mirror of
https://github.com/hwchase17/langchain.git
synced 2026-06-09 18:50:33 +00:00
2716 lines
99 KiB
Python
2716 lines
99 KiB
Python
"""Test ChatAnthropic chat model."""
|
|
|
|
from __future__ import annotations
|
|
|
|
import asyncio
|
|
import json
|
|
import os
|
|
from base64 import b64encode
|
|
from typing import TYPE_CHECKING, Any, Literal, cast
|
|
|
|
import anthropic
|
|
import httpx
|
|
import pytest
|
|
import requests
|
|
from langchain.agents import create_agent
|
|
from langchain.agents.structured_output import ProviderStrategy
|
|
from langchain_core.callbacks import CallbackManager
|
|
from langchain_core.exceptions import OutputParserException
|
|
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.tools import tool
|
|
|
|
if TYPE_CHECKING:
|
|
from collections.abc import Awaitable
|
|
|
|
from langchain_core.language_models.chat_model_stream import (
|
|
AsyncChatModelStream,
|
|
ChatModelStream,
|
|
)
|
|
from langchain_tests.utils.stream_lifecycle import assert_valid_event_stream
|
|
from pydantic import BaseModel, Field
|
|
from typing_extensions import TypedDict
|
|
|
|
from langchain_anthropic import ChatAnthropic
|
|
from langchain_anthropic._compat import _convert_from_v1_to_anthropic
|
|
from tests.unit_tests._utils import FakeCallbackHandler
|
|
|
|
MODEL_NAME = "claude-haiku-4-5-20251001"
|
|
|
|
|
|
def test_stream() -> None:
|
|
"""Test streaming tokens from Anthropic."""
|
|
llm = ChatAnthropic(model_name=MODEL_NAME) # type: ignore[call-arg, call-arg]
|
|
|
|
full: BaseMessageChunk | None = None
|
|
chunks_with_input_token_counts = 0
|
|
chunks_with_output_token_counts = 0
|
|
chunks_with_model_name = 0
|
|
for token in llm.stream("I'm Pickle Rick"):
|
|
assert isinstance(token.content, str)
|
|
full = cast("BaseMessageChunk", 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
|
|
if token.usage_metadata.get("output_tokens"):
|
|
chunks_with_output_token_counts += 1
|
|
chunks_with_model_name += int("model_name" in token.response_metadata)
|
|
if chunks_with_input_token_counts != 1 or chunks_with_output_token_counts != 1:
|
|
msg = (
|
|
"Expected exactly one chunk with input or output token counts. "
|
|
"AIMessageChunk aggregation adds counts. Check that "
|
|
"this is behaving properly."
|
|
)
|
|
raise AssertionError(
|
|
msg,
|
|
)
|
|
assert chunks_with_model_name == 1
|
|
# check token usage is populated
|
|
assert isinstance(full, AIMessageChunk)
|
|
assert len(full.content_blocks) == 1
|
|
assert full.content_blocks[0]["type"] == "text"
|
|
assert full.content_blocks[0]["text"]
|
|
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
|
|
assert "model_name" in full.response_metadata
|
|
|
|
|
|
async def test_astream() -> None:
|
|
"""Test streaming tokens from Anthropic."""
|
|
llm = ChatAnthropic(model_name=MODEL_NAME) # type: ignore[call-arg, call-arg]
|
|
|
|
full: BaseMessageChunk | None = 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 = cast("BaseMessageChunk", 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
|
|
if 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:
|
|
msg = (
|
|
"Expected exactly one chunk with input or output token counts. "
|
|
"AIMessageChunk aggregation adds counts. Check that "
|
|
"this is behaving properly."
|
|
)
|
|
raise AssertionError(
|
|
msg,
|
|
)
|
|
# check token usage is populated
|
|
assert isinstance(full, AIMessageChunk)
|
|
assert len(full.content_blocks) == 1
|
|
assert full.content_blocks[0]["type"] == "text"
|
|
assert full.content_blocks[0]["text"]
|
|
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
|
|
|
|
# Check expected raw API output
|
|
async_client = llm._async_client
|
|
params: dict = {
|
|
"model": MODEL_NAME,
|
|
"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
|
|
# Different models may report different initial output token counts
|
|
# in the message_start event. Ensure it's a positive value.
|
|
assert event.message.usage.output_tokens >= 1
|
|
elif event.type == "message_delta":
|
|
assert event.usage.output_tokens >= 1
|
|
else:
|
|
pass
|
|
|
|
|
|
async def test_stream_usage() -> None:
|
|
"""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
|
|
|
|
|
|
async def test_stream_usage_override() -> 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
|
|
|
|
|
|
async def test_abatch() -> None:
|
|
"""Test streaming tokens."""
|
|
llm = ChatAnthropic(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."""
|
|
llm = ChatAnthropic(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(
|
|
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 = 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: list[BaseMessage | BaseMessageChunk] = []
|
|
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[assignment]
|
|
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."""
|
|
llm = ChatAnthropic(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."""
|
|
llm = ChatAnthropic(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)
|
|
assert "model_name" in result.response_metadata
|
|
|
|
|
|
def test_invoke() -> None:
|
|
"""Test invoke tokens."""
|
|
llm = ChatAnthropic(model_name=MODEL_NAME) # type: ignore[call-arg, call-arg]
|
|
|
|
result = llm.invoke("I'm Pickle Rick", config={"tags": ["foo"]})
|
|
assert isinstance(result.content, str)
|
|
|
|
|
|
def test_system_invoke() -> None:
|
|
"""Test invoke tokens with a system message."""
|
|
llm = ChatAnthropic(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_handle_empty_aimessage() -> None:
|
|
# Anthropic can generate empty AIMessages, which are not valid unless in the last
|
|
# message in a sequence.
|
|
llm = ChatAnthropic(model=MODEL_NAME)
|
|
messages = [
|
|
HumanMessage("Hello"),
|
|
AIMessage([]),
|
|
HumanMessage("My name is Bob."),
|
|
]
|
|
_ = llm.invoke(messages)
|
|
|
|
# Test tool call sequence
|
|
llm_with_tools = llm.bind_tools(
|
|
[
|
|
{
|
|
"name": "get_weather",
|
|
"description": "Get weather report for a city",
|
|
"input_schema": {
|
|
"type": "object",
|
|
"properties": {"location": {"type": "string"}},
|
|
},
|
|
},
|
|
],
|
|
)
|
|
_ = llm_with_tools.invoke(
|
|
[
|
|
HumanMessage("What's the weather in Boston?"),
|
|
AIMessage(
|
|
content=[],
|
|
tool_calls=[
|
|
{
|
|
"name": "get_weather",
|
|
"args": {"location": "Boston"},
|
|
"id": "toolu_01V6d6W32QGGSmQm4BT98EKk",
|
|
"type": "tool_call",
|
|
},
|
|
],
|
|
),
|
|
ToolMessage(
|
|
content="It's sunny.", tool_call_id="toolu_01V6d6W32QGGSmQm4BT98EKk"
|
|
),
|
|
AIMessage([]),
|
|
HumanMessage("Thanks!"),
|
|
]
|
|
)
|
|
|
|
|
|
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(
|
|
model=MODEL_NAME, # type: ignore[call-arg]
|
|
callbacks=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(
|
|
model=MODEL_NAME, # type: ignore[call-arg]
|
|
callbacks=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: list[BaseMessage] = [
|
|
HumanMessage(
|
|
content=[
|
|
{
|
|
"type": "image_url",
|
|
"image_url": {
|
|
# langchain logo
|
|
