test_web_fetch() — langchain Function Reference

Architecture documentation for the test_web_fetch() function in test_chat_models.py from the langchain codebase.

Function python LangChainCore LanguageModelBase

Entity Profile

LangChainCore→ LanguageModelBase→ test_web_fetch() — langchain Function Reference

Dependency Diagram

graph TD
  d02978a8_b24f_d6c7_0881_60266f6ef777["test_web_fetch()"]
  f27640dd_3870_5548_d153_f9504ae1021f["test_chat_models.py"]
  d02978a8_b24f_d6c7_0881_60266f6ef777 -->|defined in| f27640dd_3870_5548_d153_f9504ae1021f
  style d02978a8_b24f_d6c7_0881_60266f6ef777 fill:#6366f1,stroke:#818cf8,color:#fff

Relationship Graph

Source Code

libs/partners/anthropic/tests/integration_tests/test_chat_models.py lines 1334–1592

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