mirror of
https://github.com/hwchase17/langchain.git
synced 2026-06-09 18:50:33 +00:00
1560 lines
59 KiB
Python
1560 lines
59 KiB
Python
"""OpenRouter chat models."""
|
|
|
|
from __future__ import annotations
|
|
|
|
import json
|
|
import warnings
|
|
from collections.abc import AsyncIterator, Callable, Iterator, Mapping, Sequence
|
|
from operator import itemgetter
|
|
from typing import Any, Literal, cast
|
|
|
|
from langchain_core.callbacks import (
|
|
AsyncCallbackManagerForLLMRun,
|
|
CallbackManagerForLLMRun,
|
|
)
|
|
from langchain_core.language_models import (
|
|
LanguageModelInput,
|
|
ModelProfile,
|
|
ModelProfileRegistry,
|
|
)
|
|
from langchain_core.language_models.chat_models import (
|
|
BaseChatModel,
|
|
LangSmithParams,
|
|
agenerate_from_stream,
|
|
generate_from_stream,
|
|
)
|
|
from langchain_core.messages import (
|
|
AIMessage,
|
|
AIMessageChunk,
|
|
BaseMessage,
|
|
BaseMessageChunk,
|
|
ChatMessage,
|
|
ChatMessageChunk,
|
|
HumanMessage,
|
|
HumanMessageChunk,
|
|
InvalidToolCall,
|
|
SystemMessage,
|
|
SystemMessageChunk,
|
|
ToolCall,
|
|
ToolMessage,
|
|
ToolMessageChunk,
|
|
is_data_content_block,
|
|
)
|
|
from langchain_core.messages.ai import (
|
|
InputTokenDetails,
|
|
OutputTokenDetails,
|
|
UsageMetadata,
|
|
)
|
|
from langchain_core.messages.block_translators.openai import (
|
|
convert_to_openai_data_block,
|
|
)
|
|
from langchain_core.messages.tool import tool_call_chunk
|
|
from langchain_core.output_parsers import JsonOutputParser, PydanticOutputParser
|
|
from langchain_core.output_parsers.base import OutputParserLike
|
|
from langchain_core.output_parsers.openai_tools import (
|
|
JsonOutputKeyToolsParser,
|
|
PydanticToolsParser,
|
|
make_invalid_tool_call,
|
|
parse_tool_call,
|
|
)
|
|
from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
|
|
from langchain_core.runnables import Runnable, RunnableMap, RunnablePassthrough
|
|
from langchain_core.tools import BaseTool
|
|
from langchain_core.utils import from_env, get_pydantic_field_names, secret_from_env
|
|
from langchain_core.utils.function_calling import (
|
|
convert_to_json_schema,
|
|
convert_to_openai_tool,
|
|
)
|
|
from langchain_core.utils.pydantic import is_basemodel_subclass
|
|
from pydantic import BaseModel, ConfigDict, Field, SecretStr, model_validator
|
|
from typing_extensions import Self
|
|
|
|
from langchain_openrouter.data._profiles import _PROFILES
|
|
|
|
_MODEL_PROFILES = cast("ModelProfileRegistry", _PROFILES)
|
|
|
|
# LangChain-internal kwargs that must not be forwarded to the SDK.
|
|
_INTERNAL_KWARGS = frozenset({"ls_structured_output_format"})
|
|
|
|
|
|
def _get_default_model_profile(model_name: str) -> ModelProfile:
|
|
default = _MODEL_PROFILES.get(model_name) or {}
|
|
return default.copy()
|
|
|
|
|
|
class ChatOpenRouter(BaseChatModel):
|
|
"""OpenRouter chat model integration.
|
|
|
|
OpenRouter is a unified API that provides access to hundreds of models from
|
|
multiple providers (OpenAI, Anthropic, Google, Meta, etc.).
|
|
|
|
???+ info "Setup"
|
|
|
|
Install `langchain-openrouter` and set environment variable
|
|
`OPENROUTER_API_KEY`.
|
|
|
|
```bash
|
|
pip install -U langchain-openrouter
|
|
```
|
|
|
|
```bash
|
|
export OPENROUTER_API_KEY="your-api-key"
|
|
```
|
|
|
|
??? info "Key init args — completion params"
|
|
|
|
| Param | Type | Description |
|
|
| ----- | ---- | ----------- |
|
|
| `model` | `str` | Model name |
|
|
| `temperature` | `float | None` | Sampling temperature. |
|
|
| `max_tokens` | `int | None` | Max tokens to generate. |
|
|
|
|
??? info "Key init args — client params"
|
|
|
|
| Param | Type | Description |
|
|
| ----- | ---- | ----------- |
|
|
| `api_key` | `str | None` | OpenRouter API key. |
|
|
| `base_url` | `str | None` | Base URL for API requests. |
|
|
| `timeout` | `int | None` | Timeout in milliseconds. |
|
|
| `app_url` | `str | None` | App URL for attribution. |
|
|
| `app_title` | `str | None` | App title for attribution. |
|
|
| `app_categories` | `list[str] | None` | Marketplace attribution categories. |
|
|
| `session_id` | `str | None` | Group related requests for observability. |
|
|
| `trace` | `dict[str, Any] | None` | Trace metadata for broadcasts. |
|
|
| `max_retries` | `int` | Max retries (default `2`). Set to `0` to disable. |
|
|
|
|
??? info "Instantiate"
|
|
|
|
```python
|
|
from langchain_openrouter import ChatOpenRouter
|
|
|
|
model = ChatOpenRouter(
|
|
model="anthropic/claude-sonnet-4-5",
|
|
temperature=0,
|
|
# api_key="...",
|
|
# openrouter_provider={"order": ["Anthropic"]},
|
|
)
|
|
```
|
|
|
|
See https://openrouter.ai/docs for platform documentation.
|
|
"""
|
|
|
|
client: Any = Field(default=None, exclude=True)
|
|
"""Underlying SDK client (`openrouter.OpenRouter`)."""
|
|
|
|
openrouter_api_key: SecretStr | None = Field(
|
|
alias="api_key",
|
|
default_factory=secret_from_env("OPENROUTER_API_KEY", default=None),
|
|
)
|
|
"""OpenRouter API key."""
|
|
|
|
openrouter_api_base: str | None = Field(
|
|
default_factory=from_env("OPENROUTER_API_BASE", default=None),
|
|
alias="base_url",
|
|
)
|
|
"""OpenRouter API base URL. Maps to SDK `server_url`."""
|
|
|
|
app_url: str | None = Field(
|
|
default_factory=from_env(
|
|
"OPENROUTER_APP_URL",
|
|
default="https://docs.langchain.com",
|
|
),
|
|
)
|
|
"""Application URL for OpenRouter attribution.
|
|
|
|
Maps to `HTTP-Referer` header.
|
|
|
|
Defaults to LangChain docs URL. Set this to your app's URL to get
|
|
attribution for API usage in the OpenRouter dashboard.
|
|
|
|
See https://openrouter.ai/docs/app-attribution for details.
|
|
"""
|
|
|
|
app_title: str | None = Field(
|
|
default_factory=from_env("OPENROUTER_APP_TITLE", default="LangChain"),
|
|
)
|
|
"""Application title for OpenRouter attribution.
|
|
|
|
Maps to `X-Title` header.
|
|
|
|
Defaults to `'LangChain'`. Set this to your app's name to get attribution
|
|
for API usage in the OpenRouter dashboard.
|
|
|
|
See https://openrouter.ai/docs/app-attribution for details.
|
|
"""
|
|
|
|
app_categories: list[str] | None = Field(
|
|
default=None,
|
|
)
|
|
"""Marketplace categories for OpenRouter attribution.
|
|
|
|
Maps to `X-OpenRouter-Categories` header. Pass a list of lowercase,
|
|
hyphen-separated category strings (max 30 characters each),
|
|
e.g. `['cli-agent', 'programming-app']`.
|
|
|
|
Only recognized categories are accepted (unrecognized values are silently
|
|
dropped by OpenRouter).
|
|
|
|
See https://openrouter.ai/docs/app-attribution for recognized categories.
