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Update Docs (#3520)

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Deshraj Yadav
2025-09-30 08:41:36 -07:00
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docs/components/vectordbs/dbs/azure.mdx
+6 -6
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@@ -27,7 +27,7 @@ config = {
m = Memory.from_config(config)
messages = [
{"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
{"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
{"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
{"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
{"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
]
@@ -107,13 +107,13 @@ To enable Role-Based Access Control (RBAC) for Azure AI Search, follow these ste
- Click **Add** > **Add role assignment**.
6. **Choose Role:**
- Mem0 requires the **Search Index Data Contributor** and **Search Service Contributor** role.
7. **Choose Member**
- To assign to a User, Group, Service Principle or Managed Identity:
7. **Choose Member**
- To assign to a User, Group, Service Principal or Managed Identity:
- For production it is recommended to use a service principal or managed identity.
- For a service principal: select **User, group, or service principal** and search for the service principal.
- For a managed identity: select **Managed identity** and choose the managed identity.
- For development, you can assign the role to a user account.
- For development: select ***User, group, or service principal** and pick a Azure Entra ID account (the same used with `az login`).
- For development: select **User, group, or service principal** and pick an Azure Entra ID account (the same used with `az login`).
8. **Complete the Assignment:**
- Click **Review + Assign**.
@@ -133,7 +133,7 @@ config = {
}
```
### Environment Variables to set to use Azure Identity Credential:
### Environment Variables to Use Azure Identity Credential
* For an Environment Credential, you will need to setup a Service Principal and set the following environment variables:
- `AZURE_TENANT_ID`: Your Azure Active Directory tenant ID.
- `AZURE_CLIENT_ID`: The client ID of your service principal or managed identity.
@@ -142,7 +142,7 @@ config = {
- `AZURE_CLIENT_ID`: The client ID of the user-assigned managed identity.
* For a System-Assigned Managed Identity, no additional environment variables are needed.
### Developer logins to use for a Azure Identity Credential:
### Developer Logins for Azure Identity Credential
* For an Azure CLI Credential, you need to have the Azure CLI installed and logged in with `az login`.
* For an Azure PowerShell Credential, you need to have the Azure PowerShell module installed and logged in with `Connect-AzAccount`.
* For an Azure Developer CLI Credential, you need to have the Azure Developer CLI installed and logged in with `azd auth login`.
docs/components/vectordbs/dbs/baidu.mdx
+1 -1
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@@ -37,7 +37,7 @@ m.add(messages, user_id="alice", metadata={"category": "movies"})
### Config
Here are the available parameters for the `mochow` config:
Here are the parameters available for configuring Baidu VectorDB:
| Parameter | Description | Default Value |
| --- | --- | --- |
docs/components/vectordbs/dbs/chroma.mdx
+1 -1
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@@ -26,7 +26,7 @@ config = {
m = Memory.from_config(config)
messages = [
{"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
{"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
{"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
{"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
{"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
]
docs/components/vectordbs/dbs/elasticsearch.mdx
+2 -2
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@@ -31,7 +31,7 @@ config = {
m = Memory.from_config(config)
messages = [
{"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
{"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
{"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
{"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
{"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
]
@@ -40,7 +40,7 @@ m.add(messages, user_id="alice", metadata={"category": "movies"})
### Config
Let's see the available parameters for the `elasticsearch` config:
Here are the parameters available for configuring Elasticsearch:
| Parameter | Description | Default Value |
| ---------------------- | -------------------------------------------------- | ------------- |
docs/components/vectordbs/dbs/faiss.mdx
+1 -1
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@@ -22,7 +22,7 @@ config = {
m = Memory.from_config(config)
messages = [
{"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
{"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
{"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
{"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
{"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
]
docs/components/vectordbs/dbs/langchain.mdx
+3 -3
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@@ -38,7 +38,7 @@ config = {
m = Memory.from_config(config)
messages = [
{"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
{"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
{"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
{"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
{"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
]
@@ -64,12 +64,12 @@ const memory = new Memory(config);
const messages = [
{ role: "user", content: "I'm planning to watch a movie tonight. Any recommendations?" },
{ role: "assistant", content: "How about a thriller movies? They can be quite engaging." },
{ role: "assistant", content: "How about thriller movies? They can be quite engaging." },
{ role: "user", content: "I'm not a big fan of thriller movies but I love sci-fi movies." },
{ role: "assistant", content: "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future." }
]
memory.add(messages, user_id="alice", metadata={"category": "movies"})
await memory.add(messages, { userId: "alice", metadata: { category: "movies" } });
```
</CodeGroup>
docs/components/vectordbs/dbs/milvus.mdx
+3 -3
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@@ -1,4 +1,4 @@
[Milvus](https://milvus.io/) Milvus is an open-source vector database that suits AI applications of every size from running a demo chatbot in Jupyter notebook to building web-scale search that serves billions of users.
