iYdZddlmZddlZddlZddlmZmZmZm Z m Z m Z ddl Z ddl mZddlmZmZmZddlmZddlmZdd lmZmZdd lmZer dd l mZdd lmZGd deeZy)a+ S3 Vectors-specific RAG Ingestion implementation. S3 Vectors is AWS's native vector storage service that provides: - Purpose-built vector buckets for storing and querying vectors - Vector indexes with configurable dimensions and distance metrics - Metadata filtering for semantic search This implementation: 1. Auto-creates vector buckets and indexes if not provided 2. Uses LiteLLM's embedding API (supports any provider) 3. Uses httpx + AWS SigV4 signing (no boto3 dependency for S3 Vectors APIs) 4. Stores vectors with metadata using PutVectors API ) annotationsN) TYPE_CHECKINGAnyDictListOptionalTuple)verbose_logger)S3_VECTORS_DEFAULT_DIMENSION"S3_VECTORS_DEFAULT_DISTANCE_METRIC/S3_VECTORS_DEFAULT_NON_FILTERABLE_METADATA_KEYS) safe_dumps) BaseAWSLLM)get_async_httpx_clienthttpxSpecialProvider)BaseRAGIngestion)Router)RAGIngestOptionsceZdZdZ d ddZddZddZdZ d ddZdZ d Z dd Z dd Z dd Z d dd Zy)S3VectorsRAGIngestiona S3 Vectors RAG ingestion using httpx + AWS SigV4 signing. Workflow: 1. Auto-create vector bucket if needed (CreateVectorBucket API) 2. Auto-create vector index if needed (CreateVectorIndex API) 3. Generate embeddings using LiteLLM (supports any provider) 4. Store vectors with PutVectors API Configuration: - vector_bucket_name: S3 vector bucket name (required) - index_name: Vector index name (auto-creates if not provided) - dimension: Vector dimension (default: S3_VECTORS_DEFAULT_DIMENSION) - distance_metric: "cosine" or "euclidean" (default: S3_VECTORS_DEFAULT_DISTANCE_METRIC) - non_filterable_metadata_keys: List of metadata keys to exclude from filtering Nctj|||tj||jd|_|jj d|_|jj dt|_|jj dt|_ |j|_ |jj d}|j|r t|nd|_|j!|jj d }t#t$j&|d|ind |_d |_y) N)ingest_optionsroutervector_bucket_name index_namedistance_metricnon_filterable_metadata_keysaws_region_name)r ssl_verify)r) llm_providerparamsF)r__init__rvector_store_configrgetrr rr r_get_dimension_from_config dimension)get_aws_region_name_for_non_llm_api_callsstrr_get_ssl_verifyrrRAGasync_httpx_client_config_initialized)selfrr _aws_regionrs {/Users/manta/Documents/Projects/TheRoad-I1/.venv/lib/python3.12/site-packages/litellm/rag/ingestion/s3_vectors_ingestion.pyr"zS3VectorsRAGIngestion.__init__<s> !!$~fUD!#'":":;O"P2266|D#77;; A -1,D,D,H,H * ;- ) 88:..223DE #MM0;C , N  ))//33LA* #9-111;1GL*-T#  $) cK|jrd|jvrtS |jd}tjd|d}|jr'|jj ||gd{}n t j ||gd{}|jrPt|jdkDr8t|jdd}tjd|d ||StS77h#t$r/}tjd |d td Yd}~tSd}~wwxYww) z Auto-detect dimension by making a test embedding request. Makes a single embedding request with a test string to determine the output dimension of the embedding model. modelz=Auto-detecting dimension by making test embedding request to testr2inputNr embeddingzAuto-detected dimension z for embedding model z6Could not auto-detect dimension from embedding model: z. Using default dimension of .) embedding_configr r debugr aembeddinglitellmdatalen Exceptionwarning)r- model_name test_inputresponser&es r/%_get_dimension_from_embedding_requestz;S3VectorsRAGIngestion._get_dimension_from_embedding_requestdsU$$t7L7L(L/ / ..w7J  OPZ|\   J{{!%!7!7$ZL"8"")!3!3$ZL" }}X]]!3a!7 a 0 => $$.yk9Nzl[! ,++   " "HL..J-K1N   ,+  sT!EAD:D; DDAD;EDD D> D9/ E9D>>EcPd|jvrt|jdSy)z Get vector dimension from config if explicitly provided. 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