diff --git a/docs/docs/guides/structured_output.ipynb b/docs/docs/guides/structured_output.ipynb index ce8c632583..54baeeccd7 100644 --- a/docs/docs/guides/structured_output.ipynb +++ b/docs/docs/guides/structured_output.ipynb @@ -358,13 +358,119 @@ "model_with_structure.invoke(\"Tell me a joke about cats\")" ] }, + { + "cell_type": "markdown", + "id": "6214781d", + "metadata": {}, + "source": [ + "## Groq\n", + "\n", + "Groq provides an OpenAI-compatible function calling API" + ] + }, { "cell_type": "code", - "execution_count": null, - "id": "3066b2af", + "execution_count": 11, + "id": "70511bc3", "metadata": {}, "outputs": [], - "source": [] + "source": [ + "from langchain_groq import ChatGroq" + ] + }, + { + "cell_type": "markdown", + "id": "6b7e97a6", + "metadata": {}, + "source": [ + "### Function Calling\n", + "\n", + "By default, we will use `function_calling`" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "be9fdf04", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/reag/src/langchain/libs/core/langchain_core/_api/beta_decorator.py:87: LangChainBetaWarning: The function `with_structured_output` is in beta. It is actively being worked on, so the API may change.\n", + " warn_beta(\n" + ] + } + ], + "source": [ + "model = ChatGroq()\n", + "model_with_structure = model.with_structured_output(Joke)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "e13f4676", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Joke(setup=\"Why don't cats play poker in the jungle?\", punchline='Too many cheetahs!')" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model_with_structure.invoke(\"Tell me a joke about cats\")" + ] + }, + { + "cell_type": "markdown", + "id": "a82c2f55", + "metadata": {}, + "source": [ + "### JSON Mode\n", + "\n", + "We also support JSON mode. Note that we need to specify in the prompt the format that it should respond in." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "86574fb8", + "metadata": {}, + "outputs": [], + "source": [ + "model_with_structure = model.with_structured_output(Joke, method=\"json_mode\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "01dced9c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Joke(setup=\"Why don't cats play poker in the jungle?\", punchline='Too many cheetahs!')" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model_with_structure.invoke(\n", + " \"Tell me a joke about cats, respond in JSON with `setup` and `punchline` keys\"\n", + ")" + ] } ], "metadata": { diff --git a/libs/partners/groq/langchain_groq/chat_models.py b/libs/partners/groq/langchain_groq/chat_models.py index 8c34386076..e9ad923974 100644 --- a/libs/partners/groq/langchain_groq/chat_models.py +++ b/libs/partners/groq/langchain_groq/chat_models.py @@ -4,24 +4,31 @@ from __future__ import annotations import os 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 beta from langchain_core.callbacks import ( AsyncCallbackManagerForLLMRun, CallbackManagerForLLMRun, ) +from langchain_core.language_models import LanguageModelInput from langchain_core.language_models.chat_models import ( BaseChatModel, agenerate_from_stream, @@ -43,13 +50,28 @@ from langchain_core.messages import ( 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, +) from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult from langchain_core.pydantic_v1 import BaseModel, Field, SecretStr, root_validator +from langchain_core.runnables import Runnable, RunnableMap, RunnablePassthrough +from langchain_core.tools import BaseTool from langchain_core.utils import ( convert_to_secret_str, get_from_dict_or_env, get_pydantic_field_names, ) +from langchain_core.utils.function_calling import ( + convert_to_openai_function, + convert_to_openai_tool, +) class ChatGroq(BaseChatModel): @@ -390,6 +412,334 @@ class ChatGroq(BaseChatModel): combined["system_fingerprint"] = system_fingerprint return combined + 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 the + :class:`~langchain.runnable.Runnable` constructor. + """ + + 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. + Can be a dictionary, pydantic model, callable, or BaseTool. Pydantic + models, callables, and BaseTools will be automatically converted to + their schema dictionary representation. + 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": <>}}. + **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 isinstance(tool_choice, str) and ( + tool_choice not in ("auto", "any", "none") + ): + tool_choice = {"type": "function", "function": {"name": tool_choice}} + if isinstance(tool_choice, dict) and (len(formatted_tools) != 1): + raise ValueError( + "When specifying `tool_choice`, you must provide exactly one " + f"tool. Received {len(formatted_tools)} tools." + ) + if isinstance(tool_choice, dict) and ( + formatted_tools[0]["function"]["name"] + != tool_choice["function"]["name"] + ): + raise