|
|
"""
|
|
|
|
request_timeout: int | None = Field(default=None, alias="timeout")
|
|
"""Timeout for requests in milliseconds. Maps to SDK `timeout_ms`."""
|
|
|
|
max_retries: int = 2
|
|
"""Maximum number of retries.
|
|
|
|
Each unit adds ~150 seconds to the backoff window via the SDK's
|
|
`max_elapsed_time` (e.g. `max_retries=2` allows up to ~300 s).
|
|
|
|
Set to `0` to disable retries.
|
|
"""
|
|
|
|
model_name: str = Field(alias="model")
|
|
"""The name of the model, e.g. `'anthropic/claude-sonnet-4-5'`."""
|
|
|
|
@property
|
|
def model(self) -> str:
|
|
"""Same as model_name."""
|
|
return self.model_name
|
|
|
|
temperature: float | None = None
|
|
"""Sampling temperature."""
|
|
|
|
max_tokens: int | None = None
|
|
"""Maximum number of tokens to generate."""
|
|
|
|
max_completion_tokens: int | None = None
|
|
"""Maximum number of completion tokens to generate."""
|
|
|
|
top_p: float | None = None
|
|
"""Nucleus sampling parameter."""
|
|
|
|
frequency_penalty: float | None = None
|
|
"""Frequency penalty for generation."""
|
|
|
|
presence_penalty: float | None = None
|
|
"""Presence penalty for generation."""
|
|
|
|
seed: int | None = None
|
|
"""Random seed for reproducibility."""
|
|
|
|
stop: list[str] | str | None = Field(default=None, alias="stop_sequences")
|
|
"""Default stop sequences."""
|
|
|
|
n: int = Field(default=1, ge=1)
|
|
"""Number of chat completions to generate for each prompt."""
|
|
|
|
streaming: bool = False
|
|
"""Whether to stream the results or not."""
|
|
|
|
stream_usage: bool = True
|
|
"""Whether to include usage metadata in streaming output.
|
|
|
|
If `True`, additional message chunks will be generated during the stream including
|
|
usage metadata.
|
|
"""
|
|
|
|
model_kwargs: dict[str, Any] = Field(default_factory=dict)
|
|
"""Any extra model parameters for the OpenRouter API."""
|
|
|
|
reasoning: dict[str, Any] | None = None
|
|
"""Reasoning settings to pass to OpenRouter.
|
|
|
|
Controls how many tokens the model allocates for internal chain-of-thought
|
|
reasoning.
|
|
|
|
Accepts an `openrouter.components.OpenResponsesReasoningConfig` or an
|
|
equivalent dict.
|
|
|
|
Supported keys:
|
|
|
|
- `effort`: Controls reasoning token budget.
|
|
|
|
Values: `'xhigh'`, `'high'`, `'medium'`, `'low'`, `'minimal'`, `'none'`.
|
|
- `summary`: Controls verbosity of the reasoning summary returned in the
|
|
response.
|
|
|
|
Values: `'auto'`, `'concise'`, `'detailed'`.
|
|
|
|
Example: `{"effort": "high", "summary": "auto"}`
|
|
|
|
See https://openrouter.ai/docs/guides/best-practices/reasoning-tokens
|
|
"""
|
|
|
|
openrouter_provider: dict[str, Any] | None = None
|
|
"""Provider preferences to pass to OpenRouter.
|
|
|
|
Example: `{"order": ["Anthropic", "OpenAI"]}`
|
|
"""
|
|
|
|
route: str | None = None
|
|
"""Route preference for OpenRouter, e.g. `'fallback'`."""
|
|
|
|
plugins: list[dict[str, Any]] | None = None
|
|
"""Plugins configuration for OpenRouter."""
|
|
|
|
session_id: str | None = Field(
|
|
default_factory=from_env("OPENROUTER_SESSION_ID", default=None),
|
|
)
|
|
"""Identifier used by OpenRouter to group related requests together.
|
|
|
|
Useful any time multiple requests should share an observability
|
|
grouping (e.g. a conversation, an agent workflow, a batch job, or a CI
|
|
run). Equivalent to setting the `x-session-id` HTTP header on the
|
|
underlying request. OpenRouter rejects values longer than 128
|
|
characters.
|
|
|
|
Falls back to the `OPENROUTER_SESSION_ID` environment variable when
|
|
unset, so callers can group all requests from a process without
|
|
threading the value through application code. Empty strings are
|
|
treated as unset.
|
|
|
|
Example: `"conv-2026-04-30-abc"`
|
|
|
|
See https://openrouter.ai/docs/guides/features/broadcast/overview
|
|
"""
|
|
|
|
trace: dict[str, Any] | None = None
|
|
"""Trace metadata for observability tools (e.g. Langfuse, LangSmith).
|
|
|
|
Forwarded by OpenRouter to configured broadcast destinations. Common
|
|
keys include `trace_id`, `trace_name`, `span_name`, `generation_name`,
|
|
and `parent_span_id`; see the OpenRouter broadcast docs for the
|
|
current full set. Unknown keys are forwarded as custom metadata.
|
|
|
|
No environment-variable fallback — set per-call or on the constructor.
|
|
|
|
Example: `{"trace_id": "abc-123", "span_name": "summarize"}`
|
|
|
|
See https://openrouter.ai/docs/guides/features/broadcast/overview
|
|
"""
|
|
|
|
model_config = ConfigDict(populate_by_name=True)
|
|
|
|
@model_validator(mode="before")
|
|
@classmethod
|
|
def build_extra(cls, values: dict[str, Any]) -> Any:
|
|
"""Build extra kwargs from additional params that were passed in."""
|
|
all_required_field_names = get_pydantic_field_names(cls)
|
|
extra = values.get("model_kwargs", {})
|
|
for field_name in list(values):
|
|
if field_name in extra:
|
|
msg = f"Found {field_name} supplied twice."
|
|
raise ValueError(msg)
|
|
if field_name not in all_required_field_names:
|
|
warnings.warn(
|
|
f"""WARNING! {field_name} is not default parameter.
|
|
{field_name} was transferred to model_kwargs.
|
|
Please confirm that {field_name} is what you intended.""",
|
|
stacklevel=2,
|
|
)
|
|
extra[field_name] = values.pop(field_name)
|
|
|
|
invalid_model_kwargs = all_required_field_names.intersection(extra.keys())
|
|
if invalid_model_kwargs:
|
|
msg = (
|
|
f"Parameters {invalid_model_kwargs} should be specified explicitly. "
|
|
f"Instead they were passed in as part of `model_kwargs` parameter."
|
|
)
|
|
raise ValueError(msg)
|
|
|
|
values["model_kwargs"] = extra
|
|
return values
|
|
|
|
def _build_client(self) -> Any:
|
|
"""Build and return an `openrouter.OpenRouter` SDK client.
|
|
|
|
Returns:
|
|
An `openrouter.OpenRouter` SDK client instance.
|
|
"""
|
|
import openrouter # noqa: PLC0415
|
|
from openrouter.utils import ( # noqa: PLC0415
|
|
BackoffStrategy,
|
|
RetryConfig,
|
|
)
|
|
|
|
client_kwargs: dict[str, Any] = {
|
|
"api_key": self.openrouter_api_key.get_secret_value(), # type: ignore[union-attr]
|
|
}
|
|
if self.openrouter_api_base:
|
|
client_kwargs["server_url"] = self.openrouter_api_base
|
|
extra_headers: dict[str, str] = {}
|
|
if self.app_url:
|
|
extra_headers["HTTP-Referer"] = self.app_url
|
|
if self.app_title:
|
|
extra_headers["X-Title"] = self.app_title
|
|
if self.app_categories:
|
|
extra_headers["X-OpenRouter-Categories"] = ",".join(self.app_categories)
|
|
if extra_headers:
|
|
import httpx # noqa: PLC0415
|
|
|
|
client_kwargs["client"] = httpx.Client(
|
|
headers=extra_headers, follow_redirects=True
|
|
)
|
|
client_kwargs["async_client"] = httpx.AsyncClient(
|
|
headers=extra_headers, follow_redirects=True
|
|
)
|
|
if self.request_timeout is not None:
|
|
client_kwargs["timeout_ms"] = self.request_timeout
|
|
if self.max_retries > 0:
|
|
client_kwargs["retry_config"] = RetryConfig(
|
|
strategy="backoff",
|
|
backoff=BackoffStrategy(
|
|
initial_interval=500,
|
|
max_interval=60000,
|
|
exponent=1.5,
|
|
max_elapsed_time=self.max_retries * 150_000,
|
|
),
|
|
retry_connection_errors=True,
|
|
)
|
|
return openrouter.OpenRouter(**client_kwargs)
|
|
|
|
@model_validator(mode="after")
|
|
def validate_environment(self) -> Self:
|
|
"""Validate configuration and build the SDK client."""
