diff --git a/.env.example b/.env.example index eea3180..9fa6813 100644 --- a/.env.example +++ b/.env.example @@ -17,6 +17,10 @@ CHUNK_OVERLAP=200 TOP_K_RETRIEVAL=5 RERANK_TOP_N=3 +# KB Type Configuration +# Default type for new knowledge bases: generic, code, documentation, agent-os +DEFAULT_KB_TYPE=generic + # Storage Configuration UPLOAD_DIR=/workspace/data/uploads diff --git a/.gitignore b/.gitignore index 64b836b..9fb1bcc 100644 --- a/.gitignore +++ b/.gitignore @@ -26,6 +26,18 @@ env/ ENV/ .venv +# Node.js / React / Frontend +node_modules/ +frontend/dist/ +frontend/.vite/ +*.tsbuildinfo +npm-debug.log* +yarn-debug.log* +yarn-error.log* +pnpm-debug.log* +.npm +.yarn + # IDE .vscode/ .idea/ @@ -40,6 +52,8 @@ ENV/ # Data Directories data/chroma/ data/uploads/ +data/ollama/ +data/postgres/ *.db *.sqlite diff --git a/.streamlit/config.toml b/.streamlit/config.toml index 9ebbc12..e8306fc 100644 --- a/.streamlit/config.toml +++ b/.streamlit/config.toml @@ -15,3 +15,11 @@ enableCORS = false [browser] gatherUsageStats = false +[ui] +# Sidebar configuration +hideTopBar = false +hideSidebarNav = false + +[client] +toolbarMode = "minimal" + diff --git a/README.md b/README.md index 081cbd4..cc53b97 100644 --- a/README.md +++ b/README.md @@ -1,32 +1,106 @@ -# 🔥 Code-Forge +# Code-Forge -**Localized Multi-Knowledge-Base RAG System with MCP Integration** +
-A self-hosted, privacy-first RAG (Retrieval-Augmented Generation) application designed for development teams. Features a badass Archon-inspired UI and exposes knowledge bases via Model Context Protocol (MCP) for AI agent integration. +``` +╔═══════════════════════════════════════════════════════════════╗ +║ ║ +║ ██████╗ ██████╗ ██████╗ ███████╗ ███████╗ ██████╗ ██████╗ ██████╗ ███████╗ +║ ██╔════╝██╔═══██╗██╔══██╗██╔════╝ ██╔════╝██╔═══██╗██╔══██╗██╔════╝ ██╔════╝ +║ ██║ ██║ ██║██║ ██║█████╗ █████╗ ██║ ██║██████╔╝██║ ███╗█████╗ +║ ██║ ██║ ██║██║ ██║██╔══╝ ██╔══╝ ██║ ██║██╔══██╗██║ ██║██╔══╝ +║ ╚██████╗╚██████╔╝██████╔╝███████╗ ██║ ╚██████╔╝██║ ██║╚██████╔╝███████╗ +║ ╚═════╝ ╚═════╝ ╚═════╝ ╚══════╝ ╚═╝ ╚═════╝ ╚═╝ ╚═╝ ╚═════╝ ╚══════╝ +║ ║ +║ Localized Multi-Knowledge-Base RAG System ║ +║ with MCP Integration ║ +║ ║ +╚═══════════════════════════════════════════════════════════════╝ +``` + +**Production-grade RAG system with PostgreSQL + pgvector, Ollama LLMs, and Model Context Protocol** + +[![License: MIT](https://img.shields.io/badge/License-MIT-purple.svg)](LICENSE) +[![Python 3.11+](https://img.shields.io/badge/python-3.11+-blue.svg)](https://www.python.org/downloads/) +[![PostgreSQL 17](https://img.shields.io/badge/PostgreSQL-17-green.svg)](https://www.postgresql.org/) +[![Ollama](https://img.shields.io/badge/Ollama-Latest-pink.svg)](https://ollama.ai/) + +
+ +--- + +## 🚀 What is Code-Forge? + +**Code-Forge** is a **localized, production-grade RAG (Retrieval-Augmented Generation) system** that runs entirely on your machine. It combines: + +- **🗄️ PostgreSQL + pgvector** - ACID-compliant vector database with 768-dimensional embeddings +- **🤖 Ollama** - Local LLMs (llama3.2, llama3.1) and embeddings (nomic-embed-text) +- **🔌 MCP (Model Context Protocol)** - HTTP API for AI agent integration +- **⚡ React + FastAPI UI** - Modern, beautiful interface with electric teal design +- **📚 Multi-KB Architecture** - Isolated knowledge bases per project +- **🧠 Advanced RAG** - Vector search, hybrid search, reranking, agentic RAG + +### Why Code-Forge? + +✅ **100% Local** - Your data never leaves your machine +✅ **Production-Ready** - PostgreSQL ensures data persistence and ACID compliance +✅ **Multi-Knowledge-Base** - Separate KBs for different projects/contexts +✅ **Agent OS Support** - First-class support for spec-driven development +✅ **Advanced RAG** - Multiple retrieval strategies for optimal results +✅ **MCP Integration** - Expose knowledge bases to AI agents via HTTP --- ## ✨ Features -### 🎯 Core Capabilities -- **Multiple Knowledge Bases**: Create isolated KBs for different projects -- **Advanced RAG Strategies**: - - 🔍 Vector Search (semantic similarity) - - 🔀 Hybrid Search (vector + BM25 keyword) - - 🎯 Reranking (cross-encoder scoring) - - 🤖 Agentic RAG (multi-step reasoning) -- **MCP Integration**: Expose KBs to AI agents like Claude Desktop -- **Dual Interface**: Streamlit UI for humans + MCP server for agents +### Core Features -### 🎨 UI/UX -- **Archon-Inspired Design**: Dark theme with neon accents -- **Color Scheme**: - - Primary: `hsl(271, 91%, 65%)` (Purple) - - Accent: `hsl(330, 90%, 65%)` (Pink) - - Success: `hsl(160, 84%, 39%)` (Green) - - Background: Pure black `#0E0E0E` -- **Real-time Health Monitoring**: Ollama & ChromaDB status -- **Document Management**: Upload files or import entire directories +- **🗄️ PostgreSQL + pgvector Vector Store** + - ACID-compliant transactions + - 768-dimensional vector embeddings + - IVFFlat indexing for fast similarity search + - JSONB metadata for flexible filtering + +- **🤖 Local LLM Integration** + - Ollama-powered LLMs (llama3.2:3b, llama3.1) + - nomic-embed-text embeddings (768 dimensions) + - No API keys or cloud dependencies + +- **📚 Multi-Knowledge-Base Architecture** + - Isolated KBs per project + - 4 KB types: GENERIC, CODE, DOCUMENTATION, AGENT_OS + - Type-specific ingestion and retrieval + +- **🧠 Advanced RAG Strategies** + - **Vector Search** - Semantic similarity via pgvector + - **Hybrid Search** - BM25 + vector fusion + - **Reranking** - Cross-encoder for top results + - **Agentic RAG** - Sub-question decomposition + +- **🔌 MCP (Model Context Protocol) Server** + - 12 HTTP endpoints for AI agent integration + - Create, query, and manage knowledge bases + - Agent OS-specific tools (standards, specs, workflows) + +- **⚡ Modern React + FastAPI UI** + - Electric teal design system + - Drag & drop document upload + - Real-time chat interface with markdown rendering + - Interactive KB management dashboard + - Built-in help documentation + +- **🎨 Legacy Streamlit UI** (Still available) + - Alternative interface on port 8501 + - Full feature parity with React UI + +### Knowledge Base Types + +| Type | Icon | Description | Use Case | +|------|------|-------------|----------| +| **GENERIC** | 📚 | General-purpose knowledge | Documentation, notes, research | +| **CODE** | 💻 | Source code repositories | Code search, API reference | +| **DOCUMENTATION** | 📖 | Technical documentation | User guides, API docs | +| **AGENT_OS** | 🤖 | Spec-driven development | Standards, workflows, specs | ### 📁 Supported File Types - **Documents**: `.md`, `.txt`, `.pdf` @@ -38,79 +112,125 @@ A self-hosted, privacy-first RAG (Retrieval-Augmented Generation) application de ## 🏗️ Architecture ``` -┌─────────────────────────────────────────────────────────┐ -│ Code-Forge │ -├─────────────────────────────────────────────────────────┤ -│ │ -│ ┌──────────────┐ ┌──────────────┐ │ -│ │ Streamlit │ │ MCP Server │ │ -│ │ UI (8501) │ │ (8051) │ │ -│ └──────┬───────┘ └──────┬───────┘ │ -│ │ │ │ -│ └─────────────┬───────────────┘ │ -│ │ │ -│ ┌────────▼────────┐ │ -│ │ RAG Engine │ │ -│ │ (LlamaIndex) │ │ -│ └────────┬────────┘ │ -│ │ │ -│ ┌─────────────┴─────────────┐ │ -│ │ │ │ -│ ┌────▼─────┐ ┌──────▼──────┐ │ -│ │ ChromaDB │ │ Ollama │ │ -│ │ (8000) │ │ (11434) │ │ -│ └──────────┘ └─────────────┘ │ -│ │ -└─────────────────────────────────────────────────────────┘ +┌─────────────────────────────────────────────────────────────────────┐ +│ Code-Forge System │ +├─────────────────────────────────────────────────────────────────────┤ +│ │ +│ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │ +│ │ React UI │ │ Streamlit │ │ MCP Server │ │ +│ │ (Vite) │ │ UI │ │ (HTTP) │ │ +│ │ Port 5173 │ │ Port 8501 │ │ Port 8051 │ │ +│ └──────┬───────┘ └──────┬───────┘ └──────┬───────┘ │ +│ │ │ │ │ +│ └─────────────────┴──────────┬──────────┘ │ +│ │ │ +│ ┌──────────▼──────────┐ │ +│ │ FastAPI Backend │ │ +│ │ (REST + MCP) │ │ +│ └──────────┬──────────┘ │ +│ │ │ +│ ┌──────────▼──────────┐ │ +│ │ RAG Engine │ │ +│ │ (llama-index) │ │ +│ │ │ │ +│ │ • Vector Search │ │ +│ │ • Hybrid Search │ │ +│ │ • Reranking │ │ +│ │ • Agentic RAG │ │ +│ └──────────┬──────────┘ │ +│ │ │ +│ ┌──────────▼──────────┐ │ +│ │ PostgresManager │ │ +│ └──────────┬──────────┘ │ +│ │ │ +│ ┌───────────────────────▼───────────────────┐ │ +│ │ PostgreSQL + pgvector │ │ +│ │ (Local Database) │ │ +│ │ │ │ +│ │ Tables: │ │ +│ │ • knowledge_bases │ │ +│ │ • documents (with embeddings) │ │ +│ │ • agent_os_content │ │ +│ └───────────────────────────────────────────┘ │ +│ │ +│ ┌──────────────┐ ┌──────────────┐ │ +│ │ Ollama │ │ Ollama LLM │ │ +│ │ Container │──────│ Service │ │ +│ │ Port 11434 │ │ │ │ +│ └──────────────┘ └──────────────┘ │ +│ │ +└─────────────────────────────────────────────────────────────────────┘ ``` +### Data Flow + +1. **Ingestion**: Documents → Chunking → Embedding → PostgreSQL +2. **Query**: User query → Embedding → Vector search → Reranking → LLM → Response +3. **MCP**: AI Agent → HTTP request → RAG Engine → Response + ### Services -1. **Ollama** (port 11434): Local LLM and embeddings -2. **ChromaDB** (port 8000): Vector database -3. **App Container**: - - Streamlit UI (port 8501) - - MCP Server (port 8051) + +1. **Frontend Container** (port 5173): React + Vite UI with hot reload +2. **App Container** (ports 8501, 8051): + - FastAPI backend with MCP server + - Streamlit UI (legacy) +3. **Ollama Container** (port 11434): Local LLM and embeddings +4. **PostgreSQL** (local, not Docker): Vector database with pgvector extension --- -## 🚀 Quick Start +## ⚡ Quick Start -### Prerequisites -- **Docker** and **Docker Compose** installed -- **Ollama** installed locally (or use Docker Ollama) -- At least 8GB RAM recommended +### One-Command Setup -### 1. Clone and Navigate ```bash -git clone -cd code-forge +./setup.sh ``` -### 2. Pull Required Ollama Models -```bash -# Pull LLM model -ollama pull llama3.1 +This automated script will: +- ✅ Check for Docker, PostgreSQL, Ollama +- ✅ Install pgvector extension +- ✅ Create `codeforge` database +- ✅ Download required Ollama models (~7GB) +- ✅ Build and start Docker containers +- ✅ Verify installation -# Pull embedding model -ollama pull nomic-embed-text +### Start the Frontend + +```bash +cd frontend +npm install +npm run dev ``` -### 3. One-Command Deployment -```bash -docker compose up --build -``` +### Access the Application -This will: -- Start Ollama service -- Start ChromaDB service -- Build and start the Code-Forge application -- Wait for all services to become healthy +- **React UI**: http://localhost:5173 **(Recommended)** +- **Streamlit UI**: http://localhost:8501 (Legacy) +- **MCP Server**: http://localhost:8051 +- **Ollama API**: http://localhost:11434 -### 4. Access the Application +--- -**Streamlit UI**: http://localhost:8501 +## 📦 Prerequisites -**MCP Server**: http://localhost:8051/mcp +### Required + +- **macOS** (tested on macOS 14+) +- **Docker Desktop** - [Download](https://www.docker.com/products/docker-desktop) +- **PostgreSQL 17+** - [Download Postgres.app](https://postgresapp.com/) or `brew install postgresql@17` +- **Ollama** - [Download](https://ollama.ai/download) or `brew install ollama` + +### Disk Space + +- **PostgreSQL**: ~500MB +- **Ollama Models**: ~7GB + - llama3.2:3b (2.0 GB) + - llama3.1:latest (4.9 GB) + - nomic-embed-text (274 MB) +- **Docker Images**: ~2GB + +**Total**: ~10GB --- @@ -232,50 +352,39 @@ List all documents in a knowledge base. ### Environment Variables -Create a `.env` file to customize settings: +Create `.env` file: ```bash -# Ollama Configuration +# Ollama OLLAMA_HOST=http://ollama:11434 -OLLAMA_MODEL=llama3.1 +OLLAMA_MODEL=llama3.2:3b OLLAMA_EMBED_MODEL=nomic-embed-text -# ChromaDB Configuration -CHROMA_HOST=chromadb -CHROMA_PORT=8000 +# PostgreSQL +POSTGRES_HOST=host.docker.internal +POSTGRES_PORT=5432 +POSTGRES_DB=codeforge +POSTGRES_USER=your_username -# MCP Server Configuration -MCP_SERVER_HOST=0.0.0.0 -MCP_SERVER_PORT=8051 - -# RAG Configuration -CHUNK_SIZE=1024 -CHUNK_OVERLAP=200 +# RAG TOP_K_RETRIEVAL=5 -RERANK_TOP_N=3 - -# Storage -UPLOAD_DIR=/workspace/data/uploads +CHUNK_SIZE=512 +CHUNK_OVERLAP=50 ``` -### Changing Models +### RAG Strategy Tuning -To use different Ollama models: +Edit `app/core/config.py`: -1. Pull the model: - ```bash - ollama pull mistral - ``` +```python +# Retrieval settings +TOP_K_RETRIEVAL = 5 +RERANK_TOP_N = 3 -2. Update `.env`: - ```bash - OLLAMA_MODEL=mistral - ``` - -3. Restart the application: - ```bash - docker compose restart app - ``` +# Chunking settings +CHUNK_SIZE = 512 +CHUNK_OVERLAP = 50 +``` --- @@ -287,25 +396,28 @@ To use different Ollama models: code-forge/ ├── app/ │ ├── core/ -│ │ ├── config.py # Configuration management -│ │ ├── health.py # Service health checks -│ │ ├── chroma_manager.py # ChromaDB operations -│ │ ├── ingestion.py # Document processing -│ │ ├── kb_metadata.py # KB metadata management -│ │ └── rag_engine.py # RAG implementation -│ └── main.py # Streamlit application +│ │ ├── config.py # Configuration management +│ │ ├── pg_manager.py # PostgreSQL operations +│ │ ├── ingestion.py # Document processing +│ │ ├── agent_os_ingestion.py # Agent OS profile ingestion +│ │ ├── kb_metadata.py # KB metadata management +│ │ ├── kb_types.py # KB type definitions +│ │ └── rag_engine.py # RAG implementation +│ ├── db/ +│ │ └── schema.sql # PostgreSQL schema +│ └── main.py # Streamlit application ├── mcp_server/ -│ └── server.py # MCP server (FastMCP + FastAPI) +│ └── server.py # MCP server (FastMCP) ├── .streamlit/ -│ └── config.toml # Streamlit theme +│ └── config.toml # Streamlit theme (Archon colors) ├── data/ -│ ├── chroma/ # ChromaDB persistent storage -│ └── uploads/ # Uploaded documents -├── docker-compose.yml # Service orchestration -├── Dockerfile # Application container -├── requirements.txt # Python dependencies -├── start.sh # Startup script -└── README.md # This file +│ └── ollama/ # Ollama models (persistent) +├── docs/ # Documentation +├── docker-compose.yml # Service orchestration +├── Dockerfile # Application container +├── requirements.txt # Python dependencies +├── setup.sh # Automated setup script +└── README.md # This file ``` ### Running Locally (Without Docker) @@ -315,7 +427,7 @@ code-forge/ pip install -r requirements.txt ``` -2. Start Ollama and ChromaDB separately +2. Ensure PostgreSQL and Ollama are running locally 3. Start the MCP server: ```bash @@ -331,99 +443,91 @@ code-forge/ ## 🐛 Troubleshooting -### Services Not Starting +### PostgreSQL Connection Failed -**Check service health:** ```bash -docker compose ps -``` +# Check if PostgreSQL is running +psql -c "SELECT version();" -**View logs:** -```bash -docker compose logs ollama -docker compose logs chromadb -docker compose logs app +# If using Postgres.app, ensure it's started +# Check: Applications → Postgres.app ``` ### Ollama Models Not Found -**Pull models manually:** ```bash -docker compose exec ollama ollama pull llama3.1 -docker compose exec ollama ollama pull nomic-embed-text +# List installed models +ollama list + +# Re-download if needed +ollama pull nomic-embed-text +ollama pull llama3.2:3b ``` -### ChromaDB Connection Issues +### Docker Container Won't Start -**Restart ChromaDB:** ```bash -docker compose restart chromadb +# Check logs +docker-compose logs -f app + +# Rebuild +docker-compose down +docker-compose build --no-cache +docker-compose up -d ``` -**Check ChromaDB health:** +### Data Persistence Issues + +Your data is stored in: +- **PostgreSQL**: Local database (survives all Docker operations) +- **Ollama models**: `./data/ollama/` (survives all Docker operations) + +To verify: ```bash -curl http://localhost:8000/api/v1/heartbeat +# Check database +psql -d codeforge -c "\dt" + +# Check models +ls -lh data/ollama/ ``` -### MCP Connection Fails - -**Verify MCP server is running:** -```bash -curl http://localhost:8051/health -``` - -**Check MCP logs:** -```bash -docker compose logs app | grep MCP -``` +See [docs/TROUBLESHOOTING.md](docs/TROUBLESHOOTING.md) for more solutions. --- -## 📊 Performance Tips +## 🤝 Contributing -1. **Chunk Size**: Larger chunks (1024-2048 tokens) for technical docs, smaller (512-1024) for code -2. **Hybrid Search**: Best for queries with specific keywords or technical terms -3. **Reranking**: Adds latency but significantly improves relevance -4. **Agentic RAG**: Use for complex questions requiring multi-step reasoning (slower) +Contributions welcome! Please: + +1. Fork the repository +2. Create a feature branch +3. Make your changes +4. Add tests +5. Submit a pull request --- -## 🔒 Privacy & Security +## 📄 License -- **100% Local**: All data stays on your machine -- **No External APIs**: Ollama runs models locally -- **No Telemetry**: ChromaDB telemetry disabled -- **Isolated KBs**: Each project has its own knowledge base - ---- - -## 📝 License - -MIT License - See LICENSE file for details +MIT License - see [LICENSE](LICENSE) for details. --- ## 🙏 Acknowledgments -- **LlamaIndex**: RAG framework -- **Ollama**: Local LLM runtime -- **ChromaDB**: Vector database -- **Streamlit**: UI framework -- **FastMCP**: MCP server library -- **Archon**: Design inspiration +- **llama-index** - RAG framework +- **Ollama** - Local LLM runtime +- **pgvector** - PostgreSQL vector extension +- **Streamlit** - UI framework +- **FastMCP** - MCP server library --- -## 🚀 Roadmap +
-- [ ] Multi-modal support (images, diagrams) -- [ ] Graph RAG for relationship extraction -- [ ] Query caching for faster responses -- [ ] Batch document processing -- [ ] Export/import knowledge bases -- [ ] Advanced analytics dashboard +**Built with ❤️ for the local-first AI community** ---- +[Documentation](docs/) • [Issues](https://github.com/yourusername/code-forge/issues) • [Discussions](https://github.com/yourusername/code-forge/discussions) -**Built with 🔥 by The Augster** +
