mirror of https://github.com/hwchase17/langchain
You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
522 lines
26 KiB
Python
522 lines
26 KiB
Python
"""Test ChatAnthropic chat model."""
|
|
|
|
import json
|
|
from typing import List, Optional
|
|
|
|
import pytest
|
|
from langchain_core.callbacks import CallbackManager
|
|
from langchain_core.messages import (
|
|
AIMessage,
|
|
AIMessageChunk,
|
|
BaseMessage,
|
|
BaseMessageChunk,
|
|
HumanMessage,
|
|
SystemMessage,
|
|
ToolMessage,
|
|
)
|
|
from langchain_core.outputs import ChatGeneration, LLMResult
|
|
from langchain_core.prompts import ChatPromptTemplate
|
|
from langchain_core.pydantic_v1 import BaseModel, Field
|
|
from langchain_core.tools import tool
|
|
|
|
from langchain_anthropic import ChatAnthropic, ChatAnthropicMessages
|
|
from tests.unit_tests._utils import FakeCallbackHandler
|
|
|
|
MODEL_NAME = "claude-3-sonnet-20240229"
|
|
|
|
|
|
def test_stream() -> None:
|
|
"""Test streaming tokens from Anthropic."""
|
|
llm = ChatAnthropicMessages(model_name=MODEL_NAME) # type: ignore[call-arg, call-arg]
|
|
|
|
full: Optional[BaseMessageChunk] = None
|
|
chunks_with_input_token_counts = 0
|
|
chunks_with_output_token_counts = 0
|
|
for token in llm.stream("I'm Pickle Rick"):
|
|
assert isinstance(token.content, str)
|
|
full = token if full is None else full + token
|
|
assert isinstance(token, AIMessageChunk)
|
|
if token.usage_metadata is not None:
|
|
if token.usage_metadata.get("input_tokens"):
|
|
chunks_with_input_token_counts += 1
|
|
elif token.usage_metadata.get("output_tokens"):
|
|
chunks_with_output_token_counts += 1
|
|
if chunks_with_input_token_counts != 1 or chunks_with_output_token_counts != 1:
|
|
raise AssertionError(
|
|
"Expected exactly one chunk with input or output token counts. "
|
|
"AIMessageChunk aggregation adds counts. Check that "
|
|
"this is behaving properly."
|
|
)
|
|
# check token usage is populated
|
|
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["total_tokens"] > 0
|
|
assert (
|
|
full.usage_metadata["input_tokens"] + full.usage_metadata["output_tokens"]
|
|
== full.usage_metadata["total_tokens"]
|
|
)
|
|
assert "stop_reason" in full.response_metadata
|
|
assert "stop_sequence" in full.response_metadata
|
|
|
|
|
|
async def test_astream() -> None:
|
|
"""Test streaming tokens from Anthropic."""
|
|
llm = ChatAnthropicMessages(model_name=MODEL_NAME) # type: ignore[call-arg, call-arg]
|
|
|
|
full: Optional[BaseMessageChunk] = None
|
|
chunks_with_input_token_counts = 0
|
|
chunks_with_output_token_counts = 0
|
|
async for token in llm.astream("I'm Pickle Rick"):
|
|
assert isinstance(token.content, str)
|
|
full = token if full is None else full + token
|
|
assert isinstance(token, AIMessageChunk)
|
|
if token.usage_metadata is not None:
|
|
if token.usage_metadata.get("input_tokens"):
|
|
chunks_with_input_token_counts += 1
|
|
elif token.usage_metadata.get("output_tokens"):
|
|
chunks_with_output_token_counts += 1
|
|
if chunks_with_input_token_counts != 1 or chunks_with_output_token_counts != 1:
|
|
raise AssertionError(
|
|
"Expected exactly one chunk with input or output token counts. "
|
|
"AIMessageChunk aggregation adds counts. Check that "
|
|
"this is behaving properly."
