mirror of
https://github.com/hwchase17/langchain.git
synced 2025-04-27 19:46:55 +00:00
b91daf06eb
The default model for `ChatGroq`, `"mixtral-8x7b-32768"`, is being retired on March 20, 2025. Here we remove the default, such that model names must be explicitly specified (being explicit is a good practice here, and avoids the need for breaking changes down the line). This change will be released in a minor version bump to 0.3. This follows https://github.com/langchain-ai/langchain/pull/30161 (released in version 0.2.5), where we began generating warnings to this effect. 
1236 lines
49 KiB
Python
1236 lines
49 KiB
Python
"""Groq Chat wrapper."""
|
|
|
|
from __future__ import annotations
|
|
|
|
import json
|
|
import warnings
|
|
from operator import itemgetter
|
|
from typing import (
|
|
Any,
|
|
AsyncIterator,
|
|
Callable,
|
|
Dict,
|
|
Iterator,
|
|
List,
|
|
Literal,
|
|
Mapping,
|
|
Optional,
|
|
Sequence,
|
|
Tuple,
|
|
Type,
|
|
TypedDict,
|
|
Union,
|
|
cast,
|
|
)
|
|
|
|
from langchain_core._api import deprecated
|
|
from langchain_core.callbacks import (
|
|
AsyncCallbackManagerForLLMRun,
|
|
CallbackManagerForLLMRun,
|
|
)
|
|
from langchain_core.language_models import LanguageModelInput
|
|
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,
|
|
FunctionMessage,
|
|
FunctionMessageChunk,
|
|
HumanMessage,
|
|
HumanMessageChunk,
|
|
InvalidToolCall,
|
|
SystemMessage,
|
|
SystemMessageChunk,
|
|
ToolCall,
|
|
ToolMessage,
|
|
ToolMessageChunk,
|
|
)
|
|
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_openai_function,
|
|
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_groq.version import __version__
|
|
|
|
|
|
class ChatGroq(BaseChatModel):
|
|
"""`Groq` Chat large language models API.
|
|
|
|
To use, you should have the
|
|
environment variable ``GROQ_API_KEY`` set with your API key.
|
|
|
|
Any parameters that are valid to be passed to the groq.create call
|
|
can be passed in, even if not explicitly saved on this class.
|
|
|
|
Setup:
|
|
Install ``langchain-groq`` and set environment variable
|
|
``GROQ_API_KEY``.
|
|
|
|
.. code-block:: bash
|
|
|
|
pip install -U langchain-groq
|
|
export GROQ_API_KEY="your-api-key"
|
|
|
|
Key init args — completion params:
|
|
model: str
|
|
Name of Groq model to use. E.g. "llama-3.1-8b-instant".
|
|
temperature: float
|
|
Sampling temperature. Ranges from 0.0 to 1.0.
|
|
max_tokens: Optional[int]
|
|
Max number of tokens to generate.
|
|
model_kwargs: Dict[str, Any]
|
|
Holds any model parameters valid for create call not
|
|
explicitly specified.
|
|
|
|
Key init args — client params:
|
|
timeout: Union[float, Tuple[float, float], Any, None]
|
|
Timeout for requests.
|
|
max_retries: int
|
|
Max number of retries.
|
|
api_key: Optional[str]
|
|
Groq API key. If not passed in will be read from env var GROQ_API_KEY.
|
|
base_url: Optional[str]
|
|
Base URL path for API requests, leave blank if not using a proxy
|
|
or service emulator.
|
|
custom_get_token_ids: Optional[Callable[[str], List[int]]]
|
|
Optional encoder to use for counting tokens.
|
|
|
|
See full list of supported init args and their descriptions in the params
|
|
section.
|
|
|
|
Instantiate:
|
|
.. code-block:: python
|
|
|
|
from langchain_groq import ChatGroq
|
|
|
|
llm = ChatGroq(
|
|
model="llama-3.1-8b-instant",
|
|
temperature=0.0,
|
|
max_retries=2,
|
|
# other params...
|
|
)
|
|
|
|
Invoke:
|
|
.. code-block:: python
|
|
|
|
messages = [
|
|
("system", "You are a helpful translator. Translate the user
|
|
sentence to French."),
|
|
("human", "I love programming."),
|
|
]
|
|
llm.invoke(messages)
|
|
|
|
.. code-block:: python
|
|
|
|
AIMessage(content='The English sentence "I love programming" can
|
|
be translated to French as "J\'aime programmer". The word
|
|
"programming" is translated as "programmer" in French.',
|
|
response_metadata={'token_usage': {'completion_tokens': 38,
|
|
'prompt_tokens': 28, 'total_tokens': 66, 'completion_time':
|
|
0.057975474, 'prompt_time': 0.005366091, 'queue_time': None,
|
|
'total_time': 0.063341565}, 'model_name': 'llama-3.1-8b-instant',
|
|
'system_fingerprint': 'fp_c5f20b5bb1', 'finish_reason': 'stop',
|
|
'logprobs': None}, id='run-ecc71d70-e10c-4b69-8b8c-b8027d95d4b8-0')
|
|
|
|
Stream:
|
|
.. code-block:: python
|
|
|
|
for chunk in llm.stream(messages):
|
|
print(chunk.text(), end="")
|
|
|
|
.. code-block:: python
|
|
|
|
content='' id='run-4e9f926b-73f5-483b-8ef5-09533d925853'
|
|
content='The' id='run-4e9f926b-73f5-483b-8ef5-09533d925853'
|
|
content=' English' id='run-4e9f926b-73f5-483b-8ef5-09533d925853'
|
|
content=' sentence' id='run-4e9f926b-73f5-483b-8ef5-09533d925853'
|
|
...