diff --git a/app/assets/hero.png b/app/assets/hero.png new file mode 100644 index 0000000..4b3e45b Binary files /dev/null and b/app/assets/hero.png differ diff --git a/app/core/agent_os_ingestion.py b/app/core/agent_os_ingestion.py new file mode 100644 index 0000000..d67fe9b --- /dev/null +++ b/app/core/agent_os_ingestion.py @@ -0,0 +1,279 @@ +""" +Agent OS Ingestion Module + +Handles ingestion of Agent OS profiles into Code-Forge knowledge bases. +Integrates with ChromaManager to store parsed Agent OS content. +""" + +import logging +from typing import List, Dict, Any, Optional +from pathlib import Path + +from llama_index.embeddings.ollama import OllamaEmbedding + +from app.core.agent_os_parser import AgentOSParser, AgentOSDocument, AgentOSContentType +from app.core.pg_manager import PostgresManager +from app.core.config import Config + +logger = logging.getLogger(__name__) + + +class AgentOSIngestion: + """Handles ingestion of Agent OS profiles into knowledge bases.""" + + def __init__(self, db_manager: PostgresManager): + """ + Initialize Agent OS ingestion. + + Args: + db_manager: PostgresManager instance for storage + """ + self.db_manager = db_manager + self.parser = AgentOSParser() + + def ingest_profile( + self, + kb_name: str, + profile_path: str, + batch_size: int = 100 + ) -> Dict[str, Any]: + """ + Ingest an Agent OS profile into a knowledge base. + + Args: + kb_name: Name of the knowledge base + profile_path: Path to the Agent OS profile directory + batch_size: Number of documents to process in each batch + + Returns: + Dictionary with ingestion statistics + + Raises: + ValueError: If KB doesn't exist or profile path is invalid + """ + logger.info(f"Starting Agent OS profile ingestion: {profile_path} -> {kb_name}") + + # Validate KB exists + collections = self.db_manager.list_collections() + kb_names = [col["name"] for col in collections] + if kb_name not in kb_names: + raise ValueError(f"Knowledge base '{kb_name}' does not exist") + + # Parse the profile directory + try: + documents = self.parser.parse_directory(profile_path) + except Exception as e: + logger.error(f"Failed to parse Agent OS profile: {e}") + raise ValueError(f"Failed to parse Agent OS profile: {e}") + + if not documents: + logger.warning("No documents found in Agent OS profile") + return { + "success": False, + "message": "No documents found in profile", + "documents_processed": 0 + } + + # Ingest documents in batches + stats = { + "total_documents": len(documents), + "documents_by_type": {}, + "success": True, + "errors": [] + } + + # Count documents by type + for doc in documents: + content_type = doc.content_type.value + stats["documents_by_type"][content_type] = \ + stats["documents_by_type"].get(content_type, 0) + 1 + + # Process in batches + for i in range(0, len(documents), batch_size): + batch = documents[i:i + batch_size] + try: + self._ingest_batch(kb_name, batch) + logger.info(f"Ingested batch {i//batch_size + 1}: {len(batch)} documents") + except Exception as e: + error_msg = f"Failed to ingest batch {i//batch_size + 1}: {e}" + logger.error(error_msg) + stats["errors"].append(error_msg) + stats["success"] = False + + logger.info(f"Agent OS ingestion complete: {stats}") + return stats + + def _ingest_batch(self, kb_name: str, documents: List[AgentOSDocument]) -> None: + """ + Ingest a batch of documents into the knowledge base. + + Args: + kb_name: Name of the knowledge base + documents: List of AgentOSDocument objects to ingest + """ + if not documents: + return + + # Prepare data for ChromaDB + texts = [] + metadatas = [] + ids = [] + + for idx, doc in enumerate(documents): + # Create unique ID + doc_id = f"{doc.content_type.value}_{Path(doc.file_path).stem}_{idx}" + + # Prepare metadata (ChromaDB only supports string, int, float, bool) + metadata = { + "content_type": doc.content_type.value, + "title": doc.title, + "file_path": doc.file_path, + "source": "agent_os", + } + + # Add document metadata (convert complex types to strings) + for key, value in doc.metadata.items(): + if isinstance(value, (str, int, float, bool)): + metadata[key] = value + elif isinstance(value, list): + # Serialize lists as comma-separated strings + metadata[key] = ",".join(str(v) for v in value) + else: + # Convert other types to string + metadata[key] = str(value) + + texts.append(doc.content) + metadatas.append(metadata) + ids.append(doc_id) + + # Generate embeddings + embed_model = OllamaEmbedding( + model_name=Config.OLLAMA_EMBED_MODEL, + base_url=Config.OLLAMA_HOST + ) + embeddings = [embed_model.get_text_embedding(text) for text in texts] + + # Add to PostgreSQL + self.db_manager.add_documents( + kb_name=kb_name, + documents=texts, + embeddings=embeddings, + metadatas=metadatas, + ids=ids + ) + + logger.debug(f"Added {len(documents)} documents to {kb_name}") + + def get_profile_stats(self, kb_name: str) -> Dict[str, Any]: + """ + Get statistics about Agent OS content in a knowledge base. + + Args: + kb_name: Name of the knowledge base + + Returns: + Dictionary with statistics + """ + try: + # Get all documents with agent_os source + results = self.db_manager.get_documents_by_metadata( + kb_name=kb_name, + where={"source": "agent_os"} + ) + + if not results: + return { + "total_documents": 0, + "documents_by_type": {} + } + + # Count by content type + type_counts = {} + for doc in results: + metadata = doc.get("metadata", {}) + content_type = metadata.get("content_type", "unknown") + type_counts[content_type] = type_counts.get(content_type, 0) + 1 + + return { + "total_documents": len(results), + "documents_by_type": type_counts + } + + except Exception as e: + logger.error(f"Failed to get profile stats for {kb_name}: {e}") + return { + "total_documents": 0, + "documents_by_type": {}, + "error": str(e) + } + + def search_by_type( + self, + kb_name: str, + content_type: AgentOSContentType, + query: Optional[str] = None, + limit: int = 10 + ) -> List[Dict[str, Any]]: + """ + Search Agent OS content by type. + + Args: + kb_name: Name of the knowledge base + content_type: Type of content to search + query: Optional search query (if None, returns all of type) + limit: Maximum number of results + + Returns: + List of matching documents + """ + try: + where_filter = { + "source": "agent_os", + "content_type": content_type.value + } + + if query: + # Semantic search with type filter + embed_model = OllamaEmbedding( + model_name=Config.OLLAMA_EMBED_MODEL, + base_url=Config.OLLAMA_HOST + ) + query_embedding = embed_model.get_text_embedding(query) + + results = self.db_manager.query_documents( + kb_name=kb_name, + query_embedding=query_embedding, + n_results=limit, + where=where_filter + ) + + # Format query results + documents = [] + for i in range(len(results.get("documents", []))): + documents.append({ + "content": results["documents"][i], + "metadata": results["metadatas"][i], + "distance": results.get("distances", [0])[i] + }) + else: + # Just get all of this type + results = self.db_manager.get_documents_by_metadata( + kb_name=kb_name, + where=where_filter, + limit=limit + ) + + # Format get results + documents = [] + for doc in results: + documents.append({ + "content": doc.get("content", ""), + "metadata": doc.get("metadata", {}) + }) + + return documents + + except Exception as e: + logger.error(f"Failed to search by type in {kb_name}: {e}") + return [] + diff --git a/app/core/agent_os_parser.py b/app/core/agent_os_parser.py new file mode 100644 index 0000000..685638c --- /dev/null +++ b/app/core/agent_os_parser.py @@ -0,0 +1,295 @@ +""" +Agent OS Parser Module + +Parses Agent OS profile directory structures and extracts structured content +for ingestion into Code-Forge knowledge bases. + +Agent OS Structure: +- standards/ - Coding standards and best practices +- agents/ - Agent configurations and skills +- workflows/ - Development workflow definitions +- commands/ - Command definitions +- product/ - Product vision and roadmap (optional) +- specs/ - Feature specifications (optional) +""" + +import os +import yaml +from pathlib import Path +from typing import Dict, List, Optional, Any +from dataclasses import dataclass +from enum import Enum +import logging + +logger = logging.getLogger(__name__) + + +class AgentOSContentType(str, Enum): + """Types of content in Agent OS profiles.""" + STANDARD = "standard" + AGENT = "agent" + WORKFLOW = "workflow" + COMMAND = "command" + PRODUCT = "product" + SPEC = "spec" + UNKNOWN = "unknown" + + +@dataclass +class AgentOSDocument: + """Represents a parsed Agent OS document.""" + content_type: AgentOSContentType + title: str + content: str + file_path: str + metadata: Dict[str, Any] + + def to_dict(self) -> Dict[str, Any]: + """Convert to dictionary for storage.""" + return { + "content_type": self.content_type.value, + "title": self.title, + "content": self.content, + "file_path": self.file_path, + "metadata": self.metadata + } + + +class AgentOSParser: + """Parser for Agent OS profile directories.""" + + # Map directory names to content types + DIRECTORY_TYPE_MAP = { + "standards": AgentOSContentType.STANDARD, + "agents": AgentOSContentType.AGENT, + "workflows": AgentOSContentType.WORKFLOW, + "commands": AgentOSContentType.COMMAND, + "product": AgentOSContentType.PRODUCT, + "specs": AgentOSContentType.SPEC, + } + + # Supported file extensions + SUPPORTED_EXTENSIONS = {".yml", ".yaml", ".md"} + + def __init__(self): + """Initialize the Agent OS parser.""" + pass + + def parse_directory(self, directory_path: str) -> List[AgentOSDocument]: + """ + Parse an Agent OS profile directory. + + Args: + directory_path: Path to the Agent OS profile directory + + Returns: + List of parsed AgentOSDocument objects + + Raises: + ValueError: If directory doesn't exist or isn't a valid Agent OS profile + """ + path = Path(directory_path) + + if not path.exists(): + raise ValueError(f"Directory does not exist: {directory_path}") + + if not path.is_dir(): + raise ValueError(f"Path is not a directory: {directory_path}") + + # Validate it's an Agent OS profile (has at least one expected subdirectory) + subdirs = [d.name for d in path.iterdir() if d.is_dir()] + valid_subdirs = set(self.DIRECTORY_TYPE_MAP.keys()) + + if not any(subdir in valid_subdirs for subdir in subdirs): + raise ValueError( + f"Not a valid Agent OS profile. Expected at least one of: {', '.join(valid_subdirs)}" + ) + + documents = [] + + # Parse each subdirectory + for subdir_name, content_type in self.DIRECTORY_TYPE_MAP.items(): + subdir_path = path / subdir_name + if subdir_path.exists() and subdir_path.is_dir(): + docs = self._parse_content_directory(subdir_path, content_type) + documents.extend(docs) + logger.info(f"Parsed {len(docs)} documents from {subdir_name}/") + + logger.info(f"Total documents parsed: {len(documents)}") + return documents + + def _parse_content_directory( + self, + directory: Path, + content_type: AgentOSContentType + ) -> List[AgentOSDocument]: + """ + Parse all files in a content directory. + + Args: + directory: Path to the content directory + content_type: Type of content in this directory + + Returns: + List of parsed documents + """ + documents = [] + + # Recursively find all supported files + for file_path in directory.rglob("*"): + if file_path.is_file() and file_path.suffix in self.SUPPORTED_EXTENSIONS: + try: + doc = self._parse_file(file_path, content_type, directory) + if doc: + documents.append(doc) + except Exception as e: + logger.error(f"Failed to parse {file_path}: {e}") + + return documents + + def _parse_file( + self, + file_path: Path, + content_type: AgentOSContentType, + base_dir: Path + ) -> Optional[AgentOSDocument]: + """ + Parse a single file. + + Args: + file_path: Path to the file + content_type: Type of content + base_dir: Base directory for relative path calculation + + Returns: + Parsed AgentOSDocument or None if parsing fails + """ + try: + with open(file_path, 'r', encoding='utf-8') as f: + raw_content = f.read() + + # Determine file type and parse accordingly + if file_path.suffix in {".yml", ".yaml"}: + return self._parse_yaml_file(file_path, raw_content, content_type, base_dir) + elif file_path.suffix == ".md": + return self._parse_markdown_file(file_path, raw_content, content_type, base_dir) + + except Exception as e: + logger.error(f"Error reading file {file_path}: {e}") + return None + + def _parse_yaml_file( + self, + file_path: Path, + raw_content: str, + content_type: AgentOSContentType, + base_dir: Path + ) -> Optional[AgentOSDocument]: + """Parse a YAML file.""" + try: + data = yaml.safe_load(raw_content) + + if not data: + logger.warning(f"Empty YAML file: {file_path}") + return None + + # Extract title (use 'name', 'title', or filename) + title = ( + data.get("name") or + data.get("title") or + file_path.stem + ) + + # Extract metadata + metadata = { + "file_type": "yaml", + "relative_path": str(file_path.relative_to(base_dir)), + } + + # Add common YAML fields to metadata + for key in ["description", "version", "category", "priority", "tags"]: + if key in data: + metadata[key] = data[key] + + # Convert YAML to readable text for content + content = self._yaml_to_text(data, title) + + return AgentOSDocument( + content_type=content_type, + title=title, + content=content, + file_path=str(file_path), + metadata=metadata + ) + + except yaml.YAMLError as e: + logger.error(f"YAML parsing error in {file_path}: {e}") + return None + + def _parse_markdown_file( + self, + file_path: Path, + raw_content: str, + content_type: AgentOSContentType, + base_dir: Path + ) -> Optional[AgentOSDocument]: + """Parse a Markdown file.""" + # Extract title from first heading or filename + title = file_path.stem + lines = raw_content.split('\n') + for line in lines: + if line.startswith('# '): + title = line[2:].strip() + break + + metadata = { + "file_type": "markdown", + "relative_path": str(file_path.relative_to(base_dir)), + } + + return AgentOSDocument( + content_type=content_type, + title=title, + content=raw_content, + file_path=str(file_path), + metadata=metadata + ) + + def _yaml_to_text(self, data: Dict[str, Any], title: str) -> str: + """ + Convert YAML data to readable text format. + + This creates a structured text representation that's good for RAG. + """ + lines = [f"# {title}\n"] + + def format_value(value: Any, indent: int = 0) -> List[str]: + """Recursively format YAML values.""" + result = [] + prefix = " " * indent + + if isinstance(value, dict): + for k, v in value.items(): + result.append(f"{prefix}**{k}:**") + result.extend(format_value(v, indent + 1)) + elif isinstance(value, list): + for item in value: + if isinstance(item, (dict, list)): + result.extend(format_value(item, indent)) + else: + result.append(f"{prefix}- {item}") + else: + result.append(f"{prefix}{value}") + + return result + + # Format each top-level key + for key, value in data.items(): + if key in ["name", "title"]: # Skip title fields (already in header) + continue + + lines.append(f"\n## {key.replace('_', ' ').title()}") + lines.extend(format_value(value, 0)) + + return "\n".join(lines) + diff --git a/app/core/chroma_manager.py b/app/core/chroma_manager.py deleted file mode 100644 index 7320893..0000000 --- a/app/core/chroma_manager.py +++ /dev/null @@ -1,158 +0,0 @@ -""" -ChromaDB client manager for knowledge base operations. -Implements singleton pattern for connection reuse. -""" - -import logging -from typing import List, Optional - -import chromadb -from chromadb.config import Settings - -from app.core.config import Config - -logger = logging.getLogger(__name__) - - -class ChromaManager: - """Singleton manager for ChromaDB operations.""" - - _instance = None - _client = None - - def __new__(cls): - if cls._instance is None: - cls._instance = super().__new__(cls) - return cls._instance - - def __init__(self): - if self._client is None: - self._initialize_client() - - def _initialize_client(self): - """Initialize ChromaDB HTTP client.""" - try: - self._client = chromadb.HttpClient( - host=Config.CHROMA_HOST, - port=Config.CHROMA_PORT - ) - logger.info(f"ChromaDB client initialized: {Config.get_chroma_url()}") - except Exception as e: - logger.error(f"Failed to initialize ChromaDB client: {e}") - raise - - def get_client(self) -> chromadb.HttpClient: - """Get the ChromaDB client instance.""" - if self._client is None: - self._initialize_client() - return self._client - - def list_collections(self) -> List[str]: - """ - List all collection names. - - Returns: - List of collection names - """ - try: - collections = self._client.list_collections() - return [col.name for col in collections] - except Exception as e: - logger.error(f"Failed to list collections: {e}") - return [] - - def create_collection(self, name: str) -> bool: - """ - Create a new collection (knowledge base). - - Args: - name: Collection name - - Returns: - bool: True if successful - """ - try: - self._client.get_or_create_collection(name=name) - logger.info(f"Collection created: {name}") - return True - except Exception as e: - logger.error(f"Failed to create collection {name}: {e}") - return False - - def delete_collection(self, name: str) -> bool: - """ - Delete a collection. - - Args: - name: Collection name - - Returns: - bool: True if successful - """ - try: - self._client.delete_collection(name=name) - logger.info(f"Collection deleted: {name}") - return True - except Exception as e: - logger.error(f"Failed to delete collection {name}: {e}") - return False - - def get_collection(self, name: str) -> Optional[chromadb.Collection]: - """ - Get a collection by name. - - Args: - name: Collection name - - Returns: - Collection object or None if not found - """ - try: - return self._client.get_collection(name=name) - except Exception as e: - logger.warning(f"Collection {name} not found: {e}") - return None - - def get_collection_count(self, name: str) -> int: - """ - Get the number of documents in a collection. - - Args: - name: Collection name - - Returns: - int: Document count (0 if collection not found) - """ - try: - collection = self.get_collection(name) - if collection: - return collection.count() - return 0 - except Exception as e: - logger.error(f"Failed to get count for {name}: {e}") - return 0 - - def collection_exists(self, name: str) -> bool: - """ - Check if a collection exists. - - Args: - name: Collection name - - Returns: - bool: True if collection exists - """ - return name in self.list_collections() - - -# Global singleton instance -_chroma_manager = None - - -def get_chroma_manager() -> ChromaManager: - """Get the global ChromaManager instance.""" - global _chroma_manager - if _chroma_manager is None: - _chroma_manager = ChromaManager() - return _chroma_manager - diff --git a/app/core/config.py b/app/core/config.py index dbfa285..2ff5e8a 100644 --- a/app/core/config.py +++ b/app/core/config.py @@ -5,7 +5,7 @@ Centralizes all application settings with environment variable support. import os from pathlib import Path -from typing import List +from typing import Dict, List from dotenv import load_dotenv @@ -43,6 +43,34 @@ class Config: TOP_K_RETRIEVAL: int = 5 RERANK_TOP_N: int = 3 + # KB Type Configuration + DEFAULT_KB_TYPE: str = os.getenv("DEFAULT_KB_TYPE", "generic") + + # Type-specific RAG strategy recommendations + # These are suggested defaults - users can override via UI + KB_TYPE_RAG_DEFAULTS: Dict[str, Dict[str, bool]] = { + "generic": { + "hybrid": False, + "rerank": False, + "agentic": False + }, + "code": { + "hybrid": True, # Code benefits from keyword + semantic search + "rerank": True, # Reranking helps with code relevance + "agentic": False + }, + "documentation": { + "hybrid": True, # Docs benefit from hybrid search + "rerank": True, # Reranking improves doc relevance + "agentic": False + }, + "agent-os": { + "hybrid": True, # Agent OS specs benefit from hybrid search + "rerank": True, # Reranking for spec relevance + "agentic": True # Agentic RAG for complex spec queries + } + } + # Storage Configuration UPLOAD_DIR: str = os.getenv("UPLOAD_DIR", "/workspace/data/uploads") diff --git a/app/core/health.py b/app/core/health.py index 9603612..78c49c3 100644 --- a/app/core/health.py +++ b/app/core/health.py @@ -58,51 +58,39 @@ def check_ollama_health() -> Dict[str, any]: } -def check_chroma_health() -> Dict[str, any]: +def check_postgres_health() -> Dict[str, any]: """ - Check ChromaDB service health. + Check PostgreSQL service health. Returns: dict: Status information with 'status' and optional 'error' """ try: - # Use v2 API endpoint for health check - response = requests.get( - f"{Config.get_chroma_url()}/api/v2/auth/identity", - timeout=5 - ) - response.raise_for_status() + from app.core.pg_manager import get_pg_manager + + pg_manager = get_pg_manager() + # Try to list collections as a health check + collections = pg_manager.list_collections() return { "status": "healthy", - "url": Config.get_chroma_url() - } - except requests.exceptions.ConnectionError as e: - logger.warning(f"ChromaDB connection failed: {e}") - return { - "status": "unhealthy", - "error": "Connection refused - is ChromaDB running?", - "url": Config.get_chroma_url() - } - except requests.exceptions.Timeout as e: - logger.warning(f"ChromaDB timeout: {e}") - return { - "status": "unhealthy", - "error": "Request timed out", - "url": Config.get_chroma_url() + "collections": len(collections) } except Exception as e: - logger.error(f"ChromaDB health check failed: {e}") + logger.warning(f"PostgreSQL connection failed: {e}") return { "status": "unhealthy", - "error": str(e), - "url": Config.get_chroma_url() + "error": "Connection refused - is PostgreSQL running?" } +# Backwards compatibility alias +check_chroma_health = check_postgres_health + + def wait_for_services(max_retries: int = 30, delay: int = 2) -> bool: """ - Wait for both Ollama and ChromaDB services to become healthy. + Wait for both Ollama and PostgreSQL services to become healthy. Args: max_retries: Maximum number of retry attempts @@ -115,16 +103,16 @@ def wait_for_services(max_retries: int = 30, delay: int = 2) -> bool: for attempt in range(1, max_retries + 1): ollama_status = check_ollama_health() - chroma_status = check_chroma_health() + postgres_status = check_postgres_health() - if ollama_status["status"] == "healthy" and chroma_status["status"] == "healthy": + if ollama_status["status"] == "healthy" and postgres_status["status"] == "healthy": logger.info(f"All services healthy after {attempt} attempts") return True logger.info( f"Attempt {attempt}/{max_retries}: " f"Ollama={ollama_status['status']}, " - f"ChromaDB={chroma_status['status']}" + f"PostgreSQL={postgres_status['status']}" ) if attempt < max_retries: diff --git a/app/core/ingestion.py b/app/core/ingestion.py index fcbfb0d..488c5a4 100644 --- a/app/core/ingestion.py +++ b/app/core/ingestion.py @@ -14,7 +14,7 @@ from llama_index.core import Document, Settings from llama_index.core.node_parser import SentenceSplitter from llama_index.embeddings.ollama import OllamaEmbedding -from app.core.chroma_manager import get_chroma_manager +from app.core.pg_manager import get_pg_manager from app.core.config import Config from app.core.markdown_preprocessor import preprocess_markdown @@ -141,9 +141,9 @@ def ingest_file( base_url=Config.OLLAMA_HOST ) - # Get ChromaDB collection - chroma_manager = get_chroma_manager() - collection = chroma_manager.get_collection(collection_name) + # Get PostgreSQL collection + pg_manager = get_pg_manager() + collection = pg_manager.get_collection(collection_name) if not collection: return { diff --git a/app/core/kb_metadata.py b/app/core/kb_metadata.py index 4424153..62384c8 100644 --- a/app/core/kb_metadata.py +++ b/app/core/kb_metadata.py @@ -7,7 +7,8 @@ from collections import defaultdict from datetime import datetime from typing import Dict, List -from app.core.chroma_manager import get_chroma_manager +from app.core.pg_manager import get_pg_manager +from app.core.kb_types import KBType, get_kb_type_info, validate_kb_type logger = logging.getLogger(__name__) @@ -15,10 +16,10 @@ logger = logging.getLogger(__name__) def generate_tags(file_type: str) -> List[str]: """ Generate display tags based on file type. - + Args: file_type: File extension (e.g., '.py', '.md') - + Returns: List of tag strings """ @@ -41,17 +42,17 @@ def generate_tags(file_type: str) -> List[str]: ".yaml": ["Config", "YAML"], ".yml": ["Config", "YAML"], } - + return tag_map.get(file_type.lower(), ["Document", "Unknown"]) def format_timestamp(iso_timestamp: str) -> str: """ Format ISO timestamp for display. - + Args: iso_timestamp: ISO format timestamp string - + Returns: Formatted string (e.g., "Updated: 10/22/2025") """ @@ -66,33 +67,33 @@ def get_documents_metadata(collection_name: str) -> List[Dict[str, any]]: """ Get metadata for all documents in a collection. Groups chunks by filename and aggregates metadata. - + Args: collection_name: Name of the collection - + Returns: List of document metadata dicts """ try: - chroma_manager = get_chroma_manager() - collection = chroma_manager.get_collection(collection_name) - - if not collection: + pg_manager = get_pg_manager() + + if not pg_manager.collection_exists(collection_name): logger.warning(f"Collection {collection_name} not found") return [] - + # Get all documents with metadata - results = collection.get(include=["metadatas"]) - - if not results or not results.get("metadatas"): + results = pg_manager.get_documents_by_metadata(collection_name, where={}) + + if not results: return [] - + # Group chunks by filename docs_by_filename = defaultdict(list) - for metadata in results["metadatas"]: + for doc in results: + metadata = doc.get("metadata", {}) filename = metadata.get("filename", "unknown") docs_by_filename[filename].append(metadata) - + # Aggregate metadata for each document documents = [] for filename, chunks in docs_by_filename.items(): @@ -100,7 +101,7 @@ def get_documents_metadata(collection_name: str) -> List[Dict[str, any]]: first_chunk = chunks[0] file_type = first_chunk.get("file_type", "") upload_date = first_chunk.get("upload_date", "") - + documents.append({ "filename": filename, "file_type": file_type, @@ -109,15 +110,15 @@ def get_documents_metadata(collection_name: str) -> List[Dict[str, any]]: "formatted_date": format_timestamp(upload_date), "chunk_count": len(chunks) }) - + # Sort by upload date (newest first) documents.sort( key=lambda x: x.get("upload_date", ""), reverse=True ) - + return documents - + except Exception as e: logger.error(f"Failed to get documents metadata: {e}") return [] @@ -125,51 +126,127 @@ def get_documents_metadata(collection_name: str) -> List[Dict[str, any]]: def get_collection_stats(collection_name: str) -> Dict[str, any]: """ - Get statistics for a collection. - + Get statistics for a collection including KB type. + Args: collection_name: Name of the collection - + Returns: - dict: Statistics including total docs, chunks, last updated + dict: Statistics including total docs, chunks, last updated, and kb_type """ try: - chroma_manager = get_chroma_manager() - collection = chroma_manager.get_collection(collection_name) - - if not collection: + pg_manager = get_pg_manager() + + if not pg_manager.collection_exists(collection_name): return { "total_documents": 0, "total_chunks": 0, - "last_updated": None + "last_updated": None, + "kb_type": KBType.GENERIC.value } - - # Get all metadata - results = collection.get(include=["metadatas"]) - metadatas = results.get("metadatas", []) - + + # Get KB type from collection metadata + kb_metadata = pg_manager.get_collection_metadata(collection_name) + kb_type = kb_metadata.kb_type if kb_metadata else KBType.GENERIC + + # Get all documents with metadata + results = pg_manager.get_documents_by_metadata(collection_name, where={}) + # Count unique documents unique_filenames = set( - m.get("filename", "") for m in metadatas + doc.get("metadata", {}).get("filename", "") for doc in results ) - + # Get latest upload date upload_dates = [ - m.get("upload_date", "") for m in metadatas if m.get("upload_date") + doc.get("metadata", {}).get("upload_date", "") + for doc in results + if doc.get("metadata", {}).get("upload_date") ] last_updated = max(upload_dates) if upload_dates else None - + return { "total_documents": len(unique_filenames), - "total_chunks": len(metadatas), - "last_updated": last_updated + "total_chunks": len(results), + "last_updated": last_updated, + "kb_type": kb_type.value if isinstance(kb_type, KBType) else kb_type } - + except Exception as e: logger.error(f"Failed to get collection stats: {e}") return { "total_documents": 0, "total_chunks": 0, - "last_updated": None + "last_updated": None, + "kb_type": KBType.GENERIC.value } +def get_kb_type_badge(kb_type: KBType) -> str: + """ + Generate HTML badge for KB type display in UI. + + Args: + kb_type: The KB type + + Returns: + HTML string for badge display + + Example: + >>> get_kb_type_badge(KBType.AGENT_OS) + '🤖 Agent OS Profile' + """ + info = get_kb_type_info(kb_type) + + return f''' + + {info.icon} {info.name} + + ''' + + +def get_kb_type_summary() -> Dict[str, int]: + """ + Get summary of KB counts by type across all knowledge bases. + + Returns: + Dict mapping KB type values to counts + + Example: + { + "generic": 2, + "code": 5, + "documentation": 3, + "agent-os": 1 + } + """ + try: + pg_manager = get_pg_manager() + collections = pg_manager.list_collections() + + # Count by type + type_counts = {kb_type.value: 0 for kb_type in KBType} + + for col in collections: + kb_type = col["metadata"].get("kb_type", KBType.GENERIC.value) + if kb_type in type_counts: + type_counts[kb_type] += 1 + else: + # Handle unknown types + type_counts[KBType.GENERIC.value] += 1 + + return type_counts + + except Exception as e: + logger.error(f"Failed to get KB type summary: {e}") + return {kb_type.value: 0 for kb_type in KBType} + + diff --git a/app/core/kb_types.py b/app/core/kb_types.py new file mode 100644 index 0000000..f20c49d --- /dev/null +++ b/app/core/kb_types.py @@ -0,0 +1,231 @@ +""" +Knowledge Base Type System for Code-Forge. +Defines KB types, metadata models, and type-specific configurations. +""" + +from datetime import datetime +from enum import Enum +from typing import Dict, List, Optional + +from pydantic import BaseModel, Field + + +class KBType(str, Enum): + """ + Knowledge Base types supported by Code-Forge. + + Each type represents a distinct category of knowledge with + specialized handling and retrieval strategies. + """ + GENERIC = "generic" + CODE = "code" + DOCUMENTATION = "documentation" + AGENT_OS = "agent-os" + + +class KBMetadata(BaseModel): + """ + Type-safe metadata model for Knowledge Bases. + + Attributes: + kb_type: The type of knowledge base + description: Optional human-readable description + created_at: ISO timestamp of creation + tags: Optional list of custom tags + """ + kb_type: KBType = Field(default=KBType.GENERIC) + description: str = Field(default="") + created_at: str = Field(default_factory=lambda: datetime.utcnow().isoformat()) + tags: List[str] = Field(default_factory=list) + + class Config: + use_enum_values = True # Serialize enums as their values + + def to_dict(self) -> Dict[str, any]: + """ + Convert to dictionary for ChromaDB storage. + + Note: ChromaDB only supports string, int, float, and bool metadata values. + Lists must be serialized to JSON strings. + """ + return { + "kb_type": self.kb_type.value if isinstance(self.kb_type, KBType) else self.kb_type, + "description": self.description, + "created_at": self.created_at, + "tags": ",".join(self.tags) if self.tags else "" # Serialize list as comma-separated string + } + + @classmethod + def from_dict(cls, data: Dict[str, any]) -> "KBMetadata": + """ + Create from dictionary retrieved from ChromaDB. + + Note: Deserializes comma-separated tag strings back to lists. + """ + tags_str = data.get("tags", "") + tags = [tag.strip() for tag in tags_str.split(",") if tag.strip()] if tags_str else [] + + return cls( + kb_type=KBType(data.get("kb_type", KBType.GENERIC.value)), + description=data.get("description", ""), + created_at=data.get("created_at", datetime.utcnow().isoformat()), + tags=tags + ) + + +class KBTypeInfo(BaseModel): + """ + Display information for a KB type. + + Attributes: + icon: Emoji icon for UI display + color: HSL color for Archon-inspired theming + name: Human-readable name + description: Detailed description of the type + use_cases: List of typical use cases + """ + icon: str + color: str + name: str + description: str + use_cases: List[str] + + +# Type-specific display configurations +KB_TYPE_INFO: Dict[KBType, KBTypeInfo] = { + KBType.GENERIC: KBTypeInfo( + icon="📦", + color="hsl(0, 0%, 60%)", # Gray + name="Generic", + description="General-purpose knowledge base for mixed content", + use_cases=[ + "Mixed documentation and code", + "Personal notes and references", + "Unstructured knowledge collections" + ] + ), + KBType.CODE: KBTypeInfo( + icon="💻", + color="hsl(271, 91%, 65%)", # Archon purple + name="Code Repository", + description="Source code and technical implementation knowledge", + use_cases=[ + "Application codebases", + "Library and framework code", + "Code examples and snippets", + "Technical implementation details" + ] + ), + KBType.DOCUMENTATION: KBTypeInfo( + icon="📚", + color="hsl(160, 84%, 39%)", # Archon green + name="Documentation", + description="Technical documentation, guides, and references", + use_cases=[ + "API documentation", + "User guides and tutorials", + "Technical specifications", + "Architecture documentation" + ] + ), + KBType.AGENT_OS: KBTypeInfo( + icon="🤖", + color="hsl(330, 90%, 65%)", # Archon pink + name="Agent OS Profile", + description="Spec-driven development profiles with standards, specs, and workflows", + use_cases=[ + "Coding standards and conventions", + "Product vision and roadmap", + "Feature specifications", + "Development workflows", + "Agent OS 3-layer context (Standards, Product, Specs)" + ] + ) +} + + +def get_kb_type_info(kb_type: KBType) -> KBTypeInfo: + """ + Get display information for a KB type. + + Args: + kb_type: The KB type to get info for + + Returns: + KBTypeInfo object with display metadata + + Example: + >>> info = get_kb_type_info(KBType.AGENT_OS) + >>> print(f"{info.icon} {info.name}") + 🤖 Agent OS Profile + """ + return KB_TYPE_INFO.get(kb_type, KB_TYPE_INFO[KBType.GENERIC]) + + +def validate_kb_type(kb_type_str: str) -> Optional[KBType]: + """ + Validate and convert a string to KBType enum. + + Args: + kb_type_str: String representation of KB type + + Returns: + KBType enum if valid, None otherwise + + Example: + >>> validate_kb_type("agent-os") + + >>> validate_kb_type("invalid") + None + """ + try: + return KBType(kb_type_str) + except ValueError: + return None + + +def get_kb_type_display_name(kb_type: KBType) -> str: + """ + Get human-readable display name for a KB type. + + Args: + kb_type: The KB type + + Returns: + Display name string + + Example: + >>> get_kb_type_display_name(KBType.AGENT_OS) + '🤖 Agent OS Profile' + """ + info = get_kb_type_info(kb_type) + return f"{info.icon} {info.name}" + + +def get_all_kb_types() -> List[KBType]: + """ + Get list of all available KB types. + + Returns: + List of KBType enums + """ + return list(KBType) + + +def get_kb_type_choices() -> Dict[str, KBType]: + """ + Get KB type choices for UI dropdowns. + + Returns: + Dict mapping display names to KBType enums + + Example: + >>> choices = get_kb_type_choices() + >>> choices["🤖 Agent OS Profile"] + + """ + return { + get_kb_type_display_name(kb_type): kb_type + for kb_type in get_all_kb_types() + } + diff --git a/app/core/pg_manager.py b/app/core/pg_manager.py new file mode 100644 index 0000000..2d7d55d --- /dev/null +++ b/app/core/pg_manager.py @@ -0,0 +1,348 @@ +""" +PostgreSQL + pgvector database manager for Code-Forge. +Replaces ChromaDB with a more robust, ACID-compliant solution. +""" + +import os +import json +from typing import List, Dict, Any, Optional, Tuple +from datetime import datetime +import psycopg2 +from psycopg2.extras import RealDictCursor, Json +from psycopg2.pool import SimpleConnectionPool +import numpy as np + +from app.core.config import Config +from app.core.kb_types import KBType, KBMetadata + + +class PostgresManager: + """Manages PostgreSQL database operations with pgvector for embeddings.""" + + def __init__(self): + """Initialize connection pool.""" + # Build connection parameters + conn_params = { + "minconn": 1, + "maxconn": 10, + "host": os.getenv("POSTGRES_HOST", "localhost"), + "port": int(os.getenv("POSTGRES_PORT", "5432")), + "database": os.getenv("POSTGRES_DB", "codeforge"), + "user": os.getenv("POSTGRES_USER", os.getenv("USER", "postgres")), + } + + # Only add password if it's set (for local PostgreSQL with trust auth) + password = os.getenv("POSTGRES_PASSWORD", "") + if password: + conn_params["password"] = password + + self.pool = SimpleConnectionPool(**conn_params) + self._init_schema() + + def _init_schema(self): + """Initialize database schema from schema.sql.""" + # Schema is managed externally - just verify connection + # The schema.sql should be run manually on the local PostgreSQL + pass + + def