View Source LangChain.ChatModels.ChatOllamaAI (LangChain v0.3.0-rc.0)

Parses and validates inputs for making a requests from the Ollama Chat API.

Converts responses into more specialized LangChain data structures.

The module's functionalities include:

Initializing a new ChatOllamaAI struct with defaults or specific attributes.
Validating and casting input data to fit the expected schema.
Preparing and sending requests to the Ollama AI service API.
Managing both streaming and non-streaming API responses.
Processing API responses to convert them into suitable message formats.

The ChatOllamaAI struct has fields to configure the AI, including but not limited to:

endpoint: URL of the Ollama AI service.
model: The AI model used, e.g., "llama2:latest".
receive_timeout: Max wait time for AI service responses.
temperature: Influences the AI's response creativity.

For detailed info on on all other parameters see documentation here: https://github.com/jmorganca/ollama/blob/main/docs/modelfile.md#valid-parameters-and-values

This module is for use within LangChain and follows the ChatModel behavior, outlining callbacks AI chat models must implement.

Usage examples and more details are in the LangChain documentation or the module's function docs.

Summary

Functions

call(ollama_ai, prompt, functions \\ [])

Calls the Ollama Chat Completion API struct with configuration, plus either a simple message or the list of messages to act as the prompt.

do_process_response(model, map)

for_api(model, messages, functions)

Return the params formatted for an API request.

new(attrs \\ %{})

Creates a new ChatOllamaAI struct with the given attributes.

new!(attrs \\ %{})

Creates a new ChatOllamaAI struct with the given attributes. Will raise an error if the changeset is invalid.

restore_from_map(data)

Restores the model from the config.

serialize_config(model)

Generate a config map that can later restore the model's configuration.

Types

t()

@type t() :: %LangChain.ChatModels.ChatOllamaAI{
  callbacks: term(),
  endpoint: term(),
  mirostat: term(),
  mirostat_eta: term(),
  mirostat_tau: term(),
  model: term(),
  num_ctx: term(),
  num_gpu: term(),
  num_gqa: term(),
  num_predict: term(),
  num_thread: term(),
  receive_timeout: term(),
  repeat_last_n: term(),
  repeat_penalty: term(),
  seed: term(),
  stop: term(),
  stream: term(),
  temperature: term(),
  tfs_z: term(),
  top_k: term(),
  top_p: term()
}

Functions

call(ollama_ai, prompt, functions \\ [])

Calls the Ollama Chat Completion API struct with configuration, plus either a simple message or the list of messages to act as the prompt.

NOTE: This API as of right now does not support functions. More information here: https://github.com/jmorganca/ollama/issues/1729

NOTE: This function can be used directly, but the primary interface should be through LangChain.Chains.LLMChain. The ChatOllamaAI module is more focused on translating the LangChain data structures to and from the Ollama API.

Another benefit of using LangChain.Chains.LLMChain is that it combines the storage of messages, adding functions, adding custom context that should be passed to functions, and automatically applying LangChain.MessageDelta structs as they are are received, then converting those to the full LangChain.Message once fully complete.

do_process_response(model, map)

for_api(model, messages, functions)

Return the params formatted for an API request.

new(attrs \\ %{})

@spec new(attrs :: map()) :: {:ok, t()} | {:error, Ecto.Changeset.t()}

Creates a new ChatOllamaAI struct with the given attributes.

new!(attrs \\ %{})

@spec new!(attrs :: map()) :: t() | no_return()

Creates a new ChatOllamaAI struct with the given attributes. Will raise an error if the changeset is invalid.

restore_from_map(data)

Restores the model from the config.

serialize_config(model)

@spec serialize_config(t()) :: %{required(String.t()) => any()}

Generate a config map that can later restore the model's configuration.