id stringlengths 14 16 | text stringlengths 36 2.73k | source stringlengths 59 127 |
|---|---|---|
63cad9945ed4-7 | {'uuid': '52cfe3e8-b800-4dd8-a7dd-8e9e4764dfc8', 'created_at': '2023-05-25T15:09:41.913856Z', 'role': 'ai', 'content': "Octavia Butler's contemporaries included Ursula K. Le Guin, Samuel R. Delany, and Joanna Russ.", 'token_count': 27} 0.852352466457884
{'uuid': 'd40da612-0867-4a43-92ec-778b86490a39', 'created_at': '20... | rtdocs_stable/api.python.langchain.com/en/stable/modules/memory/examples/zep_memory.html |
63cad9945ed4-8 | {'uuid': '862107de-8f6f-43c0-91fa-4441f01b2b3a', 'created_at': '2023-05-25T15:09:41.898149Z', 'role': 'human', 'content': 'Which books of hers were made into movies?', 'token_count': 11} 0.7954322970428519
{'uuid': '97164506-90fe-4c71-9539-69ebcd1d90a2', 'created_at': '2023-05-25T15:09:41.90887Z', 'role': 'human', 'con... | rtdocs_stable/api.python.langchain.com/en/stable/modules/memory/examples/zep_memory.html |
63cad9945ed4-9 | previous
Redis Chat Message History
next
Indexes
Contents
REACT Agent Chat Message History Example
Initialize the Zep Chat Message History Class and initialize the Agent
Add some history data
Run the agent
Inspect the Zep memory
Vector search over the Zep memory
By Harrison Chase
© Copyright 2023, Harris... | rtdocs_stable/api.python.langchain.com/en/stable/modules/memory/examples/zep_memory.html |
1225b3f64d34-0 | .ipynb
.pdf
How to add memory to a Multi-Input Chain
How to add memory to a Multi-Input Chain#
Most memory objects assume a single input. In this notebook, we go over how to add memory to a chain that has multiple inputs. As an example of such a chain, we will add memory to a question/answering chain. This chain takes ... | rtdocs_stable/api.python.langchain.com/en/stable/modules/memory/examples/adding_memory_chain_multiple_inputs.html |
1225b3f64d34-1 | {context}
{chat_history}
Human: {human_input}
Chatbot:"""
prompt = PromptTemplate(
input_variables=["chat_history", "human_input", "context"],
template=template
)
memory = ConversationBufferMemory(memory_key="chat_history", input_key="human_input")
chain = load_qa_chain(OpenAI(temperature=0), chain_type="stuff... | rtdocs_stable/api.python.langchain.com/en/stable/modules/memory/examples/adding_memory_chain_multiple_inputs.html |
958f5252040f-0 | .ipynb
.pdf
Callbacks
Contents
Callbacks
How to use callbacks
When do you want to use each of these?
Tags
Using an existing handler
Creating a custom handler
Async Callbacks
Using multiple handlers, passing in handlers
Tracing and Token Counting
Tracing
Token Counting
Callbacks#
LangChain provides a callbacks system ... | rtdocs_stable/api.python.langchain.com/en/stable/modules/callbacks/getting_started.html |
958f5252040f-1 | CallbackHandlers are objects that implement the CallbackHandler interface, which has a method for each event that can be subscribed to. The CallbackManager will call the appropriate method on each handler when the event is triggered.
class BaseCallbackHandler:
"""Base callback handler that can be used to handle cal... | rtdocs_stable/api.python.langchain.com/en/stable/modules/callbacks/getting_started.html |
958f5252040f-2 | """Run when chain errors."""
def on_tool_start(
self, serialized: Dict[str, Any], input_str: str, **kwargs: Any
) -> Any:
"""Run when tool starts running."""
def on_tool_end(self, output: str, **kwargs: Any) -> Any:
"""Run when tool ends running."""
def on_tool_error(
sel... | rtdocs_stable/api.python.langchain.com/en/stable/modules/callbacks/getting_started.html |
958f5252040f-3 | The verbose argument is available on most objects throughout the API (Chains, Models, Tools, Agents, etc.) as a constructor argument, eg. LLMChain(verbose=True), and it is equivalent to passing a ConsoleCallbackHandler to the callbacks argument of that object and all child objects. This is useful for debugging, as it w... | rtdocs_stable/api.python.langchain.com/en/stable/modules/callbacks/getting_started.html |
958f5252040f-4 | Note when the verbose flag on the object is set to true, the StdOutCallbackHandler will be invoked even without being explicitly passed in.
