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StartenVom Herausgeber
Multi-modal RAG service exposing a REST API and MCP server for document indexing and knowledge-base querying, powered by RAGAnything and LightRAG. Two retrieval pathways are available: a graph-based LightRAG pipeline and a classical RAG pipeline using multi-query generation with LLM-as-judge scoring. Files are retrieved from MinIO object storage and indexed into a PostgreSQL-backed knowledge graph. Each project is isolated via its own .
API/MCP wrapper for RagAnything
working_dirThe service also hosts the MCP server registry — a CRUD API for registering external MCP servers and generating new MCP servers on the fly from OpenAPI/Swagger documents. The registry is persisted in PostgreSQL and rehydrated at startup, so composable-agents and other clients can discover all available MCP servers from a single endpoint. See MCP Server Registry.
Branch
feat/dual-auth-rls-llm-peruserintroduces a new security model: dual authentication (JWT/OIDC via Logto or per-user API keys shared with composable-agents), Row-Level Security onmcp_serversscoped byuser_id, per-user LLM credentials (decrypted from the shareduser_llm_settingstable), and per-user RAG isolation (metadata-leveluser_idfilter on chunks). See Authentication, Configuration, and Database Schema for the details. The legacy masterAPI_KEYandOPEN_ROUTER_API_KEYfor chat/embeddings are deprecated (see Breaking changes).
Clients
(REST / MCP / Claude)
|
+-------------+-------------+
| FastAPI App |
+-------------+-------------+
|
+---------------+---------------+
| |
Application Layer MCP Servers (FastMCP)
+------------------------------+ |
| api/ | +---+--------+ +--+-----------+ +--+-------------+ +--+----------+
| indexing_routes.py | | RAGAnything | | RAGAnything | | RAGAnything | | RAGAnything |
| query_routes.py | | Query | | Files | | Classical | | Bricks |
| file_routes.py | | /rag/mcp | | /files/mcp | | /classical/mcp| | /bricks/mcp|
| health_routes.py | +---+--------+ +--+-----------+ +--+-------------+ +--+----------+
| classical_indexing_routes | | | | |
| classical_query_routes | | | classical_index_file list_bricks_documents
| use_cases/ | | | classical_index_folder read_bricks_document
| IndexFileUseCase | | | classical_query publish_section_version
| IndexFolderUseCase |
| QueryUseCase |
| ClassicalIndexFileUseCase |
| ClassicalIndexFolderUseCase |
| ClassicalQueryUseCase |
| ListFilesUseCase |
| ListFoldersUseCase |
| ReadFileUseCase |
| UploadFileUseCase |
| CreateFolderUseCase |
| DeleteFileUseCase |
| DeleteFolderUseCase |
| ListBricksDocumentsUseCase |
| ReadBricksDocumentUseCase |
| PublishSectionVersionUseCase|
| requests/ responses/ |
+------------------------------+
| | |
v v v
Domain Layer (ports)
+----------------------------------------------------------+
| RAGEnginePort StoragePort BM25EnginePort |
| DocumentReaderPort VectorStorePort LLMPort |
| BricksApiPort |
+----------------------------------------------------------+
| | | | |
v v v v v
Infrastructure Layer (adapters)
+----------------------------------------------------------+
| LightRAGAdapter MinioAdapter |
| (RAGAnything/ (minio-py) |
| KreuzbergParser) |
| |
| PostgresBM25Adapter RRFCombiner |
| (pg_textsearch) (hybrid+ fusion) |
| |
| KreuzbergAdapter LangchainPgvectorAdapter |
| (kreuzberg - 91 formats) (langchain-postgres PGVector) |
| |
| LangchainOpenAIAdapter BricksApiAdapter |
| (langchain-openai ChatOpenAI) (httpx, Bricks REST API) |
+----------------------------------------------------------+
| | | | |
v v v v v
PostgreSQL MinIO Kreuzberg OpenAI-compatible Bricks API
(pgvector + (object (document (LLM API) (analyse.bricks.co
Apache AGE storage) extraction) + section-versions)
pg_textsearch)
user_llm_settings), or an OpenRouter API key as fallback for local dev / Kreuzberg VLMsoludev-compose-apps/bricks/ stack for production deployment (provides PostgreSQL, MinIO, and this service)Production runs from the shared compose stack at soludev-compose-apps/bricks/. The docker-compose.yml in this repository is for local development only.
# 1. Install dependencies
uv sync
# 2. Start PostgreSQL and MinIO (docker-compose.yml provides Postgres;
# MinIO must be available separately or added to the compose file)
docker compose up -d postgres
# 3. Configure environment
cp .env.example .env
# Edit .env: set LOGTO_URL + JWT_AUDIENCE for JWT auth, SECRET_ENCRYPTION_KEY
# (shared with composable-agents), and adjust MINIO_HOST / POSTGRES_HOST.
# OPEN_ROUTER_API_KEY is only needed for the VLM and as a fallback when auth
# is disabled (per-user LLM credentials take precedence on authenticated requests).
# 4. Run the server
uv run python src/main.py
The API is available at http://localhost:8000. Swagger UI at http://localhost:8000/docs.
cd soludev-compose-apps/bricks/
docker compose up -d
This starts all brick services including raganything-api, postgres, and minio.