|
|
)
|
|
# check token usage is populated
|
|
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["total_tokens"] > 0
|
|
assert (
|
|
full.usage_metadata["input_tokens"] + full.usage_metadata["output_tokens"]
|
|
== full.usage_metadata["total_tokens"]
|
|
)
|
|
assert "stop_reason" in full.response_metadata
|
|
assert "stop_sequence" in full.response_metadata
|
|
|
|
# test usage metadata can be excluded
|
|
model = ChatAnthropic(model_name=MODEL_NAME, stream_usage=False) # type: ignore[call-arg]
|
|
async for token in model.astream("hi"):
|
|
assert isinstance(token, AIMessageChunk)
|
|
assert token.usage_metadata is None
|
|
# check we override with kwarg
|
|
model = ChatAnthropic(model_name=MODEL_NAME) # type: ignore[call-arg]
|
|
assert model.stream_usage
|
|
async for token in model.astream("hi", stream_usage=False):
|
|
assert isinstance(token, AIMessageChunk)
|
|
assert token.usage_metadata is None
|
|
|
|
# Check expected raw API output
|
|
async_client = model._async_client
|
|
params: dict = {
|
|
"model": "claude-3-haiku-20240307",
|
|
"max_tokens": 1024,
|
|
"messages": [{"role": "user", "content": "hi"}],
|
|
"temperature": 0.0,
|
|
}
|
|
stream = await async_client.messages.create(**params, stream=True)
|
|
async for event in stream:
|
|
if event.type == "message_start":
|
|
assert event.message.usage.input_tokens > 1
|
|
# Note: this single output token included in message start event
|
|
# does not appear to contribute to overall output token counts. It
|
|
# is excluded from the total token count.
|
|
assert event.message.usage.output_tokens == 1
|
|
elif event.type == "message_delta":
|
|
assert event.usage.output_tokens > 1
|
|
else:
|
|
pass
|
|
|
|
|
|
async def test_abatch() -> None:
|
|
"""Test streaming tokens from ChatAnthropicMessages."""
|
|
llm = ChatAnthropicMessages(model_name=MODEL_NAME) # type: ignore[call-arg, call-arg]
|
|
|
|
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 ChatAnthropicMessages."""
|
|
llm = ChatAnthropicMessages(model_name=MODEL_NAME) # type: ignore[call-arg, call-arg]
|
|
|
|
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)
|
|
|
|
|
|
async def test_async_tool_use() -> None:
|
|
llm = ChatAnthropic( # type: ignore[call-arg]
|
|
model=MODEL_NAME,
|
|
)
|
|
|
|
llm_with_tools = llm.bind_tools(
|
|
[
|
|
{
|
|
"name": "get_weather",
|
|
"description": "Get weather report for a city",
|
|
"input_schema": {
|
|
"type": "object",
|
|
"properties": {"location": {"type": "string"}},
|
|
},
|
|
}
|
|
]
|
|
)
|
|
response = await llm_with_tools.ainvoke("what's the weather in san francisco, ca")
|
|
assert isinstance(response, AIMessage)
|
|
assert isinstance(response.content, list)
|
|
assert isinstance(response.tool_calls, list)
|
|
assert len(response.tool_calls) == 1
|
|
tool_call = response.tool_calls[0]
|
|
assert tool_call["name"] == "get_weather"
|
|
assert isinstance(tool_call["args"], dict)
|
|
assert "location" in tool_call["args"]
|
|
|
|
# Test streaming
|
|
first = True
|
|
chunks = [] # type: ignore
|
|
async for chunk in llm_with_tools.astream(
|
|
"what's the weather in san francisco, ca"
|
|
):
|
|
chunks = chunks + [chunk]
|
|
if first:
|
|
gathered = chunk
|
|
first = False
|
|
else:
|
|
gathered = gathered + chunk # type: ignore
|
|
assert len(chunks) > 1
|
|
assert isinstance(gathered, AIMessageChunk)
|
|
assert isinstance(gathered.tool_call_chunks, list)
|
|
assert len(gathered.tool_call_chunks) == 1
|
|
tool_call_chunk = gathered.tool_call_chunks[0]
|
|
assert tool_call_chunk["name"] == "get_weather"
|
|
assert isinstance(tool_call_chunk["args"], str)
|
|
assert "location" in json.loads(tool_call_chunk["args"])
|
|
|
|
|
|
def test_batch() -> None:
|
|
"""Test batch tokens from ChatAnthropicMessages."""
|
|
llm = ChatAnthropicMessages(model_name=MODEL_NAME) # type: ignore[call-arg, call-arg]
|
|
|
|
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 ChatAnthropicMessages."""
