--- title: "/rag/ingest" description: "All-in-one document ingestion pipeline: **Upload → Chunk → Embed → Vector Store**" type: skill canonical_url: https://claudary.paisolsolutions.com/skills/rag-ingest source: "Claudary" difficulty: intermediate author: "Claude Code Knowledge Pack" date: 2026-07-10T11:37:26.215Z license: CC-BY-4.0 attribution: "/rag/ingest — Claudary (https://claudary.paisolsolutions.com/skills/rag-ingest)" --- # /rag/ingest All-in-one document ingestion pipeline: **Upload → Chunk → Embed → Vector Store** ## Overview # /rag/ingest All-in-one document ingestion pipeline: **Upload → Chunk → Embed → Vector Store** | Feature | Supported | |---------|-----------| | Logging | Yes | | Supported Providers | `openai`, `bedrock`, `vertex_ai`, `gemini`, `s3_vectors` | :::tip After ingesting documents, use [/rag/query](./rag_query.md) to search and generate responses with your ingested content. ::: ## Quick Start ### OpenAI ```bash showLineNumbers title="Ingest to OpenAI vector store" curl -X POST "http://localhost:4000/v1/rag/ingest" \\ -H "Authorization: Bearer sk-1234" \\ -H "Content-Type: application/json" \\ -d "{ \\"file\\": { \\"filename\\": \\"document.txt\\", \\"content\\": \\"$(base64 -i document.txt)\\", \\"content_type\\": \\"text/plain\\" }, \\"ingest_options\\": { \\"vector_store\\": { \\"custom_llm_provider\\": \\"openai\\" } } }" ``` ### Bedrock ```bash showLineNumbers title="Ingest to Bedrock Knowledge Base" curl -X POST "http://localhost:4000/v1/rag/ingest" \\ -H "Authorization: Bearer sk-1234" \\ -H "Content-Type: application/json" \\ -d "{ \\"file\\": { \\"filename\\": \\"document.txt\\", \\"content\\": \\"$(base64 -i document.txt)\\", \\"content_type\\": \\"text/plain\\" }, \\"ingest_options\\": { \\"vector_store\\": { \\"custom_llm_provider\\": \\"bedrock\\" } } }" ``` ### Vertex AI RAG Engine ```bash showLineNumbers title="Ingest to Vertex AI RAG Corpus" curl -X POST "http://localhost:4000/v1/rag/ingest" \\ -H "Authorization: Bearer sk-1234" \\ -H "Content-Type: application/json" \\ -d "{ \\"file\\": { \\"filename\\": \\"document.txt\\", \\"content\\": \\"$(base64 -i document.txt)\\", \\"content_type\\": \\"text/plain\\" }, \\"ingest_options\\": { \\"vector_store\\": { \\"custom_llm_provider\\": \\"vertex_ai\\", \\"vector_store_id\\": \\"your-corpus-id\\", \\"gcs_bucket\\": \\"your-gcs-bucket\\" } } }" ``` ### AWS S3 Vectors ```bash showLineNumbers title="Ingest to S3 Vectors" curl -X POST "http://localhost:4000/v1/rag/ingest" \\ -H "Authorization: Bearer sk-1234" \\ -H "Content-Type: application/json" \\ -d "{ \\"file\\": { \\"filename\\": \\"document.txt\\", \\"content\\": \\"$(base64 -i document.txt)\\", \\"content_type\\": \\"text/plain\\" }, \\"ingest_options\\": { \\"embedding\\": { \\"model\\": \\"text-embedding-3-small\\" }, \\"vector_store\\": { \\"custom_llm_provider\\": \\"s3_vectors\\", \\"vector_bucket_name\\": \\"my-embeddings\\", \\"aws_region_name\\": \\"us-west-2\\" } } }" ``` ## Response ```json { "id": "ingest_abc123", "status": "completed", "vector_store_id": "vs_xyz789", "file_id": "file_123" } ``` ## Query with RAG After ingestion, use the [/rag/query](./rag_query.md) endpoint to search and generate LLM responses: ```bash showLineNumbers title="RAG Query" curl -X POST "http://localhost:4000/v1/rag/query" \\ -H "Authorization: Bearer sk-1234" \\ -H "Content-Type: application/json" \\ -d '{ "model": "gpt-4o-mini", "messages": [{"role": "user", "content": "What is the main topic?"}], "retrieval_config": { "vector_store_id": "vs_xyz789", "custom_llm_provider": "openai", "top_k": 5 } }' ``` This will: 1. Search the vector store for relevant context 2. Prepend the context to your messages 3. Generate an LLM response ### Direct Vector Store Search Alternatively, search the vector store directly with `/vector_stores/{vector_store_id}/search`: ```bash showLineNumbers title="Search the vector store" curl -X POST "http://localhost:4000/v1/vector_stores/vs_xyz789/search" \\ -H "Authorization: Bearer sk-1234" \\ -H "Content-Type: application/json" \\ -d '{ "query": "What is the main topic?", "max_num_results": 5 }' ``` ## End-to-End Example ### OpenAI #### 1. Ingest Document ```bash showLineNumbers title="Step 1: Ingest" curl -X POST "http://localhost:4000/v1/rag/ingest" \\ -H "Authorization: Bearer sk-1234" \\ -H "Content-Type: application/json" \\ -d "{ \\"file\\": { \\"filename\\": \\"test_document.txt\\", \\"content\\": \\"$(base64 -i test_document.txt)\\", \\"content_type\\": \\"text/plain\\" }, \\"ingest_options\\": { \\"name\\": \\"test-basic-ingest\\", \\"vector_store\\": { \\"custom_llm_provider\\": \\"openai\\" } } }" ``` Response: ```json { "id": "ingest_d834f544-fc5e-4751-902d-fb0bcc183b85", "status": "completed", "vector_store_id": "vs_692658d337c4819183f2ad8488d12fc9", "file_id": "file-M2pJJiWH56cfUP4Fe7rJay" } ``` #### 2. Query ```bash showLineNumbers title="Step 2: Query" curl -X POST "http://localhost:4000/v1/vector_stores/vs_692658d337c4819183f2ad8488d12fc9/search" \\ -H "Authorization: Bearer sk-1234" \\ -H "Content-Type: application/json" \\ -d '{ "query": "What is LiteLLM?", "custom_llm_provider": "openai" }' ``` Response: ```json { "object": "vector_store.search_results.page", "search_query": ["What is LiteLLM?"], "data": [ { "file_id": "file-M2pJJiWH56cfUP4Fe7rJay", "filename": "test_document.txt", "score": 0.4004629778869299, "attributes": {}, "content": [ { "type": "text", "text": "Test document abc123 for RAG ingestion.\\nThis is a sample document to test the RAG ingest API.\\nLiteLLM provides a unified interface for vector stores." } ] } ], "has_more": false, "next_page": null } ``` ## Request Parameters ### Top-Level | Parameter | Type | Required | Description | |-----------|------|----------|-------------| | `file` | object | One of file/file_url/file_id required | Base64-encoded file | | `file.filename` | string | Yes | Filename with extension | | `file.content` | string | Yes | Base64-encoded content | | `file.content_type` | string | Yes | MIME type (e.g., `text/plain`) | | `file_url` | string | One of file/file_url/file_id required | URL to fetch file from | | `file_id` | string | One of file/file_url/file_id required | Existing file ID | | `ingest_options` | object | Yes | Pipeline configuration | ### ingest_options | Parameter | Type | Required | Description | |-----------|------|----------|-------------| | `vector_store` | object | Yes | Vector store configuration | | `name` | string | No | Pipeline name for logging | ### vector_store (OpenAI) | Parameter | Type | Default | Description | |-----------|------|---------|-------------| | `custom_llm_provider` | string | - | `"openai"` | | `vector_store_id` | string | auto-create | Existing vector store ID | ### vector_store (Bedrock) | Parameter | Type | Default | Description | |-----------|------|---------|-------------| | `custom_llm_provider` | string | - | `"bedrock"` | | `vector_store_id` | string | auto-create | Existing Knowledge Base ID | | `wait_for_ingestion` | boolean | `false` | Wait for indexing to complete | | `ingestion_timeout` | integer | `300` | Timeout in seconds (if waiting) | | `s3_bucket` | string | auto-create | S3 bucket for documents | | `s3_prefix` | string | `"data/"` | S3 key prefix | | `embedding_model` | string | `amazon.titan-embed-text-v2:0` | Bedrock embedding model | | `aws_region_name` | string | `us-west-2` | AWS region | :::info Bedrock Auto-Creation When `vector_store_id` is omitted, LiteLLM automatically creates: - S3 bucket for document storage - OpenSearch Serverless collection - IAM role with required permissions - Bedrock Knowledge Base - Data Source ::: ### vector_store (Vertex AI) | Parameter | Type | Default | Description | |-----------|------|---------|-------------| | `custom_llm_provider` | string | - | `"vertex_ai"` | | `vector_store_id` | string | **required** | RAG corpus ID | | `gcs_bucket` | string | **required** | GCS bucket for file uploads | | `vertex_project` | string | env `VERTEXAI_PROJECT` | GCP project ID | | `vertex_location` | string | `us-central1` | GCP region | | `vertex_credentials` | string | ADC | Path to credentials JSON | | `wait_for_import` | boolean | `true` | Wait for import to complete | | `import_timeout` | integer | `600` | Timeout in seconds (if waiting) | :::info Vertex AI Prerequisites 1. Create a RAG corpus in Vertex AI console or via API 2. Create a GCS bucket for file uploads 3. Authenticate via `gcloud auth application-default login` 4. Install: `uv add 'google-cloud-aiplatform>=1.60.0'` ::: ### vector_store (AWS S3 Vectors) | Parameter | Type | Default | Description | |-----------|------|---------|-------------| | `custom_llm_provider` | string | - | `"s3_vectors"` | | `vector_bucket_name` | string | **required** | S3 vector bucket name | | `index_name` | string | auto-create | Vector index name | | `dimension` | integer | auto-detect | Vector dimension (auto-detected from embedding model) | | `distance_metric` | string | `cosine` | Distance metric: `cosine` or `euclidean` | | `non_filterable_metadata_keys` | array | `["source_text"]` | Metadata keys excluded from filtering | | `aws_region_name` | string | `us-west-2` | AWS region | | `aws_access_key_id` | string | env | AWS access key | | `aws_secret_access_key` | string | env | AWS secret key | :::info S3 Vectors Auto-Creation When `index_name` is omitted, LiteLLM automatically creates: - S3 vector bucket (if it doesn't exist) - Vector index with auto-detected dimensions from your embedding model **Dimension Auto-Detection**: The vector dimension is automatically detected by making a test embedding request to your specified model. No need to manually specify dimensions! **Supported Embedding Models**: Works with any LiteLLM-supported embedding model (OpenAI, Cohere, Bedrock, Azure, etc.) ::: **Example with auto-detection:** ```json { "embedding": { "model": "text-embedding-3-small" // Dimension auto-detected as 1536 }, "vector_store": { "custom_llm_provider": "s3_vectors", "vector_bucket_name": "my-embeddings" } } ``` **Example with custom embedding provider:** ```json { "embedding": { "model": "cohere/embed-english-v3.0" // Dimension auto-detected as 1024 }, "vector_store": { "custom_llm_provider": "s3_vectors", "vector_bucket_name": "my-embeddings", "distance_metric": "cosine" } } ``` ## Input Examples ### File (Base64) ```json title="Request body" { "file": { "filename": "document.txt", "content": "", "content_type": "text/plain" }, "ingest_options": { "vector_store": {"custom_llm_provider": "openai"} } } ``` ### File URL ```bash showLineNumbers title="Ingest from URL" curl -X POST "http://localhost:4000/v1/rag/ingest" \\ -H "Authorization: Bearer sk-1234" \\ -H "Content-Type: application/json" \\ -d '{ "file_url": "https://example.com/document.pdf", "ingest_options": {"vector_store": {"custom_llm_provider": "openai"}} }' ``` ## Chunking Strategy Control how documents are split into chunks before embedding. Specify `chunking_strategy` in `ingest_options`. | Parameter | Type | Default | Description | |-----------|------|---------|-------------| | `chunk_size` | integer | `1000` | Maximum size of each chunk | | `chunk_overlap` | integer | `200` | Overlap between consecutive chunks | ### Vertex AI RAG Engine Vertex AI RAG Engine supports custom chunking via the `chunking_strategy` parameter. Chunks are processed server-side during import. ```bash showLineNumbers title="Vertex AI with custom chunking" curl -X POST "http://localhost:4000/v1/rag/ingest" \\ -H "Authorization: Bearer sk-1234" \\ -H "Content-Type: application/json" \\ -d "{ \\"file\\": { \\"filename\\": \\"document.txt\\", \\"content\\": \\"$(base64 -i document.txt)\\", \\"content_type\\": \\"text/plain\\" }, \\"ingest_options\\": { \\"chunking_strategy\\": { \\"chunk_size\\": 500, \\"chunk_overlap\\": 100 }, \\"vector_store\\": { \\"custom_llm_provider\\": \\"vertex_ai\\", \\"vector_store_id\\": \\"your-corpus-id\\", \\"gcs_bucket\\": \\"your-gcs-bucket\\" } } }" ``` --- Source: [Claudary](https://claudary.paisolsolutions.com/skills/rag-ingest) · https://claudary.paisolsolutions.com