Parses and validates inputs for making a request for the Google AI Chat API.
Converts response into more specialized LangChain data structures.
NOTE: The GoogleAI service is unique in how it reports TokenUsage information. So far, it's the only API that returns TokenUsage for each returned delta, where the generated token count is incremented with one. Other services return the total TokenUsage data at the end. This Chat model fires the callback each time it is received.
Google Search Integration
Starting with Gemini 2.0, this module supports Google Search as a native tool, allowing the model to automatically search the web for recent information to ground its responses and improve factuality. Check out the Google AI Documentation for more information.
Example Usage:
alias LangChain.Chains.LLMChain
alias LangChain.Message
alias LangChain.NativeTool
model = ChatGoogleAI.new!(%{temperature: 0, stream: false, model: "gemini-2.0-flash"})
{:ok, updated_chain} =
%{llm: model, verbose: false, stream: false}
|> LLMChain.new!()
|> LLMChain.add_message(
Message.new_user!("What is the current Google stock price?")
)
|> LLMChain.add_tools(NativeTool.new!(%{name: "google_search", configuration: %{}}))
|> LLMChain.run()The above call will return the current Google stock price.
When google_search is used, the model will also return grounding information in the metadata attribute of the assistant message.
Tool Schemas
Google's function declarations accept a
select subset of an OpenAPI 3.0
schema object rather than full JSON Schema. Keywords outside that subset are
rejected by the API with Unknown name "<keyword>" ... Cannot find field,
which fails the entire request rather than the single tool. Tool parameters
and the JSON response schema are therefore passed through
LangChain.Utils.GoogleSchema.sanitize/1 before being sent.
One removal changes what the API enforces: additionalProperties is not
supported, so an object schema that sets additionalProperties: false is
still sent, but without it. Gemini does not reject unexpected properties, and
a tool call's arguments may contain fields the schema did not declare. Where
that matters, validate the arguments inside the tool's own function. See
LangChain.Utils.GoogleSchema for the full list of removed keywords.
Summary
Functions
Calls the Google AI API passing the ChatGoogleAI struct with configuration, plus either a simple message or the list of messages to act as the prompt.
Return the content parts for the message.
Setup a ChatGoogleAI client configuration.
Setup a ChatGoogleAI client configuration and return it or raise an error if invalid.
Restores the model from the config.
Determine if an error should be retried. If true, a fallback LLM may be
used. If false, the error is understood to be more fundamental with the
request rather than a service issue and it should not be retried or fallback
to another service.
Generate a config map that can later restore the model's configuration.
Types
@type t() :: %LangChain.ChatModels.ChatGoogleAI{ api_key: term(), api_version: term(), callbacks: term(), endpoint: term(), json_response: term(), json_schema: term(), model: term(), receive_timeout: term(), req_config: term(), safety_settings: term(), stream: term(), temperature: term(), thinking_config: term(), top_k: term(), top_p: term(), verbose_api: term() }
Functions
Calls the Google AI API passing the ChatGoogleAI struct with configuration, plus either a simple message or the list of messages to act as the prompt.
Optionally pass in a list of tools available to the LLM for requesting execution in response.
Optionally pass in a callback function that can be executed as data is received from the API.
NOTE: This function can be used directly, but the primary interface
should be through LangChain.Chains.LLMChain. The ChatGoogleAI module is more focused on
translating the LangChain data structures to and from the Google AI API.
Another benefit of using LangChain.Chains.LLMChain is that it combines the
storage of messages, adding tools, adding custom context that should be
passed to tools, and automatically applying LangChain.MessageDelta
structs as they are are received, then converting those to the full
LangChain.Message once fully complete.
@spec get_message_contents(LangChain.MessageDelta.t() | LangChain.Message.t()) :: [%{required(String.t()) => any()}] | nil
Return the content parts for the message.
@spec new(attrs :: map()) :: {:ok, t()} | {:error, Ecto.Changeset.t()}
Setup a ChatGoogleAI client configuration.
Setup a ChatGoogleAI client configuration and return it or raise an error if invalid.
Restores the model from the config.
@spec retry_on_fallback?(LangChain.LangChainError.t()) :: boolean()
Determine if an error should be retried. If true, a fallback LLM may be
used. If false, the error is understood to be more fundamental with the
request rather than a service issue and it should not be retried or fallback
to another service.
Generate a config map that can later restore the model's configuration.