from langchain.callbacks import StdOutCallbackHandler
from langchain.chains import LLMChain
from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate
handler =... | rtdocs_stable/api.python.langchain.com/en/stable/modules/callbacks/getting_started.html |
958f5252040f-5 | from langchain.schema import HumanMessage
class MyCustomHandler(BaseCallbackHandler):
def on_llm_new_token(self, token: str, **kwargs) -> None:
print(f"My custom handler, token: {token}")
# To enable streaming, we pass in `streaming=True` to the ChatModel constructor
# Additionally, we pass in a list with o... | rtdocs_stable/api.python.langchain.com/en/stable/modules/callbacks/getting_started.html |
958f5252040f-6 | from langchain.schema import LLMResult
from langchain.callbacks.base import AsyncCallbackHandler
class MyCustomSyncHandler(BaseCallbackHandler):
def on_llm_new_token(self, token: str, **kwargs) -> None:
print(f"Sync handler being called in a `thread_pool_executor`: token: {token}")
class MyCustomAsyncHandle... | rtdocs_stable/api.python.langchain.com/en/stable/modules/callbacks/getting_started.html |
958f5252040f-7 | Sync handler being called in a `thread_pool_executor`: token: don
Sync handler being called in a `thread_pool_executor`: token: 't
Sync handler being called in a `thread_pool_executor`: token: scientists
Sync handler being called in a `thread_pool_executor`: token: trust
Sync handler being called in a `thread_pool_e... | rtdocs_stable/api.python.langchain.com/en/stable/modules/callbacks/getting_started.html |
958f5252040f-8 | However, in many cases, it is advantageous to pass in handlers instead when running the object. When we pass through CallbackHandlers using the callbacks keyword arg when executing an run, those callbacks will be issued by all nested objects involved in the execution. For example, when a handler is passed through to an... | rtdocs_stable/api.python.langchain.com/en/stable/modules/callbacks/getting_started.html |
958f5252040f-9 | ) -> Any:
print(f"on_tool_start {serialized['name']}")
def on_agent_action(self, action: AgentAction, **kwargs: Any) -> Any:
print(f"on_agent_action {action}")
class MyCustomHandlerTwo(BaseCallbackHandler):
def on_llm_start(
self, serialized: Dict[str, Any], prompts: List[str], **kwargs:... | rtdocs_stable/api.python.langchain.com/en/stable/modules/callbacks/getting_started.html |
958f5252040f-10 | Action
on_new_token :
on_new_token Calculator
on_new_token
Action
on_new_token Input
on_new_token :
on_new_token 2
on_new_token ^
on_new_token 0
on_new_token .
on_new_token 235
on_new_token
on_agent_action AgentAction(tool='Calculator', tool_input='2^0.235', log=' I need to use a calculator to solve this.\nAction:... | rtdocs_stable/api.python.langchain.com/en/stable/modules/callbacks/getting_started.html |
958f5252040f-11 | on_new_token .
on_new_token 17
on_new_token 690
on_new_token 67
on_new_token 372
on_new_token 187
on_new_token 674
on_new_token
'1.1769067372187674'
Tracing and Token Counting#
Tracing and token counting are two capabilities we provide which are built on our callbacks mechanism.
Tracing#
There are two recommended ways... | rtdocs_stable/api.python.langchain.com/en/stable/modules/callbacks/getting_started.html |
958f5252040f-12 | "Who is Beyonce's husband? What is his age raised to the 0.19 power?",
]
os.environ["LANGCHAIN_TRACING"] = "true"
# Both of the agent runs will be traced because the environment variable is set
agent.run(questions[0])
with tracing_enabled() as session:
assert session
agent.run(questions[1])
> Entering new Agent... | rtdocs_stable/api.python.langchain.com/en/stable/modules/callbacks/getting_started.html |
958f5252040f-13 | Action: Search
Action Input: "Olivia Wilde boyfriend"
Observation: Sudeikis and Wilde's relationship ended in November 2020. Wilde was publicly served with court documents regarding child custody while she was presenting Don't Worry Darling at CinemaCon 2022. In January 2021, Wilde began dating singer Harry Styles afte... | rtdocs_stable/api.python.langchain.com/en/stable/modules/callbacks/getting_started.html |
958f5252040f-14 | Action: Search
Action Input: "US Open men's final 2019 winner"
Observation: Rafael Nadal defeated Daniil Medvedev in the final, 7–5, 6–3, 5–7, 4–6, 6–4 to win the men's singles tennis title at the 2019 US Open. It was his fourth US ...
Thought: I need to find out the age of the winner
Action: Search
Action Input: "Rafa... | rtdocs_stable/api.python.langchain.com/en/stable/modules/callbacks/getting_started.html |
958f5252040f-15 | Action: Calculator
Action Input: 29^0.23
Observation: Answer: 2.169459462491557
Thought: I now know the final answer.
Final Answer: Harry Styles is Olivia Wilde's boyfriend and his current age raised to the 0.23 power is 2.169459462491557.
> Finished chain.
"Harry Styles is Olivia Wilde's boyfriend and his current age ... | rtdocs_stable/api.python.langchain.com/en/stable/modules/callbacks/getting_started.html |
958f5252040f-16 | Action: Search
Action Input: "US Open men's final 2019 winner"Rafael Nadal defeated Daniil Medvedev in the final, 7–5, 6–3, 5–7, 4–6, 6–4 to win the men's singles tennis title at the 2019 US Open. It was his fourth US ... I need to find out who Olivia Wilde's boyfriend is and then calculate his age raised to the 0.23 p... | rtdocs_stable/api.python.langchain.com/en/stable/modules/callbacks/getting_started.html |
958f5252040f-17 | I now need to calculate 38 raised to the 0.23 power
Action: Calculator
Action Input: 38^0.23Answer: 2.3086081644669734
> Finished chain.
"Rafael Nadal, aged 36, won the US Open men's final in 2019 and his age raised to the 0.334 power is 3.3098250249682484."
Token Counting#
LangChain offers a context manager that allow... | rtdocs_stable/api.python.langchain.com/en/stable/modules/callbacks/getting_started.html |
958f5252040f-18 | When do you want to use each of these?
Tags
Using an existing handler
Creating a custom handler
Async Callbacks
Using multiple handlers, passing in handlers
Tracing and Token Counting
Tracing
Token Counting
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023. | rtdocs_stable/api.python.langchain.com/en/stable/modules/callbacks/getting_started.html |
230a4236d05f-0 | .rst
.pdf
How-To Guides
How-To Guides#
A chain is made up of links, which can be either primitives or other chains.
Primitives can be either prompts, models, arbitrary functions, or other chains.