|
|
if not (self.openrouter_api_key and self.openrouter_api_key.get_secret_value()):
|
|
msg = "OPENROUTER_API_KEY must be set."
|
|
raise ValueError(msg)
|
|
if self.n > 1 and self.streaming:
|
|
msg = "n must be 1 when streaming."
|
|
raise ValueError(msg)
|
|
|
|
if not self.client:
|
|
try:
|
|
import openrouter # noqa: PLC0415, F401
|
|
|
|
self.client = self._build_client()
|
|
except ImportError as e:
|
|
msg = (
|
|
"Could not import the `openrouter` Python SDK. "
|
|
"Please install it with: pip install openrouter"
|
|
)
|
|
raise ImportError(msg) from e
|
|
return self
|
|
|
|
def _resolve_model_profile(self) -> ModelProfile | None:
|
|
return _get_default_model_profile(self.model_name) or None
|
|
|
|
#
|
|
# Serializable class method overrides
|
|
#
|
|
@property
|
|
def lc_secrets(self) -> dict[str, str]:
|
|
"""A map of constructor argument names to secret ids."""
|
|
return {"openrouter_api_key": "OPENROUTER_API_KEY"}
|
|
|
|
@classmethod
|
|
def is_lc_serializable(cls) -> bool:
|
|
"""Return whether this model can be serialized by LangChain."""
|
|
return True
|
|
|
|
#
|
|
# BaseChatModel method overrides
|
|
#
|
|
@property
|
|
def _llm_type(self) -> str:
|
|
"""Return type of chat model."""
|
|
return "openrouter-chat"
|
|
|
|
@property
|
|
def _identifying_params(self) -> dict[str, Any]:
|
|
"""Get the identifying parameters."""
|
|
return {
|
|
"model": self.model_name,
|
|
"temperature": self.temperature,
|
|
"max_tokens": self.max_tokens,
|
|
"top_p": self.top_p,
|
|
"streaming": self.streaming,
|
|
"reasoning": self.reasoning,
|
|
"openrouter_provider": self.openrouter_provider,
|
|
"route": self.route,
|
|
"model_kwargs": self.model_kwargs,
|
|
}
|
|
|
|
def _get_ls_params(
|
|
self,
|
|
stop: list[str] | None = None,
|
|
**kwargs: Any,
|
|
) -> LangSmithParams:
|
|
"""Get standard params for tracing."""
|
|
params = self._get_invocation_params(stop=stop, **kwargs)
|
|
ls_params = LangSmithParams(
|
|
ls_provider="openrouter",
|
|
ls_model_name=params.get("model", self.model_name),
|
|
ls_model_type="chat",
|
|
ls_temperature=params.get("temperature", self.temperature),
|
|
)
|
|
if ls_max_tokens := params.get("max_tokens", self.max_tokens):
|
|
ls_params["ls_max_tokens"] = ls_max_tokens
|
|
if ls_stop := stop or params.get("stop", None) or self.stop:
|
|
ls_params["ls_stop"] = ls_stop if isinstance(ls_stop, list) else [ls_stop]
|
|
return ls_params
|
|
|
|
def _generate(
|
|
self,
|
|
messages: list[BaseMessage],
|
|
stop: list[str] | None = None,
|
|
run_manager: CallbackManagerForLLMRun | None = None,
|
|
**kwargs: Any,
|
|
) -> ChatResult:
|
|
if self.streaming:
|
|
stream_iter = self._stream(
|
|
messages, stop=stop, run_manager=run_manager, **kwargs
|
|
)
|
|
return generate_from_stream(stream_iter)
|
|
message_dicts, params = self._create_message_dicts(messages, stop)
|
|
params = {**params, **kwargs}
|
|
_strip_internal_kwargs(params)
|
|
sdk_messages = _wrap_messages_for_sdk(message_dicts)
|
|
response = self.client.chat.send(messages=sdk_messages, **params)
|
|
return self._create_chat_result(response)
|
|
|
|
async def _agenerate(
|
|
self,
|
|
messages: list[BaseMessage],
|
|
stop: list[str] | None = None,
|
|
run_manager: AsyncCallbackManagerForLLMRun | None = None,
|
|
**kwargs: Any,
|
|
) -> ChatResult:
|
|
if self.streaming:
|
|
stream_iter = self._astream(
|
|
messages, stop=stop, run_manager=run_manager, **kwargs
|
|
)
|
|
return await agenerate_from_stream(stream_iter)
|
|
message_dicts, params = self._create_message_dicts(messages, stop)
|
|
params = {**params, **kwargs}
|
|
_strip_internal_kwargs(params)
|
|
sdk_messages = _wrap_messages_for_sdk(message_dicts)
|
|
response = await self.client.chat.send_async(messages=sdk_messages, **params)
|
|
return self._create_chat_result(response)
|
|
|
|
def _stream( # noqa: C901, PLR0912
|
|
self,
|
|
messages: list[BaseMessage],
|
|
stop: list[str] | None = None,
|
|
run_manager: CallbackManagerForLLMRun | None = None,
|
|
**kwargs: Any,
|
|
) -> Iterator[ChatGenerationChunk]:
|
|
message_dicts, params = self._create_message_dicts(messages, stop)
|
|
params = {**params, **kwargs, "stream": True}
|
|
if self.stream_usage:
|
|
params["stream_options"] = {"include_usage": True}
|
|
_strip_internal_kwargs(params)
|
|
sdk_messages = _wrap_messages_for_sdk(message_dicts)
|
|
|
|
default_chunk_class: type[BaseMessageChunk] = AIMessageChunk
|
|
for chunk in self.client.chat.send(messages=sdk_messages, **params):
|
|
chunk_dict = chunk.model_dump(by_alias=True)
|
|
if not chunk_dict.get("choices"):
|
|
if error := chunk_dict.get("error"):
|
|
msg = (
|
|
f"OpenRouter API returned an error during streaming: "
|
|
f"{error.get('message', str(error))} "
|
|
f"(code: {error.get('code', 'unknown')})"
|
|
)
|
|
raise ValueError(msg)
|
|
# Usage-only chunk (no choices) — emit with usage_metadata
|
|
if usage := chunk_dict.get("usage"):
|
|
usage_metadata = _create_usage_metadata(usage)
|
|
usage_chunk = AIMessageChunk(
|
|
content="", usage_metadata=usage_metadata
|
|
)
|
|
generation_chunk = ChatGenerationChunk(message=usage_chunk)
|
|
if run_manager:
|
|
run_manager.on_llm_new_token(
|
|
generation_chunk.text, chunk=generation_chunk
|
|
)
|
|
yield generation_chunk
|
|
continue
|
|
choice = chunk_dict["choices"][0]
|
|
message_chunk = _convert_chunk_to_message_chunk(
|
|
chunk_dict, default_chunk_class
|
|
)
|
|
generation_info: dict[str, Any] = {}
|
|
if finish_reason := choice.get("finish_reason"):
|
|
generation_info["finish_reason"] = finish_reason
|
|
# Include response-level metadata on the final chunk
|
|
response_model = chunk_dict.get("model")
|
|
generation_info["model_name"] = response_model or self.model_name
|
|
if system_fingerprint := chunk_dict.get("system_fingerprint"):
|
|
generation_info["system_fingerprint"] = system_fingerprint
|
|
if native_finish_reason := choice.get("native_finish_reason"):
|
|
generation_info["native_finish_reason"] = native_finish_reason
|
|
if response_id := chunk_dict.get("id"):
|
|
generation_info["id"] = response_id
|
|
if created := chunk_dict.get("created"):
|
|
generation_info["created"] = int(created)
|
|
if object_ := chunk_dict.get("object"):
|
|
generation_info["object"] = object_
|
|
logprobs = choice.get("logprobs")
|
|
if logprobs:
|
|
generation_info["logprobs"] = logprobs
|
|
|
|
if generation_info:
|
|
generation_info["model_provider"] = "openrouter"
|
|
message_chunk = message_chunk.model_copy(
|
|
update={
|
|
"response_metadata": {
|
|
**message_chunk.response_metadata,
|
|
**generation_info,
|
|
}
|
|
}
|
|
)
|
|
|
|
default_chunk_class = message_chunk.__class__
|
|
generation_chunk = ChatGenerationChunk(
|
|
message=message_chunk, generation_info=generation_info or None
|
|
)
|
|
|
|
if run_manager:
|
|
run_manager.on_llm_new_token(
|
|
generation_chunk.text,
|
|
chunk=generation_chunk,
|
|
logprobs=logprobs,
|
|
)
|
|
yield generation_chunk
|
|
|
|
async def _astream( # noqa: C901, PLR0912
|
|
self,
|
|
messages: list[BaseMessage],
|
|
stop: list[str] | None = None,
|
|
run_manager: AsyncCallbackManagerForLLMRun | None = None,
|
|
**kwargs: Any,
|
|
) -> AsyncIterator[ChatGenerationChunk]:
|
|
message_dicts, params = self._create_message_dicts(messages, stop)
|
|
params = {**params, **kwargs, "stream": True}
|
|
if self.stream_usage:
|
|
params["stream_options"] = {"include_usage": True}
|
|
_strip_internal_kwargs(params)
|
|
sdk_messages = _wrap_messages_for_sdk(message_dicts)
|
|
|
|
default_chunk_class: type[BaseMessageChunk] = AIMessageChunk
|
|
async for chunk in await self.client.chat.send_async(
|
|
messages=sdk_messages, **params
|
|
):
|
|
chunk_dict = chunk.model_dump(by_alias=True)
|
|
if not chunk_dict.get("choices"):
|
|
if error := chunk_dict.get("error"):
|
|
msg = (
|
|
f"OpenRouter API returned an error during streaming: "
|
|
f"{error.get('message', str(error))} "
|
|
f"(code: {error.get('code', 'unknown')})"
|
|
)
|
|
raise ValueError(msg)
|
|
# Usage-only chunk (no choices) — emit with usage_metadata
|
|
if usage := chunk_dict.get("usage"):
|
|
usage_metadata = _create_usage_metadata(usage)
|
|
usage_chunk = AIMessageChunk(
|
|
content="", usage_metadata=usage_metadata
|
|
)
|
|
generation_chunk = ChatGenerationChunk(message=usage_chunk)
|
|
if run_manager:
|
|
await run_manager.on_llm_new_token(
|
|
token=generation_chunk.text, chunk=generation_chunk
|
|
)
|
|
yield generation_chunk
|
|
continue
|
|
choice = chunk_dict["choices"][0]
|
|
message_chunk = _convert_chunk_to_message_chunk(
|
|
chunk_dict, default_chunk_class
|
|
)
|
|
generation_info: dict[str, Any] = {}
|
|
if finish_reason := choice.get("finish_reason"):
|
|
generation_info["finish_reason"] = finish_reason
|
|
# Include response-level metadata on the final chunk
|
|
response_model = chunk_dict.get("model")
|
|
generation_info["model_name"] = response_model or self.model_name
|
|
if system_fingerprint := chunk_dict.get("system_fingerprint"):
|
|
generation_info["system_fingerprint"] = system_fingerprint
|
|
if native_finish_reason := choice.get("native_finish_reason"):
|
|
generation_info["native_finish_reason"] = native_finish_reason
|
|
if response_id := chunk_dict.get("id"):
|
|
generation_info["id"] = response_id
|
|
if created := chunk_dict.get("created"):
|
|
generation_info["created"] = int(created) # UNIX timestamp
|
|
if object_ := chunk_dict.get("object"):
|
|
generation_info["object"] = object_
|
|
logprobs = choice.get("logprobs")
|
|
if logprobs:
|
|
generation_info["logprobs"] = logprobs
|
|
|
|
if generation_info:
|
|
generation_info["model_provider"] = "openrouter"
|
|
message_chunk = message_chunk.model_copy(
|
|
update={
|
|
"response_metadata": {
|
|
**message_chunk.response_metadata,
|
|
**generation_info,
|
|
}
|
|
}
|
|
)
|
|
|
|
default_chunk_class = message_chunk.__class__
|
|
generation_chunk = ChatGenerationChunk(
|
|
message=message_chunk, generation_info=generation_info or None
|
|
)
|
|
|
|
if run_manager:
|
|
await run_manager.on_llm_new_token(
|
|
token=generation_chunk.text,
|
|
chunk=generation_chunk,
|
|
logprobs=logprobs,
|
|
)
|
|
yield generation_chunk
|
|
|
|
#
|
|
# Internal methods
|
|
#
|
|
@property
|
|
def _default_params(self) -> dict[str, Any]: # noqa: C901, PLR0912
|
|
"""Get the default parameters for calling OpenRouter API."""
|
|
params: dict[str, Any] = {
|
|
"model": self.model_name,
|
|
"stream": self.streaming,
|
|
**self.model_kwargs,
|
|
}
|
|
if self.temperature is not None:
|
|
params["temperature"] = self.temperature
|
|
if self.max_tokens is not None:
|
|
params["max_tokens"] = self.max_tokens
|
|
if self.max_completion_tokens is not None:
|
|
params["max_completion_tokens"] = self.max_completion_tokens
|
|
if self.top_p is not None:
|
|
params["top_p"] = self.top_p
|
|
if self.frequency_penalty is not None:
|
|
params["frequency_penalty"] = self.frequency_penalty
|
|
if self.presence_penalty is not None:
|
|
params["presence_penalty"] = self.presence_penalty
|
|
if self.seed is not None:
|
|
params["seed"] = self.seed
|
|
if self.n > 1:
|
|
params["n"] = self.n
|
|
if self.stop is not None:
|
|
params["stop"] = self.stop
|
|
# OpenRouter-specific params
|
|
if self.reasoning is not None:
|
|
params["reasoning"] = self.reasoning
|
|
if self.openrouter_provider is not None:
|
|
params["provider"] = self.openrouter_provider
|
|
if self.route is not None:
|
|
params["route"] = self.route
|
|
if self.plugins is not None:
|
|
params["plugins"] = self.plugins
|
|
if self.session_id:
|
|
params["session_id"] = self.session_id
|
|
if self.trace is not None:
|
|
params["trace"] = self.trace
|
|
return params
|
|
|
|
def _create_message_dicts(
|
|
self, messages: list[BaseMessage], stop: list[str] | None
|
|
) -> tuple[list[dict[str, Any]], dict[str, Any]]:
|
|
params = self._default_params
|
|
if stop is not None:
|
|
params["stop"] = stop
|
|
message_dicts = [_convert_message_to_dict(m) for m in messages]
|
|
return message_dicts, params
|
|
|
|
def _create_chat_result(self, response: Any) -> ChatResult: # noqa: C901, PLR0912
|
|
"""Create a `ChatResult` from an OpenRouter SDK response."""
|
|
if not isinstance(response, dict):
|
|
response = response.model_dump(by_alias=True)
|
|
|
|
if error := response.get("error"):
|
|
msg = (
|
|
f"OpenRouter API returned an error: "
|
|
f"{error.get('message', str(error))} "
|
|
f"(code: {error.get('code', 'unknown')})"
|
|
)
|
|
raise ValueError(msg)
|
|
|
|
generations = []
|
|
token_usage = response.get("usage") or {}
|
|
|
|
choices = response.get("choices", [])
|
|
if not choices:
|
|
msg = (
|
|
"OpenRouter API returned a response with no choices. "
|
|
"This may indicate a problem with the request or model availability."
