logosdb

KO | EN

gitlite — search

#hacktoberfest #typescript #go #android #machine-learning #rust #linux #nodejs #java #awesome #kubernetes #vue #pytorch #mcp

(★ 13)

LogosDB is the SQLite for AI Agents.

logosdb Latest Version Download

Download Latest Version (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

[![CI](https://github.com/jose-compu/logosdb/actions/workflows/ci.yml/badge.svg?branch=main)](https://github.com/jose-compu/logosdb/actions/workflows/ci.yml)
[![Python](https://github.com/jose-compu/logosdb/actions/workflows/python.yml/badge.svg?branch=main)](https://github.com/jose-compu/logosdb/actions/workflows/python.yml)
[![PyPI](https://img.shields.io/pypi/v/logosdb.svg)](https://pypi.org/project/logosdb/)
[![Python versions](https://img.shields.io/pypi/pyversions/logosdb.svg)](https://pypi.org/project/logosdb/)

LogosDB is the SQLite for AI Agents.

LogosDB is a fast semantic vector database written in C/C++ that provides approximate nearest-neighbor search over embedding vectors with associated text metadata.

Authors: Jose ([@jose-compu](https://github.com/jose-compu))

**Contributing**: See [CONTRIBUTING.md](CONTRIBUTING.md) for build instructions, style guide, and PR workflow.

# Features

* Vectors and metadata are stored as flat binary files, memory-mapped for zero-copy reads.
  * Approximate nearest-neighbor search via [HNSW](https://arxiv.org/abs/1603.09320) (hnswlib), O(log n) query time.
  * Each vector row carries optional text and ISO 8601 timestamp metadata (JSONL sidecar).
  * The basic operations are `Put(embedding, text, timestamp)` and `Search(query, top_k)`.
  * **Timestamp range filtering**: search within a time window (e.g., "last 24 hours").
  * **Multiple distance metrics**: inner product, cosine similarity (auto-normalized), or L2 Euclidean.
  * Bulk vector access for direct tensor construction (e.g. loading into GPU memory).
  * Thread-safe API on a single open handle: an internal mutex serializes operations that touch the index and metadata (including search).
  * Crash recovery: HNSW index is automatically backfilled from the append-only vector store on open.
  * Scales to millions of vectors.
  * **Framework integrations**: LangChain and LlamaIndex VectorStore adapters.
  * **MCP server**: first-class [Claude Code](https://docs.anthropic.com/en/docs/claude-code/overview) integration via `logosdb-mcp-server`.

# Documentation

The public interface is in `include/logosdb/logosdb.h`. Callers should not include or rely on the details of any other header files in this package. Those internal APIs may be changed without warning.

**Planning a deployment?** See [docs/sizing.md](docs/sizing.md) for disk/RAM estimates based on N×dim, or run `python -m logosdb.sizing --rows 1_000_000 --dim 768`.

Guide to header files:

* **include/logosdb/logosdb.h**: Main interface to the DB. Start here. Contains:
  - C API with opaque handles and `errptr` convention (RocksDB/LevelDB style)
  - C++ convenience wrapper (`logosdb::DB`) with RAII and exceptions
  - `logosdb::Options` for HNSW tuning and distance metric selection
  - `logosdb::SearchHit` result struct
  - `logosdb_search_ts_range()` for timestamp-filtered search

# Limitations

* This is not a general-purpose vector database. It is purpose-built for embedding-based memory retrieval in LLM inference ([funes.cpp](../funes.cpp)).
  * Only a single process (possibly multi-threaded) can access a particular database at a time.
  * There is no client-server support built into the library. An application that needs such support will have to wrap their own server around the library.
  * For inner-product distance (`LOGOSDB_DIST_IP`, the default), vectors must be L2-normalized before insertion. Use `LOGOSDB_DIST_COSINE` for automatic normalization.
  * Embedding generation is external — the caller provides pre-computed float vectors.

# Roadmap

Work is tracked in [open issues](https://github.com/jose-compu/logosdb/issues?q=is%3Aissue+is%3Aopen). **1.0.0** shipped on 2026-05-17; the public C API and supported language bindings follow [Semantic Versioning](https://semver.org/) on the `1.x` line:

| Bump | When |
|------|------|
| **Patch** (`1.0.Z`) | Bug fixes, security fixes, documentation, CI/build-only changes, and internal tooling that does **not** alter the supported contract of the public C API in [`include/logosdb/logosdb.h`](include/logosdb/logosdb.h) or the stable surface of language bindings. |
| **Minor** (`1.Y.z`) | Backward-compatible additions: new functions or options, new storage or index behaviors behind explicit settings, new integrations, new platforms, and additive file-format upgrades with automatic or guided migration. |
| **Major** (`2.0.0`) | Breaking changes to the public C API, on-disk format without a migration path, or binding contracts that cannot remain compatible. |

Target versions and issue assignment match **[GitHub milestones](https://github.com/jose-compu/logosdb/milestones)** (each open roadmap issue has exactly one milestone).

## Milestones → issues

Past releases are summarized in the [CHANGELOG](CHANGELOG) and the [closed milestones](https://github.com/jose-compu/logosdb/milestones?state=closed).