"url": "data:image/jpeg;base64,/9j/4AAQSkZJRgABAQAAAQABAAD/2wCEAAMCAggHCQgGCQgICAcICAgICAgICAYICAgHDAgHCAgICAgIBggICAgICAgICBYICAgICwkKCAgNDQoIDggICQgBAwQEBgUGCgYGCBALCg0QCg0NEA0KCg8LDQoKCgoLDgoQDQoLDQoKCg4NDQ0NDgsQDw0OCg4NDQ4NDQoJDg8OCP/AABEIALAAsAMBEQACEQEDEQH/xAAdAAEAAgEFAQAAAAAAAAAAAAAABwgJAQIEBQYD/8QANBAAAgIBAwIDBwQCAgIDAAAAAQIAAwQFERIIEwYhMQcUFyJVldQjQVGBcZEJMzJiFRYk/8QAGwEBAAMAAwEAAAAAAAAAAAAAAAQFBgEDBwL/xAA5EQACAQIDBQQJBAIBBQAAAAAAAQIDEQQhMQVBUWGREhRxgRMVIjJSU8HR8CNyobFCguEGJGKi4v/aAAwDAQACEQMRAD8ApfJplBAEAQBAEAQBAEAQBAEAQBAEAQBAEAQBAEAQBAEAQBAEAQBAEAQBAEAQBAEAQBAEAQBAEAQBAEAQBAEAQBAEAQBAEAQBAEAQBAEAQBAEAQBAEAQBAEAQBAEAQBAEAQBAEAQBAEAQBAEAQBAEAQBAEAQBAEAQBAEAQBAEAQBAEAQBAEAQBAEAQBAEAQBAEAQBAEAQBAEAQBAEAQBAEAQBAEAQBAEAQBAEAQBANl16qOTEKB6kkAD+z5Tkcj0On+z7Ub1FlOmanejeavj6dqV6kfsQ1OK4IP8AIM6pVYR1kuqJdLCV6qvCnJ/6v66nL+Ems/RNc+y63+BOvvFL411O/wBW4r5T6D4Saz9E1z7Lrf4Ed4pfGuo9W4r5T6D4Saz9E1z7Lrf4Ed4pfGuo9W4r5T6D4Saz9E1z7Lrf4Ed4pfGuo9W4r5T6D4Saz9E1z7Lrf4Ed4pfGuo9W4r5T6D4Saz9E1z7Lrf4Ed4pfGuo9W4r5T6D4Saz9E1z7Lrf4Ed4pfGuo9W4r5T6D4Saz9E1z7Lrf4Ed4pfGuo9W4r5T6HE1D2e6lQpsu0zU6EXzZ8jTtSoUD9yWuxUAA/kmdkasJaSXVHRVwlekrzpyX+r+mh56m9WHJSGU+hUgg/wBjynaRORvnAEAQBAEAQBAEAQCbennpVzfER95LHE0tX4tlsnJr2B2srw6yQLCpBQ3Me1W+4/VZLKlh4jFRo5ay4cPH7f0XWA2XUxft37MONs34ffRcy/Xsu6bdG0UK2Nh1tkAbHMyAt+Wx2HIi11/SDcQe3jrTXv6IJRVcRUqe88uC0Nxhdn0MMv0458XnJ+e7wVlyJPJkYsTSAIAgCAIAgCAIBqDAIx9qHTbo2tBmycOtcgjYZmOBRlqdjxJtQDuhdye3ette/qhkmliKlP3XlwehXYrZ9DEr9SOfFZS6rXwd1yKCdQ3Srm+HT7yGOXpbPxXLVOLUMTtXXmVgkVliQgvU9qx9h+kz11Ne4fFRrZaS4cfD7f2YfH7LqYT279qHHevH76PlvhKTClEAQBAEAQBAJp6WOn0+I80i7mumYnF8x1LIbSSe3iV2DYq13ElnQ8q6gdijWUuIeKxHoY5e89PuXWy8D3qp7S9iOvN/D9+XiZRNN06uiuvHqrSqmpFrqqrVUrrrUBUREUBVVVAAUAAATNNtu7PR4xUUoxVkskloktxyCZwfRj26jetHPtzrMXSM4Uabj7Vrfj10O2ZdsDbb3bqrCKEYmpeyED8Hs53LZVwvsPg4qN6kbt+OS8t5hdobYqOo44edorK6SzfmtFpz14H16f8Arkz6cmrD1e9crBvsFZy3ropvxC2yo7NTXXXbjhtuXcTmisz91hX2yr4KLjemrNbuPXeMDtuoqihiGnF/5ZJx55ZNceF76GQSUJuhAEAQBAEAhb239WWl+H391s7mXnbAnExu2WqUjdWyLHda6Qw2IXdrCCGFZX5pMo4WdXNZLiyoxm1KOFfZl7UuCtdeN2kvzcRB4d/5JMV7OOVpWRRSWAFmPk1ZTKN9uT1PRi+QHnsj2H12DHYGXLZzS9mV3zVvuVFL/qGDlapSaXFST6qyfS/3tb4M8a4up49WoYlyZGLcCUsTf1B2ZGVgHrsRgVNbqrIwIYAjaVc4Sg+zJWZqaVWFWCnB3T0/PodnqOnV312Y9taW02o1dtViq9dlbAq6OjAqyspIKkEEGfKbTuj7lFSTjJXTyaejXAxd9U/T6fDmYBTzbTMvm+G7FnNRBHcxLLDuWankCrueVlRG5dq7nOlwuI9NHP3lr9zzjamA7rU9n3Jacn8P25eBC0mFKIAgCAIBtdwASfQDc/4nIbsZXulr2ZDR9HwsYpxybqxmZe4Xl71cquyMR69hO3jg+fy0r5n1OWxNX0lRvdovBflz1DZuG7vh4xtZtXl+55vpp5EsyKWZ5X2seH783TdRwsZgmVk4OVRQzMUUXPRYle7gEoCxA5gEqDvsdp2U5KM03omv7I+Ig6lKUIuzaaXmigPtb6HNQ0bEytTGXjZeLiKlhWuu6rINPMLbY1bFqkXHQ908b7CyK+wUqFe+pY2FSSjZpvnl+MwmJ2JVw9OVTtqUYq+Sadt+WaVtd9+W+uLLv5HzB8j/AIlgZ8yRdGfUXXq2JXpGTZtquFUE+cnfMxU2Wu9CzEvaicEsG+/MdzYLbsmexmHdOXaS9l/w+H2PQ9kY9V6apyftxVtdUtJc3x58iykrjQCAIAgFdurzqbPh+lMHFKHVspC6FuLLh427Icp0O4d2ZWREb5WZLGbktJrssMJhvSu8vdX8vh9zP7X2i8LBRp27b46Rj8Vt73JebyVnCfSz0jNqh/8AsGsrZZRcxuoxrms7ua7HmcvLYkOaXJ5Ctjvkb8n/AE+K3TcVi+x+nS6rdyX33eJTbL2S636+JTaeaTveTf8AlLlwjv35ZFmfHnSnoWo47Yo0/FxLOBWnJw8ejHuobb5GVqkUOqnY9qwOjDyI9CKyGKqwd+03ybdjS19mYarHs+jSe5pJNdP6KudBPiTIwNYz/D1jA1WJk91AWKLqGJctDWVg+QFlfdQtsGcVY+//AFgSzx0VKmqi5dJK/wCeZm9iVJ0sRPDye6WWdu1BpXWeV78M8uGd/wCURuCJuqX2YjWNHzMYJyyaKzmYm3Hl71SrOqKW8h307mOT5fLc3mPUSsNV9HUT3aPwf5crNpYbvGHlG2azj+5Zrrp5mKFHBAI9CNx/iak8vTubpwBAEAQDtPCekLk5WHiON0yczFx3H8pbkVVMP7VyJ8zfZi3wTfRHdRh26kI8ZRXk5IzREf6mPPXTSAIB1/iPQa8yjIwrVD05NFuPYrAFWrsrat1YHyIKsRsf2nMXZpo+ZR7UXF77rqYW2xHrJqsHG2smu1T6rapKWKf8OCP6mxvfNHj1nH2XqsnfW6yOVpGr241teVRY9ORS4sqtrPF67B6Mp/2NiCGBIIYMQeGlJWaujsp1JU5KcHZrQyZdK/U3X4ipONdwq1fGQNkVL5JkVbhfe8cE/wDgWKq1e5NFjKD8ttLPm8ThnSd17r0+35qej7N2hHFQs8prVfVcv6J4kIuBAKtdWnV8uj89I090fVeP/wCi8hXq05CvIcg26PmMpDCpgVqUrZaCGqrussLhPSe3P3f7/wCOf4s9tTaXd16On77/APXn48EU58OYl+RremrrRyHbJzdPbI9+LvZZjW21vUlgs5FMe4OqmshVrrscca9jtcSaVKXotydrcVr58zH04znioLFXd3G/a17L08E3u5vJEveGeobX/Cuq2YmttbbjX3NflUu7ZC1VW2OTlaZZuzDHrIbbGXZOFbV9qmwfLElh6Venelqsl4rc+fP6FtT2hicHiHDEu8W7u+ii8lKObtHL3fH/AC1tn1AdReJ4exVvJW/MyEJwcVWG9x2G1zkb8MVNwTbt83kqhmYCVVDDyqytot7/ADeanG46GFh2nm37q4/8c/qVr/4/fZ9k5Obm+J7+Xa430V2soVcrNuuW3LtT+RQUNZKjj3L2QHlRYqWOPqJRVJcvJJWRnth4epKpLE1FqnZ8XJ3b8MuG/LQvdKQ2ZqB/qAYXfFmkLjZWZiINkxszKx0H8JVkW1KP6VAJsIPtRT4pPqjyKtDsVJx4SkvJSdjq59HSIAgCAdp4T1dcbKw8tzsmNmYuQ5/hKsiq1j/SoTPma7UWuKa6o7qM+xUhLhKL8lJXM0RP+pjz100gCAIBjA6x/Y9ZpGq35KofcdSssy8ewA8Vvcl8rHJ3OzrazXAeQNVq8d+3Zx0mDrKpTS3rLy3P6HnG18I6FdzS9mWa/c9V9fPkQTJxRnf+AfHeRpOXj6pjHa/GsDhd+K2p6W0WHY/p31lqidiVDchsyqR8VIKpFxlo/wAv5EjD15UKiqw1X8revMy++DfFtOo4uNqNDcsfKprvrJ8iFZQeLD1Dod0KnzVlI/aZKcXCTi9UerUqkasFOLumk14M8T1L+0uzRdHzdRp8skKlGO2wPC+6xKUt2PkezzN3E7g8NtjvO7D01UqKL03+CzIe0MQ8Ph5VI66Lxbsv7Ks9D3ThTqG/iXOBvSvJsGHTae4L8lWDXZ2QzMzXMt7MoWzzNyW2PzPaYWeNxDj+nDLLPw4dPsZ7Y+CVb/ua3tO7tfitZPzyS5XJS6zOlu3XAmrYSh9Rpq7N2OzKozMYF3RUZyEXIqZ325lVtVyrMOFUjYPEql7MtP6f2J+1tmvE2qU/fWWusfo1/P8AVWfbjruoWabpFGrl/wD5Wq/UOyMhO3mV6QFxaU98BCuzW5dNxW2wcraqeZawku1pQjFVJOn7uWmna1y8uhmMdUqOhSjiPfTlr73o0rXfi1k96V7nq/YP0n6lr99OdqgysfS6qqKw2QbK8rKx6kWrHxcdG2toxlrUA3lU+Q71c3ta+rpr4qFJONOzlnpom9/N8vpkTMBsyriZKeITUEla+rSyUbapLyvzeZkT0fR6saqvFprSmilFrqqrUJXXWo2VEUABVUDbYSgbbd3qbyMVFWSskcucH0ag/wCoBhd8WauuTlZmWh3TIzMrIQ/yluRbap/tXBmwguzFLgkuiPIq0+3UnLjKT8nJ2Orn0dIgCAIBtdAQQfQjY/4nIauZXulr2nDWNHw8kvyyaKxh5e/Hl71SqozsF8h307eQB5fLcvkPQZbE0vR1Gt2q8H+WPUNm4nvGHjK92spfuWT66+ZLMilmIAgHm/aL4ExtVxL9PyaVvptRtkb1WwA9uyths1dqNsRYhDKf39Z905uElKLszor0YVoOE1dP86mH7R/DORdi5OeKz2sI4iZZIKtU+Q11dPJSvl+rS1ZBIKsyDY7krrXJKSjxvbyzPKY0ZuMprSNlLim21p4rPh1t6fA9ieq34Ka1RhW5OA7XKbMcC6ypq7DU/doT9cLyBPNK7ECglmT0nW60FLsN2fPnnroSI4KvKl6aMLxz0zeTavbW3hfy3Wq/4+fbVQKbPDd9wW7vWZGnK2wW2l17l9FTehsS0W5PA/M62uV5CqzhV4+i7+kS5Px4/T8z02wcXHsvDyed24+DzaXg7u3PLLSderP2f3arombi0KXyEFWVVWBu1jU2pc1SD93sqWxAP3dlkHC1FCqm9NOuRd7ToOvhpwjrk14xadv4K7dEPU5gYOI2iZ+RXiql1l2Hk2fJjtVae5ZVbaSUrsW42WB7O2jpYqg8k+exxuGnKXbgr8eOWXmUGxtpUqdP0FV9m12m9Gm72/8AFp8dfEmb22dZmlaXjv7nk42pag4K0U49q3U1t5fqZV1LFErTfl2g4st/8VCjnZXDo4Oc37ScVvv9L/iLXG7Xo0IfpyU57kndeLa0X8vRcq59OnsAzPFWY3iTVmezBa3uMbQOWo2qdhSibcUwa+IrPEBSq9pB/wBjV2GIrxoR9HT1/r/6M/s7A1MbU7ziHeN75/5tbuUF/Oml28h0oDfCAIBE/VL7TRo+j5uSr8cm6s4eJtx5e9XKyK6hvJuwncyCPP5aW8j6GVhqXpKiW7V+C/LFZtLE93w8pXzeUf3PJdNfIxQIgAAHoBsP8TUnl6VjdOAIAgCAIBNPSx1BHw5mE3c20zL4JmIoZjUQT28uusblmp5EMiDlZUTsHaulDDxWH9NHL3lp9i62Xj+61Pa9yWvJ/F9+XgZRNN1Ku+uvIqsS2m1FsqtrZXrsrYBkdHUlWVlIIYEggzNNNOzPR4yUkpRd081bRp7zkTg+jUQCH9Q8FeJjnNdVrmImmPx/QfTKXuqAVOXa2ZeTO5tAe29hWq1bpeS8lKdLs2cH2v3Zfn5kVjpYr0t1VXY4djNaaZ+OumWpGh9j2vaVi6pp+NVpep4+ouxQXY9ZzMnKybbGy8rVbNsHENdKMdiot2Raa0pbtjud/pac5RlK6a4PJJaJasivD4inCcIdmSle11m3JttyeStn/RJ/sG8A6no2LgaTaultiY+MwuuxmzUyDlFue4rek1XGxmd3yWspLvuwoTnskevONSTkr58bafm7dxJuDpVaNONOXZsln2b6+evjv4I6jVejTRLMp9TqTLw8xrRkV24eVZT7vkcuZtorKvUjM25KMj1+Z2RdzOxYuoo9l2a5rVcOJGnsnDubqxTjLVOMmrPilnG/k1yJxrXYAbkkADkdtyf5OwA3Pr5AD+APSQi5K7e1zod0nVrnzanu07KtZnuOMK3x7rWO7WPjuNlsY7sWoenmzMzB2YtLCljZ012XmuevUoMVsWhXk5puEnra1m+Nnl0tffmeY8Df8dum49iXZmZkZ4Q79gImJjv/AALQj23Mv/qt6BvRuQJU9lTaE5K0Vb+X9iNQ2BRg71JOfKyUemb/AJ/gtXhYSVIlNaLXVWqpXWiqqIigBURVACqoAAUAAASrbvmzTpJKy0PtByIBx9R1KuiuzItsSqmpGsttsZUrrrUFnd3YhVVVBJYkAATlJt2R8ykopyk7JZtvRJbzF31T9QR8R5gNPNdMxOSYaMGQ2kkdzLsrOxVruICo45V1AbhGsuQaXC4f0Mc/eev2PONqY7vVT2fcjpzfxfbl4kLSYUogCAIAgCAIBNvTz1VZvh0+7FTl6Wz8mxGfi1DE72WYdhBFZYkuaGHasfc/os9lrQ8RhY1s9JcePj9/7LrAbUnhPYt2ocN68Pto+W+/fsv6ktG1oKuNmVrkEbnDyCKMtTsOQFTkd0LuB3KGtr39HMoquHqU/eWXFaG4wu0KGJX6cs+DykvJ6+KuuZJxEjFiaQBAEAQBAEAQBANQIBGHtR6ktG0UMuTmVtkAbjDxyt+Wx2PEGpG/SDcSO5kNTXv6uJJpYepV91ZcXoV2K2hQwy/UlnwWcn5bvF2XMoL1DdVWb4iPuwU4mlq/JcRX5NewO9dmZYABYVIDilR2q32P6rJXat7h8LGjnrLjw8Pv/Rh8ftSpi/Yt2YcL5vx+2i5kJSYUogCAIAgCAIAgCAbLqFYcWAZT6hgCD/R8pyOZ6HT/AGg6lQorp1PU6EXyVMfUdSoUD9gFpykAA/gCdUqUJaxXREuli69JWhUkv9n9Tl/FvWfreufetb/PnX3el8C6Hf6yxXzX1Hxb1n63rn3rW/z47vS+BdB6yxXzX1Hxb1n63rn3rW/z47vS+BdB6yxXzX1Hxb1n63rn3rW/z47vS+BdB6yxXzX1Hxb1n63rn3rW/wA+O70vgXQessV819R8W9Z+t65961v8+O70vgXQessV819R8W9Z+t65961v8+O70vgXQessV819R8W9Z+t65961v8+O70vgXQessV819Tiah7QdRvU13anqd6N5MmRqOpXqR+4K3ZTgg/wROyNKEdIrojoqYuvVVp1JP/Z/TU89TQqjioCgegAAA/oeU7SJzN84AgCAIAgCAIAgCAIAgCAIAgCAIAgCAIAgCAIAgCAIAgCAIAgCAIAgCAIAgCAIAgCAIAgCAIAgCAIAgCAIAgCAIAgCAIAgCAIAgCAIAgCAIAgCAIAgCAIAgCAIAgCAIAgCAIAgCAIAgCAIAgCAIAgCAIAgCAIAgCAIAgCAIAgCAIAgCAIAgCAIAgCAIAgCAIAgCAIAgCAIAgCAIAgCAIAgCAIAgCAIAgCAIAgH/9k=", # noqa: E501
|
|
},
|
|
},
|
|
{"type": "text", "text": "What is this a logo for?"},
|
|
],
|
|
),
|
|
]
|
|
response = chat.invoke(messages)
|
|
assert isinstance(response, AIMessage)
|
|
assert isinstance(response.content, str)
|
|
num_tokens = chat.get_num_tokens_from_messages(messages)
|
|
assert num_tokens > 0
|
|
|
|
|
|
def test_streaming() -> None:
|
|
"""Test streaming tokens from Anthropic."""