The service supports dual authentication on every protected REST and MCP endpoint:
Authorization: Bearer <jwt> issued by a Logto instance. The token is validated against the Logto JWKS, the aud claim is checked against JWT_AUDIENCE, and the user identity is extracted from the JWT claims.X-API-Key: <user-api-key> stored in the shared api_keys table (owned by composable-agents, see Shared tables). The key is looked up, the bound user_id becomes the request identity.The middleware accepts either header on the same request. If both are present, JWT takes precedence. The resolved user_id is stored in a request-scoped contextvar (current_user_id) and is used for:
mcp_servers (see MCP Server Registry),user_id, see RAG isolation).Both modes are active as soon as the relevant env vars are set:
LOGTO_URL=https://logto.example.com
JWT_AUDIENCE=https://raganything.soludev.tech
SECRET_ENCRYPTION_KEY=<fernet-key-shared-with-composable-agents>
SECRET_ENCRYPTION_KEY must be the same Fernet key as composable-agents, because the api_keys and user_llm_settings tables store encrypted values that this service decrypts (see Shared tables).
Leave LOGTO_URL and JWT_AUDIENCE empty to disable JWT validation. Per-user API keys still work if rows exist in api_keys. For local development with no auth at all, leave everything empty — health endpoints are always public.
# JWT (Logto OIDC)
curl -H "Authorization: Bearer ${JWT}" \
-H "Content-Type: application/json" \
-X POST http://localhost:8000/api/v1/classical/query \
-d '{"working_dir": "project-alpha", "query": "test"}'
# Per-user API key
curl -H "X-API-Key: ${USER_API_KEY}" \
-H "Content-Type: application/json" \
-X POST http://localhost:8000/api/v1/classical/query \
-d '{"working_dir": "project-alpha", "query": "test"}'
Health endpoints (/api/v1/health, /api/v1/health/live) remain public regardless of the auth configuration.
The McpApiKeyMiddleware accepts both X-API-Key and Authorization: Bearer via FastMCP's get_http_headers. MCP clients must send one of them in their HTTP transport configuration. For composable-agents, add the header to the headers field of each MCP server config:
mcp_servers:
- name: bricks
transport: http
url: https://raganything.soludev.tech/bricks/mcp
headers:
Authorization: "Bearer ${LOGTO_USER_TOKEN}" # or
# X-API-Key: "${MCP_RAGANYTHING_API_KEY}"
- name: files
transport: http
url: https://raganything.soludev.tech/files/mcp
headers:
Authorization: "Bearer ${LOGTO_USER_TOKEN}"
- name: classical
transport: http
url: https://raganything.soludev.tech/classical/mcp
headers:
Authorization: "Bearer ${LOGTO_USER_TOKEN}"
When the current_user_id contextvar is set (i.e. the request was authenticated), the LLM/embeddings factories resolve credentials from the shared user_llm_settings table (owned by composable-agents) instead of the static LLMConfig env vars:
get_chat_llm_for_user(current_user_id) — returns a ChatOpenAI configured with the user's decrypted api_key, base_url, and model.get_embedding_for_user(current_user_id) — returns an OpenAIEmbeddings configured with the user's credentials.get_vector_store_for_user(current_user_id, working_dir) — returns a PGVectorStore bound to the user's embedding model + dimension.Decryption is done by AsyncpgUserLlmReader using the existing FernetSecretCipher and the shared SECRET_ENCRYPTION_KEY. If the user has no row in user_llm_settings, the request returns 422 LlmNotConfiguredError with a message telling the user to configure their LLM credentials in composable-agents.
When current_user_id is None (e.g. local dev with auth disabled), the factories fall back to the static LLMConfig env vars (OPEN_ROUTER_API_KEY, CHAT_MODEL, EMBEDDING_MODEL, etc.). This preserves the legacy local-dev workflow.
Classical RAG chunks are tagged with user_id in their langchain_metadata at index time. At query time, the langchain-postgres metadata filter {"user_id": current_user_id} is applied so a user only retrieves their own chunks. This is an application-level filter — RLS is not applied on the dynamic classical_rag_* tables because PGVectorStore uses its own connection pool and the app.user_id GUC is not propagated there (same conclusion as the LangGraph store in composable-agents).
Two tables are owned by composable-agents (created by its Alembic migrations) and read by mcp-raganything:
mcp-raganything reads these tables as a privileged auth/credential-resolution operation: the asyncpg connection sets SET LOCAL row_security = off for the duration of the lookup, then the connection is returned to the pool. RLS on these tables is enforced by composable-agents for its own writes.
The two services share the same PostgreSQL database (raganything) but use separate Alembic version tables: composable-agents uses alembic_version, mcp-raganything uses raganything_alembic_version (see Database Schema). This lets both services evolve their schemas independently without colliding.
