\nPlease note the recent change to the EmbeddingFunction interface: https://docs.trychroma.com/migration#migration-to-0416---november-7-2023 \n
```
### Expected behavior
Earlier a chromadb instance would be created, and I would be able to query it with my prompts. That is the expected behaviour.
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I_kwDOIPDwls52-M--
| 13,440
|
Request new feature for Robotic Application
|
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[] | null | 5
| 2023-11-16T04:11:33
| 2024-01-11T05:52:36
| null |
NONE
| null |
### Feature request
Hi Guys,
Here I made a new fork to [https://github.com/MetaSLAM/CyberChain](https://github.com/MetaSLAM/CyberChain), where I would like to combine the powerful Langchain ability and GPT4 into the real-world robotic challenges.
My question raised here is mainly about:
1. How can I setup the Chat GPT 4 version into Langchain, where I would like to levevarge the powerful visual inference of GPT4;
2. I there any suggestion for the memory system, because the robot may travel through a large-scale environment for lifelong navigation, I would like to construct a memory system within langchain to enhance its behaviour in the long-term operation.
Many thanks for your hard work, and Langchain is difinitely an impressive work.
Max
### Motivation
Combine real-world robotic application with the LangChain framework.
### Your contribution
I will provide my research outcomes under our MetaSLAM organization (https://github.com/MetaSLAM), hope this can benefit both robotics and AI communitry, targeting for the general AI system.
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PR_kwDOIPDwls5flT5s
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fix: model init ValueError
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[] | null | 6
| 2023-11-16T03:25:06
| 2023-12-06T06:14:49
| null |
NONE
| null |
This pr fix #13438 #11648 #10917
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model init ValueError
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[] | null | 2
| 2023-11-16T03:23:29
| 2023-11-16T03:30:30
| null |
NONE
| null |
### System Info
langchain version: 0.0.336
mac
python3.8
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```py
import langchain
from langchain.chat_models.minimax import MiniMaxChat
print(langchain.__version__)
chat = MiniMaxChat(minimax_api_host="test_host", minimax_api_key="test_api_key", minimax_group_id="test_group_id")
assert chat._client
assert chat._client.host == "test_host"
assert chat._client.group_id == "test_group_id"
assert chat._client.api_key == "test_api_key"
```
output:
```sh
0.0.336
Traceback (most recent call last):
File "/Users/hulk/code/py/workhome/test_pydantic/test_langchain.py", line 4, in <module>
chat = MiniMaxChat(minimax_api_host="test_host", minimax_api_key="test_api_key", minimax_group_id="test_group_id")
File "/Users/hulk/miniforge3/envs/py38/lib/python3.8/site-packages/langchain/llms/minimax.py", line 121, in __init__
self._client = _MinimaxEndpointClient(
File "pydantic/main.py", line 357, in pydantic.main.BaseModel.__setattr__
ValueError: "MiniMaxChat" object has no field "_client"
```
### Expected behavior
Instantiation Success
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I_kwDOIPDwls52-AYC
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[PGVector] The scores returned by 'similarity_search_with_score' are NOT proportional to the similarity
|
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[] | null | 4
| 2023-11-16T03:10:54
| 2023-11-16T04:36:56
| null |
NONE
| null |
### System Info
langchain==0.0.266
### Who can help?
@hwchase17
@eyurtsev
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
## Behavior
When I call the `vector_store.similarity_search_with_score` function:
- Expected: The returned scores will be proportional to the similarity. This means the higher score, the higher similarity.
- Actual: The scores are proportional to the the distance.
### Problem
- When I call `as_retriever` function with the `score_threshold`, the behavior is wrong. Because when `score_threshold` is declared, it will filter documents that have score greater than or equal to `score_threshold` value. So the top documents that found from pgvector will be filter out while it's the most similar in fact.
### Expected behavior
The returned scores from PGVector queries are proportional to the similarity.
In other words, the higher score, the higher similarity.
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PR_kwDOIPDwls5flDzQ
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DOCS: rag nit
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|
COLLABORATOR
| null | null |
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PR_kwDOIPDwls5fk7nO
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updated `memory` Titles
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| 2023-11-16T01:27:02
| 2023-11-16T21:57:39
| 2023-11-16T21:24:05
|
COLLABORATOR
| null |
- Fixed titles for two notebooks. They were inconsistent with other titles and clogged ToC.
- Added `Upstash` description and link
- Moved the authentication text up in the `Elasticsearch` nb, right after package installation. It was on the end of the page which was a wrong place.
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| 1,995,839,782
|
PR_kwDOIPDwls5fk2nf
| 13,434
|
updated `async-faiss` example
|
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[] | null | 6
| 2023-11-16T00:56:27
| 2023-11-17T01:47:06
| 2023-11-17T01:41:26
|
COLLABORATOR
| null |
The original notebook has the `faiss` title which is duplicated in the`faiss.jpynb`. As a result, we have two `faiss` items in the vectorstore ToC. And the first item breaks the searching order (it is placed between `A...` items).
- I updated title to `Asynchronous Faiss`.
|
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| 1,995,838,321
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I_kwDOIPDwls529hNx
| 13,433
|
Issue: problem with conversationchain take multiple inputs
|
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[] | null | 2
| 2023-11-16T00:54:37
| 2023-11-16T01:04:40
| null |
NONE
| null |
### Issue you'd like to raise.
Hi, I'm trying to make a chatbot using conversationchain. The prompt takes three variables, “history” from memory, user input: "input", and another variable say variable3. But it has an error “Got unexpected prompt input variables. The prompt expects ['history', 'input', 'variable3'], but got ['history'] as inputs from memory, and input as the normal input key. ”
This error doesn't occur if I'm using llmchain. So how can I prevent it if I want to use conversationchain?
Thanks
### Suggestion:
_No response_
|
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I_kwDOIPDwls529fgn
| 13,432
|
Custom Tool Output in a Chain
|
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[] | null | 1
| 2023-11-16T00:46:31
| 2023-11-16T00:46:54
| null |
NONE
| null |
### Feature request
Although there are many output parsers in langchain, how to custom the output in a chain agent can not find any solutions right now--yet it may be sometimes necessary. Let's say a chain agent x, the tools are Tool1, Tool2 and Tool3, if:
the output of Tool2 should be customized, and no longer be processed by GPT again,
the outputs of Tool1 and Tool3 should be normal, and be processed by GPT again,
in this case, no solution could be found: because the output parser is based on the agent other than any specific tools.
Should this feature be satisfied?
### Motivation
When multiple tools in an agent chain, and some of the tools should be output customized.
### Your contribution
no
|
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PR_kwDOIPDwls5fksmK
| 13,431
|
updated `semadb` example
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[] | null | 1
| 2023-11-16T00:12:43
| 2023-11-16T18:17:02
| 2023-11-16T17:57:22
|
COLLABORATOR
| null |
- the `SemaDB` notebook was placed in additional subfolder which breaks the vectorstore ToC. I moved file up, removed this unnecessary subfolder; updated the `vercel.json` with rerouting for the new URL
- Added SemaDB description and link
- improved text consistency
|
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I_kwDOIPDwls529Uh_
| 13,430
|
`get_openai_callback()` does not count tokens when LCEL chain used with `.stream()` method
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| 2023-11-16T00:05:51
| 2024-01-16T22:29:45
| null |
NONE
| null |
### System Info
```
langchain==0.0.335
python==3.10
```
### Who can help?
@agola11
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
From the LCEL interface [docs](https://python.langchain.com/docs/expression_language/interface) we have the following snippet:
```python
from langchain.chat_models import ChatOpenAI
from langchain.prompts import ChatPromptTemplate
model = ChatOpenAI()
prompt = ChatPromptTemplate.from_template("tell me a joke about {topic}")
chain = prompt | model
for s in chain.stream({"topic": "bears"}):
print(s.content, end="", flush=True)
```
From the token usage tracking [docs](https://python.langchain.com/docs/modules/model_io/chat/token_usage_tracking) we have the snippet
```python
from langchain.callbacks import get_openai_callback
from langchain.chat_models import ChatOpenAI
llm = ChatOpenAI(model_name="gpt-4")
with get_openai_callback() as cb:
result = llm.invoke("Tell me a joke")
print(cb)
```
that yields the following output:
```
Tokens Used: 24
Prompt Tokens: 11
Completion Tokens: 13
Successful Requests: 1
Total Cost (USD): $0.0011099999999999999
```
I am trying to combine the two concepts in the following snippet
```python
from langchain.prompts import ChatPromptTemplate
from langchain.callbacks import get_openai_callback
from langchain.chat_models import ChatOpenAI
llm = ChatOpenAI(model_name="gpt-4")
prompt = ChatPromptTemplate.from_template("tell me a joke about {topic}")
chain = prompt | llm
with get_openai_callback() as cb:
for s in chain.stream({"topic": "bears"}):
print(s.content, end="", flush=True)
print(cb)
```
but get the following result:
```
Tokens Used: 0
Prompt Tokens: 0
Completion Tokens: 0
Successful Requests: 0
Total Cost (USD): $0
```
Is token counting (and pricing) while streaming not supported at the moment?
### Expected behavior
The following but with the actual values for tokens and cost.