ValueError( + f"Tool choice {tool_choice} was specified, but the only " + f"provided tool was {formatted_tools[0]['function']['name']}." + ) + 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) + + @beta() + 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 as a dict or a Pydantic class. If a Pydantic class + then the model output will be an object of that class. If a dict then + the model output will be a dict. With a Pydantic class the returned + attributes will be validated, whereas with a dict they will not be. If + `method` is "function_calling" and `schema` is a dict, then the dict + must match the OpenAI function-calling spec. + method: The method for steering model generation, either "function_calling" + or "json_mode". If "function_calling" then the schema will be converted + to a OpenAI function and the returned model will make use of the + function-calling API. If "json_mode" then Groq'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 any ChatModel input and returns as output: + + If include_raw is True then a dict with keys: + raw: BaseMessage + parsed: Optional[_DictOrPydantic] + parsing_error: Optional[BaseException] + + If include_raw is False then just _DictOrPydantic is returned, + where _DictOrPydantic depends on the schema: + + If schema is a Pydantic class then _DictOrPydantic is the Pydantic + class. + + If schema is a dict then _DictOrPydantic is a dict. + + Example: Function-calling, Pydantic schema (method="function_calling", include_raw=False): + .. code-block:: python + + from langchain_groq import ChatGroq + from langchain_core.pydantic_v1 import BaseModel + + class AnswerWithJustification(BaseModel): + '''An answer to the user question along with justification for the answer.''' + answer: str + justification: str + + llm = ChatGroq(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='A pound of bricks and a pound of feathers weigh the same.' + # justification="Both a pound of bricks and a pound of feathers have been defined to have the same weight. The 'pound' is a unit of weight, so any two things that are described as weighing a pound will weigh the same." + # ) + + Example: Function-calling, Pydantic schema (method="function_calling", include_raw=True): + .. code-block:: python + + from langchain_groq import ChatGroq + from langchain_core.pydantic_v1 import BaseModel + + class AnswerWithJustification(BaseModel): + '''An answer to the user question along with justification for the answer.''' + answer: str + justification: str + + llm = ChatGroq(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_01htjn3cspevxbqc1d7nkk8wab', 'function': {'arguments': '{"answer": "A pound of bricks and a pound of feathers weigh the same.", "justification": "Both a pound of bricks and a pound of feathers have been defined to have the same weight. The \'pound\' is a unit of weight, so any two things that are described as weighing a pound will weigh the same.", "unit": "pounds"}', 'name': 'AnswerWithJustification'}, 'type': 'function'}]}, id='run-456beee6-65f6-4e80-88af-a6065480822c-0'), + # 'parsed': AnswerWithJustification(answer='A pound of bricks and a pound of feathers weigh the same.', justification="Both a pound of bricks and a pound of feathers have been defined to have the same weight. The 'pound' is a unit of weight, so any two things that are described as weighing a pound will weigh the same."), + # 'parsing_error': None + # } + + Example: Function-calling, dict schema (method="function_calling", include_raw=False): + .. code-block:: python + + from langchain_groq import ChatGroq + from langchain_core.pydantic_v1 import BaseModel + from langchain_core.utils.function_calling import convert_to_openai_tool + + class AnswerWithJustification(BaseModel): + '''An answer to the user question along with justification for the answer.''' + answer: str + justification: str + + dict_schema = convert_to_openai_tool(AnswerWithJustification) + llm = ChatGroq(temperature=0) + structured_llm = llm.with_structured_output(dict_schema) + + structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers") + # -> { + # 'answer': 'A pound of bricks and a pound of feathers weigh the same.', + # 'justification': "Both a pound of bricks and a pound of feathers have been defined to have the same weight. The 'pound' is a unit of weight, so any two things that are described as weighing a pound will weigh the same.", 'unit': 'pounds'} + # } + + Example: JSON mode, Pydantic schema (method="json_mode", include_raw=True): + .. code-block:: + + from langchain_groq import ChatGroq + from langchain_core.pydantic_v1 import BaseModel + + class AnswerWithJustification(BaseModel): + answer: str + justification: str + + llm = ChatGroq(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": "A pound of bricks is the same weight as a pound of feathers.",\n "justification": "Both a pound of bricks and a pound of feathers weigh one pound. The material being weighed does not affect the weight, only the volume or number of items being weighed."