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Acbox
108 lines
4.1 KiB
Markdown
108 lines
4.1 KiB
Markdown
# Agent CLI
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A command-line interface for the personal housekeeper assistant agent.
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## Setup
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1. Create a `.env` file in the project root (not in this directory) with the following variables:
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```env
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# Main chat model
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MODEL=gpt-4o
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BASE_URL=https://api.openai.com/v1
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API_KEY=your-api-key-here
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MODEL_CLIENT_TYPE=openai
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# Embedding model for memory search (using mem0ai)
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EMBEDDING_MODEL=text-embedding-3-small
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EMBEDDING_BASE_URL=https://api.openai.com/v1
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EMBEDDING_API_KEY=your-api-key-here
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EMBEDDING_CLIENT_TYPE=openai
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EMBEDDING_DIMENSIONS=1536
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# Summary model for memory generation (optional, defaults to main model)
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SUMMARY_MODEL=gpt-4o-mini
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SUMMARY_BASE_URL=https://api.openai.com/v1
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SUMMARY_API_KEY=your-api-key-here
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SUMMARY_CLIENT_TYPE=openai
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# Database
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DATABASE_URL=postgresql://user:password@localhost:5432/byte
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```
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2. Make sure the database is set up and running (required for memory storage).
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## Usage
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Run the CLI from the agent package:
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```bash
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pnpm start
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```
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Or with Bun directly:
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```bash
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bun run index.ts
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```
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## Features
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- **Interactive Chat**: Type your messages and get responses from the AI agent
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- **Long-term Memory**: Conversations are automatically saved with LLM-generated summaries
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- **Context Loading**: Automatically loads recent conversations (last 60 minutes)
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- **Memory Search**: The agent can search through past conversations using natural language and embeddings
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- **Tool Calling**: Supports automatic tool execution with multi-step reasoning
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- **Multi-Provider Support**: Works with OpenAI, Anthropic, and Google AI (via Vercel AI SDK)
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## Commands
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- Type your message and press Enter to chat
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- Type `exit` or `quit` to close the application
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## Environment Variables
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### Required
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- `MODEL`: The main LLM model ID (e.g., `gpt-4o`, `claude-3-5-sonnet-20241022`)
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- `BASE_URL`: The API base URL for the main model
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- `API_KEY`: Your API key for the main model
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- `EMBEDDING_MODEL`: The embedding model for memory search (e.g., `text-embedding-3-small`)
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- `DATABASE_URL`: PostgreSQL connection string with pgvector extension enabled
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### Optional
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- `MODEL_CLIENT_TYPE`: The model provider type (default: `openai`, options: `openai`, `anthropic`, `google`)
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- `EMBEDDING_BASE_URL`: Base URL for embedding API (default: same as `BASE_URL`)
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- `EMBEDDING_API_KEY`: API key for embedding (default: same as `API_KEY`)
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- `EMBEDDING_CLIENT_TYPE`: Provider type for embedding (default: `openai`)
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- `EMBEDDING_DIMENSIONS`: The dimensions of the embedding model (default: `1536`)
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- `SUMMARY_MODEL`: The model used to summarize conversations for memory (default: same as `MODEL`)
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- `SUMMARY_BASE_URL`: Base URL for summary model (default: same as `BASE_URL`)
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- `SUMMARY_API_KEY`: API key for summary model (default: same as `API_KEY`)
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- `SUMMARY_CLIENT_TYPE`: Provider type for summary model (default: same as `MODEL_CLIENT_TYPE`)
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## Memory System
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The agent uses [mem0ai](https://github.com/mem0ai/mem0) for sophisticated memory management:
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1. **Conversation Storage**: After each conversation, mem0ai uses an LLM to extract and store key information
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2. **Embedding Generation**: The extracted information is converted to embedding vectors using your configured embedding model
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3. **Vector Storage**: Embeddings are stored in PostgreSQL with pgvector extension
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4. **Semantic Search**: The agent can search past memories using natural language queries via vector similarity
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5. **Context Loading**: Recent conversations are automatically loaded from history into context
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### Model Recommendations
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- **Main Model**: Use a powerful model like `gpt-4o` or `claude-3-5-sonnet-20241022` for best conversation quality
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- **Summary Model**: Use a cheaper/faster model like `gpt-4o-mini` for memory extraction to save costs
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- **Embedding Model**: Use `text-embedding-3-small` (1536 dims) or `text-embedding-3-large` (3072 dims) for OpenAI
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### Database Requirements
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The memory system requires PostgreSQL with the `pgvector` extension installed. Make sure:
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1. PostgreSQL is installed and running
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2. pgvector extension is enabled: `CREATE EXTENSION IF NOT EXISTS vector;`
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3. mem0ai will automatically create the required tables on first run
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