|
|
)
|
|
raise ValueError(msg)
|
|
|
|
# Extract top-level response metadata
|
|
response_model = response.get("model")
|
|
system_fingerprint = response.get("system_fingerprint")
|
|
|
|
for res in choices:
|
|
message = _convert_dict_to_message(res["message"])
|
|
if token_usage and isinstance(message, AIMessage):
|
|
message.usage_metadata = _create_usage_metadata(token_usage)
|
|
# Surface OpenRouter cost data in response_metadata
|
|
if "cost" in token_usage:
|
|
message.response_metadata["cost"] = token_usage["cost"]
|
|
if "cost_details" in token_usage:
|
|
message.response_metadata["cost_details"] = token_usage[
|
|
"cost_details"
|
|
]
|
|
if isinstance(message, AIMessage):
|
|
if system_fingerprint:
|
|
message.response_metadata["system_fingerprint"] = system_fingerprint
|
|
if native_finish_reason := res.get("native_finish_reason"):
|
|
message.response_metadata["native_finish_reason"] = (
|
|
native_finish_reason
|
|
)
|
|
generation_info: dict[str, Any] = {
|
|
"finish_reason": res.get("finish_reason"),
|
|
}
|
|
if "logprobs" in res:
|
|
generation_info["logprobs"] = res["logprobs"]
|
|
gen = ChatGeneration(
|
|
message=message,
|
|
generation_info=generation_info,
|
|
)
|
|
generations.append(gen)
|
|
|
|
llm_output: dict[str, Any] = {
|
|
"model_name": response_model or self.model_name,
|
|
}
|
|
if response_id := response.get("id"):
|
|
llm_output["id"] = response_id
|
|
if created := response.get("created"):
|
|
llm_output["created"] = int(created)
|
|
if object_ := response.get("object"):
|
|
llm_output["object"] = object_
|
|
return ChatResult(generations=generations, llm_output=llm_output)
|
|
|
|
def bind_tools(
|
|
self,
|
|
tools: Sequence[dict[str, Any] | type[BaseModel] | Callable | BaseTool],
|
|
*,
|
|
tool_choice: dict | str | bool | None = None,
|
|
strict: bool | None = None,
|
|
**kwargs: Any,
|
|
) -> Runnable[LanguageModelInput, AIMessage]:
|
|
"""Bind tool-like objects to this chat model.
|
|
|
|
Args:
|
|
tools: A list of tool definitions to bind to this chat model.
|
|
|
|
Supports any tool definition handled by
|
|
`langchain_core.utils.function_calling.convert_to_openai_tool`.
|
|
tool_choice: Which tool to require the model to call.
|
|
strict: If `True`, model output is guaranteed to exactly match the
|
|
JSON Schema provided in the tool definition.
|
|
|
|
If `None`, the `strict` argument will not be passed to
|
|
the model.
|
|
**kwargs: Any additional parameters.
|
|
"""
|
|
formatted_tools = [
|
|
convert_to_openai_tool(tool, strict=strict) for tool in tools
|
|
]
|
|
if tool_choice is not None and tool_choice:
|
|
if tool_choice == "any":
|
|
tool_choice = "required"
|
|
if isinstance(tool_choice, str) and (
|
|
tool_choice not in ("auto", "none", "required")
|
|
):
|
|
tool_choice = {"type": "function", "function": {"name": tool_choice}}
|
|
if isinstance(tool_choice, bool):
|
|
if len(tools) > 1:
|
|
msg = (
|
|
"tool_choice can only be True when there is one tool. Received "
|
|
f"{len(tools)} tools."
|
|
)
|
|
raise ValueError(msg)
|
|
tool_name = formatted_tools[0]["function"]["name"]
|
|
tool_choice = {
|
|
"type": "function",
|
|
"function": {"name": tool_name},
|
|
}
|
|
kwargs["tool_choice"] = tool_choice
|
|
return super().bind(tools=formatted_tools, **kwargs)
|
|
|
|
def with_structured_output( # type: ignore[override]
|
|
self,
|
|
schema: dict | type[BaseModel] | None = None,
|
|
*,
|
|
method: Literal["function_calling", "json_schema"] = "function_calling",
|
|
include_raw: bool = False,
|
|
strict: bool | None = None,
|
|
**kwargs: Any,
|
|
) -> Runnable[LanguageModelInput, dict | BaseModel]:
|
|
"""Model wrapper that returns outputs formatted to match the given schema.
|
|
|
|
Args:
|
|
schema: The output schema as a Pydantic class, TypedDict, JSON Schema,
|
|
or OpenAI function schema.
|
|
method: The method for steering model generation.
|
|
include_raw: If `True` then both the raw model response and the
|
|
parsed model response will be returned.
|
|
strict: If `True`, model output is guaranteed to exactly match the
|
|
JSON Schema provided in the schema definition.
|
|
|
|
If `None`, the `strict` argument will not be passed to
|
|
the model.
|
|
**kwargs: Any additional parameters.
|
|
|
|
Returns:
|
|
A `Runnable` that takes same inputs as a `BaseChatModel`.
|
|
"""
|
|
if method == "json_mode":
|
|
warnings.warn(
|
|
"Unrecognized structured output method 'json_mode'. "
|
|
"Defaulting to 'json_schema' method.",
|
|
stacklevel=2,
|
|
)
|
|
method = "json_schema"
|
|
is_pydantic_schema = _is_pydantic_class(schema)
|
|
if method == "function_calling":
|
|
if schema is None:
|
|
msg = (
|
|
"schema must be specified when method is 'function_calling'. "
|
|
"Received None."
|
|
)
|
|
raise ValueError(msg)
|
|
formatted_tool = convert_to_openai_tool(schema)
|
|
tool_name = formatted_tool["function"]["name"]
|
|
llm = self.bind_tools(
|
|
[schema],
|
|
tool_choice=tool_name,
|
|
strict=strict,
|
|
ls_structured_output_format={
|
|
"kwargs": {"method": "function_calling", "strict": strict},
|
|
"schema": formatted_tool,
|
|
},
|
|
**kwargs,
|
|
)
|
|
if is_pydantic_schema:
|
|
output_parser: OutputParserLike = PydanticToolsParser(
|
|
tools=[schema], # type: ignore[list-item]
|
|
first_tool_only=True, # type: ignore[list-item]
|
|
)
|
|
else:
|
|
output_parser = JsonOutputKeyToolsParser(
|
|
key_name=tool_name, first_tool_only=True
|
|
)
|
|
elif method == "json_schema":
|
|
if schema is None:
|
|
msg = (
|
|
"schema must be specified when method is 'json_schema'. "
|
|
"Received None."
|
|
)
|
|
raise ValueError(msg)
|
|
json_schema = convert_to_json_schema(schema)
|
|
schema_name = json_schema.get("title", "")
|
|
json_schema_spec: dict[str, Any] = {
|
|
"name": schema_name,
|
|
"schema": json_schema,
|
|
}
|
|
if strict is not None:
|
|
json_schema_spec["strict"] = strict
|
|
response_format = {
|
|
"type": "json_schema",
|
|
"json_schema": json_schema_spec,
|
|
}
|
|
ls_format_info = {
|
|
"kwargs": {"method": "json_schema", "strict": strict},
|
|
"schema": json_schema,
|
|
}
|
|
llm = self.bind(
|
|
response_format=response_format,
|
|
ls_structured_output_format=ls_format_info,
|
|
**kwargs,
|
|
)
|
|
output_parser = (
|
|
PydanticOutputParser(pydantic_object=schema) # type: ignore[type-var, arg-type]
|
|
if is_pydantic_schema
|
|
else JsonOutputParser()
|
|
)
|
|
else:
|
|
msg = (
|
|
f"Unrecognized method argument. Expected one of 'function_calling' "
|
|
f"or 'json_schema'. Received: '{method}'"
|
|
)
|
|
raise ValueError(msg)
|
|
|
|
if include_raw:
|
|
parser_assign = RunnablePassthrough.assign(
|
|
parsed=itemgetter("raw") | output_parser, parsing_error=lambda _: None
|
|
)
|
|
parser_none = RunnablePassthrough.assign(parsed=lambda _: None)
|
|
parser_with_fallback = parser_assign.with_fallbacks(
|
|
[parser_none], exception_key="parsing_error"
|
|
)
|
|
return RunnableMap(raw=llm) | parser_with_fallback
|
|
return llm | output_parser
|
|
|
|
|
|
def _is_pydantic_class(obj: Any) -> bool:
|
|
return isinstance(obj, type) and is_basemodel_subclass(obj)
|
|
|
|
|
|
def _strip_internal_kwargs(params: dict[str, Any]) -> None:
|
|
"""Remove LangChain-internal keys that the SDK does not accept."""