|
|
llm = ChatAnthropicMessages(model_name=MODEL_NAME) # type: ignore[call-arg, call-arg]
|
|
|
|
result = await llm.ainvoke("I'm Pickle Rick", config={"tags": ["foo"]})
|
|
assert isinstance(result.content, str)
|
|
|
|
|
|
def test_invoke() -> None:
|
|
"""Test invoke tokens from ChatAnthropicMessages."""
|
|
llm = ChatAnthropicMessages(model_name=MODEL_NAME) # type: ignore[call-arg, call-arg]
|
|
|
|
result = llm.invoke("I'm Pickle Rick", config=dict(tags=["foo"]))
|
|
assert isinstance(result.content, str)
|
|
|
|
|
|
def test_system_invoke() -> None:
|
|
"""Test invoke tokens with a system message"""
|
|
llm = ChatAnthropicMessages(model_name=MODEL_NAME) # type: ignore[call-arg, call-arg]
|
|
|
|
prompt = ChatPromptTemplate.from_messages(
|
|
[
|
|
(
|
|
"system",
|
|
"You are an expert cartographer. If asked, you are a cartographer. "
|
|
"STAY IN CHARACTER",
|
|
),
|
|
("human", "Are you a mathematician?"),
|
|
]
|
|
)
|
|
|
|
chain = prompt | llm
|
|
|
|
result = chain.invoke({})
|
|
assert isinstance(result.content, str)
|
|
|
|
|
|
def test_anthropic_call() -> None:
|
|
"""Test valid call to anthropic."""
|
|
chat = ChatAnthropic(model=MODEL_NAME) # type: ignore[call-arg]
|
|
message = HumanMessage(content="Hello")
|
|
response = chat.invoke([message])
|
|
assert isinstance(response, AIMessage)
|
|
assert isinstance(response.content, str)
|
|
|
|
|
|
def test_anthropic_generate() -> None:
|
|
"""Test generate method of anthropic."""
|
|
chat = ChatAnthropic(model=MODEL_NAME) # type: ignore[call-arg]
|
|
chat_messages: List[List[BaseMessage]] = [
|
|
[HumanMessage(content="How many toes do dogs have?")]
|
|
]
|
|
messages_copy = [messages.copy() for messages in chat_messages]
|
|
result: LLMResult = chat.generate(chat_messages)
|
|
assert isinstance(result, LLMResult)
|
|
for response in result.generations[0]:
|
|
assert isinstance(response, ChatGeneration)
|
|
assert isinstance(response.text, str)
|
|
assert response.text == response.message.content
|
|
assert chat_messages == messages_copy
|
|
|
|
|
|
def test_anthropic_streaming() -> None:
|
|
"""Test streaming tokens from anthropic."""
|
|
chat = ChatAnthropic(model=MODEL_NAME) # type: ignore[call-arg]
|
|
message = HumanMessage(content="Hello")
|
|
response = chat.stream([message])
|
|
for token in response:
|
|
assert isinstance(token, AIMessageChunk)
|
|
assert isinstance(token.content, str)
|
|
|
|
|
|
def test_anthropic_streaming_callback() -> None:
|
|
"""Test that streaming correctly invokes on_llm_new_token callback."""
|
|
callback_handler = FakeCallbackHandler()
|
|
callback_manager = CallbackManager([callback_handler])
|
|
chat = ChatAnthropic( # type: ignore[call-arg]
|
|
model=MODEL_NAME,
|
|
callback_manager=callback_manager,
|
|
verbose=True,
|
|
)
|
|
message = HumanMessage(content="Write me a sentence with 10 words.")
|
|
for token in chat.stream([message]):
|
|
assert isinstance(token, AIMessageChunk)
|
|
assert isinstance(token.content, str)
|
|
assert callback_handler.llm_streams > 1
|
|
|
|
|
|
async def test_anthropic_async_streaming_callback() -> None:
|
|
"""Test that streaming correctly invokes on_llm_new_token callback."""
|
|
callback_handler = FakeCallbackHandler()
|
|
callback_manager = CallbackManager([callback_handler])
|
|
chat = ChatAnthropic( # type: ignore[call-arg]
|
|
model=MODEL_NAME,
|
|
callback_manager=callback_manager,
|
|
verbose=True,
|
|
)
|
|
chat_messages: List[BaseMessage] = [
|
|
HumanMessage(content="How many toes do dogs have?")