### **SHIPPED**  [0.9.0](https://github.com/jose-compu/logosdb/milestone/8) — minor — throughput and scale

* [#87](https://github.com/jose-compu/logosdb/issues/87) — Streaming NDJSON import/export (`logosdb_export_ndjson` / `logosdb_import_ndjson` in C / C++ / Python) with `chunk_size`, byte-offset `--checkpoint`, and `--resume`. Parquet output is a follow-up.
  * [#80](https://github.com/jose-compu/logosdb/issues/80) — Batch ingest v2: `logosdb_put_batch` is now chunked and WAL-aware (`LOGOSDB_BATCH_CHUNK_SIZE`, default 1024). Durability matches per-row `logosdb_put`; speed-up vs single put is workload-dependent (≈1.4× at dim=64 on the bench; HNSW per-row cost dominates at larger dims).

### **SHIPPED** [0.10.0](https://github.com/jose-compu/logosdb/milestone/9) — minor — search and metadata

* [#85](https://github.com/jose-compu/logosdb/issues/85) — Hybrid retrieval mode: ANN score + lexical score fusion
  * [#84](https://github.com/jose-compu/logosdb/issues/84) — Filter API v2: structured metadata predicates beyond timestamp range

### **SHIPPED** [0.11.0](https://github.com/jose-compu/logosdb/milestone/10) — minor — multi-tenancy and tooling

* [#86](https://github.com/jose-compu/logosdb/issues/86) — Multi-tenant namespaces: quota and isolation within one DB root
  * [#89](https://github.com/jose-compu/logosdb/issues/89) — Recall benchmarking utility for HNSW tuning

### **SHIPPED** [0.12.0](https://github.com/jose-compu/logosdb/milestone/11) — minor — integrations

* [#78](https://github.com/jose-compu/logosdb/issues/78) — Feature: Codex plugin marketplace integration for LogosDB

### **SHIPPED** [1.0.0](https://github.com/jose-compu/logosdb/milestone/13) — first stable release

* Smart chunking, UTF-8/NFC policy, path-hardening, and expanded security tests (#98, #108–#112). See [CHANGELOG](CHANGELOG) `1.0.0`.

### **SHIPPED** [1.0.1](https://github.com/jose-compu/logosdb/issues/116) — patch — release hygiene

* [#116](https://github.com/jose-compu/logosdb/issues/116) — Sync `CHANGELOG`, version macros, package metadata, and post-1.0 docs.

### Open — platform / hardening (post-1.0)

* [#10](https://github.com/jose-compu/logosdb/issues/10) — Windows support: abstract POSIX file I/O behind a portable layer
  * [#111](https://github.com/jose-compu/logosdb/issues/111) — Finish Python integration-adapter security audit (partially addressed in 1.0.0 for `vibe.py`)

## Stability (since 1.0.0)

As of **1.0.0**, the project commits to stable semver for the public C API and supported language bindings:

* Breaking changes to `include/logosdb/logosdb.h` or on-disk layout require a future major version, with migration notes.
  * Tier-1 platforms for 1.x are **Linux and macOS**; Windows remains tracked in [#10](https://github.com/jose-compu/logosdb/issues/10).
  * Backup/restore and doctor tooling shipped in earlier 0.x releases; further hardening continues on the 1.x line.

# Getting the Source

```bash
git clone --recurse-submodules <repository-url>
cd logosdb
```

# Building

This project supports [CMake](https://cmake.org/) out of the box.

Quick start:

```bash
mkdir -p build && cd build
cmake -DCMAKE_BUILD_TYPE=Release .. && cmake --build .
```

This builds:

| Target | Description |
|--------|-------------|
| `logosdb` | Static library (`liblogosdb.a`) |
| `logosdb-cli` | Command-line tool for put, search, info |
| `logosdb-bench` | Benchmark: HNSW vs brute-force, with ChromaDB comparison |
| `logosdb-test` | Unit tests |

# Usage (C API)

```c
#include <logosdb/logosdb.h>

char *err = NULL;
logosdb_options_t *opts = logosdb_options_create();
logosdb_options_set_dim(opts, 2048);

logosdb_t *db = logosdb_open("/tmp/mydb", opts, &err);
logosdb_options_destroy(opts);

float vec[2048] = { /* ... unnormalized vector ... */ };

// L2-normalize for inner-product distance (returns 0 on success, -1 if zero norm)
if (logosdb_l2_normalize(vec, 2048) == 0) {
    logosdb_put(db, vec, 2048, "My commute is 42 minutes",
                "2025-06-25T10:00:00Z", &err);
}

logosdb_search_result_t *res = logosdb_search(db, query_vec, 2048, 5, &err);
for (int i = 0; i < logosdb_result_count(res); i++) {
    printf("#%d score=%.4f text=%s\n", i,
           logosdb_result_score(res, i),
           logosdb_result_text(res, i));
}
logosdb_result_free(res);
logosdb_close(db);
```

