1048 lines
38 KiB
Python
1048 lines
38 KiB
Python
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from __future__ import annotations
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import warnings
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from collections.abc import AsyncIterator, Callable, Iterator, Sequence
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from importlib import util
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from typing import Any, Literal, TypeAlias, cast, overload
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from langchain_core.language_models import (
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BaseChatModel,
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LanguageModelInput,
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SimpleChatModel,
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)
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from langchain_core.language_models.chat_models import (
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agenerate_from_stream,
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generate_from_stream,
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)
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from langchain_core.messages import AIMessage, AnyMessage
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from langchain_core.runnables import Runnable, RunnableConfig, ensure_config
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from langchain_core.runnables.schema import StreamEvent
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from langchain_core.tools import BaseTool
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from langchain_core.tracers import RunLog, RunLogPatch
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from pydantic import BaseModel
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from typing_extensions import override
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__all__ = [
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# For backwards compatibility
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"BaseChatModel",
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"SimpleChatModel",
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"agenerate_from_stream",
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"generate_from_stream",
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"init_chat_model",
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]
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@overload
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def init_chat_model(
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model: str,
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*,
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model_provider: str | None = None,
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configurable_fields: None = None,
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config_prefix: str | None = None,
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**kwargs: Any,
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) -> BaseChatModel: ...
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@overload
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def init_chat_model(
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model: None = None,
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*,
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model_provider: str | None = None,
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configurable_fields: None = None,
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config_prefix: str | None = None,
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**kwargs: Any,
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) -> _ConfigurableModel: ...
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@overload
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def init_chat_model(
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model: str | None = None,
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*,
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model_provider: str | None = None,
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configurable_fields: Literal["any"] | list[str] | tuple[str, ...] = ...,
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config_prefix: str | None = None,
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**kwargs: Any,
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) -> _ConfigurableModel: ...
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# FOR CONTRIBUTORS: If adding support for a new provider, please append the provider
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# name to the supported list in the docstring below. Do *not* change the order of the
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# existing providers.
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def init_chat_model(
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model: str | None = None,
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*,
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model_provider: str | None = None,
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configurable_fields: Literal["any"] | list[str] | tuple[str, ...] | None = None,
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config_prefix: str | None = None,
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**kwargs: Any,
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) -> BaseChatModel | _ConfigurableModel:
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"""Initialize a chat model from any supported provider using a unified interface.
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**Two main use cases:**
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1. **Fixed model** – specify the model upfront and get back a ready-to-use chat
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model.
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2. **Configurable model** – choose to specify parameters (including model name) at
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runtime via `config`. Makes it easy to switch between models/providers without
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changing your code
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!!! note
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Requires the integration package for the chosen model provider to be installed.
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See the `model_provider` parameter below for specific package names
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(e.g., `pip install langchain-openai`).
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Refer to the [provider integration's API reference](https://docs.langchain.com/oss/python/integrations/providers)
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for supported model parameters to use as `**kwargs`.
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Args:
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model: The name or ID of the model, e.g. `'o3-mini'`, `'claude-sonnet-4-5-20250929'`.
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You can also specify model and model provider in a single argument using
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`'{model_provider}:{model}'` format, e.g. `'openai:o1'`.
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Will attempt to infer `model_provider` from model if not specified.
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The following providers will be inferred based on these model prefixes:
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- `gpt-...` | `o1...` | `o3...` -> `openai`
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- `claude...` -> `anthropic`
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- `amazon...` -> `bedrock`
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- `gemini...` -> `google_vertexai`
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- `command...` -> `cohere`
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- `accounts/fireworks...` -> `fireworks`
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- `mistral...` -> `mistralai`
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- `deepseek...` -> `deepseek`
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- `grok...` -> `xai`
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- `sonar...` -> `perplexity`
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model_provider: The model provider if not specified as part of the model arg
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(see above).
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Supported `model_provider` values and the corresponding integration package
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are:
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- `openai` -> [`langchain-openai`](https://docs.langchain.com/oss/python/integrations/providers/openai)
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- `anthropic` -> [`langchain-anthropic`](https://docs.langchain.com/oss/python/integrations/providers/anthropic)
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- `azure_openai` -> [`langchain-openai`](https://docs.langchain.com/oss/python/integrations/providers/openai)
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- `azure_ai` -> [`langchain-azure-ai`](https://docs.langchain.com/oss/python/integrations/providers/microsoft)
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- `google_vertexai` -> [`langchain-google-vertexai`](https://docs.langchain.com/oss/python/integrations/providers/google)
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- `google_genai` -> [`langchain-google-genai`](https://docs.langchain.com/oss/python/integrations/providers/google)
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- `bedrock` -> [`langchain-aws`](https://docs.langchain.com/oss/python/integrations/providers/aws)
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- `bedrock_converse` -> [`langchain-aws`](https://docs.langchain.com/oss/python/integrations/providers/aws)
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- `cohere` -> [`langchain-cohere`](https://docs.langchain.com/oss/python/integrations/providers/cohere)
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- `fireworks` -> [`langchain-fireworks`](https://docs.langchain.com/oss/python/integrations/providers/fireworks)
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- `together` -> [`langchain-together`](https://docs.langchain.com/oss/python/integrations/providers/together)
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- `mistralai` -> [`langchain-mistralai`](https://docs.langchain.com/oss/python/integrations/providers/mistralai)
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- `huggingface` -> [`langchain-huggingface`](https://docs.langchain.com/oss/python/integrations/providers/huggingface)
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- `groq` -> [`langchain-groq`](https://docs.langchain.com/oss/python/integrations/providers/groq)
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- `ollama` -> [`langchain-ollama`](https://docs.langchain.com/oss/python/integrations/providers/ollama)
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- `google_anthropic_vertex` -> [`langchain-google-vertexai`](https://docs.langchain.com/oss/python/integrations/providers/google)
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- `deepseek` -> [`langchain-deepseek`](https://docs.langchain.com/oss/python/integrations/providers/deepseek)
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- `ibm` -> [`langchain-ibm`](https://docs.langchain.com/oss/python/integrations/providers/ibm)
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- `nvidia` -> [`langchain-nvidia-ai-endpoints`](https://docs.langchain.com/oss/python/integrations/providers/nvidia)
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- `xai` -> [`langchain-xai`](https://docs.langchain.com/oss/python/integrations/providers/xai)
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- `perplexity` -> [`langchain-perplexity`](https://docs.langchain.com/oss/python/integrations/providers/perplexity)
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configurable_fields: Which model parameters are configurable at runtime:
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- `None`: No configurable fields (i.e., a fixed model).