|
|
content=' program' id='run-4e9f926b-73f5-483b-8ef5-09533d925853'
|
|
content='".' id='run-4e9f926b-73f5-483b-8ef5-09533d925853'
|
|
content='' response_metadata={'finish_reason': 'stop'}
|
|
id='run-4e9f926b-73f5-483b-8ef5-09533d925853
|
|
|
|
.. code-block:: python
|
|
|
|
stream = llm.stream(messages)
|
|
full = next(stream)
|
|
for chunk in stream:
|
|
full += chunk
|
|
full
|
|
|
|
.. code-block:: python
|
|
|
|
AIMessageChunk(content='The English sentence "I love programming"
|
|
can be translated to French as "J\'aime programmer".
|
|
Here\'s the breakdown of the sentence:\n\n* "J\'aime" is the
|
|
French equivalent of "I love"\n* "programmer" is the French
|
|
infinitive for "to program"\n\nSo, the literal translation
|
|
is "I love to program". However, in English we often omit the
|
|
"to" when talking about activities we love, and the same applies
|
|
to French. Therefore, "J\'aime programmer" is the correct and
|
|
natural way to express "I love programming" in French.',
|
|
response_metadata={'finish_reason': 'stop'},
|
|
id='run-a3c35ac4-0750-4d08-ac55-bfc63805de76')
|
|
|
|
Async:
|
|
.. code-block:: python
|
|
|
|
await llm.ainvoke(messages)
|
|
|
|
.. code-block:: python
|
|
|
|
AIMessage(content='The English sentence "I love programming" can
|
|
be translated to French as "J\'aime programmer". The word
|
|
"programming" is translated as "programmer" in French. I hope
|
|
this helps! Let me know if you have any other questions.',
|
|
response_metadata={'token_usage': {'completion_tokens': 53,
|
|
'prompt_tokens': 28, 'total_tokens': 81, 'completion_time':
|
|
0.083623752, 'prompt_time': 0.007365126, 'queue_time': None,
|
|
'total_time': 0.090988878}, 'model_name': 'llama-3.1-8b-instant',
|
|
'system_fingerprint': 'fp_c5f20b5bb1', 'finish_reason': 'stop',
|
|
'logprobs': None}, id='run-897f3391-1bea-42e2-82e0-686e2367bcf8-0')
|
|
|
|
Tool calling:
|
|
.. code-block:: python
|
|
|
|
from pydantic import BaseModel, Field
|
|
|
|
class GetWeather(BaseModel):
|
|
'''Get the current weather in a given location'''
|
|
|
|
location: str = Field(..., description="The city and state,
|
|
e.g. San Francisco, CA")
|
|
|
|
class GetPopulation(BaseModel):
|
|
'''Get the current population in a given location'''
|
|
|
|
location: str = Field(..., description="The city and state,
|
|
e.g. San Francisco, CA")
|
|
|
|
model_with_tools = llm.bind_tools([GetWeather, GetPopulation])
|
|
ai_msg = model_with_tools.invoke("What is the population of NY?")
|
|
ai_msg.tool_calls
|
|
|
|
.. code-block:: python
|
|
|
|
[{'name': 'GetPopulation',
|
|
'args': {'location': 'NY'},
|
|
'id': 'call_bb8d'}]
|
|
|
|
See ``ChatGroq.bind_tools()`` method for more.
|
|
|
|
Structured output:
|
|
.. code-block:: python
|
|
|
|
from typing import Optional
|
|
|
|
from pydantic import BaseModel, Field
|
|
|
|
class Joke(BaseModel):
|
|
'''Joke to tell user.'''
|
|
|
|
setup: str = Field(description="The setup of the joke")
|
|
punchline: str = Field(description="The punchline to the joke")
|
|
rating: Optional[int] = Field(description="How funny the joke
|
|
is, from 1 to 10")
|
|
|
|
structured_model = llm.with_structured_output(Joke)
|
|
structured_model.invoke("Tell me a joke about cats")
|
|
|
|
.. code-block:: python
|
|
|
|
Joke(setup="Why don't cats play poker in the jungle?",
|
|
punchline='Too many cheetahs!', rating=None)
|
|
|
|
See ``ChatGroq.with_structured_output()`` for more.
|
|
|
|
Response metadata
|
|
.. code-block:: python
|
|
|
|
ai_msg = llm.invoke(messages)
|
|
ai_msg.response_metadata
|
|
|
|
.. code-block:: python
|
|
|
|
{'token_usage': {'completion_tokens': 70,
|
|
'prompt_tokens': 28,
|
|
'total_tokens': 98,
|
|
'completion_time': 0.111956391,
|
|
'prompt_time': 0.007518279,
|
|
'queue_time': None,
|
|
'total_time': 0.11947467},
|
|
'model_name': 'llama-3.1-8b-instant',
|
|
'system_fingerprint': 'fp_c5f20b5bb1',
|
|
'finish_reason': 'stop',
|
|
'logprobs': None}
|
|
"""
|
|
|
|
client: Any = Field(default=None, exclude=True) #: :meta private:
|
|
async_client: Any = Field(default=None, exclude=True) #: :meta private:
|
|
model_name: str = Field(alias="model")
|
|
"""Model name to use."""
