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Updated databricks docs (#3336)

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Parshva Daftari
2025-08-18 13:24:32 -05:00
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docs/llms.txt
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@@ -62,6 +62,16 @@ Key differentiators:
- [AWS Bedrock](https://docs.mem0.ai/components/llms/models/aws_bedrock): Enterprise-grade AWS managed model integration
- [Azure OpenAI](https://docs.mem0.ai/components/llms/models/azure_openai): Microsoft Azure hosted OpenAI models for enterprise environments
- [Ollama](https://docs.mem0.ai/components/llms/models/ollama): Local model deployment for privacy-focused applications
- [vLLM](https://docs.mem0.ai/components/llms/models/vllm): High-performance inference framework
- [LM Studio](https://docs.mem0.ai/components/llms/models/lmstudio): Local model management and deployment
- [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
- [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
- [Azure OpenAI Structured](https://docs.mem0.ai/components/llms/models/azure_openai_structured): Azure OpenAI with structured outputs
### Supported Vector Databases
@@ -72,14 +82,30 @@ Key differentiators:
- [PGVector](https://docs.mem0.ai/components/vectordbs/dbs/pgvector): PostgreSQL extension for vector similarity search
- [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
- [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
- [MongoDB](https://docs.mem0.ai/components/vectordbs/dbs/mongodb): Document database with vector search capabilities
- [Azure AI Search](https://docs.mem0.ai/components/vectordbs/dbs/azure_ai_search): Microsoft's enterprise search service
- [Vertex AI Vector Search](https://docs.mem0.ai/components/vectordbs/dbs/vertex_ai_vector_search): Google Cloud's vector search service
- [Databricks](https://docs.mem0.ai/components/vectordbs/dbs/databricks): Delta Lake integration for vector search
- [Baidu](https://docs.mem0.ai/components/vectordbs/dbs/baidu): Baidu vector database integration
- [LangChain](https://docs.mem0.ai/components/vectordbs/dbs/langchain): LangChain vector store integration
### Supported Embeddings
- [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
- [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
- [Vertex AI](https://docs.mem0.ai/components/embedders/models/google_ai): Google Cloud's enterprise embedding models
- [Ollama Embeddings](https://docs.mem0.ai/components/embedders/models/ollama): Local embedding models for privacy-focused applications
- [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
- [LM Studio](https://docs.mem0.ai/components/embedders/models/lmstudio): Local model embeddings
- [LangChain](https://docs.mem0.ai/components/embedders/models/langchain): LangChain embedder integration
## Integrations
@@ -114,4 +140,4 @@ Key differentiators:
- [FAQs](https://docs.mem0.ai/faqs): Frequently asked questions about Mem0's capabilities and implementation details
- [Changelog](https://docs.mem0.ai/changelog): Detailed product updates and version history for tracking new features and improvements
- [Contributing Guide](https://docs.mem0.ai/contributing/development): Guidelines for contributing to Mem0's open-source development
- [OpenMemory](https://docs.mem0.ai/openmemory/overview): Open-source memory infrastructure for research and experimentation
- [OpenMemory](https://docs.mem0.ai/openmemory/overview): Open-source memory infrastructure for research and experimentation