Updated docs for typescript package (#2269)
This commit is contained in:
Saket Aryan
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---
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title: Configurations
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icon: "gear"
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iconType: "solid"
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---
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Config in Mem0 is a dictionary that specifies the settings for your embedding models. It allows you to customize the behavior and connection details of your chosen embedder.
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## How to define configurations?
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The config is defined as a TypeScript object with two main keys:
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- `embedder`: Specifies the embedder provider and its configuration
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- `provider`: The name of the embedder (e.g., "openai", "ollama")
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- `config`: A nested object containing provider-specific settings
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## How to use configurations?
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Here's a general example of how to use the config with Mem0:
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```typescript
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import { Memory } from 'mem0ai/oss';
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const config = {
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embedder: {
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provider: 'openai',
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config: {
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apiKey: 'your-openai-api-key',
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model: 'text-embedding-3-small',
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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("Your text here", { userId: "user", metadata: { category: "example" } });
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```
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## Why is Config Needed?
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Config is essential for:
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1. Specifying which embedding model to use.
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2. Providing necessary connection details (e.g., model, api_key, embedding_dims).
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3. Ensuring proper initialization and connection to your chosen embedder.
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## Master List of All Params in Config
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Here's a comprehensive list of all parameters that can be used across different embedders:
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| Parameter | Description |
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|------------------------|--------------------------------------------------|
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| `model` | Embedding model to use |
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| `apiKey` | API key of the provider |
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| `embeddingDims` | Dimensions of the embedding model |
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## Supported Embedding Models
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For detailed information on configuring specific embedders, please visit the [Embedding Models](./models) section. There you'll find information for each supported embedder with provider-specific usage examples and configuration details.
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---
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title: OpenAI
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---
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To use OpenAI embedding models, you need to provide the API key directly in your configuration. You can obtain the OpenAI API key from the [OpenAI Platform](https://platform.openai.com/account/api-keys).
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### Usage
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Here's how to configure OpenAI embedding models in your application:
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```typescript
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import { Memory } from 'mem0ai/oss';
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const config = {
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embedder: {
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provider: 'openai',
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config: {
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apiKey: 'your-openai-api-key',
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model: 'text-embedding-3-large',
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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("I'm visiting Paris", { userId: "john" });
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```
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### Config
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Here are the parameters available for configuring the OpenAI embedder:
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| Parameter | Description | Default Value |
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|------------------------|--------------------------------------------------|---------------|
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| `model` | The name of the embedding model to use | `text-embedding-3-small` |
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| `embedding_dims` | Dimensions of the embedding model | `1536` |
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| `api_key` | The OpenAI API key | `None` |
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@@ -0,0 +1,21 @@
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---
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title: Overview
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icon: "info"
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iconType: "solid"
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---
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Mem0 offers support for various embedding models, allowing users to choose the one that best suits their needs.
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## Supported Embedders
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See the list of supported embedders below.
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<CardGroup cols={1}>
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<Card title="OpenAI" href="/components/embedders/models/openai"></Card>
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</CardGroup>
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## Usage
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To utilize a embedder, you must provide a configuration to customize its usage. If no configuration is supplied, a default configuration will be applied, and `OpenAI` will be used as the embedder.
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For a comprehensive list of available parameters for embedder configuration, please refer to [Config](./config).
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---
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title: Configurations
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icon: "gear"
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iconType: "solid"
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---
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## How to define configurations?
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The `config` is defined as a TypeScript object with two main keys:
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- `llm`: Specifies the LLM provider and its configuration
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- `provider`: The name of the LLM (e.g., "openai", "groq")
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- `config`: A nested object containing provider-specific settings
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### Config Values Precedence
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Config values are applied in the following order of precedence (from highest to lowest):
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1. Values explicitly set in the `config` object
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2. Environment variables (e.g., `OPENAI_API_KEY`, `OPENAI_API_BASE`)
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3. Default values defined in the LLM implementation
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This means that values specified in the `config` object will override corresponding environment variables, which in turn override default values.
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## How to Use Config
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Here's a general example of how to use the config with Mem0:
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```typescript
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import { Memory } from 'mem0ai/oss';
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const config = {
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llm: {
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provider: 'openai',
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config: {
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apiKey: process.env.OPENAI_API_KEY || '',
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model: 'gpt-4-turbo-preview',
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temperature: 0.2,
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maxTokens: 1500,
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},
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},
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embedder: {
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provider: 'openai',
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config: {
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apiKey: process.env.OPENAI_API_KEY || '',
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model: 'text-embedding-3-small',
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},
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},
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vectorStore: {
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provider: 'memory',
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config: {
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collectionName: 'memories',
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dimension: 1536,
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},
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},
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historyDbPath: 'memory.db',
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};
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const memory = new Memory(config);
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memory.add("Your text here", { userId: "user123", metadata: { category: "example" } });
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```
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## Why is Config Needed?