|
|
callback_handler = FakeCallbackHandler()
|
|
callback_manager = CallbackManager([callback_handler])
|
|
|
|
llm = ChatAnthropic( # type: ignore[call-arg, call-arg]
|
|
model_name=MODEL_NAME,
|
|
streaming=True,
|
|
callbacks=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 = ChatAnthropic( # type: ignore[call-arg, call-arg]
|
|
model_name=MODEL_NAME,
|
|
streaming=True,
|
|
callbacks=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="claude-sonnet-4-5-20250929", # type: ignore[call-arg]
|
|
temperature=0,
|
|
)
|
|
tool_definition = {
|
|
"name": "get_weather",
|
|
"description": "Get weather report for a city",
|
|
"input_schema": {
|
|
"type": "object",
|
|
"properties": {"location": {"type": "string"}},
|
|
},
|
|
}
|
|
llm_with_tools = llm.bind_tools([tool_definition])
|
|
query = "how are you? what's the weather in san francisco, ca"
|
|
response = llm_with_tools.invoke(query)
|
|
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"]
|
|
|
|
content_blocks = response.content_blocks
|
|
assert len(content_blocks) == 2
|
|
assert content_blocks[0]["type"] == "text"
|
|
assert content_blocks[0]["text"]
|
|
assert content_blocks[1]["type"] == "tool_call"
|
|
assert content_blocks[1]["name"] == "get_weather"
|
|
assert content_blocks[1]["args"] == tool_call["args"]
|
|
|
|
# Test streaming
|
|
llm = ChatAnthropic(model="claude-sonnet-4-5-20250929") # type: ignore[call-arg]
|
|
llm_with_tools = llm.bind_tools([tool_definition])
|
|
first = True
|
|
chunks: list[BaseMessage | BaseMessageChunk] = []
|
|
for chunk in llm_with_tools.stream(query):
|
|
chunks = [*chunks, chunk]
|
|
if first:
|
|
gathered = chunk
|
|
first = False
|
|
else:
|
|
gathered = gathered + chunk # type: ignore[assignment]
|
|
for block in chunk.content_blocks:
|
|
assert block["type"] in ("text", "tool_call_chunk")
|
|
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
|
|
|
|
content_blocks = gathered.content_blocks
|
|
assert len(content_blocks) == 2
|
|
assert content_blocks[0]["type"] == "text"
|
|
assert content_blocks[0]["text"]
|
|
assert content_blocks[1]["type"] == "tool_call"
|
|
assert content_blocks[1]["name"] == "get_weather"
|
|
assert content_blocks[1]["args"]
|
|
|
|
# Test passing response back to model
|
|
stream = llm_with_tools.stream(
|
|
[
|
|
query,
|
|
gathered,
|
|
ToolMessage(content="sunny and warm", tool_call_id=tool_call["id"]),
|
|
],
|
|
)
|
|
chunks = []
|
|
first = True
|
|
for chunk in stream:
|
|
chunks = [*chunks, chunk]
|
|
if first:
|
|
gathered = chunk
|
|
first = False
|
|
else:
|
|
gathered = gathered + chunk # type: ignore[assignment]
|
|
assert len(chunks) > 1
|
|
|
|
|
|
def test_builtin_tools_text_editor() -> None:
|
|
llm = ChatAnthropic(model="claude-sonnet-4-5-20250929") # type: ignore[call-arg]
|
|
tool = {"type": "text_editor_20250728", "name": "str_replace_based_edit_tool"}
|
|
llm_with_tools = llm.bind_tools([tool])
|
|
response = llm_with_tools.invoke(
|
|
"There's a syntax error in my primes.py file. Can you help me fix it?",
|
|
)
|
|
assert isinstance(response, AIMessage)
|
|
assert response.tool_calls
|
|
|
|
content_blocks = response.content_blocks
|
|
assert len(content_blocks) == 2
|
|
assert content_blocks[0]["type"] == "text"
|
|
assert content_blocks[0]["text"]
|
|
assert content_blocks[1]["type"] == "tool_call"
|
|
assert content_blocks[1]["name"] == "str_replace_based_edit_tool"
|
|
|
|
|
|
def test_builtin_tools_computer_use() -> None:
|
|
"""Test computer use tool integration.
|
|
|
|
Beta header should be automatically appended based on tool type.
|
|
|
|
This test only verifies tool call generation.
|
|
"""
|
|
llm = ChatAnthropic(
|
|
model="claude-sonnet-4-5-20250929", # type: ignore[call-arg]
|
|
)
|
|
tool = {
|
|
"type": "computer_20250124",
|
|
"name": "computer",
|
|
"display_width_px": 1024,
|
|
"display_height_px": 768,
|
|
"display_number": 1,
|
|
}
|
|
llm_with_tools = llm.bind_tools([tool])
|
|
response = llm_with_tools.invoke(
|
|
"Can you take a screenshot to see what's on the screen?",
|
|
)
|
|
assert isinstance(response, AIMessage)
|
|
assert response.tool_calls
|
|
|
|
content_blocks = response.content_blocks
|
|
assert len(content_blocks) >= 2
|
|
assert content_blocks[0]["type"] == "text"
|
|
assert content_blocks[0]["text"]
|
|
|
|
# Check that we have a tool_call for computer use
|
|
tool_call_blocks = [b for b in content_blocks if b["type"] == "tool_call"]
|
|
assert len(tool_call_blocks) >= 1
|
|
assert tool_call_blocks[0]["name"] == "computer"
|
|
|
|
# Verify tool call has expected action (screenshot in this case)
|
|
tool_call = response.tool_calls[0]
|
|
assert tool_call["name"] == "computer"
|
|
assert "action" in tool_call["args"]
|
|
assert tool_call["args"]["action"] == "screenshot"
|
|
|
|
|
|
class GenerateUsername(BaseModel):
|
|
"""Get a username based on someone's name and hair color."""
|
|
|
|
name: str
|
|
hair_color: str
|
|
|
|
|
|
def test_disable_parallel_tool_calling() -> None:
|
|
llm = ChatAnthropic(model=MODEL_NAME) # type: ignore[call-arg]
|
|
llm_with_tools = llm.bind_tools([GenerateUsername], parallel_tool_calls=False)
|
|
result = llm_with_tools.invoke(
|
|
"Use the GenerateUsername tool to generate user names for:\n\n"
|
|
"Sally with green hair\n"
|
|
"Bob with blue hair",
|
|
)
|
|
assert isinstance(result, AIMessage)
|
|
assert len(result.tool_calls) == 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=MODEL_NAME, 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(
|
|
model=MODEL_NAME, # type: ignore[call-arg]
|
|
)
|
|
|
|
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 Person(BaseModel):
|
|
"""Person data."""
|
|
|
|
name: str
|
|
age: int
|
|
nicknames: list[str] | None
|
|
|
|
|
|
class PersonDict(TypedDict):
|
|
"""Person data as a TypedDict."""
|
|
|
|
name: str
|
|
age: int
|
|
nicknames: list[str] | None
|
|
|
|
|
|
@pytest.mark.parametrize("schema", [Person, Person.model_json_schema(), PersonDict])
|
|
def test_response_format(schema: dict | type) -> None:
|
|
model = ChatAnthropic(
|
|
model="claude-sonnet-4-5", # type: ignore[call-arg]
|
|
)
|
|
query = "Chester (a.k.a. Chet) is 100 years old."
|
|
|
|
response = model.invoke(query, response_format=schema)
|
|
parsed = json.loads(response.text)
|
|
if isinstance(schema, type) and issubclass(schema, BaseModel):
|
|
schema.model_validate(parsed)
|
|
else:
|
|
assert isinstance(parsed, dict)
|
|
assert parsed["name"]
|
|
assert parsed["age"]
|
|
|
|
|
|
@pytest.mark.vcr
|
|
def test_response_format_in_agent() -> None:
|
|
class Weather(BaseModel):
|
|
temperature: float
|
|
units: str
|
|
|
|
# no tools
|
|
agent = create_agent(
|
|
"anthropic:claude-sonnet-4-5", response_format=ProviderStrategy(Weather)
|
|
)
|
|
result = agent.invoke({"messages": [{"role": "user", "content": "75 degrees F."}]})
|
|
assert len(result["messages"]) == 2
|
|
parsed = json.loads(result["messages"][-1].text)
|
|
assert Weather(**parsed) == result["structured_response"]
|
|
|
|
# with tools
|
|
def get_weather(location: str) -> str:
|
|
"""Get the weather at a location."""
|
|
return "75 degrees Fahrenheit."
|
|
|
|
agent = create_agent(
|
|
"anthropic:claude-sonnet-4-5",
|
|
tools=[get_weather],
|
|
response_format=ProviderStrategy(Weather),
|
|
)
|
|
result = agent.invoke(
|
|
{"messages": [{"role": "user", "content": "What's the weather in SF?"}]},
|
|
)
|
|
assert len(result["messages"]) == 4
|
|
assert result["messages"][1].tool_calls
|
|
parsed = json.loads(result["messages"][-1].text)
|
|
assert Weather(**parsed) == result["structured_response"]
|
|
|
|
|
|
@pytest.mark.vcr
|
|
def test_strict_tool_use() -> None:
|
|
model = ChatAnthropic(
|
|
model="claude-sonnet-4-5", # type: ignore[call-arg]
|
|
)
|
|
|
|
def get_weather(location: str, unit: Literal["C", "F"]) -> str:
|
|
"""Get the weather at a location."""
|
|
return "75 degrees Fahrenheit."
|
|
|
|
model_with_tools = model.bind_tools([get_weather], strict=True)
|
|
|
|
response = model_with_tools.invoke("What's the weather in Boston, in Celsius?")
|
|
assert response.tool_calls
|
|
|
|
|
|
def test_get_num_tokens_from_messages() -> None:
|
|
llm = ChatAnthropic(model=MODEL_NAME) # type: ignore[call-arg]
|
|
|
|
# Test simple case
|
|
messages = [
|
|
SystemMessage(content="You are a scientist"),
|
|
HumanMessage(content="Hello, Claude"),
|
|
]
|
|
num_tokens = llm.get_num_tokens_from_messages(messages)
|
|
assert num_tokens > 0
|
|
|
|
# Test tool use
|
|
@tool(parse_docstring=True)
|
|
def get_weather(location: str) -> str:
|
|
"""Get the current weather in a given location.
|
|
|
|
Args:
|
|
location: The city and state, e.g. San Francisco, CA
|
|
|
|
"""
|
|
return "Sunny"
|
|
|
|
messages = [
|
|
HumanMessage(content="What's the weather like in San Francisco?"),
|
|
]
|
|
num_tokens = llm.get_num_tokens_from_messages(messages, tools=[get_weather])
|
|
assert num_tokens > 0
|
|
|
|
messages = [
|
|
HumanMessage(content="What's the weather like in San Francisco?"),
|
|
AIMessage(
|
|
content=[
|
|
{"text": "Let's see.", "type": "text"},
|
|
{
|
|
"id": "toolu_01V6d6W32QGGSmQm4BT98EKk",
|
|
"input": {"location": "SF"},
|
|
"name": "get_weather",
|
|
"type": "tool_use",
|
|
},
|
|
],
|
|
tool_calls=[
|
|
{
|
|
"name": "get_weather",
|
|
"args": {"location": "SF"},
|
|
"id": "toolu_01V6d6W32QGGSmQm4BT98EKk",
|
|
"type": "tool_call",
|
|
},
|
|
],
|
|
),
|
|
ToolMessage(content="Sunny", tool_call_id="toolu_01V6d6W32QGGSmQm4BT98EKk"),
|
|
]
|
|
num_tokens = llm.get_num_tokens_from_messages(messages, tools=[get_weather])
|
|
assert num_tokens > 0
|
|
|
|
|
|
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(
|
|
model=MODEL_NAME, # type: ignore[call-arg]
|
|
)
|
|
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)
|
|
|
|
|
|
def test_pdf_document_input() -> None:
|
|
url = "https://www.w3.org/WAI/ER/tests/xhtml/testfiles/resources/pdf/dummy.pdf"
|
|
data = b64encode(requests.get(url, timeout=10).content).decode()
|
|
|
|
result = ChatAnthropic(model=MODEL_NAME).invoke( # type: ignore[call-arg]
|
|
[
|
|
HumanMessage(
|
|
[
|
|
"summarize this document",
|
|
{
|
|
"type": "document",
|
|
"source": {
|
|
"type": "base64",
|
|
"data": data,
|
|
"media_type": "application/pdf",
|
|
},
|
|
},
|
|
],
|
|
),
|
|
],
|
|
)
|
|
assert isinstance(result, AIMessage)
|
|
assert isinstance(result.content, str)
|
|
assert len(result.content) > 0
|
|
|
|
|
|
@pytest.mark.default_cassette("test_agent_loop.yaml.gz")
|
|
@pytest.mark.vcr
|
|
@pytest.mark.parametrize("output_version", ["v0", "v1"])
|
|
def test_agent_loop(output_version: Literal["v0", "v1"]) -> None:
|
|
@tool
|
|
def get_weather(location: str) -> str:
|
|
"""Get the weather for a location."""
|
|
return "It's sunny."
|
|
|
|
llm = ChatAnthropic(model=MODEL_NAME, output_version=output_version) # type: ignore[call-arg]
|
|
llm_with_tools = llm.bind_tools([get_weather])
|
|
input_message = HumanMessage("What is the weather in San Francisco, CA?")
|
|
tool_call_message = llm_with_tools.invoke([input_message])
|
|
assert isinstance(tool_call_message, AIMessage)
|
|
tool_calls = tool_call_message.tool_calls
|
|
assert len(tool_calls) == 1
|
|
tool_call = tool_calls[0]
|
|
tool_message = get_weather.invoke(tool_call)
|
|
assert isinstance(tool_message, ToolMessage)
|
|
response = llm_with_tools.invoke(
|
|
[
|
|
input_message,
|
|
tool_call_message,
|
|
tool_message,
|
|
]
|
|
)
|
|
assert isinstance(response, AIMessage)
|
|
|
|
|
|
@pytest.mark.default_cassette("test_agent_loop_streaming.yaml.gz")
|
|
@pytest.mark.vcr
|
|
@pytest.mark.parametrize(
|
|
("output_version", "use_v2_stream"),
|
|
[
|
|
("v0", False),
|
|
("v1", False),
|
|
("v1", True),
|
|
],
|
|
)
|
|
def test_agent_loop_streaming(
|
|
output_version: Literal["v0", "v1"], *, use_v2_stream: bool
|
|
) -> None:
|
|
@tool
|
|
def get_weather(location: str) -> str:
|
|
"""Get the weather for a location."""