The examples here are broken up into three sections:
Generic Functionality
Covers both generic chains (that are useful in a ... | rtdocs_stable/api.python.langchain.com/en/stable/modules/chains/how_to_guides.html |
f9c45827a15f-0 | .ipynb
.pdf
Getting Started
Contents
Why do we need chains?
Quick start: Using LLMChain
Different ways of calling chains
Add memory to chains
Debug Chain
Combine chains with the SequentialChain
Create a custom chain with the Chain class
Getting Started#
In this tutorial, we will learn about creating simple chains in ... | rtdocs_stable/api.python.langchain.com/en/stable/modules/chains/getting_started.html |
f9c45827a15f-1 | print(chain.run("colorful socks"))
Colorful Toes Co.
If there are multiple variables, you can input them all at once using a dictionary.
prompt = PromptTemplate(
input_variables=["company", "product"],
template="What is a good name for {company} that makes {product}?",
)
chain = LLMChain(llm=llm, prompt=prompt)... | rtdocs_stable/api.python.langchain.com/en/stable/modules/chains/getting_started.html |
f9c45827a15f-2 | {'adjective': 'corny',
'text': 'Why did the tomato turn red? Because it saw the salad dressing!'}
By default, __call__ returns both the input and output key values. You can configure it to only return output key values by setting return_only_outputs to True.
llm_chain("corny", return_only_outputs=True)
{'text': 'Why d... | rtdocs_stable/api.python.langchain.com/en/stable/modules/chains/getting_started.html |
f9c45827a15f-3 | llm=chat,
memory=ConversationBufferMemory()
)
conversation.run("Answer briefly. What are the first 3 colors of a rainbow?")
# -> The first three colors of a rainbow are red, orange, and yellow.
conversation.run("And the next 4?")
# -> The next four colors of a rainbow are green, blue, indigo, and violet.
'The next ... | rtdocs_stable/api.python.langchain.com/en/stable/modules/chains/getting_started.html |
f9c45827a15f-4 | Human: What is ChatGPT?
AI:
> Finished chain.
'ChatGPT is an AI language model developed by OpenAI. It is based on the GPT-3 architecture and is capable of generating human-like responses to text prompts. ChatGPT has been trained on a massive amount of text data and can understand and respond to a wide range of topics.... | rtdocs_stable/api.python.langchain.com/en/stable/modules/chains/getting_started.html |
f9c45827a15f-5 | catchphrase = overall_chain.run("colorful socks")
print(catchphrase)
> Entering new SimpleSequentialChain chain...
Rainbow Socks Co.
"Put a little rainbow in your step!"
> Finished chain.
"Put a little rainbow in your step!"
Create a custom chain with the Chain class#
LangChain provides many chains out of the box, but ... | rtdocs_stable/api.python.langchain.com/en/stable/modules/chains/getting_started.html |
f9c45827a15f-6 | prompt_1 = PromptTemplate(
input_variables=["product"],
template="What is a good name for a company that makes {product}?",
)
chain_1 = LLMChain(llm=llm, prompt=prompt_1)
prompt_2 = PromptTemplate(
input_variables=["product"],
template="What is a good slogan for a company that makes {product}?",
)
chain... | rtdocs_stable/api.python.langchain.com/en/stable/modules/chains/getting_started.html |
c50ca7af02b9-0 | .ipynb
.pdf
Serialization
Contents
Saving a chain to disk
Loading a chain from disk
Saving components separately
Serialization#
This notebook covers how to serialize chains to and from disk. The serialization format we use is json or yaml. Currently, only some chains support this type of serialization. We will grow t... | rtdocs_stable/api.python.langchain.com/en/stable/modules/chains/generic/serialization.html |
c50ca7af02b9-1 | "best_of": 1,
"request_timeout": null,
"logit_bias": {},
"_type": "openai"
},
"output_key": "text",
"_type": "llm_chain"
}
Loading a chain from disk#
We can load a chain from disk by using the load_chain method.
from langchain.chains import load_chain
chain = load_chain("llm_chain.js... | rtdocs_stable/api.python.langchain.com/en/stable/modules/chains/generic/serialization.html |
c50ca7af02b9-2 | "top_p": 1,
"frequency_penalty": 0,
"presence_penalty": 0,
"n": 1,
"best_of": 1,
"request_timeout": null,
"logit_bias": {},
"_type": "openai"
}
config = {
"memory": None,
"verbose": True,
"prompt_path": "prompt.json",
"llm_path": "llm.json",
"output_key": "text",
"_ty... | rtdocs_stable/api.python.langchain.com/en/stable/modules/chains/generic/serialization.html |
ae357dc74de5-0 | .ipynb
.pdf
Loading from LangChainHub
Loading from LangChainHub#
This notebook covers how to load chains from LangChainHub.
from langchain.chains import load_chain
chain = load_chain("lc://chains/llm-math/chain.json")
chain.run("whats 2 raised to .12")
> Entering new LLMMathChain chain...
whats 2 raised to .12
Answer: ... | rtdocs_stable/api.python.langchain.com/en/stable/modules/chains/generic/from_hub.html |
ae357dc74de5-1 | chain.run(query)
" The president said that Ketanji Brown Jackson is a Circuit Court of Appeals Judge, one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, has received a broad range of support from the Fraternal Order of Police to former judges appointed by ... | rtdocs_stable/api.python.langchain.com/en/stable/modules/chains/generic/from_hub.html |
9bd6ce2ff374-0 | .ipynb
.pdf
Router Chains
Contents
LLMRouterChain
EmbeddingRouterChain
Router Chains#
This notebook demonstrates how to use the RouterChain paradigm to create a chain that dynamically selects the next chain to use for a given input.
