View Source API Reference LangChain v0.3.0-rc.2
Modules
Defines the structure of callbacks and provides utilities for executing them.
Defines the callbacks fired by an LLMChain and LLM module.
Defines an LLMChain for performing data extraction from a body of text.
Run a router based on a user's initial prompt to determine what category best matches from the given options. If there is no good match, the value "DEFAULT" is returned.
When an AI conversation has many back-and-forth messages (from user to assistant to user to assistant, etc.), the number of messages and the total token count can be large. Large token counts present the following problems
A convenience chain for turning a user's prompt text into a summarized title for the anticipated conversation.
Module for interacting with Anthropic models.
Represents a chat model hosted by Bumblebee and accessed through an
Nx.Serving
.
Parses and validates inputs for making a request for the Google AI Chat API.
Represents the Ollama AI Chat model
Represents the OpenAI ChatModel.
Parses and validates inputs for making a request for the Google AI Chat API.
Utility that handles interaction with the application's configuration.
Defines a "function" that can be provided to an LLM for the LLM to optionally execute and pass argument data to.
Define a function parameter as a struct. Used to generate the expected
JSONSchema data for describing one or more arguments being passed to a
LangChain.Function
.
A module providing Internationalization with a gettext-based API.
Functions for working with LangChain.GeneratedImage
files.
Represents a generated image where we have either the base64 encoded contents or a temporary URL to it.
Represents the OpenAI Images API endpoint for working with DALL-E-2 and DALL-E-3.
Exception used for raising LangChain specific errors.
Models a complete Message
for a chat LLM.
Models a ContentPart
. Some LLMs support combining text, images, and possibly
other content as part of a single user message. A ContentPart
represents a
block, or part, of a message's content that is all of one type.
Represents an LLM's request to use tool. It specifies the tool to execute and may provide arguments for the tool to use.
Represents a the result of running a requested tool. The LLM's requests a tool
use through a ToolCall
. A ToolResult
returns the answer or result from the
application back to the AI.
Models a "delta" message from a chat LLM. A delta is a small chunk, or piece of a much larger complete message. A series of deltas are used to construct the complete message.
A built-in Message processor that processes a received Message into the
provided Ecto.Changeset
.
A built-in Message processor that processes a received Message for JSON contents.
Enables defining a prompt, optionally as a template, but delaying the final building of it until a later time when input values are substituted in.
Defines a route or direction a prompting interaction with an LLM can take.
Contains token usage information returned from an LLM.
Defines a Calculator tool for performing basic math calculations.
Collection of helpful utilities mostly for internal use.
Decodes AWS messages in the application/vnd.amazon.eventstream content-type. Ignores the headers because on Bedrock it's the same content type, event type & message type headers in every message.
Configuration for AWS Bedrock.
Module to help when working with the results of a chain.
Functions for converting messages into the various commonly used chat template formats.