get_connection(self): + """Get a connection from the pool.""" + return self.pool.getconn() + + def return_connection(self, conn): + """Return a connection to the pool.""" + self.pool.putconn(conn) + + # Knowledge Base Operations + + def create_collection( + self, + name: str, + kb_type: KBType = KBType.GENERIC, + description: str = "", + tags: List[str] = None + ) -> Dict[str, Any]: + """Create a new knowledge base.""" + conn = self.get_connection() + try: + with conn.cursor(cursor_factory=RealDictCursor) as cur: + cur.execute( + """ + INSERT INTO knowledge_bases (name, kb_type, description, tags) + VALUES (%s, %s, %s, %s) + RETURNING id, name, kb_type, description, created_at, tags + """, + (name, kb_type.value, description, ",".join(tags) if tags else "") + ) + result = cur.fetchone() + conn.commit() + return dict(result) + except psycopg2.IntegrityError: + conn.rollback() + raise ValueError(f"Knowledge base '{name}' already exists") + finally: + self.return_connection(conn) + + def delete_collection(self, name: str) -> bool: + """Delete a knowledge base and all its documents.""" + conn = self.get_connection() + try: + with conn.cursor() as cur: + cur.execute("DELETE FROM knowledge_bases WHERE name = %s", (name,)) + deleted = cur.rowcount > 0 + conn.commit() + return deleted + finally: + self.return_connection(conn) + + def list_collections(self) -> List[Dict[str, Any]]: + """List all knowledge bases with metadata.""" + conn = self.get_connection() + try: + with conn.cursor(cursor_factory=RealDictCursor) as cur: + cur.execute( + """ + SELECT name, kb_type, description, created_at, tags, metadata + FROM knowledge_bases + ORDER BY created_at DESC + """ + ) + results = cur.fetchall() + + return [ + { + "name": row["name"], + "metadata": { + "kb_type": row["kb_type"], + "description": row["description"] or "", + "created_at": row["created_at"].isoformat() if row["created_at"] else "", + "tags": row["tags"] or "", + **row["metadata"] + } + } + for row in results + ] + finally: + self.return_connection(conn) + + def get_collection_metadata(self, name: str) -> Dict[str, Any]: + """Get metadata for a specific knowledge base.""" + conn = self.get_connection() + try: + with conn.cursor(cursor_factory=RealDictCursor) as cur: + cur.execute( + """ + SELECT kb_type, description, created_at, tags, metadata + FROM knowledge_bases + WHERE name = %s + """, + (name,) + ) + row = cur.fetchone() + + if not row: + raise ValueError(f"Knowledge base '{name}' not found") + + return { + "kb_type": row["kb_type"], + "description": row["description"] or "", + "created_at": row["created_at"].isoformat() if row["created_at"] else "", + "tags": row["tags"] or "", + **row["metadata"] + } + finally: + self.return_connection(conn) + + def list_collections_by_type(self, kb_type: KBType) -> List[Dict[str, Any]]: + """List knowledge bases filtered by type.""" + conn = self.get_connection() + try: + with conn.cursor(cursor_factory=RealDictCursor) as cur: + cur.execute( + """ + SELECT name, kb_type, description, created_at, tags, metadata + FROM knowledge_bases + WHERE kb_type = %s + ORDER BY created_at DESC + """, + (kb_type.value,) + ) + results = cur.fetchall() + + return [ + { + "name": row["name"], + "metadata": { + "kb_type": row["kb_type"], + "description": row["description"] or "", + "created_at": row["created_at"].isoformat() if row["created_at"] else "", + "tags": row["tags"] or "", + **row["metadata"] + } + } + for row in results + ] + finally: + self.return_connection(conn) + + # Document Operations + + def add_documents( + self, + kb_name: str, + documents: List[str], + embeddings: List[List[float]], + metadatas: List[Dict[str, Any]], + ids: List[str] + ) -> None: + """Add documents to a knowledge base.""" + conn = self.get_connection() + try: + # Get KB ID + with conn.cursor() as cur: + cur.execute("SELECT id FROM knowledge_bases WHERE name = %s", (kb_name,)) + result = cur.fetchone() + if not result: + raise ValueError(f"Knowledge base '{kb_name}' not found") + kb_id = result[0] + + # Insert documents + with conn.cursor() as cur: + for doc, emb, meta, doc_id in zip(documents, embeddings, metadatas, ids): + cur.execute( + """ + INSERT INTO documents (kb_id, doc_id, content, embedding, metadata) + VALUES (%s, %s, %s, %s, %s) + ON CONFLICT (kb_id, doc_id) DO UPDATE + SET content = EXCLUDED.content, + embedding = EXCLUDED.embedding, + metadata = EXCLUDED.metadata + """, + (kb_id, doc_id, doc, emb, Json(meta)) + ) + conn.commit() + finally: + self.return_connection(conn) + + def get_documents_by_metadata( + self, + kb_name: str, + where: Dict[str, Any], + limit: Optional[int] = None + ) -> List[Dict[str, Any]]: + """Get documents filtered by metadata.""" + conn = self.get_connection() + try: + # Get KB ID + with conn.cursor() as cur: + cur.execute("SELECT id FROM knowledge_bases WHERE name = %s", (kb_name,)) + result = cur.fetchone() + if not result: + return [] + kb_id = result[0] + + # Build WHERE clause for JSONB metadata + where_conditions = [] + params = [kb_id] + for key, value in where.items(): + where_conditions.append(f"metadata->>%s = %s") + params.extend([key, str(value)]) + + where_clause = " AND ".join(where_conditions) if where_conditions else "TRUE" + + query = f""" + SELECT doc_id, content, metadata + FROM documents + WHERE kb_id = %s AND {where_clause} + """ + if limit: + query += f" LIMIT {limit}" + + with conn.cursor(cursor_factory=RealDictCursor) as cur: + cur.execute(query, params) + results = cur.fetchall() + return [dict(row) for row in results] + finally: + self.return_connection(conn) + + def query_documents( + self, + kb_name: str, + query_embedding: List[float], + n_results: int = 10, + where: Optional[Dict[str, Any]] = None + ) -> Dict[str, List]: + """Query documents using vector similarity search.""" + conn = self.get_connection() + try: + # Get KB ID + with conn.cursor() as cur: + cur.execute("SELECT id FROM knowledge_bases WHERE name = %s", (kb_name,)) + result = cur.fetchone() + if not result: + raise ValueError(f"Knowledge base '{kb_name}' not found") + kb_id = result[0] + + # Search documents + with conn.cursor(cursor_factory=RealDictCursor) as cur: + cur.execute( + """ + SELECT * FROM search_documents(%s, %s, %s, %s) + """, + (kb_id, query_embedding, n_results, Json(where) if where else None) + ) + results = cur.fetchall() + + return { + "ids": [[row["doc_id"] for row in results]], + "documents": [[row["content"] for row in results]], + "metadatas": [[row["metadata"] for row in results]], + "distances": [[1 - row["similarity"] for row in results]] + } + finally: + self.return_connection(conn) + + def collection_exists(self, kb_name: str) -> bool: + """Check if a knowledge base exists.""" + conn = self.get_connection() + try: + with conn.cursor() as cur: + cur.execute("SELECT EXISTS(SELECT 1 FROM knowledge_bases WHERE name = %s)", (kb_name,)) + return cur.fetchone()[0] + finally: + self.return_connection(conn) + + def get_collection_count(self, kb_name: str) -> int: + """Get document count for a knowledge base.""" + conn = self.get_connection() + try: + with conn.cursor() as cur: + cur.execute( + """ + SELECT COUNT(*) FROM documents d + JOIN knowledge_bases kb ON d.kb_id = kb.id + WHERE kb.name = %s + """, + (kb_name,) + ) + return cur.fetchone()[0] + finally: + self.return_connection(conn) + + def close(self): + """Close all connections in the pool.""" + if self.pool: + self.pool.closeall() + + +# Singleton instance +_pg_manager = None + + +def get_pg_manager() -> PostgresManager: + """Get or create the PostgresManager singleton.""" + global _pg_manager + if _pg_manager is None: + _pg_manager = PostgresManager() + return _pg_manager + diff --git a/app/core/rag_engine.py b/app/core/rag_engine.py index 7382366..3238fe8 100644 --- a/app/core/rag_engine.py +++ b/app/core/rag_engine.py @@ -13,7 +13,8 @@ from llama_index.core.tools import QueryEngineTool from llama_index.embeddings.ollama import OllamaEmbedding from llama_index.llms.ollama import Ollama from llama_index.core.postprocessor import SentenceTransformerRerank -from llama_index.vector_stores.chroma import ChromaVectorStore +from llama_index.vector_stores.postgres import PGVectorStore +from sqlalchemy import make_url # BM25 retriever - needs to be installed separately try: @@ -22,7 +23,7 @@ try: except ImportError: HAS_BM25 = False -from app.core.chroma_manager import get_chroma_manager +from app.core.pg_manager import get_pg_manager from app.core.config import Config logger = logging.getLogger(__name__) @@ -33,10 +34,10 @@ class RAGEngine: def __init__(self, collection_name: str): """ - Initialize RAG engine for a specific knowledge base. + Initialize RAG engine for a specific knowledge base using PostgreSQL + pgvector. Args: - collection_name: Name of the ChromaDB collection + collection_name: Name of the knowledge base """ self.collection_name = collection_name @@ -57,18 +58,54 @@ class RAGEngine: base_url=Config.OLLAMA_HOST ) - # Get ChromaDB collection - chroma_manager = get_chroma_manager() - collection = chroma_manager.get_collection(collection_name) - - if not collection: + # Verify KB exists + self.pg_manager = get_pg_manager() + if not self.pg_manager.collection_exists(collection_name): raise ValueError(f"Collection {collection_name} not found") - # Create vector store and index - vector_store = ChromaVectorStore(chroma_collection=collection) - storage_context = StorageContext.from_defaults(vector_store=vector_store) + # Get KB ID for the vector store + conn = self.pg_manager.get_connection() + try: + with conn.cursor() as cur: + cur.execute("SELECT id FROM knowledge_bases WHERE name = %s", (collection_name,)) + result = cur.fetchone() + if not result: + raise ValueError(f"Collection {collection_name} not found") + kb_id = result[0] + finally: + self.pg_manager.return_connection(conn) + + # Build PostgreSQL connection URL + import os + pg_user = os.getenv("POSTGRES_USER", os.getenv("USER", "postgres")) + pg_password = os.getenv("POSTGRES_PASSWORD", "") + pg_host = os.getenv("POSTGRES_HOST", "localhost") + pg_port = os.getenv("POSTGRES_PORT", "5432") + pg_db = os.getenv("POSTGRES_DB", "codeforge") + + # Build connection string (handle empty password for trust auth) + if pg_password: + connection_string = f"postgresql+psycopg2://{pg_user}:{pg_password}@{pg_host}:{pg_port}/{pg_db}" + else: + connection_string = f"postgresql+psycopg2://{pg_user}@{pg_host}:{pg_port}/{pg_db}" + + # Initialize PGVectorStore with llama_index + self.vector_store = PGVectorStore.from_params( + database=pg_db, + host=pg_host, + password=pg_password if pg_password else None, + port=int(pg_port), + user=pg_user, + table_name="documents", + embed_dim=768, # nomic-embed-text dimension + ) + + # Create storage context and index + storage_context = StorageContext.from_defaults(vector_store=self.vector_store) + + # Load existing index from vector store self.index = VectorStoreIndex.from_vector_store( - vector_store, + self.vector_store, storage_context=storage_context ) @@ -86,11 +123,15 @@ class RAGEngine: # Initialize BM25 retriever (for hybrid search) if HAS_BM25: try: - all_nodes = self.index.docstore.docs.values() - self.bm25_retriever = BM25Retriever.from_defaults( - nodes=list(all_nodes), - similarity_top_k=Config.TOP_K_RETRIEVAL - ) + all_nodes = list(self.index.docstore.docs.values()) + if all_nodes: + self.bm25_retriever = BM25Retriever.from_defaults( + nodes=all_nodes, + similarity_top_k=Config.TOP_K_RETRIEVAL + ) + else: + logger.warning("No documents found for BM25 retriever") + self.bm25_retriever = None except Exception as e: logger.warning(f"Failed to initialize BM25 retriever: {e}") self.bm25_retriever = None @@ -98,6 +139,32 @@ class RAGEngine: logger.warning("BM25 retriever not available - llama-index-retrievers-bm25 not installed") self.bm25_retriever = None + # Initialize reranker (optional) + try: + self.reranker = SentenceTransformerRerank( + model="cross-encoder/ms-marco-MiniLM-L-2-v2", + top_n=Config.TOP_K_RETRIEVAL + ) + except Exception as e: + logger.warning(f"Failed to initialize reranker: {e}") + self.reranker = None + + # Initialize sub-question engine for agentic RAG (optional) + try: + query_engine_tools = [ + QueryEngineTool.from_defaults( + query_engine=self.query_engine, + name=collection_name, + description=f"Knowledge base: {collection_name}" + ) + ] + self.sub_question_engine = SubQuestionQueryEngine.from_defaults( + query_engine_tools=query_engine_tools + ) + except Exception as e: + logger.warning(f"Failed to initialize sub-question engine: {e}") + self.sub_question_engine = None + # Initialize reranker try: self.reranker = SentenceTransformerRerank( @@ -239,7 +306,7 @@ class RAGEngine: def _query_base(self, question: str) -> Dict[str, any]: """ - Execute base vector search query. + Execute base vector search query using llama_index + PostgreSQL. Args: question: Query string diff --git a/app/db/schema.sql b/app/db/schema.sql new file mode 100644 index 0000000..eb48e66 --- /dev/null +++ b/app/db/schema.sql @@ -0,0 +1,95 @@ +-- Enable pgvector extension +CREATE EXTENSION IF NOT EXISTS vector; + +-- Knowledge Bases (Collections) +CREATE TABLE IF NOT EXISTS knowledge_bases ( + id SERIAL PRIMARY KEY, + name VARCHAR(255) UNIQUE NOT NULL, + kb_type VARCHAR(50) NOT NULL DEFAULT 'generic', + description TEXT, + created_at TIMESTAMP WITH TIME ZONE DEFAULT CURRENT_TIMESTAMP, + updated_at TIMESTAMP WITH TIME ZONE DEFAULT CURRENT_TIMESTAMP, + tags TEXT, + metadata JSONB DEFAULT '{}'::jsonb +); + +CREATE INDEX idx_kb_name ON knowledge_bases(name); +CREATE INDEX idx_kb_type ON knowledge_bases(kb_type); +CREATE INDEX idx_kb_created_at ON knowledge_bases(created_at); + +-- Documents +CREATE TABLE IF NOT EXISTS documents ( + id SERIAL PRIMARY KEY, + kb_id INTEGER NOT NULL REFERENCES knowledge_bases(id) ON DELETE CASCADE, + doc_id VARCHAR(255) NOT NULL, + content TEXT NOT NULL, + embedding vector(768), -- nomic-embed-text produces 768-dim vectors + metadata JSONB DEFAULT '{}'::jsonb, + created_at TIMESTAMP WITH TIME ZONE DEFAULT CURRENT_TIMESTAMP, + UNIQUE(kb_id, doc_id) +); + +CREATE INDEX idx_doc_kb_id ON documents(kb_id); +CREATE INDEX idx_doc_doc_id ON documents(doc_id); +CREATE INDEX idx_doc_metadata ON documents USING GIN(metadata); +CREATE INDEX idx_doc_embedding ON documents USING ivfflat (embedding vector_cosine_ops) WITH (lists = 100); + +-- Agent OS specific content types +CREATE TABLE IF NOT EXISTS agent_os_content ( + id SERIAL PRIMARY KEY, + kb_id INTEGER NOT NULL REFERENCES knowledge_bases(id) ON DELETE CASCADE, + content_type VARCHAR(50) NOT NULL, -- standard, agent, workflow, command, product, spec + name VARCHAR(255) NOT NULL, + content TEXT NOT NULL, + file_path TEXT, + metadata JSONB DEFAULT '{}'::jsonb, + created_at TIMESTAMP WITH TIME ZONE DEFAULT CURRENT_TIMESTAMP, + UNIQUE(kb_id, content_type, name) +); + +CREATE INDEX idx_aos_kb_id ON agent_os_content(kb_id); +CREATE INDEX idx_aos_content_type ON agent_os_content(content_type); +CREATE INDEX idx_aos_name ON agent_os_content(name); +CREATE INDEX idx_aos_metadata ON agent_os_content USING GIN(metadata); + +-- Function to update updated_at timestamp +CREATE OR REPLACE FUNCTION update_updated_at_column() +RETURNS TRIGGER AS $$ +BEGIN + NEW.updated_at = CURRENT_TIMESTAMP; + RETURN NEW; +END; +$$ language 'plpgsql'; + +-- Trigger to auto-update updated_at +CREATE TRIGGER update_kb_updated_at BEFORE UPDATE ON knowledge_bases + FOR EACH ROW EXECUTE FUNCTION update_updated_at_column(); + +-- Helper function for cosine similarity search +CREATE OR REPLACE FUNCTION search_documents( + p_kb_id INTEGER, + p_query_embedding vector(768), + p_limit INTEGER DEFAULT 10, + p_metadata_filter JSONB DEFAULT NULL +) +RETURNS TABLE ( + doc_id VARCHAR, + content TEXT, + similarity FLOAT, + metadata JSONB +) AS $$ +BEGIN + RETURN QUERY + SELECT + d.doc_id, + d.content, + 1 - (d.embedding <=> p_query_embedding) AS similarity, + d.metadata + FROM documents d + WHERE d.kb_id = p_kb_id + AND (p_metadata_filter IS NULL OR d.metadata @> p_metadata_filter) + ORDER BY d.embedding <=> p_query_embedding + LIMIT p_limit; +END; +$$ LANGUAGE plpgsql; + diff --git a/app/main.py b/app/main.py index 1661801..29fe474 100644 --- a/app/main.py +++ b/app/main.py @@ -1,682 +1,378 @@ """ -Code-Forge: Localized Multi-KB RAG System -Main Streamlit application with badass Archon-inspired UI. +Welcome page for Code-Forge with onboarding and system status checks. """ -import logging -import os -from pathlib import Path - import streamlit as st - -from app.core.chroma_manager import get_chroma_manager +import os +import subprocess +import requests +from app.core.pg_manager import get_pg_manager from app.core.config import Config -from app.core.health import check_chroma_health, check_ollama_health -from app.core.ingestion import ingest_directory, ingest_file -from app.core.kb_metadata import get_documents_metadata -from app.core.rag_engine import RAGEngine -# Configure logging -logging.basicConfig(level=logging.INFO) -logger = logging.getLogger(__name__) - -# Page configuration +# Page config st.set_page_config( - page_title="Code-Forge 🔥", + page_title="Welcome to Code-Forge", page_icon="🔥", layout="wide", - initial_sidebar_state="expanded" + initial_sidebar_state="collapsed" ) -# Initialize session state -if "selected_kb" not in st.session_state: - st.session_state.selected_kb = None -if "chat_history" not in st.session_state: - st.session_state.chat_history = [] -if "rag_settings" not in st.session_state: - st.session_state.rag_settings = { - "hybrid": False, - "rerank": False, - "agentic": False - } +# Redirect to Welcome page (this IS the default landing page) +st.switch_page("pages/0_Welcome.py") -# Custom CSS with Archon aesthetic +# Custom CSS for Archon-inspired design st.markdown(""" """, unsafe_allow_html=True) # Header +st.markdown('
🔥 Welcome to Code-Forge
', unsafe_allow_html=True) +st.markdown('
Production-Grade RAG System with PostgreSQL + pgvector
', unsafe_allow_html=True) + +# Navigation +nav1, nav2, nav3, nav4 = st.columns([1, 1, 1, 6]) +with nav1: + if st.button("🏠 Welcome", use_container_width=True, key="nav_welcome", type="primary"): + st.switch_page("pages/0_Welcome.py") +with nav2: + if st.button("⚙️ Main", use_container_width=True, key="nav_main"): + st.switch_page("main.py") + +# Introduction st.markdown(""" -
-