Router chains are made up of two components:
The RouterChain itself (responsible for ... | rtdocs_stable/api.python.langchain.com/en/stable/modules/chains/generic/router.html |
9bd6ce2ff374-1 | "description": "Good for answering math questions",
"prompt_template": math_template
}
]
llm = OpenAI()
destination_chains = {}
for p_info in prompt_infos:
name = p_info["name"]
prompt_template = p_info["prompt_template"]
prompt = PromptTemplate(template=prompt_template, input_variables=["input... | rtdocs_stable/api.python.langchain.com/en/stable/modules/chains/generic/router.html |
9bd6ce2ff374-2 | physics: {'input': 'What is black body radiation?'}
> Finished chain.
Black body radiation is the term used to describe the electromagnetic radiation emitted by a “black body”—an object that absorbs all radiation incident upon it. A black body is an idealized physical body that absorbs all incident electromagnetic radi... | rtdocs_stable/api.python.langchain.com/en/stable/modules/chains/generic/router.html |
9bd6ce2ff374-3 | ("math", ["for questions about math"]),
]
router_chain = EmbeddingRouterChain.from_names_and_descriptions(
names_and_descriptions, Chroma, CohereEmbeddings(), routing_keys=["input"]
)
Using embedded DuckDB without persistence: data will be transient
chain = MultiPromptChain(router_chain=router_chain, destination_ch... | rtdocs_stable/api.python.langchain.com/en/stable/modules/chains/generic/router.html |
71f0bd480c50-0 | .ipynb
.pdf
Creating a custom Chain
Creating a custom Chain#
To implement your own custom chain you can subclass Chain and implement the following methods:
from __future__ import annotations
from typing import Any, Dict, List, Optional
from pydantic import Extra
from langchain.base_language import BaseLanguageModel
fro... | rtdocs_stable/api.python.langchain.com/en/stable/modules/chains/generic/custom_chain.html |
71f0bd480c50-1 | # Whenever you call a language model, or another chain, you should pass
# a callback manager to it. This allows the inner run to be tracked by
# any callbacks that are registered on the outer run.
# You can always obtain a callback manager for this by calling
# `run_manager.get_child()` ... | rtdocs_stable/api.python.langchain.com/en/stable/modules/chains/generic/custom_chain.html |
71f0bd480c50-2 | callbacks=run_manager.get_child() if run_manager else None
)
# If you want to log something about this run, you can do so by calling
# methods on the `run_manager`, as shown below. This will trigger any
# callbacks that are registered for that event.
if run_manager:
a... | rtdocs_stable/api.python.langchain.com/en/stable/modules/chains/generic/custom_chain.html |
cfda074507f9-0 | .ipynb
.pdf
Transformation Chain
Transformation Chain#
This notebook showcases using a generic transformation chain.
As an example, we will create a dummy transformation that takes in a super long text, filters the text to only the first 3 paragraphs, and then passes that into an LLMChain to summarize those.
from langc... | rtdocs_stable/api.python.langchain.com/en/stable/modules/chains/generic/transformation.html |
91a767214cd6-0 | .ipynb
.pdf
Sequential Chains
Contents
SimpleSequentialChain
Sequential Chain
Memory in Sequential Chains
Sequential Chains#
The next step after calling a language model is make a series of calls to a language model. This is particularly useful when you want to take the output from one call and use it as the input to... | rtdocs_stable/api.python.langchain.com/en/stable/modules/chains/generic/sequential_chains.html |
91a767214cd6-1 | synopsis_chain = LLMChain(llm=llm, prompt=prompt_template)
# This is an LLMChain to write a review of a play given a synopsis.
llm = OpenAI(temperature=.7)
template = """You are a play critic from the New York Times. Given the synopsis of play, it is your job to write a review for that play.
Play Synopsis:
{synopsis}
R... | rtdocs_stable/api.python.langchain.com/en/stable/modules/chains/generic/sequential_chains.html |
91a767214cd6-2 | The play follows the couple as they struggle to stay together and battle the forces that threaten to tear them apart. Despite the tragedy that awaits them, they remain devoted to one another and fight to keep their love alive. In the end, the couple must decide whether to take a chance on their future together or succu... | rtdocs_stable/api.python.langchain.com/en/stable/modules/chains/generic/sequential_chains.html |
91a767214cd6-3 | The play's setting of the beach at sunset adds a touch of poignancy and romanticism to the story, while the mysterious figure serves to keep the audience enthralled. Overall, Tragedy at Sunset on the Beach is an engaging and thought-provoking play that is sure to leave audiences feeling inspired and hopeful.
Sequential... | rtdocs_stable/api.python.langchain.com/en/stable/modules/chains/generic/sequential_chains.html |
91a767214cd6-4 | Play Synopsis:
{synopsis}
Review from a New York Times play critic of the above play:"""
prompt_template = PromptTemplate(input_variables=["synopsis"], template=template)
review_chain = LLMChain(llm=llm, prompt=prompt_template, output_key="review")
