openapi: 3.2.0
info:
  title: LiteLLM Rag API
  description: "Enterprise Edition \n\nProxy Server to call 100+ LLMs in the OpenAI format. [**Customize Swagger Docs**](https://docs.litellm.ai/docs/proxy/enterprise#swagger-docs---custom-routes--branding)\n\n\U0001F449 [```LiteLLM Admin Panel on /ui```](/ui). Create, Edit Keys with SSO. Having issues? Try [```Fallback Login```](/fallback/login)\n\n\U0001F4B8 [```LiteLLM Model Cost Map```](https://models.litellm.ai/).\n\n\U0001F50E [```LiteLLM Model Hub```](/ui/model_hub_table). See available models on the proxy. [**Docs**](https://docs.litellm.ai/docs/proxy/ai_hub)"
  version: 1.95.0
  x-operator: institution
  x-provenance:
    method: probed
    source: https://llmproxy.uva.nl/openapi.json
    retrieved: '2026-08-19'
    note: Document is generated by the LiteLLM proxy software the University of Amsterdam self-hosts; the deployment, the key issuance and the host (llmproxy.uva.nl, UvA Azure) are the institution's. servers[] added by API Evangelist because the served document omits it; nothing else altered.
servers:
- url: https://llmproxy.uva.nl
  description: University of Amsterdam / Amsterdam University of Applied Sciences shared AI gateway
tags:
- name: rag
paths:
  /rag/ingest:
    post:
      tags:
      - rag
      summary: Rag Ingest
      description: "RAG Ingest endpoint - all-in-one document ingestion pipeline.\n\nSupports form upload (for files) or JSON body (for URLs).\n\n## Form upload (for files):\n```bash\ncurl -X POST \"http://localhost:4000/v1/rag/ingest\" \\\n    -H \"Authorization: Bearer sk-1234\" \\\n    -F file=\"@document.pdf\" \\\n    -F 'ingest_options={\"vector_store\": {\"custom_llm_provider\": \"openai\"}}'\n```\n\n## JSON body (for URLs):\n```bash\ncurl -X POST \"http://localhost:4000/v1/rag/ingest\" \\\n    -H \"Authorization: Bearer sk-1234\" \\\n    -H \"Content-Type: application/json\" \\\n    -d '{\n        \"file_url\": \"https://example.com/document.pdf\",\n        \"ingest_options\": {\"vector_store\": {\"custom_llm_provider\": \"openai\"}}\n    }'\n```\n\n## Bedrock:\n```bash\ncurl -X POST \"http://localhost:4000/v1/rag/ingest\" \\\n    -H \"Authorization: Bearer sk-1234\" \\\n    -F file=\"@document.pdf\" \\\n    -F 'ingest_options={\"vector_store\": {\"custom_llm_provider\": \"bedrock\"}}'\n```"
      operationId: rag_ingest_rag_ingest_post
      responses:
        '200':
          description: Successful Response
          content:
            application/json:
              schema: {}
      security:
      - APIKeyHeader: []
  /v1/rag/ingest:
    post:
      tags:
      - rag
      summary: Rag Ingest
      description: "RAG Ingest endpoint - all-in-one document ingestion pipeline.\n\nSupports form upload (for files) or JSON body (for URLs).\n\n## Form upload (for files):\n```bash\ncurl -X POST \"http://localhost:4000/v1/rag/ingest\" \\\n    -H \"Authorization: Bearer sk-1234\" \\\n    -F file=\"@document.pdf\" \\\n    -F 'ingest_options={\"vector_store\": {\"custom_llm_provider\": \"openai\"}}'\n```\n\n## JSON body (for URLs):\n```bash\ncurl -X POST \"http://localhost:4000/v1/rag/ingest\" \\\n    -H \"Authorization: Bearer sk-1234\" \\\n    -H \"Content-Type: application/json\" \\\n    -d '{\n        \"file_url\": \"https://example.com/document.pdf\",\n        \"ingest_options\": {\"vector_store\": {\"custom_llm_provider\": \"openai\"}}\n    }'\n```\n\n## Bedrock:\n```bash\ncurl -X POST \"http://localhost:4000/v1/rag/ingest\" \\\n    -H \"Authorization: Bearer sk-1234\" \\\n    -F file=\"@document.pdf\" \\\n    -F 'ingest_options={\"vector_store\": {\"custom_llm_provider\": \"bedrock\"}}'\n```"
      operationId: rag_ingest_v1_rag_ingest_post
      responses:
        '200':
          description: Successful Response
