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Choose the one that best fits your needs:\n\n| VDB | Status | Local | Cloud | Best For |\n|-----|--------|-------|-------|----------|\n| **[Weaviate](docs/weaviate/SETUP.md)** | ✅ Stable | ✅ | ✅ | Production, all features, easiest setup |\n| **[Qdrant](docs/qdrant/SETUP.md)** | ✅ Stable | ✅ | ✅ | Rust performance, HNSW index, filtering |\n| **[Milvus](docs/milvus/SETUP.md)** | ✅ Stable | ✅ | ✅ | High performance, horizontal scaling |\n| **[Chroma](docs/chroma/SETUP.md)** | ✅ Stable | ✅ | ✅ | macOS only, simple setup, embeddings |\n| **[Supabase](docs/supabase/SETUP.md)** | ✅ Stable | ✅ | ✅ | PostgreSQL + pgvector, cost-effective |\n| **[Neo4j](docs/neo4j/README.md)** | ✅ Stable | ✅ | ⚠️ Untested | Graph + vector search, Cypher queries |\n| **[MongoDB](docs/mongodb/SETUP.md)** | ✅ Stable | ❌ | ✅ | Atlas Vector Search, existing MongoDB users |\n| **[Pinecone](docs/pinecone/SETUP.md)** | 🟢 Beta | ❌ | ✅ | Serverless, auto-scaling, managed only |\n| **[OpenSearch](docs/opensearch/README.md)** | ✅ Stable | ✅ | ✅ | AWS OpenSearch, k-NN + BM25 hybrid |\n| **[Elasticsearch](docs/elasticsearch/)** | 🟢 Beta | ✅ | ✅ | Elastic Cloud, HNSW vector + BM25 hybrid |\n\n📖 **See [Vector Database Support Matrix](docs/VDB_SUPPORT_MATRIX.md)\nfor detailed feature comparison**\n\n### Quick Setup (Weaviate - Recommended)\n\n```bash\n# Interactive configuration - fastest way to get started\nweave config create --env\n\n# Follow prompts to enter:\n# - WEAVIATE_URL\n# - WEAVIATE_API_KEY\n# - OPENAI_API_KEY\n\n# Verify setup\nweave health check\n```\n\nFor other databases, see their setup guides linked in the table above.\n\n### Basic Usage\n\n```bash\n# List collections (all configured VDBs)\nweave cols ls\n\n# List collections from specific database types\nweave cols ls --weaviate            # Weaviate only\nweave cols ls --qdrant-local        # Qdrant local only\nweave cols ls --qdrant-cloud        # Qdrant cloud only\nweave cols ls --milvus-local        # Milvus local only\nweave cols ls --milvus-cloud        # Milvus cloud (Zilliz) only\nweave cols ls --chroma-local        # Chroma local only\nweave cols ls --chroma-cloud        # Chroma cloud only\nweave cols ls --supabase            # Supabase only\nweave cols ls --neo4j-local         # Neo4j local only\nweave cols ls --neo4j-cloud         # Neo4j cloud (Aura) only\nweave cols ls --mongodb             # MongoDB Atlas only\nweave cols ls --pinecone            # Pinecone only\nweave cols ls --opensearch-local    # OpenSearch local only\nweave cols ls --opensearch-cloud    # OpenSearch cloud (AWS) only\nweave cols ls --elasticsearch-local # Elasticsearch local only\nweave cols ls --elasticsearch-cloud # Elasticsearch cloud (Elastic) only\nweave cols ls --mock                # Mock database only\nweave cols ls --all                 # All configured databases\n\n# Create a collection\nweave cols create MyCollection --text\n\n# Add documents\nweave docs create MyCollection document.txt\nweave docs create MyCollection document.pdf\n\n# Search with natural language\nweave cols q MyCollection \"search query\"\n\n# AI-powered Read, Evaluate, Print, Loop (REPL) mode or agent mode\nweave\n\u003e show me all my collections\n\u003e create TestDocs collection\n\u003e add README.md to TestDocs\n\n# Or doing one query at a time\nweave query \"show me all my collections\"\n\n# List available embeddings\nweave embeddings list\nweave emb ls --verbose\n\n# Create collection with specific embedding (used as default for all documents)\nweave cols create MyCollection --embedding text-embedding-3-small\nweave cols create MyCollection -e text-embedding-ada-002\n\n# Get AI-powered schema suggestions for your documents\nweave schema suggest ./docs --collection