Updated databricks docs (#3336)

2025-08-18 23:54:32 +05:30
parent 3a1eff425b
commit 49ad64708b
6 changed files with 107 additions and 18 deletions
@@ -86,7 +86,6 @@ const memory = new Memory({
 const result = await memory.add('My name is John', { userId: 'john' });
 ```

-
 ## Core API Reference

 ### Memory Class (Self-Hosted)
@@ -323,11 +322,13 @@ config = MemoryConfig(
 - **supabase** - Supabase vector
 - **baidu** - Baidu vector database
 - **langchain** - LangChain vector stores
+- **databricks** - Databricks vector stores

 #### Graph Store Providers (3 supported)
 - **neo4j** - Neo4j graph database
 - **memgraph** - Memgraph
 - **neptune** - AWS Neptune Analytics
+- **kuzu** - Kuzu Graph database

 ### Configuration Examples

@@ -415,6 +416,54 @@ config = MemoryConfig(
 )
 ```

+#### LLM Providers
+- **OpenAI** - GPT-4, GPT-3.5-turbo, and structured outputs
+- **Anthropic** - Claude models with advanced reasoning
+- **Google AI** - Gemini models for multimodal applications
+- **AWS Bedrock** - Enterprise-grade AWS managed models
+- **Azure OpenAI** - Microsoft Azure hosted OpenAI models
+- **Groq** - High-performance LPU optimized models
+- **Together** - Open-source model inference platform
+- **Ollama** - Local model deployment for privacy
+- **vLLM** - High-performance inference framework
+- **LM Studio** - Local model management
+- **DeepSeek** - Advanced reasoning models
+- **Sarvam** - Indian language models
+- **XAI** - xAI models
+- **LiteLLM** - Unified LLM interface
+- **LangChain** - LangChain LLM integration
+
+#### Vector Store Providers
+- **Chroma** - AI-native open-source vector database
+- **Qdrant** - High-performance vector similarity search
+- **Pinecone** - Managed vector database with serverless options
+- **Weaviate** - Open-source vector search engine
+- **PGVector** - PostgreSQL extension for vector search
+- **Milvus** - Open-source vector database for scale
+- **Redis** - Real-time vector storage with Redis Stack
+- **Supabase** - Open-source Firebase alternative
+- **Upstash Vector** - Serverless vector database
+- **Elasticsearch** - Distributed search and analytics
+- **OpenSearch** - Open-source search and analytics
+- **FAISS** - Facebook AI Similarity Search
+- **MongoDB** - Document database with vector search
+- **Azure AI Search** - Microsoft's search service
+- **Vertex AI Vector Search** - Google Cloud vector search
+- **Databricks Vector Search** - Delta Lake integration
+- **Baidu** - Baidu vector database
+- **LangChain** - LangChain vector store integration
+
+#### Embedding Providers
+- **OpenAI** - High-quality text embeddings
+- **Azure OpenAI** - Enterprise Azure-hosted embeddings
+- **Google AI** - Gemini embedding models
+- **AWS Bedrock** - Amazon embedding models
+- **Hugging Face** - Open-source embedding models
+- **Vertex AI** - Google Cloud enterprise embeddings
+- **Ollama** - Local embedding models
+- **Together** - Open-source model embeddings
+- **LM Studio** - Local model embeddings
+- **LangChain** - LangChain embedder integration

 ## TypeScript/JavaScript SDK

@@ -569,6 +618,7 @@ print(result["relations"])   # Graph relationships
 - **Neo4j**: Full-featured graph database with Cypher queries
 - **Memgraph**: High-performance in-memory graph database
 - **Neptune**: AWS managed graph database service
+- **kuzu** - OSS Kuzu Graph database