|
|
return "It's sunny."
|
|
|
|
llm = ChatAnthropic(
|
|
model=MODEL_NAME,
|
|
streaming=True,
|
|
output_version=output_version, # type: ignore[call-arg]
|
|
)
|
|
llm_with_tools = llm.bind_tools([get_weather])
|
|
input_message = HumanMessage("What is the weather in San Francisco, CA?")
|
|
if use_v2_stream:
|
|
tool_call_message = cast(
|
|
"ChatModelStream",
|
|
llm_with_tools.stream_events([input_message], version="v3"),
|
|
).output
|
|
else:
|
|
tool_call_message = llm_with_tools.invoke([input_message])
|
|
assert isinstance(tool_call_message, AIMessage)
|
|
|
|
tool_calls = tool_call_message.tool_calls
|
|
assert len(tool_calls) == 1
|
|
tool_call = tool_calls[0]
|
|
tool_message = get_weather.invoke(tool_call)
|
|
assert isinstance(tool_message, ToolMessage)
|
|
if use_v2_stream:
|
|
response = cast(
|
|
"ChatModelStream",
|
|
llm_with_tools.stream_events(
|
|
[input_message, tool_call_message, tool_message],
|
|
version="v3",
|
|
),
|
|
).output
|
|
else:
|
|
response = llm_with_tools.invoke(
|
|
[
|
|
input_message,
|
|
tool_call_message,
|
|
tool_message,
|
|
]
|
|
)
|
|
assert isinstance(response, AIMessage)
|
|
|
|
|
|
@pytest.mark.default_cassette("test_agent_loop_streaming.yaml.gz")
|
|
@pytest.mark.vcr
|
|
async def test_agent_loop_streaming_astream_events_v3_v1() -> None:
|
|
"""Async multi-turn through `astream_events(version="v3")`.
|
|
|
|
Mirrors `test_agent_loop_streaming` for `output_version="v1"` but
|
|
exercises `AsyncChatModelStream` end-to-end.
|
|
"""
|
|
|
|
@tool
|
|
def get_weather(location: str) -> str:
|
|
"""Get the weather for a location."""
|
|
return "It's sunny."
|
|
|
|
llm = ChatAnthropic(
|
|
model=MODEL_NAME,
|
|
streaming=True,
|
|
output_version="v1", # type: ignore[call-arg]
|
|
)
|
|
llm_with_tools = llm.bind_tools([get_weather])
|
|
input_message = HumanMessage("What is the weather in San Francisco, CA?")
|
|
tool_call_message = await (
|
|
await cast(
|
|
"Awaitable[AsyncChatModelStream]",
|
|
llm_with_tools.astream_events([input_message], version="v3"),
|
|
)
|
|
)
|
|
assert isinstance(tool_call_message, AIMessage)
|
|
tool_calls = tool_call_message.tool_calls
|
|
assert len(tool_calls) == 1
|
|
tool_call = tool_calls[0]
|
|
tool_message = get_weather.invoke(tool_call)
|
|
assert isinstance(tool_message, ToolMessage)
|
|
response = await (
|
|
await cast(
|
|
"Awaitable[AsyncChatModelStream]",
|
|
llm_with_tools.astream_events(
|
|
[input_message, tool_call_message, tool_message],
|
|
version="v3",
|
|
),
|
|
)
|
|
)
|
|
assert isinstance(response, AIMessage)
|
|
|
|
|
|
@pytest.mark.default_cassette("test_citations.yaml.gz")
|
|
@pytest.mark.vcr
|
|
@pytest.mark.parametrize(
|
|
("output_version", "use_v2_stream"),
|
|
[
|
|
("v0", False),
|
|
("v1", False),
|
|
("v1", True),
|
|
],
|
|
)
|
|
def test_citations(output_version: Literal["v0", "v1"], *, use_v2_stream: bool) -> None:
|
|
llm = ChatAnthropic(model=MODEL_NAME, output_version=output_version) # type: ignore[call-arg]
|
|
messages = [
|
|
{
|
|
"role": "user",
|
|
"content": [
|
|
{
|
|
"type": "document",
|
|
"source": {
|
|
"type": "content",
|
|
"content": [
|
|
{"type": "text", "text": "The grass is green"},
|
|
{"type": "text", "text": "The sky is blue"},
|
|
],
|
|
},
|
|
"citations": {"enabled": True},
|
|
},
|
|
{"type": "text", "text": "What color is the grass and sky?"},
|
|
],
|
|
},
|
|
]
|
|
response = llm.invoke(messages)
|
|
assert isinstance(response, AIMessage)
|
|
assert isinstance(response.content, list)
|
|
if output_version == "v1":
|
|
assert any("annotations" in block for block in response.content)
|
|
else:
|
|
assert any("citations" in block for block in response.content)
|
|
|
|
# Test streaming
|
|
full: BaseMessage
|
|
if use_v2_stream:
|
|
full = llm.stream_events(messages, version="v3").output
|
|
else:
|
|
aggregated: BaseMessageChunk | None = None
|
|
for chunk in llm.stream(messages):
|
|
aggregated = (
|
|
cast("BaseMessageChunk", chunk)
|
|
if aggregated is None
|
|
else aggregated + chunk
|
|
)
|
|
assert isinstance(aggregated, AIMessageChunk)
|
|
full = aggregated
|
|
assert isinstance(full.content, list)
|
|
assert not any("citation" in block for block in full.content)
|
|
if output_version == "v1":
|
|
assert any("annotations" in block for block in full.content)
|
|
else:
|
|
assert any("citations" in block for block in full.content)
|
|
|
|
# Test pass back in
|
|
next_message = {
|
|
"role": "user",
|
|
"content": "Can you comment on the citations you just made?",
|
|
}
|
|
_ = llm.invoke([*messages, full, next_message])
|
|
|
|
|
|
@pytest.mark.vcr
|
|
def test_thinking() -> None:
|
|
llm = ChatAnthropic(
|
|
model="claude-sonnet-4-5-20250929", # type: ignore[call-arg]
|
|
max_tokens=5_000, # type: ignore[call-arg]
|
|
thinking={"type": "enabled", "budget_tokens": 2_000},
|
|
)
|
|
|
|
input_message = {"role": "user", "content": "Hello"}
|
|
response = llm.invoke([input_message])
|
|
assert any("thinking" in block for block in response.content)
|
|
for block in response.content:
|
|
assert isinstance(block, dict)
|
|
if block["type"] == "thinking":
|
|
assert set(block.keys()) == {"type", "thinking", "signature"}
|
|
assert block["thinking"]
|
|
assert isinstance(block["thinking"], str)
|
|
assert block["signature"]
|
|
assert isinstance(block["signature"], str)
|
|
|
|
# Test streaming
|
|
full: BaseMessageChunk | None = None
|
|
for chunk in llm.stream([input_message]):
|
|
full = cast("BaseMessageChunk", chunk) if full is None else full + chunk
|
|
assert isinstance(full, AIMessageChunk)
|
|
assert isinstance(full.content, list)
|
|
assert any("thinking" in block for block in full.content)
|
|
for block in full.content:
|
|
assert isinstance(block, dict)
|
|
if block["type"] == "thinking":
|
|
assert set(block.keys()) == {"type", "thinking", "signature", "index"}
|
|
assert block["thinking"]
|
|
assert isinstance(block["thinking"], str)
|
|
assert block["signature"]
|
|
assert isinstance(block["signature"], str)
|
|
|
|
# Test pass back in
|
|
next_message = {"role": "user", "content": "How are you?"}
|
|
_ = llm.invoke([input_message, full, next_message])
|
|
|
|
|
|
@pytest.mark.default_cassette("test_thinking.yaml.gz")
|
|
@pytest.mark.vcr
|
|
@pytest.mark.parametrize("use_v2_stream", [False, True])
|
|
def test_thinking_v1(*, use_v2_stream: bool) -> None:
|
|
llm = ChatAnthropic(
|
|
model="claude-sonnet-4-5-20250929", # type: ignore[call-arg]
|
|
max_tokens=5_000, # type: ignore[call-arg]
|
|
thinking={"type": "enabled", "budget_tokens": 2_000},
|
|
output_version="v1",
|
|
)
|
|
|
|
input_message = {"role": "user", "content": "Hello"}
|
|
response = llm.invoke([input_message])
|
|
assert any("reasoning" in block for block in response.content)
|
|
for block in response.content:
|
|
assert isinstance(block, dict)
|
|
if block["type"] == "reasoning":
|
|
assert set(block.keys()) == {"type", "reasoning", "extras"}
|
|
assert block["reasoning"]
|
|
assert isinstance(block["reasoning"], str)
|
|
signature = block["extras"]["signature"]
|
|
assert signature
|
|
assert isinstance(signature, str)
|
|
|
|
# Test streaming
|
|
full: BaseMessage
|
|
if use_v2_stream:
|
|
full = llm.stream_events([input_message], version="v3").output
|
|
else:
|
|
aggregated: BaseMessageChunk | None = None
|
|
for chunk in llm.stream([input_message]):
|
|
aggregated = (
|
|
cast(BaseMessageChunk, chunk)
|
|
if aggregated is None
|
|
else aggregated + chunk
|
|
)
|
|
assert isinstance(aggregated, AIMessageChunk)
|
|
full = aggregated
|
|
assert isinstance(full.content, list)
|
|
assert any("reasoning" in block for block in full.content)
|
|
for block in full.content:
|
|
assert isinstance(block, dict)
|
|
if block["type"] == "reasoning":
|
|
assert set(block.keys()) == {"type", "reasoning", "extras", "index"}
|
|
assert block["reasoning"]
|
|
assert isinstance(block["reasoning"], str)
|
|
signature = block["extras"]["signature"]
|
|
assert signature
|
|
assert isinstance(signature, str)
|
|
|
|
# Test pass back in
|
|
next_message = {"role": "user", "content": "How are you?"}
|
|
_ = llm.invoke([input_message, full, next_message])
|
|
|
|
|
|
@pytest.mark.default_cassette("test_redacted_thinking.yaml.gz")
|
|
@pytest.mark.vcr
|
|
@pytest.mark.parametrize("output_version", ["v0", "v1"])
|
|
def test_redacted_thinking(output_version: Literal["v0", "v1"]) -> None:
|
|
llm = ChatAnthropic(
|
|
# It appears that Sonnet 4.5 either: isn't returning redacted thinking blocks,
|
|
# or the magic string is broken? Retry later once 3-7 finally removed
|
|
model="claude-3-7-sonnet-latest", # type: ignore[call-arg]
|
|
max_tokens=5_000, # type: ignore[call-arg]
|
|
thinking={"type": "enabled", "budget_tokens": 2_000},
|
|
output_version=output_version,
|
|
)
|
|
query = "ANTHROPIC_MAGIC_STRING_TRIGGER_REDACTED_THINKING_46C9A13E193C177646C7398A98432ECCCE4C1253D5E2D82641AC0E52CC2876CB" # noqa: E501
|
|
input_message = {"role": "user", "content": query}
|
|
|
|
response = llm.invoke([input_message])
|
|
value = None
|
|
for block in response.content:
|
|
assert isinstance(block, dict)
|
|
if block["type"] == "redacted_thinking":
|
|
value = block
|
|
elif (
|
|
block["type"] == "non_standard"
|
|
and block["value"]["type"] == "redacted_thinking"
|
|
):
|
|
value = block["value"]
|
|
else:
|
|
pass
|
|
if value:
|
|
assert set(value.keys()) == {"type", "data"}
|
|
assert value["data"]
|
|
assert isinstance(value["data"], str)
|
|
assert value is not None
|
|
|
|
# Test streaming
|
|
full: BaseMessageChunk | None = None
|
|
for chunk in llm.stream([input_message]):
|
|
full = cast("BaseMessageChunk", chunk) if full is None else full + chunk
|
|
assert isinstance(full, AIMessageChunk)
|
|
assert isinstance(full.content, list)
|
|
value = None
|
|
for block in full.content:
|
|
assert isinstance(block, dict)
|
|
if block["type"] == "redacted_thinking":
|
|
value = block
|
|
assert set(value.keys()) == {"type", "data", "index"}
|
|
assert "index" in block
|
|
elif (
|
|
block["type"] == "non_standard"
|
|
and block["value"]["type"] == "redacted_thinking"
|
|
):
|
|
value = block["value"]
|
|
assert isinstance(value, dict)
|
|
assert set(value.keys()) == {"type", "data"}
|
|
assert "index" in block
|
|
else:
|
|
pass
|
|
if value:
|
|
assert value["data"]
|
|
assert isinstance(value["data"], str)
|
|
assert value is not None
|
|
|
|
# Test pass back in
|
|
next_message = {"role": "user", "content": "What?"}
|
|
_ = llm.invoke([input_message, full, next_message])
|
|
|
|
|
|
def test_structured_output_thinking_enabled() -> None:
|
|
llm = ChatAnthropic(
|
|
model="claude-sonnet-4-5-20250929", # type: ignore[call-arg]
|
|
max_tokens=5_000, # type: ignore[call-arg]
|
|
thinking={"type": "enabled", "budget_tokens": 2_000},
|
|
)
|
|
with pytest.warns(match="structured output"):
|
|
structured_llm = llm.with_structured_output(GenerateUsername)
|
|
query = "Generate a username for Sally with green hair"
|
|
response = structured_llm.invoke(query)
|
|
assert isinstance(response, GenerateUsername)
|
|
|
|
with pytest.raises(OutputParserException):
|
|
structured_llm.invoke("Hello")
|
|
|
|
# Test streaming
|
|
for chunk in structured_llm.stream(query):
|
|
assert isinstance(chunk, GenerateUsername)
|
|
|
|
|
|
def test_structured_output_thinking_force_tool_use() -> None:
|
|
# Structured output currently relies on forced tool use, which is not supported
|
|
# when `thinking` is enabled. When this test fails, it means that the feature
|
|
# is supported and the workarounds in `with_structured_output` should be removed.
|
|
client = anthropic.Anthropic()
|
|
with pytest.raises(anthropic.BadRequestError):
|
|
_ = client.messages.create(
|
|
model="claude-sonnet-4-5-20250929",
|
|
max_tokens=5_000,
|
|
thinking={"type": "enabled", "budget_tokens": 2_000},
|
|
tool_choice={"type": "tool", "name": "get_weather"},
|
|
tools=[
|
|
{
|
|
"name": "get_weather",
|
|
"description": "Get the weather at a location.",
|
|
"input_schema": {
|
|
"type": "object",
|
|
"properties": {
|
|
"location": {"type": "string"},
|
|
},
|
|
"required": ["location"],
|
|
},
|
|
}
|
|
],
|
|
messages=[
|
|
{
|
|
"role": "user",
|
|
"content": "What's the weather in San Francisco?",
|
|
}
|
|
],
|
|
)
|
|
|
|
|
|
def test_effort_parameter() -> None:
|
|
"""Test that effort parameter can be passed without errors.