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- `'any'`: All fields are configurable. **See security note below.**
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- `list[str] | Tuple[str, ...]`: Specified fields are configurable.
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Fields are assumed to have `config_prefix` stripped if a `config_prefix` is
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specified.
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If `model` is specified, then defaults to `None`.
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If `model` is not specified, then defaults to `("model", "model_provider")`.
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!!! warning "Security note"
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Setting `configurable_fields="any"` means fields like `api_key`,
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`base_url`, etc., can be altered at runtime, potentially redirecting
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model requests to a different service/user.
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Make sure that if you're accepting untrusted configurations that you
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enumerate the `configurable_fields=(...)` explicitly.
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config_prefix: Optional prefix for configuration keys.
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Useful when you have multiple configurable models in the same application.
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If `'config_prefix'` is a non-empty string then `model` will be configurable
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at runtime via the `config["configurable"]["{config_prefix}_{param}"]` keys.
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See examples below.
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If `'config_prefix'` is an empty string then model will be configurable via
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`config["configurable"]["{param}"]`.
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**kwargs: Additional model-specific keyword args to pass to the underlying
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chat model's `__init__` method. Common parameters include:
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- `temperature`: Model temperature for controlling randomness.
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- `max_tokens`: Maximum number of output tokens.
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- `timeout`: Maximum time (in seconds) to wait for a response.
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- `max_retries`: Maximum number of retry attempts for failed requests.
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- `base_url`: Custom API endpoint URL.
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- `rate_limiter`: A
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[`BaseRateLimiter`][langchain_core.rate_limiters.BaseRateLimiter]
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instance to control request rate.
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Refer to the specific model provider's
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[integration reference](https://reference.langchain.com/python/integrations/)
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for all available parameters.
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Returns:
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A [`BaseChatModel`][langchain_core.language_models.BaseChatModel] corresponding
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to the `model_name` and `model_provider` specified if configurability is
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inferred to be `False`. If configurable, a chat model emulator that
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initializes the underlying model at runtime once a config is passed in.
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Raises:
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ValueError: If `model_provider` cannot be inferred or isn't supported.
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ImportError: If the model provider integration package is not installed.
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???+ example "Initialize a non-configurable model"
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```python
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# pip install langchain langchain-openai langchain-anthropic langchain-google-vertexai
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from langchain_classic.chat_models import init_chat_model
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o3_mini = init_chat_model("openai:o3-mini", temperature=0)
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claude_sonnet = init_chat_model("anthropic:claude-sonnet-4-5-20250929", temperature=0)
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gemini_2-5_flash = init_chat_model(
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"google_vertexai:gemini-2.5-flash", temperature=0
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)
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o3_mini.invoke("what's your name")
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claude_sonnet.invoke("what's your name")
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gemini_2-5_flash.invoke("what's your name")
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```
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??? example "Partially configurable model with no default"
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```python
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# pip install langchain langchain-openai langchain-anthropic
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from langchain_classic.chat_models import init_chat_model
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# (We don't need to specify configurable=True if a model isn't specified.)
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configurable_model = init_chat_model(temperature=0)
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configurable_model.invoke(
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"what's your name", config={"configurable": {"model": "gpt-4o"}}
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)
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# Use GPT-4o to generate the response
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configurable_model.invoke(
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"what's your name",
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config={"configurable": {"model": "claude-sonnet-4-5-20250929"}},
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)
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```
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??? example "Fully configurable model with a default"
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```python
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# pip install langchain langchain-openai langchain-anthropic
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from langchain_classic.chat_models import init_chat_model
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configurable_model_with_default = init_chat_model(
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"openai:gpt-4o",
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configurable_fields="any", # This allows us to configure other params like temperature, max_tokens, etc at runtime.
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config_prefix="foo",
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temperature=0,
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)
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configurable_model_with_default.invoke("what's your name")
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# GPT-4o response with temperature 0 (as set in default)
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configurable_model_with_default.invoke(
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"what's your name",
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config={
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"configurable": {
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"foo_model": "anthropic:claude-sonnet-4-5-20250929",
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"foo_temperature": 0.6,
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}
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},
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)
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# Override default to use Sonnet 4.5 with temperature 0.6 to generate response
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```
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??? example "Bind tools to a configurable model"
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You can call any chat model declarative methods on a configurable model in the
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same way that you would with a normal model:
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```python
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# pip install langchain langchain-openai langchain-anthropic
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from langchain_classic.chat_models import init_chat_model
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from pydantic import BaseModel, Field
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class GetWeather(BaseModel):
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'''Get the current weather in a given location'''
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location: str = Field(
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..., description="The city and state, e.g. San Francisco, CA"
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)
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class GetPopulation(BaseModel):
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'''Get the current population in a given location'''
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location: str = Field(
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..., description="The city and state, e.g. San Francisco, CA"
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)
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configurable_model = init_chat_model(
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"gpt-4o", configurable_fields=("model", "model_provider"), temperature=0
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)
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configurable_model_with_tools = configurable_model.bind_tools(
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[
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GetWeather,
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GetPopulation,
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]
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)
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configurable_model_with_tools.invoke(
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"Which city is hotter today and which is bigger: LA or NY?"
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)
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# Use GPT-4o
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configurable_model_with_tools.invoke(
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"Which city is hotter today and which is bigger: LA or NY?",
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config={"configurable": {"model": "claude-sonnet-4-5-20250929"}},
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)
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# Use Sonnet 4.5
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```
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!!! warning "Behavior changed in `langchain` 0.2.8"
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Support for `configurable_fields` and `config_prefix` added.