MyDocs --output schema.yaml\n\n# Get AI-powered chunking recommendations\nweave chunking suggest ./docs --collection MyDocs --output chunking.yaml\n```\n\n## Key Features\n\n- 🤖 **AI-Powered** - AI Agent mode, natural language interface with GPT-4o\n  multi-agent system, schema suggestions, and chunking recommendations\n- ⚡ **Fast \u0026 Easy** - Written in Go with simple CLI and interactive REPL\n  (AI Agent mode) with real-time progress feedback\n- 🌐 **Flexible** - Weaviate Cloud, local instances, or built-in mock database\n- 🔌 **Extensible** - Vector database abstraction layer supporting multiple\n  backends (Weaviate, Milvus, Supabase PGVector, MongoDB Atlas, Chroma, Qdrant,\n  Neo4j, OpenSearch)\n- 📦 **Batch Processing** - Parallel processing of entire directories\n- 📄 **PDF Support** - Intelligent text extraction and image processing\n- 🔍 **Semantic Search** - Vector-based similarity search with natural\n  language, including multi-collection queries\n- 🧠 **AI Schema \u0026 Chunking** - Analyze documents and get AI-powered schema and\n  optimal chunk size recommendations\n- 📊 **Embeddings** - List and explore available embedding models\n- ⏱️ **Configurable Timeouts** - Default 10s timeout, adjustable per\n  command\n\n## Documentation\n\n### Core Documentation\n\n- **[📖 User Guide](docs/USER_GUIDE.md)** - Complete feature documentation\n- **[📋 Changelog](docs/CHANGELOG.md)** - Version history and updates\n- **[🗂️ VDB Support Matrix](docs/VDB_SUPPORT.md)** - Database feature\n  comparison\n\n### Guides\n\n- **[🤖 AI Agents](docs/guides/WEAVE_CLI_AI.md)** - REPL mode with natural\n  language query system\n- **[🔌 MCP AI Tools API](docs/mcp/MCP_AI_TOOLS.md)** - Using AI tools via MCP server\n- **[📦 Batch Processing](docs/guides/BATCH_DOCS_CREATION.md)** - Directory\n  processing guide\n- **[📚 Vector DB Abstraction](docs/guides/VECTOR_DB_ABSTRACTION.md)** -\n  Multi-database support architecture\n- **[🎬 Demos](docs/guides/DEMO.md)** - Video demos and tutorials\n\n### Database-Specific\n\n- **[Chroma Documentation](docs/chroma/)** - Chroma integration guide (Stable)\n- **[Milvus Documentation](docs/milvus/)** - Milvus integration guide (Beta)\n- **[MongoDB Atlas Documentation](docs/mongodb/)** - MongoDB Atlas setup guide (Stable)\n- **[Neo4j Documentation](docs/neo4j/)** - Neo4j integration guide (Experimental)\n- **[OpenSearch Documentation](docs/opensearch/)** - OpenSearch integration\n  guide (Experimental)\n- **[Pinecone Documentation](docs/pinecone/)** - Pinecone integration guide (Beta)\n- **[Qdrant Documentation](docs/qdrant/)** - Qdrant integration guide (Stable)\n- **[Supabase Documentation](docs/supabase/)** - Supabase integration guide (Alpha)\n- **[Weaviate Documentation](docs/weaviate/)** - Weaviate integration status (Stable)\n\n## Advanced Usage\n\n### Configuration Options\n\n#### Auto-Configuration\n\nWeave CLI automatically detects missing configuration:\n\n```bash\n# Try any command - you'll get prompted to configure interactively\nweave cols ls\n\n# Or install latest release of weave-mcp for REPL mode\nweave config update --weave-mcp\n```\n\n**Configuration Precedence** (highest to lowest):\n\n1. Command-line flags - `weave query --model gpt-4`\n2. Environment variables - `export OPENAI_MODEL=gpt-4`\n3. config.yaml (optional) - For advanced customization\n4. Built-in defaults\n\n**Configuration Location** (precedence order):\n\n1. Local directory (`.env`, `config.yaml`) - Project-specific\n   configuration\n2. Global directory (`~/.weave-cli/.env`, `~/.weave-cli/config.yaml`) -\n   User-wide configuration\n\n```bash\n# Create configuration in global directory\nweave config create --env --global\n\n# Sync local configuration to global directory\nweave config sync\n\n# View which configuration location is being used\nweave config show\n```\n\nSee the [User Guide](docs/USER_GUIDE.md#configuration) for detailed\nconfiguration options.