Improve SEO metadata across documentation pages (#4447)

2026-03-20 02:52:39 -07:00
parent 401754ca65
commit f05e50d940
134 changed files with 197 additions and 14 deletions
@@ -1,5 +1,6 @@
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 title: Configurations
+description: "Reference for embedder configuration options in Mem0, including provider selection and model settings."
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@@ -1,5 +1,6 @@
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 title: AWS Bedrock
+description: "Configure AWS Bedrock as an embedding provider in Mem0 with IAM credentials and boto3 authentication."
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 To use AWS Bedrock embedding models, you need to have the appropriate AWS credentials and permissions. The embeddings implementation relies on the `boto3` library.
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 title: Azure OpenAI
+description: "Configure Azure OpenAI as an embedding provider in Mem0 with API key, deployment, and endpoint settings."
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 To use Azure OpenAI embedding models, set the `EMBEDDING_AZURE_OPENAI_API_KEY`, `EMBEDDING_AZURE_DEPLOYMENT`, `EMBEDDING_AZURE_ENDPOINT` and `EMBEDDING_AZURE_API_VERSION` environment variables. You can obtain the Azure OpenAI API key from the Azure.
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 title: Google AI
+description: "Configure Google AI as an embedding provider in Mem0 using Gemini models and the GOOGLE_API_KEY variable."
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 To use Google AI embedding models, set the `GOOGLE_API_KEY` environment variables. You can obtain the Gemini API key from [here](https://aistudio.google.com/app/apikey).
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 title: Hugging Face
+description: "Configure Hugging Face as an embedding provider in Mem0 for local embedding generation with open-source models."
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 You can use embedding models from Huggingface to run Mem0 locally.
@@ -1,5 +1,6 @@
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 title: LangChain
+description: "Use LangChain as an embedding provider in Mem0 to access a wide range of models through a unified interface."
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 Mem0 supports LangChain as a provider to access a wide range of embedding models. LangChain is a framework for developing applications powered by language models, making it easy to integrate various embedding providers through a consistent interface.
@@ -1,3 +1,7 @@
+---
+title: "LM Studio"
+description: "Configure LM Studio as an embedding provider in Mem0 for local embedding generation with models like nomic-embed-text."
+---
 You can use embedding models from LM Studio to run Mem0 locally.

 ### Usage
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+---
+title: "Ollama"
+description: "Configure Ollama as an embedding provider in Mem0 to generate embeddings locally using open-source models."
+---
 You can use embedding models from Ollama to run Mem0 locally.

 ### Usage
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 title: OpenAI
+description: "Configure OpenAI as an embedding provider in Mem0 using models like text-embedding-3-large for vector generation."
 ---

 To use OpenAI embedding models, 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).
@@ -1,5 +1,6 @@
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 title: Together
+description: "Configure Together AI as an embedding provider in Mem0 with support for 768-dimensional embedding models."
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 To use Together embedding models, set the `TOGETHER_API_KEY` environment variable. You can obtain the Together API key from the [Together Platform](https://api.together.xyz/settings/api-keys).
@@ -1,3 +1,7 @@
+---
+title: "Vertex AI"
+description: "Configure Google Cloud Vertex AI as an embedding provider in Mem0 with support for task-specific embedding types."
+---
 ### Vertex AI

 To use Google Cloud's Vertex AI for text embedding models, set the `GOOGLE_APPLICATION_CREDENTIALS` environment variable to point to the path of your service account's credentials JSON file. These credentials can be created in the [Google Cloud Console](https://console.cloud.google.com/).
@@ -1,5 +1,6 @@
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 title: Overview
+description: "Overview of all supported embedding model providers in Mem0, including OpenAI, Azure, Ollama, and more."
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 Mem0 offers support for various embedding models, allowing users to choose the one that best suits their needs.