API_KEY (master key) deprecated. The single shared X-API-Key: <master> mode is no longer the primary auth path. The env var is still read for backward compatibility but per-user API keys (from the shared api_keys table) are the recommended replacement. Migrate by creating per-user keys in composable-agents.OPEN_ROUTER_API_KEY deprecated for chat + embeddings. When a request is authenticated (current_user_id set), the per-user user_llm_settings row is used instead. OPEN_ROUTER_API_KEY is still used for the Kreuzberg VLM (vision model OCR in the LightRAG pipeline) because the VLM is not user-scoped — this is a documented limitation. To fully deprecate OPEN_ROUTER_API_KEY, the VLM must be made user-scoped too (future work).mcp_servers is now per-user. A user can only see, create, update, and delete their own MCP servers. Cross-user create with the same name returns 409 Conflict (BUG-001 fix: ON CONFLICT DO UPDATE is scoped by user_id, so a same-name server from another user is not silently overwritten).Base path: /api/v1
# Health check
curl http://localhost:8000/api/v1/health
Response:
{"message": "RAG Anything API is running"}
Both indexing endpoints accept JSON bodies and run processing in the background. Files are downloaded from MinIO, not uploaded directly.
Downloads the file identified by file_name from the configured MinIO bucket, then indexes it into the RAG knowledge graph scoped to working_dir.
curl -X POST http://localhost:8000/api/v1/file/index \
-H "Content-Type: application/json" \
-H "Authorization: Bearer ${JWT}" \
-d '{
"file_name": "project-alpha/report.pdf",
"working_dir": "project-alpha"
}'
Response (202 Accepted):
{"status": "accepted", "message": "File indexing started in background"}
Lists all objects under the working_dir prefix in MinIO, downloads them, then indexes the entire folder.
curl -X POST http://localhost:8000/api/v1/folder/index \
-H "Content-Type: application/json" \
-H "Authorization: Bearer ${JWT}" \
-d '{
"working_dir": "project-alpha",
"recursive": true,
"file_extensions": [".pdf", ".docx", ".txt"]
}'
Response (202 Accepted):
{"status": "accepted", "message": "Folder indexing started in background"}
The service automatically detects and processes the following document formats through the RAGAnything parser:
Note: File format detection is automatic. No configuration is required to specify the document type. The service will process any supported format when indexed. All document and image formats are supported out-of-the-box when installed with raganything[all].
Browse and read files directly from MinIO without indexing them into the RAG knowledge base. Powered by Kreuzberg for document text extraction (91 file formats).
# List all files in the bucket
curl -H "Authorization: Bearer ${JWT}" \
http://localhost:8000/api/v1/files/list
# List files under a specific prefix
curl -H "Authorization: Bearer ${JWT}" \
"http://localhost:8000/api/v1/files/list?prefix=documents/&recursive=true"
Response (200 OK):
[
{"object_name": "documents/report.pdf", "size": 1024, "last_modified": "2026-01-01 00:00:00+00:00"},
{"object_name": "documents/notes.txt", "size": 512, "last_modified": "2026-01-02 00:00:00+00:00"}
]
Uploads a file directly to the MinIO bucket. The file is stored at {prefix}{filename}. This endpoint does not index the file — use the POST /file/index endpoint after uploading to add it to the RAG knowledge base.
Allowed file types: .pdf, .txt, .docx, .xlsx, .pptx, .md, .csv, .png, .jpg, .jpeg, .gif, .webp, .svg, .bmp, .html, .xml, .json, .rtf, .odt, .ods
Maximum file size: 50 MB
curl -X POST http://localhost:8000/api/v1/files/upload \
-H "Authorization: Bearer ${JWT}" \
-F "[email protected]" \
-F "prefix=documents/"
Response (201 Created):
{"object_name": "documents/report.pdf", "size": 2048, "message": "File uploaded successfully"}
Error responses:
Downloads the file from MinIO, extracts its text content using Kreuzberg, and returns the result. Supports 91 file formats including PDF, Office documents, images, and HTML.
curl -X POST http://localhost:8000/api/v1/files/read \
-H "Content-Type: application/json" \
-H "Authorization: Bearer ${JWT}" \
-d '{"file_path": "documents/report.pdf"}'
Response (200 OK):
{
"content": "Extracted text from the document...",
"metadata": {"format_type": "pdf", "mime_type": "application/pdf"},
"tables": [{"markdown": "| Header | Value |\n|---|---|\n| A | 1 |"}]
}
Error responses:
Creates a folder marker in the MinIO bucket by writing a 0-byte object whose object name ends with a trailing /. The folder is purely a prefix — MinIO does not have a real folder concept. This endpoint does not index anything.
curl -X POST http://localhost:8000/api/v1/files/folders \
-H "Content-Type: application/json" \
-H "Authorization: Bearer ${JWT}" \
-d '{"prefix": "documents/reports/"}'
Response (201 Created):
{"message": "Folder created", "prefix": "documents/reports/"}
Error responses:
Deletes a single object from the MinIO bucket and removes its corresponding vectors from the pgvector store. The object_name and working_dir are both required query parameters.
Deletion is performed in two steps, strictly in order:
working_dir) are deleted from the vector store.If the MinIO deletion fails, the pgvector store is not touched, ensuring no orphaned vectors are created from a failed storage operation.
curl -X DELETE "http://localhost:8000/api/v1/files?object_name=documents/report.pdf&working_dir=project-alpha" \
-H "Authorization: Bearer ${JWT}"
Response (200 OK):
{"message": "File deleted", "object_name": "documents/report.pdf"}
Error responses:
Deletes all objects whose object names start with prefix from the MinIO bucket and removes all corresponding vectors from the pgvector store. The deletion is recursive and permanent — there is no confirmation beyond the API call. A trailing / is preserved so that a prefix like documents/ does not match documents-archive/.