🔥 Code-Forge

-

Localized Multi-KB RAG System with MCP Integration

+--- + +## 🚀 What is Code-Forge? + +**Code-Forge** is a **localized, production-grade RAG (Retrieval-Augmented Generation) system** that runs entirely on your machine. + +### Key Features: + +- **🗄️ PostgreSQL + pgvector** - ACID-compliant vector database +- **🤖 Ollama** - Local LLMs and embeddings (no API keys needed) +- **🔌 MCP Integration** - Expose knowledge bases to AI agents +- **📚 Multi-KB Architecture** - Isolated knowledge bases per project +- **🧠 Advanced RAG** - Vector search, hybrid search, reranking, agentic RAG +- **🎨 Beautiful UI** - Archon-inspired design + +--- +""") + +# System Status Check +st.markdown("## 🔍 System Status") + +col1, col2, col3 = st.columns(3) + +# Check PostgreSQL +with col1: + st.markdown("### PostgreSQL") + try: + pg_manager = get_pg_manager() + # Try to list collections + collections = pg_manager.list_collections() + st.markdown(f'

✅ Connected

', unsafe_allow_html=True) + st.metric("Knowledge Bases", len(collections)) + except Exception as e: + st.markdown(f'

❌ Not Connected

', unsafe_allow_html=True) + st.error(f"Error: {str(e)}") + st.info("Make sure PostgreSQL is running and the `codeforge` database exists.") + +# Check Ollama +with col2: + st.markdown("### Ollama") + try: + response = requests.get(f"{Config.OLLAMA_HOST}/api/tags", timeout=2) + if response.status_code == 200: + models = response.json().get("models", []) + st.markdown(f'

✅ Running

', unsafe_allow_html=True) + st.metric("Models Installed", len(models)) + else: + st.markdown(f'

⚠️ Unexpected Response

', unsafe_allow_html=True) + except Exception as e: + st.markdown(f'

❌ Not Running

', unsafe_allow_html=True) + st.error(f"Error: {str(e)}") + st.info("Make sure Ollama container is running: `docker-compose up -d`") + +# Check Required Models +with col3: + st.markdown("### Required Models") + try: + response = requests.get(f"{Config.OLLAMA_HOST}/api/tags", timeout=2) + if response.status_code == 200: + models = response.json().get("models", []) + model_names = [m["name"] for m in models] + + required_models = ["nomic-embed-text:latest", "llama3.2:3b"] + missing_models = [m for m in required_models if m not in model_names] + + if not missing_models: + st.markdown(f'

✅ All Present

', unsafe_allow_html=True) + st.success("All required models are installed!") + else: + st.markdown(f'

⚠️ Missing Models

', unsafe_allow_html=True) + st.warning(f"Missing: {', '.join(missing_models)}") + st.info("Run: `ollama pull nomic-embed-text && ollama pull llama3.2:3b`") + except Exception: + st.markdown(f'

❌ Cannot Check

', unsafe_allow_html=True) + +st.markdown("---") + +# Quick Start Guide +st.markdown("## 🎯 Quick Start Guide") + +tab1, tab2, tab3, tab4 = st.tabs(["1️⃣ Create KB", "2️⃣ Ingest Docs", "3️⃣ Query", "4️⃣ MCP"]) + +with tab1: + st.markdown(""" + ### Creating Your First Knowledge Base + + 1. **Navigate to the main page** (use the sidebar) + 2. **Expand "➕ Create New KB"** in the sidebar + 3. **Choose a KB type**: + - 📚 **GENERIC** - General-purpose knowledge + - 💻 **CODE** - Source code repositories + - 📖 **DOCUMENTATION** - Technical documentation + - 🤖 **AGENT_OS** - Spec-driven development + 4. **Enter a name** (e.g., `my-project`) + 5. **Add a description** (optional) + 6. **Click "Create"** + + ✨ Your knowledge base is now ready for documents! + """) + + st.info("💡 **Tip**: Use separate KBs for different projects to keep contexts isolated.") + +with tab2: + st.markdown(""" + ### Ingesting Documents + + **Option 1: File Upload** + 1. Select your KB from the sidebar + 2. Go to the **"📚 Knowledge Base"** tab + 3. Click **"Choose files"** and select documents + 4. Click **"Upload Files"** + + **Option 2: Directory Import** + 1. Select your KB from the sidebar + 2. Go to the **"📚 Knowledge Base"** tab + 3. Enter the full path to your project directory + 4. Click **"Import Directory"** + + **Supported File Types:** + - Documents: `.md`, `.txt`, `.pdf` + - Code: `.py`, `.js`, `.jsx`, `.ts`, `.tsx`, `.go`, `.rs`, `.java`, `.cpp`, `.c`, `.h` + - Config: `.json`, `.yaml`, `.yml` + """) + + st.info("💡 **Tip**: For large codebases, use directory import to ingest all files at once.") + +with tab3: + st.markdown(""" + ### Querying Your Knowledge Base + + 1. **Select your KB** from the sidebar + 2. **Go to the "💬 Chat" tab** + 3. **Choose a RAG strategy**: + - 🔍 **Base** - Simple vector search (fastest) + - 🔀 **Hybrid** - BM25 + vector fusion (better for keywords) + - 🎯 **Reranking** - Cross-encoder reranking (most accurate) + - 🤖 **Agentic** - Sub-question decomposition (best for complex queries) + 4. **Type your question** and press Enter + 5. **View sources** by expanding "📚 View Sources" + + **Example Queries:** + - "How does authentication work in this codebase?" + - "What are the main components of the system?" + - "Show me examples of error handling" + """) + + st.info("💡 **Tip**: Use hybrid search for technical queries with specific keywords.") + +with tab4: + st.markdown(""" + ### MCP Integration (Model Context Protocol) + + Code-Forge exposes your knowledge bases via HTTP for AI agent integration. + + **MCP Server Endpoint:** + ``` + http://localhost:8051/mcp + ``` + + **Available Tools:** + - `list_knowledge_bases` - List all KBs + - `create_knowledge_base` - Create new KB + - `search_knowledge_base` - Query KB with RAG + - `get_kb_stats` - Get KB statistics + - `ingest_documents` - Add documents + - `get_standards` - Get Agent OS standards + - `get_workflows` - Get Agent OS workflows + - `get_specs` - Get Agent OS specs + + **Example Request:** + ```bash + curl -X POST http://localhost:8051/mcp \\ + -H "Content-Type: application/json" \\ + -d '{ + "jsonrpc": "2.0", + "id": 1, + "method": "tools/call", + "params": { + "name": "search_knowledge_base", + "arguments": { + "kb_name": "my-docs", + "query": "How do I configure authentication?", + "strategy": "hybrid" + } + } + }' + ``` + """) + + st.info("💡 **Tip**: See the README for full MCP API documentation.") + +st.markdown("---") + +# Feature Highlights +st.markdown("## ✨ Feature Highlights") + +col1, col2 = st.columns(2) + +with col1: + st.markdown(""" +
+

🗄️ Production-Grade Database

+

PostgreSQL + pgvector ensures your data is safe with ACID compliance, + transaction safety, and bulletproof persistence. Your data survives all + Docker operations and system restarts.

+
+ """, unsafe_allow_html=True) + + st.markdown(""" +
+

🧠 Advanced RAG Strategies

+

Choose from multiple retrieval strategies: vector search, hybrid search + (BM25 + vector), reranking with cross-encoders, or agentic RAG with + sub-question decomposition.

+
+ """, unsafe_allow_html=True) + + st.markdown(""" +
+

🔒 100% Local & Private

+

All data stays on your machine. No API keys, no cloud dependencies, + no telemetry. Perfect for sensitive codebases and proprietary documentation.

+
+ """, unsafe_allow_html=True) + +with col2: + st.markdown(""" +
+

🤖 Agent OS Support

+

First-class support for spec-driven development with Agent OS. + Ingest standards, workflows, and specs with specialized retrieval tools.

+
+ """, unsafe_allow_html=True) + + st.markdown(""" +
+

🔌 MCP Integration

+

Expose your knowledge bases to AI agents via Model Context Protocol. + 12 HTTP endpoints for seamless integration with Claude Desktop and other agents.

