id stringlengths 14 15 | text stringlengths 101 5.26k | source stringlengths 57 120 |
|---|---|---|
26853b267f95-0 | .md
.pdf
Dependents
Dependents#
Dependents stats for hwchase17/langchain
[update: 2023-06-05; only dependent repositories with Stars > 100]
Repository
Stars
openai/openai-cookbook
38024
LAION-AI/Open-Assistant
33609
microsoft/TaskMatrix
33136
hpcaitech/ColossalAI
30032
imartinez/privateGPT
28094
reworkd/AgentGPT
23430
... | https://langchain.readthedocs.io/en/latest/dependents.html |
26853b267f95-1 | 770
corca-ai/EVAL
769
101dotxyz/GPTeam
755
noahshinn024/reflexion
706
eyurtsev/kor
695
cheshire-cat-ai/core
681
e-johnstonn/BriefGPT
656
run-llama/llama-lab
635
griptape-ai/griptape
583
namuan/dr-doc-search
555
getmetal/motorhead
550
kreneskyp/ix
543
hwchase17/chat-your-data
510
Anil-matcha/ChatPDF
501
whyiyhw/chatgpt-... | https://langchain.readthedocs.io/en/latest/dependents.html |
26853b267f95-2 | 140
gustavz/DataChad
140
Klingefjord/chatgpt-telegram
140
Jaseci-Labs/jaseci
139
handrew/browserpilot
137
jmpaz/promptlib
137
SamPink/dev-gpt
135
menloparklab/langchain-cohere-qdrant-doc-retrieval
135
hirokidaichi/wanna
135
steamship-core/vercel-examples
134
pablomarin/GPT-Azure-Search-Engine
133
ibiscp/LLM-IMDB
133
sh... | https://langchain.readthedocs.io/en/latest/dependents.html |
1ad08ca4c472-0 | .rst
.pdf
API References
API References#
Full documentation on all methods, classes, and APIs in LangChain.
Models
Prompts
Indexes
Memory
Chains
Agents
Utilities
Experimental Modules
previous
Installation
next
Models
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 08, 2023. | https://langchain.readthedocs.io/en/latest/reference.html |
866e1463e2f3-0 | .rst
.pdf
Integrations
Contents
Integrations by Module
Dependencies
All Integrations
Integrations#
LangChain integrates with many LLMs, systems, and products.
Integrations by Module#
Integrations grouped by the core LangChain module they map to:
LLM Providers
Chat Model Providers
Text Embedding Model Providers
Docume... | https://langchain.readthedocs.io/en/latest/integrations.html |
3651943d3656-0 | .md
.pdf
Interacting with APIs
Contents
Chains
Agents
Interacting with APIs#
Conceptual Guide
Lots of data and information is stored behind APIs.
This page covers all resources available in LangChain for working with APIs.
Chains#
If you are just getting started, and you have relatively simple apis, you should get st... | https://langchain.readthedocs.io/en/latest/use_cases/apis.html |
f9bf5705bdda-0 | .md
.pdf
Code Understanding
Contents
Conversational Retriever Chain
Code Understanding#
Overview
LangChain is a useful tool designed to parse GitHub code repositories. By leveraging VectorStores, Conversational RetrieverChain, and GPT-4, it can answer questions in the context of an entire GitHub repository or generat... | https://langchain.readthedocs.io/en/latest/use_cases/code.html |
fb794cf7c0fb-0 | .md
.pdf
Question Answering over Docs
Contents
Document Question Answering
Adding in sources
Additional Related Resources
End-to-end examples
Question Answering over Docs#
Conceptual Guide
Question answering in this context refers to question answering over your document data.
For question answering over other types ... | https://langchain.readthedocs.io/en/latest/use_cases/question_answering.html |
307d177f63e9-0 | .md
.pdf
Querying Tabular Data
Contents
Document Loading
Querying
Chains
Agents
Querying Tabular Data#
Conceptual Guide
Lots of data and information is stored in tabular data, whether it be csvs, excel sheets, or SQL tables.
This page covers all resources available in LangChain for working with data in this format.
D... | https://langchain.readthedocs.io/en/latest/use_cases/tabular.html |
aff90d32e4bc-0 | .md
.pdf
Extraction
Extraction#
Conceptual Guide
Most APIs and databases still deal with structured information.
Therefore, in order to better work with those, it can be useful to extract structured information from text.
Examples of this include:
Extracting a structured row to insert into a database from a sentence
Ex... | https://langchain.readthedocs.io/en/latest/use_cases/extraction.html |
b3dd1d4b749f-0 | .md
.pdf
Chatbots
Chatbots#
Conceptual Guide
Since language models are good at producing text, that makes them ideal for creating chatbots.
