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docs(vectordbs): correct vector store config defaults & imports (#5843)

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Kartik
2026-06-27 12:40:16 +05:30
committed by GitHub
parent f38608fb50
commit 7e7682a06d
11 changed files with 23 additions and 16 deletions
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docs/components/vectordbs/dbs/baidu.mdx
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@@ -46,7 +46,7 @@ Here are the parameters available for configuring Baidu VectorDB:
| `account` | Baidu VectorDB account name | `root` |
| `api_key` | API key for accessing Baidu VectorDB | Required |
| `database_name` | Name of the database | `mem0` |
| `table_name` | Name of the table | `mem0_table` |
| `table_name` | Name of the table | `mem0` |
| `embedding_model_dims` | Dimensions of the embedding model | `1536` |
| `metric_type` | Distance metric for similarity search | `L2` |
docs/components/vectordbs/dbs/elasticsearch.mdx
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@@ -56,6 +56,8 @@ Here are the parameters available for configuring Elasticsearch:
| `api_key` | API key for authentication | `None` |
| `user` | Username for basic authentication | `None` |
| `password` | Password for basic authentication | `None` |
| `use_ssl` | Whether to use SSL for the connection | `True` |
| `ca_certs` | Path to CA bundle for SSL certificate verification | `None` |
| `verify_certs` | Whether to verify SSL certificates | `True` |
| `auto_create_index` | Whether to automatically create the index | `True` |
| `custom_search_query` | Function returning a custom search query | `None` |
docs/components/vectordbs/dbs/faiss.mdx
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@@ -55,6 +55,7 @@ Here are the parameters available for configuring FAISS:
| `path` | Path to store FAISS index and metadata | `/tmp/faiss/<collection_name>` |
| `distance_strategy` | Distance metric strategy to use (options: 'euclidean', 'inner_product', 'cosine') | `euclidean` |
| `normalize_L2` | Whether to normalize L2 vectors (only applicable for euclidean distance) | `False` |
| `embedding_model_dims` | Dimensions of the embedding model | `1536` |
### Performance Considerations
docs/components/vectordbs/dbs/langchain.mdx
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@@ -47,12 +47,12 @@ m.add(messages, user_id="alice", metadata={"category": "movies"})
```
```typescript TypeScript
import { Memory } from "mem0ai";
import { Memory } from "mem0ai/oss";
import { OpenAIEmbeddings } from "@langchain/openai";
import { MemoryVectorStore as LangchainMemoryStore } from "langchain/vectorstores/memory";
import { MemoryVectorStore } from "langchain/vectorstores/memory";
const embeddings = new OpenAIEmbeddings();
const vectorStore = new LangchainVectorStore(embeddings);
const vectorStore = new MemoryVectorStore(embeddings);
const config = {
"vector_store": {
docs/components/vectordbs/dbs/mongodb.mdx
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@@ -42,8 +42,8 @@ Here are the parameters available for configuring MongoDB:
| Parameter | Description | Default Value |
| --- | --- | --- |
| db_name | Name of the MongoDB database | `"mem0_db"` |
| collection_name | Name of the MongoDB collection | `"mem0_collection"` |
| collection_name | Name of the MongoDB collection | `"mem0"` |
| embedding_model_dims | Dimensions of the embedding vectors | `1536` |
| mongo_uri | The MongoDB URI connection string | `mongodb://username:password@localhost:27017` |
| mongo_uri | The MongoDB URI connection string | `mongodb://localhost:27017` |
> **Note**: If `mongo_uri` is not provided, it will default to `mongodb://username:password@localhost:27017`.