## Search with timestamp range filter

```c
#include <logosdb/logosdb.h>

char *err = NULL;
logosdb_options_t *opts = logosdb_options_create();
logosdb_options_set_dim(opts, 2048);

logosdb_t *db = logosdb_open("/tmp/mydb", opts, &err);

// Search for top-5 matches within the last 24 hours
logosdb_search_result_t *res = logosdb_search_ts_range(
    db, query_vec, 2048, 5,
    "2025-04-21T10:00:00Z",  // from (inclusive), NULL for no lower bound
    "2025-04-22T10:00:00Z",  // to (inclusive), NULL for no upper bound
    50,                      // candidate_k: internal fetch multiplier (10x top_k recommended)
    &err);

for (int i = 0; i < logosdb_result_count(res); i++) {
    printf("#%d score=%.4f ts=%s text=%s\n", i,
           logosdb_result_score(res, i),
           logosdb_result_timestamp(res, i),
           logosdb_result_text(res, i));
}
logosdb_result_free(res);
logosdb_close(db);
```

## Using different distance metrics

```c
#include <logosdb/logosdb.h>

char *err = NULL;
logosdb_options_t *opts = logosdb_options_create();
logosdb_options_set_dim(opts, 2048);

// Use cosine similarity (automatically normalizes vectors)
logosdb_options_set_distance(opts, LOGOSDB_DIST_COSINE);

// Or use L2 Euclidean distance
// logosdb_options_set_distance(opts, LOGOSDB_DIST_L2);

// Default is LOGOSDB_DIST_IP (inner product on L2-normalized vectors)

logosdb_t *db = logosdb_open("/tmp/mydb", opts, &err);
logosdb_options_destroy(opts);

// For cosine: vectors are automatically normalized on put/search
float vec[2048] = { /* ... unnormalized vector ... */ };
logosdb_put(db, vec, 2048, "entry", "2025-04-22T10:00:00Z", &err);

logosdb_close(db);
```

# Usage (C++ wrapper)

```cpp
#include <logosdb/logosdb.h>
#include <vector>

// Basic usage with default inner-product distance
logosdb::DB db("/tmp/mydb", {.dim = 2048});

// L2-normalize your vectors before insertion (required for inner-product distance)
std::vector<float> embedding = load_some_vector();  // unnormalized
if (logosdb::l2_normalize(embedding)) {
    db.put(embedding, "My commute is 42 minutes", "2025-06-25T10:00:00Z");
}

// Or use l2_normalized() to get a normalized copy
auto normalized = logosdb::l2_normalized(query);
auto results = db.search(normalized, 5);
for (auto &r : results) {
    printf("id=%llu score=%.4f text=%s\n", r.id, r.score, r.text.c_str());
}
```

## C++: Search with timestamp filter

```cpp
#include <logosdb/logosdb.h>

logosdb::DB db("/tmp/mydb", {.dim = 2048});

// Search within a time window
auto results = db.search_ts_range(
    query, 5,
    "2025-04-21T00:00:00Z",  // from timestamp
    "2025-04-22T00:00:00Z",  // to timestamp
    50);                     // candidate_k (optional, defaults to 10x top_k)

for (auto &r : results) {
    printf("id=%llu score=%.4f ts=%s\n", r.id, r.score, r.timestamp.c_str());
}
```

## C++: Using cosine or L2 distance

```cpp
#include <logosdb/logosdb.h>

// Cosine similarity - vectors are automatically normalized
logosdb::DB db("/tmp/mydb", {.dim = 2048, .distance = LOGOSDB_DIST_COSINE});

// Put unnormalized vectors - they will be normalized automatically
db.put(unnormalized_embedding, "entry", "2025-04-22T10:00:00Z");

auto results = db.search(query, 5);
// scores are cosine similarities in [0, 1]

// L2 Euclidean distance
// logosdb::DB db("/tmp/mydb", {.dim = 2048, .distance = LOGOSDB_DIST_L2});
```

# Python

LogosDB ships Python bindings built with [pybind11](https://pybind11.readthedocs.io/) and [scikit-build-core](https://scikit-build-core.readthedocs.io/).

Install from PyPI (binary wheels provided for Linux x86_64/aarch64 and macOS x86_64/arm64 on CPython 3.9–3.13):

```bash
pip install logosdb
```

Or build from source in a clone:

```bash
pip install .
```

Usage:

```python
import logosdb
from sentence_transformers import SentenceTransformer

# Local Hugging Face embeddings model (runs on your machine)
model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
dim = model.get_sentence_embedding_dimension()

# Use cosine distance so LogosDB auto-normalizes vectors
db = logosdb.DB("/tmp/agent_memory", dim=dim, distance=logosdb.DIST_COSINE)

# Three learnings captured by an AI agent
learnings = [
    ("Retrying API calls with exponential backoff reduced transient failures by 42%.", "2026-05-06T09:00:00Z"),
    ("Splitting long tasks into smaller batches improved throughput and lowered memory spikes.", "2026-05-06T09:05:00Z"),
    ("Adding idempotency keys prevented duplicate writes during network retries.", "2026-05-06T09:10:00Z"),
]

for text, ts in learnings:
    emb = model.encode(text).astype("float32")
    db.put(emb, text=text, timestamp=ts)

# Ask a natural-language question
question = "How can we avoid duplicate writes when retries happen?"
q_emb = model.encode(question).astype("float32")

hits = db.search(q_emb, top_k=3)
for h in hits:
    print(f"{h.score:.4f}  {h.text}")
```

