mirror of
https://github.com/hwchase17/langchain
synced 2024-11-08 07:10:35 +00:00
5ee76fccd5
## PR title langchain_groq[patch]: Invoke callback prior to yielding ## PR message **Description:**Invoke callback prior to yielding token in _stream and _astream methods for groq. Issue: https://github.com/langchain-ai/langchain/issues/16913 Dependencies: None Twitter handle: None
508 lines
19 KiB
Python
508 lines
19 KiB
Python
"""Groq Chat wrapper."""
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from __future__ import annotations
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import os
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import warnings
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from typing import (
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Any,
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AsyncIterator,
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Dict,
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Iterator,
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List,
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Mapping,
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Optional,
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Tuple,
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Type,
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Union,
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cast,
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)
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from langchain_core.callbacks import (
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AsyncCallbackManagerForLLMRun,
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CallbackManagerForLLMRun,
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)
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from langchain_core.language_models.chat_models import (
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BaseChatModel,
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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 (
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AIMessage,
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AIMessageChunk,
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BaseMessage,
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BaseMessageChunk,
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ChatMessage,
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ChatMessageChunk,
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FunctionMessage,
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FunctionMessageChunk,
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HumanMessage,
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HumanMessageChunk,
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SystemMessage,
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SystemMessageChunk,
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ToolMessage,
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ToolMessageChunk,
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)
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from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
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from langchain_core.pydantic_v1 import BaseModel, Field, SecretStr, root_validator
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from langchain_core.utils import (
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convert_to_secret_str,
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get_from_dict_or_env,
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get_pydantic_field_names,
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)
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class ChatGroq(BaseChatModel):
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"""`Groq` Chat large language models API.
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To use, you should have the
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environment variable ``GROQ_API_KEY`` set with your API key.
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Any parameters that are valid to be passed to the groq.create call can be passed
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in, even if not explicitly saved on this class.
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Example:
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.. code-block:: python
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from langchain_community.chat_models import ChatGroq
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groq = ChatGroq(model_name="mixtral-8x7b-32768")
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"""
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client: Any = Field(default=None, exclude=True) #: :meta private:
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async_client: Any = Field(default=None, exclude=True) #: :meta private:
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model_name: str = Field(default="mixtral-8x7b-32768", alias="model")
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"""Model name to use."""
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temperature: float = 0.7
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"""What sampling temperature to use."""
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model_kwargs: Dict[str, Any] = Field(default_factory=dict)
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"""Holds any model parameters valid for `create` call not explicitly specified."""
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groq_api_key: Optional[SecretStr] = Field(default=None, alias="api_key")
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"""Automatically inferred from env var `groq_API_KEY` if not provided."""
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groq_api_base: Optional[str] = Field(default=None, alias="base_url")
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"""Base URL path for API requests, leave blank if not using a proxy or service
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emulator."""
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# to support explicit proxy for Groq
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groq_proxy: Optional[str] = None
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request_timeout: Union[float, Tuple[float, float], Any, None] = Field(
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default=None, alias="timeout"
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)
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"""Timeout for requests to Groq completion API. Can be float, httpx.Timeout or
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None."""
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max_retries: int = 2
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"""Maximum number of retries to make when generating."""
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streaming: bool = False
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"""Whether to stream the results or not."""
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n: int = 1
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"""Number of chat completions to generate for each prompt."""
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max_tokens: Optional[int] = None
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"""Maximum number of tokens to generate."""
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default_headers: Union[Mapping[str, str], None] = None
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default_query: Union[Mapping[str, object], None] = None
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# Configure a custom httpx client. See the
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# [httpx documentation](https://www.python-httpx.org/api/#client) for more details.
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http_client: Union[Any, None] = None
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"""Optional httpx.Client."""
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class Config:
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"""Configuration for this pydantic object."""
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allow_population_by_field_name = True
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@root_validator(pre=True)
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def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
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"""Build extra kwargs from additional params that were passed in."""
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all_required_field_names = get_pydantic_field_names(cls)
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extra = values.get("model_kwargs", {})
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for field_name in list(values):
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if field_name in extra:
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raise ValueError(f"Found {field_name} supplied twice.")
