base on :brain: 100% Local Memory layer and Knowledge base for agents with WebUI <p align="center"> <img src="./static/logo.png" alt="LocalRecall Logo" width="220"/> </p> <h3 align="center"><em>Your AI. Your Hardware. Your Rules.</em></h3> <div align="center"> [](https://goreportcard.com/report/github.com/mudler/LocalRecall) [](https://opensource.org/licenses/MIT) [](https://github.com/mudler/LocalRecall/stargazers) [](https://github.com/mudler/LocalRecall/issues) </div> A lightweight, no-frills RESTful API designed for managing knowledge bases and files stored in vector databases—**no GPU, internet, or cloud services required**! LocalRecall provides a simple and generic abstraction layer to handle knowledge retrieval, ideal for AI agents and chatbots to manage both long-term and short-term memory seamlessly. Currently, LocalRecall is batteries included and supports a local vector store powered by **Chromem**, with plans to add additional vector stores such as **Milvus** and **Qdrant**. It can easily integrate with LocalAI, LocalAGI, and other agent frameworks, offering an intuitive web UI for convenient file management, including support for raw text inputs. ## 📚🆕 Local Stack Family 🆕 LocalAI is now part of a comprehensive suite of AI tools designed to work together: <table> <tr> <td width="50%" valign="top"> <a href="https://github.com/mudler/LocalAI"> <img src="https://raw.githubusercontent.com/mudler/LocalAI/refs/heads/master/core/http/static/logo_horizontal.png" width="300" alt="LocalAI Logo"> </a> </td> <td width="50%" valign="top"> <h3><a href="https://github.com/mudler/LocalAI">LocalAI</a></h3> <p>LocalAI is the free, Open Source OpenAI alternative. LocalAI act as a drop-in replacement REST API that's compatible with OpenAI API specifications for local AI inferencing. Does not require GPU.</p> </td> </tr> <tr> <td width="50%" valign="top"> <a href="https://github.com/mudler/LocalAGI"> <img src="https://raw.githubusercontent.com/mudler/LocalAGI/refs/heads/main/webui/react-ui/public/logo_2.png" width="300" alt="LocalAGI Logo"> </a> </td> <td width="50%" valign="top"> <h3><a href="https://github.com/mudler/LocalAGI">LocalAGI</a></h3> <p>A powerful Local AI agent management platform that serves as a drop-in replacement for OpenAI's Responses API, enhanced with advanced agentic capabilities.</p> </td> </tr> </table> --- ## 🌟 Features - ⚡ **RESTful API**: Simple and intuitive REST interface for knowledge management. - 📡 **Fully Local**: Operates offline without external cloud dependencies. - 📚 **RAG Knowledgebase**: Retrieve-Augmented Generation (RAG) compatible with multiple vector databases. - 🗃️ **Memory Management**: Ideal for AI-driven applications requiring memory abstraction. - 📂 **File Support**: - ✅ Markdown - ✅ Plain Text - ✅ PDF - ⏳ More formats coming soon! --- ## ⚙️ Prerequisites - **Go** 1.16 or higher - **Docker** (optional, for containerized deployment) --- ## 🚧 Quickstart ### 📥 Clone Repository ```sh git clone https://github.com/mudler/LocalRecall.git cd LocalRecall ``` ### 🛠️ Build from Source ```sh go build -o localrecall ``` ### ▶️ Run Application ```sh ./localrecall ``` Your web UI will be available at `http://localhost:8080`. --- ## 🐳 Docker Deployment Build and run using Docker: ```sh docker build -t localrecall . docker run -ti -v $PWD/state:/state \ -e COLLECTION_DB_PATH=/state/db \ -e EMBEDDING_MODEL=granite-embedding-107m-multilingual \ -e FILE_ASSETS=/state/assets \ -e OPENAI_API_KEY=sk-1234567890 \ -e OPENAI_BASE_URL=http://localai:8080 \ -p 8080:8080 localrecall # Or use the images already built by the CI: docker run -ti -v $PWD/state:/state \ -e COLLECTION_DB_PATH=/state/db \ -e EMBEDDING_MODEL=granite-embedding-107m-multilingual \ -e FILE_ASSETS=/state/assets \ -e OPENAI_API_KEY=sk-1234567890 \ -e OPENAI_BASE_URL=http://localai:8080 \ -p 8080:8080 quay.io/mudler/localrecall ``` or with Docker compose ```sh docker compose up -d ``` --- ## 🌍 Environment Variables LocalRecall uses environment variables