## Python: Using cosine distance (no manual normalization needed)

```python
import numpy as np
import logosdb

# With cosine distance, vectors are automatically normalized
db = logosdb.DB("/tmp/mydb", dim=128, distance=logosdb.DIST_COSINE)

# No need to normalize - just put raw vectors
v = np.random.randn(128).astype(np.float32)
rid = db.put(v, text="unnormalized vector", timestamp="2025-04-22T10:00:00Z")

# Search also works with unnormalized queries
query = np.random.randn(128).astype(np.float32)
hits = db.search(query, top_k=5)
```

## Python: Batch ingest and streaming NDJSON (0.9.0)

```python
import numpy as np
import logosdb

db = logosdb.DB("/tmp/mydb", dim=128, distance=logosdb.DIST_COSINE)

# Chunked, WAL-aware batch insert (LOGOSDB_BATCH_CHUNK_SIZE controls fsync cadence).
embeddings = np.random.randn(10_000, 128).astype(np.float32)
ids = db.put_batch(embeddings, texts=[f"row {i}" for i in range(10_000)])

# Bounded-memory NDJSON export/import; --resume from a checkpoint on interruption.
db.export_ndjson("/tmp/rows.ndjson")
restored = logosdb.DB("/tmp/restored", dim=128, distance=logosdb.DIST_COSINE)
restored.import_ndjson("/tmp/rows.ndjson", chunk_size=1024,
                      checkpoint_path="/tmp/rows.cp", resume=False)
```

Run the Python tests and examples:

```bash
pip install ".[test]"
pytest tests/python/

python examples/python/basic_usage.py

# sentence-transformers demo (optional heavy dep)
pip install ".[examples]"
python examples/python/sentence_transformers_demo.py
```

## Memory-Efficient On-Prem RAG

LogosDB is designed for memory-efficient retrieval-augmented generation (RAG) that runs entirely on your hardware.

### RAM Model

LogosDB uses `mmap()` for zero-copy access. Your RAM usage scales with **query patterns**, not dataset size:

| Dataset | Dim | Disk | Typical Query RAM |
|---------|-----|------|-------------------|
| 100K | 384 | 153 MB | <20 MB |
| 1M | 384 | 1.5 GB | <100 MB |
| 10M | 384 | 15 GB | <200 MB |

The OS caches hot index pages; cold data stays on disk. No explicit loading/unloading needed.

### Quick RAG Example

```python
import numpy as np
import logosdb
from sentence_transformers import SentenceTransformer

# 1. Load model (runs locally)
model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
dim = model.get_sentence_embedding_dimension()

# 2. Create DB with cosine distance (auto-normalizes)
db = logosdb.DB("/data/knowledge", dim=dim, distance=logosdb.DIST_COSINE)

# 3. Index documents
for text in documents:
    emb = model.encode(text)
    db.put(emb, text=text)  # Auto-normalized with cosine distance

# 4. Query (only touched pages load into RAM)
query_emb = model.encode("What is HNSW?")
for hit in db.search(query_emb, top_k=3):
    print(f"{hit.score:.4f}  {hit.text}")
```

See [docs/rag-on-prem.md](docs/rag-on-prem.md) for complete guide including:
- Time-sharding for infinite retention
- External quantization patterns
- Architecture patterns for production

See [docs/sizing.md](docs/sizing.md) for detailed disk/RAM formulas and the `python -m logosdb.sizing` calculator.

Run the memory-efficient RAG example:

```bash
pip install ".[examples]"
python examples/python/memory_efficient_rag.py
```

## Python: LlamaIndex VectorStore

```bash
pip install 'logosdb[llama-index]'
```

```python
from logosdb import LogosDBIndex
from llama_index.core import Document
from llama_index.core.schema import TextNode
from llama_index.core.vector_stores import VectorStoreQuery
import numpy as np

# Create the vector store
db = LogosDBIndex(uri="/tmp/mydb", dim=128)

# Add nodes with pre-computed embeddings
node = TextNode(
    text="My commute is 42 minutes",
    embedding=np.random.randn(128).astype(np.float32).tolist(),
    metadata={"timestamp": "2025-04-28T10:00:00Z"}
)
db.add([node])

# Query
query_emb = np.random.randn(128).astype(np.float32).tolist()
query = VectorStoreQuery(query_embedding=query_emb, similarity_top_k=5)
results = db.query(query)

for node, score in zip(results.nodes, results.similarities):
    print(f"Score: {score:.4f}, Text: {node.text}")

# Timestamp range filtering
results = db.query(query, ts_from="2025-04-01T00:00:00Z", ts_to="2025-04-30T23:59:59Z")
```

The `LogosDBIndex` class implements LlamaIndex's `VectorStore` interface, supporting:

- `add(nodes)` - Add nodes with embeddings
- `delete(node_id)` - Delete by node ID
- `query(VectorStoreQuery)` - Similarity search by vector
- `count()` / `len(store)` - Number of live documents
- Timestamp filtering via `ts_from` and `ts_to` kwargs

# CLI

```bash
# Database info
logosdb-cli info /tmp/mydb

# Search with a binary query vector file
logosdb-cli search /tmp/mydb --query-file q.bin --top-k 5

# Streaming NDJSON export / import (#87) — bounded memory, optional resume
logosdb-cli export /tmp/mydb --output rows.ndjson [--start-id N --end-id M]
logosdb-cli import /tmp/mydb --dim 768 --input rows.ndjson \
    --chunk-size 1024 --checkpoint rows.cp [--resume]
```

# Node.js

## MCP Server — Claude Code integration

`logosdb-mcp-server` is a [Model Context Protocol](https://modelcontextprotocol.io/) server that
exposes LogosDB to **Claude Code** (and any other MCP client) over stdio.  It lets Claude index
files, persist knowledge across sessions, and do semantic search without leaving the conversation.

### Claude Code: complete recipe

Follow these steps in order. Claude Code spawns the MCP server with **working directory = your project root** (the folder you opened); paths in `mcp.json` and `LOGOSDB_PATH` are resolved relative to that cwd unless you use absolute paths.

#### 1. Prerequisites

- **Node.js** 18 or newer on your `PATH` (`node -v`, `npm -v`). For the published package you also need **`npx`**.
- **Claude Code** installed and signed in ([overview](https://docs.anthropic.com/en/docs/claude-code/overview)).
- Decide **where the DB lives**: `LOGOSDB_PATH` (default `./.logosdb`) is created under the project root; add the directory to `.gitignore` if you do not want it committed.

#### 2. Install the server (pick one)

**Option A — This repository (local `node`, no `npx`).** From the **repository root**:

```bash
npm install
```

That installs the `mcp` workspace and runs **`prepare`**, which compiles TypeScript to [`mcp/dist/index.js`](mcp/dist/index.js). After you change MCP sources, run `npm run mcp:build` at the root or `npm run build` inside `mcp/`.

**Option B — Any other project (published [`logosdb-mcp-server`](https://www.npmjs.com/package/logosdb-mcp-server)).** In your app’s root:

```bash
npm install logosdb-mcp-server
```

You can still invoke it without a project dependency via `npx -y logosdb-mcp-server` (see Option C).

#### 3. Register the MCP server in Claude Code

Project-local config lives in **`.claude/mcp.json`** at the repository root. User-wide config is **`~/.claude.json`** (same `mcpServers` shape). Only one definition named `logosdb` is needed.

**If you use Option A (this clone):** the repo already contains [`.claude/mcp.json`](.claude/mcp.json):

```json
{
  "mcpServers": {
    "logosdb": {
      "command": "node",
      "args": ["./mcp/dist/index.js"],
      "env": {
        "LOGOSDB_PATH": "./.logosdb"
      }
    }
  }
}
```

Open **this folder** as the Claude Code project so `./mcp/dist/index.js` resolves. If the client’s cwd is **not** the repo root, set `"args"` to an **absolute** path to `mcp/dist/index.js` (see [Installing locally — Option C](#installing-locally-without-npx)).

**If you use Option B or prefer always-latest from npm (Option C):** create or merge `.claude/mcp.json`:

```json
{
  "mcpServers": {
    "logosdb": {
      "command": "npx",
      "args": ["-y", "logosdb-mcp-server"],
      "env": {
        "LOGOSDB_PATH": "./.logosdb"
      }
    }
  }
}
```

Default embeddings are **local Transformers.js** (no API keys); first run may download model weights. Optional **Ollama / OpenAI / Voyage** env vars are documented in [`mcp/README.md`](mcp/README.md#configure).

#### 4. Path rules for `logosdb_index_file`

The server only indexes files that resolve under **`process.cwd()`** or **`LOGOSDB_INDEX_ROOT`** (if set). Symlinks that escape those roots are rejected. Keep indexing paths inside the project you opened, or set `LOGOSDB_INDEX_ROOT` to an absolute allowed root (details in [`mcp/README.md` — Path confinement](mcp/README.md#path-confinement-logosdb_index_file)).

#### 5. Reload Claude Code and verify

- **Restart** Claude Code or reload MCP configuration after editing `mcp.json`.
- The **logosdb** server starts on **first tool use** (stdio).
- Ask the agent to run **`logosdb_list`** (or check the MCP tools panel). You should see namespaces (possibly empty) rather than a spawn error.

#### 6. Index, search, delete

- **Natural language:** e.g. “Index `src/` into the `code` namespace”, “Search the codebase for JWT validation”.
- **Slash commands (optional):** if `.claude/commands/` is present in the project (this repo ships them), use `/index`, `/search`, `/forget` — see the table below.
- **Snippet memory:** the agent can call **`logosdb_index`** for short text; **`logosdb_index_file`** chunks files and directories.

Use **one embedding backend consistently** per namespace on disk (do not change model dimension on existing data). See [Environment variables](#environment-variables) and [`mcp/README.md`](mcp/README.md).