\n\n### Vector Database Selection\n\nControl which vector database(s) to operate on with these flags:\n\n**Important**: Database selection behavior depends on your configuration:\n\n- **Single Database**: If only one DB is configured, it's used automatically\n  (no flags needed!)\n- **Multiple Databases**:\n  - Read operations (ls, show, count) use all databases by default\n  - Write/delete operations use smart selection:\n    1. **Default Database**: Uses `VECTOR_DB_TYPE` from `.env` or config\n    2. **Weaviate Collection Search**: For `--weaviate`, searches all\n       Weaviate databases for the collection\n    3. **Manual Selection**: Use `--vector-db-type` (or `--vdb`) to\n       specify explicitly\n\n```bash\n# Single database setup - no flags needed!\nweave docs create MyCollection doc.txt       # Uses your only configured DB\n\n# Multiple databases with VECTOR_DB_TYPE set\nexport VECTOR_DB_TYPE=weaviate-cloud\nweave docs create MyCollection doc.txt       # Uses weaviate-cloud (default)\nweave docs delete MyCollection doc123        # Uses weaviate-cloud (default)\n\n# Override default with --vdb (short) or --vector-db-type (long)\nweave docs create MyCollection doc.txt --vdb weaviate-local\nweave docs create MyCollection doc.txt --vector-db-type supabase\n\n# --weaviate tries both weaviate-cloud and weaviate-local\nweave docs ls MyCollection --weaviate        # Searches both for collection\nweave cols delete MyCollection --weaviate    # Searches both for collection\n\n# Read operations work with specific or all databases\nweave cols ls --weaviate                     # All Weaviate databases\nweave cols ls --supabase                     # Supabase only\nweave cols ls --all                          # All configured databases (default)\n\n# Query multiple databases at once\nweave cols query MyCollection \"search\" --weaviate --supabase\n```\n\n**Database Selection Priority for Single-DB Operations**:\n\n1. If only one database configured → use it\n2. If `VECTOR_DB_TYPE` set → use as default\n3. If `--weaviate` flag used → try all Weaviate databases for the collection\n4. Otherwise → show error with available options\n\n### Summary Views and Filtering (New in v0.7.2)\n\nView database status and collections across multiple databases with summary\ntables and progressive output:\n\n```bash\n# Collections summary across all databases (default for multiple VDBs)\nweave cols ls                    # Shows summary table by default\nweave cols ls -S                 # Explicit summary flag (shorthand)\nweave cols ls --summary          # Explicit summary flag (long form)\n\n# Health check with progressive output (new in v0.7.2)\nweave health check               # Shows summary table, results appear\nweave health check -S            # Same as above (shorthand)\n\n# Filter databases by deployment type (new in v0.7.2)\nweave config list --cloud        # Show only cloud databases\nweave config list --local        # Show only local databases\n\nweave health check --cloud       # Check only cloud databases\nweave health check --local       # Check only local databases\nweave health check --local -S    # Local databases summary\n\n# Force detailed view for single database\nweave cols ls --weaviate         # Detailed list (default for single VDB)\nweave health check weaviate      # Detailed health check\n\n# Collections summary also supports filtering\nweave cols ls --cloud            # Collections from cloud databases only\nweave cols ls --local -S         # Local collections summary\n```\n\n**Summary Table Features**:\n\n- **Progressive Output**: Results appear immediately as they're\n  retrieved/checked (no waiting!)