busya
30ba70933d
- Add mask_api_key() and is_masked_or_placeholder() to llm_provider service - Return masked keys in all provider CRUD endpoints - Reject masked/placeholder keys in fetch_models and test_provider_config - Show masked key with Change button in ProviderConfig.svelte edit form - Exclude masked keys from fetch-models, test, and submit payloads on frontend - Update semantics-core skill with clarified complexity tier rules - Switch agent modes from subagent to all
52 lines
3.3 KiB
JSON
52 lines
3.3 KiB
JSON
{
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"/v1/rag/ingest": {
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"post": {
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"tags": [
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"rag"
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],
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"summary": "Rag Ingest",
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"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```",
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"operationId": "rag_ingest_v1_rag_ingest_post",
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"responses": {
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"200": {
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"description": "Successful Response",
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"content": {
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"application/json": {
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"schema": {}
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}
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}
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}
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},
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"security": [
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{
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"APIKeyHeader": []
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}
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]
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}
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},
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"/v1/rag/query": {
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"post": {
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"tags": [
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"rag"
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],
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"summary": "Rag Query",
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"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```",
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"operationId": "rag_query_v1_rag_query_post",
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"responses": {
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"200": {
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"description": "Successful Response",
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"content": {
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"application/json": {
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"schema": {}
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}
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}
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}
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},
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"security": [
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{
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"APIKeyHeader": []
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}
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]
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}
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}
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} |