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fix(docs): add redirect rules for legacy and moved documentation pages (#4413)

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This commit is contained in:
Saket Aryan
2026-03-19 13:42:18 +05:30
committed by GitHub
parent b971b61cbb
commit 0c4d0290cb
7 changed files with 205 additions and 13 deletions
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docs/llms.txt
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@@ -104,7 +104,7 @@ Key differentiators:
- [Together](https://docs.mem0.ai/components/llms/models/together): Open-source model inference platform
- [DeepSeek](https://docs.mem0.ai/components/llms/models/deepseek): Advanced reasoning models
- [Sarvam](https://docs.mem0.ai/components/llms/models/sarvam): Indian language models
- [XAI](https://docs.mem0.ai/components/llms/models/xai): xAI models integration
- [XAI](https://docs.mem0.ai/components/llms/models/xAI): xAI models integration
- [LiteLLM](https://docs.mem0.ai/components/llms/models/litellm): Unified LLM interface and proxy
- [LangChain](https://docs.mem0.ai/components/llms/models/langchain): LangChain LLM integration
- [OpenAI Structured](https://docs.mem0.ai/components/llms/models/openai_structured): OpenAI with structured output support
@@ -120,7 +120,7 @@ Key differentiators:
- [Milvus](https://docs.mem0.ai/components/vectordbs/dbs/milvus): Open-source vector database for AI applications at scale
- [Redis](https://docs.mem0.ai/components/vectordbs/dbs/redis): Real-time vector storage and search with Redis Stack
- [Supabase](https://docs.mem0.ai/components/vectordbs/dbs/supabase): Open-source Firebase alternative with vector support
- [Upstash Vector](https://docs.mem0.ai/components/vectordbs/dbs/upstash_vector): Serverless vector database
- [Upstash Vector](https://docs.mem0.ai/components/vectordbs/dbs/upstash-vector): Serverless vector database
- [Elasticsearch](https://docs.mem0.ai/components/vectordbs/dbs/elasticsearch): Distributed search and analytics engine
- [OpenSearch](https://docs.mem0.ai/components/vectordbs/dbs/opensearch): Open-source search and analytics platform
- [FAISS](https://docs.mem0.ai/components/vectordbs/dbs/faiss): Facebook AI Similarity Search library
@@ -136,9 +136,9 @@ Key differentiators:
- [OpenAI Embeddings](https://docs.mem0.ai/components/embedders/models/openai): High-quality text embeddings with customizable dimensions
- [Azure OpenAI Embeddings](https://docs.mem0.ai/components/embedders/models/azure_openai): Enterprise Azure-hosted embedding models
- [Google AI](https://docs.mem0.ai/components/embedders/models/google_ai): Gemini embedding models
- [Google AI](https://docs.mem0.ai/components/embedders/models/google_AI): Gemini embedding models
- [AWS Bedrock](https://docs.mem0.ai/components/embedders/models/aws_bedrock): Amazon embedding models through Bedrock
- [Hugging Face](https://docs.mem0.ai/components/embedders/models/hugging_face): Open-source embedding models for local deployment
- [Hugging Face](https://docs.mem0.ai/components/embedders/models/huggingface): Open-source embedding models for local deployment
- [Vertex AI](https://docs.mem0.ai/components/embedders/models/vertexai): Google Cloud's enterprise embedding models
- [Ollama](https://docs.mem0.ai/components/embedders/models/ollama): Local embedding models for privacy-focused applications
- [Together](https://docs.mem0.ai/components/embedders/models/together): Open-source model embeddings