[Milvus](https://milvus.io/) is an open-source vector database that suits AI applications of every size, from running a demo chatbot in a Jupyter notebook to building web-scale search that serves billions of users.
### Usage
@@ -22,7 +22,7 @@ config = {
m = Memory.from_config(config)
messages = [
{"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
{"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
{"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
{"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
{"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
]
@@ -31,7 +31,7 @@ m.add(messages, user_id="alice", metadata={"category": "movies"})
### Config
Here's the parameters available for configuring Milvus Database:
Here are the parameters available for configuring Milvus:
| Parameter | Description | Default Value |
| --- | --- | --- |
docs/components/vectordbs/dbs/mongodb.mdx
+3 -3
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@@ -24,7 +24,7 @@ config = {
m = Memory.from_config(config)
messages = [
{"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
{"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
{"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
{"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
{"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
]
@@ -40,6 +40,6 @@ Here are the parameters available for configuring MongoDB:
| db_name | Name of the MongoDB database | `"mem0_db"` |
| collection_name | Name of the MongoDB collection | `"mem0_collection"` |
| embedding_model_dims | Dimensions of the embedding vectors | `1536` |
| mongo_uri | The mongo URI connection string | mongodb://username:password@localhost:27017 |
| mongo_uri | The MongoDB URI connection string | `mongodb://username:password@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://username:password@localhost:27017`.
docs/components/vectordbs/dbs/opensearch.mdx
+5 -5
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@@ -58,7 +58,7 @@ config = {
m = Memory.from_config(config)
messages = [
{"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
{"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
{"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
{"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
{"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
]
@@ -74,8 +74,8 @@ results = m.search("What kind of movies does Alice like?", user_id="alice")
### Features
- Fast and Efficient Vector Search
- Can be deployed on-premises, in containers, or on cloud platforms like AWS OpenSearch Service.
- Multiple Authentication and Security Methods (Basic Authentication, API Keys, LDAP, SAML, and OpenID Connect)
- Can be deployed on-premises, in containers, or on cloud platforms like AWS OpenSearch Service
- Multiple authentication and security methods (Basic Authentication, API Keys, LDAP, SAML, and OpenID Connect)
- Automatic index creation with optimized mappings for vector search
- Memory Optimization through Disk-Based Vector Search and Quantization
- Real-Time Analytics and Observability
- Memory optimization through disk-based vector search and quantization
- Real-time analytics and observability
docs/components/vectordbs/dbs/pgvector.mdx
+4 -4
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@@ -1,4 +1,4 @@
[pgvector](https://github.com/pgvector/pgvector) is open-source vector similarity search for Postgres. After connecting with postgres run `CREATE EXTENSION IF NOT EXISTS vector;` to create the vector extension.
[pgvector](https://github.com/pgvector/pgvector) is an open-source vector similarity search extension for Postgres. After connecting to Postgres, run `CREATE EXTENSION IF NOT EXISTS vector;` to create the vector extension.