# This is the overall chain where we run these two chains in sequence.
... | rtdocs_stable/api.python.langchain.com/en/stable/modules/chains/generic/sequential_chains.html |
91a767214cd6-5 | 'era': 'Victorian England',
'synopsis': "\n\nThe play follows the story of John, a young man from a wealthy Victorian family, who dreams of a better life for himself. He soon meets a beautiful young woman named Mary, who shares his dream. The two fall in love and decide to elope and start a new life together.\n\nOn th... | rtdocs_stable/api.python.langchain.com/en/stable/modules/chains/generic/sequential_chains.html |
91a767214cd6-6 | 'review': "\n\nThe latest production from playwright X is a powerful and heartbreaking story of love and loss set against the backdrop of 19th century England. The play follows John, a young man from a wealthy Victorian family, and Mary, a beautiful young woman with whom he falls in love. The two decide to elope and st... | rtdocs_stable/api.python.langchain.com/en/stable/modules/chains/generic/sequential_chains.html |
91a767214cd6-7 | from langchain.memory import SimpleMemory
llm = OpenAI(temperature=.7)
template = """You are a social media manager for a theater company. Given the title of play, the era it is set in, the date,time and location, the synopsis of the play, and the review of the play, it is your job to write a social media post for tha... | rtdocs_stable/api.python.langchain.com/en/stable/modules/chains/generic/sequential_chains.html |
91a767214cd6-8 | 'location': 'Theater in the Park',
'social_post_text': "\nSpend your Christmas night with us at Theater in the Park and experience the heartbreaking story of love and loss that is 'A Walk on the Beach'. Set in Victorian England, this romantic tragedy follows the story of Frances and Edward, a young couple whose love i... | rtdocs_stable/api.python.langchain.com/en/stable/modules/chains/generic/sequential_chains.html |
37cb620079e7-0 | .ipynb
.pdf
Async API for Chain
Async API for Chain#
LangChain provides async support for Chains by leveraging the asyncio library.
Async methods are currently supported in LLMChain (through arun, apredict, acall) and LLMMathChain (through arun and acall), ChatVectorDBChain, and QA chains. Async support for other chain... | rtdocs_stable/api.python.langchain.com/en/stable/modules/chains/generic/async_chain.html |
37cb620079e7-1 | await generate_concurrently()
elapsed = time.perf_counter() - s
print('\033[1m' + f"Concurrent executed in {elapsed:0.2f} seconds." + '\033[0m')
s = time.perf_counter()
generate_serially()
elapsed = time.perf_counter() - s
print('\033[1m' + f"Serial executed in {elapsed:0.2f} seconds." + '\033[0m')
BrightSmile Toothpas... | rtdocs_stable/api.python.langchain.com/en/stable/modules/chains/generic/async_chain.html |
e98b75f55faf-0 | .ipynb
.pdf
LLM Chain
Contents
LLM Chain
Additional ways of running LLM Chain
Parsing the outputs
Initialize from string
LLM Chain#
LLMChain is perhaps one of the most popular ways of querying an LLM object. It formats the prompt template using the input key values provided (and also memory key values, if available),... | rtdocs_stable/api.python.langchain.com/en/stable/modules/chains/generic/llm_chain.html |
e98b75f55faf-1 | llm_chain.generate(input_list)
LLMResult(generations=[[Generation(text='\n\nSocktastic!', generation_info={'finish_reason': 'stop', 'logprobs': None})], [Generation(text='\n\nTechCore Solutions.', generation_info={'finish_reason': 'stop', 'logprobs': None})], [Generation(text='\n\nFootwear Factory.', generation_info={'... | rtdocs_stable/api.python.langchain.com/en/stable/modules/chains/generic/llm_chain.html |
e98b75f55faf-2 | template = """List all the colors in a rainbow"""
prompt = PromptTemplate(template=template, input_variables=[], output_parser=output_parser)
llm_chain = LLMChain(prompt=prompt, llm=llm)
llm_chain.predict()
'\n\nRed, orange, yellow, green, blue, indigo, violet'
With predict_and_parser:
llm_chain.predict_and_parse()
['R... | rtdocs_stable/api.python.langchain.com/en/stable/modules/chains/generic/llm_chain.html |
b80d4de316a8-0 | .ipynb
.pdf
Router Chains: Selecting from multiple prompts with MultiPromptChain
Router Chains: Selecting from multiple prompts with MultiPromptChain#
This notebook demonstrates how to use the RouterChain paradigm to create a chain that dynamically selects the prompt to use for a given input. Specifically we show how t... | rtdocs_stable/api.python.langchain.com/en/stable/modules/chains/examples/multi_prompt_router.html |
b80d4de316a8-1 | physics: {'input': 'What is black body radiation?'}
> Finished chain.
Black body radiation is the emission of electromagnetic radiation from a body due to its temperature. It is a type of thermal radiation that is emitted from the surface of all objects that are at a temperature above absolute zero. It is a spectrum of... | rtdocs_stable/api.python.langchain.com/en/stable/modules/chains/examples/multi_prompt_router.html |
b80d4de316a8-2 | None: {'input': 'What is the name of the type of cloud that rains?'}
> Finished chain.
The type of cloud that typically produces rain is called a cumulonimbus cloud. This type of cloud is characterized by its large vertical extent and can produce thunderstorms and heavy precipitation. Is there anything else you'd like ... | rtdocs_stable/api.python.langchain.com/en/stable/modules/chains/examples/multi_prompt_router.html |
ff4380adc0c6-0 | .ipynb
.pdf
PAL
Contents
Math Prompt
Colored Objects
Intermediate Steps
PAL#
Implements Program-Aided Language Models, as in https://arxiv.org/pdf/2211.10435.pdf.
from langchain.chains import PALChain
from langchain import OpenAI
llm = OpenAI(temperature=0, max_tokens=512)
Math Prompt#
pal_chain = PALChain.from_math_... | rtdocs_stable/api.python.langchain.com/en/stable/modules/chains/examples/pal.html |
ff4380adc0c6-1 | objects += [('booklet', 'purple')] * 2
objects += [('sunglasses', 'yellow')] * 2
# Remove all pairs of sunglasses
objects = [object for object in objects if object[0] != 'sunglasses']
# Count number of purple objects
num_purple = len([object for object in objects if object[1] == 'purple'])
answer = num_purple
> Finishe... | rtdocs_stable/api.python.langchain.com/en/stable/modules/chains/examples/pal.html |
ff4380adc0c6-2 | answer = num_purple
> Finished chain.