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if field_name not in all_required_field_names:
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warnings.warn(
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f"""WARNING! {field_name} is not default parameter.
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{field_name} was transferred to model_kwargs.
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Please confirm that {field_name} is what you intended."""
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)
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extra[field_name] = values.pop(field_name)
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invalid_model_kwargs = all_required_field_names.intersection(extra.keys())
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if invalid_model_kwargs:
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raise ValueError(
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f"Parameters {invalid_model_kwargs} should be specified explicitly. "
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f"Instead they were passed in as part of `model_kwargs` parameter."
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)
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values["model_kwargs"] = extra
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return values
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@root_validator()
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def validate_environment(cls, values: Dict) -> Dict:
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"""Validate that api key and python package exists in environment."""
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if values["n"] < 1:
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raise ValueError("n must be at least 1.")
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if values["n"] > 1 and values["streaming"]:
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raise ValueError("n must be 1 when streaming.")
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if values["temperature"] == 0:
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values["temperature"] = 1e-8
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values["groq_api_key"] = convert_to_secret_str(
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get_from_dict_or_env(values, "groq_api_key", "GROQ_API_KEY")
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)
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values["groq_api_base"] = values["groq_api_base"] or os.getenv("GROQ_API_BASE")
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values["groq_proxy"] = values["groq_proxy"] = os.getenv("GROQ_PROXY")
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client_params = {
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"api_key": values["groq_api_key"].get_secret_value(),
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"base_url": values["groq_api_base"],
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"timeout": values["request_timeout"],
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"max_retries": values["max_retries"],
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"default_headers": values["default_headers"],
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"default_query": values["default_query"],
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"http_client": values["http_client"],
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}
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try:
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import groq
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if not values.get("client"):
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values["client"] = groq.Groq(**client_params).chat.completions
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if not values.get("async_client"):
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values["async_client"] = groq.AsyncGroq(
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**client_params
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).chat.completions
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except ImportError:
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raise ImportError(
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"Could not import groq python package. "
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"Please install it with `pip install groq`."
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)
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return values
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#
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# Serializable class method overrides
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#
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@property
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def lc_secrets(self) -> Dict[str, str]:
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return {"groq_api_key": "GROQ_API_KEY"}
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@classmethod
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def is_lc_serializable(cls) -> bool:
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"""Return whether this model can be serialized by Langchain."""
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return True
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#
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# BaseChatModel method overrides
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#
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@property
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def _llm_type(self) -> str:
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"""Return type of model."""
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return "groq-chat"
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def _generate(
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self,
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messages: List[BaseMessage],
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stop: Optional[List[str]] = None,
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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stream: Optional[bool] = None,
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**kwargs: Any,
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) -> ChatResult:
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should_stream = stream if stream is not None else self.streaming
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if should_stream:
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stream_iter = self._stream(
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messages, stop=stop, run_manager=run_manager, **kwargs
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)
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return generate_from_stream(stream_iter)
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message_dicts, params = self._create_message_dicts(messages, stop)
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params = {
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**params,
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**({"stream": stream} if stream is not None else {}),
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**kwargs,
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}
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response = self.client.create(messages=message_dicts, **params)
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return self._create_chat_result(response)
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async def _agenerate(
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self,
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messages: List[BaseMessage],
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stop: Optional[List[str]] = None,
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run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
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stream: Optional[bool] = None,
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**kwargs: Any,
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) -> ChatResult:
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should_stream = stream if stream is not None else self.streaming
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if should_stream:
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stream_iter = self._astream(
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messages, stop=stop, run_manager=run_manager, **kwargs
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)
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return await agenerate_from_stream(stream_iter)
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message_dicts, params = self._create_message_dicts(messages, stop)
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params = {
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**params,
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**({"stream": stream} if stream is not None else {}),
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**kwargs,
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}
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response = await self.async_client.create(messages=message_dicts, **params)
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return self._create_chat_result(response)
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def _stream(
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self,
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messages: List[BaseMessage],
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stop: Optional[List[str]] = None,
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> Iterator[ChatGenerationChunk]:
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message_dicts, params = self._create_message_dicts(messages, stop)
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params = {**params, **kwargs, "stream": True}
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default_chunk_class = AIMessageChunk
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for chunk in self.client.create(messages=message_dicts, **params):
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if not isinstance(chunk, dict):
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chunk = chunk.dict()