The prefix is automatically used as the working_dir for the pgvector deletion — no separate working_dir parameter is needed on this endpoint.
Deletion is performed in two steps, strictly in order:
If the MinIO deletion fails, the pgvector store is not touched.
curl -X DELETE "http://localhost:8000/api/v1/files/folders?prefix=documents/reports/" \
-H "Authorization: Bearer ${JWT}"
Response (200 OK):
{"message": "Folder deleted", "prefix": "documents/reports/"}
Error responses:
All delete routes (DELETE /files, DELETE /files/folders) and the create-folder route (POST /files/folders) validate the supplied path before touching storage. The validation rules are shared with the existing read/upload/list endpoints:
/, no drive prefixes).., .., start with ../, or contain a /../ segment.Any violation returns 422 Unprocessable Entity with a FileValidationError body. The object_name / prefix is normalized (backslashes converted to /, trailing slashes preserved) before being forwarded to the StoragePort.
Query the indexed knowledge base. The RAG engine is initialized for the given working_dir before executing the query.
curl -X POST http://localhost:8000/api/v1/query \
-H "Content-Type: application/json" \
-H "Authorization: Bearer ${JWT}" \
-d '{
"working_dir": "project-alpha",
"query": "What are the main findings of the report?",
"mode": "naive",
"top_k": 10
}'
Response (200 OK):
{
"status": "success",
"message": "",
"data": {
"entities": [],
"relationships": [],
"chunks": [
{
"reference_id": "...",
"content": "...",
"file_path": "...",
"chunk_id": "..."
}
],
"references": []
},
"metadata": {
"query_mode": "naive",
"keywords": null,
"processing_info": null
}
}
Returns results ranked by PostgreSQL full-text search using pg_textsearch. Each chunk includes a score field with the BM25 relevance score.
curl -X POST http://localhost:8000/api/v1/query \
-H "Content-Type: application/json" \
-H "Authorization: Bearer ${JWT}" \
-d '{
"working_dir": "project-alpha",
"query": "quarterly revenue growth",
"mode": "bm25",
"top_k": 10
}'
Response (200 OK):
{
"status": "success",
"message": "",
"data": {
"entities": [],
"relationships": [],
"chunks": [
{
"chunk_id": "abc123",
"content": "Quarterly revenue grew 12% year-over-year...",
"file_path": "reports/financials-q4.pdf",
"score": 3.456,
"metadata": {}
}
],
"references": []
},
"metadata": {
"query_mode": "bm25",
"total_results": 10
}
}
Runs BM25 and vector search in parallel, then merges results using Reciprocal Rank Fusion (RRF). Each chunk includes bm25_rank, vector_rank, and combined_score fields.
curl -X POST http://localhost:8000/api/v1/query \
-H "Content-Type: application/json" \
-H "Authorization: Bearer ${JWT}" \
-d '{
"working_dir": "project-alpha",
"query": "quarterly revenue growth",
"mode": "hybrid+",
"top_k": 10
}'
Response (200 OK):
{
"status": "success",
"message": "",
"data": {
"entities": [],
"relationships": [],
"chunks": [
{
"chunk_id": "abc123",
"content": "Quarterly revenue grew 12% year-over-year...",
"file_path": "reports/financials-q4.pdf",
"score": 0.0328,
"bm25_rank": 1,
"vector_rank": 3,
"combined_score": 0.0328,
"metadata": {}
}
],
"references": []
},
"metadata": {
"query_mode": "hybrid+",
"total_results": 10,
"rrf_k": 60
}
}
The combined_score is the sum of bm25_score and vector_score, each computed as 1 / (k + rank). Results are sorted by combined_score descending. A chunk that appears in both result sets will have a higher combined score than one that appears in only one.
A second retrieval pathway alongside the graph-based LightRAG. Classical RAG uses a straightforward chunk → embed → retrieve flow with two quality-enhancing techniques: multi-query generation and LLM-as-judge relevance scoring. It stores chunks in dedicated PGVector tables (one per working_dir) and does not build a knowledge graph.
chunk_id, then an LLM judge scores each chunk's relevance on a 0–10 scale. Chunks below the relevance threshold are discarded; the rest are returned sorted by score.Both classical indexing endpoints accept JSON bodies and run processing in the background.
Downloads the file from MinIO, extracts text with Kreuzberg chunking, and embeds the chunks into a PGVector table scoped to working_dir.
curl -X POST http://localhost:8000/api/v1/classical/file/index \
-H "Content-Type: application/json" \
-H "Authorization: Bearer ${JWT}" \
-d '{
"file_name": "project-alpha/report.pdf",
"working_dir": "project-alpha",
"chunk_size": 1000,
"chunk_overlap": 200
}'
Response (202 Accepted):
{"status": "accepted", "message": "File indexing started in background"}
Lists all objects under the working_dir prefix in MinIO, downloads them, and indexes each file into the PGVector table.
curl -X POST http://localhost:8000/api/v1/classical/folder/index \
-H "Content-Type: application/json" \
-H "Authorization: Bearer ${JWT}" \
-d '{
"working_dir": "project-alpha",
"recursive": true,
"file_extensions": [".pdf", ".docx", ".txt"],
"chunk_size": 1000,
"chunk_overlap": 200
}'
Response (202 Accepted):
{"status": "accepted", "message": "Folder indexing started in background"}
Query the classical RAG pipeline. Supports two modes: vector (default) and hybrid (BM25 + vector via Reciprocal Rank Fusion).