|
|
]
|
|
async for token in chat.astream(chat_messages):
|
|
assert isinstance(token, AIMessageChunk)
|
|
assert isinstance(token.content, str)
|
|
assert callback_handler.llm_streams > 1
|
|
|
|
|
|
def test_anthropic_multimodal() -> None:
|
|
"""Test that multimodal inputs are handled correctly."""
|
|
chat = ChatAnthropic(model=MODEL_NAME) # type: ignore[call-arg]
|
|
messages = [
|
|
HumanMessage(
|
|
content=[
|
|
{
|
|
"type": "image_url",
|
|
"image_url": {
|
|
# langchain logo
|
|
"url": "data:image/jpeg;base64,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", # noqa: E501
|
|
},
|
|
},
|
|
{"type": "text", "text": "What is this a logo for?"},
|
|
]
|
|
)
|
|
]
|
|
response = chat.invoke(messages)
|
|
assert isinstance(response, AIMessage)
|
|
assert isinstance(response.content, str)
|
|
|
|
|
|
def test_streaming() -> None:
|
|
"""Test streaming tokens from Anthropic."""
|
|
callback_handler = FakeCallbackHandler()
|
|
callback_manager = CallbackManager([callback_handler])
|
|
|
|
llm = ChatAnthropicMessages( # type: ignore[call-arg, call-arg]
|
|
model_name=MODEL_NAME, streaming=True, callback_manager=callback_manager
|
|
)
|
|
|
|
response = llm.generate([[HumanMessage(content="I'm Pickle Rick")]])
|
|
assert callback_handler.llm_streams > 0
|
|
assert isinstance(response, LLMResult)
|
|
|
|
|
|
async def test_astreaming() -> None:
|
|
"""Test streaming tokens from Anthropic."""
|
|
callback_handler = FakeCallbackHandler()
|
|
callback_manager = CallbackManager([callback_handler])
|
|
|
|
llm = ChatAnthropicMessages( # type: ignore[call-arg, call-arg]
|
|
model_name=MODEL_NAME, streaming=True, callback_manager=callback_manager
|
|
)
|
|
|
|
response = await llm.agenerate([[HumanMessage(content="I'm Pickle Rick")]])
|
|
assert callback_handler.llm_streams > 0
|
|
assert isinstance(response, LLMResult)
|
|
|
|
|
|
def test_tool_use() -> None:
|
|
llm = ChatAnthropic(model=MODEL_NAME) # type: ignore[call-arg]
|
|
llm_with_tools = llm.bind_tools(
|
|
[
|
|
{
|
|
"name": "get_weather",
|
|
"description": "Get weather report for a city",
|
|
"input_schema": {
|
|
"type": "object",
|
|
"properties": {"location": {"type": "string"}},
|
|
},
|
|
}
|
|
]
|
|
)
|
|
response = llm_with_tools.invoke("what's the weather in san francisco, ca")
|
|
assert isinstance(response, AIMessage)
|
|
assert isinstance(response.content, list)
|
|
assert isinstance(response.tool_calls, list)
|
|
assert len(response.tool_calls) == 1
|
|
tool_call = response.tool_calls[0]
|
|
assert tool_call["name"] == "get_weather"
|
|
assert isinstance(tool_call["args"], dict)
|
|
assert "location" in tool_call["args"]
|
|
|
|
# Test streaming
|
|
input = "how are you? what's the weather in san francisco, ca"
|
|
first = True
|
|
chunks = [] # type: ignore
|
|
for chunk in llm_with_tools.stream(input):
|
|
chunks = chunks + [chunk]
|
|
if first:
|
|
gathered = chunk
|
|
first = False
|
|
else:
|
|
gathered = gathered + chunk # type: ignore
|
|
assert len(chunks) > 1
|
|
assert isinstance(gathered.content, list)
|
|
assert len(gathered.content) == 2
|
|
tool_use_block = None
|
|
for content_block in gathered.content:
|
|
assert isinstance(content_block, dict)
|
|
if content_block["type"] == "tool_use":
|
|
tool_use_block = content_block
|
|
break
|
|
assert tool_use_block is not None
|
|
assert tool_use_block["name"] == "get_weather"
|
|
assert "location" in json.loads(tool_use_block["partial_json"])
|
|
assert isinstance(gathered, AIMessageChunk)
|
|
assert isinstance(gathered.tool_calls, list)
|
|
assert len(gathered.tool_calls) == 1
|
|
tool_call = gathered.tool_calls[0]
|
|
assert tool_call["name"] == "get_weather"
|
|
assert isinstance(tool_call["args"], dict)
|
|
assert "location" in tool_call["args"]
|
|
assert tool_call["id"] is not None
|
|
|
|
# Test passing response back to model
|
|
stream = llm_with_tools.stream(
|
|
[
|
|
input,
|
|
gathered,
|
|
ToolMessage(content="sunny and warm", tool_call_id=tool_call["id"]),
|
|
]
|
|
)
|
|
chunks = [] # type: ignore
|
|
first = True
|
|
for chunk in stream:
|
|
chunks = chunks + [chunk]
|
|
if first:
|
|
gathered = chunk
|
|
first = False
|
|
else:
|
|
gathered = gathered + chunk # type: ignore
|
|
assert len(chunks) > 1
|
|
|
|
|
|
def test_anthropic_with_empty_text_block() -> None:
|
|
"""Anthropic SDK can return an empty text block."""