\n- **Status Indicators**: ✓ OK (green) or ✗ FAIL (red) with color\n  coding\n- **Footer Statistics**: Total count, collections/healthy count,\n  failures\n- **Auto-Selection**: Summary for multiple VDBs, detailed for single\n  VDB\n- **Cloud/Local Filtering**: Filter by deployment type with `--cloud`\n  or `--local` flags\n- **Consistent UX**: Same behavior across `cols ls`, `health check`,\n  and `config list`\n\n### RAG Agents (All Vector Databases)\n\nEnhance query results with AI-powered RAG (Retrieval-Augmented Generation)\nagents. Now works with **all supported vector databases**, not just Weaviate!\n\n```bash\n# Use RAG agent with any vector database\nweave cols query MyDocs \"What is machine learning?\" --agent rag-agent\nweave cols query MyDocs \"Summarize main topics\" --agent summarize-agent --db qdrant\nweave cols query MyDocs \"Answer this question\" --agent qa-agent --db milvus\n\n# Combine with database selection\nweave cols q MyDocs \"AI overview\" --agent rag-agent --qdrant-local\nweave cols q MyDocs \"Quick summary\" --agent summarize-agent --chroma-local\nweave cols q MyDocs \"Detailed analysis\" --agent rag-agent --mongodb\n\n# Show progress during agent execution\nweave cols q MyDocs \"complex query\" --agent rag-agent --progress\n\n# JSON output with progress (JSON Lines format)\nweave cols q MyDocs \"query\" --agent rag-agent --json --progress\n\n# Multi-collection queries (query multiple collections at once)\nweave cols query WeaveDocs WeaveImages \"weave cli\" --agent rag-agent --top_k 3\nweave cols query AuctionsDocs AuctionsImages AuctionResults \"vintage cars\" --agent rag-agent\n```\n\n**Multi-Collection Queries:**\n\nQuery multiple collections simultaneously and aggregate results. The command\nreturns top K results from EACH collection, which are then combined and passed\nto the agent for processing. Each result includes `_collection` metadata to\ntrack its source.\n\n```bash\n# Query 2 collections (returns top 3 from EACH)\nweave cols query Collection1 Collection2 \"search query\" --agent rag-agent --top_k 3\n\n# Query 3+ collections (useful for multi-modal data)\nweave cols query Docs Images Audio \"query\" --agent summarize-agent --top_k 5\n```\n\n**Cross-VDB Queries:**\n\nQuery collections from different vector databases in a single command. Use the\n`Collection:vdb-key` syntax to specify which VDB each collection resides in.\nResults include both collection name and VDB information in citations.\n\n```bash\n# Query collections from different VDBs\nweave cols query WeaveDocs:weaviate-local WeaveImages:milvus-local \"weave cli\" --agent rag-agent\n\n# AuctionsMax.ai example: Query across MongoDB, Weaviate, and Milvus\nweave cols query \\\n  AuctionListings:mongodb-cloud \\\n  AuctionResults:weaviate-cloud \\\n  AuctionImages:milvus-cloud \\\n  \"vintage Leica cameras\" \\\n  --agent rag-agent --top_k 3\n\n# Mixed: explicit VDB + default from flags\nweave cols query WeaveDocs WeaveImages:milvus-local \"query\" --weaviate-local --agent rag-agent\n```\n\nSupported VDB keys: `weaviate-local`, `weaviate-cloud`, `milvus-local`,\n`milvus-cloud`, `mongodb-cloud`, `qdrant-local`, `qdrant-cloud`,\n`neo4j-local`, `neo4j-cloud`, `chroma-local`, `chroma-cloud`,\n`supabase-cloud`, etc.\n\n**Available Agents:**\n\n- `rag-agent` - Comprehensive answers with source citations\n- `summarize-agent` - Concise summaries of retrieved content\n- `qa-agent` - Precise question answering\n\n**Requirements:**\n\n- `OPENAI_API_KEY` environment variable must be set\n- Works with: Qdrant, Milvus, Chroma, MongoDB, Neo4j, Weaviate, Supabase,\n  and more\n\nSee [User Guide](docs/USER_GUIDE.md#rag-agents) for custom agent configuration.