Aside from the base prompts/LLMs, an important concept to know for Chatbots is memory.
Most chat based applications rely on remembering what happened in previous interactions, whic... | https://langchain.readthedocs.io/en/latest/use_cases/chatbots.html |
6230a47ed8ef-0 | .md
.pdf
Agent Simulations
Contents
Simulations with One Agent
Simulations with Two Agents
Simulations with Multiple Agents
Agent Simulations#
Agent simulations involve interacting one of more agents with each other.
Agent simulations generally involve two main components:
Long Term Memory
Simulation Environment
Spec... | https://langchain.readthedocs.io/en/latest/use_cases/agent_simulations.html |
c9d147987241-0 | .md
.pdf
Agents
Contents
Create Your Own Agent
Step 1: Create Tools
(Optional) Step 2: Modify Agent
(Optional) Step 3: Modify Agent Executor
Examples
Agents#
Conceptual Guide
Agents can be used for a variety of tasks.
Agents combine the decision making ability of a language model with tools in order to create a syste... | https://langchain.readthedocs.io/en/latest/use_cases/personal_assistants.html |
1f2b0d5f4f74-0 | .md
.pdf
Summarization
Summarization#
Conceptual Guide
Summarization involves creating a smaller summary of multiple longer documents.
This can be useful for distilling long documents into the core pieces of information.
The recommended way to get started using a summarization chain is:
from langchain.chains.summarize ... | https://langchain.readthedocs.io/en/latest/use_cases/summarization.html |
f66ddc3381c2-0 | .md
.pdf
Autonomous Agents
Contents
Baby AGI (Original Repo)
AutoGPT (Original Repo)
MetaPrompt (Original Repo)
Autonomous Agents#
Autonomous Agents are agents that designed to be more long running.
You give them one or multiple long term goals, and they independently execute towards those goals.
The applications com... | https://langchain.readthedocs.io/en/latest/use_cases/autonomous_agents.html |
212bd7ff8a4b-0 | .rst
.pdf
Evaluation
Contents
The Problem
The Solution
The Examples
Other Examples
Evaluation#
Note
Conceptual Guide
This section of documentation covers how we approach and think about evaluation in LangChain.
Both evaluation of internal chains/agents, but also how we would recommend people building on top of LangCh... | https://langchain.readthedocs.io/en/latest/use_cases/evaluation.html |
212bd7ff8a4b-1 | Hugging Face Datasets: Covers an example of loading and using a dataset from Hugging Face for evaluation.
previous
Summarization
next
Agent Benchmarking: Search + Calculator
Contents
The Problem
The Solution
The Examples
Other Examples
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last upd... | https://langchain.readthedocs.io/en/latest/use_cases/evaluation.html |
648a136bd2f6-0 | .ipynb
.pdf
QA Generation
QA Generation#
This notebook shows how to use the QAGenerationChain to come up with question-answer pairs over a specific document.
This is important because often times you may not have data to evaluate your question-answer system over, so this is a cheap and lightweight way to generate it!
f... | https://langchain.readthedocs.io/en/latest/use_cases/evaluation/qa_generation.html |
0ecb9fe3d36a-0 | .ipynb
.pdf
Question Answering
Contents
Setup
Examples
Predictions
Evaluation
Customize Prompt
Evaluation without Ground Truth
Comparing to other evaluation metrics
Question Answering#
This notebook covers how to evaluate generic question answering problems. This is a situation where you have an example containing a ... | https://langchain.readthedocs.io/en/latest/use_cases/evaluation/question_answering.html |
0ecb9fe3d36a-1 | """
PROMPT = PromptTemplate(input_variables=["query", "answer", "result"], template=_PROMPT_TEMPLATE)
evalchain = QAEvalChain.from_llm(llm=llm,prompt=PROMPT)
evalchain.evaluate(examples, predictions, question_key="question", answer_key="answer", prediction_key="text")
Evaluation without Ground Truth#
Its possible to ev... | https://langchain.readthedocs.io/en/latest/use_cases/evaluation/question_answering.html |
46e7d1d65d58-0 | .ipynb
.pdf
Benchmarking Template
Contents
Loading the data
Setting up a chain
Make a prediction
Make many predictions
Evaluate performance
Benchmarking Template#
This is an example notebook that can be used to create a benchmarking notebook for a task of your choice. Evaluation is really hard, and so we greatly welc... | https://langchain.readthedocs.io/en/latest/use_cases/evaluation/benchmarking_template.html |
3d8bb23d8977-0 | .ipynb
.pdf
Question Answering Benchmarking: State of the Union Address
Contents
Loading the data
Setting up a chain
Make a prediction
Make many predictions
Evaluate performance
Question Answering Benchmarking: State of the Union Address#
Here we go over how to benchmark performance on a question answering task over ... | https://langchain.readthedocs.io/en/latest/use_cases/evaluation/qa_benchmarking_sota.html |
3d8bb23d8977-1 | next
QA Generation
Contents
Loading the data
Setting up a chain
Make a prediction
Make many predictions
Evaluate performance
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 08, 2023. | https://langchain.readthedocs.io/en/latest/use_cases/evaluation/qa_benchmarking_sota.html |
8ee62de7c3b2-0 | .ipynb
.pdf
SQL Question Answering Benchmarking: Chinook
Contents
Loading the data
Setting up a chain
Make a prediction
Make many predictions
Evaluate performance
SQL Question Answering Benchmarking: Chinook#
Here we go over how to benchmark performance on a question answering task over a SQL database.