```
Tokens Used: 0
Prompt Tokens: 0
Completion Tokens: 0
Successful Requests: 0
Total Cost (USD): $0
```
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PR_kwDOIPDwls5fklXs
| 13,429
|
Update multi-modal RAG cookbook
|
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| 2023-11-15T23:37:28
| 2023-11-16T18:34:15
| 2023-11-16T18:34:14
|
COLLABORATOR
| null |
Use example [blog](https://cloudedjudgement.substack.com/p/clouded-judgement-111023) w/ tables, charts as images.
|
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PR_kwDOIPDwls5fkj_e
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updated `Activeloop DeepMemory` notebook
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|
COLLABORATOR
| null |
- Fixed the title of the notebook. It created an ugly ToC element as `Activeloop DeepLake's DeepMemory + LangChain + ragas or how to get +27% on RAG recall.`
- Added Activeloop description
- improved consistency in text
- fixed ToC (it was using HTML tagas that break left-side in-page ToC). Now in-page ToC works
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Bagatur/callbacks refactor
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| 2023-11-20T02:22:46
|
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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PR_kwDOIPDwls5fkfKm
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updated `data_connection` index page
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|
COLLABORATOR
| null |
- the `Index` section was missed. Created it.
- text simplification
|
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PR_kwDOIPDwls5fkctO
| 13,425
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FIX: Limit Azure OpenAI embeddings chunk size
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| 2023-11-20T02:34:52
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|
CONTRIBUTOR
| null |
Hi!
This short PR aims at:
* Fixing `OpenAIEmbeddings`' check on `chunk_size` when used with Azure OpenAI (thus with openai < 1.0). Azure OpenAI embeddings support at most 16 chunks per batch, I believe we are supposed to take the min between the passed value/default value and 16, not the max - which, I suppose, was introduced by accident while refactoring the previous version of this check from this other PR of mine: #10707
* Porting this fix to the newest class (`AzureOpenAIEmbeddings`) for openai >= 1.0
This fixes #13539 (closed but the issue persists).
@baskaryan @hwchase17
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PR_kwDOIPDwls5fkYhB
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updated `clickup` example
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| 2023-11-15T22:41:45
| 2023-11-15T23:31:28
| 2023-11-15T23:11:24
|
COLLABORATOR
| null |
- Fixed headers (was more then 1 Titles)
- Removed security token value. It was OK to have it, because it is temporary token, but the automatic security swippers raise warnings on that.
- Added `ClickUp` service description and link.
|
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Fix a link in docs
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COLLABORATOR
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PR_kwDOIPDwls5fkR0U
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full template docs
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Replace this entire comment with:
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- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
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PR_kwDOIPDwls5fkNhl
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img update
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Replace this entire comment with:
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- **Issue:** the issue # it fixes (if applicable),
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- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
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PR_kwDOIPDwls5fkMLc
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bump 336, exp 44
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COLLABORATOR
| null |
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Replace this entire comment with:
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- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
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(vectorstore)/PGVectorAsync
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COLLABORATOR
| null |
- feat(vectorstore): :sparkles: Create new integration with PGVector async
- test(vectorstore): :white_check_mark: Mark test as async and create db context manager to handle fresh start on tests
- fix(vectorstore): :pencil2: Typo on async calling
- feat(vectorstore): :bug: Set expire on commit to false
- feat(vectorstore): :sparkles: Implement asimilarity_search_with_relevance_scores
- feat(vectorstore): :memo: Update documentation
Eugene:
- Caught up with master (still need to resolve issues)
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PR_kwDOIPDwls5fj84F
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Runnable with message history
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COLLABORATOR
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https://dev.smith.langchain.com/public/c1c06afb-2e4f-4b53-bf37-6926003e7fa9/r
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PR_kwDOIPDwls5fj7I3
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Use secretstr for api keys for javelin-ai-gateway
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[] | null | 2
| 2023-11-15T21:05:54
| 2023-11-15T21:12:06
| 2023-11-15T21:12:05
|
COLLABORATOR
| null |
- Make javelin_ai_gateway_api_key a SecretStr
|
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| 1,995,562,229
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I_kwDOIPDwls528dz1
| 13,416
|
TextGen is not raising Exception when response status code is not 200
|
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[] | null | 2
| 2023-11-15T21:01:18
| 2023-11-15T21:40:20
| null |
NONE
| null |
### System Info
Langchain 0.0.335
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
When status code is not 200, [TextGen ](https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/llms/textgen.py#L223)only prints the code and returns an empty string.
This is a problem if I want to use TextGen `with_retry()` and I want to retry non 200 responses such as 5xx.
- If I edit the textgen.py source code myself and raise an Exception [here](https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/llms/textgen.py#L223), then my `with_retry()` works as desired.
I tried to handle this by raising an empty string error in my Output Parser, but the TextGen `with_retry()` is not triggering.
### Expected behavior
I'd like TextGen to raise an Exception when the status code is not 200.
Perhaps consider using the `HTTPError` from `requests` package.
|
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| 1,995,385,588
|
I_kwDOIPDwls527yr0
| 13,414
|
Issue: Making sure `ConversationalRetrievalChain` only answer based on the retriever information
|
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[] | null | 7
| 2023-11-15T19:19:26
| 2023-11-29T17:57:25
| null |
NONE
| null |
### Issue you'd like to raise.
Hi,
I have been learning LangChain for the last month and I have been struggling in the last week to "_guarantee_" `ConversationalRetrievalChain` only answers based on the knowledge added on embeddings. I don't know if I am missing some LangChain configuration or if it is just a matter of tuning my prompt. I will add my code here (simplified, not the actual one, but I will try to preserve everything important).
```
chat = AzureChatOpenAI(
deployment_name="chat",
model_name="gpt-3.5-turbo",
openai_api_version=os.getenv('OPENAI_API_VERSION'),
openai_api_key=os.getenv('OPENAI_API_KEY'),
openai_api_base=os.getenv('OPENAI_API_BASE'),
openai_api_type="azure",
temperature=0
)
embeddings = OpenAIEmbeddings(deployment_id="text-embedding-ada-002", chunk_size=1)
acs = AzureSearch(azure_search_endpoint=os.getenv('AZURE_COGNITIVE_SEARCH_SERVICE_NAME'),
azure_search_key=os.getenv('AZURE_COGNITIVE_SEARCH_API_KEY'),
index_name=os.getenv('AZURE_COGNITIVE_SEARCH_INDEX_NAME'),
embedding_function=embeddings.embed_query)
custom_template = """You work for CompanyX which sells things located in United States.
If you don't know the answer, just say that you don't. Don't try to make up an answer.
Base your questions only on the knowledge provided here. Do not use any outside knowledge.
Given the following chat history and a follow up question, rephrase the follow up question to be a standalone question, in its original language.
Chat History:
{chat_history}
Follow Up Input: {question}
Standalone question:
"""
CUSTOM_QUESTION_PROMPT = PromptTemplate.from_template(custom_template)
memory = ConversationBufferMemory(memory_key="chat_history", input_key="question", return_messages=True)
qa = ConversationalRetrievalChain.from_llm(
llm=chat,
retriever=acs.as_retriever(),
condense_question_prompt=CUSTOM_QUESTION_PROMPT,
memory=memory
)
```
When I ask it something like `qa({"question": "What is an elephant?"})` it still answers it, although it is totally unrelated to the knowledge base added to the AzureSearch via embeddings.
I tried different `condense_question_prompt`, with different results, but nothing near _good_. I've been reading the documentation and API for the last 3 weeks, but nothing else seems to help in this case. I'd appreciate any suggestions.
### Suggestion:
_No response_
|
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| 1,995,308,846
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PR_kwDOIPDwls5fjCST
| 13,413
|
Fix Runnable Lambda Afunc Repr
|
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closed
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[] | null | 3
| 2023-11-15T18:28:54
| 2023-11-15T21:11:44
| 2023-11-15T21:11:43
|
COLLABORATOR
| null |
Otherwise, you get an error when using async functions.
h/t to Chris Ruppelt
|
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PR_kwDOIPDwls5fi_G1
| 13,412
|
FIX: Infer runnable agent single or multi action
|
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| 2023-11-15T18:16:59
| 2023-11-15T21:58:15
| 2023-11-15T21:58:14
|
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
|
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| 1,995,289,924
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I_kwDOIPDwls527bVE
| 13,411
|
IDE Support - Python Package - langchain.llms
|
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| 2023-11-15T18:14:01
| 2023-11-15T18:14:23
| null |
NONE
| null |
### Feature request
Add IDE auto-complete to `langchain.llm` module
Currently, IntelliJ-based IDEs (PyCharm) interpret an LLM model to be `typing.Any`. Below is an example for the `GPT4All` package
### Motivation
Not having auto-complete on a `LLM` class can be a bit frustrating as a Python developer who works on an IntelliJ product. I've been performing a more direct import of the LLM models to handle this instead:
```python
from langchain.llms.gpt4all import GPT4All
```
Adding support for the `langchain.llms` API would improve the developer experience with the top level `langchain.llms` API
### Your contribution
The existing implementation uses a lazy-loading technique implemented in #11237 to speed up imports. Maintaining this performance is important for whatever solution is implemented. I believe this can be achieved with some imports behind an `if TYPE_CHECKING` block. If the below `Proposed Implementation` is acceptable I'd be happy to open a PR to add this functionality.
