fix(langchain): extract model name for ChatHuggingFace

[vismaytiwari]

What does this PR do?

Fixes langfuse/langfuse#14103

When a generation runs through ChatHuggingFace, Langfuse recorded it without a model name and logged "Langfuse was not able to parse the LLM model". ChatHuggingFace is not LangChain-serializable, so it reaches the callback as a not_implemented stub with no kwargs; the model id is only present in the repr string as model_id='...'. _extract_model_name had no entry for it, so extraction fell through and returned None.

This adds a repr-pattern entry that reads model_id, matching how the other repr-only models (HuggingFaceHub, Ollama, etc.) are already handled. ChatHuggingFace exposes model_id regardless of the underlying backend (HuggingFaceEndpoint, HuggingFaceHub, or HuggingFacePipeline), so this covers those cases.

Type of change

• Bug fix

Verification

I confirmed the model id is only available in the repr by dumping a real ChatHuggingFace via langchain_core.load.dumpd, then wrote a deterministic unit test using that serialized shape so it needs no live HuggingFace call. The test returns None before the change and the correct model id after.

uv run --frozen pytest tests/unit/test_langchain_utils.py
uv run --frozen ruff check langfuse/langchain/utils.py tests/unit/test_langchain_utils.py
uv run --frozen ruff format --check langfuse/langchain/utils.py tests/unit/test_langchain_utils.py
uv run --frozen mypy langfuse/langchain/utils.py --no-error-summary

Checklist

• I self-reviewed the diff using code_review.md.
• I added or updated tests for behavior changes.
• I updated docs, examples, or .env.template if needed.
• I did not hand-edit generated files; if generated files changed, I used the upstream regeneration path.
• I did not commit secrets or credentials.

Greptile Summary

Adds ChatHuggingFace to the repr-based model-name extraction table so that Langfuse records a model name when LangChain callbacks arrive from ChatHuggingFace, which serialises as a not_implemented stub with the model id only available in the repr string.

• langfuse/langchain/utils.py: Inserts (\"ChatHuggingFace\", \"model_id\", None) into models_by_pattern, following the identical approach already used for HuggingFaceHub, Ollama, DeepInfra, and others that expose their model identifier only via repr.
• tests/unit/test_langchain_utils.py: Adds a new unit test file with a deterministic serialized stub matching the real dumpd shape of ChatHuggingFace, verifying the regex extracts the correct model_id.

Confidence Score: 5/5

Safe to merge — a one-line addition to a lookup table with a corresponding unit test.

The change is minimal and self-contained: one new tuple in models_by_pattern using the same repr-regex mechanism already proven by multiple other entries. The unit test confirms the extraction works correctly for the primary backend shape. The only gap is that the HuggingFacePipeline-backend repr variant is not explicitly tested, but since both occurrences carry the same value in practice this is a test-coverage gap rather than a functional defect.

No files require special attention.

Flowchart

%%{init: {'theme': 'neutral'}}%%
flowchart TD
    A[_extract_model_name called] --> B{Match by ID path\nmodels_by_id list}
    B -- match --> Z[Return model name]
    B -- no match --> C{AzureOpenAI\nspecial case?}
    C -- yes --> D[Extract from\ninvocation_params / kwargs]
    D --> Z
    C -- no --> E{Match by repr\nmodels_by_pattern list}
    E -- includes new ChatHuggingFace entry\nre.search model_id='.+' in repr --> F{repr contains\nmodel_id='...'?}
    F -- yes --> Z
    F -- no --> G[Return None default]
    E -- no match --> H{Catch-all path\nkwargs / serialized}
    H -- found --> Z
    H -- not found --> I[Return None]

Loading

%%{init: {'theme': 'base', 'themeVariables': {"darkMode": true, "background": "#0d1117", "primaryColor": "#21262d", "primaryTextColor": "#e6edf3", "primaryBorderColor": "#8b949e", "lineColor": "#8b949e", "textColor": "#e6edf3", "edgeLabelBackground": "#161b22", "actorBkg": "#21262d", "actorBorder": "#8b949e", "actorTextColor": "#e6edf3", "actorLineColor": "#8b949e", "signalColor": "#8b949e", "signalTextColor": "#e6edf3", "noteBkgColor": "#373320", "noteBorderColor": "#d4a72c", "noteTextColor": "#f0e6c0", "labelBoxBkgColor": "#21262d", "labelBoxBorderColor": "#8b949e", "labelTextColor": "#e6edf3", "loopTextColor": "#e6edf3", "activationBkgColor": "#30363d", "activationBorderColor": "#8b949e"}}}%%
flowchart TD
    A[_extract_model_name called] --> B{Match by ID path\nmodels_by_id list}
    B -- match --> Z[Return model name]
    B -- no match --> C{AzureOpenAI\nspecial case?}
    C -- yes --> D[Extract from\ninvocation_params / kwargs]
    D --> Z
    C -- no --> E{Match by repr\nmodels_by_pattern list}
    E -- includes new ChatHuggingFace entry\nre.search model_id='.+' in repr --> F{repr contains\nmodel_id='...'?}
    F -- yes --> Z
    F -- no --> G[Return None default]
    E -- no match --> H{Catch-all path\nkwargs / serialized}
    H -- found --> Z
    H -- not found --> I[Return None]

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Prompt To Fix All With AI

Fix the following 1 code review issue. Work through them one at a time, proposing concise fixes.

---

### Issue 1 of 1
tests/unit/test_langchain_utils.py:29-32
**Test only covers HuggingFaceEndpoint backend shape**

The PR description correctly notes that `ChatHuggingFace` works with three backends — `HuggingFaceEndpoint`, `HuggingFaceHub`, and `HuggingFacePipeline`. `HuggingFacePipeline` itself has a `model_id` attribute in its own repr, so a wrapped repr could look like `ChatHuggingFace(llm=HuggingFacePipeline(model_id='...', ...), model_id='...')`. The regex uses `re.search` (first match), meaning it would pick up the inner `HuggingFacePipeline`'s `model_id` rather than `ChatHuggingFace`'s. In practice both should be the same value, but adding a parameterised test for each backend shape would guard against any future repr change where the two values might differ.

_{Reviews (1): Last reviewed commit: "fix(langchain): extract model name for C..." | Re-trigger Greptile}

Greptile also left 1 inline comment on this PR.

[claude]

Claude Code Review

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[hassiebp]

Thanks for your contribution, @vismaytiwari !