It is highly r... | https://langchain.readthedocs.io/en/latest/use_cases/evaluation/sql_qa_benchmarking_chinook.html |
8ee62de7c3b2-1 | Loading the data
Setting up a chain
Make a prediction
Make many predictions
Evaluate performance
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 08, 2023. | https://langchain.readthedocs.io/en/latest/use_cases/evaluation/sql_qa_benchmarking_chinook.html |
02721d1ab90a-0 | .ipynb
.pdf
Evaluating an OpenAPI Chain
Contents
Load the API Chain
Optional: Generate Input Questions and Request Ground Truth Queries
Run the API Chain
Evaluate the requests chain
Evaluate the Response Chain
Generating Test Datasets
Evaluating an OpenAPI Chain#
This notebook goes over ways to semantically evaluate ... | https://langchain.readthedocs.io/en/latest/use_cases/evaluation/openapi_eval.html |
02721d1ab90a-1 | {'question': 'I want to buy a new skirt',
'expected_query': {'max_price': None, 'q': 'skirt'}},
{'question': 'My company is asking me to get a professional Deskopt PC - money is no object.',
'expected_query': {'max_price': 10000, 'q': 'professional desktop PC'}},
{'question': 'What are the best budget cameras?',
... | https://langchain.readthedocs.io/en/latest/use_cases/evaluation/openapi_eval.html |
02721d1ab90a-2 | "I found several Nike and Adidas shoes in the API response. Here are the links to the products: Nike Dunk Low M - Black/White: https://www.klarna.com/us/shopping/pl/cl337/3200177969/Shoes/Nike-Dunk-Low-M-Black-White/?utm_source=openai&ref-site=openai_plugin, Nike Air Jordan 4 Retro M - Midnight Navy: https://www.klarna... | https://langchain.readthedocs.io/en/latest/use_cases/evaluation/openapi_eval.html |
02721d1ab90a-3 | predicted_queries = [output["intermediate_steps"]["request_args"] for output in chain_outputs]
from langchain.prompts import PromptTemplate
template = """You are trying to answer the following question by querying an API:
> Question: {question}
The query you know you should be executing against the API is:
> Query: {tr... | https://langchain.readthedocs.io/en/latest/use_cases/evaluation/openapi_eval.html |
02721d1ab90a-4 | ' The original query is asking for shoes, so the predicted query is asking for the same thing. The original query does not specify a size, so the predicted query is not adding any additional information. The original query does not specify a price range, so the predicted query is adding additional information that is n... | https://langchain.readthedocs.io/en/latest/use_cases/evaluation/openapi_eval.html |
02721d1ab90a-5 | ' The original query is asking for laptops with a maximum price of 300. The predicted query is asking for laptops with a minimum price of 0 and a maximum price of 500. This means that the predicted query is likely to return more results than the original query, as it is asking for a wider range of prices. Therefore, th... | https://langchain.readthedocs.io/en/latest/use_cases/evaluation/openapi_eval.html |
02721d1ab90a-6 | " The API response provided a list of laptops with their prices and attributes. The user asked if there were any budget laptops, and the response provided a list of laptops that are all priced under $500. Therefore, the response was accurate and useful in answering the user's question. Final Grade: A",
" The API respo... | https://langchain.readthedocs.io/en/latest/use_cases/evaluation/openapi_eval.html |
02721d1ab90a-7 | # Re-show the examples for which the chain failed to complete
failed_examples
[]
Generating Test Datasets#
To evaluate a chain against your own endpoint, you’ll want to generate a test dataset that’s conforms to the API.
This section provides an overview of how to bootstrap the process.