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if len(chunk["choices"]) == 0:
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continue
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choice = chunk["choices"][0]
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chunk = _convert_delta_to_message_chunk(
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choice["delta"], default_chunk_class
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)
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generation_info = {}
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if finish_reason := choice.get("finish_reason"):
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generation_info["finish_reason"] = finish_reason
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logprobs = choice.get("logprobs")
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if logprobs:
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generation_info["logprobs"] = logprobs
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default_chunk_class = chunk.__class__
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chunk = ChatGenerationChunk(
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message=chunk, generation_info=generation_info or None
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)
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if run_manager:
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run_manager.on_llm_new_token(chunk.text, chunk=chunk, logprobs=logprobs)
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yield chunk
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async def _astream(
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self,
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messages: List[BaseMessage],
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stop: Optional[List[str]] = None,
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run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> AsyncIterator[ChatGenerationChunk]:
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message_dicts, params = self._create_message_dicts(messages, stop)
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params = {**params, **kwargs, "stream": True}
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default_chunk_class = AIMessageChunk
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async for chunk in await self.async_client.create(
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messages=message_dicts, **params
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):
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if not isinstance(chunk, dict):
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chunk = chunk.dict()
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if len(chunk["choices"]) == 0:
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continue
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choice = chunk["choices"][0]
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chunk = _convert_delta_to_message_chunk(
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choice["delta"], default_chunk_class
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)
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generation_info = {}
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if finish_reason := choice.get("finish_reason"):
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generation_info["finish_reason"] = finish_reason
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logprobs = choice.get("logprobs")
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if logprobs:
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generation_info["logprobs"] = logprobs
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default_chunk_class = chunk.__class__
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chunk = ChatGenerationChunk(
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message=chunk, generation_info=generation_info or None
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)
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if run_manager:
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await run_manager.on_llm_new_token(
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token=chunk.text, chunk=chunk, logprobs=logprobs
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)
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yield chunk
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#
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# Internal methods
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#
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@property
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def _default_params(self) -> Dict[str, Any]:
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"""Get the default parameters for calling Groq API."""
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params = {
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"model": self.model_name,
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"stream": self.streaming,
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"n": self.n,
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"temperature": self.temperature,
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**self.model_kwargs,
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}
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if self.max_tokens is not None:
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params["max_tokens"] = self.max_tokens
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return params
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def _create_chat_result(self, response: Union[dict, BaseModel]) -> ChatResult:
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generations = []
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if not isinstance(response, dict):
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response = response.dict()
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for res in response["choices"]:
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message = _convert_dict_to_message(res["message"])
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generation_info = dict(finish_reason=res.get("finish_reason"))
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if "logprobs" in res:
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generation_info["logprobs"] = res["logprobs"]
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gen = ChatGeneration(
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message=message,
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generation_info=generation_info,
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)
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generations.append(gen)
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token_usage = response.get("usage", {})
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llm_output = {
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"token_usage": token_usage,
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"model_name": self.model_name,
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"system_fingerprint": response.get("system_fingerprint", ""),
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}
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return ChatResult(generations=generations, llm_output=llm_output)
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def _create_message_dicts(
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self, messages: List[BaseMessage], stop: Optional[List[str]]
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) -> Tuple[List[Dict[str, Any]], Dict[str, Any]]:
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params = self._default_params
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if stop is not None:
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if "stop" in params:
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raise ValueError("`stop` found in both the input and default params.")
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params["stop"] = stop
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message_dicts = [_convert_message_to_dict(m) for m in messages]
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return message_dicts, params
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def _combine_llm_outputs(self, llm_outputs: List[Optional[dict]]) -> dict:
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overall_token_usage: dict = {}
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system_fingerprint = None
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for output in llm_outputs:
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if output is None:
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# Happens in streaming
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continue
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token_usage = output["token_usage"]
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if token_usage is not None:
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for k, v in token_usage.items():
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if k in overall_token_usage:
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overall_token_usage[k] += v
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else:
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overall_token_usage[k] = v
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if system_fingerprint is None:
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system_fingerprint = output.get("system_fingerprint")
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combined = {"token_usage": overall_token_usage, "model_name": self.model_name}
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if system_fingerprint:
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combined["system_fingerprint"] = system_fingerprint
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return combined
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#
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# Type conversion helpers
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#
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def _convert_message_to_dict(message: BaseMessage) -> dict:
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"""Convert a LangChain message to a dictionary.
