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docs(components): fix LLM & embedder model IDs and TS support lists (#5838)

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Kartik
2026-06-27 18:42:02 +05:30
committed by GitHub
parent 4b39d01ccb
commit e9c930c430
10 changed files with 20 additions and 14 deletions
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docs/components/llms/config.mdx
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@@ -98,7 +98,7 @@ Here's a comprehensive list of all parameters that can be used across different
| `max_tokens` | Tokens to generate | All |
| `top_p` | Probability threshold for nucleus sampling | All |
| `top_k` | Number of highest probability tokens to keep | All |
| `http_client_proxies`| Allow proxy server settings | AzureOpenAI |
| `http_client_proxies`| Allow proxy server settings | All |
| `models` | List of models | Openrouter |
| `route` | Routing strategy | Openrouter |
| `openrouter_base_url`| Base URL for Openrouter API | Openrouter |
@@ -110,7 +110,7 @@ Here's a comprehensive list of all parameters that can be used across different
| `deepseek_base_url` | Base URL for DeepSeek API | DeepSeek |
| `xai_base_url` | Base URL for XAI API | XAI |
| `sarvam_base_url` | Base URL for Sarvam API | Sarvam |
| `reasoning_effort` | Reasoning level (low, medium, high) | Sarvam |
| `reasoning_effort` | Reasoning level (low, medium, high) | All |
| `frequency_penalty` | Penalize frequent tokens (-2.0 to 2.0) | Sarvam |
| `presence_penalty` | Penalize existing tokens (-2.0 to 2.0) | Sarvam |
| `seed` | Seed for deterministic sampling | Sarvam |
docs/components/llms/models/anthropic.mdx
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@@ -20,7 +20,7 @@ config = {
"llm": {
"provider": "anthropic",
"config": {
"model": "claude-sonnet-4-20250514",
"model": "claude-sonnet-4-6",
"temperature": 0.1,
"max_tokens": 2000,
}
@@ -45,7 +45,7 @@ const config = {
provider: 'anthropic',
config: {
apiKey: process.env.ANTHROPIC_API_KEY || '',
model: 'claude-sonnet-4-20250514',
model: 'claude-sonnet-4-6',
temperature: 0.1,
maxTokens: 2000,
},
docs/components/llms/models/aws_bedrock.mdx
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@@ -6,7 +6,7 @@ description: "Configure AWS Bedrock as an LLM provider in Mem0 with IAM authenti
### Setup
- 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).
- 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)
- You will have to export `AWS_REGION`, `AWS_ACCESS_KEY`, and `AWS_SECRET_ACCESS_KEY` to set environment variables.
- You will have to export `AWS_REGION`, `AWS_ACCESS_KEY_ID`, and `AWS_SECRET_ACCESS_KEY` to set environment variables.
### Usage
docs/components/llms/models/groq.mdx
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@@ -21,7 +21,7 @@ config = {
"llm": {
"provider": "groq",
"config": {
"model": "mixtral-8x7b-32768",
"model": "llama-3.3-70b-versatile",
"temperature": 0.1,
"max_tokens": 2000,
}
@@ -46,7 +46,7 @@ const config = {
provider: 'groq',
config: {
apiKey: process.env.GROQ_API_KEY || '',
model: 'mixtral-8x7b-32768',
model: 'llama3-70b-8192',
temperature: 0.1,
maxTokens: 1000,
},
docs/components/llms/models/xAI.mdx
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@@ -20,7 +20,7 @@ config = {
"llm": {
"provider": "xai",
"config": {
"model": "grok-3-beta",
"model": "grok-4.3",
"temperature": 0.1,
"max_tokens": 2000,
}
docs/components/llms/overview.mdx
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@@ -16,7 +16,7 @@ For a comprehensive list of available parameters for llm configuration, please r
See the list of supported LLMs below.
<Note>
All LLMs are supported in Python. The following LLMs are also supported in TypeScript: **OpenAI**, **Anthropic**, and **Groq**.
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**.
</Note>
<CardGroup cols={4}>