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!!! warning "Behavior changed in `langchain` 0.2.12"
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Support for Ollama via langchain-ollama package added
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(`langchain_ollama.ChatOllama`). Previously,
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the now-deprecated langchain-community version of Ollama was imported
|
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(`langchain_community.chat_models.ChatOllama`).
|
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|
|
Support for AWS Bedrock models via the Converse API added
|
|||
|
|
(`model_provider="bedrock_converse"`).
|
|||
|
|
|
|||
|
|
!!! warning "Behavior changed in `langchain` 0.3.5"
|
|||
|
|
|
|||
|
|
Out of beta.
|
|||
|
|
|
|||
|
|
!!! warning "Behavior changed in `langchain` 0.3.19"
|
|||
|
|
|
|||
|
|
Support for Deepseek, IBM, Nvidia, and xAI models added.
|
|||
|
|
|
|||
|
|
""" # noqa: E501
|
|||
|
|
if not model and not configurable_fields:
|
|||
|
|
configurable_fields = ("model", "model_provider")
|
|||
|
|
config_prefix = config_prefix or ""
|
|||
|
|
if config_prefix and not configurable_fields:
|
|||
|
|
warnings.warn(
|
|||
|
|
f"{config_prefix=} has been set but no fields are configurable. Set "
|
|||
|
|
f"`configurable_fields=(...)` to specify the model params that are "
|
|||
|
|
f"configurable.",
|
|||
|
|
stacklevel=2,
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
if not configurable_fields:
|
|||
|
|
return _init_chat_model_helper(
|
|||
|
|
cast("str", model),
|
|||
|
|
model_provider=model_provider,
|
|||
|
|
**kwargs,
|
|||
|
|
)
|
|||
|
|
if model:
|
|||
|
|
kwargs["model"] = model
|
|||
|
|
if model_provider:
|
|||
|
|
kwargs["model_provider"] = model_provider
|
|||
|
|
return _ConfigurableModel(
|
|||
|
|
default_config=kwargs,
|
|||
|
|
config_prefix=config_prefix,
|
|||
|
|
configurable_fields=configurable_fields,
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
|
|||
|
|
def _init_chat_model_helper(
|
|||
|
|
model: str,
|
|||
|
|
*,
|
|||
|
|
model_provider: str | None = None,
|
|||
|
|
**kwargs: Any,
|
|||
|
|
) -> BaseChatModel:
|
|||
|
|
model, model_provider = _parse_model(model, model_provider)
|
|||
|
|
if model_provider == "openai":
|
|||
|
|
_check_pkg("langchain_openai")
|
|||
|
|
from langchain_openai import ChatOpenAI
|
|||
|
|
|
|||
|
|
return ChatOpenAI(model=model, **kwargs)
|
|||
|
|
if model_provider == "anthropic":
|
|||
|
|
_check_pkg("langchain_anthropic")
|
|||
|
|
from langchain_anthropic import ChatAnthropic
|
|||
|
|
|
|||
|
|
return ChatAnthropic(model=model, **kwargs) # type: ignore[call-arg,unused-ignore]
|
|||
|
|
if model_provider == "azure_openai":
|
|||
|
|
_check_pkg("langchain_openai")
|
|||
|
|
from langchain_openai import AzureChatOpenAI
|
|||
|
|
|
|||
|
|
return AzureChatOpenAI(model=model, **kwargs)
|
|||
|
|
if model_provider == "azure_ai":
|
|||
|
|
_check_pkg("langchain_azure_ai")
|
|||
|
|
from langchain_azure_ai.chat_models import AzureAIChatCompletionsModel
|
|||
|
|
|
|||
|
|
return AzureAIChatCompletionsModel(model=model, **kwargs)
|
|||
|
|
if model_provider == "cohere":
|
|||
|
|
_check_pkg("langchain_cohere")
|
|||
|
|
from langchain_cohere import ChatCohere
|
|||
|
|
|
|||
|
|
return ChatCohere(model=model, **kwargs)
|
|||
|
|
if model_provider == "google_vertexai":
|
|||
|
|
_check_pkg("langchain_google_vertexai")
|
|||
|
|
from langchain_google_vertexai import ChatVertexAI
|
|||
|
|
|
|||
|
|
return ChatVertexAI(model=model, **kwargs)
|
|||
|
|
if model_provider == "google_genai":
|
|||
|
|
_check_pkg("langchain_google_genai")
|
|||
|
|
from langchain_google_genai import ChatGoogleGenerativeAI
|
|||
|
|
|
|||
|
|
return ChatGoogleGenerativeAI(model=model, **kwargs)
|
|||
|
|
if model_provider == "fireworks":
|
|||
|
|
_check_pkg("langchain_fireworks")
|
|||
|
|
from langchain_fireworks import ChatFireworks
|
|||
|
|
|
|||
|
|
return ChatFireworks(model=model, **kwargs)
|
|||
|
|
if model_provider == "ollama":
|
|||
|
|
try:
|
|||
|
|
_check_pkg("langchain_ollama")
|
|||
|
|
from langchain_ollama import ChatOllama
|
|||
|
|
except ImportError:
|
|||
|
|
# For backwards compatibility
|
|||
|
|
try:
|
|||
|
|
_check_pkg("langchain_community")
|
|||
|
|
from langchain_community.chat_models import ChatOllama
|
|||
|
|
except ImportError:
|
|||
|
|
# If both langchain-ollama and langchain-community aren't available,
|
|||
|
|
# raise an error related to langchain-ollama
|
|||
|
|
_check_pkg("langchain_ollama")
|
|||
|
|
|
|||
|
|
return ChatOllama(model=model, **kwargs)
|
|||
|
|
if model_provider == "together":
|
|||
|
|
_check_pkg("langchain_together")
|
|||
|
|
from langchain_together import ChatTogether
|
|||
|
|
|
|||
|
|
