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langchain/docs/reference/integrations.md
Harrison Chase 985496f4be
Docs refactor (#480) 
Big docs refactor! Motivation is to make it easier for people to find
resources they are looking for. To accomplish this, there are now three
main sections:

- Getting Started: steps for getting started, walking through most core
functionality
- Modules: these are different modules of functionality that langchain
provides. Each part here has a "getting started", "how to", "key
concepts" and "reference" section (except in a few select cases where it
didnt easily fit).
- Use Cases: this is to separate use cases (like summarization, question
answering, evaluation, etc) from the modules, and provide a different
entry point to the code base.

There is also a full reference section, as well as extra resources
(glossary, gallery, etc)

Co-authored-by: Shreya Rajpal <ShreyaR@users.noreply.github.com>
2023-01-02 08:24:09 -08:00

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Integrations

Besides the installation of this python package, you will also need to install packages and set environment variables depending on which chains you want to use.

Note: the reason these packages are not included in the dependencies by default is that as we imagine scaling this package, we do not want to force dependencies that are not needed.

The following use cases require specific installs and api keys:

  • OpenAI:
    • Install requirements with pip install openai
    • Get an OpenAI api key and either set it as an environment variable (OPENAI_API_KEY) or pass it to the LLM constructor as openai_api_key.
  • Cohere:
    • Install requirements with pip install cohere
    • Get a Cohere api key and either set it as an environment variable (COHERE_API_KEY) or pass it to the LLM constructor as cohere_api_key.
  • Hugging Face Hub
    • Install requirements with pip install huggingface_hub
    • Get a Hugging Face Hub api token and either set it as an environment variable (HUGGINGFACEHUB_API_TOKEN) or pass it to the LLM constructor as huggingfacehub_api_token.
  • SerpAPI:
    • Install requirements with pip install google-search-results
    • Get a SerpAPI api key and either set it as an environment variable (SERPAPI_API_KEY) or pass it to the LLM constructor as serpapi_api_key.
  • GoogleSearchAPI:
    • Install requirements with pip install google-api-python-client
    • Get a Google api key and either set it as an environment variable (GOOGLE_API_KEY) or pass it to the LLM constructor as google_api_key. You will also need to set the GOOGLE_CSE_ID environment variable to your custom search engine id. You can pass it to the LLM constructor as google_cse_id as well.
  • NatBot:
    • Install requirements with pip install playwright
  • Wikipedia:
    • Install requirements with pip install wikipedia
  • Elasticsearch:
    • Install requirements with pip install elasticsearch
    • Set up Elasticsearch backend. If you want to do locally, this is a good guide.
  • FAISS:
    • Install requirements with pip install faiss for Python 3.7 and pip install faiss-cpu for Python 3.10+.
  • Manifest:
    • Install requirements with pip install manifest-ml (Note: this is only available in Python 3.8+ currently).

If you are using the NLTKTextSplitter or the SpacyTextSplitter, you will also need to install the appropriate models. For example, if you want to use the SpacyTextSplitter, you will need to install the en_core_web_sm model with python -m spacy download en_core_web_sm. Similarly, if you want to use the NLTKTextSplitter, you will need to install the punkt model with python -m nltk.downloader punkt.