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13d0df1562
### What problem does this PR solve? Adds Perplexity contextualized embeddings API as a new model provider, as requested in #13610. - `PerplexityEmbed` provider in `rag/llm/embedding_model.py` supporting both standard (`/v1/embeddings`) and contextualized (`/v1/contextualizedembeddings`) endpoints - All 4 Perplexity embedding models registered in `conf/llm_factories.json`: `pplx-embed-v1-0.6b`, `pplx-embed-v1-4b`, `pplx-embed-context-v1-0.6b`, `pplx-embed-context-v1-4b` - Frontend entries (enum, icon mapping, API key URL) in `web/src/constants/llm.ts` - Updated `docs/guides/models/supported_models.mdx` - 22 unit tests in `test/unit_test/rag/llm/test_perplexity_embed.py` Perplexity's API returns `base64_int8` encoded embeddings (not OpenAI-compatible), so this uses a custom `requests`-based implementation. Contextualized vs standard model is auto-detected from the model name. Closes #13610 ### Type of change - [x] New Feature (non-breaking change which adds functionality) - [x] Documentation Update
62 lines
2.1 KiB
Python
62 lines
2.1 KiB
Python
#
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# Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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"""
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Prevent rag.llm.__init__ from running its heavy auto-discovery loop.
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The __init__.py dynamically imports ALL model modules (chat_model,
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cv_model, ocr_model, etc.), which pull in deepdoc, xgboost, torch,
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and other heavy native deps. We pre-install a lightweight stub for
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the rag.llm package so that `from rag.llm.embedding_model import X`
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works without triggering the full init.
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"""
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import os
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import sys
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import types
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# Resolve the real path to rag/llm/ so sub-module imports can find files
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_RAGFLOW_ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", "..", ".."))
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_RAG_LLM_DIR = os.path.join(_RAGFLOW_ROOT, "rag", "llm")
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def _install_rag_llm_stub():
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"""Replace rag.llm with a minimal package stub if not yet loaded.
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The stub has __path__ pointing to the real rag/llm/ directory so that
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`from rag.llm.embedding_model import X` resolves to the actual file,
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but the __init__.py auto-discovery loop is skipped.
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"""
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if "rag.llm" in sys.modules:
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return
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# Create a stub rag.llm package that does NOT run the real __init__
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llm_pkg = types.ModuleType("rag.llm")
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llm_pkg.__path__ = [_RAG_LLM_DIR]
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llm_pkg.__package__ = "rag.llm"
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# Provide empty dicts for the mappings the real __init__ would build
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llm_pkg.EmbeddingModel = {}
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llm_pkg.ChatModel = {}
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llm_pkg.CvModel = {}
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llm_pkg.RerankModel = {}
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llm_pkg.Seq2txtModel = {}
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llm_pkg.TTSModel = {}
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llm_pkg.OcrModel = {}
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sys.modules["rag.llm"] = llm_pkg
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_install_rag_llm_stub()
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