\n\n### More Examples\n\n```bash\n# Batch process documents with parallel workers\nweave docs batch --directory ./docs --collection MyCollection --parallel 3\n\n# Convert CMYK PDFs to RGB\nweave docs pdf-convert document.pdf --rgb\n\n# Text-only PDF extraction (faster, no images)\nweave docs create MyCollection document.pdf --skip-all-images\n\n# Natural language queries with AI agents\nweave q \"find all empty collections\"\nweave query \"create TestDocs and add README.md\" --dry-run\n\n# Configure timeout for slow connections\nweave cols ls --timeout 30s\nweave health check --timeout 60s\n\n# Create collections and documents with specific embeddings\nweave cols create MyCollection --embedding text-embedding-3-small\nweave docs create MyCollection document.txt --embedding text-embedding-3-small\nweave docs create MyCollection report.pdf --embedding text-embedding-ada-002\n```\n\n## Database Support\n\nWeave CLI features a **pluggable vector database abstraction layer** that\nallows seamless switching between different vector database backends.\n\n### Support Matrix\n\n| Database | Type | Status | Maturity | Docs |\n| -------- | ---- | ------ | -------- | ---- |\n| **Weaviate Cloud** | `weaviate-cloud` | ✅ Production Ready | **Stable** | [Guide](docs/) |\n| **Weaviate Local** | `weaviate-local` | ✅ Production Ready | **Stable** | [Guide](docs/) |\n| **Milvus Local** | `milvus-local` | ✅ Functional | **Beta** - Feature complete, local testing ready | [Guide](docs/milvus/) |\n| **Milvus Cloud** | `milvus-cloud` | ✅ Functional | **Beta** - Zilliz cloud integration ready | [Guide](docs/milvus/) |\n| **Supabase** | `supabase` | ✅ Functional | **Alpha** - Feature complete, needs testing | [Guide](docs/supabase/) |\n| **MongoDB Atlas** | `mongodb` | ✅ Functional | **Experimental** - Vector search requires index setup | [Guide](docs/mongodb/) |\n| **Chroma Local** | `chroma-local` | ✅ Production Ready | **Stable** - Full CRUD, tested with v2 API ⚠️ **macOS only** | [Guide](docs/chroma/) |\n| **Chroma Cloud** | `chroma-cloud` | ✅ Functional | **Beta** - Cloud integration ⚠️ **macOS only** | [Guide](docs/chroma/) |\n| **Qdrant Local** | `qdrant-local` | ✅ Production Ready | **Stable** - HNSW vector search, full CRUD | [Guide](docs/qdrant/) |\n| **Neo4j Local** | `neo4j-local` | ✅ Functional | **Experimental** - Graph + vector search | [Guide](docs/neo4j/) |\n| **OpenSearch Local** | `opensearch-local` | ✅ Functional | **Experimental** - k-NN with HNSW algorithm | [Guide](docs/opensearch/) |\n| **OpenSearch Cloud** | `opensearch-cloud` | ✅ Functional | **Experimental** - AWS OpenSearch Service | [Guide](docs/opensearch/) |\n| **Mock** | `mock` | ✅ Testing Only | **Stable** | - |\n\n### Maturity Levels\n\n- **Stable**: Production-ready, well-tested, recommended for all use cases\n- **Beta**: Feature complete, functional, ready for testing and feedback\n- **Alpha**: Feature complete, functional, recommended for\n  development/testing\n- **Experimental**: Basic functionality working, may require manual setup,\n  use with caution\n\n### Platform Compatibility\n\n⚠️ **Important Platform Limitations:**\n\n- **Chroma**: macOS only (CGO dependency on `libtokenizers`)\n  - Linux/Windows users: Use Weaviate, Milvus, Qdrant, or Supabase instead\n  - CI/CD on Linux: Skip Chroma tests with `--skip chroma`\n- **MongoDB**: Cloud (Atlas) only - no local/self-hosted support\n  - Offline deployments: Use Weaviate, Milvus, Qdrant, or Neo4j instead\n- **All other VDBs**: Cross-platform (Linux, macOS, Windows)\n\n### Additional Resources\n\n- **[Vector DB Integrations Planning](docs/planning/VECTOR_DB_INTEGRATIONS.md)**\n  \\- Roadmap for upcoming database support\n- **[Vector DB Abstraction Guide](docs/VECTOR_DB_ABSTRACTION.md)** -\n  Architecture details and how to add new databases\n\n### Abstraction Benefits\n\n- **Unified Interface** - Same commands work across all database types\n- **Easy Migration** - Switch databases without changing workflows\n- **Extensible** - Add new vector databases with minimal code changes\n- **Type Safety** - Compile-time validation of database operations\n- **Error Handling** - Structured error types with context and recovery\n\nSee **[📚 Vector DB Abstraction Guide](docs/VECTOR_DB_ABSTRACTION.md)** for\nimplementation details and adding new database support.