<details><summary>Current Implementation</summary>
<p>
`langchain.llms.__init__.py` (abbreviated)
```python
from typing import Any, Callable, Dict, Type
from langchain.llms.base import BaseLLM
def _import_anthropic() -> Any:
from langchain.llms.anthropic import Anthropic
return Anthropic
def _import_gpt4all() -> Any:
from langchain.llms.gpt4all import GPT4All
return GPT4All
def __getattr__(name: str) -> Any:
if name == "Anthropic":
return _import_anthropic()
elif name == "GPT4All":
return _import_gpt4all()
else:
raise AttributeError(f"Could not find: {name}")
__all__ = [
"Anthropic",
"GPT4All",
]
```
</p>
</details>
<details><summary>Proposed Implementation</summary>
<p>
`langchain.llms.__init__.py` (abbreviated)
```python
from typing import Any, Callable, Dict, Type, TYPE_CHECKING
from langchain.llms.base import BaseLLM
if TYPE_CHECKING:
from langchain.llms.anthropic import Anthropic
from langchain.llms.gpt4all import GPT4All
def _import_anthropic() -> "Anthropic":
from langchain.llms.anthropic import Anthropic
return Anthropic
def _import_gpt4all() -> "GPT4All":
from langchain.llms.gpt4all import GPT4All
return GPT4All
def __getattr__(name: str) -> "BaseLLM":
if name == "Anthropic":
return _import_anthropic()
elif name == "GPT4All":
return _import_gpt4all()
else:
raise AttributeError(f"Could not find: {name}")
__all__ = [
"Anthropic",
"GPT4All",
]
```
</p>
</details>
<details><summary>IntelliJ Screenshot</summary>
<p>
Here's a screenshot after implementing the above `Proposed Implementation`
<img width="588" alt="image" src="https://github.com/langchain-ai/langchain/assets/49741340/1647f3b5-238e-4137-8fa4-f30a2234fdb0">
</p>
</details>
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I_kwDOIPDwls527Wgu
| 13,410
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Missing required arguments; Expected either ('model' and 'prompt') or ('model', 'prompt' and 'stream')
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open
| false
| null |
[] | null | 12
| 2023-11-15T18:00:46
| 2024-01-10T09:30:33
| null |
NONE
| null |
### System Info
langchain = 0.0.335
openai = 1.2.4
python = 3.11
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [x] My own modified scripts
### Related Components
- [x] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Modified example code (https://python.langchain.com/docs/integrations/llms/azure_openai) from langchain to access AzureOpenAI inferencing endpoint
```
import os
os.environ["OPENAI_API_TYPE"] = "azure"
os.environ["OPENAI_API_VERSION"] = "2023-05-15"
os.environ["OPENAI_API_BASE"] = "..."
os.environ["OPENAI_API_KEY"] = "..."
# Import Azure OpenAI
from langchain.llms import AzureOpenAI
# Create an instance of Azure OpenAI
# Replace the deployment name with your own
llm = AzureOpenAI(
deployment_name="td2",
model_name="text-davinci-002",
)
# Run the LLM
llm("Tell me a joke")
```
I get the following error:
TypeError: Missing required arguments; Expected either ('model' and 'prompt') or ('model', 'prompt' and 'stream') arguments to be given
If I modify the last line as follows:
`llm("Tell me a joke", model="text-davinci-002") `
i get a different error:
Completions.create() got an unexpected keyword argument 'engine'
It appears to be passing all keywords to the create method, the first of which is 'engine', and it appears that and other kws are being added by the code.
### Expected behavior
I expect the model to return a response, such as is shown in the example.
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| 1,995,268,049
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I_kwDOIPDwls527V_R
| 13,409
|
Persistent client open file handles
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[] | null | 2
| 2023-11-15T17:59:14
| 2023-11-15T18:04:24
| null |
NONE
| null |
### System Info
Name: langchain
Version: 0.0.327
Name: chromadb
Version: 0.4.8
Summary: Chroma.
### Who can help?
@hwchase17 I believe the chromadb don't close the file handle during persistence making it difficult to use it on cloud services like Modal Labs. What about adding a close method or similar to make sure this doesn't happen?
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I am using ModalLabs with a simple example:
```
@stub.function(volumes={CHROMA_DIR: stub.volume})
def test_chroma():
import chromadb
from importlib.metadata import version
print("ChromaDB: %s" % version('chromadb'))
# Initialize ChromaDB client
client = chromadb.PersistentClient(path=SENTENCE_DIR.as_posix())
# Create the collection
neo_collection = client.create_collection(name="neo")
# Adding raw documents
neo_collection.add(
documents=["I know kung fu.", "There is no spoon."], ids=["quote_1", "quote_2"]
)
# Counting items in a collection
item_count = neo_collection.count()
print(f"Count of items in collection: {item_count}")
stub.volume.commit()
```
Error:
`grpclib.exceptions.GRPCError: (<Status.FAILED_PRECONDITION: 9>, 'there are open files preventing the operation', None)
`
### Expected behavior
There shouldn't be any open file handles.
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PR_kwDOIPDwls5fiwrR
| 13,408
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RunnableLambda: create afunc instance from func when not provided
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[] | null | 8
| 2023-11-15T17:30:20
| 2023-11-28T11:24:40
| 2023-11-28T11:18:26
|
CONTRIBUTOR
| null |
Fixes #13407.
This workaround consists in letting the RunnableLambda create its self.afunc from its self.func when self.afunc is not provided; the change has no dependency.
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
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- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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I_kwDOIPDwls527LNe
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RunnableLambda: returned runnable called synchronously when using ainvoke
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closed
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[] | null | 2
| 2023-11-15T17:27:49
| 2023-11-28T11:18:27
| 2023-11-28T11:18:27
|
CONTRIBUTOR
| null |
### System Info
langchain on master branch
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [X] Async
### Reproduction
```
from langchain.schema.runnable import RunnableLambda
import asyncio
def idchain_sync(__input):
print(f'sync chain call: {__input}')
return __input
async def idchain_async(__input):
print(f'async chain call: {__input}')
return __input
idchain = RunnableLambda(func=idchain_sync,afunc=idchain_async)
def func(__input):
return idchain
asyncio.run(RunnableLambda(func).ainvoke('toto'))
#printss 'sync chain call: toto' instead of 'async chain call: toto'
```
### Expected behavior
LCEL's route can cause chains to be silently run synchronously, while the user uses ainvoke...
When calling a RunnableLambda A returning a chain B with ainvoke, we would expect the new chain B to be called with ainvoke;
However, if the function provided to RunnableLambda A is not async, then the chain B will be called with invoke, silently causing all the rest of the chain to be called synchronously.
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I_kwDOIPDwls527EkQ
| 13,406
|
Issue: Need Help - Implement ChatOpenAI into my LangChain Research
|
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| 2023-11-15T17:12:08
| 2023-11-28T21:44:27
| 2023-11-28T21:44:27
|
NONE
| null |
### Issue you'd like to raise.
Hi there,
I am doing a research on creating a PDF reader AI which can answer users' questions based on the PDF uploaded and the prompt user entered. I got it so far with using the OpenAI package but now want's to make it more advance by using ChatOpenAI with the LangChain Schema package (SystemMessage, HumanMessage, and AIMessage). I am kinda lost on where I should start and make the adjustments. Could you help me on that?