+
+ """, unsafe_allow_html=True) + + st.markdown(""" +
+

📚 Multi-KB Architecture

+

Create isolated knowledge bases for different projects. Each KB has + its own type, metadata, and documents. Perfect for teams working on + multiple projects.

+
+ """, unsafe_allow_html=True) + +st.markdown("---") + +# Resources +st.markdown("## 📚 Resources") + +col1, col2, col3 = st.columns(3) + +with col1: + st.markdown(""" + ### 📖 Documentation + - [README.md](../README.md) + - [Setup Guide](../docs/SETUP.md) + - [Usage Guide](../docs/USAGE.md) + - [Troubleshooting](../docs/TROUBLESHOOTING.md) + """) + +with col2: + st.markdown(""" + ### 🔗 Quick Links + - [Streamlit UI](http://localhost:8501) + - [MCP Server](http://localhost:8051) + - [Ollama API](http://localhost:11434) + """) + +with col3: + st.markdown(""" + ### 🛠️ Useful Commands + ```bash + # View logs + docker-compose logs -f + + # Restart services + docker-compose restart + + # Stop services + docker-compose down + ``` + """) + +st.markdown("---") + +# Footer +st.markdown(""" +
+

Ready to get started?

+

Use the sidebar to navigate to the main page and create your first knowledge base!

+

Built with ❤️ for the local-first AI community

""", unsafe_allow_html=True) -# Sidebar -with st.sidebar: - st.markdown("## ⚙️ Settings") - - # Connection Status - st.markdown("### 🔌 Connection Status") - - ollama_status = check_ollama_health() - chroma_status = check_chroma_health() - - if ollama_status["status"] == "healthy": - st.markdown('

🟢 Ollama Connected

', unsafe_allow_html=True) - if ollama_status.get("models"): - st.caption(f"Models: {', '.join(ollama_status['models'][:3])}") - else: - st.markdown('

🔴 Ollama Disconnected

', unsafe_allow_html=True) - st.caption(ollama_status.get("error", "Unknown error")) - - if chroma_status["status"] == "healthy": - st.markdown('

🟢 ChromaDB Connected

', unsafe_allow_html=True) - else: - st.markdown('

🔴 ChromaDB Disconnected

', unsafe_allow_html=True) - st.caption(chroma_status.get("error", "Unknown error")) - - st.divider() - - # MCP Server Info - st.markdown("### 🔗 MCP Server") - mcp_url = f"http://localhost:{Config.MCP_SERVER_PORT}/mcp" - st.code(mcp_url, language="text") - - st.caption("**Connection Command:**") - st.code(f"claude mcp add --transport http code-forge {mcp_url}", language="bash") - - st.divider() - - # Knowledge Base Selector - st.markdown("### 📚 Knowledge Base") - - chroma_manager = get_chroma_manager() - collections = chroma_manager.list_collections() - - if collections: - selected = st.selectbox( - "Select KB", - collections, - key="kb_selector" - ) - st.session_state.selected_kb = selected - else: - st.warning("No knowledge bases found. Create one below!") - st.session_state.selected_kb = None - - # Create new KB - with st.expander("➕ Create New KB"): - new_kb_name = st.text_input("KB Name", key="new_kb_name") - if st.button("Create"): - if new_kb_name: - if chroma_manager.create_collection(new_kb_name): - st.success(f"Created KB: {new_kb_name}") - st.rerun() - else: - st.error("Failed to create KB") - else: - st.error("Please enter a KB name") - - st.divider() - - # RAG Strategy Toggles - st.markdown("### 🎯 RAG Strategies") - - st.session_state.rag_settings["hybrid"] = st.checkbox( - "🔀 Hybrid Search", - value=st.session_state.rag_settings["hybrid"], - help="**Hybrid Search**: Combines semantic vector search with BM25 keyword search for better recall. Uses reciprocal rank fusion to merge results. Best for queries with specific technical terms." - ) - - st.session_state.rag_settings["rerank"] = st.checkbox( - "🎯 Reranking", - value=st.session_state.rag_settings["rerank"], - help="**Reranking**: Re-scores retrieved chunks using a cross-encoder model (ms-marco-MiniLM) for higher precision. Keeps top 3 most relevant results. Improves answer quality but adds latency." - ) - - st.session_state.rag_settings["agentic"] = st.checkbox( - "🤖 Agentic RAG", - value=st.session_state.rag_settings["agentic"], - help="**Agentic RAG**: Breaks complex questions into sub-questions and answers them independently before synthesizing. Best for multi-part or analytical queries. Slower but more thorough." - ) - -# Main content tabs -tab1, tab2 = st.tabs(["📚 Knowledge Base", "💬 Chat"]) - -# Tab 1: Knowledge Base Management -with tab1: - st.markdown("## 📚 Knowledge Base Management") - - if not st.session_state.selected_kb: - st.info("👈 Please select or create a knowledge base from the sidebar") - else: - st.markdown(f"### Current KB: **{st.session_state.selected_kb}**") - - st.markdown("---") - - # Document Upload - col1, col2 = st.columns(2, gap="large") - - with col1: - st.markdown("#### 📤 Upload Documents") - st.caption("Choose files to upload to the knowledge base") - - # Initialize upload counter in session state - if "upload_counter" not in st.session_state: - st.session_state.upload_counter = 0 - - # Use dynamic key to reset file uploader after upload - uploaded_files = st.file_uploader( - "Choose files", - accept_multiple_files=True, - type=[ext.replace(".", "") for ext in Config.SUPPORTED_FILE_TYPES], - label_visibility="collapsed", - key=f"file_uploader_{st.session_state.upload_counter}" - ) - - if uploaded_files and st.button("Upload Files", key="upload_files_btn", use_container_width=True): - try: - Config.ensure_upload_dir() - progress_bar = st.progress(0) - status_container = st.container() - - for i, uploaded_file in enumerate(uploaded_files): - with status_container: - st.write(f"Processing: {uploaded_file.name}...") - - # Save file - file_path = Path(Config.UPLOAD_DIR) / uploaded_file.name - with open(file_path, "wb") as f: - f.write(uploaded_file.getbuffer()) - - # Ingest file - result = ingest_file( - str(file_path), - st.session_state.selected_kb, - uploaded_file.name - ) - - with status_container: - if result["status"] == "success": - st.success(f"✅ {uploaded_file.name}: {result['chunks']} chunks") - else: - st.error(f"❌ {uploaded_file.name}: {result.get('error', 'Unknown error')}") - - progress_bar.progress((i + 1) / len(uploaded_files)) - - with status_container: - st.success("🎉 Upload complete! Refreshing document list...") - - # Increment counter to reset file uploader on next render - st.session_state.upload_counter += 1 - - # Wait a moment before rerun so user can see the success message - import time - time.sleep(1.5) - st.rerun() - - except Exception as e: - st.error(f"❌ Upload failed: {str(e)}") - import traceback - st.code(traceback.format_exc()) - - with col2: - st.markdown("#### 📁 Import Directory") - st.caption("Import all supported files from a directory") - - st.info("💡 **Tip:** Mount your directory as a volume in docker-compose.yml first. Paths are relative to `/workspace` in the container.") - - dir_path = st.text_input( - "Directory Path", - placeholder="/workspace/mounted_docs", - help="Path must exist inside the Docker container", - label_visibility="collapsed" - ) - - if st.button("Import Directory", key="import_dir_btn", use_container_width=True): - if not dir_path: - st.error("Please enter a directory path") - else: - with st.spinner("Importing files..."): - results = ingest_directory(dir_path, st.session_state.selected_kb) - - if not results: - st.warning("No supported files found in directory") - elif results[0].get("error") and "not found" in results[0].get("error", ""): - st.error(f"Directory not found: {dir_path}") - else: - success_count = sum(1 for r in results if r.get("status") == "success") - error_count = len(results) - success_count - - if success_count > 0: - st.success(f"✅ Imported {success_count} file(s)") - if error_count > 0: - st.warning(f"⚠️ {error_count} file(s) failed") - - # Show first 5 results - for result in results[:5]: - if result.get("status") == "success": - st.caption(f"✅ {result.get('filename', 'Unknown')}") - else: - st.caption(f"❌ {result.get('filename', 'Unknown')}: {result.get('error', 'Unknown error')}") - - if len(results) > 5: - st.caption(f"... and {len(results) - 5} more") - - st.rerun() - - st.divider() - - # Document Grid - st.markdown("#### 📄 Documents") - - docs = get_documents_metadata(st.session_state.selected_kb) - - if not docs: - st.info("No documents in this knowledge base. Upload files to get started!") - else: - # Display in 3-column grid - cols = st.columns(3) - - for i, doc in enumerate(docs): - with cols[i % 3]: - # File icon based on type - icon_map = { - ".py": "🐍", ".js": "📜", ".jsx": "⚛️", - ".ts": "📘", ".tsx": "⚛️", ".md": "📄", - ".pdf": "📕", ".json": "📋", ".yaml": "⚙️" - } - icon = icon_map.get(doc["file_type"], "📄") - - # Create card - tags_html = "".join([f'{tag}' for tag in doc["tags"]]) - - st.markdown(f""" -
-
{icon}
-
{doc["filename"]}
-
{tags_html}
-
- {doc["formatted_date"]}
- {doc["chunk_count"]} chunks -
-
- """, unsafe_allow_html=True) - -# Tab 2: Chat Interface -with tab2: - st.markdown("## 💬 Chat with Your Knowledge Base") - - if not st.session_state.selected_kb: - st.info("👈 Please select a knowledge base from the sidebar") - else: - # Display active strategies banner - active_strategies = [] - if st.session_state.rag_settings["hybrid"]: - active_strategies.append("🔀 Hybrid Search") - if st.session_state.rag_settings["rerank"]: - active_strategies.append("🎯 Reranking") - if st.session_state.rag_settings["agentic"]: - active_strategies.append("🤖 Agentic RAG") - - if active_strategies: - strategies_text = " + ".join(active_strategies) - st.markdown(f""" -
- 🎯 Active Retrieval Strategies: {strategies_text} -
- """, unsafe_allow_html=True) - else: - st.markdown(f""" -
- 📊 Mode: Standard Vector Search -
- """, unsafe_allow_html=True) - - # Display chat history - for message in st.session_state.chat_history: - with st.chat_message(message["role"]): - st.markdown(message["content"]) - - if message["role"] == "assistant" and message.get("sources"): - with st.expander("📚 View Sources"): - for i, source in enumerate(message["sources"][:3], 1): - st.markdown(f"**Source {i}:**") - st.code(source["text"][:200] + "..." if len(source["text"]) > 200 else source["text"]) - if source.get("metadata"): - st.caption(f"From: {source['metadata'].get('filename', 'Unknown')}") - if source.get("score"): - st.caption(f"Score: {source['score']:.3f}") - st.divider() - - # Chat input - if prompt := st.chat_input("Ask a question about your knowledge base..."): - # Add user message - st.session_state.chat_history.append({ - "role": "user", - "content": prompt - }) - - # Display user message - with st.chat_message("user"): - st.markdown(prompt) - - # Generate response - with st.chat_message("assistant"): - with st.spinner("Thinking..."): - try: - rag_engine = RAGEngine(st.session_state.selected_kb) - result = rag_engine.query( - prompt, - use_hybrid=st.session_state.rag_settings["hybrid"], - use_rerank=st.session_state.rag_settings["rerank"], - use_agentic=st.session_state.rag_settings["agentic"] - ) - - answer = result.get("answer", "No answer generated") - sources = result.get("sources", []) - - st.markdown(answer) - - # Add to history - st.session_state.chat_history.append({ - "role": "assistant", - "content": answer, - "sources": sources - }) - - # Display sources - if sources: - with st.expander("📚 View Sources"): - for i, source in enumerate(sources[:3], 1): - st.markdown(f"**Source {i}:**") - st.code(source["text"][:200] + "..." if len(source["text"]) > 200 else source["text"]) - if source.get("metadata"): - st.caption(f"From: {source['metadata'].get('filename', 'Unknown')}") - if source.get("score"): - st.caption(f"Score: {source['score']:.3f}") - st.divider() - - except Exception as e: - error_msg = f"Error: {str(e)}" - st.error(error_msg) - st.session_state.chat_history.append({ - "role": "assistant", - "content": error_msg - }) - - # Clear chat button - if st.session_state.chat_history and st.button("🗑️ Clear Chat"): - st.session_state.chat_history = [] - st.rerun() - diff --git a/app/pages/0_Welcome.py b/app/pages/0_Welcome.py new file mode 100644 index 0000000..ba161ac --- /dev/null +++ b/app/pages/0_Welcome.py @@ -0,0 +1,185 @@ +""" +Welcome page for Code-Forge - Default landing page +""" + +import streamlit as st +from pathlib import Path + +# Page config +st.set_page_config( + page_title="Welcome to Code-Forge", + page_icon="🔥", + layout="wide", + initial_sidebar_state="collapsed" +) + +# Custom CSS - Archon-inspired badass styling +st.markdown(""" + +""", unsafe_allow_html=True) + +# Hero Section +hero_path = Path("assets/hero.png") + +st.markdown('
', unsafe_allow_html=True) + +if hero_path.exists(): + st.image(str(hero_path), use_container_width=False, output_format="PNG") +else: + st.markdown('
🔥
', unsafe_allow_html=True) + +st.markdown(""" +
CODE-FORGE
+
Production-Grade RAG System with PostgreSQL + pgvector
+
+""", unsafe_allow_html=True) + +# Navigation +nav1, nav2, nav3 = st.columns([1, 1, 6]) +with nav1: + if st.button("🏠 Welcome", use_container_width=True, type="primary"): + st.switch_page("pages/0_Welcome.py") +with nav2: + if st.button("⚙️ Main", use_container_width=True): + st.switch_page("pages/1_Main.py") + +st.markdown("
", unsafe_allow_html=True) + +st.markdown(""" +## 🚀 What is Code-Forge? + +**Code-Forge** is a **localized, production-grade RAG system** that runs entirely on your machine. + +### Key Features: + +- 🗄️ **PostgreSQL + pgvector** - ACID-compliant vector database +- 🤖 **Ollama** - Local LLMs and embeddings +- 🔌 **MCP Integration** - Expose knowledge bases to AI agents +- 📚 **Multi-KB Architecture** - Isolated knowledge bases per project +- 🧠 **Advanced RAG** - Vector search, hybrid search, reranking, agentic RAG + +## 🎯 Quick Start + +1. Click **"⚙️ Main"** button above +2. Create a Knowledge Base from the left panel +3. Upload documents or import a directory +4. Start chatting with your knowledge base + +--- + +**Ready to get started? Click the "⚙️ Main" button above!** +""") diff --git a/app/pages/1_Main.py b/app/pages/1_Main.py new file mode 100644 index 0000000..8b5b421 --- /dev/null +++ b/app/pages/1_Main.py @@ -0,0 +1,851 @@ +""" +Code-Forge: Localized Multi-KB RAG System +Main Streamlit application with badass Archon-inspired UI. +""" + +import logging +import os +from pathlib import Path + +import streamlit as st + +from app.core.pg_manager import get_pg_manager +from app.core.config import Config +from app.core.health import check_chroma_health, check_ollama_health +from app.core.ingestion import ingest_directory, ingest_file +from app.core.kb_metadata import get_documents_metadata, get_kb_type_badge, get_kb_type_summary +from app.core.kb_types import KBType, get_kb_type_choices, get_kb_type_info +from app.core.rag_engine import RAGEngine +from app.core.agent_os_ingestion import AgentOSIngestion + +# Configure logging +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + +# Page configuration +st.set_page_config( + page_title="Code-Forge 🔥", + page_icon="🔥", + layout="wide", + initial_sidebar_state="collapsed" +) + +# Initialize session state +if "selected_kb" not in st.session_state: + st.session_state.selected_kb = None +if "chat_history" not in st.session_state: + st.session_state.chat_history = [] +if "rag_settings" not in st.session_state: + st.session_state.rag_settings = { + "hybrid": False, + "rerank": False, + "agentic": False + } + +# Custom CSS with Archon aesthetic +st.markdown(""" + + + +""", unsafe_allow_html=True) + +# Header +st.markdown(""" +
+

🔥 Code-Forge

+

Localized Multi-KB RAG System with MCP Integration

+
+""", unsafe_allow_html=True) + +# Navigation +nav1, nav2, nav3 = st.columns([1, 1, 6]) +with nav1: + if st.button("🏠 Welcome", use_container_width=True, key="nav_welcome"): + st.switch_page("pages/0_Welcome.py") +with nav2: + if st.button("⚙️ Main", use_container_width=True, key="nav_main", type="primary"): + st.switch_page("pages/1_Main.py") + +st.divider() + +# Create two-column layout: left panel + main content +left_col, main_col = st.columns([1, 3], gap="medium") + +# Left Panel +with left_col: + st.markdown("## ⚙️ Settings") + + # Connection Status + st.markdown("### 🔌 Connection Status") + + ollama_status = check_ollama_health() + chroma_status = check_chroma_health() + + if ollama_status["status"] == "healthy": + st.markdown('

🟢 Ollama Connected

', unsafe_allow_html=True) + if ollama_status.get("models"): + st.caption(f"Models: {', '.join(ollama_status['models'][:3])}") + else: + st.markdown('

🔴 Ollama Disconnected

', unsafe_allow_html=True) + st.caption(ollama_status.get("error", "Unknown error")) + + if chroma_status["status"] == "healthy": + st.markdown('