to configure its behavior. These variables allow you to customize paths, models, and integration settings without modifying the code. | Variable | Description | | -------------------- | --------------------------------------------------------------------------------------------------------------- | | `COLLECTION_DB_PATH` | Path to the vector database directory where collections are stored. | | `EMBEDDING_MODEL` | Name of the embedding model used for vectorization (e.g., `granite-embedding-107m-multilingual`). | | `FILE_ASSETS` | Directory path to store and retrieve uploaded file assets. | | `OPENAI_API_KEY` | API key for embedding services (such as LocalAI or OpenAI-compatible APIs). | | `OPENAI_BASE_URL` | Base URL for the embedding model API (commonly `http://localai:8080`). | | `LISTENING_ADDRESS` | Address the server listens on (default: `:8080`). Useful for deployments on custom ports or network interfaces. | | `VECTOR_ENGINE` | Vector database engine to use (`chromem` by default; support for others like Milvus and Qdrant planned). | | `MAX_CHUNKING_SIZE` | Maximum size (in characters) for breaking down documents into chunks. Affects performance and accuracy. | | `API_KEYS` | Comma-separated list of API keys for securing access to the REST API (optional). | | `GIT_PRIVATE_KEY` | Base64-encoded SSH private key for accessing private Git repositories (optional). | These variables can be passed directly when running the binary or inside your Docker container for easy configuration. You can use an `.env` file to set the variables. The Docker compose file is configured to use an `.env` file in the root of the project if available. --- ## 📖 REST API **Base URL**: `http://localhost:8080/api` ### 🔧 Manage Collections - **Create Collection**: ```sh curl -X POST $BASE_URL/collections \ -H "Content-Type: application/json" \ -d '{"name":"myCollection"}' ``` - **Upload File**: ```sh curl -X POST $BASE_URL/collections/myCollection/upload \ -F "file=@/path/to/file.txt" ``` - **List Collections**: ```sh curl -X GET $BASE_URL/collections ``` - **List Files in Collection**: ```sh curl -X GET $BASE_URL/collections/myCollection/entries ``` - **Search Collection**: ```sh curl -X POST $BASE_URL/collections/myCollection/search \ -H "Content-Type: application/json" \ -d '{"query":"search term", "max_results":5}' ``` - **Reset Collection**: ```sh curl -X POST $BASE_URL/collections/myCollection/reset ``` - **Delete Entry**: ```sh curl -X DELETE $BASE_URL/collections/myCollection/entry/delete \ -H "Content-Type: application/json" \ -d '{"entry":"file.txt"}' ``` - **Add External Source**: ```sh curl -X POST $BASE_URL/collections/myCollection/sources \ -H "Content-Type: application/json" \ -d '{"url":"https://example.com", "update_interval":30}' ``` The `update_interval` is specified in minutes. If not provided, it defaults to 60 minutes. External sources support various URL types: - Web pages (https://example.com) - Git repositories (https://github.com/user/repo.git or [email protected]:user/repo.git) - Sitemaps (https://example.com/sitemap.xml) For private Git repositories, set the `GIT_PRIVATE_KEY` environment variable with a base64-encoded SSH private key: ```sh # Encode your private key export GIT_PRIVATE_KEY=$(cat /path/to/private_key | base64 -w 0) ``` - **Remove External Source**: ```sh curl -X DELETE $BASE_URL/collections/myCollection/sources \ -H "Content-Type: application/json" \ -d '{"url":"https://example.com"}' ``` External sources are automatically monitored and updated in the background. The content is periodically fetched and added to the collection, making it searchable through the regular search endpoint. --- ## 📝 License Released under the [MIT License](LICENSE). --- ## 🤝 Contributing We welcome contributions! Please feel free to: - ✅ Open an issue for suggestions or bugs. - ✅ Submit a pull request with enhancements. ", Assign "at most 3 tags" to the expected json: {"id":"14326","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"