|
|
|
|
@tool
|
|
def type_letter(letter: str) -> str:
|
|
"""Type the given letter."""
|
|
return "OK"
|
|
|
|
model = ChatAnthropic(model="claude-3-opus-20240229", temperature=0).bind_tools( # type: ignore[call-arg]
|
|
[type_letter]
|
|
)
|
|
|
|
messages = [
|
|
SystemMessage(
|
|
content="Repeat the given string using the provided tools. Do not write "
|
|
"anything else or provide any explanations. For example, "
|
|
"if the string is 'abc', you must print the "
|
|
"letters 'a', 'b', and 'c' one at a time and in that order. "
|
|
),
|
|
HumanMessage(content="dog"),
|
|
AIMessage(
|
|
content=[
|
|
{"text": "", "type": "text"},
|
|
{
|
|
"id": "toolu_01V6d6W32QGGSmQm4BT98EKk",
|
|
"input": {"letter": "d"},
|
|
"name": "type_letter",
|
|
"type": "tool_use",
|
|
},
|
|
],
|
|
tool_calls=[
|
|
{
|
|
"name": "type_letter",
|
|
"args": {"letter": "d"},
|
|
"id": "toolu_01V6d6W32QGGSmQm4BT98EKk",
|
|
"type": "tool_call",
|
|
},
|
|
],
|
|
),
|
|
ToolMessage(content="OK", tool_call_id="toolu_01V6d6W32QGGSmQm4BT98EKk"),
|
|
]
|
|
|
|
model.invoke(messages)
|
|
|
|
|
|
def test_with_structured_output() -> None:
|
|
llm = ChatAnthropic( # type: ignore[call-arg]
|
|
model="claude-3-opus-20240229",
|
|
)
|
|
|
|
structured_llm = llm.with_structured_output(
|
|
{
|
|
"name": "get_weather",
|
|
"description": "Get weather report for a city",
|
|
"input_schema": {
|
|
"type": "object",
|
|
"properties": {"location": {"type": "string"}},
|
|
},
|
|
}
|
|
)
|
|
response = structured_llm.invoke("what's the weather in san francisco, ca")
|
|
assert isinstance(response, dict)
|
|
assert response["location"]
|
|
|
|
|
|
class GetWeather(BaseModel):
|
|
"""Get the current weather in a given location"""
|
|
|
|
location: str = Field(..., description="The city and state, e.g. San Francisco, CA")
|
|
|
|
|
|
@pytest.mark.parametrize("tool_choice", ["GetWeather", "auto", "any"])
|
|
def test_anthropic_bind_tools_tool_choice(tool_choice: str) -> None:
|
|
chat_model = ChatAnthropic( # type: ignore[call-arg]
|
|
model=MODEL_NAME,
|
|
)
|
|
chat_model_with_tools = chat_model.bind_tools([GetWeather], tool_choice=tool_choice)
|
|
response = chat_model_with_tools.invoke("what's the weather in ny and la")
|
|
assert isinstance(response, AIMessage)
|