\n}', id='run-e5453bc5-5025-4833-95f9-4967bf6d5c4f-0'), + # 'parsed': AnswerWithJustification(answer='A pound of bricks is the same weight as a pound of feathers.', justification='Both a pound of bricks and a pound of feathers weigh one pound. The material being weighed does not affect the weight, only the volume or number of items being weighed.'), + # 'parsing_error': None + # } + + Example: JSON mode, no schema (schema=None, method="json_mode", include_raw=True): + .. code-block:: + + from langchain_groq import ChatGroq + + llm = ChatGroq(temperature=0) + 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": "A pound of bricks is the same weight as a pound of feathers.",\n "justification": "Both a pound of bricks and a pound of feathers weigh one pound. The material doesn\'t change the weight, only the volume or space that the material takes up."\n}', id='run-a4abbdb6-c20e-456f-bfff-da906a7e76b5-0'), + # 'parsed': { + # 'answer': 'A pound of bricks is the same weight as a pound of feathers.', + # 'justification': "Both a pound of bricks and a pound of feathers weigh one pound. The material doesn't change the weight, only the volume or space that the material takes up."}, + # '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." + ) + llm = self.bind_tools([schema], tool_choice=True) + if is_pydantic_schema: + output_parser: OutputParserLike = PydanticToolsParser( + tools=[schema], first_tool_only=True + ) + else: + key_name = convert_to_openai_tool(schema)["function"]["name"] + output_parser = JsonOutputKeyToolsParser( + key_name=key_name, first_tool_only=True + ) + elif method == "json_mode": + llm = self.bind(response_format={"type": "json_object"}) + output_parser = ( + PydanticOutputParser(pydantic_object=schema) + if is_pydantic_schema + else JsonOutputParser() + ) + else: + raise ValueError( + f"Unrecognized method argument. Expected one of 'function_calling' or " + f"'json_format'. 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 issubclass(obj, BaseModel) + + +class _FunctionCall(TypedDict): + name: str + # # Type conversion helpers @@ -480,17 +830,18 @@ def _convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage: 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", "") + content = _dict.get("content", "") or "" additional_kwargs: Dict = {} if function_call := _dict.get("function_call"): additional_kwargs["function_call"] = dict(function_call) if tool_calls := _dict.get("tool_calls"): additional_kwargs["tool_calls"] = tool_calls - return AIMessage(content=content, additional_kwargs=additional_kwargs) + return AIMessage(content=content, id=id_, additional_kwargs=additional_kwargs) elif role == "system": return SystemMessage(content=_dict.get("content", "")) elif role == "function": diff --git a/libs/partners/groq/pyproject.toml b/libs/partners/groq/pyproject.toml index 7ca84d7387..a57efe7706 100644 --- a/libs/partners/groq/pyproject.toml +++ b/libs/partners/groq/pyproject.toml @@ -89,7 +89,9 @@ markers = [ ] filterwarnings = [ "error", + 'ignore::ResourceWarning:', + 'ignore:The function `with_structured_output` is in beta', # Maintain support for pydantic 1.X - 'default:The `dict` method is deprecated; use `model_dump` instead.*:DeprecationWarning', + 'default:The `dict` method is deprecated; use `model_dump` instead:DeprecationWarning', ] asyncio_mode = "auto" diff --git a/libs/partners/groq/tests/integration_tests/test_chat_models.py b/libs/partners/groq/tests/integration_tests/test_chat_models.py index d0b5925d63..3ef7fb1639 100644 --- a/libs/partners/groq/tests/integration_tests/test_chat_models.py +++ b/libs/partners/groq/tests/integration_tests/test_chat_models.py @@ -1,15 +1,18 @@ """Test ChatGroq chat model.""" +import json from typing import Any import pytest from langchain_core.messages import ( + AIMessage, BaseMessage, BaseMessageChunk, HumanMessage, SystemMessage, ) from langchain_core.outputs import ChatGeneration, LLMResult +from langchain_core.pydantic_v1 import BaseModel, Field from langchain_groq import ChatGroq from tests.unit_tests.fake.callbacks import ( @@ -45,9 +48,9 @@ def test_invoke() -> None: @pytest.mark.scheduled async def test_ainvoke() -> None: """Test ainvoke tokens from ChatGroq.""" - llm = ChatGroq(max_tokens=10) + chat = ChatGroq(max_tokens=10) - result = await llm.ainvoke("Welcome to the Groqetship!", config={"tags": ["foo"]}) + result = await chat.ainvoke("Welcome to the Groqetship!", config={"tags": ["foo"]}) assert isinstance(result, BaseMessage) assert isinstance(result.content, str) @@ -55,9 +58,9 @@ async def test_ainvoke() -> None: @pytest.mark.scheduled def test_batch() -> None: """Test batch tokens from ChatGroq.""" - llm = ChatGroq(max_tokens=10) + chat = ChatGroq(max_tokens=10) - result = llm.batch(["Hello!", "Welcome to the Groqetship!"]) + result = chat.batch(["Hello!", "Welcome to the Groqetship!"]) for token in result: assert isinstance(token, BaseMessage) assert isinstance(token.content, str) @@ -66,9 +69,9 @@ def test_batch() -> None: @pytest.mark.scheduled async def test_abatch() -> None: """Test abatch tokens from ChatGroq.""" - llm = ChatGroq(max_tokens=10) + chat = ChatGroq(max_tokens=10) - result = await llm.abatch(["Hello!", "Welcome to the Groqetship!"]) + result = await chat.abatch(["Hello!", "Welcome to the Groqetship!"]) for token in result: assert isinstance(token, BaseMessage) assert isinstance(token.content, str) @@ -77,9 +80,9 @@ async def test_abatch() -> None: @pytest.mark.scheduled async def test_stream() -> None: """Test streaming tokens from Groq.""" - llm = ChatGroq(max_tokens=10) + chat = ChatGroq(max_tokens=10) - for token in llm.stream("Welcome to the Groqetship!"): + for token in chat.stream("Welcome to the Groqetship!"): assert isinstance(token, BaseMessageChunk) assert isinstance(token.content, str) @@ -87,9 +90,9 @@ async def test_stream() -> None: @pytest.mark.scheduled async def test_astream() -> None: """Test streaming tokens from Groq.""" - llm = ChatGroq(max_tokens=10) + chat = ChatGroq(max_tokens=10) - async for token in llm.astream("Welcome to the Groqetship!"): + async for token in chat.astream("Welcome to the Groqetship!"): assert isinstance(token, BaseMessageChunk) assert isinstance(token.content, str) @@ -202,11 +205,11 @@ def test_streaming_generation_info() -> None: temperature=0, callbacks=[callback], ) - list(chat.stream("Respond with the single word Hello")) + list(chat.stream("Respond with the single word Hello", stop=["o"])) generation = callback.saved_things["generation"] # `Hello!` is two tokens, assert that that is what is returned assert isinstance(generation, LLMResult) - assert generation.generations[0][0].text == "Hello" + assert generation.generations[0][0].text == "Hell" def test_system_message() -> None: @@ -219,6 +222,75 @@ def test_system_message() -> None: assert isinstance(response.content, str) +@pytest.mark.scheduled +def test_tool_choice() -> None: + """Test that tool choice is respected.""" + llm = ChatGroq() + + class MyTool(BaseModel): + name: str + age: int + + with_tool = llm.bind_tools([MyTool], tool_choice="MyTool") + + resp = with_tool.invoke("Who was the 27 year old named Erick?") + assert isinstance(resp, AIMessage) + assert resp.content == "" # should just be tool call + tool_calls = resp.additional_kwargs["tool_calls"] + assert len(tool_calls) == 1 + tool_call = tool_calls[0] + assert tool_call["function"]["name"] == "MyTool" + assert json.loads(tool_call["function"]["arguments"]) == { + "age": 27, + "name": "Erick", + } + assert tool_call["type"] == "function" + + +@pytest.mark.scheduled +def test_tool_choice_bool() -> None: + """Test that tool choice is respected just passing in True.""" + llm = ChatGroq() + + class MyTool(BaseModel): + name: str + age: int + + with_tool = llm.bind_tools([MyTool], tool_choice=True) + + resp = with_tool.invoke("Who was the 27 year old named Erick?") + assert isinstance(resp, AIMessage) + assert resp.content == "" # should just be tool call + tool_calls = resp.additional_kwargs["tool_calls"] + assert len(tool_calls) == 1 + tool_call = tool_calls[0] + assert tool_call["function"]["name"] == "MyTool" + assert json.loads(tool_call["function"]["arguments"]) == { + "age": 27, + "name": "Erick", + } + assert tool_call["type"] == "function" + + +@pytest.mark.scheduled +def test_json_mode_structured_output() -> None: + """Test with_structured_output with json""" + + class Joke(BaseModel): + """Joke to tell user.""" + + setup: str = Field(description="question to set up a joke") + punchline: str = Field(description="answer to resolve the joke") + + chat = ChatGroq().with_structured_output(Joke, method="json_mode") + result = chat.invoke( + "Tell me a joke about cats, respond in JSON with `setup` and `punchline` keys" + ) + assert type(result) == Joke + assert len(result.setup) != 0 + assert len(result.punchline) != 0 + + # Groq does not currently support N > 1 # @pytest.mark.scheduled # def test_chat_multiple_completions() -> None: diff --git a/libs/partners/groq/tests/unit_tests/test_chat_models.py b/libs/partners/groq/tests/unit_tests/test_chat_models.py index 7695151640..35c50ab9a7 100644 --- a/libs/partners/groq/tests/unit_tests/test_chat_models.py +++ b/libs/partners/groq/tests/unit_tests/test_chat_models.py @@ -16,7 +16,8 @@ from langchain_core.messages import ( from langchain_groq.chat_models import ChatGroq, _convert_dict_to_message -os.environ["GROQ_API_KEY"] = "fake-key" +if "GROQ_API_KEY" not in os.environ: + os.environ["GROQ_API_KEY"] = "fake-key" def test_groq_model_param() -> None: