uPjG>dZddlmZddlZddlmZddlmZmZddl m Z m Z ddl m Z ddlmZmZdd lmZdd lmZdd lmZer dd lmZdd lmZddZGdde dZGdde dZGdde dZGdde ZGdde Z GddeeZ!y)aG Vertex AI-specific RAG Ingestion implementation. Vertex AI RAG Engine handles embedding and chunking internally when files are uploaded, so this implementation skips the embedding step and directly uploads files to RAG corpora. Based on: https://docs.cloud.google.com/vertex-ai/generative-ai/docs/model-reference/rag-api-v1 ) annotationsN)Mapping) TYPE_CHECKINGFinal)ReadOnly TypedDict)verbose_logger)get_async_httpx_clienthttpxSpecialProvider)get_vertex_base_url) VertexBase)BaseRAGIngestion)Router)RAGIngestOptionsc@|Dcic] \}}|s || c}}Scc}}wN)fieldsnamevalues n/home/oem/CyberBrain26-unified/.venv/lib/python3.12/site-packages/litellm/rag/ingestion/vertex_ai_ingestion.py_present_fieldsr s +1 ;KD%UD%K ;; ;s ceZdZUded<y)VertexRagResourceName ReadOnly[str]rN)__name__ __module__ __qualname____annotations__rrrr$s r rF)totalc:eZdZUdZded<ded<ded<ded <y ) VertexRagOperationzNA Vertex AI long-running operation resource, as the RAG Engine API returns it.zReadOnly[bool]donerrzReadOnly[object]errorReadOnly[VertexRagResourceName]responseNrrr__doc__rrr rr#r#(sX   --r r#c&eZdZUdZded<ded<y)VertexRagFileUploadz'Body of a ``ragFiles:upload`` response.rrr&ragFileNr(rr rr+r+1s1  ,,r r+ceZdZUdZded<y)_RagOperationViewzKHolds one decoded long-running operation so the JSON body reads back typed.zReadOnly[VertexRagOperation] operationNr(rr rr.r.8s U++r r.ceZdZUdZded<y)_RagFileUploadViewzMHolds one decoded ``ragFiles:upload`` body so the JSON body reads back typed.zReadOnly[VertexRagFileUpload]uploadNr(rr rr1r1>s W ))r r1ceZdZdZ d d dZ d dZ d d dZ d ddZ d ddZ ddZ dd Z y)VertexAIRAGIngestiona Vertex AI RAG Engine ingestion implementation. Key differences from base: - Embedding is handled by Vertex AI RAG Engine when files are uploaded - Files are uploaded using the RAG API (import or upload) - Chunking is done by Vertex AI RAG Engine (supports custom chunking config) - Supports Google Cloud Storage (GCS) and Google Drive sources - Supports custom parsing configurations (layout parser, LLM parser) Nctj|||tj|t|j}|j ||_|j|xsd|_|j||_ y)N)ingest_optionsrouterz us-central1) r__init__r dictvector_store_configsafe_get_vertex_ai_project project_idget_vertex_ai_locationlocationsafe_get_vertex_ai_credentialsvertex_credentials)selfr6r7litellm_paramss rr8zVertexAIRAGIngestion.__init__Pst !!$~fUD!!%T%=%= >99.I33NCT} "&"E"En"Ur c Kyw)z Vertex AI RAG Engine handles embedding internally - skip this step. Returns: None (Vertex AI embeds when files are uploaded to RAG corpus) Nr)rAchunkss rembedzVertexAIRAGIngestion.embed`s scPK|js td|jjd}|sB|j |j xsd|jjdd{}d}|r!|r|r|j ||||d{}||fS7-7 w)a] Store content in Vertex AI RAG corpus. Vertex AI workflow: 1. Create RAG corpus (if not provided) 2. Upload file using RAG API (Vertex AI handles chunking/embedding) Args: file_content: Raw file bytes filename: Name of the file content_type: MIME type chunks: Ignored - Vertex AI handles chunking embeddings: Ignored - Vertex AI handles embedding existing_file_id: Existing provider file ID, unsupported for Vertex AI Returns: Tuple of (rag_corpus_id, file_id) zVvertex_project is required for Vertex AI RAG ingestion. Set it in vector_store config.vector_store_idzlitellm-rag-corpus description) display_namerHN) rag_corpus_idfilename file_content content_type)r< ValueErrorr:get_create_rag_corpus ingest_name_upload_file_to_corpus) rArLrKrMrD embeddingsexisting_file_idrJresult_file_ids rstorezVertexAIRAGIngestion.storems6uv v00445FG "&"9"9!--E1E 4488G#:#M  H#'#>#>+!)) $?$Nn,,s$A2B&4B"5$B&B$ B&$B&cK|j|j|jd\}}|js||_t|j}|d|jd|jd}|j j d}|j j d}|rdd d |iiind } | r i|xsi| n|} d |itd |fd| ff} tjd|tjdtj| dttjddi} | j|| d|ddd {} | j dvr/d| j"}tj$|t'|d| ji}|d}tjdtj|d|j dr#|j dij d d!}nC|j d d!}tjd"||j)||#d {}tjd$||S77w)%a Create a Vertex AI RAG corpus. Args: display_name: Display name for the corpus description: Optional description Returns: RAG corpus ID (format: projects/{project}/locations/{location}/ragCorpora/{corpus_id}) vertex_ai credentialsr<custom_llm_providerz/v1beta1/projects/z /locations/z /ragCorporavector_db_configembedding_modelragEmbeddingModelConfigvertexPredictionEndpointendpointN displayNamerHvectorDbConfigzCreating RAG corpus: %sRequest body: %sindenttimeoutN@ llm_providerparamsBearer application/json Authorizationz Content-TypejsonheaderszFailed to create RAG corpus: r/zCreate corpus response: %sr$r'rzPolling operation: %s)operation_name access_tokenzCreated RAG corpus: %s)_ensure_access_tokenr@r<r r>r:rOrr debugrqdumpsr r RAGpost status_codetextr% Exception_poll_operation)rArIrHrxr<base_urlurlr\r]embedding_model_configvector_db_section request_bodyclientr' error_msgoperation_view response_data corpus_namerws rrPz'VertexAIRAGIngestion._create_rag_corpuss $(#<#<// +$=$  j(DO.dmm< z!3DOO3DKPTP]P]^ij#'":":">">?Q"R!%!9!9!=!=>O!P')CjRaEb(c d  G] B &B B+A Bbr  <  {;>NPa=bcd 6</LQR1ST.-11t$  !' #*<.!9 2!,!     z 1!>x}}oNI   +I& &4?3Q-k: 94::m\];^_   V $'++J;??KK%2$5$5fb$AN  !8. I $ 4 4-)!5!K 5{CA 4s%EI-I(C5I- I+ I-+I-cKddl}t|j}|d|}ttj ddi}t |D]} |j|dd|i d{} | jd k7r/d | j} tj| t| d | ji} | d } | jd rYd| vr| jd}td|| jdijdd}|r|cStd| tjd| dz||j|d{td|d7 7w)a Poll a long-running operation until it completes. Args: operation_name: The operation name (e.g., "operations/123456") access_token: Access token for authentication max_retries: Maximum number of polling attempts retry_delay: Delay between polling attempts in seconds Returns: The corpus name from the completed operation Raises: Exception: If operation fails or times out rN /v1beta1/rgrhrirorl)rrrtzFailed to poll operation: r/r$r%zOperation failed: r'rrvz&No corpus name in operation response: z%Operation not done yet, attempt %s/%szOperation timed out after z attempts)asyncior r>r r r|rangerOr~rr r%rrqrzsleep)rArwrx max_retries retry_delayrrrrattemptr'rroperation_datar%rs rrz$VertexAIRAGIngestion._poll_operations, -dmm<!z>*:;.-11t$  [) -G#ZZ#w|n%=(H##s*8H $$Y/ **1r:rOrchunking_strategyr rzrqr{r r r|r}r~rr%rwarning)rArJrKrLrMrx_rrrHrrrrrrrrrr'r upload_viewr rag_file_namefile_ides rrRz+VertexAIRAGIngestion._upload_file_to_corpus;s(33// +4  a.dmm< z!1-@PQ"5599:LM H  {;=>  :>9O9O-11,? 044_E '6z8RUdfsTt7u'v% 1.1HJ_0`3      8:PQST  ?E^TZZ-KLtzz(35GH: : /-11u%  !' #*<.!9*5!,!     z 1!8HI   +I& & 6> 5PK#.x#8M#0#4#4Y#C#G#GPR#SM*Km.?.?.KG  !? IN/ 0   " "#H! L s>E0I 2H3AI 5A&HI  I'I=I II cK|j|j|jd\}}t|j}|d|d}|j }|j d}|j d} |jj d} |s| r |xsd| xsd d nd } d d |iitd| fd| ff} d| i} tjd|tjdtj| dttjddi}|j!|| d|ddd {}|j"dvr/d|j$}tj&|t)|d|ji}|d}|j dd }tjd!||S7w)"a Import files from Google Cloud Storage into RAG corpus. Args: rag_corpus_id: RAG corpus resource name gcs_uris: List of GCS URIs (e.g., ["gs://bucket/file.pdf"]) Returns: Operation name for tracking import progress rXrYrz/ragFiles:importrrmax_embedding_requests_per_minirt) chunkSize chunkOverlapN gcsSourceurisragFileChunkingConfigmaxEmbeddingRequestsPerMinimportRagFilesConfigzImporting files from GCS: %srcrdrergrhrirlrmrnrprszFailed to import files: r/rrvzImport operation started: %s)ryr@r<r r>rrOr:rr rzrqr{r r r|r}r~rr%r)rArJgcs_urisrxrrrrrrmax_embedding_qpmchunking_config import_configrrr'rrrrws r_import_files_from_gcsz+VertexAIRAGIngestion._import_files_from_gcss 33// +4  a.dmm< z=/9IJ:>9O9O-11,? 044_E $(#;#;#?#?@`#a ]%,m>Rs S  &(+ ,o>13DE  6}E ;SA/LQR1ST.-11t$  !' #*<.!9 2!,!     z 1!9(--II   +I& &4?3Q-k: - 1 1&" =;^L' sD;G=G>BGr)r6rr7z Router | None)rD list[str]returnlist[list[float]] | None)rLz bytes | NonerK str | NonerMrrDrrSrrTrrztuple[str | None, str | None])rIstrrHrrr)g@) rwrrxrrintrfloatrr) rJrrKrrLbytesrMrrr)rJrrrrr) rrrr)r8rErVrPrrRrrr rr4r4Dsb !%V(VV   " ((,0-"0-0-! 0-  0- - 0-%0- '0-j#'VV V Vx BMBMBM BM  BM BMHccc c ! c cJNNN Nr r4)rztuple[tuple[str, object], ...]rzMapping[str, object])"r) __future__rrqcollections.abcrtypingrrtyping_extensionsrrlitellm._loggingr &litellm.llms.custom_httpx.http_handlerr r #litellm.llms.vertex_ai.common_utilsr &litellm.llms.vertex_ai.vertex_llm_baser $litellm.rag.ingestion.base_ingestionrlitellmrlitellm.types.ragrrrr#r+r.r1r4rr rrs# #'1+D=A2<IU.%.-)5-, , ** j+Zjr