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เปิดHigh-Performance Legal & Code Intelligence for the Terminal.
A "Native & Lean" implementation of a Retrieval-Augmented Generation (RAG) server written in Rust. This server exposes high-precision search logic via the Model Context Protocol (MCP), specifically optimized for Void GNU/Linux and Intel iGPU (Vulkan) hardware.
By bypassing the "PCIe dinosaur" and utilizing Unified Memory Architecture (UMA), this server provides the lightning-fast "memory" required for autonomous agents like Goose and Aider.
The Rust RAG server is not an HTTP server. It communicates via:
HTTP is only used internally – the RAG server makes outgoing HTTP requests to local services:
Agentgateway is an HTTP proxy that handles cloud LLM access (port 4000), but the RAG server never serves HTTP – it only consumes.
The server communicates directly with local Vulkan-accelerated engines over native STDIO pipes, ensuring 0% data leakage and minimum latency.
graph TD
A[Goose / Aider / CLI] -->|STDIO| B(Rust RAG Server)
B -->|HTTP| C[LLM Server :11434]
B -->|HTTP| D[BGE-M3 :11435]
B -->|HTTP| E[Reranker :11436/11437]
B -->|Dynamic Loading| F[vec0.so Extension]
F <--> G[(SQLite-Vec DB)]
B -->|FTS5| H[(BM25 Index)]
B -->|Cache| I[(Query Cache)]
J[Agentgateway :4000] -.->|HTTP Proxy| C
J -.->|HTTP Proxy| D
J -.->|HTTP Proxy| E
Explanation:
Note: The RAG server makes outgoing HTTP calls to the LLM, embedding, and reranker services. It does not accept incoming HTTP requests. All MCP communication with Goose is via STDIO.
Ensure you have the Rust toolchain and the specialized vec0 extension for SQLite.
# Sync the XBPS package repository index
sudo xbps-install -S
# Install system dependencies (build tools, Rust toolchain, SQLite headers, PDF tooling)
sudo xbps-install -y base-devel rust cargo sqlite-devel poppler-utils curl wget git
# Download the specific vec0.so (v0.1.9+)
mkdir -p ~/.config/rag-server/extensions
cd /tmp
curl -L -O https://github.com/asg017/sqlite-vec/releases/download/v0.1.9/sqlite-vec-0.1.9-loadable-linux-x86_64.tar.gz
tar -xvf sqlite-vec-0.1.9-loadable-linux-x86_64.tar.gz
mv vec0.so ~/.config/rag-server/extensions/
Download the BGE-M3 metadata required for exact tokenization.
wget https://huggingface.co/BAAI/bge-m3/resolve/main/tokenizer.json -O ~/.config/rag-server/tokenizer.json
cd ~/.config/rag-server
cargo build --release
# Optional: build with CPU-specific optimizations (not portable to other machines)
RUSTFLAGS="-C target-cpu=native" cargo build --release
If you have an existing database, add FTS5 support for BM25 keyword search:
sqlite3 ~/.config/rag-server/vectors.db "INSERT OR IGNORE INTO docs_fts(rowid, id, collection, text) SELECT rowid, id, collection, text FROM docs;"
v2.4.0 contains DB-affecting fixes — see the Change Log. Two upgrade notes:
ingest_directory now uses absolute paths as doc_ids. Files previously indexed with the bare filename will re-appear under a new id on the next directory ingest. If you don't want duplicates, clear (or re-ingest) the affected collection.LIKE 'doc_id%' deletion could accidentally touch sibling docs named doc_id_*. The new code deletes by exact parent_id. Existing DBs are usable as-is, but a fresh rm ~/.config/rag-server/vectors.db followed by re-ingestion is the cleanest path.The server defaults to optimized values for a 16GB U-series workstation but can be overridden via environment variables.
~/.config/rag-server/env)The compiled-in defaults below are typically overridden by a shell environment file that is sourced before launching the server:
# ~/.config/rag-server/env
export RAG_EMBED_MODEL="local-llama-server-embed"
export RAG_RERANK_MODEL="local-llama-server-rerank"
export RAG_RERANK_URL="http://127.0.0.1:11436/rerank"
export RAG_RERANK_K=10
export RAG_RERANK_MIN_CANDIDATES=3
export RAG_MAX_CONCURRENT_FILES=4
export RAG_MAX_CONCURRENT_REQUESTS=4
# Optional: override default paths
# export RAG_DB_PATH="$HOME/.config/rag-server/vectors.db"
# export RAG_TOKENIZER_PATH="$HOME/.config/rag-server/tokenizer.json"
Source it in your shell profile or in the Goose launch wrapper (_goose_session). See the full example in the Goose section below.
These are the compiled-in defaults from src/config.rs. Override them via environment variables (e.g., in ~/.config/rag-server/env).
Compiled-in settings (not overridable via env):
14400 seconds (hard-coded)Note:
RAG_MAX_CONCURRENT_REQUESTSwas previously unsafe with values> 1— the batch combiner usedbuffer_unordered, so embeddings could be attached to the wrong chunks. Fixed in v2.4.0 withbuffered. It is now safe to set this to4(or higher) for faster ingestion.
.zshenv)Set these in your ~/.zshenv for all sessions:
# ~/.zshenv
export OPENAI_API_KEY="sk-unused"
export OPENAI_BASE_URL="http://localhost:4000/v1"
export OPENAI_TIMEOUT=7200
export SQLITE_VEC_PATH="$HOME/.config/rag-server/extensions/vec0.so"
~/.config/goose/config.yaml)Integrate the server as a stdio extension:
extensions:
rag:
enabled: true
name: rag
type: stdio
cmd: $HOME/.config/rag-server/target/release/rag-server
env:
SQLITE_VEC_PATH: $HOME/.config/rag-server/extensions/vec0.so
timeout: 7200
Note: The RAG server reads all
RAG_*environment variables. These can be set in~/.config/rag-server/envand sourced in your session.
Configure your ~/.zshrc with these aliases for seamless reranker switching:
# ~/.zshrc
_ensure_agentgateway() {
if ! ss -tulpn | grep -q ":4000 "; then
echo "🚀 Starting Agentgateway..."
agentgateway -f ~/.config/agentgateway/config.yaml > /dev/null 2>&1 &
local count=0
while ! ss -tulpn | grep -q ":4000 "; do
if [ $count -gt 10 ]; then
echo "❌ Error: Agentgateway failed to start."
return 1
fi
sleep 1
((count++))
done
fi
}
_goose_session() {
local model="${1:-local-llama-server}"
local embed="${2}" # No default; let ~/.config/rag-server/env handle it
local rerank="${3}" # No default; let ~/.config/rag-server/env handle it
_ensure_agentgateway || return 1
# Source RAG environment variables (sets defaults)
source ~/.config/rag-server/env
# Override RAG_EMBED_MODEL and RAG_RERANK_MODEL if provided
if [ -n "$embed" ]; then
export RAG_EMBED_MODEL="$embed"
fi
if [ -n "$rerank" ]; then
export RAG_RERANK_MODEL="$rerank"
# Reload RAG_RERANK_URL from env file
unset RAG_RERANK_URL
source ~/.config/rag-server/env
fi
GOOSE_MODEL="$model" goose session
}
# ==============================================================================
# --- 1. SWEDISH STACK (Primary BGE Reranker on Port 11436) ---
# ==============================================================================
alias goose-local='_goose_session local-llama-server'
alias goose-deepseek='_goose_session local-llama-server'
alias goose-qwen-coder='_goose_session local-llama-server'
alias goose-phi='_goose_session local-llama-server'
alias goose-mistral='_goose_session mistral-large-latest'
alias goose-codestral='_goose_session codestral-latest'
alias goose-gemini='_goose_session gemini-3.5-flash'
# ==============================================================================
# --- 2. ENGLISH STACK (Mxbai Reranker on Port 11437) ---
# ==============================================================================
alias goose-local-en='_goose_session local-llama-server "" local-llama-server-rerank-2'
alias goose-deepseek-en='_goose_session local-llama-server "" local-llama-server-rerank-2'
alias goose-qwen-coder-en='_goose_session local-llama-server "" local-llama-server-rerank-2'
alias goose-phi-en='_goose_session local-llama-server "" local-llama-server-rerank-2'
alias goose-mistral-en='_goose_session mistral-large-latest "" local-llama-server-rerank-2'
alias goose-codestral-en='_goose_session codestral-latest "" local-llama-server-rerank-2'
alias goose-gemini-en='_goose_session gemini-3.5-flash "" local-llama-server-rerank-2'
create_collection – Initializes a new KB (e.g., juridik or rust-src).
ingest_file – Chunks, tokenizes, and indexes a file via the iGPU.
ingest_directory – Bulk‑ingests all matching files in a directory with parallel processing.
add_documents – Indexes raw text strings directly from JSON.
query – Performs hybrid semantic + BM25 keyword search with reranking for 97% citation accuracy.
list_collections – Displays all local libraries and document counts.
delete_documents – Deletes one or more documents from a collection.
delete_collection – Removes an entire collection and all its data.
clear_cache – Clears the in‑memory query cache.
The same binary acts as a full‑featured command‑line tool. Run it without arguments to start the MCP server (stdin/stdout). Run it with a subcommand to perform operations directly.
./target/release/rag-server --help
./target/release/rag-server query --help
# Create a new collection
./target/release/rag-server create-collection --name juridik
# List all collections
./target/release/rag-server list-collections
# Delete a collection
./target/release/rag-server delete-collection --name juridik
# Ingest a single file
./target/release/rag-server ingest-file --collection juridik --file-path ~/dokument/lag.pdf
# Ingest all .txt and .pdf files in a directory
./target/release/rag-server ingest-directory --collection juridik --directory-path ~/dokument/ --extensions txt,pdf
# Force re-indexing
./target/release/rag-server ingest-file --collection juridik --file-path ~/dokument/lag.pdf --force
# Semantic search (vector only)
./target/release/rag-server query --collection juridik --query "vad är regeringsformen?" --top-k 3
# Hybrid search (BM25 + vector)
./target/release/rag-server query --collection juridik --query "tolkningsregler" --hybrid
# Hybrid search with custom weights (more BM25 weight)
./target/release/rag-server query --collection juridik --query "tolkningsregler" --hybrid --vector-weight 0.4 --bm25-weight 0.6
# Specify reranker URL per query
./target/release/rag-server query --collection juridik --query "constitutional law" --rerank-url http://127.0.0.1:11437/rerank
# Bypass cache (force fresh search)
./target/release/rag-server query --collection juridik --query "tolkningsregler" --hybrid --no-cache
# Clear the query cache
./target/release/rag-server clear-cache
{
"name": "query",
"description": "Search the collection using hybrid search (BM25 + vector) and reranking",
"inputSchema": {
"type": "object",
"properties": {
"collection": { "type": "string" },
"query": { "type": "string" },
"top_k": { "type": "integer", "default": 5 },
"rerank_url": {
"type": "string",
"description": "Optional reranker-URL (e.g., http://127.0.0.1:11437/rerank)"
},
"hybrid": {
"type": "boolean",
"description": "Use hybrid search (BM25 + vector)",
"default": false
},
"vector_weight": {
"type": "number",
"description": "Weight for vector score (0-1)",
"default": 0.7
},
"bm25_weight": {
"type": "number",
"description": "Weight for BM25 score (0-1)",
"default": 0.3
},
"no_cache": {
"type": "boolean",
"description": "Bypass cache",
"default": false
}
},
"required": ["collection", "query"]
}
}
By utilizing Quantization-Aware Training (QAT-UD) and N-Gram software speculation, this Rust implementation delivers:
800% Speedup: Indexing 700 project chunks reduced from 45+ mins (CPU) to ~5 mins (iGPU).
100% Speedup on Repeated Queries: LRU query cache serves identical queries instantly.
Linguistic Precision: Handles Swedish legal nuances and complex Rust traits without semantic drift.
Zero Leakage: 100% of data remains on local silicon.
Hybrid Search Recall: BM25 + vector search significantly improves recall for both exact keywords and semantic concepts.
Comprehensive testing with real legal documents validated all core features:
All critical tests passed. The server is production-ready for legal, codebase, and technical QA deployments.
Correctness, concurrency and cleanup release.
~ in the config-dir path is now expanded via dirs::config_dir() instead of producing a literal ./~ directory. RAG_DB_PATH and RAG_TOKENIZER_PATH env vars are now honoured.embedder.rs uses buffered() instead of buffer_unordered(). With RAG_MAX_CONCURRENT_REQUESTS > 1, previous builds could attach embeddings to the wrong chunks when batches completed out of order.LIKE 'doc_id%' with exact parent_id-derived id deletion. A doc named test can no longer wipe chunks of a doc named testing. Added index idx_docs_parent on docs(collection, parent_id).insert_chunks clears all existing chunks for a parent_id before inserting, so re-indexing a file with a smaller chunk count leaves no orphans behind.top_n is honoured on the small-candidate skip path (previously returned every candidate).ingest_directory: uses absolute paths as doc_ids, avoiding filename collisions (README.md vs readme.md) and multi-directory clashes.add_documents: batch existence check via a single query instead of one lock+query per document.expander: Lazy<Regex> instead of recompiling per call.--pipeline: the producer/consumer pipeline was correct only by accident (it defeated its own concurrency, embedding one batch at a time). After the buffered() fix, the straight-line chunk→embed path is faster and simpler. pipeline.rs is deleted; the --pipeline flag is removed from ingest_file and from the MCP tool schema.Cargo.toml and src/main.rs serverInfo.version → 2.4.0.rusqlite 0.32 → 0.40, reqwest 0.12 → 0.13, pdf-extract 0.6 → 0.12, tokenizers 0.21 → 0.23, dirs 5 → 7, dashmap 6.1 → 6.2.usize → i64 casts in db.rs (search(), bm25_search(), insert_chunks()) — required by rusqlite 0.40, which removed ToSql/FromSql for unsigned integers.native-tls feature on reqwest 0.13 to keep the OpenSSL-based TLS stack (0.13 defaults to rustls).tokenizers uses default-features = false, features = ["fancy-regex"], dropping spm_precompiled, base64 0.13, nom 7, onig, and onig_sys. Duplicated crates: 11 → 6.expander.rs preserved (they match Swedish user queries).Cargo.toml and src/main.rs serverInfo.version → 2.3.7.linux/amd64 and linux/arm64.Query Cache: In‑memory cache (5 min TTL) for repeated query results; added --no-cache and clear-cache command.
Parallel Embedding: Concurrent HTTP requests for embedding generation (configurable with RAG_MAX_CONCURRENT_REQUESTS).
Parallel Chunking: Automatic parallel processing for documents with >500 sentences.
Adaptive Reranking: Skips reranking if fewer candidates than RAG_RERANK_MIN_CANDIDATES (default 3).
Connection Pooling: Reuses HTTP connections for better performance.
Environment Management: Added dedicated ~/.config/rag-server/env file for cleaner configuration.
Initial Rust port with BGE-M3 embeddings and BGE-Reranker-v2-M3.
Hybrid search (BM25 + vector) and per-query reranker selection.
The project is continuously built, tested, and published via GitHub Actions.
The container image is built for both amd64 (x86_64) and arm64 (Apple Silicon, AWS Graviton, Raspberry Pi) architectures.
Docker automatically selects the correct architecture when you pull the image.
Registry: ghcr.io
Image: ghcr.io/bengtfrost/rag-server
Storage: Free for public repositories.
# Pull the latest image (auto‑selects your architecture)
docker pull ghcr.io/bengtfrost/rag-server:latest
# Pull a specific version
docker pull ghcr.io/bengtfrost/rag-server:v2.4.0
# Run the binary
docker run --rm ghcr.io/bengtfrost/rag-server:latest --help
# Run in foreground (STDIO MCP server mode)
docker run --rm -i ghcr.io/bengtfrost/rag-server:latest
# Inspect the multi‑arch manifest
docker manifest inspect ghcr.io/bengtfrost/rag-server:latest
# Check which arch you pulled
docker inspect ghcr.io/bengtfrost/rag-server:latest | grep Architecture
Author: Bengt Frost
Philosophy: Native & Lean | Unified Memory | Sovereign AI
License: MIT
High-performance RAG server in Rust. Hybrid BM25 + vector search, dual rerankers (BGE/Mxbai), exact BPE tokenization, MCP tools for Goose/Aider, LRU query cache, 100% local. 800% iGPU speedup on Void
ภาพรวม
High-performance RAG server in Rust. Hybrid BM25 + vector search, dual rerankers (BGE/Mxbai), exact BPE tokenization, MCP tools for Goose/Aider, LRU query cache, 100% local. 800% iGPU speedup on Void Linux + Intel Vulkan.
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Rag Server ทำงานแบบโลคัลผ่าน stdio
ตรวจสอบล่าสุดเมื่อ 2026-10-28 High-performance RAG server in Rust. Hybrid BM25 + vector search, dual rerankers (BGE/Mxbai), exact BPE tokenization, MCP tools for Goose/Aider, LRU query cache, 100% local. 800% iGPU speedup on Void Linux + Intel Vulkan.