return ChatTogether(model=model, **kwargs)
|
|||
|
|
if model_provider == "mistralai":
|
|||
|
|
_check_pkg("langchain_mistralai")
|
|||
|
|
from langchain_mistralai import ChatMistralAI
|
|||
|
|
|
|||
|
|
return ChatMistralAI(model=model, **kwargs) # type: ignore[call-arg,unused-ignore]
|
|||
|
|
|
|||
|
|
if model_provider == "huggingface":
|
|||
|
|
_check_pkg("langchain_huggingface")
|
|||
|
|
from langchain_huggingface import ChatHuggingFace
|
|||
|
|
|
|||
|
|
return ChatHuggingFace.from_model_id(model_id=model, **kwargs)
|
|||
|
|
|
|||
|
|
if model_provider == "groq":
|
|||
|
|
_check_pkg("langchain_groq")
|
|||
|
|
from langchain_groq import ChatGroq
|
|||
|
|
|
|||
|
|
return ChatGroq(model=model, **kwargs)
|
|||
|
|
if model_provider == "bedrock":
|
|||
|
|
_check_pkg("langchain_aws")
|
|||
|
|
from langchain_aws import ChatBedrock
|
|||
|
|
|
|||
|
|
# TODO: update to use model= once ChatBedrock supports
|
|||
|
|
return ChatBedrock(model_id=model, **kwargs)
|
|||
|
|
if model_provider == "bedrock_converse":
|
|||
|
|
_check_pkg("langchain_aws")
|
|||
|
|
from langchain_aws import ChatBedrockConverse
|
|||
|
|
|
|||
|
|
return ChatBedrockConverse(model=model, **kwargs)
|
|||
|
|
if model_provider == "google_anthropic_vertex":
|
|||
|
|
_check_pkg("langchain_google_vertexai")
|
|||
|
|
from langchain_google_vertexai.model_garden import ChatAnthropicVertex
|
|||
|
|
|
|||
|
|
return ChatAnthropicVertex(model=model, **kwargs)
|
|||
|
|
if model_provider == "deepseek":
|
|||
|
|
_check_pkg("langchain_deepseek", pkg_kebab="langchain-deepseek")
|
|||
|
|
from langchain_deepseek import ChatDeepSeek
|
|||
|
|
|
|||
|
|
return ChatDeepSeek(model=model, **kwargs)
|
|||
|
|
if model_provider == "nvidia":
|
|||
|
|
_check_pkg("langchain_nvidia_ai_endpoints")
|
|||
|
|
from langchain_nvidia_ai_endpoints import ChatNVIDIA
|
|||
|
|
|
|||
|
|
return ChatNVIDIA(model=model, **kwargs)
|
|||
|
|
if model_provider == "ibm":
|
|||
|
|
_check_pkg("langchain_ibm")
|
|||
|
|
from langchain_ibm import ChatWatsonx
|
|||
|
|
|
|||
|
|
return ChatWatsonx(model_id=model, **kwargs)
|
|||
|
|
if model_provider == "xai":
|
|||
|
|
_check_pkg("langchain_xai")
|
|||
|
|
from langchain_xai import ChatXAI
|
|||
|
|
|
|||
|
|
return ChatXAI(model=model, **kwargs)
|
|||
|
|
if model_provider == "perplexity":
|
|||
|
|
_check_pkg("langchain_perplexity")
|
|||
|
|
from langchain_perplexity import ChatPerplexity
|
|||
|
|
|
|||
|
|
return ChatPerplexity(model=model, **kwargs)
|
|||
|
|
if model_provider == "upstage":
|
|||
|
|
_check_pkg("langchain_upstage")
|
|||
|
|
from langchain_upstage import ChatUpstage
|
|||
|
|
|
|||
|
|
return ChatUpstage(model=model, **kwargs)
|
|||
|
|
supported = ", ".join(_SUPPORTED_PROVIDERS)
|
|||
|
|
msg = (
|
|||
|
|
f"Unsupported {model_provider=}.\n\nSupported model providers are: {supported}"
|
|||
|
|
)
|
|||
|
|
raise ValueError(msg)
|
|||
|
|
|
|||
|
|
|
|||
|
|
_SUPPORTED_PROVIDERS = {
|
|||
|
|
"openai",
|
|||
|
|
"anthropic",
|
|||
|
|
"azure_openai",
|
|||
|
|
"azure_ai",
|
|||
|
|
"cohere",
|
|||
|
|
"google_vertexai",
|
|||
|
|
"google_genai",
|
|||
|
|
"fireworks",
|
|||
|
|
"ollama",
|
|||
|
|
"together",
|
|||
|
|
"mistralai",
|
|||
|
|
"huggingface",
|
|||
|
|
"groq",
|
|||
|
|
"bedrock",
|
|||
|
|
"bedrock_converse",
|
|||
|
|
"google_anthropic_vertex",
|
|||
|
|
"deepseek",
|
|||
|
|
"ibm",
|
|||
|
|
"xai",
|
|||
|
|
"perplexity",
|
|||
|
|
"upstage",
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
|
|||
|
|
def _attempt_infer_model_provider(model_name: str) -> str | None:
|
|||
|
|
"""Attempt to infer model provider from model name.
|
|||
|
|
|
|||
|
|
Args:
|
|||
|
|
model_name: The name of the model to infer provider for.
|
|||
|
|
|
|||
|
|
Returns:
|
|||
|
|
The inferred provider name, or `None` if no provider could be inferred.
|
|||
|
|
"""
|
|||
|
|
model_lower = model_name.lower()
|
|||
|
|
|
|||
|
|
# OpenAI models (including newer models and aliases)
|
|||
|
|
if any(
|
|||
|
|
model_lower.startswith(pre)
|
|||
|
|
for pre in (
|
|||
|
|
"gpt-",
|
|||
|
|
"o1",
|
|||
|
|
"o3",
|
|||
|
|
"chatgpt",
|
|||
|
|
"text-davinci",
|
|||
|
|
)
|
|||
|
|
):
|
|||
|
|
return "openai"
|
|||
|
|
|
|||
|
|
# Anthropic models
|
|||
|
|
if model_lower.startswith("claude"):
|
|||
|
|
return "anthropic"
|
|||
|
|
|
|||
|
|
# Cohere models
|
|||
|
|
if model_lower.startswith("command"):
|
|||
|
|
return "cohere"
|
|||
|
|
|
|||
|
|
# Fireworks models
|
|||
|
|
if model_name.startswith("accounts/fireworks"):
|
|||
|
|
return "fireworks"
|
|||
|
|
|
|||
|
|
# Google models
|
|||
|
|
if model_lower.startswith("gemini"):
|
|||
|
|
return "google_vertexai"
|
|||
|
|
|
|||
|
|
# AWS Bedrock models
|
|||
|
|
if model_name.startswith("amazon.") or model_lower.startswith(
|
|||
|
|
("anthropic.", "meta.")