Below is my code so far:
## Imports
import streamlit as st
import os
from apikey import apikey
import pickle
from PyPDF2 import PdfReader
from streamlit_extras.add_vertical_space import add_vertical_space
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import FAISS
from langchain.llms import OpenAI
from langchain.chains.question_answering import load_qa_chain
from langchain.callbacks import get_openai_callback
from langchain.chat_models import ChatOpenAI
from langchain.schema import (SystemMessage, HumanMessage, AIMessage)
os.environ['OPENAI_API_KEY'] = apikey
## User Interface
# Side Bar
with st.sidebar:
st.title('🚀 Zi-GPT Version 2.0')
st.markdown('''
## About
This app is an LLM-powered chatbot built using:
- [Streamlit](https://streamlit.io/)
- [LangChain](https://python.langchain.com/)
- [OpenAI](https://platform.openai.com/docs/models) LLM model
''')
add_vertical_space(5)
st.write('Made with ❤️ by Zi')
# Main Page
def main():
st.header("Zi's PDF Helper: Chat with PDF")
# upload a PDF file
pdf = st.file_uploader("Please upload your PDF here", type='pdf')
# st.write(pdf)
# read PDF
if pdf is not None:
pdf_reader = PdfReader(pdf)
# split document into chunks
# also can use text split: good for PDFs that do not contains charts and visuals
sections = []
for page in pdf_reader.pages:
# Split the page text by paragraphs (assuming two newlines indicate a new paragraph)
page_sections = page.extract_text().split('\n\n')
sections.extend(page_sections)
chunks = sections
# st.write(chunks)
# embeddings
file_name = pdf.name[:-4]
# comvert into pickle file
# wb: open in binary mode
# rb: read the file
# Note: only create new vectors for new files updated
if os.path.exists(f"{file_name}.pkl"):
with open(f"{file_name}.pkl", "rb") as f:
VectorStore = pickle.load(f)
st.write('Embeddings Loaded from the Disk')
else:
embeddings = OpenAIEmbeddings(model="text-embedding-ada-002")
VectorStore = FAISS.from_texts(chunks,embedding=embeddings)
with open(f"{file_name}.pkl", "wb") as f:
pickle.dump(VectorStore, f)
st.write('Embeddings Computation Completed')
# Create chat history
if pdf:
# generate chat history
chat_history_file = f"{pdf.name}_chat_history.pkl"
# load history if exist
if os.path.exists(chat_history_file):
with open(chat_history_file, "rb") as f:
chat_history = pickle.load(f)
else:
chat_history = []
# Initialize chat_history in session_state if not present
if 'chat_history' not in st.session_state:
st.session_state.chat_history = []
# Check if 'prompt' is in session state
if 'last_input' not in st.session_state:
st.session_state.last_input = ''
# User Input
current_prompt = st.session_state.get('user_input', '')
prompt_placeholder = st.empty()
prompt = prompt_placeholder.text_area("Ask questions about your PDF:", value=current_prompt, placeholder="Send a message", key="user_input")
submit_button = st.button("Submit")
if submit_button and prompt:
# Update the last input in session state
st.session_state.last_input = prompt
docs = VectorStore.similarity_search(query=prompt, k=3)
#llm = OpenAI(temperature=0.9, model_name='gpt-3.5-turbo')
chat = ChatOpenAI(model='gpt-3.5-turbo', temperature=0.7)
chain = load_qa_chain(llm=chat, chain_type="stuff")
with get_openai_callback() as cb:
response = chain.run(input_documents=docs, question=prompt)
print(cb)
# st.write(response)
# st.write(docs)
# Add to chat history
st.session_state.chat_history.append((prompt, response))
# Save chat history
with open(chat_history_file, "wb") as f:
pickle.dump(st.session_state.chat_history, f)
# Clear the input after processing
prompt_placeholder.text_area("Ask questions about your PDF:", value='', placeholder="Send a message", key="pdf_prompt")
# Display the entire chat
chat_content = ""
for user_msg, bot_resp in st.session_state.chat_history:
chat_content += f"<div style='background-color: #222222; color: white; padding: 10px;'>**You:** {user_msg}</div>"
chat_content += f"<div style='background-color: #333333; color: white; padding: 10px;'>**Zi GPT:** {bot_resp}</div>"
st.markdown(chat_content, unsafe_allow_html=True)
if __name__ == '__main__':
main()
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5fiaM5
| 13,405
|
Passthrough kwargs in runnable lambda
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[] | null | 1
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| 2023-11-15T20:32:07
| 2023-11-15T19:45:16
|
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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PR_kwDOIPDwls5fiW3h
| 13,404
|
add azure ai data document loader
|
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| 2023-11-15T16:20:11
| 2023-12-02T03:30:53
| 2023-12-02T03:25:55
|
CONTRIBUTOR
| null |
This PR adds an "Azure AI data" document loader, which allows Azure AI users to load their registered data assets as a document object in langchain.
|
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PR_kwDOIPDwls5fiKy3
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Add llama2-13b-chat-v1 support to `chat_models.BedrockChat`
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| 2023-11-15T15:54:35
| 2023-11-21T21:43:40
| 2023-11-20T02:44:59
|
CONTRIBUTOR
| null |
Hi 👋 We are working with Llama2 on Bedrock, and would like to add it to Langchain. We saw a [pull request](https://github.com/langchain-ai/langchain/pull/13322) to add it to the `llm.Bedrock` class, but since it concerns a chat model, we would like to add it to `BedrockChat` as well.
- **Description:** Add support for Llama2 to `BedrockChat` in `chat_models`
- **Issue:** the issue # it fixes (if applicable) [#13316](https://github.com/langchain-ai/langchain/issues/13316)
- **Dependencies:** any dependencies required for this change `None`
- **Tag maintainer:** /
- **Twitter handle:** `@SimonBockaert @WouterDurnez`
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PR_kwDOIPDwls5fiGOV
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|
WebResearchRetriever error handling in urls with connection error
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| 2023-11-15T15:43:55
| 2023-11-17T22:02:26
| 2023-11-17T22:02:26
|
CONTRIBUTOR
| null |
- **Description:** Added a method `fetch_valid_documents` to `WebResearchRetriever` class that will test the connection for every url in `new_urls` and remove those that raise a `ConnectionError`.
- **Issue:** [Previous PR](https://github.com/langchain-ai/langchain/pull/13353),
- **Dependencies:** None,
- **Tag maintainer:** @efriis
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
|
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I_kwDOIPDwls526KBV
| 13,400
|
Add support for multimodal embeddings from Google Vertex AI
|
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| 2023-11-15T15:02:35
| 2023-11-17T09:36:37
| null |
NONE
| null |
### Feature request
Currently no support for multi-modal embeddings from VertexAI exists. However, I did stumble upon this experimental implementation of [GoogleVertexAIMultimodalEmbeddings](https://js.langchain.com/docs/modules/data_connection/experimental/multimodal_embeddings/google_vertex_ai) in LangChain for Javascript. Hence, I think this would also be a very nice feature to implement in the Python version of LangChain.
### Motivation
Using multi-modal embeddings could positively affect applications that rely on information of different modalities. One example could be product search in a web catalogue. Since more cloud providers are making [endpoints for multi-modal embeddings](https://cloud.google.com/vertex-ai/docs/generative-ai/embeddings/get-multimodal-embeddings) available, it makes sense to incorporate these into LangChain as well. The embeddings of these endpoints could be stored in vector stores and hence be used in downstream applications that are built using LangChain.
### Your contribution
I can contribute to this feature.
|
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PR_kwDOIPDwls5fh1C-
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Update PlanAndExecute agent to include intermediate steps
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NONE
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- **Description:** update PlanAndExecute agent to include intermediate steps.
- **Issue:** [issue 4485](https://github.com/langchain-ai/langchain/issues/4485).
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I_kwDOIPDwls526Ajh
| 13,398
|
ElasticSearch allow for multiple vector_query_fields than default text & make it a kwarg in search functions
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[] | null | 1
| 2023-11-15T14:42:10
| 2023-11-24T15:12:23
| null |
NONE
| null |
### Feature request
Elastic supports natively to have multiple DenseVectors in a document, which can be selected during query time / search. The langchain vector search interface enables to pass additional keyword args. But at the moment, the implementation of _search in the ElasticSearch implementation does not consider the `vector_query_field` variable, which could be passed through the kwargs. Furthermore, there should be a solution, to allow a document to have multiple text fields that get passed as a queryable vector, not just the standard text field.
### Motivation
If you have multiple vector fields in one index, this feature could simplify the query of the right one, like it's natively allowed in Elastic. In the current implementation one would need to add additional vector fields in the metadata and change the `vector_query_field` of ElasticSearchStore the whole class every time before you call the search. This is not a clean solution and I would vote for a more generic and clean solution.
### Your contribution
I could help by implementing this issue, although I need to state that I am not an expert in Elastic. I saw this issue when we tried to use an existing index in Elastic to add and retrieve Documents within the Langchain Framework.
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I_kwDOIPDwls52592q
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Missing escape in `llms.utils.enforce_stop_tokens()`
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| 2023-11-15T14:36:39
| 2023-11-17T22:09:17
| 2023-11-17T22:09:17
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NONE
| null |
### System Info
Not relevant.
### Who can help?
@hwchase17
@agola11
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Use any LLM relying on stop words with special regex characters.
For example instanciate a `HuggingFacePipeline` LLM instance with [openchat](https://huggingface.co/openchat/openchat_3.5) model. This model uses `<|end_of_turn|>` stop words.
Since the `llms.utils.enforce_stop_tokens()` function doesn't escape the provided stop words strings the `|` chars are interpreted as part of the regex instead of the stop word. So in this case any single `<` chars in the output would trigger the
split.
### Expected behavior
Stop words should be escaped with `re.escape()` so the split only happens on the complete words.
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PR_kwDOIPDwls5fhkmk
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fix: OpenAPISpec.parse_obj unlimited recursion
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CONTRIBUTOR
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<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** fix OpenAPISpec.parse_obj unlimited recursion, when parse openapi spec with field not exist, the recursion can not stop
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** none,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
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Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
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If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
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PR_kwDOIPDwls5fhia7
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Make pirate-speak-configurable template not require env vars for alte…
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COLLABORATOR
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…rnative LLMs until used
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
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- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
|
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I_kwDOIPDwls525cLe
| 13,393
|
[Enhancement] Timestamp supported Message and/or Memory
|
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[] | null | 2
| 2023-11-15T13:21:24
| 2023-11-15T13:40:39
| 2023-11-15T13:40:38
|
NONE
| null |
### Feature request
Make `Message` and/or `Memory` to support `Timestamp`
### Motivation
To provide context and clarity regarding the timing of conversation. This can be helpful for reference and coordination, especially when discussing time-sensitive topics.
