zhanghongyu_0626
2945108f66
- Added new configuration for knowledge base root directory in `.env.example` and `settings.py`. - Implemented `find_products` method in `CoreReadOnlyRepository` for fuzzy product search based on user queries. - Introduced `search_cs_knowledge` function in `rag_service.py` to facilitate semantic search across new `fin_*` collections. - Updated document parsing to support Markdown and YAML front-matter for knowledge base entries. - Created multiple new FAQ and policy documents in the `data/kb_collections` directory to enrich the knowledge base. This update significantly improves the knowledge retrieval capabilities for customer service interactions, ensuring more relevant and accurate responses.
23 lines
593 B
Python
23 lines
593 B
Python
"""向量化：Ollama bge-m3，1024 维（客服 KB 灌库 / fin_* 检索用）。
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薄封装 `app.service.embedding`，与 T-21 共用同一 Ollama 配置。
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"""
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from __future__ import annotations
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from app.service import embedding
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class Embedder:
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"""批量/单条 embedding（build_collections / test_search 调用面）。"""
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def embed(self, text: str) -> list[float]:
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return embedding.embed_text(text)
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def embed_batch(self, texts: list[str]) -> list[list[float]]:
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return embedding.embed_texts(texts)
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def get_embedder() -> Embedder:
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return Embedder()
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