mirror of
https://github.com/tiennm99/DocsGPT.git
synced 2026-10-04 16:13:23 +00:00
Alex
2565906f88
Bumps the Python backend dependency set, holding back the ones that are
resolver-blocked or that regress behaviour this repo depends on.
Notable upgrades:
cryptography 46.0.7 -> 50.0.0 (requires msal 1.37.0, which relaxes its cap)
protobuf 6.33.6 -> 7.35.1 (floats opentelemetry-* to 1.44.0)
openai 2.32.0 -> 2.53.0, anthropic 0.88.0 -> 0.121.0
google-genai 1.73.1 -> 2.17.0 (the 2.0 break is scoped to the Interactions
API, which this repo does not use)
fastmcp 3.2.4 -> 3.4.6, gunicorn 25.3.0 -> 26.0.0
starlette 1.0.0 -> 1.6.0, uvicorn 0.42.0 -> 0.52.1
sentence-transformers 5.3.0 -> 5.7.0, numpy 2.4.4 -> 2.5.1
faiss-cpu 1.13.2 -> 1.15.0, pillow -> 12.3.0 (pinned; security release)
pypdf 6.9.2 -> 6.15.0, lxml 6.0.2 -> 6.1.1 (CVE-2026-41066)
Code changes needed by the bumps:
- openai >= 2.53 rejects a falsy api_key at construction. Keyless
OpenAI-compatible backends (Ollama, llama.cpp, vLLM) legitimately have
none, and pydantic-settings yields "" for a bare `API_KEY=` in .env, so
both call sites now fall back to a placeholder.
- Flask >= 3.1.2 tears a stream_with_context request down twice, and a
ContextVar token may only be reset once. The log-context teardown hook
is now idempotent.
- sentence-transformers renamed get_sentence_embedding_dimension to
get_embedding_dimension in 5.4; use the new name with a fallback.
Held back deliberately:
torch 2.11.0 - 2.13.0 drags torchvision 0.26 -> 0.28 and the whole
docling stack; torch is only probed for cuda.empty_cache()
tokenizers 0.22.2 - transformers pins <=0.23.0 and no stable 0.23.0 exists
transformers - capped <5.9.0: 5.9+ breaks docling's PDF layout model on
Apple Silicon (MPS float64), matching docling-core's own
darwin pin
docling 2.84.0 - 2.118.1 needs rapidocr >=3.9.1 and conflicts with
transformers on macOS; wants its own PR with a
golden-corpus diff
redis 7.4.0 - 8.x defaults socket_timeout to 5s, silently capping the
blocking reads in the device broker and SSE tail
websockets 16.0 - google-genai caps <17.0
marshmallow - dataclasses-json hard-caps <4; spec tightened to match
langchain block - langchain-community 0.4.2 deletes the Qdrant vectorstore
this repo imports; langchain 1.3.x needs websockets <16
55 lines
2.0 KiB
Python
55 lines
2.0 KiB
Python
"""
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Local embeddings using SentenceTransformer.
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This module is only imported when EMBEDDINGS_BASE_URL is not set,
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to avoid loading SentenceTransformer into memory when using remote embeddings.
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"""
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import logging
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from sentence_transformers import SentenceTransformer
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class EmbeddingsWrapper:
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def __init__(self, model_name, *args, **kwargs):
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logging.info(f"Initializing EmbeddingsWrapper with model: {model_name}")
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try:
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kwargs.setdefault("trust_remote_code", True)
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self.model = SentenceTransformer(
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model_name,
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config_kwargs={"allow_dangerous_deserialization": True},
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*args,
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**kwargs,
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)
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if self.model is None or self.model._first_module() is None:
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raise ValueError(
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f"SentenceTransformer model failed to load properly for: {model_name}"
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)
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# Renamed in sentence-transformers 5.4; keep the old name as a fallback.
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get_dimension = getattr(
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self.model,
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"get_embedding_dimension",
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getattr(self.model, "get_sentence_embedding_dimension", None),
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)
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self.dimension = get_dimension()
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logging.info(f"Successfully loaded model with dimension: {self.dimension}")
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except Exception as e:
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logging.error(
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f"Failed to initialize SentenceTransformer with model {model_name}: {str(e)}",
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exc_info=True,
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)
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raise
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def embed_query(self, query: str):
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return self.model.encode(query).tolist()
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def embed_documents(self, documents: list):
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return self.model.encode(documents).tolist()
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def __call__(self, text):
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if isinstance(text, str):
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return self.embed_query(text)
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elif isinstance(text, list):
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return self.embed_documents(text)
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else:
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raise ValueError("Input must be a string or a list of strings")
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