#### 7. Optional: agent instructions

Copy the **`CLAUDE.md`** template block from [Agent instructions (`CLAUDE.md` and similar)](#agent-instructions-claudemd-and-similar) into your project so the agent indexes and searches without being reminded every session.

#### 8. Troubleshooting

| Symptom | What to try |
|--------|----------------|
| MCP fails to start / “Cannot find module” | From the same cwd Claude uses, run `node ./mcp/dist/index.js` (Option A) or `npx -y logosdb-mcp-server` (Option C) in a terminal; fix missing `npm install` or broken path. |
| Relative `./mcp/dist/index.js` not found | Use an **absolute** `args` path to `index.js`, or open the correct folder as the project root. |
| `logosdb_index_file` rejects a path | Path is outside cwd / `LOGOSDB_INDEX_ROOT`; use a path inside the project or set `LOGOSDB_INDEX_ROOT`. |
| Search looks wrong after changing model | New embeddings must use a **fresh** `LOGOSDB_PATH` or new namespace; dimensions must match. |

**Google Antigravity:** same stdio MCP pattern (`node` + local script, or `npx` + package). Step-by-step: [`mcp/README.md` — Google Antigravity](mcp/README.md#google-antigravity).

### Claude Code slash commands

This repo ships **project slash commands** under [.claude/commands/](.claude/commands/) (in addition to [`.claude/mcp.json`](.claude/mcp.json) for the MCP server):

| Command | Role |
|---------|------|
| `/index` | Index a **file or directory** via `logosdb_index_file` with **`incremental: true`** (new/changed files only; `commands/index.md`) |
| `/search` | Semantic search; optional ISO `ts_from` / `ts_to` on the MCP tool (`commands/search.md`) |
| `/forget` | Delete by row `id` **or** by natural-language `query` (`commands/forget.md`) |

### Agent instructions (`CLAUDE.md` and similar)

The MCP server does not index the repository by itself: **the agent must call the tools** (or you rely on slash commands / hooks). To make LogosDB feel automatic without typing `/index` every time, add a block like the following to your project’s **`CLAUDE.md`** (or any instructions file your agent reads on every session). Adjust namespaces and paths to your repo.

````markdown
## LogosDB (semantic memory via MCP)

The **logosdb** MCP server is configured. Data lives on disk under `LOGOSDB_PATH` (see `.claude/mcp.json`); it **persists across sessions**.

**Namespaces:** Use separate namespaces for different concerns (e.g. `code` for `src/`, `docs` for `docs/`, `decisions` for short architectural notes). Search only the namespace that matches the user’s task.

**When starting substantive work on this codebase:**
1. If the user has not indexed recently and you need broad code context, call **`logosdb_index_file`** with **`incremental: true`** on the smallest useful path (e.g. `src/` or a package directory), not the whole monorepo unless asked.
2. Before answering “where is X implemented?” or similar, call **`logosdb_search`** with a tight natural-language `query`, `namespace` set appropriately, and `top_k` between **3** and **8**. Do not paste entire trees into the chat—retrieve, then read only the cited files.
3. For “what did we decide recently?” style questions, use **`logosdb_search`** with optional **`ts_from` / `ts_to`** (ISO 8601 inclusive bounds) on the `decisions` or `docs` namespace when timestamps matter.
4. When the user states a durable fact worth remembering (API contract, policy, workaround), call **`logosdb_index`** into the right namespace with concise text (timestamps are stored automatically; optional **`metadata`** can label the source).

**After large refactors or dependency upgrades:** Re-run **`logosdb_index_file`** with **`incremental: true`** on affected paths so search stays aligned without duplicating chunks for unchanged files.

**Deletion:** Use **`logosdb_delete`** with **`id`** from a prior search hit, or with **`query`** + optional **`match_rank`** / **`search_top_k`** to remove a semantically matched row when the user asks to forget something.
````

### Environment variables

| Variable | Default | Description |
|---|---|---|
| `LOGOSDB_PATH` | `./.logosdb` | Root directory for all namespace databases |
| `EMBEDDING_PROVIDER` | *(local)* | Omit for Transformers.js on-device; or `ollama`, `openai`, `voyage` |
| `TRANSFORMERS_MODEL` | `Xenova/all-MiniLM-L6-v2` | Local embedding model (bundled MCP path) |
| `OLLAMA_*` | — | See `mcp/README.md` when using Ollama |
| `OPENAI_API_KEY` | — | Required when `EMBEDDING_PROVIDER=openai` |
| `VOYAGE_API_KEY` | — | Required when `EMBEDDING_PROVIDER=voyage` |
| `LOGOSDB_CHUNK_SIZE` | `800` | Target characters per chunk for `"legacy"` and `"section"` chunking modes |
| `LOGOSDB_CHUNK_MODE` | `auto` | Chunking strategy: `auto` (per-extension), `line` (code), `section` (markdown/rst), `legacy` (original paragraph/char). See [`mcp/README.md` — Chunking strategies](mcp/README.md#chunking-strategies). |