result['intermediate_steps']
"# Put objects into a list to record ordering\nobjects = []\nobjects += [('booklet', 'blue')] * 2\nobjects += [('booklet', 'purple')] * 2\nobjects += [('sunglasses', 'yellow')] * 2\n\n# Remove all pairs of sunglasses\nobjects = [object for object in obj... | rtdocs_stable/api.python.langchain.com/en/stable/modules/chains/examples/pal.html |
808b16fa5fc3-0 | .ipynb
.pdf
FLARE
Contents
Imports
Retriever
FLARE Chain
FLARE#
This notebook is an implementation of Forward-Looking Active REtrieval augmented generation (FLARE).
Please see the original repo here.
The basic idea is:
Start answering a question
If you start generating tokens the model is uncertain about, look up rel... | rtdocs_stable/api.python.langchain.com/en/stable/modules/chains/examples/flare.html |
808b16fa5fc3-1 | min_prob: Any tokens generated with probability below this will be considered uncertain
Imports#
import os
os.environ["SERPER_API_KEY"] = ""
import re
import numpy as np
from langchain.schema import BaseRetriever
from langchain.utilities import GoogleSerperAPIWrapper
from langchain.embeddings import OpenAIEmbeddings
fr... | rtdocs_stable/api.python.langchain.com/en/stable/modules/chains/examples/flare.html |
808b16fa5fc3-2 | >>> RESPONSE:
> Entering new QuestionGeneratorChain chain...
Prompt after formatting:
Given a user input and an existing partial response as context, ask a question to which the answer is the given term/entity/phrase:
>>> USER INPUT: explain in great detail the difference between the langchain framework and baby agi
>... | rtdocs_stable/api.python.langchain.com/en/stable/modules/chains/examples/flare.html |
808b16fa5fc3-3 | Baby AGI, on the other hand, is an artificial general intelligence (AGI) platform. It uses a combination of deep learning and reinforcement learning to create an AI system that can learn and adapt to new tasks. Baby AGI is designed to be a general-purpose AI system that can be used for a variety of applications, includ... | rtdocs_stable/api.python.langchain.com/en/stable/modules/chains/examples/flare.html |
808b16fa5fc3-4 | >>> USER INPUT: explain in great detail the difference between the langchain framework and baby agi
>>> EXISTING PARTIAL RESPONSE:
The Langchain Framework is a decentralized platform for natural language processing (NLP) applications. It uses a blockchain-based distributed ledger to store and process data, allowing f... | rtdocs_stable/api.python.langchain.com/en/stable/modules/chains/examples/flare.html |
808b16fa5fc3-5 | Baby AGI, on the other hand, is an artificial general intelligence (AGI) platform. It uses a combination of deep learning and reinforcement learning to create an AI system that can learn and adapt to new tasks. Baby AGI is designed to be a general-purpose AI system that can be used for a variety of applications, includ... | rtdocs_stable/api.python.langchain.com/en/stable/modules/chains/examples/flare.html |
808b16fa5fc3-6 | >>> USER INPUT: explain in great detail the difference between the langchain framework and baby agi
>>> EXISTING PARTIAL RESPONSE:
The Langchain Framework is a decentralized platform for natural language processing (NLP) applications. It uses a blockchain-based distributed ledger to store and process data, allowing f... | rtdocs_stable/api.python.langchain.com/en/stable/modules/chains/examples/flare.html |
808b16fa5fc3-7 | Baby AGI, on the other hand, is an artificial general intelligence (AGI) platform. It uses a combination of deep learning and reinforcement learning to create an AI system that can learn and adapt to new tasks. Baby AGI is designed to be a general-purpose AI system that can be used for a variety of applications, includ... | rtdocs_stable/api.python.langchain.com/en/stable/modules/chains/examples/flare.html |
808b16fa5fc3-8 | >>> CONTEXT: LangChain: Software. LangChain is a software development framework designed to simplify the creation of applications using large language models. LangChain Initial release date: October 2022. LangChain Programming languages: Python and JavaScript. LangChain Developer(s): Harrison Chase. LangChain License: ... | rtdocs_stable/api.python.langchain.com/en/stable/modules/chains/examples/flare.html |
808b16fa5fc3-9 | LangChain is a framework for including AI from large language models inside data pipelines and applications. This tutorial provides an overview of what you ... Missing: secure | Must include:secure. Blockchain is the best way to secure the data of the shared community. Utilizing the capabilities of the blockchain nobod... | rtdocs_stable/api.python.langchain.com/en/stable/modules/chains/examples/flare.html |
808b16fa5fc3-10 | LangChain is a framework for including AI from large language models inside data pipelines and applications. This tutorial provides an overview of what you ... LangChain is an intuitive framework created to assist in developing applications driven by a language model, such as OpenAI or Hugging Face. This documentation ... | rtdocs_stable/api.python.langchain.com/en/stable/modules/chains/examples/flare.html |
808b16fa5fc3-11 | Blockchain is one type of a distributed ledger. Distributed ledgers use independent computers (referred to as nodes) to record, share and ... Missing: Langchain | Must include:Langchain. Blockchain is used in distributed storage software where huge data is broken down into chunks. This is available in encrypted data ac... | rtdocs_stable/api.python.langchain.com/en/stable/modules/chains/examples/flare.html |
808b16fa5fc3-12 | LangChain is an intuitive framework created to assist in developing applications driven by a language model, such as OpenAI or Hugging Face. Missing: decentralized | Must include:decentralized. LangChain, created by Harrison Chase, is a Python library that provides out-of-the-box support to build NLP applications using... | rtdocs_stable/api.python.langchain.com/en/stable/modules/chains/examples/flare.html |
808b16fa5fc3-13 | LangChain is a powerful tool that can be used to work with Large Language ... If an API key has been provided, create an OpenAI language model instance At its core, LangChain is a framework built around LLMs. We can use it for chatbots, Generative Question-Answering (GQA), summarization, and much more. A tutorial of th... | rtdocs_stable/api.python.langchain.com/en/stable/modules/chains/examples/flare.html |
808b16fa5fc3-14 | At its core, LangChain is a framework built around LLMs. We can use it for chatbots, Generative Question-Answering (GQA), summarization, and much more. The core idea of the library is that we can “chain” together different components to create more advanced use cases around LLMs.