|
|
for key in _INTERNAL_KWARGS:
|
|
params.pop(key, None)
|
|
|
|
|
|
def _has_file_content_blocks(message_dicts: list[dict[str, Any]]) -> bool:
|
|
"""Return `True` if any message dict contains a `file` content block."""
|
|
for msg in message_dicts:
|
|
content = msg.get("content")
|
|
if isinstance(content, list):
|
|
for block in content:
|
|
if isinstance(block, dict) and block.get("type") == "file":
|
|
return True
|
|
return False
|
|
|
|
|
|
def _wrap_messages_for_sdk(
|
|
message_dicts: list[dict[str, Any]],
|
|
) -> list[dict[str, Any]] | list[Any]:
|
|
"""Wrap message dicts as SDK Pydantic models when file blocks are present.
|
|
|
|
The OpenRouter Python SDK does not include `file` in its
|
|
`ChatMessageContentItem` discriminated union, so Pydantic validation
|
|
rejects file content blocks even though the OpenRouter **API** supports
|
|
them. Using `model_construct` on the SDK's message classes bypasses
|
|
validation while still producing the correct JSON payload.
|
|
|
|
When no file blocks are detected the original dicts are returned unchanged
|
|
so the normal (validated) code path is preserved.
|
|
|
|
Args:
|
|
message_dicts: Message dicts produced by `_convert_message_to_dict`.
|
|
|
|
Returns:
|
|
The original list when no file blocks are present, or a list of SDK
|
|
Pydantic model instances otherwise.
|
|
"""
|
|
if not _has_file_content_blocks(message_dicts):
|
|
return message_dicts
|
|
|
|
try:
|
|
from openrouter import components # noqa: PLC0415
|
|
except ImportError:
|
|
warnings.warn(
|
|
"Could not import openrouter.components; file content blocks "
|
|
"will be sent as raw dicts which may cause validation errors.",
|
|
stacklevel=2,
|
|
)
|
|
return message_dicts
|
|
|
|
role_to_model: dict[str, type[BaseModel]] = {
|
|
"user": components.ChatUserMessage,
|
|
"system": components.ChatSystemMessage,
|
|
"assistant": components.ChatAssistantMessage,
|
|
"tool": components.ChatToolMessage,
|
|
"developer": components.ChatDeveloperMessage,
|
|
}
|
|
|
|
wrapped: list[Any] = []
|
|
for msg in message_dicts:
|
|
model_cls = role_to_model.get(msg.get("role", ""))
|
|
if model_cls is None:
|
|
warnings.warn(
|
|
f"Unknown message role {msg.get('role')!r} encountered during "
|
|
f"SDK wrapping; passing raw dict to the API.",
|
|
stacklevel=2,
|
|
)
|
|
wrapped.append(msg)
|
|
continue
|
|
wrapped.append(model_cls.model_construct(**msg))
|
|
return wrapped
|
|
|
|
|
|
#
|
|
# Type conversion helpers
|
|
#
|
|
def _convert_video_block_to_openrouter(block: dict[str, Any]) -> dict[str, Any]:
|
|
"""Convert a LangChain video content block to OpenRouter's `video_url` format.
|
|
|
|
Args:
|
|
block: A LangChain `VideoContentBlock`.
|
|
|
|
Returns:
|
|
A dict in OpenRouter's `video_url` format.
|
|
|
|
Raises:
|
|
ValueError: If no video source is provided.
|
|
"""
|
|
if "url" in block:
|
|
return {"type": "video_url", "video_url": {"url": block["url"]}}
|
|
if "base64" in block or block.get("source_type") == "base64":
|
|
base64_data = block["data"] if "source_type" in block else block["base64"]
|
|
mime_type = block.get("mime_type", "video/mp4")
|
|
return {
|
|
"type": "video_url",
|
|
"video_url": {"url": f"data:{mime_type};base64,{base64_data}"},
|
|
}
|
|
msg = "Video block must have either 'url' or 'base64' data."
|
|
raise ValueError(msg)
|
|
|
|
|
|
def _convert_file_block_to_openrouter(block: dict[str, Any]) -> dict[str, Any]:
|
|
"""Convert a LangChain file content block to OpenRouter's `file` format.
|
|
|
|
OpenRouter accepts files as::
|
|
|
|
{"type": "file", "file": {"filename": "...", "file_data": "..."}}
|
|
|
|
where `file_data` is either a public URL or a `data:` URI.
|
|
|
|
Args:
|
|
block: A LangChain file content block.
|
|
|
|
Returns:
|
|
A dict in OpenRouter's `file` format.
|
|
|
|
Raises:
|
|
ValueError: If the block contains neither a URL, base64 data, nor a
|
|
file ID.
|
|
"""
|
|
file: dict[str, str] = {}
|
|
|
|
# --- resolve file_data ---------------------------------------------------
|
|
if "url" in block:
|
|
file["file_data"] = block["url"]
|
|
elif block.get("source_type") == "base64" or "base64" in block:
|
|
base64_data = block["data"] if "source_type" in block else block["base64"]
|
|
mime_type = block.get("mime_type", "application/octet-stream")
|
|
file["file_data"] = f"data:{mime_type};base64,{base64_data}"
|
|
elif block.get("source_type") == "id" or "file_id" in block:
|
|
msg = "OpenRouter does not support file IDs."
|
|
raise ValueError(msg)
|
|
else:
|
|
msg = "File block must have either 'url' or 'base64' data."
|
|
raise ValueError(msg)
|
|
|
|
# --- resolve filename ----------------------------------------------------
|
|
if filename := block.get("filename"):
|
|
file["filename"] = filename
|
|
elif ((extras := block.get("extras")) and "filename" in extras) or (
|
|
(extras := block.get("metadata")) and "filename" in extras
|
|
):
|
|
file["filename"] = extras["filename"]
|
|
|
|
return {"type": "file", "file": file}
|
|
|
|
|
|
def _format_message_content(content: Any) -> Any:
|
|
"""Format message content for OpenRouter API.
|
|
|
|
Converts LangChain data content blocks to the expected format.
|
|
|
|
Args:
|
|
content: The message content (string or list of content blocks).
|
|
|
|
Returns:
|
|
Formatted content suitable for the OpenRouter API.
|
|
"""
|
|
if content and isinstance(content, list):
|
|
formatted: list = []
|
|
for block in content:
|
|
if isinstance(block, dict) and is_data_content_block(block):
|
|
if block.get("type") == "video":
|
|
formatted.append(_convert_video_block_to_openrouter(block))
|
|
elif block.get("type") == "file":
|
|
formatted.append(_convert_file_block_to_openrouter(block))
|
|
else:
|
|
formatted.append(convert_to_openai_data_block(block))
|
|
else:
|
|
formatted.append(block)
|
|
return formatted
|
|
return content
|
|
|
|
|
|
def _merge_reasoning_run(run: list[dict[str, Any]]) -> dict[str, Any]:
|
|
"""Merge a run of consecutive same-`(type, index)` reasoning fragments."""
|
|
merged_entry: dict[str, Any] = {}
|
|
text_parts: list[str] = []
|
|
has_text = False
|
|
for frag in run:
|
|
for k, v in frag.items():
|
|
if k == "text":
|
|
has_text = True
|
|
if v:
|
|
text_parts.append(v)
|
|
elif v is not None:
|
|
merged_entry[k] = v
|
|
if has_text:
|
|
merged_entry["text"] = "".join(text_parts)
|
|
return merged_entry
|
|
|
|
|
|
def _merge_reasoning_details(
|
|
details: list[dict[str, Any]],
|
|
) -> list[dict[str, Any]]:
|
|
"""Merge fragmented `reasoning_details` from streaming chunk concatenation.