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Config is essential for:
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1. Specifying which LLM to use.
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2. Providing necessary connection details (e.g., model, api_key, temperature).
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3. Ensuring proper initialization and connection to your chosen LLM.
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## Master List of All Params in Config
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Here's a comprehensive list of all parameters that can be used across different LLMs:
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| Parameter | Description | Provider |
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|----------------------|-----------------------------------------------|-------------------|
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| `model` | Embedding model to use | All |
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| `temperature` | Temperature of the model | All |
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| `apiKey` | API key to use | All |
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| `maxTokens` | Tokens to generate | All |
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| `topP` | Probability threshold for nucleus sampling | All |
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| `topK` | Number of highest probability tokens to keep | All |
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| `openaiBaseUrl` | Base URL for OpenAI API | OpenAI |
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## Supported LLMs
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For detailed information on configuring specific LLMs, please visit the [LLMs](./models) section. There you'll find information for each supported LLM with provider-specific usage examples and configuration details.
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---
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title: Anthropic
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---
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To use Anthropic's models, please set the `ANTHROPIC_API_KEY`, which you can find on their [Account Settings Page](https://console.anthropic.com/account/keys).
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## Usage
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```typescript
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import { Memory } from 'mem0ai/oss';
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const config = {
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llm: {
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provider: 'anthropic',
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config: {
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apiKey: process.env.ANTHROPIC_API_KEY || '',
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model: 'claude-3-7-sonnet-latest',
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temperature: 0.1,
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maxTokens: 2000,
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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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## Config
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All available parameters for the `anthropic` config are present in the [Master List of All Params in Config](../config).
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---
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title: Groq
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---
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[Groq](https://groq.com/) is the creator of the world's first Language Processing Unit (LPU), providing exceptional speed performance for AI workloads running on their LPU Inference Engine.
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In order to use LLMs from Groq, go to their [platform](https://console.groq.com/keys) and get the API key. Set the API key as `GROQ_API_KEY` environment variable to use the model as given below in the example.
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## Usage
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```typescript
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import { Memory } from 'mem0ai/oss';
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const config = {
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llm: {
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provider: 'groq',
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config: {
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apiKey: process.env.GROQ_API_KEY || '',
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model: 'mixtral-8x7b-32768',
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temperature: 0.1,
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maxTokens: 1000,
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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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## Config
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All available parameters for the `groq` config are present in the [Master List of All Params in Config](../config).
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---
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title: OpenAI
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---
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To use OpenAI LLM models, you need to set the `OPENAI_API_KEY` environment variable. You can obtain the OpenAI API key from the [OpenAI Platform](https://platform.openai.com/account/api-keys).
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## Usage
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```typescript
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import { Memory } from 'mem0ai/oss';
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const config = {
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llm: {
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provider: 'openai',
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config: {
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apiKey: process.env.OPENAI_API_KEY || '',
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model: 'gpt-4-turbo-preview',
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temperature: 0.2,
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maxTokens: 1500,
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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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## Config
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All available parameters for the `openai` config are present in the [Master List of All Params in Config](../config).
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@@ -0,0 +1,45 @@
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---
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title: Overview
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icon: "info"
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iconType: "solid"
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---
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Mem0 includes built-in support for various popular large language models. Memory can utilize the LLM provided by the user, ensuring efficient use for specific needs.
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## Usage
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To use a llm, you must provide a configuration to customize its usage. If no configuration is supplied, a default configuration will be applied, and `OpenAI` will be used as the llm.
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For a comprehensive list of available parameters for llm configuration, please refer to [Config](./config).
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To view all supported llms, visit the [Supported LLMs](./models).
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<CardGroup cols={3}>
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<Card title="OpenAI" href="/open-source-typescript/components/llms/models/openai"></Card>
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<Card title="Anthropic" href="/open-source-typescript/components/llms/models/anthropic"></Card>
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<Card title="Groq" href="/open-source-typescript/components/llms/models/groq"></Card>
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</CardGroup>
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## Structured vs Unstructured Outputs
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Mem0 supports two types of OpenAI LLM formats, each with its own strengths and use cases:
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### Structured Outputs
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Structured outputs are LLMs that align with OpenAI's structured outputs model:
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- **Optimized for:** Returning structured responses (e.g., JSON objects)
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- **Benefits:** Precise, easily parseable data
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- **Ideal for:** Data extraction, form filling, API responses
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- **Learn more:** [OpenAI Structured Outputs Guide](https://platform.openai.com/docs/guides/structured-outputs/introduction)
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### Unstructured Outputs
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Unstructured outputs correspond to OpenAI's standard, free-form text model:
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- **Flexibility:** Returns open-ended, natural language responses
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- **Customization:** Use the `response_format` parameter to guide output
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- **Trade-off:** Less efficient than structured outputs for specific data needs
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- **Best for:** Creative writing, explanations, general conversation
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Choose the format that best suits your application's requirements for optimal performance and usability.