|
|
|
|
Only Opus 4.5 supports currently.
|
|
"""
|
|
llm = ChatAnthropic(
|
|
model="claude-opus-4-5-20251101",
|
|
effort="medium",
|
|
max_tokens=100,
|
|
)
|
|
|
|
result = llm.invoke("Say hello in one sentence")
|
|
|
|
# Verify we got a response
|
|
assert isinstance(result.content, str)
|
|
assert len(result.content) > 0
|
|
|
|
# Verify response metadata is present
|
|
assert "model_name" in result.response_metadata
|
|
assert result.usage_metadata is not None
|
|
assert result.usage_metadata["input_tokens"] > 0
|
|
assert result.usage_metadata["output_tokens"] > 0
|
|
|
|
|
|
def test_image_tool_calling() -> None:
|
|
"""Test tool calling with image inputs."""
|
|
|
|
class color_picker(BaseModel): # noqa: N801
|
|
"""Input your fav color and get a random fact about it."""
|
|
|
|
fav_color: str
|
|
|
|
human_content: list[dict] = [
|
|
{
|
|
"type": "text",
|
|
"text": "what's your favorite color in this image",
|
|
},
|
|
]
|
|
image_url = "https://raw.githubusercontent.com/langchain-ai/docs/4d11d08b6b0e210bd456943f7a22febbd168b543/src/images/agentic-rag-output.png"
|
|
image_data = b64encode(httpx.get(image_url, timeout=10.0).content).decode("utf-8")
|
|
human_content.append(
|
|
{
|
|
"type": "image",
|
|
"source": {
|
|
"type": "base64",
|
|
"media_type": "image/png",
|
|
"data": image_data,
|
|
},
|
|
},
|
|
)
|
|
messages = [
|
|
SystemMessage("you're a good assistant"),
|
|
HumanMessage(human_content), # type: ignore[arg-type]
|
|
AIMessage(
|
|
[
|
|
{"type": "text", "text": "Hmm let me think about that"},
|
|
{
|
|
"type": "tool_use",
|
|
"input": {"fav_color": "purple"},
|
|
"id": "foo",
|
|
"name": "color_picker",
|
|
},
|
|
],
|
|
),
|
|
HumanMessage(
|
|
[
|
|
{
|
|
"type": "tool_result",
|
|
"tool_use_id": "foo",
|
|
"content": [
|
|
{
|
|
"type": "text",
|
|
"text": "purple is a great pick! that's my sister's favorite color", # noqa: E501
|
|
},
|
|
],
|
|
"is_error": False,
|
|
},
|
|
{"type": "text", "text": "what's my sister's favorite color"},
|
|
],
|
|
),
|
|
]
|
|
llm = ChatAnthropic(model=MODEL_NAME) # type: ignore[call-arg]
|
|
_ = llm.bind_tools([color_picker]).invoke(messages)
|
|
|
|
|
|
@pytest.mark.default_cassette("test_web_search.yaml.gz")
|
|
@pytest.mark.vcr
|
|
@pytest.mark.parametrize("output_version", ["v0", "v1"])
|
|
def test_web_search(output_version: Literal["v0", "v1"]) -> None:
|
|
llm = ChatAnthropic(
|
|
model=MODEL_NAME, # type: ignore[call-arg]
|
|
max_tokens=1024,
|
|
output_version=output_version,
|
|
)
|
|
|
|
tool = {"type": "web_search_20250305", "name": "web_search", "max_uses": 1}
|
|
llm_with_tools = llm.bind_tools([tool])
|
|
|
|
input_message = {
|
|
"role": "user",
|
|
"content": [
|
|
{
|
|
"type": "text",
|
|
"text": "How do I update a web app to TypeScript 5.5?",
|
|
},
|
|
],
|
|
}
|
|
response = llm_with_tools.invoke([input_message])
|
|
assert all(isinstance(block, dict) for block in response.content)
|
|
block_types = {block["type"] for block in response.content} # type: ignore[index]
|
|
if output_version == "v0":
|
|
assert block_types == {"text", "server_tool_use", "web_search_tool_result"}
|
|
else:
|
|
assert block_types == {"text", "server_tool_call", "server_tool_result"}
|
|
|
|
# Test streaming
|
|
full: BaseMessageChunk | None = None
|
|
for chunk in llm_with_tools.stream([input_message]):
|
|
assert isinstance(chunk, AIMessageChunk)
|
|
full = chunk if full is None else full + chunk
|
|
|
|
assert isinstance(full, AIMessageChunk)
|
|
assert isinstance(full.content, list)
|
|
block_types = {block["type"] for block in full.content} # type: ignore[index]
|
|
if output_version == "v0":
|
|
assert block_types == {"text", "server_tool_use", "web_search_tool_result"}
|
|
else:
|
|
assert block_types == {"text", "server_tool_call", "server_tool_result"}
|
|
|
|
# Test we can pass back in
|
|
next_message = {
|
|
"role": "user",
|
|
"content": "Please repeat the last search, but focus on sources from 2024.",
|
|
}
|
|
_ = llm_with_tools.invoke(
|
|
[input_message, full, next_message],
|
|
)
|
|
|
|
|
|
@pytest.mark.vcr
|
|
def test_web_fetch() -> None:
|
|
"""Note: this is a beta feature.
|
|
|
|
TODO: Update to remove beta once it's generally available.
|
|
"""
|
|
llm = ChatAnthropic(
|
|
model=MODEL_NAME, # type: ignore[call-arg]
|
|
max_tokens=1024,
|
|
betas=["web-fetch-2025-09-10"],
|
|
)
|
|
tool = {"type": "web_fetch_20250910", "name": "web_fetch", "max_uses": 1}
|
|
llm_with_tools = llm.bind_tools([tool])
|
|
|
|
input_message = {
|
|
"role": "user",
|
|
"content": [
|
|
{
|
|
"type": "text",
|
|
"text": "Fetch the content at https://docs.langchain.com and analyze",
|
|
},
|
|
],
|
|
}
|
|
response = llm_with_tools.invoke([input_message])
|
|
assert all(isinstance(block, dict) for block in response.content)
|
|
block_types = {
|
|
block["type"] for block in response.content if isinstance(block, dict)
|
|
}
|
|
|
|
# A successful fetch call should include:
|
|
# 1. text response from the model (e.g. "I'll fetch that for you")
|
|
# 2. server_tool_use block indicating the tool was called (using tool "web_fetch")
|
|
# 3. web_fetch_tool_result block with the results of said fetch
|
|
assert block_types == {"text", "server_tool_use", "web_fetch_tool_result"}
|
|
|
|
# Verify web fetch result structure
|
|
web_fetch_results = [
|
|
block
|
|
for block in response.content
|
|
if isinstance(block, dict) and block.get("type") == "web_fetch_tool_result"
|
|
]
|
|
assert len(web_fetch_results) == 1 # Since max_uses=1
|
|
fetch_result = web_fetch_results[0]
|
|
assert "content" in fetch_result
|
|
assert "url" in fetch_result["content"]
|
|
assert "retrieved_at" in fetch_result["content"]
|
|
|
|
# Fetch with citations enabled
|
|
tool_with_citations = tool.copy()
|
|
tool_with_citations["citations"] = {"enabled": True}
|
|
llm_with_citations = llm.bind_tools([tool_with_citations])
|
|
|
|
citation_message = {
|
|
"role": "user",
|
|
"content": (
|
|
"Fetch https://docs.langchain.com and provide specific quotes with "
|
|
"citations"
|
|
),
|
|
}
|
|
citation_response = llm_with_citations.invoke([citation_message])
|
|
|
|
citation_results = [
|
|
block
|
|
for block in citation_response.content
|
|
if isinstance(block, dict) and block.get("type") == "web_fetch_tool_result"
|
|
]
|
|
assert len(citation_results) == 1 # Since max_uses=1
|
|
citation_result = citation_results[0]
|
|
assert citation_result["content"]["content"]["citations"]["enabled"]
|
|
text_blocks = [
|
|
block
|
|
for block in citation_response.content
|
|
if isinstance(block, dict) and block.get("type") == "text"
|
|
]
|
|
|
|
# Check that the response contains actual citations in the content
|
|
has_citations = False
|
|
for block in text_blocks:
|
|
citations = block.get("citations", [])
|
|
for citation in citations:
|
|
if citation.get("type") and citation.get("start_char_index"):
|
|
has_citations = True
|
|
break
|
|
assert has_citations, (
|
|
"Expected inline citation tags in response when citations are enabled for "
|
|
"web fetch"
|
|
)
|
|
|
|
# Max content tokens param
|
|
tool_with_limit = tool.copy()
|
|
tool_with_limit["max_content_tokens"] = 1000
|
|
llm_with_limit = llm.bind_tools([tool_with_limit])
|
|
|
|
limit_response = llm_with_limit.invoke([input_message])
|
|
# Response should still work even with content limits
|
|
assert any(
|
|
block["type"] == "web_fetch_tool_result"
|
|
for block in limit_response.content
|
|
if isinstance(block, dict)
|
|
)
|
|
|
|
# Domains filtering (note: only one can be set at a time)
|
|
tool_with_allowed_domains = tool.copy()
|
|
tool_with_allowed_domains["allowed_domains"] = ["docs.langchain.com"]
|
|
llm_with_allowed = llm.bind_tools([tool_with_allowed_domains])
|
|
|
|
allowed_response = llm_with_allowed.invoke([input_message])
|
|
assert any(
|
|
block["type"] == "web_fetch_tool_result"
|
|
for block in allowed_response.content
|
|
if isinstance(block, dict)
|
|
)
|
|
|
|
# Test that a disallowed domain doesn't work
|
|
tool_with_disallowed_domains = tool.copy()
|
|
tool_with_disallowed_domains["allowed_domains"] = [
|
|
"example.com"
|
|
] # Not docs.langchain.com
|
|
llm_with_disallowed = llm.bind_tools([tool_with_disallowed_domains])
|
|
|
|
disallowed_response = llm_with_disallowed.invoke([input_message])
|
|
|
|
# We should get an error result since the domain (docs.langchain.com) is not allowed
|
|
disallowed_results = [
|
|
block
|
|
for block in disallowed_response.content
|
|
if isinstance(block, dict) and block.get("type") == "web_fetch_tool_result"
|
|
]
|
|
if disallowed_results:
|
|
disallowed_result = disallowed_results[0]
|
|
if disallowed_result.get("content", {}).get("type") == "web_fetch_tool_error":
|
|
assert disallowed_result["content"]["error_code"] in [
|
|
"invalid_url",
|
|
"fetch_failed",
|
|
]
|
|
|
|
# Blocked domains filtering
|
|
tool_with_blocked_domains = tool.copy()
|
|
tool_with_blocked_domains["blocked_domains"] = ["example.com"]
|
|
llm_with_blocked = llm.bind_tools([tool_with_blocked_domains])
|
|
|
|
blocked_response = llm_with_blocked.invoke([input_message])
|
|
assert any(
|
|
block["type"] == "web_fetch_tool_result"
|
|
for block in blocked_response.content
|
|
if isinstance(block, dict)
|
|
)
|
|
|
|
# Test fetching from a blocked domain fails
|
|
blocked_domain_message = {
|
|
"role": "user",
|
|
"content": "Fetch https://example.com and analyze",
|
|
}
|
|
tool_with_blocked_example = tool.copy()
|
|
tool_with_blocked_example["blocked_domains"] = ["example.com"]
|
|
llm_with_blocked_example = llm.bind_tools([tool_with_blocked_example])
|
|
|
|
blocked_domain_response = llm_with_blocked_example.invoke([blocked_domain_message])
|
|
|
|
# Should get an error when trying to access a blocked domain
|
|
blocked_domain_results = [
|
|
block
|
|
for block in blocked_domain_response.content
|
|
if isinstance(block, dict) and block.get("type") == "web_fetch_tool_result"
|
|
]
|
|
if blocked_domain_results:
|
|
blocked_result = blocked_domain_results[0]
|
|
if blocked_result.get("content", {}).get("type") == "web_fetch_tool_error":
|
|
assert blocked_result["content"]["error_code"] in [
|
|
"invalid_url",
|
|
"fetch_failed",
|
|
]
|
|
|
|
# Max uses parameter - test exceeding the limit
|
|
multi_fetch_message = {
|
|
"role": "user",
|
|
"content": (
|
|
"Fetch https://docs.langchain.com and then try to fetch "
|
|
"https://langchain.com"
|
|
),
|
|
}
|
|
max_uses_response = llm_with_tools.invoke([multi_fetch_message])
|
|
|
|
# Should contain at least one fetch result and potentially an error for the second
|
|
fetch_results = [
|
|
block
|
|
for block in max_uses_response.content
|
|
if isinstance(block, dict) and block.get("type") == "web_fetch_tool_result"
|
|
] # type: ignore[index]
|
|
assert len(fetch_results) >= 1
|
|
error_results = [
|
|
r
|
|
for r in fetch_results
|
|
if r.get("content", {}).get("type") == "web_fetch_tool_error"
|
|
]
|
|
if error_results:
|
|
assert any(
|
|
r["content"]["error_code"] == "max_uses_exceeded" for r in error_results
|
|
)
|
|
|
|
# Streaming
|
|
full: BaseMessageChunk | None = None
|
|
for chunk in llm_with_tools.stream([input_message]):
|
|
assert isinstance(chunk, AIMessageChunk)
|
|
full = chunk if full is None else full + chunk
|
|
assert isinstance(full, AIMessageChunk)
|
|
assert isinstance(full.content, list)
|
|
block_types = {block["type"] for block in full.content if isinstance(block, dict)}
|
|
assert block_types == {"text", "server_tool_use", "web_fetch_tool_result"}
|
|
|
|
# Test that URLs from context can be used in follow-up
|
|
next_message = {
|
|
"role": "user",
|
|
"content": "What does the site you just fetched say about models?",
|
|
}
|
|
follow_up_response = llm_with_tools.invoke(
|
|
[input_message, full, next_message],
|
|
)
|
|
# Should work without issues since URL was already in context
|
|
assert isinstance(follow_up_response.content, (list, str))
|
|
|
|
# Error handling - test with an invalid URL format
|
|
error_message = {
|
|
"role": "user",
|
|
"content": "Try to fetch this invalid URL: not-a-valid-url",
|
|
}
|
|
error_response = llm_with_tools.invoke([error_message])
|
|
|
|
# Should handle the error gracefully
|
|
assert isinstance(error_response.content, (list, str))
|
|
|
|
# PDF document fetching
|
|
pdf_message = {
|
|
"role": "user",
|
|
"content": (
|
|
"Fetch this PDF: "
|
|
"https://www.w3.org/WAI/ER/tests/xhtml/testfiles/resources/pdf/dummy.pdf "
|
|
"and summarize its content",
|
|
),
|
|
}
|
|
pdf_response = llm_with_tools.invoke([pdf_message])
|
|
|
|
assert any(
|
|
block["type"] == "web_fetch_tool_result"
|
|
for block in pdf_response.content
|
|
if isinstance(block, dict)
|
|
)
|
|
|
|
# Verify PDF content structure (should have base64 data for PDFs)
|
|
pdf_results = [
|
|
block
|
|
for block in pdf_response.content
|
|
if isinstance(block, dict) and block.get("type") == "web_fetch_tool_result"
|
|
]
|
|
if pdf_results:
|
|
pdf_result = pdf_results[0]
|
|
content = pdf_result.get("content", {})
|
|
if content.get("content", {}).get("source", {}).get("type") == "base64":
|
|
assert content["content"]["source"]["media_type"] == "application/pdf"
|
|
assert "data" in content["content"]["source"]
|
|
|
|
|
|
@pytest.mark.default_cassette("test_web_fetch_v1.yaml.gz")
|
|
@pytest.mark.vcr
|
|
@pytest.mark.parametrize("output_version", ["v0", "v1"])
|
|
def test_web_fetch_v1(output_version: Literal["v0", "v1"]) -> None:
|
|
"""Test that http calls are unchanged between v0 and v1."""
