mirror of
https://github.com/hwchase17/langchain.git
synced 2025-04-27 19:46:55 +00:00
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."""
|
|
|
|
import json
|
|
import logging
|
|
import time
|
|
from typing import Any, Optional
|
|
|
|
import pytest
|
|
from httpx import ReadTimeout
|
|
from langchain_core.messages import (
|
|
AIMessage,
|
|
AIMessageChunk,
|
|
BaseMessageChunk,
|
|
HumanMessage,
|
|
)
|
|
from pydantic import BaseModel
|
|
from typing_extensions import TypedDict
|
|
|
|
from langchain_mistralai.chat_models import ChatMistralAI
|
|
|
|
|
|
def test_stream() -> None:
|
|
"""Test streaming tokens from ChatMistralAI."""
|
|
llm = ChatMistralAI()
|
|
|
|
for token in llm.stream("Hello"):
|
|
assert isinstance(token.content, str)
|
|
|
|
|
|
async def test_astream() -> None:
|
|
"""Test streaming tokens from ChatMistralAI."""
|
|
llm = ChatMistralAI()
|
|
|
|
full: Optional[BaseMessageChunk] = None
|
|
chunks_with_token_counts = 0
|
|
chunks_with_response_metadata = 0
|
|
async for token in llm.astream("Hello"):
|
|
assert isinstance(token, AIMessageChunk)
|
|
assert isinstance(token.content, str)
|
|
full = token if full is None else full + token
|
|
if token.usage_metadata is not None:
|
|
chunks_with_token_counts += 1
|
|
if token.response_metadata:
|
|
chunks_with_response_metadata += 1
|
|
if chunks_with_token_counts != 1 or chunks_with_response_metadata != 1:
|
|
raise AssertionError(
|
|
"Expected exactly one chunk with token counts or response_metadata. "
|
|
"AIMessageChunk aggregation adds / appends counts and metadata. Check that "
|
|
"this is behaving properly."
|
|
)
|
|
assert isinstance(full, AIMessageChunk)
|
|
assert full.usage_metadata is not None
|
|
assert full.usage_metadata["input_tokens"] > 0
|
|
assert full.usage_metadata["output_tokens"] > 0
|
|
assert (
|
|
full.usage_metadata["input_tokens"] + full.usage_metadata["output_tokens"]
|
|
== full.usage_metadata["total_tokens"]
|
|
)
|
|
assert isinstance(full.response_metadata["model_name"], str)
|
|
assert full.response_metadata["model_name"]
|
|
|
|
|
|
async def test_abatch() -> None:
|
|
"""Test streaming tokens from ChatMistralAI"""
|
|
llm = ChatMistralAI()
|
|
|
|
result = await llm.abatch(["I'm Pickle Rick", "I'm not Pickle Rick"])
|
|
for token in result:
|
|
assert isinstance(token.content, str)
|
|
|
|
|
|
async def test_abatch_tags() -> None:
|
|
"""Test batch tokens from ChatMistralAI"""
|
|
llm = ChatMistralAI()
|
|
|
|
result = await llm.abatch(
|
|
["I'm Pickle Rick", "I'm not Pickle Rick"], config={"tags": ["foo"]}
|
|
)
|
|
for token in result:
|
|
assert isinstance(token.content, str)
|
|
|
|
|
|
def test_batch() -> None:
|
|
"""Test batch tokens from ChatMistralAI"""
|
|
llm = ChatMistralAI()
|
|
|
|
result = llm.batch(["I'm Pickle Rick", "I'm not Pickle Rick"])
|
|
for token in result:
|
|
assert isinstance(token.content, str)
|
|
|
|
|
|
async def test_ainvoke() -> None:
|
|
"""Test invoke tokens from ChatMistralAI"""
|
|
llm = ChatMistralAI()
|
|
|
|
result = await llm.ainvoke("I'm Pickle Rick", config={"tags": ["foo"]})
|
|
assert isinstance(result.content, str)
|
|
assert "model_name" in result.response_metadata
|
|
|
|
|
|
def test_invoke() -> None:
|
|
"""Test invoke tokens from ChatMistralAI"""
|
|
llm = ChatMistralAI()
|
|
|
|
result = llm.invoke("I'm Pickle Rick", config=dict(tags=["foo"]))
|
|
assert isinstance(result.content, str)
|
|
|
|
|
|
def test_chat_mistralai_llm_output_contains_model_name() -> None:
|
|
"""Test llm_output contains model_name."""
|
|
chat = ChatMistralAI(max_tokens=10)
|
|
message = HumanMessage(content="Hello")
|
|
llm_result = chat.generate([[message]])
|
|
assert llm_result.llm_output is not None
|
|
assert llm_result.llm_output["model_name"] == chat.model
|
|
|
|
|
|
def test_chat_mistralai_streaming_llm_output_contains_model_name() -> None:
|
|
"""Test llm_output contains model_name."""