I noticed that [one agent in opengpts](https://opengpts-example-vz4y4ooboq-uc.a.run.app/) has supported this feature.
### Your contribution
I have not made a clear outline of adding the `Timestamps` feature.
The following is some possible ways to support it for discussion:
Proposal 1: Add `Timestamps` to `Message Schema`. This way every `Memory Entity` should support `Timestamps`.
Proposal 2: Create a new `TimestampedMemory`. This way has a better backward compatibility.
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PR_kwDOIPDwls5fg1Ji
| 13,392
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TextGen api endpoint update
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[] | null | 6
| 2023-11-15T12:14:59
| 2023-11-23T03:40:58
| null |
NONE
| null |
- **Description:** The end point that TextGen exposes is different to the one that the current version of langchain makes a call to
- **Issue:** Failure when using TextGen to get LLM response
- **Tag maintainer:** @hwchase17
@agola11
Any guidance you can provide would be greatly appreciated.
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I_kwDOIPDwls524SZ6
| 13,391
|
Doc2txtLoader not working for web paths
|
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| 2023-11-15T10:15:47
| 2023-11-15T10:23:15
| null |
NONE
| null |
### System Info
Windows 11, Langchain 0.0327, Python 3.10.
The Doc2txtLoader does not work for web paths, as a PermissionError occurs when self.tempfile attempts to write content to the tempfile:
if not os.path.isfile(self.file_path) and self._is_valid_url(self.file_path):
r = requests.get(self.file_path)
if r.status_code != 200:
raise ValueError(
"Check the url of your file; returned status code %s"
% r.status_code
)
self.web_path = self.file_path
self.temp_file = tempfile.NamedTemporaryFile()
**self.temp_file.write(r.content)**
self.file_path = self.temp_file.name
elif not os.path.isfile(self.file_path):
raise ValueError("File path %s is not a valid file or url" % self.file_path)
It produces a Permission Error: _[Errno 13] Permission denied:_ as the file is already open, and will be deleted on close.
I can work around this by replacing this section of the code with the following:
if not os.path.isfile(self.file_path) and self._is_valid_url(self.file_path):
self.temp_dir = tempfile.TemporaryDirectory()
_, suffix = os.path.splitext(self.file_path)
temp_pdf = os.path.join(self.temp_dir.name, f"tmp{suffix}")
self.web_path = self.file_path
if not self._is_s3_url(self.file_path):
r = requests.get(self.file_path, headers=self.headers)
if r.status_code != 200:
raise ValueError(
"Check the url of your file; returned status code %s"
% r.status_code
)
with open(temp_pdf, mode="wb") as f:
f.write(r.content)
self.file_path = str(temp_pdf)
elif not os.path.isfile(self.file_path):
raise ValueError("File path %s is not a valid file or url" % self.file_path)
This is the method that works for the PDF loader.
The workaround is fine for now but will cause a problem if I need to update the langchain version any time in the future.
### Who can help?
@hwchase17 @eyurtsev
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
from langchain.document_loaders import Docx2txtLoader
loader = Docx2txtLoader("https://file-examples.com/wp-content/storage/2017/02/file-sample_100kB.docx")
doc = loader.load()[0]
Traceback (most recent call last):
File "C:\Program Files\JetBrains\PyCharm Community Edition 2022.3.2\plugins\python-ce\helpers\pydev\pydevconsole.py", line 364, in runcode
coro = func()
File "<input>", line 3, in <module>
File "C:\Users\crawleyb\PycharmProjects\SharepointGPT\venv\lib\site-packages\langchain\document_loaders\word_document.py", line 55, in load
return [
File "C:\Users\crawleyb\PycharmProjects\SharepointGPT\venv\lib\site-packages\docx2txt\docx2txt.py", line 76, in process
zipf = zipfile.ZipFile(docx)
File "C:\Program Files\WindowsApps\PythonSoftwareFoundation.Python.3.10_3.10.3056.0_x64__qbz5n2kfra8p0\lib\zipfile.py", line 1251, in __init__
self.fp = io.open(file, filemode)
PermissionError: [Errno 13] Permission denied: 'C:\\Users\\
### Expected behavior
The DocumentLoader should be able to get the contents of the docx file, loader.load()[0] should return a Document object.
|
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I_kwDOIPDwls524Lxn
| 13,390
|
DOC: Clarify how to handle runs and linked calls with run_managers
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| 2023-11-15T09:59:55
| 2023-11-15T11:37:39
| null |
NONE
| null |
### Issue with current documentation:
I'm trying to work through the streaming parameters around run_manager and callbacks.
Here's a minimal setup of what I'm trying to establish
```
class MyTool(BaseTool)
name: "my_extra_tool"
async def _arun(self, query: str, run_manager: Optional[AsyncCallbackManagerForToolRun] = None) -> str:
"""Use the tool asynchronously."""
nested_manager = run_manager.get_child() # run_manager doesn't have an `on_llm_start` method, only supports `on_tool_end` / `on_tool_error` and `on_text` (which has no callback in LangchainTracer
llm_run_manager = await nested_manager.on_llm_start({"llm": self.llm, "name": self.name+"_substep"}, prompts=[query]) # need to give
# do_stuff, results in main_response
main_response = "<gets created in the tool, might be part of streaming output in the future>"
await llm_run_manager[0].on_llm_new_token(main_response) # can't use llm_run_manager directly as it's a list
await llm_run_manager[0].on_llm_end(response=main_response)
```
I'm seeing that on_llm_new_token callback is being called in my custom callback handler, but I don't see the response in Langsmith.
The docs aren't fully clear on how to make sure these run_ids should be propagated.
### Idea or request for content:
It would be fantastic to have a detailed example of how to correctly nest runs with arbitrary tools.
|
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Feature: Add iFlyTek Spark LLM chat model support
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| 2023-11-15T09:51:35
| 2024-01-17T02:20:09
| null |
NONE
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
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Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
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If you're adding a new integration, please include:
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2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
- **Description:** This PR enables LangChain to access the iFlyTek's Spark LLM via the chat_models wrapper.
- **Dependencies:** websocket-client ^1.6.1
- **Tag maintainer:** @baskaryan
### SparkLLM chat model usage
Get SparkLLM's app_id, api_key and api_secret from [iFlyTek SparkLLM API Console](https://console.xfyun.cn/services/bm3) (for more info, see [iFlyTek SparkLLM Intro](https://xinghuo.xfyun.cn/sparkapi) ), then set environment variables `IFLYTEK_SPARK_APP_ID`, `IFLYTEK_SPARK_API_KEY` and `IFLYTEK_SPARK_API_SECRET` or pass parameters when using it like the demo below:
```python3
from langchain.chat_models.sparkllm import ChatSparkLLM
client = ChatSparkLLM(
spark_app_id="<app_id>",
spark_api_key="<api_key>",
spark_api_secret="<api_secret>"
)
```
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PR_kwDOIPDwls5ffzxr
| 13,388
|
DeepLake Backwards compatibility fix
|
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| 2023-11-20T05:46:02
| 2023-11-20T05:46:01
|
CONTRIBUTOR
| null |
- **Description:** during search with DeepLake some people are facing backwards compatibility issues, this PR fixes it by making search accessible for the older datasets
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MySQL : SQL Query generated contains enclosing single quotes leading to SQL Alchemy giving Programming Error, 1064
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| 2023-11-15T09:09:20
| 2023-11-15T09:51:03
| 2023-11-15T09:44:20
|
NONE
| null |
### System Info
Langchain version: 0.0.321
Python: 3.10
### Who can help?
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I am using Langchain to generate and execute SQL queries for MySql database.
The SQL Query generated is enclosed in single quotes
Generate SQL Query: **'**"SELECT * FROM EMPLOYEE WHERE ID = 123"**'**
Expected SQL Query: "SELECT * FROM EMPLOYEE WHERE ID = 123"
Hence though the query is correct, sql alchemy is unable to execute query and gives Programming error
(pymysql.err.ProgrammingError) (1064, 'You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use
### Expected behavior
Expected SQL Query should be without enclosing single quotes.
I did some debugging as looks like we get single quotes while invoking predict method of LLM - https://github.com/langchain-ai/langchain/blob/master/libs/experimental/langchain_experimental/sql/base.py#L156
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Updated clarifai-grpc with Clarifai python SDK functions (#1)
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| 2023-11-15T07:27:43
| 2023-11-21T17:27:31
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CONTRIBUTOR
| null |
Description :
1. Updated the functions with new clarifai python SDK.
2. Enabled initialisation of clarifai class with model URL.
3. Updated docs with new functions examples.
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PR_kwDOIPDwls5fe_Ln
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Multi-modal RAG template
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COLLABORATOR
| null |
Template that follow recipe for multi-modal RAG using multi-vector retriever as shown in recent [cookbook](https://github.com/langchain-ai/langchain/blob/master/cookbook/Multi_modal_RAG.ipynb).
Applies RAG on [this blog post](https://cloudedjudgement.substack.com/p/clouded-judgement-111023).
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Issue: <error faq() takes 1 positional argument but 2 were given>
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NONE
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### Issue you'd like to raise.
