{
  "/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": []
        }
      ]
    }
  },
  "/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": []
        }
      ]
    }
  }
}