QidZddlZddlmZmZddlmZddlmZee Z GddeZ Gdd e Z Gd d e Z d e fd ZdS)z4Embedding providers for the Second Brain RAG system.N)ABCabstractmethod)List) setup_loggercreZdZdZedeedeeefdZede fdZ dS)EmbeddingProviderz,Abstract base class for embedding providers.textsreturncdS)zGenerate embeddings for a list of texts. Args: texts: List of text strings to embed. Returns: List of embedding vectors (one per text). N)selfr s 6/root/projects/butler/slack_bot/obsidian/embeddings.pyembedzEmbeddingProvider.embeds  cdS)znReturn the embedding vector dimension. Returns: Embedding vector dimension size. Nr r s r get_dimensionzEmbeddingProvider.get_dimensions  rN) __name__ __module__ __qualname____doc__rrstrfloatrintrr rrrr sz66  49  d5k):    ^   s   ^   rrcZeZdZdZddZdeedeeefdZde fdZ dS) QwenEmbeddingProviderz>Cloud embedding provider using Qwen via OpenAI-compatible API.r Nc tjd}tjdd}tjdd|_|st dddlm}||| |_dS) NEMBEDDING_API_KEYEMBEDDING_BASE_URLzhttps://openrouter.ai/apiEMBEDDING_MODELzqwen/qwen3-embedding-8bz9EMBEDDING_API_KEY is required for cloud embedding backendr)OpenAI)api_keybase_url)osenvirongetmodel ValueErroropenair!client)r r"r#r!s r__init__zQwenEmbeddingProvider.__init__(s*..!455:>>"68STTZ^^$57PQQ  ZXYY Y!!!!!!fWx@@@ rr cp|jj||j}d|jDS)zEmbed texts using Qwen via OpenAI-compatible API. Args: texts: List of text strings to embed. Returns: List of embedding vectors. )inputr'cg|] }|j Sr ) embedding).0items r z/QwenEmbeddingProvider.embed..=s9994999r)r* embeddingscreater'data)r r responses rrzQwenEmbeddingProvider.embed3s9;)00uDJ0OO998=9999rcdS)zwReturn embedding dimension for Qwen embedding model. Returns: Embedding dimension (4096). ir rs rrz#QwenEmbeddingProvider.get_dimension?s trr N) rrrrr+rrrrrrr rrrr%s|HH A A A A :49 :d5k): : : : :srrc`eZdZdZd dZdZdeedeeefdZ de fdZ dS) SentenceTransformerProviderzCLocal embedding provider using sentence-transformers (lazy-loaded).r Nc^tjdd|_d|_dS)NEMBEDDING_LOCAL_MODELzall-MiniLM-L6-v2)r$r%r& model_name_modelrs rr+z$SentenceTransformerProvider.__init__Ks&*..)@BTUU rc|j\ ddlm}td|j||j|_n#t $rt dwxYw|jS)z(Lazy-load the SentenceTransformer model.Nr)SentenceTransformerzLoading local embedding model: zsentence-transformers is not installed. Run: pip install sentence-transformers Or set EMBEDDING_BACKEND=cloud to use a remote API instead.)r>sentence_transformersr@loggerinfor= ImportError)r r@s r _get_modelz&SentenceTransformerProvider._get_modelOs ;  EEEEEE OdoOOPPP11$/BB    !R  {s =AA!r c|}||d}|S)zEmbed texts using a local SentenceTransformer model. Args: texts: List of text strings to embed. Returns: List of embedding vectors. T)convert_to_numpy)rEencodetolist)r r r'r3s rrz!SentenceTransformerProvider.embed^s:!!\\%$\??   """rcdS)zrReturn embedding dimension for all-MiniLM-L6-v2. Returns: Embedding dimension (384). ir rs rrz)SentenceTransformerProvider.get_dimensionks srr8) rrrrr+rErrrrrrr rrr:r:HsMM    #49 #d5k): # # # #srr:r ctjdd}|dkr(tdt StdtS)zFactory: return the configured embedding provider. Reads EMBEDDING_BACKEND env var (default: "local"). Returns: Configured EmbeddingProvider instance. EMBEDDING_BACKENDlocalcloudz%Using cloud (Qwen) embedding providerz4Using local (SentenceTransformer) embedding provider)r$r%r&lowerrBrCrr:)backends rget_embedding_providerrQtsojnn0'::@@BBG' ;<<<$&&& KKFGGG & ( ((r)rr$abcrrtypingrhealth.utils.logging_configrrrBrrr:rQr rrrUs:: ########444444 h          2     -   F)))))"3)))X ) 1 ) ) ) ) ) )r