|
|
llm = ChatAnthropic(
|
|
model=MODEL_NAME, # type: ignore[call-arg]
|
|
betas=["web-fetch-2025-09-10"],
|
|
output_version=output_version,
|
|
)
|
|
|
|
if output_version == "v0":
|
|
call_key = "server_tool_use"
|
|
result_key = "web_fetch_tool_result"
|
|
else:
|
|
# v1
|
|
call_key = "server_tool_call"
|
|
result_key = "server_tool_result"
|
|
|
|
tool = {
|
|
"type": "web_fetch_20250910",
|
|
"name": "web_fetch",
|
|
"max_uses": 1,
|
|
"citations": {"enabled": True},
|
|
}
|
|
llm_with_tools = llm.bind_tools([tool])
|
|
|
|
input_message = {
|
|
"role": "user",
|
|
"content": [
|
|
{
|
|
"type": "text",
|
|
"text": "Fetch the content at https://docs.langchain.com and analyze",
|
|
},
|
|
],
|
|
}
|
|
response = llm_with_tools.invoke([input_message])
|
|
assert all(isinstance(block, dict) for block in response.content)
|
|
block_types = {block["type"] for block in response.content} # type: ignore[index]
|
|
assert block_types == {"text", call_key, result_key}
|
|
|
|
# Test streaming
|
|
full: BaseMessageChunk | None = None
|
|
for chunk in llm_with_tools.stream([input_message]):
|
|
assert isinstance(chunk, AIMessageChunk)
|
|
full = chunk if full is None else full + chunk
|
|
|
|
assert isinstance(full, AIMessageChunk)
|
|
assert isinstance(full.content, list)
|
|
block_types = {block["type"] for block in full.content} # type: ignore[index]
|
|
assert block_types == {"text", call_key, result_key}
|
|
|
|
# Test we can pass back in
|
|
next_message = {
|
|
"role": "user",
|
|
"content": "What does the site you just fetched say about models?",
|
|
}
|
|
_ = llm_with_tools.invoke(
|
|
[input_message, full, next_message],
|
|
)
|
|
|
|
|
|
@pytest.mark.default_cassette("test_code_execution_old.yaml.gz")
|
|
@pytest.mark.vcr
|
|
@pytest.mark.parametrize("output_version", ["v0", "v1"])
|
|
def test_code_execution_old(output_version: Literal["v0", "v1"]) -> None:
|
|
"""Note: this tests the `code_execution_20250522` tool, which is now legacy.
|
|
|
|
See the `test_code_execution` test below to test the current
|
|
`code_execution_20250825` tool.
|
|
|
|
Migration guide: https://platform.claude.com/docs/en/agents-and-tools/tool-use/code-execution-tool#upgrade-to-latest-tool-version
|
|
"""
|
|
llm = ChatAnthropic(
|
|
model=MODEL_NAME, # type: ignore[call-arg]
|
|
betas=["code-execution-2025-05-22"],
|
|
output_version=output_version,
|
|
)
|
|
|
|
tool = {"type": "code_execution_20250522", "name": "code_execution"}
|
|
llm_with_tools = llm.bind_tools([tool])
|
|
|
|
input_message = {
|
|
"role": "user",
|
|
"content": [
|
|
{
|
|
"type": "text",
|
|
"text": (
|
|
"Calculate the mean and standard deviation of "
|
|
"[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]"
|
|
),
|
|
},
|
|
],
|
|
}
|
|
response = llm_with_tools.invoke([input_message])
|
|
assert all(isinstance(block, dict) for block in response.content)
|
|
block_types = {block["type"] for block in response.content} # type: ignore[index]
|
|
if output_version == "v0":
|
|
assert block_types == {"text", "server_tool_use", "code_execution_tool_result"}
|
|
else:
|
|
assert block_types == {"text", "server_tool_call", "server_tool_result"}
|
|
|
|
# Test streaming
|
|
full: BaseMessageChunk | None = None
|
|
for chunk in llm_with_tools.stream([input_message]):
|
|
assert isinstance(chunk, AIMessageChunk)
|
|
full = chunk if full is None else full + chunk
|
|
assert isinstance(full, AIMessageChunk)
|
|
assert isinstance(full.content, list)
|
|
block_types = {block["type"] for block in full.content} # type: ignore[index]
|
|
if output_version == "v0":
|
|
assert block_types == {"text", "server_tool_use", "code_execution_tool_result"}
|
|
else:
|
|
assert block_types == {"text", "server_tool_call", "server_tool_result"}
|
|
|
|
# Test we can pass back in
|
|
next_message = {
|
|
"role": "user",
|
|
"content": "Please add more comments to the code.",
|
|
}
|
|
_ = llm_with_tools.invoke(
|
|
[input_message, full, next_message],
|
|
)
|
|
|
|
|
|
@pytest.mark.default_cassette("test_code_execution.yaml.gz")
|
|
@pytest.mark.vcr
|
|
@pytest.mark.parametrize("output_version", ["v0", "v1"])
|
|
def test_code_execution(output_version: Literal["v0", "v1"]) -> None:
|
|
"""Note: this is a beta feature.
|
|
|
|
TODO: Update to remove beta once generally available.
|
|
"""
|
|
llm = ChatAnthropic(
|
|
model=MODEL_NAME, # type: ignore[call-arg]
|
|
betas=["code-execution-2025-08-25"],
|
|
output_version=output_version,
|
|
)
|
|
|
|
tool = {"type": "code_execution_20250825", "name": "code_execution"}
|
|
llm_with_tools = llm.bind_tools([tool])
|
|
|
|
input_message = {
|
|
"role": "user",
|
|
"content": [
|
|
{
|
|
"type": "text",
|
|
"text": (
|
|
"Calculate the mean and standard deviation of "
|
|
"[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]"
|
|
),
|
|
},
|
|
],
|
|
}
|
|
response = llm_with_tools.invoke([input_message])
|
|
assert all(isinstance(block, dict) for block in response.content)
|
|
block_types = {block["type"] for block in response.content} # type: ignore[index]
|
|
if output_version == "v0":
|
|
assert block_types == {
|
|
"text",
|
|
"server_tool_use",
|
|
"bash_code_execution_tool_result",
|
|
}
|
|
else:
|
|
assert block_types == {"text", "server_tool_call", "server_tool_result"}
|
|
|
|
# Test streaming
|
|
full: BaseMessageChunk | None = None
|
|
for chunk in llm_with_tools.stream([input_message]):
|
|
assert isinstance(chunk, AIMessageChunk)
|
|
full = chunk if full is None else full + chunk
|
|
assert isinstance(full, AIMessageChunk)
|
|
assert isinstance(full.content, list)
|
|
block_types = {block["type"] for block in full.content} # type: ignore[index]
|
|
if output_version == "v0":
|
|
assert block_types == {
|
|
"text",
|
|
"server_tool_use",
|
|
"bash_code_execution_tool_result",
|
|
}
|
|
else:
|
|
assert block_types == {"text", "server_tool_call", "server_tool_result"}
|
|
|
|
# Test we can pass back in
|
|
next_message = {
|
|
"role": "user",
|
|
"content": "Please add more comments to the code.",
|
|
}
|
|
_ = llm_with_tools.invoke(
|
|
[input_message, full, next_message],
|
|
)
|
|
|
|
|
|
@pytest.mark.default_cassette("test_remote_mcp.yaml.gz")
|
|
@pytest.mark.vcr
|
|
@pytest.mark.parametrize("output_version", ["v0", "v1"])
|
|
def test_remote_mcp(output_version: Literal["v0", "v1"]) -> None:
|
|
"""Note: this is a beta feature.
|
|
|
|
TODO: Update to remove beta once generally available.
|
|
"""
|
|
mcp_servers = [
|
|
{
|
|
"type": "url",
|
|
"url": "https://mcp.deepwiki.com/mcp",
|
|
"name": "deepwiki",
|
|
"authorization_token": "PLACEHOLDER",
|
|
},
|
|
]
|
|
|
|
llm = ChatAnthropic(
|
|
model="claude-sonnet-4-5-20250929", # type: ignore[call-arg]
|
|
mcp_servers=mcp_servers,
|
|
output_version=output_version,
|
|
).bind_tools([{"type": "mcp_toolset", "mcp_server_name": "deepwiki"}])
|
|
|
|
input_message = {
|
|
"role": "user",
|
|
"content": [
|
|
{
|
|
"type": "text",
|
|
"text": (
|
|
"What transport protocols does the 2025-03-26 version of the MCP "
|
|
"spec (modelcontextprotocol/modelcontextprotocol) support?"
|
|
),
|
|
},
|
|
],
|
|
}
|
|
response = llm.invoke([input_message])
|
|
assert all(isinstance(block, dict) for block in response.content)
|
|
block_types = {block["type"] for block in response.content} # type: ignore[index]
|
|
if output_version == "v0":
|
|
assert block_types == {"text", "mcp_tool_use", "mcp_tool_result"}
|
|
else:
|
|
assert block_types == {"text", "server_tool_call", "server_tool_result"}
|
|
|
|
# Test streaming
|
|
full: BaseMessageChunk | None = None
|
|
for chunk in llm.stream([input_message]):
|
|
assert isinstance(chunk, AIMessageChunk)
|
|
full = chunk if full is None else full + chunk
|
|
assert isinstance(full, AIMessageChunk)
|
|
assert isinstance(full.content, list)
|
|
assert all(isinstance(block, dict) for block in full.content)
|
|
block_types = {block["type"] for block in full.content} # type: ignore[index]
|
|
if output_version == "v0":
|
|
assert block_types == {"text", "mcp_tool_use", "mcp_tool_result"}
|
|
else:
|
|
assert block_types == {"text", "server_tool_call", "server_tool_result"}
|
|
|
|
# Test we can pass back in
|
|
next_message = {
|
|
"role": "user",
|
|
"content": "Please query the same tool again, but add 'please' to your query.",
|
|
}
|
|
_ = llm.invoke(
|
|
[input_message, full, next_message],
|
|
)
|
|
|
|
|
|
@pytest.mark.parametrize("block_format", ["anthropic", "standard"])
|
|
def test_files_api_image(block_format: str) -> None:
|
|
"""Note: this is a beta feature.
