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