|
|
|
|
During streaming, `AIMessageChunk.__add__` list-concatenates
|
|
`reasoning_details` in `additional_kwargs`, fragmenting a single entry
|
|
into many. When serialized back to the API via
|
|
`_convert_message_to_dict`, these fragments cause
|
|
`BadRequestResponseError` on multi-turn conversations (the provider
|
|
rejects the malformed thinking block with `Invalid signature`).
|
|
|
|
Streaming deltas tag each fragment with the `index` of the entry it
|
|
belongs to in the original (non-streamed) array, so this function groups
|
|
consecutive entries by `(type, index)` and merges each group into one.
|
|
Entries without an `index` are preserved as-is, since non-streaming
|
|
responses can legitimately contain multiple entries.
|
|
|
|
Within a merged group, `text` values are concatenated in order. Other
|
|
metadata fields (e.g. `format`, `signature`) use last-non-`None`-wins
|
|
semantics, which preserves stable provider metadata without concatenating
|
|
repeated strings — Anthropic-style reasoning streams emit a single
|
|
signature-bearing fragment at the end of the block.
|
|
|
|
A list with zero or one items passes through unchanged.
|
|
"""
|
|
if not isinstance(details, list) or len(details) <= 1:
|
|
return details
|
|
|
|
merged: list[dict[str, Any]] = []
|
|
i = 0
|
|
while i < len(details):
|
|
entry = details[i]
|
|
# Without an index we cannot distinguish streaming fragments from
|
|
# distinct non-streaming entries, so leave them alone. Same for any
|
|
# non-dict items that may have slipped in upstream.
|
|
if not isinstance(entry, dict) or entry.get("index") is None:
|
|
merged.append(entry)
|
|
i += 1
|
|
continue
|
|
|
|
entry_type = entry.get("type", "")
|
|
entry_index = entry["index"]
|
|
run = [entry]
|
|
i += 1
|
|
while i < len(details):
|
|
nxt = details[i]
|
|
if (
|
|
isinstance(nxt, dict)
|
|
and nxt.get("type", "") == entry_type
|
|
and nxt.get("index") == entry_index
|
|
):
|
|
run.append(nxt)
|
|
i += 1
|
|
else:
|
|
break
|
|
|
|
merged.append(entry if len(run) == 1 else _merge_reasoning_run(run))
|
|
|
|
return merged
|
|
|
|
|
|
def _convert_message_to_dict(message: BaseMessage) -> dict[str, Any]: # noqa: C901, PLR0912
|
|
"""Convert a LangChain message to an OpenRouter-compatible dict payload.
|
|
|
|
Handles role mapping, multimodal content formatting, tool call
|
|
serialization, and reasoning content preservation for multi-turn
|
|
conversations.
|
|
|
|
Args:
|
|
message: The LangChain message.
|
|
|
|
Returns:
|
|
A dict suitable for the OpenRouter chat API `messages` parameter.
|
|
"""
|
|
message_dict: dict[str, Any]
|
|
if isinstance(message, ChatMessage):
|
|
message_dict = {"role": message.role, "content": message.content}
|
|
elif isinstance(message, HumanMessage):
|
|
message_dict = {
|
|
"role": "user",
|
|
"content": _format_message_content(message.content),
|
|
}
|
|
elif isinstance(message, AIMessage):
|
|
message_dict = {"role": "assistant", "content": message.content}
|
|
# Filter out non-text blocks from list content
|
|
if isinstance(message.content, list):
|
|
text_blocks = [
|
|
block
|
|
for block in message.content
|
|
if isinstance(block, dict) and block.get("type") == "text"
|
|
]
|
|
message_dict["content"] = text_blocks or ""
|
|
if message.tool_calls or message.invalid_tool_calls:
|
|
message_dict["tool_calls"] = [
|
|
_lc_tool_call_to_openrouter_tool_call(tc) for tc in message.tool_calls
|
|
] + [
|
|
_lc_invalid_tool_call_to_openrouter_tool_call(tc)
|
|
for tc in message.invalid_tool_calls
|
|
]
|
|
if message_dict["content"] == "" or (
|
|
isinstance(message_dict["content"], list)
|
|
and not message_dict["content"]
|
|
):
|
|
message_dict["content"] = None
|
|
elif "tool_calls" in message.additional_kwargs:
|
|
message_dict["tool_calls"] = message.additional_kwargs["tool_calls"]
|
|
if message_dict["content"] == "" or (
|
|
isinstance(message_dict["content"], list)
|
|
and not message_dict["content"]
|
|
):
|
|
message_dict["content"] = None
|
|
# Preserve reasoning content for multi-turn conversations (e.g.
|
|
# tool-calling loops). OpenRouter stores reasoning text in `reasoning`
|
|
# and structured fragment details in `reasoning_details`; the latter
|
|
# is merged before serialization to undo streaming fragmentation.
|
|
if "reasoning_content" in message.additional_kwargs:
|
|
message_dict["reasoning"] = message.additional_kwargs["reasoning_content"]
|
|
if "reasoning_details" in message.additional_kwargs:
|
|
message_dict["reasoning_details"] = _merge_reasoning_details(
|
|
message.additional_kwargs["reasoning_details"]
|
|
)
|
|
elif isinstance(message, SystemMessage):
|
|
message_dict = {"role": "system", "content": message.content}
|
|
elif isinstance(message, ToolMessage):
|
|
message_dict = {
|
|
"role": "tool",
|
|
"content": message.content,
|
|
"tool_call_id": message.tool_call_id,
|
|
}
|
|
else:
|
|
msg = f"Got unknown type {message}"
|
|
raise TypeError(msg)
|
|
if "name" in message.additional_kwargs:
|
|
message_dict["name"] = message.additional_kwargs["name"]
|
|
return message_dict
|
|
|
|
|
|
def _convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage: # noqa: C901
|
|
"""Convert an OpenRouter API response message dict to a LangChain message.
|
|
|
|
Extracts tool calls, reasoning content, and maps roles to the appropriate
|
|
LangChain message type (`HumanMessage`, `AIMessage`, `SystemMessage`,
|
|
`ToolMessage`, or `ChatMessage`).
|
|
|
|
Args:
|
|
_dict: The message dictionary from the API response.
|
|
|
|
Returns:
|
|
The corresponding LangChain message.
|
|
"""
|
|
id_ = _dict.get("id")
|
|
role = _dict.get("role")
|
|
if role == "user":
|
|
return HumanMessage(content=_dict.get("content", ""))
|
|
if role == "assistant":
|
|
content = _dict.get("content", "") or ""
|
|
additional_kwargs: dict = {}
|
|
if reasoning := _dict.get("reasoning"):
|
|
additional_kwargs["reasoning_content"] = reasoning
|
|
if reasoning_details := _dict.get("reasoning_details"):
|
|
additional_kwargs["reasoning_details"] = reasoning_details
|
|
tool_calls = []
|
|
invalid_tool_calls = []
|
|
if raw_tool_calls := _dict.get("tool_calls"):
|
|
for raw_tool_call in raw_tool_calls:
|
|
try:
|
|
tool_calls.append(parse_tool_call(raw_tool_call, return_id=True))
|
|
except Exception as e: # noqa: BLE001, PERF203
|
|
invalid_tool_calls.append(
|
|
make_invalid_tool_call(raw_tool_call, str(e))
|
|
)
|
|
return AIMessage(
|
|
content=content,
|
|
id=id_,
|
|
additional_kwargs=additional_kwargs,
|
|
tool_calls=tool_calls,
|
|
invalid_tool_calls=invalid_tool_calls,
|
|
response_metadata={"model_provider": "openrouter"},
|
|
)
|
|
if role == "system":
|
|
return SystemMessage(content=_dict.get("content", ""))
|
|
if role == "tool":
|
|
additional_kwargs = {}
|
|
if "name" in _dict:
|
|
additional_kwargs["name"] = _dict["name"]
|
|
return ToolMessage(
|
|
content=_dict.get("content", ""),
|
|
tool_call_id=_dict.get("tool_call_id"),
|
|
additional_kwargs=additional_kwargs,
|
|
)
|
|
if role is None:
|
|
msg = (
|
|
f"OpenRouter response message is missing the 'role' field. "
|
|
f"Message keys: {list(_dict.keys())}"
|
|
)
|
|
raise ValueError(msg)
|
|
warnings.warn(
|
|
f"Unrecognized message role '{role}' from OpenRouter. "
|
|
f"Falling back to ChatMessage.",
|
|
stacklevel=2,
|
|
)
|
|
return ChatMessage(content=_dict.get("content", ""), role=role)
|
|
|
|
|
|
def _convert_chunk_to_message_chunk( # noqa: C901, PLR0911, PLR0912
|
|
chunk: Mapping[str, Any], default_class: type[BaseMessageChunk]
|
|
) -> BaseMessageChunk:
|
|
"""Convert a streaming chunk dict to a LangChain message chunk.