Voyage AI (`voyage-3`, dim=1024) is Anthropic's recommended cloud embedding model:

```json
"env": {
  "LOGOSDB_PATH": "./.logosdb",
  "EMBEDDING_PROVIDER": "voyage",
  "VOYAGE_API_KEY": "<your-voyage-api-key>"
}
```

### Available tools

| Tool | Description |
|---|---|
| `logosdb_index` | Embed and store a text snippet in a namespace |
| `logosdb_index_file` | Chunk, embed, and store a file or tree; optional **`incremental: true`** (skip unchanged, replace changed, prune deleted under a directory). When the path lives inside a Git working tree the walker honours `.gitignore` (root + nested + `.git/info/exclude` + global excludes) by default; disable with `respect_gitignore: false` or `LOGOSDB_RESPECT_GITIGNORE=0`. |
| `logosdb_search` | Semantic search; optional `ts_from` / `ts_to` (ISO 8601) for timestamp-window filter |
| `logosdb_list` | List all namespaces |
| `logosdb_info` | Stats for a namespace (count, dimension, path) |
| `logosdb_delete` | Delete by row `id`, or by natural-language `query` (`search_top_k`, `match_rank`) |

### Installing locally (without npx)

**Option A — global CLI on your machine**

```bash
npm install -g logosdb-mcp-server
```

In `.claude/mcp.json`, point the server at the global binary:

```json
"logosdb": {
  "command": "logosdb-mcp-server",
  "args": [],
  "env": { "LOGOSDB_PATH": "./.logosdb" }
}
```

Ensure the directory containing the global npm binaries is on your `PATH` when Claude Code spawns the process (same shell you use for `npm install -g`).

**Option B — project-local `node_modules` (no global install)**

From your app repo:

```bash
npm install logosdb-mcp-server
```

Then use `npx` so the binary resolves from `./node_modules`:

```json
"logosdb": {
  "command": "npx",
  "args": ["-y", "logosdb-mcp-server"],
  "env": { "LOGOSDB_PATH": "./.logosdb" }
}
```

Omit `-y` if you prefer a fixed local install only. You can also call the entry script explicitly:

```json
"command": "node",
"args": ["./node_modules/logosdb-mcp-server/dist/index.js"],
"env": { "LOGOSDB_PATH": "./.logosdb" }
```

**Option C — development build from a LogosDB clone**

```bash
cd mcp && npm install && npm run build
```

This repository’s checked-in [`.claude/mcp.json`](.claude/mcp.json) uses a **relative** path `./mcp/dist/index.js` after **`npm install` from the repo root** (workspace `prepare`). For **other** clients or if the process cwd is not the repo root, use an **absolute** path to `mcp/dist/index.js`:

```json
"logosdb": {
  "command": "node",
  "args": ["/absolute/path/to/logosdb/mcp/dist/index.js"],
  "env": { "LOGOSDB_PATH": "./.logosdb" }
}
```

The same `env` block (`LOGOSDB_PATH`, embedding variables) applies to every option. Restart Claude Code or reload MCP config after editing `.claude/mcp.json`.

# Performance

Here is a performance report from the included `logosdb-bench` program. The results are somewhat noisy, but should be enough to get a ballpark performance estimate.

## Setup

We use databases with 1K, 10K, and 100K vectors. Each vector has 2048 dimensions (matching typical LLM embedding sizes). Vectors are L2-normalized random unit vectors.

*(Numbers below are illustrative ballparks; absolute values depend on hardware and build flags. Relative HNSW vs brute-force behavior is what to focus on.)*

LogosDB:    version 1.0.1
    CPU:        Apple M-series (ARM64)
    Dim:        2048
    HNSW M:     16, ef_construction: 200, ef_search: 50

## Write performance

put (1K vectors):    ~50 µs/op   (~20,000 inserts/sec)
    put (10K vectors):   ~80 µs/op   (~12,500 inserts/sec)
    put (100K vectors):  ~120 µs/op  (~8,300 inserts/sec)

Each "op" above corresponds to a write of a single vector + metadata + HNSW index update.

## Search performance

HNSW top-5 (1K):     ~0.1 ms/query
    HNSW top-5 (10K):    ~0.3 ms/query
    HNSW top-5 (100K):   ~1.2 ms/query

Brute-force top-5 (1K):    ~0.3 ms/query
    Brute-force top-5 (10K):   ~2.5 ms/query
    Brute-force top-5 (100K):  ~25 ms/query

HNSW maintains sub-linear scaling while brute-force grows linearly with database size. At 100K vectors, HNSW is roughly 20x faster.

