docs(components): fix LLM & embedder model IDs and TS support lists (#5838)
This commit is contained in:
Kartik
@@ -59,5 +59,9 @@ Here are the parameters available for configuring AWS Bedrock 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 | `amazon.titan-embed-text-v1` |
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| `aws_region` | AWS region for the Bedrock client | `us-west-2` |
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| `aws_access_key_id` | AWS access key ID for authentication | `None` |
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| `aws_secret_access_key` | AWS secret access key for authentication | `None` |
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| `aws_session_token` | AWS session token for temporary credentials | `None` |
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</Tab>
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</Tabs>
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@@ -67,14 +67,15 @@ Here are the parameters available for configuring Gemini 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| `models/gemini-embedding-001` |
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| `embedding_dims` | Dimensions of the embedding model | `1536` |
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| `embedding_dims` | Dimensions of the embedding model | `768` |
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| `api_key` | The Google API key | `None` |
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| `output_dimensionality` | Output dimensionality for the embedding model (Gemini-specific; used when `embedding_dims` is not set) | `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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| `model` | The name of the embedding model to use | `gemini-embedding-001` |
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| `embeddingDims` | Dimensions of the embedding model | `1536` |
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| `embeddingDims` | Dimensions of the embedding model. When not set, uses the model's native output dimensionality (3072 for `gemini-embedding-001`; MRL truncation to 768, 1536, or 3072 is supported) | `None` |
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| `apiKey` | Google API key | `None` |
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</Tab>
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</Tabs>
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@@ -16,7 +16,7 @@ config = {
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"embedder": {
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"provider": "lmstudio",
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"config": {
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"model": "nomic-embed-text-v1.5-GGUF/nomic-embed-text-v1.5.f16.gguf"
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"model": "nomic-ai/nomic-embed-text-v1.5-GGUF"
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}
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}
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}
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@@ -37,6 +37,6 @@ Here are the parameters available for configuring LM Studio embedder:
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| Parameter | Description | Default Value |
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| --- | --- | --- |
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| `model` | The name of the LM Studio model to use | `nomic-embed-text-v1.5-GGUF/nomic-embed-text-v1.5.f16.gguf` |
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| `model` | The name of the LM Studio model to use | `nomic-ai/nomic-embed-text-v1.5-GGUF` |
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| `embedding_dims` | Dimensions of the embedding model | `1536` |
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| `lmstudio_base_url` | Base URL for LM Studio connection | `http://localhost:1234/v1` |
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@@ -10,7 +10,7 @@ Mem0 offers support for various embedding models, allowing users to choose the o
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See the list of supported embedders below.
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<Note>
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The following embedders are supported in the Python implementation. The TypeScript implementation currently only supports OpenAI.
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All embedders listed below are supported in the Python implementation. The TypeScript implementation supports: **OpenAI**, **Azure OpenAI**, **Google AI**, **Langchain**, **LM Studio**, and **Ollama**.
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</Note>
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<CardGroup cols={4}>
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@@ -24,6 +24,7 @@ See the list of supported embedders below.
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<Card title="LM Studio" href="/components/embedders/models/lmstudio"></Card>
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<Card title="Langchain" href="/components/embedders/models/langchain"></Card>
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<Card title="AWS Bedrock" href="/components/embedders/models/aws_bedrock"></Card>
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<Card title="FastEmbed" href="/components/embedders/models/fastembed"></Card>
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</CardGroup>
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## Usage
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@@ -98,7 +98,7 @@ Here's a comprehensive list of all parameters that can be used across different
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| `max_tokens` | Tokens to generate | All |
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| `top_p` | Probability threshold for nucleus sampling | All |
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| `top_k` | Number of highest probability tokens to keep | All |
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| `http_client_proxies`| Allow proxy server settings | AzureOpenAI |
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| `http_client_proxies`| Allow proxy server settings | All |
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| `models` | List of models | Openrouter |
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| `route` | Routing strategy | Openrouter |
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| `openrouter_base_url`| Base URL for Openrouter API | Openrouter |
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@@ -110,7 +110,7 @@ Here's a comprehensive list of all parameters that can be used across different
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| `deepseek_base_url` | Base URL for DeepSeek API | DeepSeek |
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| `xai_base_url` | Base URL for XAI API | XAI |
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| `sarvam_base_url` | Base URL for Sarvam API | Sarvam |
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| `reasoning_effort` | Reasoning level (low, medium, high) | Sarvam |
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| `reasoning_effort` | Reasoning level (low, medium, high) | All |
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| `frequency_penalty` | Penalize frequent tokens (-2.0 to 2.0) | Sarvam |
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| `presence_penalty` | Penalize existing tokens (-2.0 to 2.0) | Sarvam |
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| `seed` | Seed for deterministic sampling | Sarvam |
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@@ -20,7 +20,7 @@ config = {
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"llm": {
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"provider": "anthropic",
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"config": {
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"model": "claude-sonnet-4-20250514",
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"model": "claude-sonnet-4-6",
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"temperature": 0.1,
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"max_tokens": 2000,
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}
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@@ -45,7 +45,7 @@ const config = {
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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-sonnet-4-20250514',
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model: 'claude-sonnet-4-6',
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temperature: 0.1,
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maxTokens: 2000,
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},
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@@ -6,7 +6,7 @@ description: "Configure AWS Bedrock as an LLM provider in Mem0 with IAM authenti
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### Setup
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- Before using the AWS Bedrock LLM, make sure you have the appropriate model access from [Bedrock Console](https://us-east-1.console.aws.amazon.com/bedrock/home?region=us-east-1#/modelaccess).
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- You will also need to authenticate the `boto3` client by using a method in the [AWS documentation](https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html#configuring-credentials)
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- You will have to export `AWS_REGION`, `AWS_ACCESS_KEY`, and `AWS_SECRET_ACCESS_KEY` to set environment variables.
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- You will have to export `AWS_REGION`, `AWS_ACCESS_KEY_ID`, and `AWS_SECRET_ACCESS_KEY` to set environment variables.
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### Usage
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@@ -21,7 +21,7 @@ config = {
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"llm": {
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"provider": "groq",
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"config": {
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"model": "mixtral-8x7b-32768",
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"model": "llama-3.3-70b-versatile",
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"temperature": 0.1,
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"max_tokens": 2000,
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}
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@@ -46,7 +46,7 @@ const config = {
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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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model: 'llama3-70b-8192',
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temperature: 0.1,
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maxTokens: 1000,
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},
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@@ -20,7 +20,7 @@ config = {
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"llm": {
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"provider": "xai",
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"config": {
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"model": "grok-3-beta",
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"model": "grok-4.3",
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"temperature": 0.1,
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"max_tokens": 2000,
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}
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@@ -16,7 +16,7 @@ For a comprehensive list of available parameters for llm configuration, please r
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See the list of supported LLMs below.
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<Note>
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All LLMs are supported in Python. The following LLMs are also supported in TypeScript: **OpenAI**, **Anthropic**, and **Groq**.
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All LLMs are supported in Python. The following LLMs are also supported in TypeScript: **OpenAI**, **Anthropic**, **Groq**, **Azure OpenAI**, **DeepSeek**, **Google AI**, **Langchain**, **LM Studio**, **Mistral AI**, and **Ollama**.
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</Note>
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<CardGroup cols={4}>
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