80 lines
2.0 KiB
Plaintext
80 lines
2.0 KiB
Plaintext
[Redis](https://redis.io/) is a scalable, real-time database that can store, search, and analyze vector data.
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### Installation
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```bash
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pip install redis redisvl
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```
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Redis Stack using Docker:
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```bash
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docker run -d --name redis-stack -p 6379:6379 -p 8001:8001 redis/redis-stack:latest
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```
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### Usage
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<CodeGroup>
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```python Python
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import os
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from mem0 import Memory
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os.environ["OPENAI_API_KEY"] = "sk-xx"
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config = {
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"vector_store": {
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"provider": "redis",
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"config": {
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"collection_name": "mem0",
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"embedding_model_dims": 1536,
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"redis_url": "redis://localhost:6379"
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}
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},
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"version": "v1.1"
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}
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m = Memory.from_config(config)
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m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
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```
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```typescript TypeScript
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import { Memory } from 'mem0ai/oss';
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const config = {
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vectorStore: {
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provider: 'redis',
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config: {
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collectionName: 'memories',
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embeddingModelDims: 1536,
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redisUrl: 'redis://localhost:6379',
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username: 'your-redis-username',
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password: 'your-redis-password',
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},
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},
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};
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const memory = new Memory(config);
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await memory.add("Likes to play cricket on weekends", { userId: "alice", metadata: { category: "hobbies" } });
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```
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</CodeGroup>
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### Config
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Let's see the available parameters for the `redis` config:
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<Tabs>
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<Tab title="Python">
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| Parameter | Description | Default Value |
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| --- | --- | --- |
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| `collection_name` | The name of the collection to store the vectors | `mem0` |
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| `embedding_model_dims` | Dimensions of the embedding model | `1536` |
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| `redis_url` | The URL of the Redis server | `None` |
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</Tab>
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<Tab title="TypeScript">
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| Parameter | Description | Default Value |
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| --- | --- | --- |
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| `collectionName` | The name of the collection to store the vectors | `mem0` |
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| `embeddingModelDims` | Dimensions of the embedding model | `1536` |
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| `redisUrl` | The URL of the Redis server | `None` |
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| `username` | Username for Redis connection | `None` |
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| `password` | Password for Redis connection | `None` |
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</Tab>
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</Tabs> |