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@@ -0,0 +1,100 @@
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---
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title: Configurations
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||||
icon: "gear"
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||||
iconType: "solid"
|
||||
---
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||||
|
||||
## How to define configurations?
|
||||
|
||||
The `config` is defined as a TypeScript object with two main keys:
|
||||
- `vectorStore`: Specifies the vector database provider and its configuration
|
||||
- `provider`: The name of the vector database (e.g., "chroma", "pgvector", "qdrant", "milvus", "azure_ai_search", "redis", "memory")
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- `config`: A nested object containing provider-specific settings
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## In-Memory Storage Option
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We also support an in-memory storage option for the vector store, which is useful for reduced overhead and faster access times. Here's how to configure it:
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### Example for In-Memory Storage
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```typescript
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const configMemory = {
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vector_store: {
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provider: 'memory',
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config: {
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collectionName: 'memories',
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dimension: 1536,
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},
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},
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};
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const memory = new Memory(configMemory);
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await memory.add("Your text here", { userId: "user", metadata: { category: "example" } });
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```
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## How to Use Config
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||||
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Here's a general example of how to use the config with Mem0:
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||||
|
||||
### Example for qdrant
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||||
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```typescript
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import { Memory } from 'mem0ai/oss';
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const config = {
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vector_store: {
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provider: 'qdrant',
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config: {
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collectionName: 'memories',
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embeddingModelDims: 1536,
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host: 'localhost',
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port: 6333,
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url: 'https://your-qdrant-url.com',
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apiKey: 'your-qdrant-api-key',
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onDisk: true,
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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("Your text here", { userId: "user", metadata: { category: "example" } });
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||||
```
|
||||
|
||||
## Why is Config Needed?
|
||||
|
||||
Config is essential for:
|
||||
1. Specifying which vector database to use.
|
||||
2. Providing necessary connection details (e.g., host, port, credentials).
|
||||
3. Customizing database-specific settings (e.g., collection name, path).
|
||||
4. Ensuring proper initialization and connection to your chosen vector store.
|
||||
|
||||
## Master List of All Params in Config
|
||||
|
||||
Here's a comprehensive list of all parameters that can be used across different vector databases:
|
||||
|
||||
| Parameter | Description |
|
||||
|------------------------|--------------------------------------|
|
||||
| `collectionName` | Name of the collection |
|
||||
| `dimension` | Dimensions of the embedding model |
|
||||
| `host` | Host where the server is running |
|
||||
| `port` | Port where the server is running |
|
||||
| `embeddingModelDims` | Dimensions of the embedding model |
|
||||
| `url` | URL for the Qdrant server |
|
||||
| `apiKey` | API key for the Qdrant server |
|
||||
| `path` | Path for the Qdrant server |
|
||||
| `onDisk` | Enable persistent storage (for Qdrant) |
|
||||
| `redisUrl` | URL for the Redis server |
|
||||
| `username` | Username for Redis connection |
|
||||
| `password` | Password for Redis connection |
|
||||
|
||||
## Customizing Config
|
||||
|
||||
Each vector database has its own specific configuration requirements. To customize the config for your chosen vector store:
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||||
|
||||
1. Identify the vector database you want to use from [supported vector databases](./dbs).
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||||
2. Refer to the `Config` section in the respective vector database's documentation.
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||||
3. Include only the relevant parameters for your chosen database in the `config` object.
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||||
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||||
## Supported Vector Databases
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||||
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||||
For detailed information on configuring specific vector databases, please visit the [Supported Vector Databases](./dbs) section. There you'll find individual pages for each supported vector store with provider-specific usage examples and configuration details.
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||||
@@ -0,0 +1,44 @@
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||||
[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.