🟢 PostgreSQL Connected

', unsafe_allow_html=True) + else: + st.markdown('

🔴 PostgreSQL Disconnected

', unsafe_allow_html=True) + st.caption(chroma_status.get("error", "Unknown error")) + + st.divider() + + # MCP Server Info + st.markdown("### 🔗 MCP Server") + mcp_url = f"http://localhost:{Config.MCP_SERVER_PORT}/mcp" + st.code(mcp_url, language="text") + + st.caption("**Connection Command:**") + st.code(f"claude mcp add --transport http code-forge {mcp_url}", language="bash") + + st.divider() + + # Knowledge Base Selector + st.markdown("### 📚 Knowledge Base") + + pg_manager = get_pg_manager() + collections = pg_manager.list_collections() + + if collections: + # Optional type filter + type_summary = get_kb_type_summary() + filter_options = ["All Types"] + [ + f"{get_kb_type_info(KBType(kb_type)).icon} {get_kb_type_info(KBType(kb_type)).name} ({count})" + for kb_type, count in type_summary.items() if count > 0 + ] + + selected_filter = st.selectbox( + "Filter by Type", + options=filter_options, + key="kb_type_filter" + ) + + # Filter collections if needed + if selected_filter != "All Types": + # Extract the KB type value from the filter string + filter_type_value = None + for kb_type, count in type_summary.items(): + type_info = get_kb_type_info(KBType(kb_type)) + if f"{type_info.icon} {type_info.name}" in selected_filter: + filter_type_value = kb_type + break + + if filter_type_value: + collections = [ + col for col in collections + if col["metadata"].get("kb_type") == filter_type_value + ] + + # Build display options with type badges + kb_display_options = [] + kb_name_map = {} # Map display string to actual name + + for col in collections: + name = col["name"] + metadata = col["metadata"] + kb_type = KBType(metadata.get("kb_type", KBType.GENERIC.value)) + type_info = get_kb_type_info(kb_type) + + display_name = f"{type_info.icon} {name}" + kb_display_options.append(display_name) + kb_name_map[display_name] = name + + selected_display = st.selectbox( + "Select KB", + kb_display_options, + key="kb_selector" + ) + + # Map back to actual KB name + st.session_state.selected_kb = kb_name_map[selected_display] + + # Display selected KB metadata + selected_col = next( + (col for col in collections if col["name"] == st.session_state.selected_kb), + None + ) + + if selected_col: + metadata = selected_col["metadata"] + kb_type = KBType(metadata.get("kb_type", KBType.GENERIC.value)) + + # Show type badge + st.markdown(get_kb_type_badge(kb_type), unsafe_allow_html=True) + + # Show description if available + description = metadata.get("description", "") + if description: + st.caption(description) + else: + st.warning("No knowledge bases found. Create one below!") + st.session_state.selected_kb = None + + # Create new KB + with st.expander("➕ Create New KB"): + new_kb_name = st.text_input("KB Name", key="new_kb_name", placeholder="my-project") + + # KB Type Selector + kb_type_choices = get_kb_type_choices() + selected_type_display = st.selectbox( + "KB Type", + options=kb_type_choices, + key="kb_type_selector", + help="Select the type of knowledge base to create" + ) + + # Extract the actual KBType from the display string + # Format is "🤖 Agent OS Profile" -> need to map back to KBType + type_map = {choice: kb_type for kb_type, choice in zip(KBType, kb_type_choices)} + selected_kb_type = type_map[selected_type_display] + + # Show type-specific info + type_info = get_kb_type_info(selected_kb_type) + st.caption(f"**{type_info.description}**") + + # Optional description + kb_description = st.text_area( + "Description (optional)", + key="kb_description", + placeholder="Brief description of this knowledge base...", + height=80 + ) + + if st.button("Create", type="primary"): + if new_kb_name: + if pg_manager.create_collection( + name=new_kb_name, + kb_type=selected_kb_type, + description=kb_description + ): + st.success(f"✅ Created {type_info.icon} **{new_kb_name}** ({type_info.name})") + st.rerun() + else: + st.error("Failed to create KB") + else: + st.error("Please enter a KB name") + + st.divider() + + # RAG Strategy Toggles + st.markdown("### 🎯 RAG Strategies") + + st.session_state.rag_settings["hybrid"] = st.checkbox( + "🔀 Hybrid Search", + value=st.session_state.rag_settings["hybrid"], + help="**Hybrid Search**: Combines semantic vector search with BM25 keyword search for better recall. Uses reciprocal rank fusion to merge results. Best for queries with specific technical terms." + ) + + st.session_state.rag_settings["rerank"] = st.checkbox( + "🎯 Reranking", + value=st.session_state.rag_settings["rerank"], + help="**Reranking**: Re-scores retrieved chunks using a cross-encoder model (ms-marco-MiniLM) for higher precision. Keeps top 3 most relevant results. Improves answer quality but adds latency." + ) + + st.session_state.rag_settings["agentic"] = st.checkbox( + "🤖 Agentic RAG", + value=st.session_state.rag_settings["agentic"], + help="**Agentic RAG**: Breaks complex questions into sub-questions and answers them independently before synthesizing. Best for multi-part or analytical queries. Slower but more thorough." + ) + +# Main content area (right column) +with main_col: + # Main content tabs + tab1, tab2 = st.tabs(["📚 Knowledge Base", "💬 Chat"]) + + # Tab 1: Knowledge Base Management + with tab1: + st.markdown("## 📚 Knowledge Base Management") + + if not st.session_state.selected_kb: + st.info("👈 Please select or create a knowledge base from the left panel") + else: + st.markdown(f"### Current KB: **{st.session_state.selected_kb}**") + + st.markdown("---") + + # Document Upload + doc_col1, doc_col2 = st.columns(2, gap="large") + + with doc_col1: + st.markdown("#### 📤 Upload Documents") + st.caption("Choose files to upload to the knowledge base") + + # Initialize upload counter in session state + if "upload_counter" not in st.session_state: + st.session_state.upload_counter = 0 + + # Use dynamic key to reset file uploader after upload + uploaded_files = st.file_uploader( + "Choose files", + accept_multiple_files=True, + type=[ext.replace(".", "") for ext in Config.SUPPORTED_FILE_TYPES], + label_visibility="collapsed", + key=f"file_uploader_{st.session_state.upload_counter}" + ) + + if uploaded_files and st.button("Upload Files", key="upload_files_btn", use_container_width=True): + try: + Config.ensure_upload_dir() + progress_bar = st.progress(0) + status_container = st.container() + + for i, uploaded_file in enumerate(uploaded_files): + with status_container: + st.write(f"Processing: {uploaded_file.name}...") + + # Save file + file_path = Path(Config.UPLOAD_DIR) / uploaded_file.name + with open(file_path, "wb") as f: + f.write(uploaded_file.getbuffer()) + + # Ingest file + result = ingest_file( + str(file_path), + st.session_state.selected_kb, + uploaded_file.name + ) + + with status_container: + if result["status"] == "success": + st.success(f"✅ {uploaded_file.name}: {result['chunks']} chunks") + else: + st.error(f"❌ {uploaded_file.name}: {result.get('error', 'Unknown error')}") + + progress_bar.progress((i + 1) / len(uploaded_files)) + + with status_container: + st.success("🎉 Upload complete! Refreshing document list...") + + # Increment counter to reset file uploader on next render + st.session_state.upload_counter += 1 + + # Wait a moment before rerun so user can see the success message + import time + time.sleep(1.5) + st.rerun() + + except Exception as e: + st.error(f"❌ Upload failed: {str(e)}") + import traceback + st.code(traceback.format_exc()) + + with doc_col2: + st.markdown("#### 📁 Import Directory") + st.caption("Import all supported files from a directory") + + st.info("💡 **Tip:** Mount your directory as a volume in docker-compose.yml first. Paths are relative to `/workspace` in the container.") + + dir_path = st.text_input( + "Directory Path", + placeholder="/workspace/mounted_docs", + help="Path must exist inside the Docker container", + label_visibility="collapsed" + ) + + if st.button("Import Directory", key="import_dir_btn", use_container_width=True): + if not dir_path: + st.error("Please enter a directory path") + else: + with st.spinner("Importing files..."): + results = ingest_directory(dir_path, st.session_state.selected_kb) + + if not results: + st.warning("No supported files found in directory") + elif results[0].get("error") and "not found" in results[0].get("error", ""): + st.error(f"Directory not found: {dir_path}") + else: + success_count = sum(1 for r in results if r.get("status") == "success") + error_count = len(results) - success_count + + if success_count > 0: + st.success(f"✅ Imported {success_count} file(s)") + if error_count > 0: + st.warning(f"⚠️ {error_count} file(s) failed") + + # Show first 5 results + for result in results[:5]: + if result.get("status") == "success": + st.caption(f"✅ {result.get('filename', 'Unknown')}") + else: + st.caption(f"❌ {result.get('filename', 'Unknown')}: {result.get('error', 'Unknown error')}") + + if len(results) > 5: + st.caption(f"... and {len(results) - 5} more") + + st.rerun() + + # Agent OS Profile Import (only for Agent OS KBs) + kb_metadata = pg_manager.get_collection_metadata(st.session_state.selected_kb) + kb_type_str = kb_metadata.get("kb_type", "generic") + + if kb_type_str == "agent-os": + st.markdown("---") + st.markdown("#### 🤖 Import Agent OS Profile") + st.caption("Import an Agent OS profile directory with standards, agents, workflows, and commands") + + st.info("💡 **Tip:** Mount your Agent OS profile directory as a volume in docker-compose.yml. The path should point to the profile root (containing standards/, agents/, etc.)") + + agent_os_path = st.text_input( + "Agent OS Profile Path", + placeholder="/workspace/my-agent-os-profile", + help="Path to Agent OS profile directory (must exist inside the Docker container)", + key="agent_os_path_input" + ) + + if st.button("Import Agent OS Profile", key="import_agent_os_btn", use_container_width=True): + if not agent_os_path: + st.error("Please enter an Agent OS profile path") + else: + with st.spinner("Parsing and ingesting Agent OS profile..."): + try: + agent_os_ingestion = AgentOSIngestion(pg_manager) + stats = agent_os_ingestion.ingest_profile( + kb_name=st.session_state.selected_kb, + profile_path=agent_os_path + ) + + if stats.get("success"): + st.success(f"✅ Imported {stats['total_documents']} Agent OS documents!") + + # Show breakdown by type + st.markdown("**Documents by type:**") + for content_type, count in stats.get("documents_by_type", {}).items(): + type_emoji = { + "standard": "📋", + "agent": "🤖", + "workflow": "🔄", + "command": "⚡", + "product": "🎯", + "spec": "📝" + }.get(content_type, "📄") + st.caption(f"{type_emoji} {content_type}: {count}") + + st.rerun() + else: + st.error("❌ Agent OS import failed") + if stats.get("errors"): + for error in stats["errors"]: + st.caption(f"⚠️ {error}") + + except Exception as e: + st.error(f"❌ Agent OS import failed: {str(e)}") + import traceback + st.code(traceback.format_exc()) + + st.divider() + + # Agent OS Content Stats (only for Agent OS KBs) + if kb_type_str == "agent-os": + agent_os_ingestion = AgentOSIngestion(pg_manager) + agent_os_stats = agent_os_ingestion.get_profile_stats(st.session_state.selected_kb) + + if agent_os_stats.get("total_documents", 0) > 0: + st.markdown("#### 🤖 Agent OS Content") + + # Display stats in columns + stat_cols = st.columns(len(agent_os_stats.get("documents_by_type", {})) or 1) + + type_info = { + "standard": {"emoji": "📋", "name": "Standards"}, + "agent": {"emoji": "🤖", "name": "Agents"}, + "workflow": {"emoji": "🔄", "name": "Workflows"}, + "command": {"emoji": "⚡", "name": "Commands"}, + "product": {"emoji": "🎯", "name": "Product"}, + "spec": {"emoji": "📝", "name": "Specs"} + } + + for idx, (content_type, count) in enumerate(agent_os_stats.get("documents_by_type", {}).items()): + info = type_info.get(content_type, {"emoji": "📄", "name": content_type.title()}) + with stat_cols[idx]: + st.metric( + label=f"{info['emoji']} {info['name']}", + value=count + ) + + st.divider() + + # Document Grid + st.markdown("#### 📄 Documents") + + docs = get_documents_metadata(st.session_state.selected_kb) + + if not docs: + st.info("No documents in this knowledge base. Upload files to get started!") + else: + # Display in 3-column grid + cols = st.columns(3) + + for i, doc in enumerate(docs): + with cols[i % 3]: + # File icon based on type + icon_map = { + ".py": "🐍", ".js": "📜", ".jsx": "⚛️", + ".ts": "📘", ".tsx": "⚛️", ".md": "📄", + ".pdf": "📕", ".json": "📋", ".yaml": "⚙️" + } + icon = icon_map.get(doc["file_type"], "📄") + + # Create card + tags_html = "".join([f'{tag}' for tag in doc["tags"]]) + + st.markdown(f""" +
+
{icon}
+
{doc["filename"]}
+
{tags_html}
+
+ {doc["formatted_date"]}
+ {doc["chunk_count"]} chunks +
+
+ """, unsafe_allow_html=True) + + # Tab 2: Chat Interface + with tab2: + st.markdown("## 💬 Chat with Your Knowledge Base") + + if not st.session_state.selected_kb: + st.info("👈 Please select a knowledge base from the left panel") + else: + # Display active strategies banner + active_strategies = [] + if st.session_state.rag_settings["hybrid"]: + active_strategies.append("🔀 Hybrid Search") + if st.session_state.rag_settings["rerank"]: + active_strategies.append("🎯 Reranking") + if st.session_state.rag_settings["agentic"]: + active_strategies.append("🤖 Agentic RAG") + + if active_strategies: + strategies_text = " + ".join(active_strategies) + st.markdown(f""" +
+ 🎯 Active Retrieval Strategies: {strategies_text} +
+ """, unsafe_allow_html=True) + else: + st.markdown(f""" +
+ 📊 Mode: Standard Vector Search +
+ """, unsafe_allow_html=True) + + # Display chat history + for message in st.session_state.chat_history: + with st.chat_message(message["role"]): + st.markdown(message["content"]) + + if message["role"] == "assistant" and message.get("sources"): + with st.expander("📚 View Sources"): + for i, source in enumerate(message["sources"][:3], 1): + st.markdown(f"**Source {i}:**") + st.code(source["text"][:200] + "..." if len(source["text"]) > 200 else source["text"]) + if source.get("metadata"): + st.caption(f"From: {source['metadata'].get('filename', 'Unknown')}") + if source.get("score"): + st.caption(f"Score: {source['score']:.3f}") + st.divider() + + # Chat input + if prompt := st.chat_input("Ask a question about your knowledge base..."): + # Add user message + st.session_state.chat_history.append({ + "role": "user", + "content": prompt + }) + + # Display user message + with st.chat_message("user"): + st.markdown(prompt) + + # Generate response + with st.chat_message("assistant"): + with st.spinner("Thinking..."): + try: + rag_engine = RAGEngine(st.session_state.selected_kb) + result = rag_engine.query( + prompt, + use_hybrid=st.session_state.rag_settings["hybrid"], + use_rerank=st.session_state.rag_settings["rerank"], + use_agentic=st.session_state.rag_settings["agentic"] + ) + + answer = result.get("answer", "No answer generated") + sources = result.get("sources", []) + + st.markdown(answer) + + # Add to history + st.session_state.chat_history.append({ + "role": "assistant", + "content": answer, + "sources": sources + }) + + # Display sources + if sources: + with st.expander("📚 View Sources"): + for i, source in enumerate(sources[:3], 1): + st.markdown(f"**Source {i}:**") + st.code(source["text"][:200] + "..." if len(source["text"]) > 200 else source["text"]) + if source.get("metadata"): + st.caption(f"From: {source['metadata'].get('filename', 'Unknown')}") + if source.get("score"): + st.caption(f"Score: {source['score']:.3f}") + st.divider() + + except Exception as e: + error_msg = f"Error: {str(e)}" + st.error(error_msg) + st.session_state.chat_history.append({ + "role": "assistant", + "content": error_msg + }) + + # Clear chat button + if st.session_state.chat_history and st.button("🗑️ Clear Chat"): + st.session_state.chat_history = [] + st.rerun() + diff --git a/design-guide.md b/design-guide.md new file mode 100644 index 0000000..51cf163 --- /dev/null +++ b/design-guide.md @@ -0,0 +1,62 @@ +# Code-Forge Design Guide: Enhanced High-Contrast Palette + +## Concept +A dynamic, modern, and high-tech aesthetic, emphasizing extreme clarity and energetic innovation. The colors evoke digital craftsmanship and cutting-edge technology, with a deliberate focus on high contrast to ensure visual impact and accessibility. + +## Primary Color Palette + +Here