|
|||
|
|
):
|
|||
|
|
return "bedrock"
|
|||
|
|
|
|||
|
|
# Mistral models
|
|||
|
|
if model_lower.startswith(("mistral", "mixtral")):
|
|||
|
|
return "mistralai"
|
|||
|
|
|
|||
|
|
# DeepSeek models
|
|||
|
|
if model_lower.startswith("deepseek"):
|
|||
|
|
return "deepseek"
|
|||
|
|
|
|||
|
|
# xAI models
|
|||
|
|
if model_lower.startswith("grok"):
|
|||
|
|
return "xai"
|
|||
|
|
|
|||
|
|
# Perplexity models
|
|||
|
|
if model_lower.startswith("sonar"):
|
|||
|
|
return "perplexity"
|
|||
|
|
|
|||
|
|
# Upstage models
|
|||
|
|
if model_lower.startswith("solar"):
|
|||
|
|
return "upstage"
|
|||
|
|
|
|||
|
|
return None
|
|||
|
|
|
|||
|
|
|
|||
|
|
def _parse_model(model: str, model_provider: str | None) -> tuple[str, str]:
|
|||
|
|
"""Parse model name and provider, inferring provider if necessary."""
|
|||
|
|
if not model_provider and ":" in model:
|
|||
|
|
prefix, suffix = model.split(":", 1)
|
|||
|
|
if prefix in _SUPPORTED_PROVIDERS:
|
|||
|
|
model_provider = prefix
|
|||
|
|
model = suffix
|
|||
|
|
else:
|
|||
|
|
inferred = _attempt_infer_model_provider(prefix)
|
|||
|
|
if inferred:
|
|||
|
|
model_provider = inferred
|
|||
|
|
model = suffix
|
|||
|
|
|
|||
|
|
model_provider = model_provider or _attempt_infer_model_provider(model)
|
|||
|
|
if not model_provider:
|
|||
|
|
supported_list = ", ".join(sorted(_SUPPORTED_PROVIDERS))
|
|||
|
|
msg = (
|
|||
|
|
f"Unable to infer model provider for {model=}. "
|
|||
|
|
f"Please specify 'model_provider' directly.\n\n"
|
|||
|
|
f"Supported providers: {supported_list}\n\n"
|
|||
|
|
f"For help with specific providers, see: "
|
|||
|
|
f"https://docs.langchain.com/oss/python/integrations/providers"
|
|||
|
|
)
|
|||
|
|
raise ValueError(msg)
|
|||
|
|
|
|||
|
|
# Normalize provider name
|
|||
|
|
model_provider = model_provider.replace("-", "_").lower()
|
|||
|
|
return model, model_provider
|
|||
|
|
|
|||
|
|
|
|||
|
|
def _check_pkg(pkg: str, *, pkg_kebab: str | None = None) -> None:
|
|||
|
|
if not util.find_spec(pkg):
|
|||
|
|
pkg_kebab = pkg_kebab if pkg_kebab is not None else pkg.replace("_", "-")
|
|||
|
|
msg = (
|
|||
|
|
f"Unable to import {pkg}. Please install with `pip install -U {pkg_kebab}`"
|
|||
|
|
)
|
|||
|
|
raise ImportError(msg)
|
|||
|
|
|
|||
|
|
|
|||
|
|
_DECLARATIVE_METHODS = ("bind_tools", "with_structured_output")
|
|||
|
|
|
|||
|
|
|
|||
|
|
class _ConfigurableModel(Runnable[LanguageModelInput, Any]):
|
|||
|
|
def __init__(
|
|||
|
|
self,
|
|||
|
|
*,
|
|||
|
|
default_config: dict | None = None,
|
|||
|
|
configurable_fields: Literal["any"] | list[str] | tuple[str, ...] = "any",
|
|||
|
|
config_prefix: str = "",
|
|||
|
|
queued_declarative_operations: Sequence[tuple[str, tuple, dict]] = (),
|
|||
|
|
) -> None:
|
|||
|
|
self._default_config: dict = default_config or {}
|
|||
|
|
self._configurable_fields: Literal["any"] | list[str] = (
|
|||
|
|
configurable_fields
|
|||
|
|
if configurable_fields == "any"
|
|||
|
|
else list(configurable_fields)
|
|||
|
|
)
|
|||
|
|
self._config_prefix = (
|
|||
|
|
config_prefix + "_"
|
|||
|
|
if config_prefix and not config_prefix.endswith("_")
|
|||
|
|
else config_prefix
|
|||
|
|
)
|
|||
|
|
self._queued_declarative_operations: list[tuple[str, tuple, dict]] = list(
|
|||
|
|
queued_declarative_operations,
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
def __getattr__(self, name: str) -> Any:
|
|||
|
|
if name in _DECLARATIVE_METHODS:
|
|||
|
|
# Declarative operations that cannot be applied until after an actual model
|
|||
|
|
# object is instantiated. So instead of returning the actual operation,
|
|||
|
|
# we record the operation and its arguments in a queue. This queue is
|
|||
|
|
# then applied in order whenever we actually instantiate the model (in
|
|||
|
|
# self._model()).
|
|||
|
|
def queue(*args: Any, **kwargs: Any) -> _ConfigurableModel:
|
|||
|
|
queued_declarative_operations = list(
|
|||
|
|
self._queued_declarative_operations,
|
|||
|
|
)
|
|||
|
|
queued_declarative_operations.append((name, args, kwargs))
|
|||
|
|
return _ConfigurableModel(
|
|||
|
|
default_config=dict(self._default_config),
|
|||
|
|
configurable_fields=list(self._configurable_fields)
|
|||
|
|
if isinstance(self._configurable_fields, list)
|
|||
|
|
else self._configurable_fields,
|
|||
|
|
config_prefix=self._config_prefix,
|
|||
|
|
queued_declarative_operations=queued_declarative_operations,
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
return queue
|
|||
|
|
if self._default_config and (model := self._model()) and hasattr(model, name):
|
|||
|
|
return getattr(model, name)
|
|||
|
|
msg = f"{name} is not a BaseChatModel attribute"
|
|||
|
|
if self._default_config:
|
|||
|
|
msg += " and is not implemented on the default model"
|
|||
|
|
msg += "."
|
|||
|
|
raise AttributeError(msg)
|
|||
|
|
|
|||
|
|
def _model(self, config: RunnableConfig | None = None) -> Runnable:
|
|||
|
|
params = {**self._default_config, **self._model_params(config)}
|
|||
|
|
model = _init_chat_model_helper(**params)
|
|||
|
|
for name, args, kwargs in self._queued_declarative_operations:
|
|||
|
|
model = getattr(model, name)(*args, **kwargs)
|
|||
|
|
return model
|
|||
|
|
|
|||
|
|
def _model_params(self, config: RunnableConfig | None) -> dict:
|
|||
|
|
config = ensure_config(config)
|
|||
|
|
model_params = {
|
|||
|
|
k.removeprefix(self._config_prefix): v
|
|||
|
|
for k, v in config.get("configurable", {}).items()
|
|||
|
|
if k.startswith(self._config_prefix)
|
|||
|
|
}
|
|||
|
|
if self._configurable_fields != "any":
|
|||
|
|
model_params = {
|
|||
|
|
k: v for k, v in model_params.items() if k in self._configurable_fields
|
|||
|
|
}
|
|||
|
|
return model_params
|
|||
|
|
|
|||
|
|
def with_config(
|
|||
|
|
self,
|
|||
|
|
config: RunnableConfig | None = None,
|
|||
|
|
**kwargs: Any,
|
|||
|
|
) -> _ConfigurableModel:
|
|||
|
|
"""Bind config to a `Runnable`, returning a new `Runnable`."""
|
|||
|
|
config = RunnableConfig(**(config or {}), **cast("RunnableConfig", kwargs))
|
|||
|
|
model_params = self._model_params(config)
|
|||
|
|
remaining_config = {k: v for k, v in config.items() if k != "configurable"}
|
|||
|
|
remaining_config["configurable"] = {
|
|||
|
|
k: v
|
|||
|
|
for k, v in config.get("configurable", {}).items()
|
|||
|
|
if k.removeprefix(self._config_prefix) not in model_params
|
|||
|
|
}
|
|||
|
|
queued_declarative_operations = list(self._queued_declarative_operations)
|
|||
|
|
if remaining_config:
|
|||
|
|
queued_declarative_operations.append(
|
|||
|
|
(
|
|||
|
|
"with_config",
|
|||
|
|
(),
|
|||
|
|
{"config": remaining_config},
|
|||
|
|
),
|
|||
|
|
)
|
|||
|
|
return _ConfigurableModel(
|
|||
|
|
default_config={**self._default_config, **model_params},
|
|||
|
|
configurable_fields=list(self._configurable_fields)
|
|||
|
|
if isinstance(self._configurable_fields, list)
|
|||
|
|
else self._configurable_fields,
|
|||
|
|
config_prefix=self._config_prefix,
|
|||
|
|
queued_declarative_operations=queued_declarative_operations,
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
@property
|
|||
|
|
@override
|
|||
|
|
def InputType(self) -> TypeAlias:
|
|||
|
|
"""Get the input type for this `Runnable`."""
|
|||
|
|
from langchain_core.prompt_values import (
|
|||
|
|
ChatPromptValueConcrete,
|
|||
|
|
StringPromptValue,
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
# This is a version of LanguageModelInput which replaces the abstract
|
|||
|
|
# base class BaseMessage with a union of its subclasses, which makes
|
|||
|
|
# for a much better schema.
|
|||
|
|
return str | StringPromptValue | ChatPromptValueConcrete | list[AnyMessage]
|
|||
|
|
|
|||
|
|
@override
|
|||
|
|
def invoke(
|
|||
|
|
self,
|
|||
|
|
input: LanguageModelInput,
|
|||
|
|
config: RunnableConfig | None = None,
|
|||
|
|
**kwargs: Any,
|
|||
|
|
) -> Any:
|
|||
|
|
return self._model(config).invoke(input, config=config, **kwargs)
|
|||
|
|
|
|||
|
|
@override
|
|||
|
|
async def ainvoke(
|
|||
|
|
self,
|
|||
|
|
input: LanguageModelInput,
|
|||
|
|
config: RunnableConfig | None = None,
|
|||
|
|
**kwargs: Any,
|
|||
|
|
) -> Any:
|
|||
|
|
return await self._model(config).ainvoke(input, config=config, **kwargs)
|
|||
|
|
|
|||
|
|
@override
|
|||
|
|
def stream(
|
|||
|
|
self,
|
|||
|
|
input: LanguageModelInput,
|
|||
|
|
config: RunnableConfig | None = None,
|
|||
|
|
**kwargs: Any | None,
|
|||
|
|
) -> Iterator[Any]:
|
|||
|
|
yield from self._model(config).stream(input, config=config, **kwargs)
|
|||
|
|
|
|||
|
|
@override
|
|||
|
|
async def astream(
|
|||
|
|
self,
|
|||
|
|
input: LanguageModelInput,
|
|||
|
|
config: RunnableConfig | None = None,
|
|||
|
|
**kwargs: Any | None,
|
|||
|
|
) -> AsyncIterator[Any]:
|
|||
|
|
async for x in self._model(config).astream(input, config=config, **kwargs):
|
|||
|
|
yield x
|
|||
|
|
|
|||
|
|
def batch(
|
|||
|
|
self,
|
|||
|
|
inputs: list[LanguageModelInput],
|
|||
|
|
config: RunnableConfig | list[RunnableConfig] | None = None,
|
|||
|
|
*,
|
|||
|
|
return_exceptions: bool = False,
|
|||
|
|
**kwargs: Any | None,
|
|||
|
|
) -> list[Any]:
|
|||
|
|
config = config or None
|
|||
|
|
# If <= 1 config use the underlying models batch implementation.
|
|||
|
|
if config is None or isinstance(config, dict) or len(config) <= 1:
|
|||
|
|
if isinstance(config, list):
|
|||
|
|
config = config[0]
|
|||
|
|
return self._model(config).batch(
|
|||
|
|
inputs,
|
|||
|
|
config=config,
|
|||
|
|
return_exceptions=return_exceptions,
|
|||
|
|
**kwargs,
|
|||
|
|
)
|
|||
|
|
# If multiple configs default to Runnable.batch which uses executor to invoke
|
|||
|
|
# in parallel.
|
|||
|
|
return super().batch(
|
|||
|
|
inputs,
|
|||
|
|
config=config,
|
|||
|
|
return_exceptions=return_exceptions,
|
|||
|
|
**kwargs,
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
async def abatch(
|
|||
|
|
self,
|
|||
|
|
inputs: list[LanguageModelInput],
|
|||
|
|
config: RunnableConfig | list[RunnableConfig] | None = None,
|
|||
|
|
*,
|
|||
|
|
return_exceptions: bool = False,
|
|||
|
|
**kwargs: Any | None,
|
|||
|
|
) -> list[Any]:
|
|||
|
|
config = config or None
|
|||
|
|
# If <= 1 config use the underlying models batch implementation.
|
|||
|
|
if config is None or isinstance(config, dict) or len(config) <= 1:
|
|||
|
|
if isinstance(config, list):
|
|||
|
|
config = config[0]
|
|||
|
|
return await self._model(config).abatch(
|
|||
|
|
inputs,
|
|||
|
|
config=config,
|
|||
|
|
return_exceptions=return_exceptions,
|
|||
|
|
**kwargs,
|
|||
|
|
)
|
|||
|
|
# If multiple configs default to Runnable.batch which uses executor to invoke
|
|||
|
|
# in parallel.
|
|||
|
|
return await super().abatch(
|
|||
|
|
inputs,
|
|||
|
|
config=config,
|
|||
|
|
return_exceptions=return_exceptions,
|
|||
|
|
**kwargs,
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
def batch_as_completed(
|
|||
|
|
self,
|
|||
|
|
inputs: Sequence[LanguageModelInput],
|
|||
|
|
config: RunnableConfig | Sequence[RunnableConfig] | None = None,
|
|||
|
|
*,
|
|||
|
|
return_exceptions: bool = False,
|
|||
|
|
**kwargs: Any,
|
|||
|
|
) -> Iterator[tuple[int, Any | Exception]]:
|
|||
|
|
config = config or None
|
|||
|
|
# If <= 1 config use the underlying models batch implementation.
|
|||
|
|
if config is None or isinstance(config, dict) or len(config) <= 1:
|
|||
|
|
if isinstance(config, list):
|
|||
|
|
config = config[0]
|
|||
|
|
yield from self._model(cast("RunnableConfig", config)).batch_as_completed( # type: ignore[call-overload]
|
|||
|
|
inputs,
|
|||
|
|
config=config,
|
|||
|
|
return_exceptions=return_exceptions,
|
|||
|
|
**kwargs,
|
|||
|
|
)
|
|||
|
|
# If multiple configs default to Runnable.batch which uses executor to invoke
|
|||
|
|
# in parallel.
|
|||
|
|
else:
|
|||
|
|
yield from super().batch_as_completed( # type: ignore[call-overload]
|
|||
|
|
inputs,
|
|||
|
|
config=config,
|
|||
|
|
return_exceptions=return_exceptions,
|
|||
|
|
**kwargs,
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
async def abatch_as_completed(
|
|||
|
|
self,
|
|||
|
|
inputs: Sequence[LanguageModelInput],
|
|||
|
|
config: RunnableConfig | Sequence[RunnableConfig] | None = None,
|
|||
|
|
*,
|
|||
|
|
return_exceptions: bool = False,
|
|||
|
|
**kwargs: Any,
|
|||
|
|
) -> AsyncIterator[tuple[int, Any]]:
|
|||
|
|
config = config or None
|
|||
|
|
# If <= 1 config use the underlying models batch implementation.
|
|||
|
|
if config is None or isinstance(config, dict) or len(config) <= 1:
|
|||
|
|
if isinstance(config, list):
|
|||
|
|
config = config[0]
|
|||
|
|
async for x in self._model(
|
|||
|
|
cast("RunnableConfig", config),
|
|||
|
|
).abatch_as_completed( # type: ignore[call-overload]
|
|||
|
|
inputs,
|
|||
|
|
config=config,
|
|||
|
|
return_exceptions=return_exceptions,
|
|||
|
|
**kwargs,
|
|||
|
|
):