### Usage
@@ -24,7 +24,7 @@ config = {
m = Memory.from_config(config)
messages = [
{"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
{"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
{"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
{"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
{"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
]
@@ -54,7 +54,7 @@ const config = {
const memory = new Memory(config);
const messages = [
{"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
{"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
{"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
{"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
{"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
]
@@ -64,7 +64,7 @@ await memory.add(messages, { userId: "alice", metadata: { category: "movies" } }
### Config
Here's the parameters available for configuring pgvector:
Here are the parameters available for configuring pgvector:
| Parameter | Description | Default Value |
| --- | --- | --- |
docs/components/vectordbs/dbs/pinecone.mdx
+1 -1
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@@ -33,7 +33,7 @@ config = {
m = Memory.from_config(config)
messages = [
{"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
{"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
{"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
{"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
{"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
]
docs/components/vectordbs/dbs/qdrant.mdx
+2 -2
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@@ -23,7 +23,7 @@ config = {
m = Memory.from_config(config)
messages = [
{"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
{"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
{"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
{"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
{"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
]
@@ -48,7 +48,7 @@ const config = {
const memory = new Memory(config);
const messages = [
{"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
{"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
{"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
{"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
{"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
]
docs/components/vectordbs/dbs/redis.mdx
+2 -2
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@@ -34,7 +34,7 @@ config = {
m = Memory.from_config(config)
messages = [
{"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
{"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
{"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
{"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
{"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
]
@@ -60,7 +60,7 @@ const config = {
const memory = new Memory(config);
const messages = [
{"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
{"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
{"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
{"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
{"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
]
docs/components/vectordbs/dbs/s3_vectors.mdx
+1 -1
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@@ -48,7 +48,7 @@ m.add(messages, user_id="alice", metadata={"category": "movies"})
### Config
Here are the available parameters for the `s3_vectors` config:
Here are the parameters available for configuring Amazon S3 Vectors:
| Parameter | Description | Default Value |
| ---------------------- | -------------------------------------------------------------------------------- | ------------------------------------- |
docs/components/vectordbs/dbs/supabase.mdx
+3 -3
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@@ -26,7 +26,7 @@ config = {
m = Memory.from_config(config)
messages = [
{"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
{"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
{"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
{"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
{"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
]
@@ -53,7 +53,7 @@ const memory = new Memory(config);
const messages = [
{"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
{"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
{"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
{"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
{"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
]
@@ -109,7 +109,7 @@ end;
$$;
```
Goto [Supabase](https://supabase.com/dashboard/projects) and run the above SQL migrations inside the SQL Editor.
Go to [Supabase](https://supabase.com/dashboard/projects) and run the above SQL migrations in the SQL Editor.
### Config
docs/components/vectordbs/dbs/valkey.mdx
+2 -2
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@@ -26,7 +26,7 @@ config = {
m = Memory.from_config(config)
messages = [
{"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
{"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
{"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
{"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
{"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
]
@@ -35,7 +35,7 @@ m.add(messages, user_id="alice", metadata={"category": "movies"})
## Parameters
Let's see the available parameters for the `valkey` config:
Here are the parameters available for configuring Valkey:
| Parameter | Description | Default Value |
| --- | --- | --- |
docs/components/vectordbs/dbs/vectorize.mdx
+1 -1
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@@ -31,7 +31,7 @@ await memory.add(messages, { userId: "bob", metadata: { interest: "books" } });
### Config
Let's see the available parameters for the `vectorize` config:
Here are the parameters available for configuring Vectorize:
<Tabs>
<Tab title="TypeScript">
docs/components/vectordbs/dbs/weaviate.mdx
+1 -1
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@@ -37,7 +37,7 @@ m.add(messages, user_id="alice", metadata={"category": "movies"})
### Config
Let's see the available parameters for the `weaviate` config:
Here are the parameters available for configuring Weaviate:
| Parameter | Description | Default Value |
| --- | --- | --- |
docs/components/vectordbs/overview.mdx
+4 -5
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@@ -17,7 +17,7 @@ See the list of supported vector databases below.
<CardGroup cols={3}>
<Card title="Qdrant" href="/components/vectordbs/dbs/qdrant"></Card>
<Card title="Chroma" href="/components/vectordbs/dbs/chroma"></Card>
<Card title="Pgvector" href="/components/vectordbs/dbs/pgvector"></Card>
<Card title="PGVector" href="/components/vectordbs/dbs/pgvector"></Card>
<Card title="Upstash Vector" href="/components/vectordbs/dbs/upstash-vector"></Card>
<Card title="Milvus" href="/components/vectordbs/dbs/milvus"></Card>
<Card title="Pinecone" href="/components/vectordbs/dbs/pinecone"></Card>
@@ -44,12 +44,11 @@ For a comprehensive list of available parameters for vector database configurati
## Common issues
### Using model with different dimensions
### Using Model with Different Dimensions
If you are using customized model, which is having different dimensions other than 1536
for example 768, you may encounter below error:
If you are using a customized model with different dimensions other than 1536 (for example, 768), you may encounter the following error:
`ValueError: shapes (0,1536) and (768,) not aligned: 1536 (dim 1) != 768 (dim 0)`
you could add `"embedding_model_dims": 768,` to the config of the vector_store to overcome this issue.
You can add `"embedding_model_dims": 768,` to the config of the vector_store to resolve this issue.