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63673b765b
**partners: Enable max_retries in ChatMistralAI** **Description** - This pull request reactivates the retry logic in the completion_with_retry method of the ChatMistralAI class, restoring the intended functionality of the previously ineffective max_retries parameter. New unit test that mocks failed/successful retry calls and an integration test to confirm end-to-end functionality. **Issue** - Closes #30362 **Dependencies** - No additional dependencies required Co-authored-by: andrasfe <andrasf94@gmail.com>
343 lines
11 KiB
Python
343 lines
11 KiB
Python
"""Test ChatMistral chat model."""
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import json
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import logging
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import time
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from typing import Any, Optional
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import pytest
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from httpx import ReadTimeout
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from langchain_core.messages import (
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AIMessage,
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AIMessageChunk,
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BaseMessageChunk,
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HumanMessage,
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)
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from pydantic import BaseModel
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from typing_extensions import TypedDict
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from langchain_mistralai.chat_models import ChatMistralAI
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def test_stream() -> None:
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"""Test streaming tokens from ChatMistralAI."""
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llm = ChatMistralAI()
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for token in llm.stream("Hello"):
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assert isinstance(token.content, str)
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async def test_astream() -> None:
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"""Test streaming tokens from ChatMistralAI."""
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llm = ChatMistralAI()
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full: Optional[BaseMessageChunk] = None
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chunks_with_token_counts = 0
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chunks_with_response_metadata = 0
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async for token in llm.astream("Hello"):
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assert isinstance(token, AIMessageChunk)
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assert isinstance(token.content, str)
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full = token if full is None else full + token
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if token.usage_metadata is not None:
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chunks_with_token_counts += 1
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if token.response_metadata:
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chunks_with_response_metadata += 1
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if chunks_with_token_counts != 1 or chunks_with_response_metadata != 1:
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raise AssertionError(
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"Expected exactly one chunk with token counts or response_metadata. "
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"AIMessageChunk aggregation adds / appends counts and metadata. Check that "
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"this is behaving properly."
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)
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assert isinstance(full, AIMessageChunk)
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assert full.usage_metadata is not None
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assert full.usage_metadata["input_tokens"] > 0
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assert full.usage_metadata["output_tokens"] > 0
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assert (
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full.usage_metadata["input_tokens"] + full.usage_metadata["output_tokens"]
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== full.usage_metadata["total_tokens"]
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)
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assert isinstance(full.response_metadata["model_name"], str)
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assert full.response_metadata["model_name"]
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async def test_abatch() -> None:
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"""Test streaming tokens from ChatMistralAI"""
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llm = ChatMistralAI()
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result = await llm.abatch(["I'm Pickle Rick", "I'm not Pickle Rick"])
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for token in result:
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assert isinstance(token.content, str)
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async def test_abatch_tags() -> None:
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"""Test batch tokens from ChatMistralAI"""
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llm = ChatMistralAI()
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result = await llm.abatch(
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["I'm Pickle Rick", "I'm not Pickle Rick"], config={"tags": ["foo"]}
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)
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for token in result:
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assert isinstance(token.content, str)
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def test_batch() -> None:
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"""Test batch tokens from ChatMistralAI"""
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llm = ChatMistralAI()
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result = llm.batch(["I'm Pickle Rick", "I'm not Pickle Rick"])
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for token in result:
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assert isinstance(token.content, str)
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async def test_ainvoke() -> None:
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"""Test invoke tokens from ChatMistralAI"""
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llm = ChatMistralAI()
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result = await llm.ainvoke("I'm Pickle Rick", config={"tags": ["foo"]})
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assert isinstance(result.content, str)
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assert "model_name" in result.response_metadata
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def test_invoke() -> None:
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"""Test invoke tokens from ChatMistralAI"""
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llm = ChatMistralAI()
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result = llm.invoke("I'm Pickle Rick", config=dict(tags=["foo"]))
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assert isinstance(result.content, str)
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def test_chat_mistralai_llm_output_contains_model_name() -> None:
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"""Test llm_output contains model_name."""