|
|||
|
|
yield x
|
|||
|
|
# If multiple configs default to Runnable.batch which uses executor to invoke
|
|||
|
|
# in parallel.
|
|||
|
|
else:
|
|||
|
|
async for x in super().abatch_as_completed( # type: ignore[call-overload]
|
|||
|
|
inputs,
|
|||
|
|
config=config,
|
|||
|
|
return_exceptions=return_exceptions,
|
|||
|
|
**kwargs,
|
|||
|
|
):
|
|||
|
|
yield x
|
|||
|
|
|
|||
|
|
@override
|
|||
|
|
def transform(
|
|||
|
|
self,
|
|||
|
|
input: Iterator[LanguageModelInput],
|
|||
|
|
config: RunnableConfig | None = None,
|
|||
|
|
**kwargs: Any | None,
|
|||
|
|
) -> Iterator[Any]:
|
|||
|
|
yield from self._model(config).transform(input, config=config, **kwargs)
|
|||
|
|
|
|||
|
|
@override
|
|||
|
|
async def atransform(
|
|||
|
|
self,
|
|||
|
|
input: AsyncIterator[LanguageModelInput],
|
|||
|
|
config: RunnableConfig | None = None,
|
|||
|
|
**kwargs: Any | None,
|
|||
|
|
) -> AsyncIterator[Any]:
|
|||
|
|
async for x in self._model(config).atransform(input, config=config, **kwargs):
|
|||
|
|
yield x
|
|||
|
|
|
|||
|
|
@overload
|
|||
|
|
def astream_log(
|
|||
|
|
self,
|
|||
|
|
input: Any,
|
|||
|
|
config: RunnableConfig | None = None,
|
|||
|
|
*,
|
|||
|
|
diff: Literal[True] = True,
|
|||
|
|
with_streamed_output_list: bool = True,
|
|||
|
|
include_names: Sequence[str] | None = None,
|
|||
|
|
include_types: Sequence[str] | None = None,
|
|||
|
|
include_tags: Sequence[str] | None = None,
|
|||
|
|
exclude_names: Sequence[str] | None = None,
|
|||
|
|
exclude_types: Sequence[str] | None = None,
|
|||
|
|
exclude_tags: Sequence[str] | None = None,
|
|||
|
|
**kwargs: Any,
|
|||
|
|
) -> AsyncIterator[RunLogPatch]: ...
|
|||
|
|
|
|||
|
|
@overload
|
|||
|
|
def astream_log(
|
|||
|
|
self,
|
|||
|
|
input: Any,
|
|||
|
|
config: RunnableConfig | None = None,
|
|||
|
|
*,
|
|||
|
|
diff: Literal[False],
|
|||
|
|
with_streamed_output_list: bool = True,
|
|||
|
|
include_names: Sequence[str] | None = None,
|
|||
|
|
include_types: Sequence[str] | None = None,
|
|||
|
|
include_tags: Sequence[str] | None = None,
|
|||
|
|
exclude_names: Sequence[str] | None = None,
|
|||
|
|
exclude_types: Sequence[str] | None = None,
|
|||
|
|
exclude_tags: Sequence[str] | None = None,
|
|||
|
|
**kwargs: Any,
|
|||
|
|
) -> AsyncIterator[RunLog]: ...
|
|||
|
|
|
|||
|
|
@override
|
|||
|
|
async def astream_log(
|
|||
|
|
self,
|
|||
|
|
input: Any,
|
|||
|
|
config: RunnableConfig | None = None,
|
|||
|
|
*,
|
|||
|
|
diff: bool = True,
|
|||
|
|
with_streamed_output_list: bool = True,
|
|||
|
|
include_names: Sequence[str] | None = None,
|
|||
|
|
include_types: Sequence[str] | None = None,
|
|||
|
|
include_tags: Sequence[str] | None = None,
|
|||
|
|
exclude_names: Sequence[str] | None = None,
|
|||
|
|
exclude_types: Sequence[str] | None = None,
|
|||
|
|
exclude_tags: Sequence[str] | None = None,
|
|||
|
|
**kwargs: Any,
|
|||
|
|
) -> AsyncIterator[RunLogPatch] | AsyncIterator[RunLog]:
|
|||
|
|
async for x in self._model(config).astream_log( # type: ignore[call-overload, misc]
|
|||
|
|
input,
|
|||
|
|
config=config,
|
|||
|
|
diff=diff,
|
|||
|
|
with_streamed_output_list=with_streamed_output_list,
|
|||
|
|
include_names=include_names,
|
|||
|
|
include_types=include_types,
|
|||
|
|
include_tags=include_tags,
|
|||
|
|
exclude_tags=exclude_tags,
|
|||
|
|
exclude_types=exclude_types,
|
|||
|
|
exclude_names=exclude_names,
|
|||
|
|
**kwargs,
|
|||
|
|
):
|
|||
|
|
yield x
|
|||
|
|
|
|||
|
|
@override
|
|||
|
|
async def astream_events(
|
|||
|
|
self,
|
|||
|
|
input: Any,
|
|||
|
|
config: RunnableConfig | None = None,
|
|||
|
|
*,
|
|||
|
|
version: Literal["v1", "v2"] = "v2",
|
|||
|
|
include_names: Sequence[str] | None = None,
|
|||
|
|
include_types: Sequence[str] | None = None,
|
|||
|
|
include_tags: Sequence[str] | None = None,
|
|||
|
|
exclude_names: Sequence[str] | None = None,
|
|||
|
|
exclude_types: Sequence[str] | None = None,
|
|||
|
|
exclude_tags: Sequence[str] | None = None,
|
|||
|
|
**kwargs: Any,
|
|||
|
|
) -> AsyncIterator[StreamEvent]:
|
|||
|
|
async for x in self._model(config).astream_events(
|
|||
|
|
input,
|
|||
|
|
config=config,
|
|||
|
|
version=version,
|
|||
|
|
include_names=include_names,
|
|||
|
|
include_types=include_types,
|
|||
|
|
include_tags=include_tags,
|
|||
|
|
exclude_tags=exclude_tags,
|
|||
|
|
exclude_types=exclude_types,
|
|||
|
|
exclude_names=exclude_names,
|
|||
|
|
**kwargs,
|
|||
|
|
):
|
|||
|
|
yield x
|
|||
|
|
|
|||
|
|
# Explicitly added to satisfy downstream linters.
|
|||
|
|
def bind_tools(
|
|||
|
|
self,
|
|||
|
|
tools: Sequence[dict[str, Any] | type[BaseModel] | Callable | BaseTool],
|
|||
|
|
**kwargs: Any,
|
|||
|
|
) -> Runnable[LanguageModelInput, AIMessage]:
|
|||
|
|
return self.__getattr__("bind_tools")(tools, **kwargs)
|
|||
|
|
|
|||
|
|
# Explicitly added to satisfy downstream linters.
|
|||
|
|
def with_structured_output(
|
|||
|
|
self,
|
|||
|
|
schema: dict | type[BaseModel],
|
|||
|
|
**kwargs: Any,
|
|||
|
|
) -> Runnable[LanguageModelInput, dict | BaseModel]:
|
|||
|
|
return self.__getattr__("with_structured_output")(schema, **kwargs)
|