|
|
temperature: float = 0.7
|
|
"""What sampling temperature to use."""
|
|
stop: Optional[Union[List[str], str]] = Field(default=None, alias="stop_sequences")
|
|
"""Default stop sequences."""
|
|
model_kwargs: Dict[str, Any] = Field(default_factory=dict)
|
|
"""Holds any model parameters valid for `create` call not explicitly specified."""
|
|
groq_api_key: Optional[SecretStr] = Field(
|
|
alias="api_key", default_factory=secret_from_env("GROQ_API_KEY", default=None)
|
|
)
|
|
"""Automatically inferred from env var `GROQ_API_KEY` if not provided."""
|
|
groq_api_base: Optional[str] = Field(
|
|
alias="base_url", default_factory=from_env("GROQ_API_BASE", default=None)
|
|
)
|
|
"""Base URL path for API requests, leave blank if not using a proxy or service
|
|
emulator."""
|
|
# to support explicit proxy for Groq
|
|
groq_proxy: Optional[str] = Field(
|
|
default_factory=from_env("GROQ_PROXY", default=None)
|
|
)
|
|
request_timeout: Union[float, Tuple[float, float], Any, None] = Field(
|
|
default=None, alias="timeout"
|
|
)
|
|
"""Timeout for requests to Groq completion API. Can be float, httpx.Timeout or
|
|
None."""
|
|
max_retries: int = 2
|
|
"""Maximum number of retries to make when generating."""
|
|
streaming: bool = False
|
|
"""Whether to stream the results or not."""
|
|
n: int = 1
|
|
"""Number of chat completions to generate for each prompt."""
|
|
max_tokens: Optional[int] = None
|
|
"""Maximum number of tokens to generate."""
|
|
default_headers: Union[Mapping[str, str], None] = None
|
|
default_query: Union[Mapping[str, object], None] = None
|
|
# Configure a custom httpx client. See the
|
|
# [httpx documentation](https://www.python-httpx.org/api/#client) for more details.
|
|
http_client: Union[Any, None] = None
|
|
"""Optional httpx.Client."""
|
|
http_async_client: Union[Any, None] = None
|
|
"""Optional httpx.AsyncClient. Only used for async invocations. Must specify
|
|
http_client as well if you'd like a custom client for sync invocations."""
|
|
|
|
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:
|
|
raise ValueError(f"Found {field_name} supplied twice.")
|
|
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."""
|
|
)
|
|
extra[field_name] = values.pop(field_name)
|
|
|
|
invalid_model_kwargs = all_required_field_names.intersection(extra.keys())
|
|
if invalid_model_kwargs:
|
|
raise ValueError(
|
|
f"Parameters {invalid_model_kwargs} should be specified explicitly. "
|
|
f"Instead they were passed in as part of `model_kwargs` parameter."
|
|
)
|
|
|
|
values["model_kwargs"] = extra
|
|
return values
|
|
|
|
@model_validator(mode="after")
|
|
def validate_environment(self) -> Self:
|
|
"""Validate that api key and python package exists in environment."""
|
|
if self.n < 1:
|
|
raise ValueError("n must be at least 1.")
|
|
if self.n > 1 and self.streaming:
|
|
raise ValueError("n must be 1 when streaming.")
|
|
if self.temperature == 0:
|
|
self.temperature = 1e-8
|
|
|
|
default_headers = {"User-Agent": f"langchain/{__version__}"} | dict(
|
|
self.default_headers or {}
|
|
)
|
|
|
|
client_params: Dict[str, Any] = {
|
|
"api_key": (
|
|
self.groq_api_key.get_secret_value() if self.groq_api_key else None
|
|
),
|
|
"base_url": self.groq_api_base,
|
|
"timeout": self.request_timeout,
|
|
"max_retries": self.max_retries,
|
|
"default_headers": default_headers,
|
|
"default_query": self.default_query,
|
|
}
|
|
|
|
try:
|
|
import groq
|
|
|
|
sync_specific: Dict[str, Any] = {"http_client": self.http_client}
|
|
if not self.client:
|
|
self.client = groq.Groq(
|
|
**client_params, **sync_specific
|
|
).chat.completions
|
|
if not self.async_client:
|
|
async_specific: Dict[str, Any] = {"http_client": self.http_async_client}
|
|
self.async_client = groq.AsyncGroq(
|
|
**client_params, **async_specific
|
|
).chat.completions
|
|
except ImportError:
|
|
raise ImportError(
|
|
"Could not import groq python package. "
|
|
"Please install it with `pip install groq`."
|
|
)
|
|
return self
|
|
|
|
#
|
|
# Serializable class method overrides
|
|
#
|
|
@property
|
|
def lc_secrets(self) -> Dict[str, str]:
|
|
return {"groq_api_key": "GROQ_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 model."""
|
|
return "groq-chat"
|
|
|
|
def _get_ls_params(
|
|
self, stop: Optional[List[str]] = None, **kwargs: Any
|
|
) -> LangSmithParams:
|
|
"""Get standard params for tracing."""