> **Note**: If `mongo_uri` is not provided, it will default to `mongodb://localhost:27017`.
docs/components/vectordbs/dbs/neptune_analytics.mdx
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@@ -10,7 +10,7 @@ description: "Use AWS Neptune Analytics as a vector store in Mem0, combining gra
## Installation
```bash
pip install mem0ai[vector_stores]
pip install mem0ai[vector-stores]
```
## Usage
docs/components/vectordbs/dbs/upstash-vector.mdx
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@@ -18,7 +18,9 @@ os.environ["UPSTASH_VECTOR_REST_TOKEN"] = "..."
config = {
"vector_store": {
"provider": "upstash_vector",
"enable_embeddings": True,
"config": {
"enable_embeddings": True,
}
}
}
docs/components/vectordbs/dbs/valkey.mdx
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@@ -9,7 +9,7 @@ description: "Use Valkey as an open-source vector store in Mem0 for high-perform
## Installation
```bash
pip install mem0ai[vector_stores]
pip install mem0ai[vector-stores]
```
## Usage
@@ -51,7 +51,7 @@ Here are the parameters available for configuring Valkey:
| `hnsw_ef_construction` | Size of dynamic candidate list for HNSW | `200` |
| `hnsw_ef_runtime` | Size of dynamic candidate list for search | `10` |
| `cluster_mode` | Enable cluster mode for Valkey cluster (CME) deployments | `false` |
| `distance_metric` | Distance metric for vector similarity | `cosine` |
| `timezone` | Timezone for timestamp handling | `UTC` |
## Cluster Mode
docs/components/vectordbs/dbs/vertex_ai.mdx
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@@ -24,7 +24,7 @@ config = {
"deployment_index_id": "YOUR_DEPLOYMENT_INDEX_ID", # Required: Deployment-specific ID
"project_id": "YOUR_PROJECT_ID", # Required: Google Cloud project ID
"project_number": "YOUR_PROJECT_NUMBER", # Required: Google Cloud project number
"region": "YOUR_REGION", # Optional: Defaults to GOOGLE_CLOUD_REGION
"region": "YOUR_REGION", # Required: Google Cloud region
"credentials_path": "path/to/credentials.json", # Optional: Defaults to GOOGLE_APPLICATION_CREDENTIALS
"vector_search_api_endpoint": "YOUR_API_ENDPOINT" # Required for get operations
}
@@ -45,5 +45,6 @@ m.add("Your text here", user_id="user", metadata={"category": "example"})
| `project_id` | Google Cloud project ID | Yes |
| `project_number` | Google Cloud project number | Yes |
| `vector_search_api_endpoint` | Vector search API endpoint | Yes (for get operations) |
| `region` | Google Cloud region | No (defaults to GOOGLE_CLOUD_REGION) |
| `region` | Google Cloud region | Yes |
| `credentials_path` | Path to service account credentials | No (defaults to GOOGLE_APPLICATION_CREDENTIALS) |
| `service_account_json` | Service account credentials as a dictionary (alternative to `credentials_path`) | `None` |
docs/components/vectordbs/dbs/weaviate.mdx
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@@ -7,7 +7,7 @@ description: "Use Weaviate as an open-source vector search engine in Mem0 for st
### Installation
```bash
pip install weaviate weaviate-client
pip install weaviate-client
```
### Usage
@@ -48,4 +48,5 @@ Here are the parameters available for configuring Weaviate:
| `collection_name` | The name of the collection to store the vectors | `mem0` |
| `embedding_model_dims` | Dimensions of the embedding model | `1536` |
| `cluster_url` | URL for the Weaviate server | `None` |
| `auth_client_secret` | API key for Weaviate authentication | `None` |
| `auth_client_secret` | API key for Weaviate authentication | `None` |
| `additional_headers` | Additional headers to include in requests (`Dict[str, str]`) | `None` |
docs/components/vectordbs/overview.mdx
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@@ -10,7 +10,7 @@ Mem0 includes built-in support for various popular databases. Memory can utilize
See the list of supported vector databases below.
<Note>
The following vector databases are supported in the Python implementation. The TypeScript implementation currently only supports Qdrant, Redis, Valkey, Vectorize and in-memory vector database.
The following vector databases are supported in the Python implementation. The TypeScript implementation currently supports Qdrant, Redis, PGVector, Supabase, LangChain, Azure AI Search, Vectorize, and an in-memory store.
</Note>
<CardGroup cols={3}>