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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/embedders/models/aws_bedrock.mdx
+4
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@@ -59,5 +59,9 @@ Here are the parameters available for configuring AWS Bedrock embedder:
| Parameter | Description | Default Value |
| --- | --- | --- |
| `model` | The name of the embedding model to use | `amazon.titan-embed-text-v1` |
| `aws_region` | AWS region for the Bedrock client | `us-west-2` |
| `aws_access_key_id` | AWS access key ID for authentication | `None` |
| `aws_secret_access_key` | AWS secret access key for authentication | `None` |
| `aws_session_token` | AWS session token for temporary credentials | `None` |
</Tab>
</Tabs>
docs/components/embedders/models/google_AI.mdx
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@@ -67,14 +67,15 @@ Here are the parameters available for configuring Gemini embedder:
| Parameter | Description | Default Value |
| ---------------- | ------------------------------------ | ----------------------- |
| `model` | The name of the embedding model to use| `models/gemini-embedding-001` |
| `embedding_dims` | Dimensions of the embedding model | `1536` |
| `embedding_dims` | Dimensions of the embedding model | `768` |
| `api_key` | The Google API key | `None` |
| `output_dimensionality` | Output dimensionality for the embedding model (Gemini-specific; used when `embedding_dims` is not set) | `None` |
</Tab>
<Tab title="TypeScript">
| Parameter | Description | Default Value |
| ----------------- | --------------------------------------------- | -------------------------- |
| `model` | The name of the embedding model to use | `gemini-embedding-001` |
| `embeddingDims` | Dimensions of the embedding model | `1536` |
| `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` |
| `apiKey` | Google API key | `None` |
</Tab>
</Tabs>
docs/components/embedders/models/lmstudio.mdx
+2 -2
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@@ -16,7 +16,7 @@ config = {
"embedder": {
"provider": "lmstudio",
"config": {
"model": "nomic-embed-text-v1.5-GGUF/nomic-embed-text-v1.5.f16.gguf"
"model": "nomic-ai/nomic-embed-text-v1.5-GGUF"
}
}
}
@@ -37,6 +37,6 @@ Here are the parameters available for configuring LM Studio embedder:
| Parameter | Description | Default Value |
| --- | --- | --- |
| `model` | The name of the LM Studio model to use | `nomic-embed-text-v1.5-GGUF/nomic-embed-text-v1.5.f16.gguf` |
| `model` | The name of the LM Studio model to use | `nomic-ai/nomic-embed-text-v1.5-GGUF` |
| `embedding_dims` | Dimensions of the embedding model | `1536` |
| `lmstudio_base_url` | Base URL for LM Studio connection | `http://localhost:1234/v1` |
docs/components/embedders/overview.mdx
+2 -1
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@@ -10,7 +10,7 @@ Mem0 offers support for various embedding models, allowing users to choose the o
See the list of supported embedders below.
<Note>
The following embedders are supported in the Python implementation. The TypeScript implementation currently only supports OpenAI.
All embedders listed below are supported in the Python implementation. The TypeScript implementation supports: **OpenAI**, **Azure OpenAI**, **Google AI**, **Langchain**, **LM Studio**, and **Ollama**.
</Note>
<CardGroup cols={4}>
@@ -24,6 +24,7 @@ See the list of supported embedders below.
<Card title="LM Studio" href="/components/embedders/models/lmstudio"></Card>
<Card title="Langchain" href="/components/embedders/models/langchain"></Card>
<Card title="AWS Bedrock" href="/components/embedders/models/aws_bedrock"></Card>
<Card title="FastEmbed" href="/components/embedders/models/fastembed"></Card>
</CardGroup>
## Usage