klas/t6_mem0
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Update Docs (#2277)

Browse Source
This commit is contained in:
Saket Aryan
2025-03-01 06:07:05 +05:30
committed by GitHub
parent c1aba35884
commit 5606c3ffb8
30 changed files with 437 additions and 877 deletions
Download Patch File Download Diff File Expand all files Collapse all files

41
docs/components/vectordbs/dbs/qdrant.mdx
View File

@@ -2,7 +2,8 @@
### Usage
```python
<CodeGroup>
```python Python
import os
from mem0 import Memory
@@ -23,10 +24,32 @@ m = Memory.from_config(config)
m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
```
```typescript TypeScript
import { Memory } from 'mem0ai/oss';
const config = {
vectorStore: {
provider: 'qdrant',
config: {
collectionName: 'memories',
embeddingModelDims: 1536,
host: 'localhost',
port: 6333,
},
},
};
const memory = new Memory(config);
await memory.add("Likes to play cricket on weekends", { userId: "alice", metadata: { category: "hobbies" } });
```
</CodeGroup>
### Config
Let's see the available parameters for the `qdrant` config:
<Tabs>
<Tab title="Python">
| Parameter | Description | Default Value |
| --- | --- | --- |
| `collection_name` | The name of the collection to store the vectors | `mem0` |
@@ -37,4 +60,18 @@ Let's see the available parameters for the `qdrant` config:
| `path` | Path for the qdrant database | `/tmp/qdrant` |
| `url` | Full URL for the qdrant server | `None` |
| `api_key` | API key for the qdrant server | `None` |
| `on_disk` | For enabling persistent storage | `False` |
| `on_disk` | For enabling persistent storage | `False` |
</Tab>
<Tab title="TypeScript">
| Parameter | Description | Default Value |
| --- | --- | --- |
| `collectionName` | The name of the collection to store the vectors | `mem0` |
| `embeddingModelDims` | Dimensions of the embedding model | `1536` |
| `host` | The host where the Qdrant server is running | `None` |
| `port` | The port where the Qdrant server is running | `None` |
| `path` | Path for the Qdrant database | `/tmp/qdrant` |
| `url` | Full URL for the Qdrant server | `None` |
| `apiKey` | API key for the Qdrant server | `None` |
| `onDisk` | For enabling persistent storage | `False` |
</Tab>
</Tabs>

39
docs/components/vectordbs/dbs/redis.mdx
View File

@@ -12,7 +12,8 @@ docker run -d --name redis-stack -p 6379:6379 -p 8001:8001 redis/redis-stack:lat
### Usage
```python
<CodeGroup>
```python Python
import os
from mem0 import Memory
@@ -34,12 +35,46 @@ m = Memory.from_config(config)
m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
```
```typescript TypeScript
import { Memory } from 'mem0ai/oss';
const config = {
vectorStore: {
provider: 'redis',
config: {
collectionName: 'memories',
embeddingModelDims: 1536,
redisUrl: 'redis://localhost:6379',
username: 'your-redis-username',
password: 'your-redis-password',
},
},
};
const memory = new Memory(config);
await memory.add("Likes to play cricket on weekends", { userId: "alice", metadata: { category: "hobbies" } });
```
</CodeGroup>
### Config
Let's see the available parameters for the `redis` config:
<Tabs>
<Tab title="Python">
| Parameter | Description | Default Value |
| --- | --- | --- |
| `collection_name` | The name of the collection to store the vectors | `mem0` |
| `embedding_model_dims` | Dimensions of the embedding model | `1536` |
| `redis_url` | The URL of the Redis server | `None` |
| `redis_url` | The URL of the Redis server | `None` |
</Tab>
<Tab title="TypeScript">
| Parameter | Description | Default Value |
| --- | --- | --- |
| `collectionName` | The name of the collection to store the vectors | `mem0` |
| `embeddingModelDims` | Dimensions of the embedding model | `1536` |
| `redisUrl` | The URL of the Redis server | `None` |
| `username` | Username for Redis connection | `None` |
| `password` | Password for Redis connection | `None` |
</Tab>
</Tabs>