First, we’ll parse the OpenAPI S... | https://langchain.readthedocs.io/en/latest/use_cases/evaluation/openapi_eval.html |
02721d1ab90a-8 | "Can you tell me how to say 'goodbye' in Chinese?",
"I'm trying to learn the Arabic word for 'please'."]
# Define the generation chain to get hypotheses
api_chain = OpenAPIEndpointChain.from_api_operation(
operation,
llm,
requests=Requests(),
verbose=verbose,
return_intermediate_steps=True # Ret... | https://langchain.readthedocs.io/en/latest/use_cases/evaluation/openapi_eval.html |
02721d1ab90a-9 | Query: Can you tell me how to say 'thank you' in German?
Request: {"task_description": "say 'thank you'", "learning_language": "German", "native_language": "English", "full_query": "Can you tell me how to say 'thank you' in German?"}
Requested changes:
Query: I'm trying to learn the Italian word for 'please'.
Request:... | https://langchain.readthedocs.io/en/latest/use_cases/evaluation/openapi_eval.html |
02721d1ab90a-10 | '{"task_description": "say goodbye", "learning_language": "Chinese", "native_language": "English", "full_query": "Can you tell me how to say \'goodbye\' in Chinese?"}',
'{"task_description": "Learn the Arabic word for \'please\'", "learning_language": "Arabic", "native_language": "English", "full_query": "I\'m trying ... | https://langchain.readthedocs.io/en/latest/use_cases/evaluation/openapi_eval.html |
bb7fca69529f-0 | .ipynb
.pdf
Data Augmented Question Answering
Contents
Setup
Examples
Evaluate
Evaluate with Other Metrics
Data Augmented Question Answering#
This notebook uses some generic prompts/language models to evaluate an question answering system that uses other sources of data besides what is in the model. For example, this... | https://langchain.readthedocs.io/en/latest/use_cases/evaluation/data_augmented_question_answering.html |
bb7fca69529f-1 | Real Answer: He praised her legal ability and said he nominated her for the supreme court.
Predicted Answer: The president said that she is one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, and from a family of public school educators and police officers... | https://langchain.readthedocs.io/en/latest/use_cases/evaluation/data_augmented_question_answering.html |
bb7fca69529f-2 | for k, v in metrics.items()
}
Finally, we can print out the results. We can see that overall the scores are higher when the output is semantically correct, and also when the output closely matches with the gold-standard answer.
for i, eg in enumerate(examples):
score_string = ", ".join([f"{k}={v['examples'][i]['val... | https://langchain.readthedocs.io/en/latest/use_cases/evaluation/data_augmented_question_answering.html |
bb7fca69529f-3 | Benchmarking Template
next
Generic Agent Evaluation
Contents
Setup
Examples
Evaluate
Evaluate with Other Metrics
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 08, 2023. | https://langchain.readthedocs.io/en/latest/use_cases/evaluation/data_augmented_question_answering.html |
7dee49eda043-0 | .ipynb
.pdf
Generic Agent Evaluation
Contents
Setup
Testing the Agent
Evaluating the Agent
Generic Agent Evaluation#
Good evaluation is key for quickly iterating on your agent’s prompts and tools. Here we provide an example of how to use the TrajectoryEvalChain to evaluate your agent.
Setup#
Let’s start by defining o... | https://langchain.readthedocs.io/en/latest/use_cases/evaluation/generic_agent_evaluation.html |
7dee49eda043-1 | Answer: 14901.234567901234
> Finished chain.
Observation: Answer: 14901.234567901234
Thought:{
"action": "Calculator",
"action_input": "The length of the Eiffel Tower is 324 meters. The distance from coast to coast in the US is approximately 4,828 kilometers. First, we need to convert 4,828 kilometers to meters... | https://langchain.readthedocs.io/en/latest/use_cases/evaluation/generic_agent_evaluation.html |
7dee49eda043-2 | Reasoning: i. Is the final answer helpful?
Yes, the final answer is helpful as it provides an approximate number of Eiffel Towers needed to cover the US from coast to coast.
ii. Does the AI language use a logical sequence of tools to answer the question?
No, the AI language model does not use a logical sequence of too... | https://langchain.readthedocs.io/en/latest/use_cases/evaluation/generic_agent_evaluation.html |
035b736ea39e-0 | .ipynb
.pdf
LLM Math
Contents
Setting up a chain
LLM Math#
Evaluating chains that know how to do math.
