LangChain

LangChain is a framework for developing applications powered by language models. We believe that the most powerful and differentiated applications will not only call out to a language model via an API, but will also: Be data-aware: connect a language model to other sources of data; Be agentic: allow a language model to interact with its environment. https://langchain.com/ cf LLM

Python and Typescripts varieties

Getting Started — 🦜🔗 LangChain 0.0.128

Agents use an LLM to determine which actions to take and in what order. An action can either be using a tool and observing its output, or returning to the user.

In order to load agents, you should understand the following concepts:

• Tool: A function that performs a specific duty. This can be things like: Google Search, Database lookup, Python REPL
• LLM: The language model powering the agent.
• Agent: The agent to use. This should be a string that references a support agent class.
• Agents: For a list of supported agents and their specifications, see here.

Tools — 🦜🔗 LangChain 0.0.129

examples of tools and how to use them: Apify; Bash; Bing Search; ChatGPT Plugins; Google Search; Google Serper API; Human as a tool; IFTTT WebHooks; OpenWeatherMap API; Python REPL; Requests; Search Tools; SearxNG Search API; SerpAPI; Wikipedia API; Wolfram Alpha; Zapier Natural Language Actions API

Example with SimpleSequentialChain

Agents — 🦜🔗 LangChain 0.0.129

We also have documentation for an in-depth dive into each agent type. Conversation Agent (for Chat Models); Conversation Agent; MRKL; MRKL Chat; ReAct; Self Ask With Search