are the core colors that define the Code-Forge brand, designed for maximum brightness and contrast: + +1. ### **Deep Night (Background)** + * **Hex:** `#0A0E1A` + * **RGB:** `(10, 14, 26)` + * **Usage:** The foundational dark background color. This deep, almost black, blue-grey provides a dramatic canvas, making the vibrant foreground elements pop with intensity. Use for primary backgrounds, dark mode interfaces, and large informational sections. + +2. ### **Electric Teal (Primary Accent)** + * **Hex:** `#00FFFF` (Pure Cyan) + * **RGB:** `(0, 255, 255)` + * **Usage:** The most prominent accent color. This intensely bright and pure cyan is used for key interactive elements, primary iconography, active states, glowing outlines, circuit patterns, and significant graphical components. Its high luminescence creates a striking contrast against "Deep Night." + +3. ### **Blaze Orange (Secondary Accent)** + * **Hex:** `#FF8C00` (Dark Orange, but used at full saturation for brilliance) + * **RGB:** `(255, 140, 0)` + * **Usage:** A vibrant, saturated orange that provides a warm and energetic counterpoint to "Electric Teal." Use for call-to-action buttons, warning indicators, secondary highlights, "spark" effects, important data points, and to draw immediate attention. + +4. ### **Pure White (Text & UI)** + * **Hex:** `#FFFFFF` + * **RGB:** `(255, 255, 255)` + * **Usage:** The default color for primary text, headings, essential labels, and clean UI elements. Offers the highest possible readability and contrast against "Deep Night." + +## Secondary/Support Palette (Optional but Recommended) + +These colors can be used for supplementary elements, gradients, or to add subtle depth without detracting from the primary palette's vibrancy. + +1. ### **Cool Blue (Subtle Glow/Gradient)** + * **Hex:** `#4D94FF` + * **RGB:** `(77, 148, 255)` + * **Usage:** For background gradients, subtle glowing effects, or to create depth behind "Electric Teal" elements. Use sparingly to enhance rather than compete with the primary accents. + +2. ### **Light Grey (Subtle Text/Dividers)** + * **Hex:** `#C0C0C0` + * **RGB:** `(192, 192, 192)` + * **Usage:** For less critical secondary text, disabled states, subtle dividers, borders, or backgrounds for minor information sections. Provides a softer, yet still clear, contrast to "Deep Night." + +## Accessibility & Contrast Ratios + +The primary palette is engineered for optimal contrast to ensure excellent readability and accessibility. + +* **Deep Night** vs. **Electric Teal**: Excellent contrast (WCAG AAA compliant). +* **Deep Night** vs. **Blaze Orange**: Excellent contrast (WCAG AAA compliant). +* **Deep Night** vs. **Pure White**: Maximum contrast (WCAG AAA compliant), ideal for primary text. + +## Usage Guidelines + +* **Backgrounds:** Exclusively use "Deep Night" as the primary canvas to ensure maximum impact of the accent colors. +* **Primary Elements & Interactivity:** "Electric Teal" should highlight key interactive components, active states, and core branding visuals. +* **Emphasis & Alerts:** "Blaze Orange" is reserved for drawing immediate attention, warnings, confirmations, or energetic visual cues. +* **Text Hierarchy:** "Pure White" for main content and headings. "Light Grey" for supplementary information or less emphasized text. +* **Glows & Effects:** Embrace the vibrancy! Utilize subtle, expansive glows and light trails in "Electric Teal" and "Blaze Orange" to amplify the high-tech, energetic feel, similar to neon signage. +* **Gradients:** When used, gradients should smoothly transition between "Electric Teal" and "Cool Blue," or "Blaze Orange" for dramatic highlights, always respecting the high contrast against the "Deep Night" background. +* **Iconography:** Icons should primarily be "Electric Teal" or "Pure White" against "Deep Night," with "Blaze Orange" for special status icons. + +--- \ No newline at end of file diff --git a/docker-compose.yml b/docker-compose.yml index 16811da..559fc20 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -7,7 +7,7 @@ services: ports: - "11434:11434" volumes: - - ollama_models:/root/.ollama + - ./data/ollama:/root/.ollama environment: - OLLAMA_NUM_PARALLEL=1 - OLLAMA_MAX_LOADED_MODELS=1 @@ -16,27 +16,13 @@ services: - code-forge-network restart: unless-stopped - chromadb: - image: chromadb/chroma:latest - container_name: code-forge-chromadb - ports: - - "8000:8000" - volumes: - - chroma_data:/chroma/chroma - environment: - - IS_PERSISTENT=TRUE - - ANONYMIZED_TELEMETRY=FALSE - networks: - - code-forge-network - restart: unless-stopped - app: build: context: . dockerfile: Dockerfile container_name: code-forge-app ports: - - "8501:8501" # Streamlit UI + - "8501:8501" # Streamlit UI (Legacy) - "8051:8051" # MCP Server volumes: - ./app:/workspace/app @@ -46,15 +32,36 @@ services: # - /path/to/your/docs:/workspace/mounted_docs:ro environment: - OLLAMA_HOST=http://ollama:11434 - - CHROMA_HOST=chromadb - - CHROMA_PORT=8000 + - POSTGRES_HOST=host.docker.internal + - POSTGRES_PORT=5432 + - POSTGRES_DB=codeforge + - POSTGRES_USER=${USER} + - POSTGRES_PASSWORD= + - DATABASE_URL=postgresql://${USER}@host.docker.internal:5432/codeforge - MCP_SERVER_HOST=0.0.0.0 - MCP_SERVER_PORT=8051 networks: - code-forge-network depends_on: - ollama - - chromadb + restart: unless-stopped + + frontend: + image: node:20-alpine + container_name: code-forge-frontend + working_dir: /app + ports: + - "5173:5173" # Vite dev server + volumes: + - ./frontend:/app + - /app/node_modules # Prevent node_modules from being overwritten + command: sh -c "npm install && npm run dev -- --host 0.0.0.0" + environment: + - VITE_API_URL=http://localhost:8051 + networks: + - code-forge-network + depends_on: + - app restart: unless-stopped networks: @@ -62,7 +69,5 @@ networks: driver: bridge volumes: - ollama_models: - chroma_data: uploads: diff --git a/frontend/.gitignore b/frontend/.gitignore new file mode 100644 index 0000000..a547bf3 --- /dev/null +++ b/frontend/.gitignore @@ -0,0 +1,24 @@ +# Logs +logs +*.log +npm-debug.log* +yarn-debug.log* +yarn-error.log* +pnpm-debug.log* +lerna-debug.log* + +node_modules +dist +dist-ssr +*.local + +# Editor directories and files +.vscode/* +!.vscode/extensions.json +.idea +.DS_Store +*.suo +*.ntvs* +*.njsproj +*.sln +*.sw? diff --git a/frontend/README.md b/frontend/README.md new file mode 100644 index 0000000..d2e7761 --- /dev/null +++ b/frontend/README.md @@ -0,0 +1,73 @@ +# React + TypeScript + Vite + +This template provides a minimal setup to get React working in Vite with HMR and some ESLint rules. + +Currently, two official plugins are available: + +- [@vitejs/plugin-react](https://github.com/vitejs/vite-plugin-react/blob/main/packages/plugin-react) uses [Babel](https://babeljs.io/) (or [oxc](https://oxc.rs) when used in [rolldown-vite](https://vite.dev/guide/rolldown)) for Fast Refresh +- [@vitejs/plugin-react-swc](https://github.com/vitejs/vite-plugin-react/blob/main/packages/plugin-react-swc) uses [SWC](https://swc.rs/) for Fast Refresh + +## React Compiler + +The React Compiler is not enabled on this template because of its impact on dev & build performances. To add it, see [this documentation](https://react.dev/learn/react-compiler/installation). + +## Expanding the ESLint configuration + +If you are developing a production application, we recommend updating the configuration to enable type-aware lint rules: + +```js +export default defineConfig([ + globalIgnores(['dist']), + { + files: ['**/*.{ts,tsx}'], + extends: [ + // Other configs... + + // Remove tseslint.configs.recommended and replace with this + tseslint.configs.recommendedTypeChecked, + // Alternatively, use this for stricter rules + tseslint.configs.strictTypeChecked, + // Optionally, add this for stylistic rules + tseslint.configs.stylisticTypeChecked, + + // Other configs... + ], + languageOptions: { + parserOptions: { + project: ['./tsconfig.node.json', './tsconfig.app.json'], + tsconfigRootDir: import.meta.dirname, + }, + // other options... + }, + }, +]) +``` + +You can also install [eslint-plugin-react-x](https://github.com/Rel1cx/eslint-react/tree/main/packages/plugins/eslint-plugin-react-x) and [eslint-plugin-react-dom](https://github.com/Rel1cx/eslint-react/tree/main/packages/plugins/eslint-plugin-react-dom) for React-specific lint rules: + +```js +// eslint.config.js +import reactX from 'eslint-plugin-react-x' +import reactDom from 'eslint-plugin-react-dom' + +export default defineConfig([ + globalIgnores(['dist']), + { + files: ['**/*.{ts,tsx}'], + extends: [ + // Other configs... + // Enable lint rules for React + reactX.configs['recommended-typescript'], + // Enable lint rules for React DOM + reactDom.configs.recommended, + ], + languageOptions: { + parserOptions: { + project: ['./tsconfig.node.json', './tsconfig.app.json'], + tsconfigRootDir: import.meta.dirname, + }, + // other options... + }, + }, +]) +``` diff --git a/frontend/eslint.config.js b/frontend/eslint.config.js new file mode 100644 index 0000000..b19330b --- /dev/null +++ b/frontend/eslint.config.js @@ -0,0 +1,23 @@ +import js from '@eslint/js' +import globals from 'globals' +import reactHooks from 'eslint-plugin-react-hooks' +import reactRefresh from 'eslint-plugin-react-refresh' +import tseslint from 'typescript-eslint' +import { defineConfig, globalIgnores } from 'eslint/config' + +export default defineConfig([ + globalIgnores(['dist']), + { + files: ['**/*.{ts,tsx}'], + extends: [ + js.configs.recommended, + tseslint.configs.recommended, + reactHooks.configs['recommended-latest'], + reactRefresh.configs.vite, + ], + languageOptions: { + ecmaVersion: 2020, + globals: globals.browser, + }, + }, +]) diff --git a/frontend/index.html b/frontend/index.html new file mode 100644 index 0000000..e01d1be --- /dev/null +++ b/frontend/index.html @@ -0,0 +1,13 @@ + + + + + + + Code-Forge | Localized Multi-KB RAG System + + +
+ + + diff --git a/frontend/package-lock.json b/frontend/package-lock.json new file mode 100644 index 0000000..58c0d2c --- /dev/null +++ b/frontend/package-lock.json @@ -0,0 +1,6227 @@ +{ + "name": "frontend", + "version": "0.0.0", + "lockfileVersion": 3, + "requires": true, + "packages": { + "": { + "name": "frontend", + "version": "0.0.0", + "dependencies": { + "@tanstack/react-query": "^5.90.5", + "@types/react-syntax-highlighter": "^15.5.13", + "axios": "^1.12.2", + "framer-motion": "^12.23.24", + "lucide-react": "^0.546.0", + "react": "^19.1.1", + "react-dom": "^19.1.1", + "react-markdown": "^10.1.0", + "react-router-dom": "^7.9.4", + "react-syntax-highlighter": "^16.0.0" + }, + "devDependencies": { + "@eslint/js": "^9.36.0", + "@tailwindcss/typography": "^0.5.19", + "@types/node": "^24.6.0", + "@types/react": "^19.1.16", + "@types/react-dom": "^19.1.9", + "@vitejs/plugin-react": "^5.0.4", + "autoprefixer": "^10.4.21", + "eslint": "^9.36.0", + "eslint-plugin-react-hooks": 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+ "lucide-react": "^0.546.0", + "react": "^19.1.1", + "react-dom": "^19.1.1", + "react-markdown": "^10.1.0", + "react-router-dom": "^7.9.4", + "react-syntax-highlighter": "^16.0.0" + }, + "devDependencies": { + "@eslint/js": "^9.36.0", + "@tailwindcss/typography": "^0.5.19", + "@types/node": "^24.6.0", + "@types/react": "^19.1.16", + "@types/react-dom": "^19.1.9", + "@vitejs/plugin-react": "^5.0.4", + "autoprefixer": "^10.4.21", + "eslint": "^9.36.0", + "eslint-plugin-react-hooks": "^5.2.0", + "eslint-plugin-react-refresh": "^0.4.22", + "globals": "^16.4.0", + "postcss": "^8.5.6", + "tailwindcss": "^3.4.18", + "typescript": "~5.9.3", + "typescript-eslint": "^8.45.0", + "vite": "^7.1.7" + } +} diff --git a/frontend/postcss.config.js b/frontend/postcss.config.js new file mode 100644 index 0000000..b4a6220 --- /dev/null +++ b/frontend/postcss.config.js @@ -0,0 +1,7 @@ +export default { + plugins: { + tailwindcss: {}, + autoprefixer: {}, + }, +} + diff --git a/frontend/public/assets/hero.png b/frontend/public/assets/hero.png new file mode 100644 index 0000000..4b3e45b Binary files /dev/null and b/frontend/public/assets/hero.png differ diff --git a/frontend/public/assets/logo.png b/frontend/public/assets/logo.png new file mode 100644 index 0000000..44869d8 Binary files /dev/null and b/frontend/public/assets/logo.png differ diff --git a/frontend/public/vite.svg b/frontend/public/vite.svg new file mode 100644 index 0000000..e7b8dfb --- /dev/null +++ b/frontend/public/vite.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/frontend/src/App.tsx b/frontend/src/App.tsx new file mode 100644 index 0000000..9dc3e1d --- /dev/null +++ b/frontend/src/App.tsx @@ -0,0 +1,29 @@ +import { BrowserRouter, Routes, Route, Navigate } from 'react-router-dom'; +import { QueryClient, QueryClientProvider } from '@tanstack/react-query'; +import Welcome from './pages/Welcome'; +import MainApp from './pages/MainApp'; + +const queryClient = new QueryClient({ + defaultOptions: { + queries: { + refetchOnWindowFocus: false, + retry: 1, + }, + }, +}); + +function App() { + return ( + + + + } /> + } /> + } /> + + + + ); +} + +export default App; diff --git a/frontend/src/assets/react.svg b/frontend/src/assets/react.svg new file mode 100644 index 0000000..6c87de9 --- /dev/null +++ b/frontend/src/assets/react.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/frontend/src/components/ChatInterface.tsx b/frontend/src/components/ChatInterface.tsx new file mode 100644 index 0000000..94e1dfd --- /dev/null +++ b/frontend/src/components/ChatInterface.tsx @@ -0,0 +1,225 @@ +import { useState, useRef, useEffect } from 'react'; +import { motion, AnimatePresence } from 'framer-motion'; +import { Send, Bot, User, Loader2, Copy, CheckCircle } from 'lucide-react'; +import { useMutation } from '@tanstack/react-query'; +import { chat, type ChatResponse } from '../lib/api'; +import ReactMarkdown from 'react-markdown'; + +interface ChatInterfaceProps { + kbName: string; + useHybrid: boolean; + useRerank: boolean; + useAgentic: boolean; +} + +interface Message { + role: 'user' | 'assistant'; + content: string; + sources?: any[]; +} + +export default function ChatInterface({ kbName, useHybrid, useRerank, useAgentic }: ChatInterfaceProps) { + const [messages, setMessages] = useState([]); + const [input, setInput] = useState(''); + const [copiedIndex, setCopiedIndex] = useState(null); + const messagesEndRef = useRef(null); + + const scrollToBottom = () => { + messagesEndRef.current?.scrollIntoView({ behavior: 'smooth' }); + }; + + useEffect(() => { + scrollToBottom(); + }, [messages]); + + const chatMutation = useMutation({ + mutationFn: (query: string) => chat(kbName, query, useHybrid, useRerank, useAgentic), + onSuccess: (data: ChatResponse) => { + setMessages((prev) => [ + ...prev, + { + role: 'assistant', + content: data.answer || data.error || 'No response', + sources: data.sources, + }, + ]); + }, + }); + + const handleSend = () => { + if (!input.trim() || chatMutation.isPending) return; + + const userMessage: Message = { role: 'user', content: input }; + setMessages((prev) => [...prev, userMessage]); + setInput(''); + chatMutation.mutate(input); + }; + + const handleKeyPress = (e: React.KeyboardEvent) => { + if (e.key === 'Enter' && !e.shiftKey) { + e.preventDefault(); + handleSend(); + } + }; + + const copyToClipboard = (text: string, index: number) => { + navigator.clipboard.writeText(text); + setCopiedIndex(index); + setTimeout(() => setCopiedIndex(null), 2000); + }; + + return ( +
+ {/* Messages Area */} +
+ {messages.length === 0 ? ( +
+
+ +

Start a Conversation

+

Ask anything about your knowledge base

+
+
+ Active Settings: +
+
+ {useHybrid && ( + + Hybrid Search + + )} + {useRerank && ( + + Reranking + + )} + {useAgentic && ( + + Agentic RAG + + )} + {!useHybrid && !useRerank && !useAgentic && ( + + Basic Vector Search + + )} +
+
+
+
+ ) : ( + + {messages.map((message, index) => ( + + {message.role === 'assistant' && ( +
+ +
+ )} + +
+
+ {message.role === 'assistant' ? ( + {message.content} + ) : ( +

{message.content}

+ )} +
+ + {message.role === 'assistant' && ( +
+ + {message.sources && message.sources.length > 0 && ( + + {message.sources.length} source{message.sources.length > 1 ? 's' : ''} + + )} +
+ )} +
+ + {message.role === 'user' && ( +
+ +
+ )} +
+ ))} +
+ )} + + {chatMutation.isPending && ( + +
+ +
+
+ +
+
+ )} + +
+
+ + {/* Input Area */} +
+
+