```
openai.proxy = {
"http": "http://127.0.0.1:7890",
"https": "http://127.0.0.1:7890"
}
callback = AsyncIteratorCallbackHandler()
llm = OpenAI(
openai_api_key= os.environ["OPENAI_API_KEY"],
temperature=0,
streaming=True,
callbacks=[callback]
)
embeddings = OpenAIEmbeddings()
# faq
loader = TextLoader("static/faq/ecommerce_faq.txt")
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_documents(documents)
docsearch = Chroma.from_documents(texts, embeddings)
faq_chain = RetrievalQA.from_chain_type(
llm,
chain_type="stuff",
retriever=docsearch.as_retriever(),
)
@tool("FAQ")
def faq(input) -> str:
""""useful for when you need to answer questions about shopping policies, like return policy, shipping policy, etc."""
print('faq input', input)
return faq_chain.acall(input)
tools = [faq]
memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
conversation_agent = initialize_agent(
tools,
llm,
agent="conversational-react-description",
memory=memory,
verbose=True,
)
async def wait_done(fn, event):
try:
await fn
except Exception as e:
print('error', e)
# event.set()
finally:
event.set()
async def call_openai(question):
# chain = faq(question)
chain = conversation_agent.acall(question)
coroutine = wait_done(chain, callback.done)
task = asyncio.create_task(coroutine)
async for token in callback.aiter():
# print('token', token)
yield f"{token}"
await task
app = FastAPI()
@app.get("/")
async def homepage():
return FileResponse('static/index.html')
@app.post("/ask")
def ask(body: dict):
return StreamingResponse(call_openai(body['question']), media_type="text/event-stream")
if __name__ == "__main__":
uvicorn.run(host="127.0.0.1", port=8888, app=app)
```
RuntimeWarning: Enable tracemalloc to get the object allocation traceback
Thought: Do I need to use a tool? No
AI: 你好！很高兴认识你！
> Finished chain.
INFO: 127.0.0.1:65193 - "POST /ask HTTP/1.1" 200 OK
conversation_agent <coroutine object Chain.acall at 0x1326b78a0>
coroutine <async_generator object AsyncIteratorCallbackHandler.aiter at 0x133b49340>
> Entering new AgentExecutor chain...
Thought: Do I need to use a tool? Yes
Action: FAQ
Action Input: 如何更改帐户信息error faq() takes 1 positional argument but 2 were given
### Suggestion:
_No response_
|
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I_kwDOIPDwls522Yd8
| 13,382
|
Custom Tool Output
|
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open
| false
| null |
[] | null | 3
| 2023-11-15T03:27:22
| 2023-11-15T03:57:32
| null |
NONE
| null |
### Issue you'd like to raise.
Hi guys,
Here is the situation, I have 3 tools to use one by one. But, the outputs of which are not in the same types. Say:
Tool 1: output normally, and can be further thought by GPT in the chain,
Tool 2: output specially, which means the output should not be thought further by GPT again, because in that way the output format and data will be not correctly,
Tool 3: output normally, like Tool 1.
In this way, I found it hard to get the correct answer for myself. If all the tools are set to return_direct=False, the answer to Tool 2 will not be right, and if I set return_dircect=True, the chain of Tools 1->2->3 would be lost...
What should I do?
Any help will be highly appreciated.
Best
### Suggestion:
_No response_
|
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| 1,993,952,712
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PR_kwDOIPDwls5febAP
| 13,381
|
Minor redis improvements
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[] | null | 5
| 2023-11-15T03:14:18
| 2023-11-20T18:07:42
| 2023-11-20T03:15:45
|
CONTRIBUTOR
| null |
- **Description:**
- Fixes a `key_prefix` bug where passing it in on `Redis.from_existing(...)` did not work properly. Updates doc strings accordingly.
- Updates Redis filter classes logic with best practices on typing, string formatting, and handling "empty" filters.
- Fixes a bug that would prevent multiple tag filters from being applied together in some scenarios.
- Added a whole new filter unit testing module. Also updated code formatting for a number of modules that were failing the `make` commands.
- **Issue:** N/A
- **Dependencies:** N/A
- **Tag maintainer:** @baskaryan
- **Twitter handle:** @tchutch94
|
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| 1,993,916,716
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PR_kwDOIPDwls5feTUy
| 13,380
|
docs: install nit
|
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[] | null | 1
| 2023-11-15T02:32:09
| 2023-11-15T18:27:01
| 2023-11-15T18:27:00
|
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
|
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| 1,993,906,368
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PR_kwDOIPDwls5feRFo
| 13,379
|
docs integration cards site
|
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[] | null | 1
| 2023-11-15T02:20:50
| 2023-11-15T16:38:53
| 2023-11-15T03:49:18
|
COLLABORATOR
| null |
The `Integrations` site is hidden now.
I've added it into the `More` menu.
The name is `Integration Cards` otherwise, it is confused with the `Integrations` menu.
|
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PR_kwDOIPDwls5feL0H
| 13,378
|
api doc newlines
|
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| 2023-11-15T01:59:06
| 2023-11-20T16:38:24
| 2023-11-15T03:16:31
|
COLLABORATOR
| null |
cc @leo-gan
Deploying at https://api.python.langchain.com/en/erick-api-doc-newlines-/api_reference.html (will take a bit)
|
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PR_kwDOIPDwls5feGxl
| 13,377
|
Set openai_api_type to be passed as values to leverage azure openai's default
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[] | null | 4
| 2023-11-15T01:33:47
| 2023-11-16T21:38:59
| 2023-11-15T03:48:56
|
NONE
| null |
- **Description:**
Recently, this was introduced: https://github.com/langchain-ai/langchain/pull/10707
However, the changes are not taking effect unless you create the AzureOpenAIEmbeddings class with parameter openai_api_type="azure".. In the code it's already getting set by default if openAI version > 1 but it's never getting accessed when the embed_documents is actually happening because it was never surfaced up
- **Issue:** Not so much an issue but leveraging existing default code of api type of azure when used.
- **Twitter handle:** @kevin_neum
|
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PR_kwDOIPDwls5fdvP_
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corrected handling of response in _generate/_agenerate
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NONE
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- **Description:** corrects handling of Prediction object returned from the predict method of the PredictionServiceClient in _generate/_agenerate method of the VertexAIModelGarden class
- **Issue:** #13370,
- **Dependencies:** None
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PR_kwDOIPDwls5fdt4v
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`Yi` model from `01.ai` , example
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COLLABORATOR
| null |
Added an example with new soa `Yi` model to `HuggingFace-hub` notebook
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PR_kwDOIPDwls5fdrGu
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Updated VertexAIModelGarden class generate method vertexai.py to align with VertexAI API
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NONE
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Replace this entire comment with:
- **Description:** corrects handling of Prediction object returned from the predict method of the PredictionServiceClient in _generate method of the VertexAIModelGarden class
- **Issue:** #13370,
- **Dependencies:** None
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PR_kwDOIPDwls5fdn3k
| 13,373
|
fix cli release
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| 2023-11-14T23:08:36
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|
COLLABORATOR
| null |
My thought is that the ==version would prevent pip from finding the package on regular [pypi.org](http://pypi.org/), so it would look at [test.pypi.org](http://test.pypi.org/) for that. Otherwise it'll pull package from [pypi.org](http://pypi.org/) (e.g. sub deps)
Right now, the cli release is failing because it's going to test.pypi.org by default, so it finds this incorrect FASTAPI package instead of the real one: https://test.pypi.org/project/FASTAPI/
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PR_kwDOIPDwls5fdg2z
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callback refactor
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PR_kwDOIPDwls5fdZ7j
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fmt
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COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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I_kwDOIPDwls521IDN
| 13,370
|
VertexAIModelGarden _generate method not in sync with VertexAI API
|
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open
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[] | null | 7
| 2023-11-14T21:55:57
| 2023-12-04T15:53:27
| null |
NONE
| null |
### System Info
LangChain version:
Platform: WSL for Windows (Linux 983G3J3 5.15.90.1-microsoft-standard-WSL2)
Python version: 3.10.6
### Who can help?
@agola11
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1a. Create a project in GCP and deploy an open source model from Vertex AI Model Garden (follow the provided Colab notebook to deploy to an endpoint)
1b. Instantiate a VertexAIModelGarden object
```python
llm = VertexAIModelGarden(project=PROJECT_ID, endpoint_id=ENDPOINT_ID)
```
2. Create a prompt string
```python
prompt = "This is an example prompt"
```
3. Call the generate method on the VertexAIModelGarden object
```python
llm.generate([prompt])
```
4. The following error will be produced:
```python
../python3.10/site-packages/langchain/llms/vertexai.py", line 452, in <listcomp>
[Generation(text=prediction[self.result_arg]) for prediction in result]
TypeError: string indices must be integers
```
### Expected behavior
Expecting the generate method to return an LLMResult object that contains the model's response in the 'generations' property
In order to align with Vertex AI api the _generate method should iterate through response.predictions and set text property of Generation object to the iterator variable since response.predictions is a list data type that contains the output strings.
```python
for result in response.predictions:
generations.append(
[Generation(text=result)]
)
```
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PR_kwDOIPDwls5fdPTU
| 13,369
|
Fix the bedrock documentation
|
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[] | null | 2
| 2023-11-14T21:29:55
| 2023-11-15T03:00:53
| 2023-11-15T03:00:52
|
NONE
| null |
- **Description:** As I was working through bedrock llm for RetreivalQAChain I noticed that the imports were wrong and is corrected for documentation.