## VectorDB Bench

[VectorDB Bench](https://github.com/zilliztech/VectorDBBench) is the standard open-source harness for comparing vector databases at realistic scale (load, optimize, serial/concurrent search, recall). LogosDB is integrated as a first-class backend:

| Location | Role |
|----------|------|
| `vectordb_bench/backend/clients/logosdb/` | Client: `config.py`, `logosdb.py`, `cli.py` |
| `vectordb_bench/backend/clients/__init__.py` | `DB.LogosDB` enum + `init_cls` / `config_cls` / `case_config_cls` mappings |
| `vectordb_bench/cli/vectordbbench.py` | CLI subcommand `logosdb` |
| `pyproject.toml` | Optional extra `logosdb` (installs the Python bindings used by the client) |

### Results — `Performance1536D50K` (2026-05-17)

OpenAI embeddings, **50K** vectors, **1536** dimensions, **cosine** distance. Serial search only (`--skip-search-concurrent`). Absolute timings depend on hardware; recall/NDCG are the primary cross-database comparators.

| Metric | Value |
|--------|-------|
| Load duration | 340.4 s |
| Insert duration | 339.6 s |
| Optimize duration | 0.8 s |
| Serial latency p99 | 4.6 ms |
| Serial latency p95 | 4.0 ms |
| Recall@100 | **0.9347** |
| NDCG | **0.9464** |

Raw JSON: `vectordb_bench/results/LogosDB/result_20260517_*.json` (under your VectorDB Bench checkout).

### Re-run

From a VectorDB Bench tree with the LogosDB client installed (`pip install -e '.[logosdb]'` or equivalent):

```bash
.venv/bin/python3.12 -m vectordb_bench.cli.vectordbbench logosdb \
  --uri /tmp/vectordbbench_logosdb \
  --case-type Performance1536D50K \
  --skip-search-concurrent
```

Use a fresh `--uri` directory per run. Set `OPENAI_API_KEY` (or the embedding provider your case expects) before starting.

## Results — `glove-25-angular` (qdrant/vector-db-benchmark)

Standard [qdrant/vector-db-benchmark](https://github.com/qdrant/vector-db-benchmark) harness, **1.18M × 25** vectors, **cosine** distance (angular). Serial search.

| Metric | Value |
|--------|-------|
| Upload time | 345 s |
| Mean precision@10 | 0.939 |
| RPS | 9,401 |
| Mean latency | 0.092 ms |
| p99 latency | 0.146 ms |

Source: [qdrant/vector-db-benchmark](https://github.com/qdrant/vector-db-benchmark).

## Benchmark vs ChromaDB

```bash
logosdb-bench --dim 2048 --counts 1000,10000,100000
```

| Metric | ChromaDB | LogosDB |
|--------|----------|---------|
| Language | Python + C (hnswlib) | Pure C/C++ |
| Search algorithm | HNSW | HNSW (same hnswlib) |
| Storage | SQLite + Parquet | Binary mmap + JSONL |
| Startup overhead | Python runtime + deps | Zero (linked library) |
| Embedding generation | Built-in (Sentence Transformers) | External (caller provides vectors) |
| Target use case | General-purpose vector store | Embedded LLM inference memory |
| Search latency (100K, dim=2048) | ~5-10 ms | ~1-3 ms |
| Memory footprint (100K, dim=2048) | ~1.5 GB (Python + SQLite) | ~800 MB (mmap) |
| Cold start | ~2-5 s (Python imports) | <10 ms |
| Dependencies | Python, NumPy, SQLite, hnswlib | hnswlib (header-only, vendored) |

LogosDB uses the same HNSW implementation as ChromaDB (hnswlib) but eliminates Python overhead, SQLite serialization, and Sentence Transformer coupling. The result is a leaner library optimized for the single use case of embedded semantic memory for LLM inference.

# Repository contents

include/logosdb/logosdb.h     Public C/C++ API (start here)
    src/logosdb.cpp               Core engine: wires storage + index + metadata
    src/storage.h / storage.cpp   Fixed-stride binary vector file with mmap
    src/metadata.h / metadata.cpp Append-only JSONL text + timestamp store
    src/hnsw_index.h / .cpp       Thin wrapper around hnswlib
    tools/logosdb-cli.cpp         Command-line interface
    tools/logosdb-bench.cpp       Benchmark tool
    tests/test_doctest.cpp        C++ unit tests (doctest, incl. CLI integration)
    tests/test_stress.cpp         Stress/property tests (opt-in via --test-suite=stress)
    tests/python/test_smoke.py    Python smoke tests (pytest)
    python/src/bindings.cpp       pybind11 Python bindings
    python/logosdb/               Python package (logosdb._core + stubs)
    examples/python/              Python usage examples
    pyproject.toml                Python build/config (scikit-build-core)
    third_party/hnswlib/          Vendored hnswlib (header-only)
    package.json / package-lock.json   Private npm workspace (MCP + `nodejs` logosdb); `npm install` at repo root only
    mcp/                          MCP server (logosdb-mcp-server npm package)
    .claude/mcp.json              Claude Code MCP config (local `node ./mcp/dist/index.js`)
    .claude/commands/             Slash command prompts (/index, /search, /forget)
    CHANGELOG                     Release history
    LICENSE                       MIT license text

# Contributing

We welcome contributions. See [CONTRIBUTING.md](CONTRIBUTING.md) for:
- Building from source
- Running tests and benchmarks
- Code style and PR workflow

Please review our [Code of Conduct](CODE_OF_CONDUCT.md) and [Security Policy](SECURITY.md).

# License

MIT — see [LICENSE](LICENSE) for the full text.

logosdb

logosdb Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

logosdb

logosdb Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

Download README