BrainPalace RAG Server
BrainPalace (formerly doc-serve) is an intelligent document indexing and semantic search system designed to give AI agents long-term memory.
AI agents need persistent memory to be truly useful. BrainPalace provides the retrieval infrastructure that enables context-aware, knowledge-grounded AI interactions.
Installation
pip install brainpalace-rag
Quick Start
-
Set environment variables:
export OPENAI_API_KEY=your-key export ANTHROPIC_API_KEY=your-key
-
Start the server:
brainpalace-serve
The server will start at http://127.0.0.1:8000.
Note: The legacy command
doc-serveis still available but deprecated. Please usebrainpalace-servefor new installations.
Search Capabilities
BrainPalace provides multiple search strategies to match your retrieval needs:
| Search Type | Description | Best For |
|---|---|---|
| Semantic Search | Natural language queries using OpenAI embeddings (text-embedding-3-large) |
Conceptual questions, finding related content |
| Keyword Search (BM25) | Traditional keyword matching with TF-IDF ranking | Exact matches, technical terms, code identifiers |
| Hybrid Search | Combines vector + BM25 for best of both approaches | General-purpose queries, balanced recall/precision |
| GraphRAG | Knowledge graph-based retrieval for relationship-aware queries | Understanding connections, multi-hop reasoning |
Features
- Document Indexing: Load and index documents from folders (PDF, Markdown, TXT, DOCX, HTML)
- AST-Aware Code Ingestion: Smart parsing for Python, TypeScript, JavaScript, Java, Go, Rust, C, C++
- Multi-Strategy Retrieval: Semantic, keyword, hybrid, and graph-based search
- OpenAI Embeddings: Uses
text-embedding-3-largefor high-quality embeddings - Claude Summarization: AI-powered code summaries for better context
- Chroma Vector Store: Persistent, thread-safe vector database
- FastAPI: Modern, high-performance REST API with OpenAPI documentation
Prerequisites
- Python 3.10+
- OpenAI API key (for embeddings)
- Anthropic API key (for summarization)
GraphRAG Configuration (Feature 113)
BrainPalace supports optional GraphRAG (Graph-based Retrieval-Augmented Generation) for enhanced relationship-aware queries.
Enabling GraphRAG
Set the environment variable to enable graph indexing:
export ENABLE_GRAPH_INDEX=true
Configuration Options
| Variable | Default | Description |
|---|---|---|
ENABLE_GRAPH_INDEX |
true |
Enable/disable GraphRAG features (brainpalace init writes graphrag.enabled: true) |
GRAPH_STORE_TYPE |
sqlite |
Graph backend: sqlite (persistent + temporal, default) or simple (in-memory JSON) |
GRAPH_MAX_TRIPLETS_PER_CHUNK |
10 |
Maximum entities to extract per document chunk |
GRAPH_USE_CODE_METADATA |
true |
Extract relationships from code AST metadata |
GRAPH_TRAVERSAL_DEPTH |
2 |
Default traversal depth for graph queries |
LLM entity extraction has no environment variable — enable it with the
extraction.mode key in config.yaml (off | subagent | auto | provider).
Query Modes
With GraphRAG enabled, you have access to additional query modes:
graph: Query using only the knowledge graph (entity relationships)multi: Combines vector search, BM25, and graph results using RRF fusion
Example: Graph Query
# CLI
brainpalace query "authentication service" --mode graph
# API
curl -X POST http://localhost:8000/query \
-H "Content-Type: application/json" \
-d '{"query": "authentication service", "mode": "graph", "top_k": 10}'
Example: Multi-Mode Query
# CLI
brainpalace query "user login flow" --mode multi
# API
curl -X POST http://localhost:8000/query \
-H "Content-Type: application/json" \
-d '{"query": "user login flow", "mode": "multi", "top_k": 5}'
Rebuilding the Graph Index
To rebuild only the graph index without re-indexing documents:
curl -X POST "http://localhost:8000/index?rebuild_graph=true" \
-H "Content-Type: application/json" \
-d '{"folder_path": "."}'
Optional Dependencies
For enhanced GraphRAG features, install optional dependency groups:
The default sqlite graph store needs no extra dependency (Python stdlib —
persistent + temporal). Only the enhanced extractor is optional:
# For enhanced (LLM/LangExtract) entity extraction
poetry install --with graphrag
Two-Stage Reranking (Feature 123)
BrainPalace supports optional two-stage retrieval with reranking for improved search precision. When enabled, the system:
- Stage 1: Retrieves more candidates than requested (e.g., 50 candidates for top_k=5)
- Stage 2: Reranks candidates using a cross-encoder model for more accurate relevance scoring
Enabling Reranking
Set the following environment variables:
# Enable two-stage reranking (default: false)
ENABLE_RERANKING=true
# Choose provider (default: sentence-transformers)
RERANKER_PROVIDER=sentence-transformers # or "ollama"
# Choose model (default: cross-encoder/ms-marco-MiniLM-L-6-v2)
RERANKER_MODEL=cross-encoder/ms-marco-MiniLM-L-6-v2
# Stage 1 retrieval multiplier (default: 10)
RERANKER_TOP_K_MULTIPLIER=10
# Maximum candidates for Stage 1 (default: 100)
RERANKER_MAX_CANDIDATES=100
# Batch size for reranking inference (default: 32)
RERANKER_BATCH_SIZE=32
Provider Options
| Provider | Model | Latency | Description |
|---|---|---|---|
| sentence-transformers | cross-encoder/ms-marco-MiniLM-L-6-v2 | ~50ms | Recommended. Fast, accurate cross-encoder. |
| sentence-transformers | cross-encoder/ms-marco-MiniLM-L-12-v2 | ~100ms | Slower but more accurate. |
| ollama | llama3.2:1b | ~500ms | Fully local, no HuggingFace download. |
YAML Configuration
You can also configure reranking in config.yaml:
reranker:
provider: sentence-transformers
model: cross-encoder/ms-marco-MiniLM-L-6-v2
params:
batch_size: 32
Graceful Degradation
If the reranker fails (model unavailable, timeout, etc.), the system automatically falls back to Stage 1 results. This ensures queries never fail due to reranking issues.
Response Fields
When reranking is enabled, query results include additional fields:
rerank_score: The cross-encoder relevance scoreoriginal_rank: The position before reranking (1-indexed)
Example response:
{
"results": [
{
"text": "Document content...",
"source": "docs/guide.md",
"score": 0.95,
"rerank_score": 0.95,
"original_rank": 5,
"chunk_id": "chunk_abc123"
}
]
}
Development Installation
cd brainpalace-server
poetry install
Configuration
Copy the environment template and configure:
cp ../.env.example .env
# Edit .env with your API keys
Required environment variables:
OPENAI_API_KEY: Your OpenAI API key for embeddingsANTHROPIC_API_KEY: Your Anthropic API key for summarization
Running the Server
# Development mode
poetry run uvicorn brainpalace_server.api.main:app --reload
# Or use the entry point
poetry run brainpalace-serve
API Documentation
Once running, visit:
- Swagger UI: http://127.0.0.1:8000/docs
- ReDoc: http://127.0.0.1:8000/redoc
- OpenAPI JSON: http://127.0.0.1:8000/openapi.json
API Endpoints
Health
GET /health- Server health statusGET /health/status- Detailed indexing status
Indexing
POST /index- Start indexing documents from a folderPOST /index/add- Add documents to existing indexDELETE /index- Reset the index
Querying
POST /query- Semantic search queryGET /query/count- Get indexed document count
Example Usage
Index Documents
curl -X POST http://localhost:8000/index \
-H "Content-Type: application/json" \
-d '{"folder_path": "/path/to/docs"}'
Query Documents
curl -X POST http://localhost:8000/query \
-H "Content-Type: application/json" \
-d '{"query": "How do I configure authentication?", "top_k": 5}'
Architecture
brainpalace_server/
├── api/
│ ├── main.py # FastAPI application
│ └── routers/ # Endpoint handlers
├── config/
│ └── settings.py # Configuration management
├── models/ # Pydantic request/response models
├── indexing/
│ ├── document_loader.py # Document loading
│ ├── chunking.py # Text chunking
│ └── embedding.py # Embedding generation
├── services/
│ ├── indexing_service.py # Indexing orchestration
│ └── query_service.py # Query execution
└── storage/
└── vector_store.py # Chroma vector store
Development
Running Tests
poetry run pytest
Code Formatting
poetry run black brainpalace_server/
poetry run ruff check brainpalace_server/
Type Checking
poetry run mypy brainpalace_server/
Documentation
- User Guide - Getting started and usage
- Developer Guide - Contributing and development
- API Reference - Full API documentation
Release Information
- Current Version: See pyproject.toml
- Release Notes: GitHub Releases
- Changelog: Latest Release
Related Packages
- brainpalace-cli - Command-line interface for BrainPalace
License
MIT
1 maintainer
Metadata
1 maintainer
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