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Args:
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message: The LangChain message.
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Returns:
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The dictionary.
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"""
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message_dict: Dict[str, Any]
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if isinstance(message, ChatMessage):
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message_dict = {"role": message.role, "content": message.content}
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elif isinstance(message, HumanMessage):
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message_dict = {"role": "user", "content": message.content}
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elif isinstance(message, AIMessage):
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message_dict = {"role": "assistant", "content": message.content}
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if "function_call" in message.additional_kwargs:
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message_dict["function_call"] = message.additional_kwargs["function_call"]
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# If function call only, content is None not empty string
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if message_dict["content"] == "":
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message_dict["content"] = None
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if "tool_calls" in message.additional_kwargs:
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message_dict["tool_calls"] = message.additional_kwargs["tool_calls"]
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# If tool calls only, content is None not empty string
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if message_dict["content"] == "":
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message_dict["content"] = None
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elif isinstance(message, SystemMessage):
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message_dict = {"role": "system", "content": message.content}
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elif isinstance(message, FunctionMessage):
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message_dict = {
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"role": "function",
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"content": message.content,
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"name": message.name,
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}
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elif isinstance(message, ToolMessage):
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message_dict = {
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"role": "tool",
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"content": message.content,
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"tool_call_id": message.tool_call_id,
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}
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else:
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raise TypeError(f"Got unknown type {message}")
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if "name" in message.additional_kwargs:
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message_dict["name"] = message.additional_kwargs["name"]
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return message_dict
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def _convert_delta_to_message_chunk(
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_dict: Mapping[str, Any], default_class: Type[BaseMessageChunk]
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) -> BaseMessageChunk:
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role = cast(str, _dict.get("role"))
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content = cast(str, _dict.get("content") or "")
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additional_kwargs: Dict = {}
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if _dict.get("function_call"):
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function_call = dict(_dict["function_call"])
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if "name" in function_call and function_call["name"] is None:
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function_call["name"] = ""
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additional_kwargs["function_call"] = function_call
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if _dict.get("tool_calls"):
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additional_kwargs["tool_calls"] = _dict["tool_calls"]
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if role == "user" or default_class == HumanMessageChunk:
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return HumanMessageChunk(content=content)
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elif role == "assistant" or default_class == AIMessageChunk:
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return AIMessageChunk(content=content, additional_kwargs=additional_kwargs)
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elif role == "system" or default_class == SystemMessageChunk:
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return SystemMessageChunk(content=content)
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elif role == "function" or default_class == FunctionMessageChunk:
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return FunctionMessageChunk(content=content, name=_dict["name"])
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elif role == "tool" or default_class == ToolMessageChunk:
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return ToolMessageChunk(content=content, tool_call_id=_dict["tool_call_id"])
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elif role or default_class == ChatMessageChunk:
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return ChatMessageChunk(content=content, role=role)
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else:
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return default_class(content=content) # type: ignore
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def _convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage:
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"""Convert a dictionary to a LangChain message.
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Args:
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_dict: The dictionary.
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Returns:
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The LangChain message.
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"""
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role = _dict.get("role")
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if role == "user":
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return HumanMessage(content=_dict.get("content", ""))
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elif role == "assistant":
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content = _dict.get("content", "")
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additional_kwargs: Dict = {}
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if function_call := _dict.get("function_call"):
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additional_kwargs["function_call"] = dict(function_call)
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if tool_calls := _dict.get("tool_calls"):
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additional_kwargs["tool_calls"] = tool_calls
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return AIMessage(content=content, additional_kwargs=additional_kwargs)
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elif role == "system":
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return SystemMessage(content=_dict.get("content", ""))
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elif role == "function":
|
|
return FunctionMessage(content=_dict.get("content", ""), name=_dict.get("name"))
|
|
elif 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,
|
|
)
|
|
else:
|
|
return ChatMessage(content=_dict.get("content", ""), role=role)
|