>>> USER INPUT: explain in great detail... | rtdocs_stable/api.python.langchain.com/en/stable/modules/chains/examples/flare.html |
808b16fa5fc3-15 | llm = OpenAI()
llm(query)
'\n\nThe Langchain framework and Baby AGI are both artificial intelligence (AI) frameworks that are used to create intelligent agents. The Langchain framework is a supervised learning system that is based on the concept of “language chains”. It uses a set of rules to map natural language input... | rtdocs_stable/api.python.langchain.com/en/stable/modules/chains/examples/flare.html |
808b16fa5fc3-16 | >>> USER INPUT: how are the origin stories of langchain and bitcoin similar or different?
>>> EXISTING PARTIAL RESPONSE:
Langchain and Bitcoin have very different origin stories. Bitcoin was created by the mysterious Satoshi Nakamoto in 2008 as a decentralized digital currency. Langchain, on the other hand, was creat... | rtdocs_stable/api.python.langchain.com/en/stable/modules/chains/examples/flare.html |
808b16fa5fc3-17 | FINISHED
The question to which the answer is the term/entity/phrase " developers as a platform for creating and managing decentralized language learning applications." is:
> Finished chain.
Generated Questions: ['How would you describe the origin stories of Langchain and Bitcoin in terms of their similarities or differ... | rtdocs_stable/api.python.langchain.com/en/stable/modules/chains/examples/flare.html |
808b16fa5fc3-18 | >>> CONTEXT: Bitcoin and Ethereum have many similarities but different long-term visions and limitations. Ethereum changed from proof of work to proof of ... Bitcoin will be around for many years and examining its white paper origins is a great exercise in understanding why. Satoshi Nakamoto's blueprint describes ... B... | rtdocs_stable/api.python.langchain.com/en/stable/modules/chains/examples/flare.html |
808b16fa5fc3-19 | At its core, LangChain is a framework built around LLMs. We can use it for chatbots, Generative Question-Answering (GQA), summarization, and much more. The core idea of the library is that we can “chain” together different components to create more advanced use cases around LLMs.
>>> USER INPUT: how are the origin stor... | rtdocs_stable/api.python.langchain.com/en/stable/modules/chains/examples/flare.html |
91cf89266cf6-0 | .ipynb
.pdf
GraphCypherQAChain
Contents
Seeding the database
Refresh graph schema information
Querying the graph
Limit the number of results
Return intermediate results
Return direct results
GraphCypherQAChain#
This notebook shows how to use LLMs to provide a natural language interface to a graph database you can que... | rtdocs_stable/api.python.langchain.com/en/stable/modules/chains/examples/graph_cypher_qa.html |
91cf89266cf6-1 | """
MERGE (m:Movie {name:"Top Gun"})
WITH m
UNWIND ["Tom Cruise", "Val Kilmer", "Anthony Edwards", "Meg Ryan"] AS actor
MERGE (a:Actor {name:actor})
MERGE (a)-[:ACTED_IN]->(m)
"""
)
[]
Refresh graph schema information#
If the schema of database changes, you can refresh the schema information needed to generate Cypher s... | rtdocs_stable/api.python.langchain.com/en/stable/modules/chains/examples/graph_cypher_qa.html |
91cf89266cf6-2 | Limit the number of results#
You can limit the number of results from the Cypher QA Chain using the top_k parameter.
The default is 10.
chain = GraphCypherQAChain.from_llm(
ChatOpenAI(temperature=0), graph=graph, verbose=True, top_k=2
)
chain.run("Who played in Top Gun?")
> Entering new GraphCypherQAChain chain...
... | rtdocs_stable/api.python.langchain.com/en/stable/modules/chains/examples/graph_cypher_qa.html |
91cf89266cf6-3 | > Finished chain.
Intermediate steps: [{'query': "MATCH (a:Actor)-[:ACTED_IN]->(m:Movie {name: 'Top Gun'})\nRETURN a.name"}, {'context': [{'a.name': 'Val Kilmer'}, {'a.name': 'Anthony Edwards'}, {'a.name': 'Meg Ryan'}, {'a.name': 'Tom Cruise'}]}]
Final answer: Val Kilmer, Anthony Edwards, Meg Ryan, and Tom Cruise playe... | rtdocs_stable/api.python.langchain.com/en/stable/modules/chains/examples/graph_cypher_qa.html |
13efa52e14c6-0 | .ipynb
.pdf
SQL Chain example
Contents
Use Query Checker
Customize Prompt
Return Intermediate Steps
Choosing how to limit the number of rows returned
Adding example rows from each table
Custom Table Info
SQLDatabaseSequentialChain
Using Local Language Models
SQL Chain example#
This example demonstrates the use of the... | rtdocs_stable/api.python.langchain.com/en/stable/modules/chains/examples/sqlite.html |
13efa52e14c6-1 | db_chain.run("How many employees are there?")