The LLM generates query variations, runs vector similarity search for each, deduplicates results, then scores and filters them with an LLM judge.
curl -X POST http://localhost:8000/api/v1/classical/query \
-H "Content-Type: application/json" \
-H "Authorization: Bearer ${JWT}" \
-d '{
"working_dir": "project-alpha",
"query": "What are the main findings of the report?",
"top_k": 10,
"num_variations": 3,
"relevance_threshold": 5.0,
"mode": "vector"
}'
Runs BM25 full-text search and multi-query vector search in parallel, merges results using Reciprocal Rank Fusion (RRF), then scores with an LLM judge. Chunks include bm25_score, vector_score, and combined_score fields.
curl -X POST http://localhost:8000/api/v1/classical/query \
-H "Content-Type: application/json" \
-H "Authorization: Bearer ${JWT}" \
-d '{
"working_dir": "project-alpha",
"query": "What are the main findings of the report?",
"mode": "hybrid"
}'
Response (200 OK):
{
"status": "success",
"message": "",
"queries": [
"What are the main findings of the report?",
"What key results does the report present?",
"Summarize the primary conclusions from the report"
],
"chunks": [
{
"chunk_id": "a1b2c3d4-...",
"content": "The primary finding indicates that...",
"file_path": "project-alpha/report.pdf",
"relevance_score": 8.5,
"metadata": {"chunk_index": 0},
"bm25_score": 0.0164,
"vector_score": 0.0164,
"combined_score": 0.0328
}
],
"mode": "hybrid"
}
If BM25 is unavailable (BM25_ENABLED=false or pg_textsearch extension missing), hybrid mode falls back to vector mode and logs a warning.
The service exposes four MCP servers, all using streamable HTTP transport:
/rag/mcpQuery-focused tools for searching the indexed knowledge base.
query_knowledge_basequery_knowledge_base_multimodal/files/mcpFile browsing tools for listing and reading files from MinIO storage.
list_filesread_fileDownloads the file from MinIO, extracts its text content using Kreuzberg, and returns the extracted text along with metadata and any detected tables.
/bricks/mcpBricks integration tools for accessing project documents from the Bricks platform and publishing structured section versions.
list_bricks_documentsReturns a list of documents for the specified Bricks project, including metadata like file name, MIME type, size, status, and presigned download URLs.
read_bricks_documentDownloads the document from the presigned S3 URL, extracts its text content using Kreuzberg, and returns the extracted text, metadata, and detected tables. No authentication is required — the URL is already signed.
publish_section_versionPublishes a structured section version back to the Bricks platform. When BRICKS_PUBLISH_DRY_RUN=true (default), the tool returns a preview of the payload without making an API call. Set BRICKS_PUBLISH_DRY_RUN=false to enable real publishing.
Dry-run response example:
{
"success": true,
"message": "DRY RUN — no API call made",
"dry_run": true,
"payload_preview": {
"project_unique_id": "abc-123",
"section_key": "summary",
"content": {"title": "Analysis Summary"},
"workflow_id": "agent-haiku-files-v1",
"workflow_name": "haiku-files",
"workflow_metadata": {}
}
}
/classical/mcpClassical RAG tools for indexing and querying without a knowledge graph.
classical_index_fileclassical_index_folderclassical_queryAll MCP servers use streamable HTTP transport exclusively. Connect MCP clients to the mount paths:
http://localhost:8000/rag/mcp # RAGAnythingQuery
http://localhost:8000/files/mcp # RAGAnythingFiles
http://localhost:8000/classical/mcp # RAGAnythingClassical
http://localhost:8000/bricks/mcp # RAGAnythingBricks
In addition to the four built-in MCP servers above, mcp-raganything owns the MCP server registry — a CRUD service that lets you register external MCP servers (by URL) or generate new MCP servers on the fly from any OpenAPI/Swagger document. Registered servers are persisted in the mcp_servers PostgreSQL table (shared with composable-agents) and rehydrated at startup, so composable-agents (and other clients) only need to know the registry URL to discover and connect to every available MCP server.
This registry was previously hosted in the composable-agents brick; it has been migrated here so that the RAG service is the single owner of MCP server lifecycle (registration, generation, mounting, crash recovery) and the mcp_servers table schema.
Since branch feat/dual-auth-rls-llm-peruser, the mcp_servers table is row-level isolated by user_id:
user_id column.ALTER TABLE mcp_servers ENABLE ROW LEVEL SECURITY + FORCE ROW LEVEL SECURITY and creates a policy mcp_servers_user_isolation that restricts rows to user_id = current_setting('app.user_id').McpRegistryStore sets the GUC app.user_id = <current_user_id> on the asyncpg connection before any query, so every read/write is automatically scoped to the authenticated user. The middleware resolves current_user_id from JWT or per-user API key (see Authentication).