|
|
params = self._get_invocation_params(stop=stop, **kwargs)
|
|
ls_params = LangSmithParams(
|
|
ls_provider="groq",
|
|
ls_model_name=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: Optional[List[str]] = None,
|
|
run_manager: Optional[CallbackManagerForLLMRun] = 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,
|
|
}
|
|
response = self.client.create(messages=message_dicts, **params)
|
|
return self._create_chat_result(response)
|
|
|
|
async def _agenerate(
|
|
self,
|
|
messages: List[BaseMessage],
|
|
stop: Optional[List[str]] = None,
|
|
run_manager: Optional[AsyncCallbackManagerForLLMRun] = 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,
|
|
}
|
|
response = await self.async_client.create(messages=message_dicts, **params)
|
|
return self._create_chat_result(response)
|
|
|
|
def _stream(
|
|
self,
|
|
messages: List[BaseMessage],
|
|
stop: Optional[List[str]] = None,
|
|
run_manager: Optional[CallbackManagerForLLMRun] = None,
|
|
**kwargs: Any,
|
|
) -> Iterator[ChatGenerationChunk]:
|
|
message_dicts, params = self._create_message_dicts(messages, stop)
|
|
|
|
params = {**params, **kwargs, "stream": True}
|
|
|
|
default_chunk_class: Type[BaseMessageChunk] = AIMessageChunk
|
|
for chunk in self.client.create(messages=message_dicts, **params):
|
|
if not isinstance(chunk, dict):
|
|
chunk = chunk.model_dump()
|
|
if len(chunk["choices"]) == 0:
|
|
continue
|
|
choice = chunk["choices"][0]
|
|
message_chunk = _convert_chunk_to_message_chunk(chunk, default_chunk_class)
|
|
generation_info = {}
|
|
if finish_reason := choice.get("finish_reason"):
|
|
generation_info["finish_reason"] = finish_reason
|
|
logprobs = choice.get("logprobs")
|
|
if logprobs:
|
|
generation_info["logprobs"] = logprobs
|
|
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(
|
|
self,
|
|
messages: List[BaseMessage],
|
|
stop: Optional[List[str]] = None,
|
|
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
|
|
**kwargs: Any,
|
|
) -> AsyncIterator[ChatGenerationChunk]:
|
|
message_dicts, params = self._create_message_dicts(messages, stop)
|
|
|
|
params = {**params, **kwargs, "stream": True}
|
|
|
|
default_chunk_class: Type[BaseMessageChunk] = AIMessageChunk
|
|
async for chunk in await self.async_client.create(
|
|
messages=message_dicts, **params
|
|
):
|
|
if not isinstance(chunk, dict):
|
|
chunk = chunk.model_dump()
|
|
if len(chunk["choices"]) == 0:
|
|
continue
|
|
choice = chunk["choices"][0]
|
|
message_chunk = _convert_chunk_to_message_chunk(chunk, default_chunk_class)
|
|
generation_info = {}
|
|
if finish_reason := choice.get("finish_reason"):
|
|
generation_info["finish_reason"] = finish_reason
|
|
logprobs = choice.get("logprobs")
|
|
if logprobs:
|
|
generation_info["logprobs"] = logprobs
|
|
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]:
|
|
"""Get the default parameters for calling Groq API."""
|
|
params = {
|
|
"model": self.model_name,
|
|
"stream": self.streaming,
|
|
"n": self.n,
|
|
"temperature": self.temperature,
|
|
"stop": self.stop,
|
|
**self.model_kwargs,
|
|
}
|
|
if self.max_tokens is not None:
|
|
params["max_tokens"] = self.max_tokens
|
|
return params
|
|
|
|
def _create_chat_result(self, response: Union[dict, BaseModel]) -> ChatResult:
|
|
generations = []
|
|
if not isinstance(response, dict):
|
|
response = response.model_dump()
|
|
token_usage = response.get("usage", {})
|
|
for res in response["choices"]:
|
|
message = _convert_dict_to_message(res["message"])
|
|
if token_usage and isinstance(message, AIMessage):
|
|
input_tokens = token_usage.get("prompt_tokens", 0)
|
|
output_tokens = token_usage.get("completion_tokens", 0)
|
|
message.usage_metadata = {
|
|
"input_tokens": input_tokens,
|
|
"output_tokens": output_tokens,
|
|
"total_tokens": token_usage.get(
|
|
"total_tokens", input_tokens + output_tokens
|
|
),
|
|
}
|
|
generation_info = dict(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 = {
|
|
"token_usage": token_usage,
|
|
"model_name": self.model_name,
|
|
"system_fingerprint": response.get("system_fingerprint", ""),
|
|
}
|
|
return ChatResult(generations=generations, llm_output=llm_output)
|
|
|
|
def _create_message_dicts(
|
|
self, messages: List[BaseMessage], stop: Optional[List[str]]
|
|
) -> 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 _combine_llm_outputs(self, llm_outputs: List[Optional[dict]]) -> dict:
|
|
overall_token_usage: dict = {}
|
|
system_fingerprint = None
|
|
for output in llm_outputs:
|
|
if output is None:
|
|
# Happens in streaming
|
|
continue
|
|
token_usage = output["token_usage"]
|
|
if token_usage is not None:
|
|
for k, v in token_usage.items():
|
|
if k in overall_token_usage and v is not None:
|
|
overall_token_usage[k] += v
|
|
else:
|
|
overall_token_usage[k] = v
|
|
if system_fingerprint is None:
|
|
system_fingerprint = output.get("system_fingerprint")
|
|
combined = {"token_usage": overall_token_usage, "model_name": self.model_name}
|
|
if system_fingerprint:
|
|
combined["system_fingerprint"] = system_fingerprint
|
|
return combined
|
|
|
|
@deprecated(
|
|
since="0.2.1",
|
|
alternative="langchain_groq.chat_models.ChatGroq.bind_tools",
|
|
removal="1.0.0",
|
|
)
|
|
def bind_functions(
|
|
self,
|
|
functions: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable, BaseTool]],
|
|
function_call: Optional[
|
|
Union[_FunctionCall, str, Literal["auto", "none"]]
|
|
] = None,
|
|
**kwargs: Any,
|
|
) -> Runnable[LanguageModelInput, BaseMessage]:
|
|
"""Bind functions (and other objects) to this chat model.