# Comment this out if you are NOT using tracing
import os
os.environ["LANGCHAIN_HANDLER"] = "langchain"
from langchain.evaluation.loading import load_dataset
dataset = load_dataset("llm-math")
Downloading and prepar... | https://langchain.readthedocs.io/en/latest/use_cases/evaluation/llm_math.html |
5ea0f344b8c8-0 | .ipynb
.pdf
Agent Benchmarking: Search + Calculator
Contents
Loading the data
Setting up a chain
Make a prediction
Make many predictions
Evaluate performance
Agent Benchmarking: Search + Calculator#
Here we go over how to benchmark performance of an agent on tasks where it has access to a calculator and a search tool... | https://langchain.readthedocs.io/en/latest/use_cases/evaluation/agent_benchmarking.html |
c370bd0d57c6-0 | .ipynb
.pdf
Using Hugging Face Datasets
Contents
Setup
Examples
Predictions
Evaluation
Using Hugging Face Datasets#
This example shows how to use Hugging Face datasets to evaluate models. Specifically, we show how to load examples to evaluate models on from Hugging Face’s dataset package.
Setup#
For demonstration pur... | https://langchain.readthedocs.io/en/latest/use_cases/evaluation/huggingface_datasets.html |
7c18f4dfba05-0 | .ipynb
.pdf
Question Answering Benchmarking: Paul Graham Essay
Contents
Loading the data
Setting up a chain
Make a prediction
Make many predictions
Evaluate performance
Question Answering Benchmarking: Paul Graham Essay#
Here we go over how to benchmark performance on a question answering task over a Paul Graham essa... | https://langchain.readthedocs.io/en/latest/use_cases/evaluation/qa_benchmarking_pg.html |
0652bd5278bc-0 | .ipynb
.pdf
Agent VectorDB Question Answering Benchmarking
Contents
Loading the data
Setting up a chain
Make a prediction
Make many predictions
Evaluate performance
Agent VectorDB Question Answering Benchmarking#
Here we go over how to benchmark performance on a question answering task using an agent to route between... | https://langchain.readthedocs.io/en/latest/use_cases/evaluation/agent_vectordb_sota_pg.html |
0652bd5278bc-1 | agent.run(dataset[0]['question'])
'The purpose of the NATO Alliance is to secure peace and stability in Europe after World War 2.'
Make many predictions#
Now we can make predictions
predictions = []
predicted_dataset = []
error_dataset = []
for data in dataset:
new_data = {"input": data["question"], "answer": data[... | https://langchain.readthedocs.io/en/latest/use_cases/evaluation/agent_vectordb_sota_pg.html |
65ca9d682ea1-0 | .md
.pdf
BiliBili
Contents
Installation and Setup
Document Loader
BiliBili#
Bilibili is one of the most beloved long-form video sites in China.
Installation and Setup#
pip install bilibili-api-python
Document Loader#
See a usage example.
from langchain.document_loaders import BiliBiliLoader
previous
Beam
next
Blackbo... | https://langchain.readthedocs.io/en/latest/integrations/bilibili.html |
071a61162ae3-0 | .md
.pdf
2Markdown
Contents
Installation and Setup
Document Loader
2Markdown#
2markdown service transforms website content into structured markdown files.
Installation and Setup#
We need the API key. See instructions how to get it.
Document Loader#
See a usage example.
from langchain.document_loaders import ToMarkdow... | https://langchain.readthedocs.io/en/latest/integrations/tomarkdown.html |
7f491add78fb-0 | .md
.pdf
spaCy
Contents
Installation and Setup
Text Splitter
spaCy#
spaCy is an open-source software library for advanced natural language processing, written in the programming languages Python and Cython.
Installation and Setup#
pip install spacy
Text Splitter#
See a usage example.
from langchain.llms import SpacyT... | https://langchain.readthedocs.io/en/latest/integrations/spacy.html |
f525b3271c43-0 | .md
.pdf
Obsidian
Contents
Installation and Setup
Document Loader
Obsidian#
Obsidian is a powerful and extensible knowledge base
that works on top of your local folder of plain text files.
Installation and Setup#
All instructions are in examples below.
Document Loader#
See a usage example.
from langchain.document_loa... | https://langchain.readthedocs.io/en/latest/integrations/obsidian.html |
c10bcb7ab9ef-0 | .md
.pdf
Anyscale
Contents
Installation and Setup
Wrappers
LLM
Anyscale#
This page covers how to use the Anyscale ecosystem within LangChain.
It is broken into two parts: installation and setup, and then references to specific Anyscale wrappers.
Installation and Setup#
Get an Anyscale Service URL, route and API key a... | https://langchain.readthedocs.io/en/latest/integrations/anyscale.html |
96017268d80b-0 | .md
.pdf
Shale Protocol
Contents
How to
1. Find the link to our Discord on https://shaleprotocol.com. Generate an API key through the “Shale Bot” on our Discord. No credit card is required and no free trials. It’s a forever free tier with 1K limit per day per API key.