> Entering new SQLDatabaseChain chain...
How many employees are there?
SQLQuery:
/workspace/langchain/langchain/sql_database.py:191: SAWarning: Dialect sqlite+pysqlite does *not* support Decimal objects natively, and SQLAlchemy must convert from floating point - rounding er... | rtdocs_stable/api.python.langchain.com/en/stable/modules/chains/examples/sqlite.html |
13efa52e14c6-2 | Use the following format:
Question: "Question here"
SQLQuery: "SQL Query to run"
SQLResult: "Result of the SQLQuery"
Answer: "Final answer here"
Only use the following tables:
{table_info}
If someone asks for the table foobar, they really mean the employee table.
Question: {input}"""
PROMPT = PromptTemplate(
input_... | rtdocs_stable/api.python.langchain.com/en/stable/modules/chains/examples/sqlite.html |
13efa52e14c6-3 | Answer:There are 8 employees in the foobar table.
> Finished chain.
[{'input': 'How many employees are there in the foobar table?\nSQLQuery:SELECT COUNT(*) FROM Employee;\nSQLResult: [(8,)]\nAnswer:',
'top_k': '5',
'dialect': 'sqlite', | rtdocs_stable/api.python.langchain.com/en/stable/modules/chains/examples/sqlite.html |
13efa52e14c6-4 | 'table_info': '\nCREATE TABLE "Artist" (\n\t"ArtistId" INTEGER NOT NULL, \n\t"Name" NVARCHAR(120), \n\tPRIMARY KEY ("ArtistId")\n)\n\n/*\n3 rows from Artist table:\nArtistId\tName\n1\tAC/DC\n2\tAccept\n3\tAerosmith\n*/\n\n\nCREATE TABLE "Employee" (\n\t"EmployeeId" INTEGER NOT NULL, \n\t"LastName" NVARCHAR(20) NOT NULL... | rtdocs_stable/api.python.langchain.com/en/stable/modules/chains/examples/sqlite.html |
13efa52e14c6-5 | 2N1\t+1 (780) 428-9482\t+1 (780) 428-3457\tandrew@chinookcorp.com\n2\tEdwards\tNancy\tSales Manager\t1\t1958-12-08 00:00:00\t2002-05-01 00:00:00\t825 8 Ave SW\tCalgary\tAB\tCanada\tT2P 2T3\t+1 (403) 262-3443\t+1 (403) 262-3322\tnancy@chinookcorp.com\n3\tPeacock\tJane\tSales Support Agent\t2\t1973-08-29 00:00:00\t2002-0... | rtdocs_stable/api.python.langchain.com/en/stable/modules/chains/examples/sqlite.html |
13efa52e14c6-6 | TABLE "Playlist" (\n\t"PlaylistId" INTEGER NOT NULL, \n\t"Name" NVARCHAR(120), \n\tPRIMARY KEY ("PlaylistId")\n)\n\n/*\n3 rows from Playlist table:\nPlaylistId\tName\n1\tMusic\n2\tMovies\n3\tTV Shows\n*/\n\n\nCREATE TABLE "Album" (\n\t"AlbumId" INTEGER NOT NULL, \n\t"Title" NVARCHAR(160) NOT NULL, \n\t"ArtistId" INTEGE... | rtdocs_stable/api.python.langchain.com/en/stable/modules/chains/examples/sqlite.html |
13efa52e14c6-7 | REFERENCES "Employee" ("EmployeeId")\n)\n\n/*\n3 rows from Customer table:\nCustomerId\tFirstName\tLastName\tCompany\tAddress\tCity\tState\tCountry\tPostalCode\tPhone\tFax\tEmail\tSupportRepId\n1\tLuís\tGonçalves\tEmbraer - Empresa Brasileira de Aeronáutica S.A.\tAv. Brigadeiro Faria Lima, 2170\tSão José dos Campos\tSP... | rtdocs_stable/api.python.langchain.com/en/stable/modules/chains/examples/sqlite.html |
13efa52e14c6-8 | KEY ("InvoiceId"), \n\tFOREIGN KEY("CustomerId") REFERENCES "Customer" ("CustomerId")\n)\n\n/*\n3 rows from Invoice table:\nInvoiceId\tCustomerId\tInvoiceDate\tBillingAddress\tBillingCity\tBillingState\tBillingCountry\tBillingPostalCode\tTotal\n1\t2\t2009-01-01 00:00:00\tTheodor-Heuss-Straße 34\tStuttgart\tNone\tGerman... | rtdocs_stable/api.python.langchain.com/en/stable/modules/chains/examples/sqlite.html |
13efa52e14c6-9 | Those About To Rock (We Salute You)\t1\t1\t1\tAngus Young, Malcolm Young, Brian Johnson\t343719\t11170334\t0.99\n2\tBalls to the Wall\t2\t2\t1\tNone\t342562\t5510424\t0.99\n3\tFast As a Shark\t3\t2\t1\tF. Baltes, S. Kaufman, U. Dirkscneider & W. Hoffman\t230619\t3990994\t0.99\n*/\n\n\nCREATE TABLE "InvoiceLine" (\n\t"I... | rtdocs_stable/api.python.langchain.com/en/stable/modules/chains/examples/sqlite.html |
13efa52e14c6-10 | "Playlist" ("PlaylistId")\n)\n\n/*\n3 rows from PlaylistTrack table:\nPlaylistId\tTrackId\n1\t3402\n1\t3389\n1\t3390\n*/', | rtdocs_stable/api.python.langchain.com/en/stable/modules/chains/examples/sqlite.html |
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