|
|
|
|
Args:
|
|
chunk: The streaming chunk dictionary.
|
|
default_class: Default message chunk class.
|
|
|
|
Returns:
|
|
The LangChain message chunk.
|
|
"""
|
|
choice = chunk["choices"][0]
|
|
_dict = choice.get("delta", {})
|
|
role = cast("str", _dict.get("role"))
|
|
content = cast("str", _dict.get("content") or "")
|
|
additional_kwargs: dict = {}
|
|
tool_call_chunks: list = []
|
|
|
|
if raw_tool_calls := _dict.get("tool_calls"):
|
|
for rtc in raw_tool_calls:
|
|
try:
|
|
tool_call_chunks.append(
|
|
tool_call_chunk(
|
|
name=rtc["function"].get("name"),
|
|
args=rtc["function"].get("arguments"),
|
|
id=rtc.get("id"),
|
|
index=rtc["index"],
|
|
)
|
|
)
|
|
except (KeyError, TypeError, AttributeError): # noqa: PERF203
|
|
warnings.warn(
|
|
f"Skipping malformed tool call chunk during streaming: "
|
|
f"unexpected structure in {rtc!r}.",
|
|
stacklevel=2,
|
|
)
|
|
|
|
if role == "user" or default_class == HumanMessageChunk:
|
|
return HumanMessageChunk(content=content)
|
|
if role == "assistant" or default_class == AIMessageChunk:
|
|
if reasoning := _dict.get("reasoning"):
|
|
additional_kwargs["reasoning_content"] = reasoning
|
|
if reasoning_details := _dict.get("reasoning_details"):
|
|
additional_kwargs["reasoning_details"] = reasoning_details
|
|
usage_metadata = None
|
|
response_metadata: dict[str, Any] = {"model_provider": "openrouter"}
|
|
if usage := chunk.get("usage"):
|
|
usage_metadata = _create_usage_metadata(usage)
|
|
# Surface OpenRouter cost data in response_metadata
|
|
if "cost" in usage:
|
|
response_metadata["cost"] = usage["cost"]
|
|
if "cost_details" in usage:
|
|
response_metadata["cost_details"] = usage["cost_details"]
|
|
return AIMessageChunk(
|
|
content=content,
|
|
additional_kwargs=additional_kwargs,
|
|
tool_call_chunks=tool_call_chunks, # type: ignore[arg-type]
|
|
usage_metadata=usage_metadata, # type: ignore[arg-type]
|
|
response_metadata=response_metadata,
|
|
)
|
|
if role == "system" or default_class == SystemMessageChunk:
|
|
return SystemMessageChunk(content=content)
|
|
if role == "tool" or default_class == ToolMessageChunk:
|
|
return ToolMessageChunk(
|
|
content=content, tool_call_id=_dict.get("tool_call_id", "")
|
|
)
|
|
if role:
|
|
warnings.warn(
|
|
f"Unrecognized streaming chunk role '{role}' from OpenRouter. "
|
|
f"Falling back to ChatMessageChunk.",
|
|
stacklevel=2,
|
|
)
|
|
return ChatMessageChunk(content=content, role=role)
|
|
if default_class is ChatMessageChunk:
|
|
return ChatMessageChunk(content=content, role=role or "")
|
|
return default_class(content=content) # type: ignore[call-arg]
|
|
|
|
|
|
def _lc_tool_call_to_openrouter_tool_call(tool_call: ToolCall) -> dict[str, Any]:
|
|
"""Convert a LangChain ``ToolCall`` to an OpenRouter tool call dict.
|
|
|
|
Serializes `args` (a dict) via `json.dumps`.
|
|
"""
|
|
return {
|
|
"type": "function",
|
|
"id": tool_call["id"],
|
|
"function": {
|
|
"name": tool_call["name"],
|
|
"arguments": json.dumps(tool_call["args"], ensure_ascii=False),
|
|
},
|
|
}
|
|
|
|
|
|
def _lc_invalid_tool_call_to_openrouter_tool_call(
|
|
invalid_tool_call: InvalidToolCall,
|
|
) -> dict[str, Any]:
|
|
"""Convert a LangChain `InvalidToolCall` to an OpenRouter tool call dict.
|
|
|
|
Unlike the valid variant, `args` is already a raw string (not a dict) and
|
|
is passed through as-is.
|
|
"""
|
|
return {
|
|
"type": "function",
|
|
"id": invalid_tool_call["id"],
|
|
"function": {
|
|
"name": invalid_tool_call["name"],
|
|
"arguments": invalid_tool_call["args"],
|
|
},
|
|
}
|
|
|
|
|
|
def _create_usage_metadata(token_usage: dict[str, Any]) -> UsageMetadata:
|
|
"""Create usage metadata from OpenRouter token usage response.
|
|
|
|
OpenRouter may return token counts as floats rather than ints, so all
|
|
values are explicitly cast to int.
|
|
|
|
Args:
|
|
token_usage: Token usage dict from the API response.
|
|
|
|
Returns:
|
|
Usage metadata with input/output token details.
|
|
"""
|
|
_input = token_usage.get("prompt_tokens")
|
|
input_tokens = int(
|
|
_input if _input is not None else (token_usage.get("input_tokens") or 0)
|
|
)
|
|
_output = token_usage.get("completion_tokens")
|
|
output_tokens = int(
|
|
_output if _output is not None else (token_usage.get("output_tokens") or 0)
|
|
)
|
|
_total = token_usage.get("total_tokens")
|
|
total_tokens = int(_total if _total is not None else input_tokens + output_tokens)
|
|
|
|
input_details_dict = (
|
|
token_usage.get("prompt_tokens_details")
|
|
or token_usage.get("input_tokens_details")
|
|
or {}
|
|
)
|
|
output_details_dict = (
|
|
token_usage.get("completion_tokens_details")
|
|
or token_usage.get("output_tokens_details")
|
|
or {}
|
|
)
|
|
|
|
cache_read = input_details_dict.get("cached_tokens")
|
|
cache_creation = input_details_dict.get("cache_write_tokens")
|
|
input_token_details: dict = {
|
|
"cache_read": int(cache_read) if cache_read is not None else None,
|
|
"cache_creation": int(cache_creation) if cache_creation is not None else None,
|
|
}
|
|
reasoning_tokens = output_details_dict.get("reasoning_tokens")
|
|
output_token_details: dict = {
|
|
"reasoning": int(reasoning_tokens) if reasoning_tokens is not None else None,
|
|
}
|
|
usage_metadata: UsageMetadata = {
|
|
"input_tokens": input_tokens,
|
|
"output_tokens": output_tokens,
|
|
"total_tokens": total_tokens,
|
|
}
|
|
|
|
filtered_input = {k: v for k, v in input_token_details.items() if v is not None}
|
|
if filtered_input:
|
|
usage_metadata["input_token_details"] = InputTokenDetails(**filtered_input) # type: ignore[typeddict-item]
|
|
filtered_output = {k: v for k, v in output_token_details.items() if v is not None}
|
|
if filtered_output:
|
|
usage_metadata["output_token_details"] = OutputTokenDetails(**filtered_output) # type: ignore[typeddict-item]
|
|
return usage_metadata
|