|
|
|
|
TODO: Update to remove beta once generally available.
|
|
"""
|
|
image_file_id = os.getenv("ANTHROPIC_FILES_API_IMAGE_ID")
|
|
if not image_file_id:
|
|
pytest.skip()
|
|
llm = ChatAnthropic(
|
|
model=MODEL_NAME, # type: ignore[call-arg]
|
|
betas=["files-api-2025-04-14"],
|
|
)
|
|
if block_format == "anthropic":
|
|
block = {
|
|
"type": "image",
|
|
"source": {
|
|
"type": "file",
|
|
"file_id": image_file_id,
|
|
},
|
|
}
|
|
else:
|
|
# standard block format
|
|
block = {
|
|
"type": "image",
|
|
"file_id": image_file_id,
|
|
}
|
|
input_message = {
|
|
"role": "user",
|
|
"content": [
|
|
{"type": "text", "text": "Describe this image."},
|
|
block,
|
|
],
|
|
}
|
|
_ = llm.invoke([input_message])
|
|
|
|
|
|
@pytest.mark.parametrize("block_format", ["anthropic", "standard"])
|
|
def test_files_api_pdf(block_format: str) -> None:
|
|
"""Note: this is a beta feature.
|
|
|
|
TODO: Update to remove beta once generally available.
|
|
"""
|
|
pdf_file_id = os.getenv("ANTHROPIC_FILES_API_PDF_ID")
|
|
if not pdf_file_id:
|
|
pytest.skip()
|
|
llm = ChatAnthropic(
|
|
model=MODEL_NAME, # type: ignore[call-arg]
|
|
betas=["files-api-2025-04-14"],
|
|
)
|
|
if block_format == "anthropic":
|
|
block = {"type": "document", "source": {"type": "file", "file_id": pdf_file_id}}
|
|
else:
|
|
# standard block format
|
|
block = {
|
|
"type": "file",
|
|
"file_id": pdf_file_id,
|
|
}
|
|
input_message = {
|
|
"role": "user",
|
|
"content": [
|
|
{"type": "text", "text": "Describe this document."},
|
|
block,
|
|
],
|
|
}
|
|
_ = llm.invoke([input_message])
|
|
|
|
|
|
@pytest.mark.vcr
|
|
def test_search_result_tool_message() -> None:
|
|
"""Test that we can pass a search result tool message to the model."""
|
|
llm = ChatAnthropic(
|
|
model=MODEL_NAME, # type: ignore[call-arg]
|
|
)
|
|
|
|
@tool
|
|
def retrieval_tool(query: str) -> list[dict]:
|
|
"""Retrieve information from a knowledge base."""
|
|
return [
|
|
{
|
|
"type": "search_result",
|
|
"title": "Leave policy",
|
|
"source": "HR Leave Policy 2025",
|
|
"citations": {"enabled": True},
|
|
"content": [
|
|
{
|
|
"type": "text",
|
|
"text": (
|
|
"To request vacation days, submit a leave request form "
|
|
"through the HR portal. Approval will be sent by email."
|
|
),
|
|
},
|
|
],
|
|
},
|
|
]
|
|
|
|
tool_call = {
|
|
"type": "tool_call",
|
|
"name": "retrieval_tool",
|
|
"args": {"query": "vacation days request process"},
|
|
"id": "toolu_abc123",
|
|
}
|
|
|
|
tool_message = retrieval_tool.invoke(tool_call)
|
|
assert isinstance(tool_message, ToolMessage)
|
|
assert isinstance(tool_message.content, list)
|
|
|
|
messages = [
|
|
HumanMessage("How do I request vacation days?"),
|
|
AIMessage(
|
|
[{"type": "text", "text": "Let me look that up for you."}],
|
|
tool_calls=[tool_call],
|
|
),
|
|
tool_message,
|
|
]
|
|
|
|
result = llm.invoke(messages)
|
|
assert isinstance(result, AIMessage)
|
|
assert isinstance(result.content, list)
|
|
assert any("citations" in block for block in result.content)
|
|
|
|
assert (
|
|
_convert_from_v1_to_anthropic(result.content_blocks, [], "anthropic")
|
|
== result.content
|
|
)
|
|
|
|
|
|
def test_search_result_top_level() -> None:
|
|
llm = ChatAnthropic(
|
|
model=MODEL_NAME, # type: ignore[call-arg]
|
|
)
|
|
input_message = HumanMessage(
|
|
[
|
|
{
|
|
"type": "search_result",
|
|
"title": "Leave policy",
|
|
"source": "HR Leave Policy 2025 - page 1",
|
|
"citations": {"enabled": True},
|
|
"content": [
|
|
{
|
|
"type": "text",
|
|
"text": (
|
|
"To request vacation days, submit a leave request form "
|
|
"through the HR portal. Approval will be sent by email."
|
|
),
|
|
},
|
|
],
|
|
},
|
|
{
|
|
"type": "search_result",
|
|
"title": "Leave policy",
|
|
"source": "HR Leave Policy 2025 - page 2",
|
|
"citations": {"enabled": True},
|
|
"content": [
|
|
{
|
|
"type": "text",
|
|
"text": "Managers have 3 days to approve a request.",
|
|
},
|
|
],
|
|
},
|
|
{
|
|
"type": "text",
|
|
"text": "How do I request vacation days?",
|
|
},
|
|
],
|
|
)
|
|
result = llm.invoke([input_message])
|
|
assert isinstance(result, AIMessage)
|
|
assert isinstance(result.content, list)
|
|
assert any("citations" in block for block in result.content)
|
|
|
|
assert (
|
|
_convert_from_v1_to_anthropic(result.content_blocks, [], "anthropic")
|
|
== result.content
|
|
)
|
|
|
|
|
|
def test_memory_tool() -> None:
|
|
llm = ChatAnthropic(
|
|
model="claude-sonnet-4-5-20250929", # type: ignore[call-arg]
|
|
betas=["context-management-2025-06-27"],
|
|
)
|
|
llm_with_tools = llm.bind_tools([{"type": "memory_20250818", "name": "memory"}])
|
|
response = llm_with_tools.invoke("What are my interests?")
|
|
assert isinstance(response, AIMessage)
|
|
assert response.tool_calls
|
|
assert response.tool_calls[0]["name"] == "memory"
|
|
|
|
|
|
@pytest.mark.vcr
|
|
def test_context_management() -> None:
|
|
# TODO: update example to trigger action
|
|
llm = ChatAnthropic(
|
|
model="claude-sonnet-4-5-20250929", # type: ignore[call-arg]
|
|
betas=["context-management-2025-06-27"],
|
|
context_management={
|
|
"edits": [
|
|
{
|
|
"type": "clear_tool_uses_20250919",
|
|
"trigger": {"type": "input_tokens", "value": 10},
|
|
"clear_at_least": {"type": "input_tokens", "value": 5},
|
|
}
|
|
]
|
|
},
|
|
max_tokens=1024, # type: ignore[call-arg]
|
|
)
|
|
llm_with_tools = llm.bind_tools(
|
|
[{"type": "web_search_20250305", "name": "web_search"}]
|
|
)
|
|
input_message = {"role": "user", "content": "Search for recent developments in AI"}
|
|
response = llm_with_tools.invoke([input_message])
|
|
assert response.response_metadata.get("context_management")
|
|
|
|
# Test streaming
|
|
full: BaseMessageChunk | None = None
|
|
for chunk in llm_with_tools.stream([input_message]):
|
|
assert isinstance(chunk, AIMessageChunk)
|
|
full = chunk if full is None else full + chunk
|
|
assert isinstance(full, AIMessageChunk)
|
|
assert full.response_metadata.get("context_management")
|
|
|
|
|
|
@pytest.mark.default_cassette("test_tool_search.yaml.gz")
|
|
@pytest.mark.vcr
|
|
@pytest.mark.parametrize("output_version", ["v0", "v1"])
|
|
def test_tool_search(output_version: str) -> None:
|
|
"""Test tool search with LangChain tools using extras parameter."""
|
|
|
|
@tool(parse_docstring=True, extras={"defer_loading": True})
|
|
def get_weather(location: str, unit: str = "fahrenheit") -> str:
|
|
"""Get the current weather for a location.
|
|
|
|
Args:
|
|
location: City name
|
|
unit: Temperature unit (celsius or fahrenheit)
|
|
"""
|
|
return f"The weather in {location} is sunny and 72°{unit[0].upper()}"
|
|
|
|
@tool(parse_docstring=True, extras={"defer_loading": True})
|
|
def search_files(query: str) -> str:
|
|
"""Search through files in the workspace.
|
|
|
|
Args:
|
|
query: Search query
|
|
"""
|
|
return f"Found 3 files matching '{query}'"
|
|
|
|
model = ChatAnthropic(
|
|
model="claude-opus-4-5-20251101", output_version=output_version
|
|
)
|
|
|
|
agent = create_agent( # type: ignore[var-annotated]
|
|
model,
|
|
tools=[
|
|
{
|
|
"type": "tool_search_tool_regex_20251119",
|
|
"name": "tool_search_tool_regex",
|
|
},
|
|
get_weather,
|
|
search_files,
|
|
],
|
|
)
|
|
|
|
# Test with actual API call
|
|
input_message = {
|
|
"role": "user",
|
|
"content": "What's the weather in San Francisco? Find and use a tool.",
|
|
}
|
|
result = agent.invoke({"messages": [input_message]})
|
|
first_response = result["messages"][1]
|
|
content_types = [block["type"] for block in first_response.content]
|
|
if output_version == "v0":
|
|
assert content_types == [
|
|
"text",
|
|
"server_tool_use",
|
|
"tool_search_tool_result",
|
|
"text",
|
|
"tool_use",
|
|
]
|
|
else:
|
|
# v1
|
|
assert content_types == [
|
|
"text",
|
|
"server_tool_call",
|
|
"server_tool_result",
|
|
"text",
|
|
"tool_call",
|
|
]
|
|
|
|
answer = result["messages"][-1]
|
|
assert not answer.tool_calls
|
|
assert answer.text
|
|
|
|
|
|
@pytest.mark.default_cassette("test_programmatic_tool_use.yaml.gz")
|
|
@pytest.mark.vcr
|
|
@pytest.mark.parametrize("output_version", ["v0", "v1"])
|
|
def test_programmatic_tool_use(output_version: str) -> None:
|
|
"""Test programmatic tool use.
|
|
|
|
Implicitly checks that `allowed_callers` in tool extras works.
|
|
"""
|
|
|
|
@tool(extras={"allowed_callers": ["code_execution_20250825"]})
|
|
def get_weather(location: str) -> str:
|
|
"""Get the weather at a location."""
|
|
return "It's sunny."
|
|
|
|
tools: list = [
|
|
{"type": "code_execution_20250825", "name": "code_execution"},
|
|
get_weather,
|
|
]
|
|
|
|
model = ChatAnthropic(
|
|
model="claude-sonnet-4-5",
|
|
betas=["advanced-tool-use-2025-11-20"],
|
|
reuse_last_container=True,
|
|
output_version=output_version,
|
|
)
|
|
|
|
agent = create_agent(model, tools=tools) # type: ignore[var-annotated]
|
|
|
|
input_query = {
|
|
"role": "user",
|
|
"content": "What's the weather in Boston?",
|
|
}
|
|
|
|
result = agent.invoke({"messages": [input_query]})
|
|
assert len(result["messages"]) == 4
|
|
tool_call_message = result["messages"][1]
|
|
response_message = result["messages"][-1]
|
|
|
|
if output_version == "v0":
|
|
server_tool_use_block = next(
|
|
block
|
|
for block in tool_call_message.content
|
|
if block["type"] == "server_tool_use"
|
|
)
|
|
assert server_tool_use_block
|
|
|
|
tool_use_block = next(
|
|
block for block in tool_call_message.content if block["type"] == "tool_use"
|
|
)
|
|
assert "caller" in tool_use_block
|
|
|
|
code_execution_result = next(
|
|
block
|
|
for block in response_message.content
|
|
if block["type"] == "code_execution_tool_result"
|
|
)
|
|
assert code_execution_result["content"]["return_code"] == 0
|
|
else:
|
|
server_tool_call_block = next(
|
|
block
|
|
for block in tool_call_message.content
|
|
if block["type"] == "server_tool_call"
|
|
)
|
|
assert server_tool_call_block
|
|
|
|
tool_call_block = next(
|
|
block for block in tool_call_message.content if block["type"] == "tool_call"
|
|
)
|
|
assert "caller" in tool_call_block["extras"]
|
|
|
|
server_tool_result = next(
|
|
block
|
|
for block in response_message.content
|
|
if block["type"] == "server_tool_result"
|
|
)
|
|
assert server_tool_result["output"]["return_code"] == 0
|
|
|
|
|
|
@pytest.mark.default_cassette("test_programmatic_tool_use_streaming.yaml.gz")
|
|
@pytest.mark.vcr
|
|
@pytest.mark.parametrize("output_version", ["v0", "v1"])
|
|
def test_programmatic_tool_use_streaming(output_version: str) -> None:
|
|
@tool(extras={"allowed_callers": ["code_execution_20250825"]})
|
|
def get_weather(location: str) -> str:
|
|
"""Get the weather at a location."""
|
|
return "It's sunny."
