Added Mem0 TS Library (#2270)

mem0-ts/src/oss/README.md

@@ -0,0 +1,177 @@
+# mem0-ts
+
+A TypeScript implementation of the mem0 memory system, using OpenAI for embeddings and completions.
+
+## Features
+
+- Memory storage and retrieval using vector embeddings
+- Fact extraction from text using GPT-4
+- SQLite-based history tracking
+- Optional graph-based memory relationships
+- TypeScript type safety
+- Built-in OpenAI integration with default configuration
+- In-memory vector store implementation
+- Extensible architecture with interfaces for custom implementations
+
+## Installation
+
+1. Clone the repository:
+
+```bash
+git clone <repository-url>
+cd mem0-ts
+```
+
+2. Install dependencies:
+
+```bash
+npm install
+```
+
+3. Set up environment variables:
+
+```bash
+cp .env.example .env
+# Edit .env with your OpenAI API key
+```
+
+4. Build the project:
+
+```bash
+npm run build
+```
+
+## Usage
+
+### Basic Example
+
+```typescript
+import { Memory } from "mem0-ts";
+
+// Create a memory instance with default OpenAI configuration
+const memory = new Memory();
+
+// Or with minimal configuration (only API key)
+const memory = new Memory({
+  embedder: {
+    config: {
+      apiKey: process.env.OPENAI_API_KEY,
+    },
+  },
+  llm: {
+    config: {
+      apiKey: process.env.OPENAI_API_KEY,
+    },
+  },
+});
+
+// Or with custom configuration
+const memory = new Memory({
+  embedder: {
+    provider: "openai",
+    config: {
+      apiKey: process.env.OPENAI_API_KEY,
+      model: "text-embedding-3-small",
+    },
+  },
+  vectorStore: {
+    provider: "memory",
+    config: {
+      collectionName: "custom-memories",
+    },
+  },
+  llm: {
+    provider: "openai",
+    config: {
+      apiKey: process.env.OPENAI_API_KEY,
+      model: "gpt-4-turbo-preview",
+    },
+  },
+});
+
+// Add a memory
+await memory.add("The sky is blue", "user123");
+
+// Search memories
+const results = await memory.search("What color is the sky?", "user123");
+```
+
+### Default Configuration
+
+The memory system comes with sensible defaults:
+
+- OpenAI embeddings with `text-embedding-3-small` model
+- In-memory vector store
+- OpenAI GPT-4 Turbo for LLM operations
+- SQLite for history tracking
+
+You only need to provide API keys - all other settings are optional.
+
+### Methods
+
+- `add(messages: string | Message[], userId?: string, ...): Promise<SearchResult>`
+- `search(query: string, userId?: string, ...): Promise<SearchResult>`
+- `get(memoryId: string): Promise<MemoryItem | null>`
+- `update(memoryId: string, data: string): Promise<{ message: string }>`
+- `delete(memoryId: string): Promise<{ message: string }>`
+- `deleteAll(userId?: string, ...): Promise<{ message: string }>`
+- `history(memoryId: string): Promise<any[]>`
+- `reset(): Promise<void>`
+
+### Try the Example
+
+We provide a comprehensive example in `examples/basic.ts` that demonstrates all the features including:
+
+- Default configuration usage
+- In-memory vector store
+- PGVector store (with PostgreSQL)
+- Qdrant vector store
+- Redis vector store
+- Memory operations (add, search, update, delete)
+
+To run the example:
+
+```bash
+npm run example
+```
+
+You can use this example as a template and modify it according to your needs. The example includes:
+
+- Different vector store configurations
+- Various memory operations
+- Error handling
+- Environment variable usage
+
+## Development
+
+1. Build the project:
+
+```bash
+npm run build
+```
+
+2. Clean build files:
+
+```bash
+npm run clean
+```
+
+## Extending
+
+The system is designed to be extensible. You can implement your own:
+
+- Embedders by implementing the `Embedder` interface
+- Vector stores by implementing the `VectorStore` interface
+- Language models by implementing the `LLM` interface
+
+## License
+
+MIT
+
+## Contributing
+
+1. Fork the repository
+2. Create your feature branch
+3. Commit your changes
+4. Push to the branch
+5. Create a new Pull Request