BUG-001 fix: the ON CONFLICT (name) DO UPDATE upsert is scoped by user_id. If user A creates a server named weather and user B tries to create another weather, the conflict check only matches A's row through RLS, so B's insert is not a conflict — it creates a new row. If B already has a weather row, the upsert updates B's own row. There is no silent cross-user overwrite. A duplicate-name conflict within the same user returns 409 Conflict.
fastmcp.Client, validates reachability, and records the discovered tool_count. Composable-agents reads these entries and connects to the URL at agent build time./generated/{name}/mcp.SECRET_ENCRYPTION_KEY before being written to mcp_servers. They are only decrypted on the /reveal endpoint or when the server is mounted.openapi row from mcp_servers, re-fetches the spec, rebuilds the FastMCP server, and remounts it. External http servers do not need rehydration (the client connects to them lazily), so only openapi rows are rebuilt.All registry endpoints live under /api/v1/mcp/servers and are protected by the dual-auth middleware (JWT Authorization: Bearer or per-user X-API-Key). All reads/writes are scoped to the authenticated user_id via RLS — a user only sees and manages their own servers.
A GET/PUT/DELETE on a {name} that exists but is owned by another user returns 404 Not Found (RLS hides the row). A POST with a name already used by the same user returns 409 Conflict.
The request body for POST and PUT extends the McpServerConfig shape with two fields:
When source_type="openapi" and the fetched document is a Swagger 2.0 spec (swagger: "2.0"), the service converts it to OpenAPI 3.0 in-process, in pure Python, with no external service call (offline). The converter handles:
swagger: "2.0" → openapi: "3.0.x"host + basePath + schemes → servers[].urldefinitions → components.schemasresponses / parameters / securityDefinitions → components.*produces / consumes → per-operation requestBody.content / responses.*.contentx-... vendor extensions are preservedAfter conversion, the resulting OpenAPI 3.0 document is fed to FastMCP to build the generated server. If the document is already OpenAPI 3.0, no conversion is performed. Validation errors (malformed spec, unreachable URL, unsupported version) are returned as 422 responses on /validate and POST.
For source_type="openapi" servers, the service:
openapi_url (with optional headers for upstream auth)./generated/{name}/mcp using streamable HTTP transport, with proper lifespan management via an AsyncExitStack so that HTTP clients and sessions opened by FastMCP are closed cleanly on shutdown or unmount.mcp_servers with url = {GENERATED_MCP_BASE_URL}/generated/{name}/mcp.The returned url is built from GENERATED_MCP_BASE_URL so that clients outside the container can reach it. Connect an MCP client to:
{GENERATED_MCP_BASE_URL}/generated/{name}/mcp
For local development the default is http://localhost:8020/generated/{name}/mcp. In Docker the default is http://raganything-api:8000/generated/{name}/mcp (set GENERATED_MCP_BASE_URL to the public/ingress URL if clients are external).
On startup, the FastAPI lifespan reads all rows from mcp_servers and, for each row with source_type="openapi", re-fetches the OpenAPI spec, rebuilds the FastMCP server, and remounts it at /generated/{name}/mcp. External http/stdio servers are not rebuilt (the client connects to them lazily on first tool call). If SECRET_ENCRYPTION_KEY is not set, the registry is disabled at startup and a warning is logged — the rest of the RAG service still starts.
# JWT (Logto OIDC)
curl -X POST http://localhost:8000/api/v1/mcp/servers \
-H "Content-Type: application/json" \
-H "Authorization: Bearer ${JWT}" \
-d '{
"name": "weather",
"source_type": "external",
"url": "https://weather.example.com/mcp",
"auth_token": "super-secret"
}'
# Per-user API key
curl -X POST http://localhost:8000/api/v1/mcp/servers \
-H "Content-Type: application/json" \
-H "X-API-Key: ${USER_API_KEY}" \
-d '{
"name": "weather",
"source_type": "external",
"url": "https://weather.example.com/mcp",
"auth_token": "super-secret"
}'
Response (201 Created, secrets masked) — note the tool_count reflects the tools discovered by connecting to the remote server:
{
"name": "weather",
"source_type": "external",
"url": "https://weather.example.com/mcp",
"auth_token": null,
"headers": {},
"tool_count": 3
}
curl -X POST http://localhost:8000/api/v1/mcp/servers \
-H "Content-Type: application/json" \
-H "Authorization: Bearer ${JWT}" \
-d '{
"name": "petstore",
"source_type": "openapi",
"openapi_url": "https://petstore.swagger.io/v2/swagger.json",
"headers": {"Authorization": "Bearer upstream-token"}
}'
Response (201 Created) — note the generated url:
{
"name": "petstore",
"source_type": "openapi",
"url": "http://localhost:8020/generated/petstore/mcp",
"openapi_url": "https://petstore.swagger.io/v2/swagger.json",
"headers": {"Authorization": "***"}
}
The mounted server is immediately reachable at http://localhost:8020/generated/petstore/mcp and will be rehydrated automatically on the next startup.
All configuration is via environment variables, loaded through Pydantic Settings. See .env.example for a complete reference.
AppConfig)DatabaseConfig)LLMConfig)Deprecated for chat + embeddings on authenticated requests. When
current_user_idis set (JWT or per-user API key), the per-useruser_llm_settingsrow (decrypted viaSECRET_ENCRYPTION_KEY) takes precedence over these env vars. The env vars remain the fallback whencurrent_user_idisNone(local dev, auth disabled) and are still required for the Kreuzberg VLM (vision OCR in the LightRAG pipeline), which is not user-scoped — see Per-user LLM credentials and Breaking changes.