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||||
|
||||
### Usage
|
||||
|
||||
Here's how to configure pgvector in your application:
|
||||
|
||||
```typescript
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||||
import { Memory } from 'mem0ai/oss';
|
||||
|
||||
const config = {
|
||||
vector_store: {
|
||||
provider: 'pgvector',
|
||||
config: {
|
||||
collectionName: 'memories',
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||||
dimension: 1536,
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||||
dbname: 'vectordb',
|
||||
user: 'postgres',
|
||||
password: 'postgres',
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host: 'localhost',
|
||||
port: 5432,
|
||||
embeddingModelDims: 1536,
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||||
hnsw: true,
|
||||
},
|
||||
},
|
||||
};
|
||||
|
||||
const memory = new Memory(config);
|
||||
await memory.add("Likes to play cricket on weekends", { userId: "alice", metadata: { category: "hobbies" } });
|
||||
```
|
||||
|
||||
### Config
|
||||
|
||||
Here's the parameters available for configuring pgvector:
|
||||
|
||||
| Parameter | Description | Default Value |
|
||||
|------------------------|--------------------------------------------------|---------------|
|
||||
| `dbname` | The name of the database | `postgres` |
|
||||
| `collectionName` | The name of the collection | `mem0` |
|
||||
| `embeddingModelDims` | Dimensions of the embedding model | `1536` |
|
||||
| `user` | Username to connect to the database | `None` |
|
||||
| `password` | Password to connect to the database | `None` |
|
||||
| `host` | The host where the Postgres server is running | `None` |
|
||||
| `port` | The port where the Postgres server is running | `None` |
|
||||
| `hnsw` | Enable HNSW indexing | `False` |
|
||||
@@ -0,0 +1,42 @@
|
||||
[Qdrant](https://qdrant.tech/) is an open-source vector search engine. It is designed to work with large-scale datasets and provides a high-performance search engine for vector data.
|
||||
|
||||
### Usage
|
||||
|
||||
Here's how to configure Qdrant in your application:
|
||||
|
||||
```typescript
|
||||
import { Memory } from 'mem0ai/oss';
|
||||
|
||||
const config = {
|
||||
vector_store: {
|
||||
provider: 'qdrant',
|
||||
config: {
|
||||
collectionName: 'memories',
|
||||
embeddingModelDims: 1536,
|
||||
host: 'localhost',
|
||||
port: 6333,
|
||||
url: 'https://your-qdrant-url.com',
|
||||
apiKey: 'your-qdrant-api-key',
|
||||
onDisk: true,
|
||||
},
|
||||
},
|
||||
};
|
||||
|
||||
const memory = new Memory(config);
|
||||
await memory.add("Likes to play cricket on weekends", { userId: "alice", metadata: { category: "hobbies" } });
|
||||
```
|
||||
|
||||
### Config
|
||||
|
||||
Let's see the available parameters for the `qdrant` config:
|
||||
|
||||
| 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` |
|
||||
@@ -0,0 +1,47 @@
|
||||
[Redis](https://redis.io/) is a scalable, real-time database that can store, search, and analyze vector data.
|
||||
|
||||
### Installation
|
||||
```bash
|
||||
pip install redis redisvl
|
||||
```
|
||||
|
||||
Redis Stack using Docker:
|
||||
```bash
|
||||
docker run -d --name redis-stack -p 6379:6379 -p 8001:8001 redis/redis-stack:latest
|
||||
```
|
||||
|
||||
### Usage
|
||||
|
||||
Here's how to configure Redis in your application:
|
||||
|
||||
```typescript
|
||||
import { Memory } from 'mem0ai/oss';
|
||||
|
||||
const config = {
|
||||
vector_store: {
|
||||
provider: 'redis',
|
||||
config: {
|
||||
collectionName: 'memories',
|
||||
embeddingModelDims: 1536,
|
||||
redisUrl: 'redis://localhost:6379',
|
||||
username: 'your-redis-username',
|
||||
password: 'your-redis-password',
|
||||
},
|
||||
},
|
||||
};
|
||||
|
||||
const memoryRedis = new Memory(config);
|
||||
await memoryRedis.add("Likes to play cricket on weekends", { userId: "alice", metadata: { category: "hobbies" } });
|
||||
```
|
||||
|
||||
### Config
|
||||
|
||||
Let's see the available parameters for the `redis` config:
|
||||
|
||||
| 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` |
|
||||
@@ -0,0 +1,36 @@
|
||||
---
|
||||
title: Overview
|
||||
icon: "info"
|
||||
iconType: "solid"
|
||||
---
|
||||
|
||||
Mem0 includes built-in support for various popular databases. Memory can utilize the database provided by the user, ensuring efficient use for specific needs.
|
||||
|
||||
## Supported Vector Databases
|
||||
|
||||
See the list of supported vector databases below.
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="Memory" href="/components/vectordbs/dbs/memory"></Card>
|
||||
<Card title="Qdrant" href="/components/vectordbs/dbs/qdrant"></Card>
|
||||
<Card title="Redis" href="/components/vectordbs/dbs/redis"></Card>
|
||||
<Card title="Pgvector" href="/components/vectordbs/dbs/pgvector"></Card>
|
||||
</CardGroup>
|
||||
|
||||
## Usage
|
||||
|
||||
To utilize a vector database, you must provide a configuration to customize its usage. If no configuration is supplied, a default configuration will be applied, and `Memory` will be used as the vector database.
|
||||
|
||||
For a comprehensive list of available parameters for vector database configuration, please refer to [Config](./config).
|
||||
|
||||
## Common issues
|
||||
|
||||
### 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:
|
||||
|
||||
`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.
|
||||
|
||||
Reference in New Issue
Block a user