- **Issue:** As mentioned in the documentation for langchain. The import says from `langchain.llms import Bedrock` which should rather be `from langchain.llms.bedrock import Bedrock`
- **Dependencies:** None
|
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I_kwDOIPDwls520tX0
| 13,368
|
module 'openai' has no attribute 'error'
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| false
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[] | null | 12
| 2023-11-14T20:37:27
| 2024-01-03T09:27:48
| null |
NONE
| null |
### System Info
openai==1.2.4
langchain==0.0.325
llama_index==0.8.69
### Who can help?
@hwchase17 @agola11
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
/usr/local/lib/python3.10/site-packages/llama_index/indices/base.py:102: in from_documents
return cls(
/usr/local/lib/python3.10/site-packages/llama_index/indices/vector_store/base.py:49: in __init__
super().__init__(
/usr/local/lib/python3.10/site-packages/llama_index/indices/base.py:71: in __init__
index_struct = self.build_index_from_nodes(nodes)
/usr/local/lib/python3.10/site-packages/llama_index/indices/vector_store/base.py:254: in build_index_from_nodes
return self._build_index_from_nodes(nodes, **insert_kwargs)
/usr/local/lib/python3.10/site-packages/llama_index/indices/vector_store/base.py:235: in _build_index_from_nodes
self._add_nodes_to_index(
/usr/local/lib/python3.10/site-packages/llama_index/indices/vector_store/base.py:188: in _add_nodes_to_index
nodes = self._get_node_with_embedding(nodes, show_progress)
/usr/local/lib/python3.10/site-packages/llama_index/indices/vector_store/base.py:100: in _get_node_with_embedding
id_to_embed_map = embed_nodes(
/usr/local/lib/python3.10/site-packages/llama_index/indices/utils.py:137: in embed_nodes
new_embeddings = embed_model.get_text_embedding_batch(
/usr/local/lib/python3.10/site-packages/llama_index/embeddings/base.py:250: in get_text_embedding_batch
embeddings = self._get_text_embeddings(cur_batch)
/usr/local/lib/python3.10/site-packages/llama_index/embeddings/langchain.py:82: in _get_text_embeddings
return self._langchain_embedding.embed_documents(texts)
/usr/local/lib/python3.10/site-packages/langchain/embeddings/openai.py:490: in embed_documents
return self._get_len_safe_embeddings(texts, engine=self.deployment)
/usr/local/lib/python3.10/site-packages/langchain/embeddings/openai.py:374: in _get_len_safe_embeddings
response = embed_with_retry(
/usr/local/lib/python3.10/site-packages/langchain/embeddings/openai.py:100: in embed_with_retry
retry_decorator = _create_retry_decorator(embeddings)
/usr/local/lib/python3.10/site-packages/langchain/embeddings/openai.py:47: in _create_retry_decorator
retry_if_exception_type(openai.error.Timeout)
E AttributeError: module 'openai' has no attribute 'error'
```
### Expected behavior
I suppose it should run, I'll provide some reproducible code here in a minute.
|
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| 1,993,497,679
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PR_kwDOIPDwls5fc3lu
| 13,367
|
Add limit_to_domains to APIChain based tools
|
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[] | null | 2
| 2023-11-14T20:15:54
| 2023-11-15T03:07:17
| 2023-11-15T03:07:16
|
CONTRIBUTOR
| null |
- **Description:** Adds `limit_to_domains` param to the APIChain based tools (open_meteo, TMDB, podcast_docs, and news_api)
- **Issue:** I didn't open an issue, but after upgrading to 0.0.328 using these tools would throw an error.
- **Dependencies:** N/A
- **Tag maintainer:** @baskaryan
**Note**: I included the trailing / simply because the docs here did https://github.com/langchain-ai/langchain/blob/fc886cc3039c2479bb13287f48fcbdb097a44c70/docs/docs/use_cases/apis.ipynb#L246 , but I checked the code and it is using `urlparse`. SoI followed the docs since it comes down to stylee.
|
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PR_kwDOIPDwls5fcwxt
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Bedrock cohere embedding support
|
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| 2023-11-14T19:58:24
| 2023-11-15T19:12:43
| 2023-11-15T18:19:13
|
CONTRIBUTOR
| null |
- **Description:** adding cohere embedding support to bedrock embedding class
- **Issue:** N/A
- **Dependencies:** None
- **Tag maintainer:** @3coins
- **Twitter handle:** celmore25
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I_kwDOIPDwls520WwJ
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PGVector don't have indices what kills postgres in production
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[] | null | 1
| 2023-11-14T19:38:34
| 2023-11-14T19:39:02
| null |
CONTRIBUTOR
| null |
### System Info
after getting million embedding records in postgres, everything became ridiculously slow and postgres cpu usage went to 100%
fix was simple:
```
CREATE INDEX CONCURRENTLY langchain_pg_embedding_collection_id ON langchain_pg_embedding(collection_id);
CREATE INDEX CONCURRENTLY langchain_pg_collection_name ON langchain_pg_collection(name);
```
I think it's important to include index creation in basic setup.
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Just run PGVector, and it will create tables without indices.
### Expected behavior
Should create indices
|
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PR_kwDOIPDwls5fcm6q
| 13,364
|
Update `rag-timescale-conversation` to dependencies without CVEs.
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| 2023-11-14T19:28:43
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| 2023-11-15T03:05:12
|
COLLABORATOR
| null |
Just `poetry lock` and moving `langchain` to the latest version, in case folks copy this template.
This resolves some vulnerable dependency alerts GitHub code scanning was flagging.
|
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PR_kwDOIPDwls5fceww
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Bump pyarrow from 13.0.0 to 14.0.1 in /libs/langchain
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| 2023-11-14T19:07:27
| 2023-11-14T19:23:54
| 2023-11-14T19:23:53
|
CONTRIBUTOR
| null |
Bumps [pyarrow](https://github.com/apache/arrow) from 13.0.0 to 14.0.1.
<details>
<summary>Commits</summary>
<ul>
<li><a href="https://github.com/apache/arrow/commit/ba537483618196f50c67a90a473039e4d5dc35e0"><code>ba53748</code></a> MINOR: [Release] Update versions for 14.0.1</li>
<li><a href="https://github.com/apache/arrow/commit/529f3768fa4fce80781cd1a3cbdcf3a826281b14"><code>529f376</code></a> MINOR: [Release] Update .deb/.rpm changelogs for 14.0.1</li>
<li><a href="https://github.com/apache/arrow/commit/b84bbcac64e184a2b58661385506cff23006cc10"><code>b84bbca</code></a> MINOR: [Release] Update CHANGELOG.md for 14.0.1</li>
<li><a href="https://github.com/apache/arrow/commit/f14170976372436ec1d03a724d8d3f3925484ecf"><code>f141709</code></a> <a href="https://redirect.github.com/apache/arrow/issues/38607">GH-38607</a>: [Python] Disable PyExtensionType autoload (<a href="https://redirect.github.com/apache/arrow/issues/38608">#38608</a>)</li>
<li><a href="https://github.com/apache/arrow/commit/5a37e741987e4cba41dfdad2331a308c95b62083"><code>5a37e74</code></a> <a href="https://redirect.github.com/apache/arrow/issues/38431">GH-38431</a>: [Python][CI] Update fs.type_name checks for s3fs tests (<a href="https://redirect.github.com/apache/arrow/issues/38455">#38455</a>)</li>
<li><a href="https://github.com/apache/arrow/commit/2dcee3f82c6cf54b53a64729fd81840efa583244"><code>2dcee3f</code></a> MINOR: [Release] Update versions for 14.0.0</li>
<li><a href="https://github.com/apache/arrow/commit/297428cbf2fc84a224654eb0b336614e6543d1aa"><code>297428c</code></a> MINOR: [Release] Update .deb/.rpm changelogs for 14.0.0</li>
<li><a href="https://github.com/apache/arrow/commit/3e9734f8830797fe09b883f00d349116d95c51f9"><code>3e9734f</code></a> MINOR: [Release] Update CHANGELOG.md for 14.0.0</li>
<li><a href="https://github.com/apache/arrow/commit/9f90995c8cee0d9906349f421f2445ab9adcb7ac"><code>9f90995</code></a> <a href="https://redirect.github.com/apache/arrow/issues/38332">GH-38332</a>: [CI][Release] Resolve symlinks in RAT lint (<a href="https://redirect.github.com/apache/arrow/issues/38337">#38337</a>)</li>
<li><a href="https://github.com/apache/arrow/commit/bd61239a32c94e37b9510071c0ffacad455798c0"><code>bd61239</code></a> <a href="https://redirect.github.com/apache/arrow/issues/35531">GH-35531</a>: [Python] C Data Interface PyCapsule Protocol (<a href="https://redirect.github.com/apache/arrow/issues/37797">#37797</a>)</li>
<li>Additional commits viewable in <a href="https://github.com/apache/arrow/compare/go/v13.0.0...go/v14.0.1">compare view</a></li>
</ul>
</details>
<br />
[](https://docs.github.com/en/github/managing-security-vulnerabilities/about-dependabot-security-updates#about-compatibility-scores)
Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting `@dependabot rebase`.