RAGConfig)BM25Config)When BM25_ENABLED is false or the pg_textsearch extension is not available, hybrid+ mode falls back to naive (vector-only) and bm25 mode returns an error.
ClassicalRAGConfig)The classical RAG adapters resolve LLM/embeddings per user via get_chat_llm_for_user / get_embedding_for_user / get_vector_store_for_user when current_user_id is set (see Per-user LLM credentials). If the user has no row in user_llm_settings, the request returns 422 LlmNotConfiguredError. When auth is disabled (current_user_id is None), the adapters fall back to the static LLMConfig env vars (OPEN_ROUTER_API_KEY, CHAT_MODEL, EMBEDDING_MODEL, EMBEDDING_DIM). If initialization fails (e.g. missing API key in fallback mode), the classical endpoints return 503 Service Unavailable with a descriptive error. Chunks are tagged with user_id in langchain_metadata at index time and filtered by {"user_id": current_user_id} at query time (see Per-user RAG isolation).
MinioConfig)BricksConfig)uv sync # Install all dependencies (including dev)
uv run python src/main.py # Run the server locally
uv run pytest tests/unit -q # Run unit tests (557 pass on feat/dual-auth-rls-llm-peruser)
uv run ruff check src/ # Lint
uv run ruff format src/ # Format
uv run mypy src/ # Type checking
The soludev-compose-apps/bricks/ stack auto-seeds the shared api_keys and user_llm_settings tables (the same seed data is used by composable-agents), so end-to-end QA of dual-auth + per-user LLM + RAG isolation can be run without manual seeding:
cd soludev-compose-apps/bricks/
docker compose up -d
# Then exercise the API with a JWT or a seeded per-user API key.
docker compose up -d # Start Postgres + API
docker compose logs -f raganything-api # Follow API logs
docker compose down -v # Stop and remove volumes
mcp-raganything uses Alembic for the mcp_servers table (the MCP server registry). Migrations live in src/alembic/versions/ and run automatically at startup (via asyncio.to_thread(_run_alembic_upgrade) in the FastAPI lifespan). The migration state is tracked in the raganything_alembic_version table, which is separate from composable-agents' alembic_version table so both services can share the same database without colliding.
The classical RAG tables are not managed by Alembic:
classical_rag_*) — created at runtime by LangchainPgvectorAdapter (langchain-postgres PGVectorStore) the first time a collection is indexed. RLS is not applied on these tables (PGVectorStore uses its own connection pool and the app.user_id GUC is not propagated); isolation is enforced at the application level via the {"user_id": current_user_id} langchain-postgres metadata filter (see Per-user RAG isolation).ClassicalBM25Adapter._ensure_bm25_index the first time a classical_rag_* table is queried, using CREATE INDEX ... USING bm25(content).The shared tables api_keys and user_llm_settings are not managed by this service's Alembic — they are created by composable-agents' Alembic. mcp-raganything only reads them with SET LOCAL row_security = off (see Shared tables).
The PostgreSQL server must have the pg_textsearch extension installed and loaded. In production, this requires:
Dockerfile.db builds a custom PostgreSQL image that compiles pg_textsearch from source (along with pgvector and Apache AGE).
docker-compose.yml must configure shared_preload_libraries=pg_textsearch for the bricks-db service. The local dev docker-compose.yml in this repository includes this by default.
ClassicalBM25Adapter checks for the extension at pool-creation time and logs a warning if it is missing; BM25 queries will then fail at runtime. Ensure the database image is built from Dockerfile.db and the shared library is preloaded.
src/
main.py -- FastAPI app, triple MCP mounts, entry point
config.py -- Pydantic Settings config classes
dependencies.py -- Dependency injection wiring
domain/
entities/
indexing_result.py -- FileIndexingResult, FolderIndexingResult
ports/
rag_engine.py -- RAGEnginePort (abstract)
storage_port.py -- StoragePort (abstract, methods: list, read, upload, create_folder, remove_object, remove_prefix) + FileInfo
bm25_engine.py -- BM25EnginePort (abstract)
document_reader_port.py -- DocumentReaderPort (abstract) + DocumentContent
vector_store_port.py -- VectorStorePort (abstract) + SearchResult
llm_port.py -- LLMPort (abstract)
bricks_api_port.py -- BricksApiPort (abstract) + BricksDocumentInfo + SectionVersionResult
application/
api/
health_routes.py -- GET /health
indexing_routes.py -- POST /file/index, /folder/index
query_routes.py -- POST /query
file_routes.py -- GET /files/list, GET /files/folders, POST /files/read, POST /files/upload, POST /files/folders, DELETE /files, DELETE /files/folders
classical_indexing_routes.py -- POST /classical/file/index, /classical/folder/index
classical_query_routes.py -- POST /classical/query
mcp_query_tools.py -- MCP tools: query_knowledge_base, query_knowledge_base_multimodal
mcp_file_tools.py -- MCP tools: list_files, read_file
mcp_classical_tools.py -- MCP tools: classical_index_file, classical_index_folder, classical_query
mcp_bricks_tools.py -- MCP tools: list_bricks_documents, read_bricks_document, publish_section_version
requests/
indexing_request.py -- IndexFileRequest, IndexFolderRequest
classical_indexing_request.py -- ClassicalIndexFileRequest, ClassicalIndexFolderRequest
classical_query_request.py -- ClassicalQueryRequest
query_request.py -- QueryRequest, MultimodalQueryRequest
file_request.py -- ListFilesRequest, ReadFileRequest
responses/
query_response.py -- QueryResponse, QueryDataResponse
classical_query_response.py -- ClassicalQueryResponse, ClassicalChunkResponse
file_response.py -- FileInfoResponse, FileContentResponse
use_cases/
index_file_use_case.py -- Downloads from MinIO, indexes single file (LightRAG)
index_folder_use_case.py -- Downloads from MinIO, indexes folder (LightRAG)
query_use_case.py -- Query with bm25/hybrid+ support
classical_index_file_use_case.py -- Classical: download → Kreuzberg chunk → PGVector
classical_index_folder_use_case.py -- Classical: folder batch index
classical_query_use_case.py -- Classical: multi-query + LLM judge + hybrid BM25
_classical_helpers.py -- validate_path, build_documents_from_extraction
list_files_use_case.py -- Lists files with metadata from MinIO
list_folders_use_case.py -- Lists folder prefixes from MinIO
read_file_use_case.py -- Reads file from MinIO, extracts content via Kreuzberg
upload_file_use_case.py -- Uploads file to MinIO storage
create_folder_use_case.py -- Creates a folder marker (0-byte trailing-slash object) in MinIO
delete_file_use_case.py -- Deletes a single object from MinIO storage
delete_folder_use_case.py -- Recursively deletes all objects under a prefix in MinIO storage
list_bricks_documents_use_case.py -- Lists documents from Bricks API
read_bricks_document_use_case.py -- Downloads Bricks document via presigned URL, extracts via Kreuzberg
publish_section_version_use_case.py -- Publishes section version (dry-run aware)
infrastructure/
rag/
lightrag_adapter.py -- LightRAGAdapter (RAGAnything/LightRAG)
kreuzberg_raganything_parser.py -- KreuzbergRAGAnythingParser (kreuzberg, custom parser for RAGAnything)
storage/
minio_adapter.py -- MinioAdapter (minio-py client)
document_reader/
kreuzberg_adapter.py -- KreuzbergAdapter (kreuzberg, 91 formats)
bm25/
pg_textsearch_adapter.py -- PostgresBM25Adapter (pg_textsearch, LightRAG tables)
classical_bm25_adapter.py -- ClassicalBM25Adapter (pg_textsearch, classical_rag_* tables)
hybrid/
rrf_combiner.py -- RRFCombiner (Reciprocal Rank Fusion)
vector_store/
langchain_pgvector_adapter.py -- LangchainPgvectorAdapter (langchain-postgres PGVectorStore)
llm/
langchain_openai_adapter.py -- LangchainOpenAIAdapter (langchain-openai ChatOpenAI)
bricks/
bricks_api_adapter.py -- BricksApiAdapter (httpx, Bricks REST API + section-versions)
alembic/
env.py -- Alembic migration environment (async, version_table=raganything_alembic_version)
versions/
001_create_mcp_servers_table.py -- mcp_servers table (Fernet-encrypted secrets, source_type, openapi_url)
Railway is a deployment platform that supports Docker-based services with managed PostgreSQL. The service requires PostgreSQL with pgvector and pg_textsearch extensions.
Railway project
├── PostgreSQL (Railway managed plugin — requires pgvector + pg_textsearch)
├── mcp-raganything (Dockerfile deploy)
└── (MinIO — use Railway's external S3-compatible storage or a separate container)
Create a Railway project and add a PostgreSQL plugin.
Provision pgvector + pg_textsearch:
postgres image without pgvector or pg_textsearch.Dockerfile.db from this repo to build a pgvector/pgvector:pg17-based image with pg_textsearch compiled in.Dockerfile.db, and disable the managed plugin.Deploy mcp-raganything:
Dockerfile automatically.8000 (Railway auto-detects EXPOSE 8000).Configure environment variables in the Railway dashboard:
MinIO: Railway does not offer managed MinIO. Options:
minio/minio).soludev-compose-apps stack if you need all services together.Verify deployment:
curl https://mcp-raganything.xxx.up.railway.app/api/v1/health
Connect composable-agents to this Railway deployment by pointing agent MCP URLs to the Railway domain and authenticating with either a Logto user JWT (Authorization: Bearer) or a per-user API key from the shared api_keys table (X-API-Key). The legacy MCP_RAGANYTHING_API_KEY env var (master API_KEY) still works but is deprecated.
mcp-raganything-production.up.railway.app).pg_textsearch extension must be available on the PostgreSQL server. If using Railway's managed PostgreSQL, you will need to replace it with a custom image built from Dockerfile.db.MIT
API/MCP wrapper for RagAnything
Tools
Noch keine Tool-Liste. Remote-Server erhalten eine nach dem ersten Health-Check.
FAQ
Kopieren Sie auf dieser Seite die stdio-Konfiguration für Cursor, Claude Desktop, Claude Code oder VS Code. Sie nutzt die Installation des Herausgebers, keinen PlugMCP-Proxy.
In diesem Eintrag ist keine erforderliche Umgebungsvariable aufgeführt.
MCP Raganything läuft lokal über stdio.
Zuletzt geprüft: 2026-10-28. API/MCP wrapper for RagAnything