2. Use https://shale.live/v1 as OpenAI API drop-i... | https://langchain.readthedocs.io/en/latest/integrations/shaleprotocol.html |
30e371053c87-0 | .md
.pdf
Diffbot
Contents
Installation and Setup
Document Loader
Diffbot#
Diffbot is a service to read web pages. Unlike traditional web scraping tools,
Diffbot doesn’t require any rules to read the content on a page.
It starts with computer vision, which classifies a page into one of 20 possible types. Content is th... | https://langchain.readthedocs.io/en/latest/integrations/diffbot.html |
0c87c25672eb-0 | .md
.pdf
Chroma
Contents
Installation and Setup
VectorStore
Retriever
Chroma#
Chroma is a database for building AI applications with embeddings.
Installation and Setup#
pip install chromadb
VectorStore#
There exists a wrapper around Chroma vector databases, allowing you to use it as a vectorstore,
whether for semanti... | https://langchain.readthedocs.io/en/latest/integrations/chroma.html |
a6429254bd97-0 | .md
.pdf
LanceDB
Contents
Installation and Setup
Wrappers
VectorStore
LanceDB#
This page covers how to use LanceDB within LangChain.
It is broken into two parts: installation and setup, and then references to specific LanceDB wrappers.
Installation and Setup#
Install the Python SDK with pip install lancedb
Wrappers#
... | https://langchain.readthedocs.io/en/latest/integrations/lancedb.html |
adeb39b3fc13-0 | .md
.pdf
Runhouse
Contents
Installation and Setup
Self-hosted LLMs
Self-hosted Embeddings
Runhouse#
This page covers how to use the Runhouse ecosystem within LangChain.
It is broken into three parts: installation and setup, LLMs, and Embeddings.
Installation and Setup#
Install the Python SDK with pip install runhouse... | https://langchain.readthedocs.io/en/latest/integrations/runhouse.html |
6502157766d3-0 | .md
.pdf
Weather
Contents
Installation and Setup
Document Loader
Weather#
OpenWeatherMap is an open source weather service provider.
Installation and Setup#
pip install pyowm
We must set up the OpenWeatherMap API token.
Document Loader#
See a usage example.
from langchain.document_loaders import WeatherDataLoader
pre... | https://langchain.readthedocs.io/en/latest/integrations/weather.html |
81c640847846-0 | .md
.pdf
Google Drive
Contents
Installation and Setup
Document Loader
Google Drive#
Google Drive is a file storage and synchronization service developed by Google.
Currently, only Google Docs are supported.
Installation and Setup#
First, you need to install several python package.
pip install google-api-python-client... | https://langchain.readthedocs.io/en/latest/integrations/google_drive.html |
596a55209956-0 | .md
.pdf
OpenSearch
Contents
Installation and Setup
Wrappers
VectorStore
OpenSearch#
This page covers how to use the OpenSearch ecosystem within LangChain.
It is broken into two parts: installation and setup, and then references to specific OpenSearch wrappers.
Installation and Setup#
Install the Python package with ... | https://langchain.readthedocs.io/en/latest/integrations/opensearch.html |
70dba12033dc-0 | .md
.pdf
Roam
Contents
Installation and Setup
Document Loader
Roam#
ROAM is a note-taking tool for networked thought, designed to create a personal knowledge base.
Installation and Setup#
There isn’t any special setup for it.
Document Loader#
See a usage example.
from langchain.document_loaders import RoamLoader
prev... | https://langchain.readthedocs.io/en/latest/integrations/roam.html |
77b8f73e4c1d-0 | .md
.pdf
Discord
Contents
Installation and Setup
Document Loader
Discord#
Discord is a VoIP and instant messaging social platform. Users have the ability to communicate
with voice calls, video calls, text messaging, media and files in private chats or as part of communities called
“servers”. A server is a collection ... | https://langchain.readthedocs.io/en/latest/integrations/discord.html |
21b1da85cd68-0 | .md
.pdf
AI21 Labs
Contents
Installation and Setup
Wrappers
LLM
AI21 Labs#
This page covers how to use the AI21 ecosystem within LangChain.
It is broken into two parts: installation and setup, and then references to specific AI21 wrappers.
Installation and Setup#
Get an AI21 api key and set it as an environment varia... | https://langchain.readthedocs.io/en/latest/integrations/ai21.html |
37b6f2a38659-0 | .ipynb
.pdf
Ray Serve
Contents
Goal of this notebook
Setup Ray Serve
General Skeleton
Example of deploying and OpenAI chain with custom prompts
Ray Serve#
Ray Serve is a scalable model serving library for building online inference APIs. Serve is particularly well suited for system composition, enabling you to build a... | https://langchain.readthedocs.io/en/latest/integrations/ray_serve.html |
d4e51a0da15e-0 | .md
.pdf
NLPCloud
Contents
Installation and Setup
Wrappers
LLM
NLPCloud#
This page covers how to use the NLPCloud ecosystem within LangChain.