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chat = ChatMistralAI(max_tokens=10)
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message = HumanMessage(content="Hello")
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llm_result = chat.generate([[message]])
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assert llm_result.llm_output is not None
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assert llm_result.llm_output["model_name"] == chat.model
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def test_chat_mistralai_streaming_llm_output_contains_model_name() -> None:
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"""Test llm_output contains model_name."""
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chat = ChatMistralAI(max_tokens=10, streaming=True)
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message = HumanMessage(content="Hello")
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llm_result = chat.generate([[message]])
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assert llm_result.llm_output is not None
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assert llm_result.llm_output["model_name"] == chat.model
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def test_chat_mistralai_llm_output_contains_token_usage() -> None:
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"""Test llm_output contains model_name."""
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chat = ChatMistralAI(max_tokens=10)
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message = HumanMessage(content="Hello")
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llm_result = chat.generate([[message]])
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assert llm_result.llm_output is not None
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assert "token_usage" in llm_result.llm_output
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token_usage = llm_result.llm_output["token_usage"]
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assert "prompt_tokens" in token_usage
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assert "completion_tokens" in token_usage
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assert "total_tokens" in token_usage
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def test_chat_mistralai_streaming_llm_output_not_contain_token_usage() -> None:
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"""Mistral currently doesn't return token usage when streaming."""
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chat = ChatMistralAI(max_tokens=10, streaming=True)
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message = HumanMessage(content="Hello")
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llm_result = chat.generate([[message]])
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assert llm_result.llm_output is not None
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assert "token_usage" in llm_result.llm_output
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token_usage = llm_result.llm_output["token_usage"]
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assert not token_usage
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def test_structured_output() -> None:
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llm = ChatMistralAI(model="mistral-large-latest", temperature=0) # type: ignore[call-arg]
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schema = {
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"title": "AnswerWithJustification",
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"description": (
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"An answer to the user question along with justification for the answer."
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),
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"type": "object",
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"properties": {
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"answer": {"title": "Answer", "type": "string"},
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"justification": {"title": "Justification", "type": "string"},
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},
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"required": ["answer", "justification"],
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}
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structured_llm = llm.with_structured_output(schema)
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result = structured_llm.invoke(
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"What weighs more a pound of bricks or a pound of feathers"
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)
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assert isinstance(result, dict)
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def test_streaming_structured_output() -> None:
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llm = ChatMistralAI(model="mistral-large-latest", temperature=0) # type: ignore[call-arg]
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class Person(BaseModel):
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name: str
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age: int
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structured_llm = llm.with_structured_output(Person)
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strm = structured_llm.stream("Erick, 27 years old")
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chunk_num = 0
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for chunk in strm:
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assert chunk_num == 0, "should only have one chunk with model"
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assert isinstance(chunk, Person)
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assert chunk.name == "Erick"
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assert chunk.age == 27
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chunk_num += 1
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class Book(BaseModel):
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name: str
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authors: list[str]
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class BookDict(TypedDict):
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name: str
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authors: list[str]
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def _check_parsed_result(result: Any, schema: Any) -> None:
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if schema == Book:
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assert isinstance(result, Book)
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else:
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assert all(key in ["name", "authors"] for key in result.keys())
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@pytest.mark.parametrize("schema", [Book, BookDict, Book.model_json_schema()])
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def test_structured_output_json_schema(schema: Any) -> None:
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llm = ChatMistralAI(model="ministral-8b-latest") # type: ignore[call-arg]
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structured_llm = llm.with_structured_output(schema, method="json_schema")
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messages = [
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{"role": "system", "content": "Extract the book's information."},
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{
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"role": "user",
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"content": "I recently read 'To Kill a Mockingbird' by Harper Lee.",
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},
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]
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# Test invoke
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result = structured_llm.invoke(messages)
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_check_parsed_result(result, schema)
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# Test stream
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for chunk in structured_llm.stream(messages):
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_check_parsed_result(chunk, schema)