          content:
            application/json:
              schema: {}
      security:
      - APIKeyHeader: []
  /rag/query:
    post:
      tags:
      - rag
      summary: Rag Query
      description: "RAG Query endpoint - search vector store, optionally rerank, and generate LLM response.\n\nThis endpoint:\n1. Extracts the query from the last user message\n2. Searches the vector store for relevant context\n3. Optionally reranks the results\n4. Generates an LLM response with the retrieved context\n\n## Example Request:\n```bash\ncurl -X POST \"http://localhost:4000/v1/rag/query\" \\\n    -H \"Authorization: Bearer sk-1234\" \\\n    -H \"Content-Type: application/json\" \\\n    -d '{\n        \"model\": \"gpt-4o-mini\",\n        \"messages\": [{\"role\": \"user\", \"content\": \"What is LiteLLM?\"}],\n        \"retrieval_config\": {\n            \"vector_store_id\": \"vs_abc123\",\n            \"custom_llm_provider\": \"openai\",\n            \"top_k\": 5\n        }\n    }'\n```\n\n## With Reranking:\n```bash\ncurl -X POST \"http://localhost:4000/v1/rag/query\" \\\n    -H \"Authorization: Bearer sk-1234\" \\\n    -H \"Content-Type: application/json\" \\\n    -d '{\n        \"model\": \"gpt-4o-mini\",\n        \"messages\": [{\"role\": \"user\", \"content\": \"What is LiteLLM?\"}],\n        \"retrieval_config\": {\n            \"vector_store_id\": \"vs_abc123\",\n            \"custom_llm_provider\": \"openai\",\n            \"top_k\": 10\n        },\n        \"rerank\": {\n            \"enabled\": true,\n            \"model\": \"cohere/rerank-english-v3.0\",\n            \"top_n\": 3\n        }\n    }'\n```"
      operationId: rag_query_rag_query_post
      responses:
        '200':
          description: Successful Response
          content:
            application/json:
              schema: {}
      security:
      - APIKeyHeader: []
  /v1/rag/query:
    post:
      tags:
      - rag
      summary: Rag Query
      description: "RAG Query endpoint - search vector store, optionally rerank, and generate LLM response.\n\nThis endpoint:\n1. Extracts the query from the last user message\n2. Searches the vector store for relevant context\n3. Optionally reranks the results\n4. Generates an LLM response with the retrieved context\n\n## Example Request:\n```bash\ncurl -X POST \"http://localhost:4000/v1/rag/query\" \\\n    -H \"Authorization: Bearer sk-1234\" \\\n    -H \"Content-Type: application/json\" \\\n    -d '{\n        \"model\": \"gpt-4o-mini\",\n        \"messages\": [{\"role\": \"user\", \"content\": \"What is LiteLLM?\"}],\n        \"retrieval_config\": {\n            \"vector_store_id\": \"vs_abc123\",\n            \"custom_llm_provider\": \"openai\",\n            \"top_k\": 5\n        }\n    }'\n```\n\n## With Reranking:\n```bash\ncurl -X POST \"http://localhost:4000/v1/rag/query\" \\\n    -H \"Authorization: Bearer sk-1234\" \\\n    -H \"Content-Type: application/json\" \\\n    -d '{\n        \"model\": \"gpt-4o-mini\",\n        \"messages\": [{\"role\": \"user\", \"content\": \"What is LiteLLM?\"}],\n        \"retrieval_config\": {\n            \"vector_store_id\": \"vs_abc123\",\n            \"custom_llm_provider\": \"openai\",\n            \"top_k\": 10\n        },\n        \"rerank\": {\n            \"enabled\": true,\n            \"model\": \"cohere/rerank-english-v3.0\",\n            \"top_n\": 3\n        }\n    }'\n```"
      operationId: rag_query_v1_rag_query_post
      responses:
        '200':
          description: Successful Response
          content:
            application/json:
              schema: {}
      security:
      - APIKeyHeader: []
components:
  securitySchemes:
    APIKeyHeader:
      type: apiKey
      description: Bearer token
      in: header
      name: x-litellm-api-key