It is broken into two parts: installation and setup, and then references to specific NLPCloud wrappers.
Installation and Setup#
Install the Python SDK with pip install nlpcloud... | https://langchain.readthedocs.io/en/latest/integrations/nlpcloud.html |
426ebb075280-0 | .md
.pdf
College Confidential
Contents
Installation and Setup
Document Loader
College Confidential#
College Confidential gives information on 3,800+ colleges and universities.
Installation and Setup#
There isn’t any special setup for it.
Document Loader#
See a usage example.
from langchain.document_loaders import Col... | https://langchain.readthedocs.io/en/latest/integrations/college_confidential.html |
4228cf3576be-0 | .md
.pdf
Google BigQuery
Contents
Installation and Setup
Document Loader
Google BigQuery#
Google BigQuery is a serverless and cost-effective enterprise data warehouse that works across clouds and scales with your data.
BigQuery is a part of the Google Cloud Platform.
Installation and Setup#
First, you need to install... | https://langchain.readthedocs.io/en/latest/integrations/google_bigquery.html |
78fe4838470e-0 | .md
.pdf
Gutenberg
Contents
Installation and Setup
Document Loader
Gutenberg#
Project Gutenberg is an online library of free eBooks.
Installation and Setup#
There isn’t any special setup for it.
Document Loader#
See a usage example.
from langchain.document_loaders import GutenbergLoader
previous
Graphsignal
next
Hack... | https://langchain.readthedocs.io/en/latest/integrations/gutenberg.html |
dec4581a8e6b-0 | .md
.pdf
Zilliz
Contents
Installation and Setup
Vectorstore
Zilliz#
Zilliz Cloud is a fully managed service on cloud for LF AI Milvus®,
Installation and Setup#
Install the Python SDK:
pip install pymilvus
Vectorstore#
A wrapper around Zilliz indexes allows you to use it as a vectorstore,
whether for semantic search o... | https://langchain.readthedocs.io/en/latest/integrations/zilliz.html |
3cefe94ab96a-0 | .md
.pdf
Vectara
Contents
Installation and Setup
VectorStore
Vectara#
What is Vectara?
Vectara Overview:
Vectara is developer-first API platform for building conversational search applications
To use Vectara - first sign up and create an account. Then create a corpus and an API key for indexing and searching.
You can... | https://langchain.readthedocs.io/en/latest/integrations/vectara.html |
e930bc109771-0 | .ipynb
.pdf
Weights & Biases
Weights & Biases#
This notebook goes over how to track your LangChain experiments into one centralized Weights and Biases dashboard. To learn more about prompt engineering and the callback please refer to this Report which explains both alongside the resultant dashboards you can expect to s... | https://langchain.readthedocs.io/en/latest/integrations/wandb_tracking.html |
e930bc109771-1 | wandb_callback.flush_tracker(llm, name="simple_sequential")
Waiting for W&B process to finish... (success). View run llm at: https://wandb.ai/harrison-chase/langchain_callback_demo/runs/e47j1914Synced 5 W&B file(s), 2 media file(s), 5 artifact file(s) and 0 other file(s)Find logs at: ./wandb/run-20230318_150408-e47j191... | https://langchain.readthedocs.io/en/latest/integrations/wandb_tracking.html |
e930bc109771-2 | > Finished chain.
Waiting for W&B process to finish... (success). View run agent at: https://wandb.ai/harrison-chase/langchain_callback_demo/runs/wzy59zjqSynced 5 W&B file(s), 2 media file(s), 7 artifact file(s) and 0 other file(s)Find logs at: ./wandb/run-20230318_150550-wzy59zjq/logs
previous
Vespa
next
Weather
By Ha... | https://langchain.readthedocs.io/en/latest/integrations/wandb_tracking.html |
e587b87346cb-0 | .md
.pdf
ForefrontAI
Contents
Installation and Setup
Wrappers
LLM
ForefrontAI#
This page covers how to use the ForefrontAI ecosystem within LangChain.
It is broken into two parts: installation and setup, and then references to specific ForefrontAI wrappers.
Installation and Setup#
Get an ForefrontAI api key and set i... | https://langchain.readthedocs.io/en/latest/integrations/forefrontai.html |
9b8271206a6c-0 | .md
.pdf
DeepInfra
Contents
Installation and Setup
Available Models
Wrappers
LLM
DeepInfra#
This page covers how to use the DeepInfra ecosystem within LangChain.