[//]: # (dependabot-automerge-start)
[//]: # (dependabot-automerge-end)
---
<details>
<summary>Dependabot commands and options</summary>
<br />
You can trigger Dependabot actions by commenting on this PR:
- `@dependabot rebase` will rebase this PR
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You can disable automated security fix PRs for this repo from the [Security Alerts page](https://github.com/langchain-ai/langchain/network/alerts).
</details>
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PR_kwDOIPDwls5fcdPo
| 13,362
|
Update `templates/rag-self-query` with newer dependencies without CVEs.
|
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COLLABORATOR
| null |
The `langchain` repo was being flagged for using vulnerable dependencies, some of which were in this template's lockfile. Updating to newer versions should fix that.
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CosmosDBHistoryMessage
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| 2023-11-14T19:55:32
| null |
NONE
| null |
### System Info
CosmosDBHistory messages are wiping out the session of each run.
Need the library to not recreate the object for the same session or else this cannot be used as chatbot which is stateless
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [X] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
`import os
from time import perf_counter
from langchain.chat_models import AzureChatOpenAI
from langchain.memory.chat_message_histories import CosmosDBChatMessageHistory
from langchain.schema import (
SystemMessage,
HumanMessage
)
from logger_setup import logger
llm = AzureChatOpenAI(
deployment_name=os.getenv('OPENAI_GPT4_DEPLOYMENT_NAME'),
model=os.getenv('OPENAI_GPT4_MODEL_NAME')
)
cosmos_history = CosmosDBChatMessageHistory(
cosmos_endpoint=os.getenv('AZ_COSMOS_ENDPOINT'),
cosmos_database=os.getenv('AZ_COSMOS_DATABASE'),
cosmos_container=os.getenv('AZ_COSMOS_CONTAINER'),
session_id='1234',
user_id='user001',
connection_string=os.getenv('AZ_COSMOS_CS')
)
# cosmos_history.prepare_cosmos()
sys_msg = SystemMessage(content='You are a helpful bot that can run various SQL queries')
# cosmos_history.add_message(sys_msg)
human_msg = HumanMessage(content='Can you tell me how things are done in database')
cosmos_history.add_message(human_msg)
messages = []
messages.append(sys_msg)
messages.append(human_msg)
start_time = perf_counter()
response = llm.predict_messages(messages=messages)
end_time = perf_counter()
logger.info('Total time taken %d s', (end_time - start_time))
print(response)
messages.append(response)
cosmos_history.add_message(response)
`
### Expected behavior
Need this to save it for each subsequent web service calls with the same session id
Also this code does not retrieve data
try:
logger.info(f"Reading item with session_id: {self.session_id}, user_id: {self.user_id}")
item = self._container.read_item(
item=self.session_id,
partition_key=self.user_id
)
except CosmosHttpResponseError as ex:
logger.error(f"Error reading item from CosmosDB: {ex}")
return
But using sql does
query = f"SELECT * FROM c WHERE c.id = '{self.session_id}' AND c.user_id = '{self.user_id}'"
items = list(self._container.query_items(query, enable_cross_partition_query=True))
if items:
item = items[0]
# Continue with reading the item
else:
logger.info("Item not found in CosmosDB")
return
|
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more cli interactivity, bugfix
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PR_kwDOIPDwls5fcDiI
| 13,359
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cli 0.0.17
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PR_kwDOIPDwls5fcAMy
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Add some properties to NotionDBLoader
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|
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
fix #13356
Add supports following properties for metadata to NotionDBLoader.
- `checkbox`
- `email`
- `number`
- `select`
There are no relevant tests for this code to be updated.
|
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PR_kwDOIPDwls5fb6jv
| 13,357
|
Bagatur/rm return direct error
|
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I_kwDOIPDwls52zc1M
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Issue: Notion DB loader for doesn't supports some properties
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| 2023-11-14T17:20:22
| 2023-11-15T04:31:13
| 2023-11-15T04:31:13
|
CONTRIBUTOR
| null |
### Issue you'd like to raise.
notion page properties
https://developers.notion.com/reference/page-property-values
Current version Notion DB loader for doesn't supports following properties for metadata
- `checkbox`
- `email`
- `number`
- `select`
### Suggestion:
I would like to make a PR to fix this issue if it's okay.
|
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PR_kwDOIPDwls5fbtAa
| 13,355
|
Fix latest message index
|
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|
CONTRIBUTOR
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There is a bug which caused the earliest message rather than the latest message being sent
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PR_kwDOIPDwls5fblPF
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AsyncHtmlLoader update due to fetching errors in not available webpages
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|
CONTRIBUTOR
| null |
- **Description:** When running the `qa_chain` with the `WebResearchRetriever` class as a retriever, the code would throw an error even with other possible/valid sources. This would happen when there was no access/connection to an URL.
- **Issue:** None,
- **Dependencies:** None,
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
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PR_kwDOIPDwls5fbTEq
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Pass filters and other search_kwargs through retrieval chain at query time (#9195)
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| 2023-12-09T20:56:57
| null |
NONE
| null |
**Description:**
Adds the ability to pass filters and other search_kwargs to a vector store when *calling* a conversational retriever . (#9195)
Existing functionality allows setting the search_kwargs only when *constructing* the retriever - which limits the ability to create a single retriever then alter filters during chat sessions.
My use case (backed by the others in the Issue) is to be able to ingest documents into a vector database with different 'tags', then during a long-running RAG0-based chat session, specify the tags to filter ingested documents on with each question.
The existing support for search_kwargs is still useful - args like 'k', the number of results, are likely to be definitely determined at construction time. Hence this fix *merges* the search_kwargs provided with the query with any that were set during construction. That is: query-time values *override* construction-time values, but only those with the same key. Hence you can set 'k', 'score_threshold', etc, during construction, then pass in a custom 'filter' with the query.
Query-time search_kwargs are provided in the `inputs` parameter, but are not listed in the input_keys, since they are not mandatory. This preserves compatibility, and makes sense, given the search_kwargs, very much are inputs.
The documentation is updated with an example on the vector_db_qa.mdx page.
Details on the rationale behind this PR:
1. Many of the vector store implementations take “filter” as a parameter in their similarity_search (chroma, elastic, pgvector, open_search), and some, like weaviate, take more complex kwargs, accepting in them “where_filter”.
We need a way to pass down arguments for these parameters from the original __call__ method on the base chain, in such a way as they get to - and only to - the vector stores that can use them.
2. The generic VectorStoreRetriever has a search_kwargs field (Pydantic) that is intended to hold just this sort of parameter, and passes it into similarty_search() (and other search methods). But is limited in that it can only be set when the VectorRetriever is constructed.
3. The abstract BaseRetriever._get_relevant_documents doesn’t take extra args, but BaseRetriever allows for the concrete implementations in different retrievers to have extra args (which only just slightly freaks me out as an old OO Java programmer :)). BaseRetriever detects this and sets _expects_other_args to True which causes it to pass on kwargs from its get_relevant_documents() method to the specific retrievers _get_relevant_documents implementation.
So… to get our filter down where it’s needed, we need to get it in a more dynamic search_kwargs dictionary, and get that merged with the one passed in from VectorStoreRetriever, without interfering with the base retriever.
For this we need one change to VectoreStoreRetriever:
- add a search_kwargs dictionary to its _get_relevant_documents method
- merge that with the base search kwargs (dictionary update - to override, treating the construction-time search_kwargs as defaults
We have to change one more class though, to get the search_kwargs into the get_relevant_documents incoming kwargs parameter: we can do that in ConversationalRetrievalChain._get_docs which currently does not call it with any kwargs. But _get_docs just happens to get the inputs dictionary from which history and question are taken, which is passed in from the original __call__. Hence we:
- Modifiy ConversationalRetrievalChain._get_docs to retrieve the search_kwargs, if there are any, from the inputs[] list and add them to the kwargs going into get_relevant_documents
So, we can put search_kwargs in inputs- because they are just as much inputs as the question and chat_history, then we can extract them in _get_docs and pass them into get_relevant_documents, and we get them where they need to go with only two changes.
The modifications needed for base retrievers is slightly different, but similar enough.
**Issue:**
Passing filter through ConversationalRetrievalChain to the underlying vector store #9195
**Tag maintainer:** @baskaryan
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I_kwDOIPDwls52yvPt
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Ability to suppress warning when supplying a "not default parameter" to OpenAI Chat Models
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[] | null | 1
| 2023-11-14T15:43:35
| 2023-11-14T15:43:59
| null |
NONE
| null |
### Feature request
Supplying a not default parameter to a model is not something that will require always showing a warning. It should be easy to suppress that specific warning.
### Motivation
The logging gets full of those warnings just because you are supplying not default parameters such as "top_p", "frequency_penalty" or "presence_penalty".
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PR_kwDOIPDwls5fayuF
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Bump all libraries to the latest `ruff` version.
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|
COLLABORATOR
| null |
This version of `ruff` is the one we'll be using to lint the docs and cookbooks (#12677), so I'm making it used everywhere else too.
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