|
|
chat = ChatMistralAI(max_tokens=10, streaming=True)
|
|
message = HumanMessage(content="Hello")
|
|
llm_result = chat.generate([[message]])
|
|
assert llm_result.llm_output is not None
|
|
assert llm_result.llm_output["model_name"] == chat.model
|
|
|
|
|
|
def test_chat_mistralai_llm_output_contains_token_usage() -> None:
|
|
"""Test llm_output contains model_name."""
|
|
chat = ChatMistralAI(max_tokens=10)
|
|
message = HumanMessage(content="Hello")
|
|
llm_result = chat.generate([[message]])
|
|
assert llm_result.llm_output is not None
|
|
assert "token_usage" in llm_result.llm_output
|
|
token_usage = llm_result.llm_output["token_usage"]
|
|
assert "prompt_tokens" in token_usage
|
|
assert "completion_tokens" in token_usage
|
|
assert "total_tokens" in token_usage
|
|
|
|
|
|
def test_chat_mistralai_streaming_llm_output_not_contain_token_usage() -> None:
|
|
"""Mistral currently doesn't return token usage when streaming."""
|
|
chat = ChatMistralAI(max_tokens=10, streaming=True)
|
|
message = HumanMessage(content="Hello")
|
|
llm_result = chat.generate([[message]])
|
|
assert llm_result.llm_output is not None
|
|
assert "token_usage" in llm_result.llm_output
|
|
token_usage = llm_result.llm_output["token_usage"]
|
|
assert not token_usage
|
|
|
|
|
|
def test_structured_output() -> None:
|
|
llm = ChatMistralAI(model="mistral-large-latest", temperature=0) # type: ignore[call-arg]
|
|
schema = {
|
|
"title": "AnswerWithJustification",
|
|
"description": (
|
|
"An answer to the user question along with justification for the answer."
|
|
),
|
|
"type": "object",
|
|
"properties": {
|
|
"answer": {"title": "Answer", "type": "string"},
|
|
"justification": {"title": "Justification", "type": "string"},
|
|
},
|
|
"required": ["answer", "justification"],
|
|
}
|
|
structured_llm = llm.with_structured_output(schema)
|
|
result = structured_llm.invoke(
|
|
"What weighs more a pound of bricks or a pound of feathers"
|
|
)
|
|
assert isinstance(result, dict)
|
|
|
|
|
|
def test_streaming_structured_output() -> None:
|
|
llm = ChatMistralAI(model="mistral-large-latest", temperature=0) # type: ignore[call-arg]
|
|
|
|
class Person(BaseModel):
|
|
name: str
|
|
age: int
|
|
|
|
structured_llm = llm.with_structured_output(Person)
|
|
strm = structured_llm.stream("Erick, 27 years old")
|
|
chunk_num = 0
|
|
for chunk in strm:
|
|
assert chunk_num == 0, "should only have one chunk with model"
|
|
assert isinstance(chunk, Person)
|
|
assert chunk.name == "Erick"
|
|
assert chunk.age == 27
|
|
chunk_num += 1
|
|
|
|
|
|
class Book(BaseModel):
|
|
name: str
|
|
authors: list[str]
|
|
|
|
|
|
class BookDict(TypedDict):
|
|
name: str
|
|
authors: list[str]
|
|
|
|
|
|
def _check_parsed_result(result: Any, schema: Any) -> None:
|
|
if schema == Book:
|
|
assert isinstance(result, Book)
|
|
else:
|
|
assert all(key in ["name", "authors"] for key in result.keys())
|
|
|
|
|
|
@pytest.mark.parametrize("schema", [Book, BookDict, Book.model_json_schema()])
|
|
def test_structured_output_json_schema(schema: Any) -> None:
|
|
llm = ChatMistralAI(model="ministral-8b-latest") # type: ignore[call-arg]
|
|
structured_llm = llm.with_structured_output(schema, method="json_schema")
|
|
|
|
messages = [
|
|
{"role": "system", "content": "Extract the book's information."},
|
|
{
|
|
"role": "user",
|
|
"content": "I recently read 'To Kill a Mockingbird' by Harper Lee.",
|
|
},
|
|
]
|
|
# Test invoke
|
|
result = structured_llm.invoke(messages)
|
|
_check_parsed_result(result, schema)
|
|
|
|
# Test stream
|
|
for chunk in structured_llm.stream(messages):
|
|
_check_parsed_result(chunk, schema)
|
|
|
|
|
|
@pytest.mark.parametrize("schema", [Book, BookDict, Book.model_json_schema()])
|
|
async def test_structured_output_json_schema_async(schema: Any) -> None:
|
|
llm = ChatMistralAI(model="ministral-8b-latest") # type: ignore[call-arg]
|
|
structured_llm = llm.with_structured_output(schema, method="json_schema")
|
|
|
|
messages = [
|
|
{"role": "system", "content": "Extract the book's information."},
|
|
{
|
|
"role": "user",
|
|
"content": "I recently read 'To Kill a Mockingbird' by Harper Lee.",
|
|
},
|
|
]
|
|
# Test invoke
|
|
result = await structured_llm.ainvoke(messages)
|
|
_check_parsed_result(result, schema)
|
|
|
|
# Test stream
|
|
async for chunk in structured_llm.astream(messages):
|
|
_check_parsed_result(chunk, schema)
|
|
|
|
|
|
def test_tool_call() -> None:
|
|
llm = ChatMistralAI(model="mistral-large-latest", temperature=0) # type: ignore[call-arg]
|
|
|
|
class Person(BaseModel):
|
|
name: str
|
|
age: int
|
|
|
|
tool_llm = llm.bind_tools([Person])
|
|
|
|
result = tool_llm.invoke("Erick, 27 years old")
|
|
assert isinstance(result, AIMessage)
|
|
assert len(result.tool_calls) == 1
|
|
tool_call = result.tool_calls[0]
|
|
assert tool_call["name"] == "Person"
|
|
assert tool_call["args"] == {"name": "Erick", "age": 27}
|
|
|
|
|
|
def test_streaming_tool_call() -> None:
|
|
llm = ChatMistralAI(model="mistral-large-latest", temperature=0) # type: ignore[call-arg]
|
|
|
|
class Person(BaseModel):
|
|
name: str
|
|
age: int
|
|
|
|
tool_llm = llm.bind_tools([Person])
|
|
|
|
# where it calls the tool
|
|
strm = tool_llm.stream("Erick, 27 years old")
|
|
|
|
additional_kwargs = None
|
|
for chunk in strm:
|
|
assert isinstance(chunk, AIMessageChunk)
|
|
assert chunk.content == ""
|
|
additional_kwargs = chunk.additional_kwargs
|
|
|
|
assert additional_kwargs is not None
|
|
assert "tool_calls" in additional_kwargs
|
|
assert len(additional_kwargs["tool_calls"]) == 1
|
|
assert additional_kwargs["tool_calls"][0]["function"]["name"] == "Person"
|
|
assert json.loads(additional_kwargs["tool_calls"][0]["function"]["arguments"]) == {
|
|
"name": "Erick",
|
|
"age": 27,
|
|
}
|
|
|
|
assert isinstance(chunk, AIMessageChunk)
|
|
assert len(chunk.tool_call_chunks) == 1
|
|
tool_call_chunk = chunk.tool_call_chunks[0]
|
|
assert tool_call_chunk["name"] == "Person"
|
|
assert tool_call_chunk["args"] == '{"name": "Erick", "age": 27}'
|
|
|
|
# where it doesn't call the tool
|
|
strm = tool_llm.stream("What is 2+2?")
|
|
acc: Any = None
|
|
for chunk in strm:
|
|
assert isinstance(chunk, AIMessageChunk)
|
|
acc = chunk if acc is None else acc + chunk
|
|
assert acc.content != ""
|
|
assert "tool_calls" not in acc.additional_kwargs
|
|
|
|
|
|
def test_retry_parameters(caplog: pytest.LogCaptureFixture) -> None:
|
|
"""Test that retry parameters are honored in ChatMistralAI."""
|
|
# Create a model with intentionally short timeout and multiple retries
|
|
mistral = ChatMistralAI(
|
|
timeout=1, # Very short timeout to trigger timeouts
|
|
max_retries=3, # Should retry 3 times
|
|
)
|
|
|
|
# Simple test input that should take longer than 1 second to process
|
|
test_input = "Write a 2 sentence story about a cat"
|
|
|
|
# Measure start time
|
|
t0 = time.time()
|
|
|
|
try:
|
|
# Try to get a response
|
|
response = mistral.invoke(test_input)
|
|
|
|
# If successful, validate the response
|
|
elapsed_time = time.time() - t0
|
|
logging.info(f"Request succeeded in {elapsed_time:.2f} seconds")
|
|
# Check that we got a valid response
|
|
assert response.content
|
|
assert isinstance(response.content, str)
|
|
assert "cat" in response.content.lower()
|
|
|
|
except ReadTimeout:
|
|
elapsed_time = time.time() - t0
|
|
logging.info(f"Request timed out after {elapsed_time:.2f} seconds")
|
|
assert elapsed_time >= 3.0
|
|
pytest.skip("Test timed out as expected with short timeout")
|
|
except Exception as e:
|
|
logging.error(f"Unexpected exception: {e}")
|
|
raise
|