\n\n## Development\n\n```bash\n# Setup development environment (installs linters, PDF tools, etc.)\n./setup.sh\n\n# Build, test, and lint\n./build.sh\n./test.sh\n./lint.sh\n```\n\nSee [User Guide](docs/USER_GUIDE.md) for detailed development instructions.\n\n## Testing \u0026 Quality\n\n### Test Coverage\n\nComprehensive integration tests ensure reliability across all supported vector databases:\n\n| VectorDB | Coverage | Integration Tests | Status |\n|----------|----------|-------------------|--------|\n| **Milvus** | 51.5% | 15/15 ✅ | Production Ready |\n| **Qdrant** | 45.8% | 14/14 ✅ | Production Ready |\n| **MongoDB** | 59.1% | 13/13 ✅ | Production Ready |\n| **Weaviate** | 23.6% | 13/13 ✅ | Production Ready |\n| **Neo4j** | 37.9% | 13/13 ✅ | Production Ready |\n| **Chroma** | 49.1% | 12/12 ✅ | Production Ready |\n| **Supabase** | 30%+ | 11/11 ✅ | Production Ready |\n\n**Total**: 91 integration tests covering CRUD operations, collections,\nsearch functionality (semantic, BM25, hybrid, metadata), and end-to-end\nworkflows.\n\n### Running Tests\n\n```bash\n# Unit tests only (fast)\ngo test ./...\n\n# Integration tests (requires Docker)\ngo test -tags=integration ./src/pkg/vectordb/...\n\n# Full test suite with coverage\n./test.sh integration\n\n# Specific VDB tests\ngo test -tags=integration -run TestIntegration_Milvus ./src/pkg/vectordb/milvus/\n```\n\nSee [Integration Test Plan](docs/planning/INTEGRATION_TEST_PLAN.md) for\ndetailed testing strategy.\n\n## Contributing\n\nContributions welcome! Please:\n\n1. Fork the repository\n2. Create a feature branch\n3. Make your changes with tests\n4. Run `./test.sh` and `./lint.sh`\n5. Submit a pull request\n\n## Links\n\n### Video Demos\n\n- **[Full Demo (5 min)](https://asciinema.org/a/LrKzmThBfDbTPISZzr8biP4dt)** -\n  Complete feature walkthrough\n- **[Quick Demo (2 min)](https://asciinema.org/a/HiAU7h1iJvZ2QdJe70ae3Cc0b)** -\n  Quick overview\n- **[REPL Demo](https://asciinema.org/a/U504HN4FSeMsOA0qS0os0NWUE)** -\n  AI-powered natural language interface\n\n### Interactive Demos\n\nRun these scripts locally for hands-on demonstrations:\n\n- **[Configuration Demo](demos/config-demo.sh)** - Interactive setup and\n  configuration management\n- **[Supabase Demo](demos/supabase-demo.sh)** - Supabase (PostgreSQL +\n  pgvector) integration\n\nSee [demos/README.md](demos/README.md) for details.\n\n### Resources\n\n- **[GitHub Repository](https://github.com/maximilien/weave-cli)**\n- **[Documentation](docs/)**\n- **[User Guide](docs/USER_GUIDE.md)**\n\n## Presentations\n\nRecent presentations about Weave CLI:\n\n- **[AI by the Bay (Nov 2025)](presentations/vdbs-ai-by-the-bay-11-25.pptx)** -\n  Vector database management with AI agents\n  - Event: [AI by the Bay Conference](https://ai.bythebay.io/)\n\n- **[SV AI Demo 2 (Nov 2025)](presentations/vdbs-svai-demo-11-25.pptx)** -\n  Multi-database vector management demo\n  - Event: [SV AI Demo Meetup](https://luma.com/bs4fkbjs?tk=bNbVMV)\n\n## License\n\nMIT License - see [LICENSE](LICENSE) file for details.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmaximilien%2Fweave-cli","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmaximilien%2Fweave-cli","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmaximilien%2Fweave-cli/lists"}