It is broken into two parts: installation and setup, and then references to specific DeepInfra wrappers.
Installation and Setup#
Get your DeepInfra api key ... | https://langchain.readthedocs.io/en/latest/integrations/deepinfra.html |
f8215f0035bb-0 | .md
.pdf
Zep
Contents
Installation and Setup
Retriever
Zep#
Zep - A long-term memory store for LLM applications.
Zep stores, summarizes, embeds, indexes, and enriches conversational AI chat histories, and exposes them via simple, low-latency APIs.
Long-term memory persistence, with access to historical messages irres... | https://langchain.readthedocs.io/en/latest/integrations/zep.html |
e5eac0d833af-0 | .md
.pdf
Redis
Contents
Installation and Setup
Wrappers
Cache
Standard Cache
Semantic Cache
VectorStore
Retriever
Memory
Vector Store Retriever Memory
Chat Message History Memory
Redis#
This page covers how to use the Redis ecosystem within LangChain.
It is broken into two parts: installation and setup, and then refe... | https://langchain.readthedocs.io/en/latest/integrations/redis.html |
7a38156386a8-0 | .md
.pdf
Beam
Contents
Installation and Setup
LLM
Example of the Beam app
Deploy the Beam app
Call the Beam app
Beam#
Beam makes it easy to run code on GPUs, deploy scalable web APIs,
schedule cron jobs, and run massively parallel workloads — without managing any infrastructure.
Installation and Setup#
Create an acco... | https://langchain.readthedocs.io/en/latest/integrations/beam.html |
ec9a01e3a967-0 | .md
.pdf
Elasticsearch
Contents
Installation and Setup
Retriever
Elasticsearch#
Elasticsearch is a distributed, RESTful search and analytics engine.
It provides a distributed, multi-tenant-capable full-text search engine with an HTTP web interface and schema-free
JSON documents.
Installation and Setup#
pip install el... | https://langchain.readthedocs.io/en/latest/integrations/elasticsearch.html |
4e3546ca5bb0-0 | .ipynb
.pdf
Rebuff
Contents
Installation and Setup
Example
Use in a chain
Rebuff#
Rebuff is a self-hardening prompt injection detector.
It is designed to protect AI applications from prompt injection (PI) attacks through a multi-stage defense.
Homepage
Playground
Docs
GitHub Repository
Installation and Setup#
# !pip3... | https://langchain.readthedocs.io/en/latest/integrations/rebuff.html |
66de1c5d99b3-0 | .md
.pdf
Wikipedia
Contents
Installation and Setup
Document Loader
Retriever
Wikipedia#
Wikipedia is a multilingual free online encyclopedia written and maintained by a community of volunteers, known as Wikipedians, through open collaboration and using a wiki-based editing system called MediaWiki. Wikipedia is the la... | https://langchain.readthedocs.io/en/latest/integrations/wikipedia.html |
b150644a3322-0 | .md
.pdf
MediaWikiDump
Contents
Installation and Setup
Document Loader
MediaWikiDump#
MediaWiki XML Dumps contain the content of a wiki
(wiki pages with all their revisions), without the site-related data. A XML dump does not create a full backup
of the wiki database, the dump does not contain user accounts, images, ... | https://langchain.readthedocs.io/en/latest/integrations/mediawikidump.html |
57c137cffb1f-0 | .md
.pdf
Milvus
Contents
Installation and Setup
Wrappers
VectorStore
Milvus#
This page covers how to use the Milvus ecosystem within LangChain.
It is broken into two parts: installation and setup, and then references to specific Milvus wrappers.
Installation and Setup#
Install the Python SDK with pip install pymilvus... | https://langchain.readthedocs.io/en/latest/integrations/milvus.html |
fed286aa1fb1-0 | .md
.pdf
Hazy Research
Contents
Installation and Setup
Wrappers
LLM
Hazy Research#
This page covers how to use the Hazy Research ecosystem within LangChain.
It is broken into two parts: installation and setup, and then references to specific Hazy Research wrappers.
Installation and Setup#
To use the manifest, install... | https://langchain.readthedocs.io/en/latest/integrations/hazy_research.html |
8e825e89ffd9-0 | .md
.pdf
Hacker News
Contents
Installation and Setup
Document Loader
Hacker News#
Hacker News (sometimes abbreviated as HN) is a social news
website focusing on computer science and entrepreneurship. It is run by the investment fund and startup
incubator Y Combinator. In general, content that can be submitted is defi... | https://langchain.readthedocs.io/en/latest/integrations/hacker_news.html |
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