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@pytest.mark.parametrize("schema", [Book, BookDict, Book.model_json_schema()])
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async def test_structured_output_json_schema_async(schema: Any) -> None:
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llm = ChatMistralAI(model="ministral-8b-latest") # type: ignore[call-arg]
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structured_llm = llm.with_structured_output(schema, method="json_schema")
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messages = [
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{"role": "system", "content": "Extract the book's information."},
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{
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"role": "user",
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"content": "I recently read 'To Kill a Mockingbird' by Harper Lee.",
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},
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]
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# Test invoke
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result = await structured_llm.ainvoke(messages)
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_check_parsed_result(result, schema)
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# Test stream
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async for chunk in structured_llm.astream(messages):
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_check_parsed_result(chunk, schema)
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def test_tool_call() -> None:
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llm = ChatMistralAI(model="mistral-large-latest", temperature=0) # type: ignore[call-arg]
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class Person(BaseModel):
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name: str
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age: int
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tool_llm = llm.bind_tools([Person])
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result = tool_llm.invoke("Erick, 27 years old")
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assert isinstance(result, AIMessage)
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assert len(result.tool_calls) == 1
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tool_call = result.tool_calls[0]
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assert tool_call["name"] == "Person"
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assert tool_call["args"] == {"name": "Erick", "age": 27}
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def test_streaming_tool_call() -> None:
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llm = ChatMistralAI(model="mistral-large-latest", temperature=0) # type: ignore[call-arg]
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class Person(BaseModel):
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name: str
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age: int
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tool_llm = llm.bind_tools([Person])
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# where it calls the tool
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strm = tool_llm.stream("Erick, 27 years old")
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additional_kwargs = None
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for chunk in strm:
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assert isinstance(chunk, AIMessageChunk)
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assert chunk.content == ""
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additional_kwargs = chunk.additional_kwargs
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assert additional_kwargs is not None
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assert "tool_calls" in additional_kwargs
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assert len(additional_kwargs["tool_calls"]) == 1
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assert additional_kwargs["tool_calls"][0]["function"]["name"] == "Person"
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assert json.loads(additional_kwargs["tool_calls"][0]["function"]["arguments"]) == {
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"name": "Erick",
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"age": 27,
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}
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assert isinstance(chunk, AIMessageChunk)
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assert len(chunk.tool_call_chunks) == 1
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tool_call_chunk = chunk.tool_call_chunks[0]
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assert tool_call_chunk["name"] == "Person"
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assert tool_call_chunk["args"] == '{"name": "Erick", "age": 27}'
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# where it doesn't call the tool
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strm = tool_llm.stream("What is 2+2?")
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acc: Any = None
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for chunk in strm:
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assert isinstance(chunk, AIMessageChunk)
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acc = chunk if acc is None else acc + chunk
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assert acc.content != ""
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assert "tool_calls" not in acc.additional_kwargs
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def test_retry_parameters(caplog: pytest.LogCaptureFixture) -> None:
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"""Test that retry parameters are honored in ChatMistralAI."""
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# Create a model with intentionally short timeout and multiple retries
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mistral = ChatMistralAI(
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timeout=1, # Very short timeout to trigger timeouts
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max_retries=3, # Should retry 3 times
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)
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# Simple test input that should take longer than 1 second to process
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test_input = "Write a 2 sentence story about a cat"
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# Measure start time
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t0 = time.time()
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try:
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# Try to get a response
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response = mistral.invoke(test_input)
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# If successful, validate the response
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elapsed_time = time.time() - t0
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logging.info(f"Request succeeded in {elapsed_time:.2f} seconds")
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# Check that we got a valid response
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assert response.content
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assert isinstance(response.content, str)
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assert "cat" in response.content.lower()
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except ReadTimeout:
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elapsed_time = time.time() - t0
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logging.info(f"Request timed out after {elapsed_time:.2f} seconds")
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assert elapsed_time >= 3.0
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pytest.skip("Test timed out as expected with short timeout")
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except Exception as e:
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logging.error(f"Unexpected exception: {e}")
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raise
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