|
|
|
|
Model is compatible with OpenAI function-calling API.
|
|
|
|
NOTE: Using bind_tools is recommended instead, as the `functions` and
|
|
`function_call` request parameters are officially deprecated.
|
|
|
|
Args:
|
|
functions: A list of function definitions to bind to this chat model.
|
|
Can be a dictionary, pydantic model, or callable. Pydantic
|
|
models and callables will be automatically converted to
|
|
their schema dictionary representation.
|
|
function_call: Which function to require the model to call.
|
|
Must be the name of the single provided function or
|
|
"auto" to automatically determine which function to call
|
|
(if any).
|
|
**kwargs: Any additional parameters to pass to
|
|
:meth:`~langchain_groq.chat_models.ChatGroq.bind`.
|
|
"""
|
|
|
|
formatted_functions = [convert_to_openai_function(fn) for fn in functions]
|
|
if function_call is not None:
|
|
function_call = (
|
|
{"name": function_call}
|
|
if isinstance(function_call, str)
|
|
and function_call not in ("auto", "none")
|
|
else function_call
|
|
)
|
|
if isinstance(function_call, dict) and len(formatted_functions) != 1:
|
|
raise ValueError(
|
|
"When specifying `function_call`, you must provide exactly one "
|
|
"function."
|
|
)
|
|
if (
|
|
isinstance(function_call, dict)
|
|
and formatted_functions[0]["name"] != function_call["name"]
|
|
):
|
|
raise ValueError(
|
|
f"Function call {function_call} was specified, but the only "
|
|
f"provided function was {formatted_functions[0]['name']}."
|
|
)
|
|
kwargs = {**kwargs, "function_call": function_call}
|
|
return super().bind(
|
|
functions=formatted_functions,
|
|
**kwargs,
|
|
)
|
|
|
|
def bind_tools(
|
|
self,
|
|
tools: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable, BaseTool]],
|
|
*,
|
|
tool_choice: Optional[
|
|
Union[dict, str, Literal["auto", "any", "none"], bool]
|
|
] = None,
|
|
**kwargs: Any,
|
|
) -> Runnable[LanguageModelInput, BaseMessage]:
|
|
"""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
|
|
:meth:`langchain_core.utils.function_calling.convert_to_openai_tool`.
|
|
tool_choice: Which tool to require the model to call.
|
|
Must be the name of the single provided function,
|
|
"auto" to automatically determine which function to call
|
|
with the option to not call any function, "any" to enforce that some
|
|
function is called, or a dict of the form:
|
|
{"type": "function", "function": {"name": <<tool_name>>}}.
|
|
**kwargs: Any additional parameters to pass to the
|
|
:class:`~langchain.runnable.Runnable` constructor.
|
|
"""
|
|
|
|
formatted_tools = [convert_to_openai_tool(tool) 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:
|
|
raise ValueError(
|
|
"tool_choice can only be True when there is one tool. Received "
|
|
f"{len(tools)} tools."
|
|
)
|
|
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(
|
|
self,
|
|
schema: Optional[Union[Dict, Type[BaseModel]]] = None,
|
|
*,
|
|
method: Literal["function_calling", "json_mode"] = "function_calling",
|
|
include_raw: bool = False,
|
|
**kwargs: Any,
|
|
) -> Runnable[LanguageModelInput, Union[Dict, BaseModel]]:
|
|
"""Model wrapper that returns outputs formatted to match the given schema.
|
|
|
|
Args:
|
|
schema:
|
|
The output schema. Can be passed in as:
|
|
- an OpenAI function/tool schema,
|
|
- a JSON Schema,
|
|
- a TypedDict class (supported added in 0.1.9),
|
|
- or a Pydantic class.
|
|
If ``schema`` is a Pydantic class then the model output will be a
|
|
Pydantic instance of that class, and the model-generated fields will be
|
|
validated by the Pydantic class. Otherwise the model output will be a
|
|
dict and will not be validated. See :meth:`langchain_core.utils.function_calling.convert_to_openai_tool`
|
|
for more on how to properly specify types and descriptions of
|
|
schema fields when specifying a Pydantic or TypedDict class.
|
|
|
|
.. versionchanged:: 0.1.9
|
|
|
|
Added support for TypedDict class.
|
|
method:
|
|
The method for steering model generation, either "function_calling"
|
|
or "json_mode". If "function_calling" then the schema will be converted
|
|
to an OpenAI function and the returned model will make use of the
|
|
function-calling API. If "json_mode" then OpenAI's JSON mode will be
|
|
used. Note that if using "json_mode" then you must include instructions
|
|
for formatting the output into the desired schema into the model call.
|
|
include_raw:
|
|
If False then only the parsed structured output is returned. If
|
|
an error occurs during model output parsing it will be raised. If True
|
|
then both the raw model response (a BaseMessage) and the parsed model
|
|
response will be returned. If an error occurs during output parsing it
|
|
will be caught and returned as well. The final output is always a dict
|
|
with keys "raw", "parsed", and "parsing_error".
|
|
|
|
Returns:
|
|
A Runnable that takes same inputs as a :class:`langchain_core.language_models.chat.BaseChatModel`.
|
|
|
|
If ``include_raw`` is False and ``schema`` is a Pydantic class, Runnable outputs
|
|
an instance of ``schema`` (i.e., a Pydantic object).
|
|
|
|
Otherwise, if ``include_raw`` is False then Runnable outputs a dict.
|
|
|
|
If ``include_raw`` is True, then Runnable outputs a dict with keys:
|
|
- ``"raw"``: BaseMessage
|
|
- ``"parsed"``: None if there was a parsing error, otherwise the type depends on the ``schema`` as described above.
|
|
- ``"parsing_error"``: Optional[BaseException]
|
|
|
|
Example: schema=Pydantic class, method="function_calling", include_raw=False:
|
|
.. code-block:: python
|
|
|
|
from typing import Optional
|
|
|
|
from langchain_groq import ChatGroq
|
|
from pydantic import BaseModel, Field
|
|
|
|
|
|
class AnswerWithJustification(BaseModel):
|
|
'''An answer to the user question along with justification for the answer.'''
|
|
|
|
answer: str
|
|
# If we provide default values and/or descriptions for fields, these will be passed
|
|
# to the model. This is an important part of improving a model's ability to
|
|
# correctly return structured outputs.
|
|
justification: Optional[str] = Field(
|
|
default=None, description="A justification for the answer."
|
|
)
|
|
|
|
|
|
llm = ChatGroq(model="llama-3.1-405b-reasoning", temperature=0)
|
|
structured_llm = llm.with_structured_output(AnswerWithJustification)
|
|
|
|
structured_llm.invoke(
|
|
"What weighs more a pound of bricks or a pound of feathers"
|
|
)
|
|
|
|
# -> AnswerWithJustification(
|
|
# answer='They weigh the same',
|
|
# justification='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.'
|
|
# )
|
|
|
|
Example: schema=Pydantic class, method="function_calling", include_raw=True:
|
|
.. code-block:: python
|
|
|
|
from langchain_groq import ChatGroq
|
|
from pydantic import BaseModel
|
|
|
|
|
|
class AnswerWithJustification(BaseModel):
|
|
'''An answer to the user question along with justification for the answer.'''
|
|
|
|
answer: str
|
|
justification: str
|
|
|
|
|
|
llm = ChatGroq(model="llama-3.1-405b-reasoning", temperature=0)
|
|
structured_llm = llm.with_structured_output(
|
|
AnswerWithJustification, include_raw=True
|
|
)
|
|
|
|
structured_llm.invoke(
|
|
"What weighs more a pound of bricks or a pound of feathers"
|
|
)
|
|
# -> {
|
|
# 'raw': AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_Ao02pnFYXD6GN1yzc0uXPsvF', 'function': {'arguments': '{"answer":"They weigh the same.","justification":"Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ."}', 'name': 'AnswerWithJustification'}, 'type': 'function'}]}),
|
|
# 'parsed': AnswerWithJustification(answer='They weigh the same.', justification='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.'),
|
|
# 'parsing_error': None
|
|
# }
|
|
|
|
Example: schema=TypedDict class, method="function_calling", include_raw=False:
|
|
.. code-block:: python
|
|
|
|
# IMPORTANT: If you are using Python <=3.8, you need to import Annotated
|
|
# from typing_extensions, not from typing.
|
|
from typing_extensions import Annotated, TypedDict
|
|
|
|
from langchain_groq import ChatGroq
|
|
|
|
|
|
class AnswerWithJustification(TypedDict):
|
|
'''An answer to the user question along with justification for the answer.'''
|
|
|
|
answer: str
|
|
justification: Annotated[
|
|
Optional[str], None, "A justification for the answer."
|
|
]
|
|
|
|
|
|
llm = ChatGroq(model="llama-3.1-405b-reasoning", temperature=0)
|
|
structured_llm = llm.with_structured_output(AnswerWithJustification)
|
|
|
|
structured_llm.invoke(
|
|
"What weighs more a pound of bricks or a pound of feathers"
|
|
)
|
|
# -> {
|
|
# 'answer': 'They weigh the same',
|
|
# 'justification': 'Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume and density of the two substances differ.'
|
|
# }
|
|
|
|
Example: schema=OpenAI function schema, method="function_calling", include_raw=False:
|
|
.. code-block:: python
|
|
|
|
from langchain_groq import ChatGroq
|
|
|
|
oai_schema = {
|
|
'name': 'AnswerWithJustification',
|
|
'description': 'An answer to the user question along with justification for the answer.',
|
|
'parameters': {
|
|
'type': 'object',
|
|
'properties': {
|
|
'answer': {'type': 'string'},
|
|
'justification': {'description': 'A justification for the answer.', 'type': 'string'}
|
|
},
|
|
'required': ['answer']
|
|
}
|
|
}
|
|
|
|
llm = ChatGroq(model="llama-3.1-405b-reasoning", temperature=0)
|
|
structured_llm = llm.with_structured_output(oai_schema)
|
|
|
|
structured_llm.invoke(
|
|
"What weighs more a pound of bricks or a pound of feathers"
|
|
)
|
|
# -> {
|
|
# 'answer': 'They weigh the same',
|
|
# 'justification': 'Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume and density of the two substances differ.'
|
|
# }
|
|
|
|
Example: schema=Pydantic class, method="json_mode", include_raw=True:
|
|
.. code-block::
|
|
|
|
from langchain_groq import ChatGroq
|
|
from pydantic import BaseModel
|
|
|
|
class AnswerWithJustification(BaseModel):
|
|
answer: str
|
|
justification: str
|
|
|
|
llm = ChatGroq(model="llama-3.1-405b-reasoning", temperature=0)
|
|
structured_llm = llm.with_structured_output(
|
|
AnswerWithJustification,
|
|
method="json_mode",
|
|
include_raw=True
|
|
)
|
|
|
|
structured_llm.invoke(
|
|
"Answer the following question. "
|
|
"Make sure to return a JSON blob with keys 'answer' and 'justification'.\n\n"
|
|
"What's heavier a pound of bricks or a pound of feathers?"
|
|
)
|
|
# -> {
|
|
# 'raw': AIMessage(content='{\n "answer": "They are both the same weight.",\n "justification": "Both a pound of bricks and a pound of feathers weigh one pound. The difference lies in the volume and density of the materials, not the weight." \n}'),
|
|
# 'parsed': AnswerWithJustification(answer='They are both the same weight.', justification='Both a pound of bricks and a pound of feathers weigh one pound. The difference lies in the volume and density of the materials, not the weight.'),
|
|
# 'parsing_error': None
|
|
# }
|
|
|
|
Example: schema=None, method="json_mode", include_raw=True:
|
|
.. code-block::
|
|
|
|
structured_llm = llm.with_structured_output(method="json_mode", include_raw=True)
|
|
|
|
structured_llm.invoke(
|
|
"Answer the following question. "
|
|
"Make sure to return a JSON blob with keys 'answer' and 'justification'.\n\n"
|
|
"What's heavier a pound of bricks or a pound of feathers?"
|
|
)
|
|
# -> {
|
|
# 'raw': AIMessage(content='{\n "answer": "They are both the same weight.",\n "justification": "Both a pound of bricks and a pound of feathers weigh one pound. The difference lies in the volume and density of the materials, not the weight." \n}'),
|
|
# 'parsed': {
|
|
# 'answer': 'They are both the same weight.',
|
|
# 'justification': 'Both a pound of bricks and a pound of feathers weigh one pound. The difference lies in the volume and density of the materials, not the weight.'
|
|
# },
|
|
# 'parsing_error': None
|
|
# }
|
|
""" # noqa: E501
|
|
if kwargs:
|
|
raise ValueError(f"Received unsupported arguments {kwargs}")
|
|
is_pydantic_schema = _is_pydantic_class(schema)
|
|
if method == "function_calling":
|
|
if schema is None:
|
|
raise ValueError(
|
|
"schema must be specified when method is 'function_calling'. "
|
|
"Received None."
|
|
)
|
|
formatted_tool = convert_to_openai_tool(schema)
|
|
tool_name = formatted_tool["function"]["name"]
|
|
llm = self.bind_tools(
|
|
[schema],
|
|
tool_choice=tool_name,
|
|
ls_structured_output_format={
|
|
"kwargs": {"method": "function_calling"},
|
|
"schema": formatted_tool,
|
|
},
|
|
)
|
|
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_mode":
|
|
llm = self.bind(
|
|
response_format={"type": "json_object"},
|
|
ls_structured_output_format={
|
|
"kwargs": {"method": "json_mode"},
|
|
"schema": schema,
|
|
},
|
|
)
|
|
output_parser = (
|
|
PydanticOutputParser(pydantic_object=schema) # type: ignore[type-var, arg-type]
|
|
if is_pydantic_schema
|
|
else JsonOutputParser()
|
|
)
|
|
else:
|
|
raise ValueError(
|
|
f"Unrecognized method argument. Expected one of 'function_calling' or "
|
|
f"'json_mode'. Received: '{method}'"
|
|
)
|
|
|
|
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
|
|
else:
|
|
return llm | output_parser
|
|
|
|
|
|
def _is_pydantic_class(obj: Any) -> bool:
|
|
return isinstance(obj, type) and is_basemodel_subclass(obj)
|
|
|
|
|
|
class _FunctionCall(TypedDict):
|
|
name: str
|
|
|
|
|
|
#
|
|
# Type conversion helpers
|
|
#
|
|
def _convert_message_to_dict(message: BaseMessage) -> dict:
|
|
"""Convert a LangChain message to a dictionary.
|
|
|
|
Args:
|
|
message: The LangChain message.
|
|
|
|
Returns:
|
|
The dictionary.
|
|
"""
|
|
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": message.content}
|
|
elif isinstance(message, AIMessage):
|
|
message_dict = {"role": "assistant", "content": message.content}
|
|
if "function_call" in message.additional_kwargs:
|
|
message_dict["function_call"] = message.additional_kwargs["function_call"]
|
|
# If function call only, content is None not empty string
|
|
if message_dict["content"] == "":
|
|
message_dict["content"] = None
|
|
if message.tool_calls or message.invalid_tool_calls:
|
|
message_dict["tool_calls"] = [
|
|
_lc_tool_call_to_groq_tool_call(tc) for tc in message.tool_calls
|
|
] + [
|
|
_lc_invalid_tool_call_to_groq_tool_call(tc)
|
|
for tc in message.invalid_tool_calls
|
|
]
|
|
elif "tool_calls" in message.additional_kwargs:
|
|
message_dict["tool_calls"] = message.additional_kwargs["tool_calls"]
|
|
# If tool calls only, content is None not empty string
|
|
if message_dict["content"] == "":
|
|
message_dict["content"] = None
|
|
elif isinstance(message, SystemMessage):
|
|
message_dict = {"role": "system", "content": message.content}
|
|
elif isinstance(message, FunctionMessage):
|
|
message_dict = {
|
|
"role": "function",
|
|
"content": message.content,
|
|
"name": message.name,
|
|
}
|
|
elif isinstance(message, ToolMessage):
|
|
message_dict = {
|
|
"role": "tool",
|
|
"content": message.content,
|
|
"tool_call_id": message.tool_call_id,
|
|
}
|
|
else:
|
|
raise TypeError(f"Got unknown type {message}")
|
|
if "name" in message.additional_kwargs:
|
|
message_dict["name"] = message.additional_kwargs["name"]
|
|
return message_dict
|
|
|
|
|
|
def _convert_chunk_to_message_chunk(
|
|
chunk: Mapping[str, Any], default_class: Type[BaseMessageChunk]
|
|
) -> BaseMessageChunk:
|
|
choice = chunk["choices"][0]
|
|
_dict = choice["delta"]
|
|
role = cast(str, _dict.get("role"))
|
|
content = cast(str, _dict.get("content") or "")
|
|
additional_kwargs: Dict = {}
|
|
if _dict.get("function_call"):
|
|
function_call = dict(_dict["function_call"])
|
|
if "name" in function_call and function_call["name"] is None:
|
|
function_call["name"] = ""
|
|
additional_kwargs["function_call"] = function_call
|
|
if _dict.get("tool_calls"):
|
|
additional_kwargs["tool_calls"] = _dict["tool_calls"]
|
|
|
|
if role == "user" or default_class == HumanMessageChunk:
|
|
return HumanMessageChunk(content=content)
|
|
elif role == "assistant" or default_class == AIMessageChunk:
|
|
if usage := (chunk.get("x_groq") or {}).get("usage"):
|
|
input_tokens = usage.get("prompt_tokens", 0)
|
|
output_tokens = usage.get("completion_tokens", 0)
|
|
usage_metadata = {
|
|
"input_tokens": input_tokens,
|
|
"output_tokens": output_tokens,
|
|
"total_tokens": usage.get("total_tokens", input_tokens + output_tokens),
|
|
}
|
|
else:
|
|
usage_metadata = None
|
|
return AIMessageChunk(
|
|
content=content,
|
|
additional_kwargs=additional_kwargs,
|
|
usage_metadata=usage_metadata, # type: ignore[arg-type]
|
|
)
|
|
elif role == "system" or default_class == SystemMessageChunk:
|
|
return SystemMessageChunk(content=content)
|
|
elif role == "function" or default_class == FunctionMessageChunk:
|
|
return FunctionMessageChunk(content=content, name=_dict["name"])
|
|
elif role == "tool" or default_class == ToolMessageChunk:
|
|
return ToolMessageChunk(content=content, tool_call_id=_dict["tool_call_id"])
|
|
elif role or default_class == ChatMessageChunk:
|
|
return ChatMessageChunk(content=content, role=role)
|
|
else:
|
|
return default_class(content=content) # type: ignore
|
|
|
|
|
|
def _convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage:
|
|
"""Convert a dictionary to a LangChain message.
|
|
|
|
Args:
|
|
_dict: The dictionary.
|
|
|
|
Returns:
|
|
The LangChain message.
|
|
"""
|
|
id_ = _dict.get("id")
|
|
role = _dict.get("role")
|
|
if role == "user":
|
|
return HumanMessage(content=_dict.get("content", ""))
|
|
elif role == "assistant":
|
|
content = _dict.get("content", "") or ""
|
|
additional_kwargs: Dict = {}
|
|
if function_call := _dict.get("function_call"):
|
|
additional_kwargs["function_call"] = dict(function_call)
|
|
tool_calls = []
|
|
invalid_tool_calls = []
|
|
if raw_tool_calls := _dict.get("tool_calls"):
|
|
additional_kwargs["tool_calls"] = raw_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:
|
|
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,
|
|
)
|
|
elif role == "system":
|
|
return SystemMessage(content=_dict.get("content", ""))
|
|
elif role == "function":
|
|
return FunctionMessage(content=_dict.get("content", ""), name=_dict.get("name")) # type: ignore[arg-type]
|
|
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) # type: ignore[arg-type]
|
|
|
|
|
|
def _lc_tool_call_to_groq_tool_call(tool_call: ToolCall) -> dict:
|
|
return {
|
|
"type": "function",
|
|
"id": tool_call["id"],
|
|
"function": {
|
|
"name": tool_call["name"],
|
|
"arguments": json.dumps(tool_call["args"]),
|
|
},
|
|
}
|
|
|
|
|
|
def _lc_invalid_tool_call_to_groq_tool_call(
|
|
invalid_tool_call: InvalidToolCall,
|
|
) -> dict:
|
|
return {
|
|
"type": "function",
|
|
"id": invalid_tool_call["id"],
|
|
"function": {
|
|
"name": invalid_tool_call["name"],
|
|
"arguments": invalid_tool_call["args"],
|
|
},
|
|
}
|