 ### Multimodal Memory

@@ -7,21 +7,33 @@ mode: "wide"
 <Tabs>
 <Tab title="Python">

+<Update label="2025-07-24" description="v0.1.116">

 **New Features:**
- **Databricks Vector Search:** Added comprehensive support for Databricks Vector Search as a vector store provider
-  - Delta Sync Index integration with automatic synchronization from Delta tables
-  - Dual authentication support (Service Principal and Personal Access Token)
-  - Both STANDARD and STORAGE_OPTIMIZED endpoint types supported
-  - Self-managed and Databricks-computed embedding options
-  - Auto-creation of endpoints and indexes when not present
-  - Unity Catalog integration for secure data governance
+- **Pinecone:** Added namespace support and improved type safety
+- **Milvus:** Added db_name field to MilvusDBConfig
+- **Vector Stores:** Added multi-id filters support
+- **Vercel AI SDK:** Migration to AI SDK V5.0
+- **Python Support:** Added Python 3.12 support
+- **Graph Memory:** Added sanitizer methods for nodes and relationships
+- **LLM Monitoring:** Added monitoring callback support

 **Improvements:**
- **Documentation:**
-  - Added detailed Databricks Vector Search configuration guide with authentication methods
-  - Updated vector databases overview to include Databricks option
-  - Enhanced vector store documentation with embedding configuration examples
+- **Performance:** Improved async handling in AsyncMemory class
+- **Documentation:** Added async add announcement, personalized search docs, Neptune examples, V5 migration docs
+- **Configuration:** Refactored base class config for LLMs, added sslmode for pgvector
+- **Dependencies:** Updated psycopg to version 3, updated Docker compose
+
+**Bug Fixes:**
+- **Tests:** Fixed failing tests and restricted package versions
+- **Memgraph:** Fixed async attribute errors, n_embeddings usage, and indexing issues
+- **Vector Stores:** Fixed Qdrant cloud indexing, Neo4j Cypher syntax, and LLM parameters
+- **Graph Store:** Fixed LM config prioritization
+- **Dependencies:** Fixed JSON import for psycopg
+
+**Refactoring:**
+- **Google AI:** Refactored from Gemini to Google AI
+- **Base Classes:** Refactored LLM base class configuration

 </Update>

@@ -23,7 +23,6 @@ See the list of supported vector databases below.
  <Card title="Pinecone" href="/components/vectordbs/dbs/pinecone"></Card>
  <Card title="MongoDB" href="/components/vectordbs/dbs/mongodb"></Card>
  <Card title="Azure" href="/components/vectordbs/dbs/azure"></Card>
-  <Card title="Databricks" href="/components/vectordbs/dbs/databricks"></Card>
  <Card title="Redis" href="/components/vectordbs/dbs/redis"></Card>
  <Card title="Elasticsearch" href="/components/vectordbs/dbs/elasticsearch"></Card>
  <Card title="OpenSearch" href="/components/vectordbs/dbs/opensearch"></Card>
@@ -32,6 +31,7 @@ See the list of supported vector databases below.
  <Card title="Weaviate" href="/components/vectordbs/dbs/weaviate"></Card>
  <Card title="FAISS" href="/components/vectordbs/dbs/faiss"></Card>
  <Card title="LangChain" href="/components/vectordbs/dbs/langchain"></Card>
+  <Card title="Databricks" href="/components/vectordbs/dbs/databricks"></Card>
 </CardGroup>

 ## Usage
@@ -160,7 +160,8 @@
                          "components/vectordbs/dbs/weaviate",
                          "components/vectordbs/dbs/faiss",
                          "components/vectordbs/dbs/langchain",
-                          "components/vectordbs/dbs/baidu"
+                          "components/vectordbs/dbs/baidu",
+                          "components/vectordbs/dbs/databricks"
                        ]
                      }
                    ]
@@ -1,5 +1,5 @@
 ---
-title: Amazon Stack: AWS Bedrock, AOSS, and Neptune Analytics
+title: "Amazon Stack: AWS Bedrock, AOSS, and Neptune Analytics"
 ---

 This example demonstrates how to configure and use the `mem0ai` SDK with **AWS Bedrock**, **OpenSearch Service (AOSS)**, and **AWS Neptune Analytics** for persistent memory capabilities in Python.
@@ -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