|
|
|
|
tools: list = [
|
|
{"type": "code_execution_20250825", "name": "code_execution"},
|
|
get_weather,
|
|
]
|
|
|
|
model = ChatAnthropic(
|
|
model="claude-sonnet-4-5",
|
|
betas=["advanced-tool-use-2025-11-20"],
|
|
reuse_last_container=True,
|
|
streaming=True,
|
|
output_version=output_version,
|
|
)
|
|
|
|
agent = create_agent(model, tools=tools) # type: ignore[var-annotated]
|
|
|
|
input_query = {
|
|
"role": "user",
|
|
"content": "What's the weather in Boston?",
|
|
}
|
|
|
|
result = agent.invoke({"messages": [input_query]})
|
|
assert len(result["messages"]) == 4
|
|
tool_call_message = result["messages"][1]
|
|
response_message = result["messages"][-1]
|
|
|
|
if output_version == "v0":
|
|
server_tool_use_block = next(
|
|
block
|
|
for block in tool_call_message.content
|
|
if block["type"] == "server_tool_use"
|
|
)
|
|
assert server_tool_use_block
|
|
|
|
tool_use_block = next(
|
|
block for block in tool_call_message.content if block["type"] == "tool_use"
|
|
)
|
|
assert "caller" in tool_use_block
|
|
|
|
code_execution_result = next(
|
|
block
|
|
for block in response_message.content
|
|
if block["type"] == "code_execution_tool_result"
|
|
)
|
|
assert code_execution_result["content"]["return_code"] == 0
|
|
else:
|
|
server_tool_call_block = next(
|
|
block
|
|
for block in tool_call_message.content
|
|
if block["type"] == "server_tool_call"
|
|
)
|
|
assert server_tool_call_block
|
|
|
|
tool_call_block = next(
|
|
block for block in tool_call_message.content if block["type"] == "tool_call"
|
|
)
|
|
assert "caller" in tool_call_block["extras"]
|
|
|
|
server_tool_result = next(
|
|
block
|
|
for block in response_message.content
|
|
if block["type"] == "server_tool_result"
|
|
)
|
|
assert server_tool_result["output"]["return_code"] == 0
|
|
|
|
|
|
def test_async_shared_client() -> None:
|
|
llm = ChatAnthropic(model=MODEL_NAME) # type: ignore[call-arg]
|
|
_ = asyncio.run(llm.ainvoke("Hello"))
|
|
_ = asyncio.run(llm.ainvoke("Hello"))
|
|
|
|
|
|
def test_fine_grained_tool_streaming() -> None:
|
|
"""Test fine-grained tool streaming reduces latency for tool parameter streaming.
|
|
|
|
Fine-grained tool streaming enables Claude to stream tool parameter values.
|
|
|
|
https://platform.claude.com/docs/en/agents-and-tools/tool-use/fine-grained-tool-streaming
|
|
"""
|
|
llm = ChatAnthropic(
|
|
model=MODEL_NAME, # type: ignore[call-arg]
|
|
temperature=0,
|
|
betas=["fine-grained-tool-streaming-2025-05-14"],
|
|
)
|
|
|
|
# Define a tool that requires a longer text parameter
|
|
tool_definition = {
|
|
"name": "write_document",
|
|
"description": "Write a document with the given content",
|
|
"input_schema": {
|
|
"type": "object",
|
|
"properties": {
|
|
"title": {"type": "string", "description": "Document title"},
|
|
"content": {
|
|
"type": "string",
|
|
"description": "The full document content",
|
|
},
|
|
},
|
|
"required": ["title", "content"],
|
|
},
|
|
}
|
|
|
|
llm_with_tools = llm.bind_tools([tool_definition])
|
|
query = (
|
|
"Write a document about the benefits of streaming APIs. "
|
|
"Include at least 3 paragraphs."
|
|
)
|
|
|
|
# Test streaming with fine-grained tool streaming
|
|
first = True
|
|
chunks: list[BaseMessage | BaseMessageChunk] = []
|
|
tool_call_chunks = []
|
|
|
|
for chunk in llm_with_tools.stream(query):
|
|
chunks.append(chunk)
|
|
if first:
|
|
gathered = chunk
|
|
first = False
|
|
else:
|
|
gathered = gathered + chunk # type: ignore[assignment]
|
|
|
|
# Collect tool call chunks
|
|
tool_call_chunks.extend(
|
|
[
|
|
block
|
|
for block in chunk.content_blocks
|
|
if block["type"] == "tool_call_chunk"
|
|
]
|
|
)
|
|
|
|
# Verify we got chunks
|
|
assert len(chunks) > 1
|
|
|
|
# Verify final message has tool call
|
|
assert isinstance(gathered, AIMessageChunk)
|
|
assert isinstance(gathered.tool_calls, list)
|
|
assert len(gathered.tool_calls) >= 1
|
|
|
|
# Find the write_document tool call
|
|
write_doc_call = None
|
|
for tool_call in gathered.tool_calls:
|
|
if tool_call["name"] == "write_document":
|
|
write_doc_call = tool_call
|
|
break
|
|
|
|
assert write_doc_call is not None, "write_document tool call not found"
|
|
assert isinstance(write_doc_call["args"], dict)
|
|
assert "title" in write_doc_call["args"]
|
|
assert "content" in write_doc_call["args"]
|
|
assert (
|
|
len(write_doc_call["args"]["content"]) > 100
|
|
) # Should have substantial content
|
|
|
|
# Verify tool_call_chunks were received
|
|
# With fine-grained streaming, we should get tool call chunks
|
|
assert len(tool_call_chunks) > 0
|
|
|
|
# Verify content_blocks in final message
|
|
content_blocks = gathered.content_blocks
|
|
assert len(content_blocks) >= 1
|
|
|
|
# Should have at least one tool_call block
|
|
tool_call_blocks = [b for b in content_blocks if b["type"] == "tool_call"]
|
|
assert len(tool_call_blocks) >= 1
|
|
|
|
write_doc_block = None
|
|
for block in tool_call_blocks:
|
|
if block["name"] == "write_document":
|
|
write_doc_block = block
|
|
break
|
|
|
|
assert write_doc_block is not None
|
|
assert write_doc_block["name"] == "write_document"
|
|
assert "args" in write_doc_block
|
|
|
|
|
|
@pytest.mark.vcr
|
|
def test_compaction() -> None:
|
|
"""Test the compaction beta feature."""
|
|
llm = ChatAnthropic(
|
|
model="claude-opus-4-6", # type: ignore[call-arg]
|
|
betas=["compact-2026-01-12"],
|
|
max_tokens=4096,
|
|
context_management={
|
|
"edits": [
|
|
{
|
|
"type": "compact_20260112",
|
|
"trigger": {"type": "input_tokens", "value": 50000},
|
|
"pause_after_compaction": True,
|
|
}
|
|
]
|
|
},
|
|
)
|
|
|
|
input_message = {
|
|
"role": "user",
|
|
"content": f"Generate a one-sentence summary of this:\n\n{'a' * 100000}",
|
|
}
|
|
messages: list = [input_message]
|
|
|
|
first_response = llm.invoke(messages)
|
|
messages.append(first_response)
|
|
|
|
second_message = {
|
|
"role": "user",
|
|
"content": f"Generate a one-sentence summary of this:\n\n{'b' * 100000}",
|
|
}
|
|
messages.append(second_message)
|
|
|
|
second_response = llm.invoke(messages)
|
|
messages.append(second_response)
|
|
|
|
content_blocks = second_response.content_blocks
|
|
compaction_block = next(
|
|
(block for block in content_blocks if block["type"] == "non_standard"),
|
|
None,
|
|
)
|
|
assert compaction_block
|
|
assert compaction_block["value"].get("type") == "compaction"
|
|
|
|
third_message = {
|
|
"role": "user",
|
|
"content": "What are we talking about?",
|
|
}
|
|
messages.append(third_message)
|
|
third_response = llm.invoke(messages)
|
|
content_blocks = third_response.content_blocks
|
|
assert [block["type"] for block in content_blocks] == ["text"]
|
|
|
|
|
|
@pytest.mark.vcr
|
|
def test_compaction_streaming() -> None:
|
|
"""Test the compaction beta feature."""
|
|
llm = ChatAnthropic(
|
|
model="claude-opus-4-6", # type: ignore[call-arg]
|
|
betas=["compact-2026-01-12"],
|
|
max_tokens=4096,
|
|
context_management={
|
|
"edits": [
|
|
{
|
|
"type": "compact_20260112",
|
|
"trigger": {"type": "input_tokens", "value": 50000},
|
|
"pause_after_compaction": False,
|
|
}
|
|
]
|
|
},
|
|
streaming=True,
|
|
)
|
|
|
|
input_message = {
|
|
"role": "user",
|
|
"content": f"Generate a one-sentence summary of this:\n\n{'a' * 100000}",
|
|
}
|
|
messages: list = [input_message]
|
|
|
|
first_response = llm.invoke(messages)
|
|
messages.append(first_response)
|
|
|
|
second_message = {
|
|
"role": "user",
|
|
"content": f"Generate a one-sentence summary of this:\n\n{'b' * 100000}",
|
|
}
|
|
messages.append(second_message)
|
|
|
|
second_response = llm.invoke(messages)
|
|
messages.append(second_response)
|
|
|
|
content_blocks = second_response.content_blocks
|
|
compaction_block = next(
|
|
(block for block in content_blocks if block["type"] == "non_standard"),
|
|
None,
|
|
)
|
|
assert compaction_block
|
|
assert compaction_block["value"].get("type") == "compaction"
|
|
|
|
third_message = {
|
|
"role": "user",
|
|
"content": "What are we talking about?",
|
|
}
|
|
messages.append(third_message)
|
|
third_response = llm.invoke(messages)
|
|
content_blocks = third_response.content_blocks
|
|
assert [block["type"] for block in content_blocks] == ["text"]
|
|
|
|
|
|
class _Person(BaseModel):
|
|
"""A person with a name and age."""
|
|
|
|
name: str = Field(description="The person's name")
|
|
age: int = Field(description="The person's age in years")
|
|
|
|
|
|
def _stable_blocks(blocks: Any) -> list[dict[str, Any]]:
|
|
"""Drop fields that vary between API calls so blocks can be compared.
|
|
|
|
Tool-call ids, wire indices, and provider extras are not path- or call-
|
|
stable; strip them so the comparison targets the semantic content.
|
|
"""
|
|
volatile = {"id", "index", "extras"}
|
|
return [{k: v for k, v in b.items() if k not in volatile} for b in blocks]
|
|
|
|
|
|
@pytest.mark.default_cassette("test_streaming_tool_call_v1_v2_parity.yaml.gz")
|
|
@pytest.mark.vcr
|
|
def test_streaming_tool_call_v1_v2_parity() -> None:
|
|
"""`AIMessage` parity between `stream()` and `stream_events(version="v3")` output.
|
|
|
|
Runs the same forced-tool-call prompt through both the legacy chunk
|
|
stream (reduced with `AIMessageChunk.__add__`) and the `stream_events(version="v3")`
|
|
bridge path on a `v1`-output `ChatAnthropic`, then compares the
|
|
resulting messages on path-independent invariants:
|
|
|
|
- tool call name and args (ids vary between calls and are ignored)
|
|
- exactly one tool call, no invalid tool calls
|
|
- `content_blocks` (the v1 projection, stripped of volatile fields)
|
|
- a valid tool-use `finish_reason`
|
|
|
|
The v2 path is additionally validated against the full protocol
|
|
lifecycle via `assert_valid_event_stream`.
|
|
"""
|
|
llm = ChatAnthropic(
|
|
model=MODEL_NAME,
|
|
output_version="v1", # type: ignore[call-arg]
|
|
)
|
|
with_tool = llm.bind_tools(
|
|
[_Person],
|
|
tool_choice={"type": "tool", "name": "_Person"},
|
|
)
|
|
prompt = "Extract: Erick is 27 years old."
|
|
|
|
v1_full: AIMessageChunk | None = None
|
|
for chunk in with_tool.stream(prompt):
|
|
assert isinstance(chunk, AIMessageChunk)
|
|
v1_full = chunk if v1_full is None else v1_full + chunk
|
|
assert isinstance(v1_full, AIMessageChunk)
|
|
|
|
stream = cast("ChatModelStream", with_tool.stream_events(prompt, version="v3"))
|
|
events = list(stream)
|
|
assert_valid_event_stream(events)
|
|
v2_message = stream.output
|
|
assert isinstance(v2_message, AIMessage)
|
|
|
|
assert len(v1_full.tool_calls) == len(v2_message.tool_calls) == 1
|
|
assert not v1_full.invalid_tool_calls
|
|
assert not v2_message.invalid_tool_calls
|
|
|
|
v1_tc = v1_full.tool_calls[0]
|
|
v2_tc = v2_message.tool_calls[0]
|
|
assert v1_tc["name"] == v2_tc["name"] == "_Person"
|
|
assert v1_tc["args"] == v2_tc["args"] == {"name": "Erick", "age": 27}
|
|
|
|
v1_blocks = _stable_blocks(v1_full.content_blocks)
|
|
v2_blocks = _stable_blocks(v2_message.content_blocks)
|
|
assert v1_blocks == v2_blocks
|
|
assert v1_blocks == [
|
|
{
|
|
"type": "tool_call",
|
|
"name": "_Person",
|
|
"args": {"name": "Erick", "age": 27},
|
|
}
|
|
]
|
|
|
|
# The compat bridge passes the provider's raw terminal reason through
|
|
# unchanged — Anthropic surfaces it under `stop_reason` on both paths.
|
|
# Accept either key on both sides rather than asserting a specific
|
|
# normalization that the bridge does not perform.
|
|
v1_finish = v1_full.response_metadata.get(
|
|
"finish_reason"
|
|
) or v1_full.response_metadata.get("stop_reason")
|
|
v2_finish = v2_message.response_metadata.get(
|
|
"finish_reason"
|
|
) or v2_message.response_metadata.get("stop_reason")
|
|
assert v1_finish is not None
|
|
assert v2_finish is not None
|
|
assert any(k in v1_finish for k in ("tool_use", "tool_calls", "stop"))
|
|
assert any(k in v2_finish for k in ("tool_use", "tool_calls", "stop"))
|