MAC Vectors Connector Overview
MAC Vectors provides access to a broad number of external Vector Stores and Databases. It is built to be leveraged by the MAC Projects AI Connector (Amazon Bedrock, MuleSoft AI Chain & Einstein AI)
What is MAC Vectors Connector?
MAC Vectors is a custom connector for MuleSoft, to provide MuleSoft users access to Vector Databases.
Supported Vector Stores
The MAC Vectors connector supports the following embedding stores.
- Azure AI Search (opens in a new tab) (Microsoft): Cloud-based AI-powered search with semantic search capabilities.
- Chroma (opens in a new tab) (Open Source): Open-source vector database for AI and embeddings management.
- Elasticsearch (opens in a new tab) (Elastic): Scalable search engine for structured/unstructured data and analytics.
- Milvus (opens in a new tab) (Zilliz): Vector database optimized for similarity search and AI workloads.
- Amazon OpenSearch (opens in a new tab) (Amazon Web Services): Managed search service for full-text, structured data queries.
- PGVector (opens in a new tab) (Open Source): PostgreSQL extension for storing and querying vector embeddings.
- Pinecone (opens in a new tab) (Pinecone): Scalable vector database with high-speed similarity search capabilities.
- Qdrant (opens in a new tab) (Qdrant): Vector database with advanced filtering for semantic search applications.
Supported Model Providers
The MAC Vectors connector supports the following model providers to generate embeddings.
- Azure OpenAI (opens in a new tab) (Microsoft): Managed access to OpenAI models through Azure's cloud platform.
- Einstein (opens in a new tab) (Salesforce): AI platform integrated into Salesforce for CRM automation and insights.
- OpenAI (opens in a new tab) (OpenAI): Developer of advanced AI models like GPT and DALL·E for diverse applications.
- Mistral AI (opens in a new tab) (Mistral): Open-weight LLMs optimized for efficiency and customization in AI projects.
- Nomic (opens in a new tab) (Nomic): Tools for understanding and visualizing large datasets with embeddings and AI.
- Hugging Face (opens in a new tab) (Hugging Face): Community-driven hub for machine learning models, datasets, and tools.
Supported Storage Options
- Local: Allows to load data from application local storage
- Azure Blob Storage (opens in a new tab): Allows to load data from Azure Blob Storage
- Amazon S3 (opens in a new tab): Allows to load data from Amazon S3 Buckets
- Google Cloud Storage (opens in a new tab): Allows to load data from Google Cloud Storage
Operations
The MAC Vectors connector offers a range of features to implement advanced RAG use cases:
- Load, parse and split single Document
- Load, parse and split list of Documents
- Generate Embeddings from text
- Generate Embeddings from document
- Add embeddings to Store
- List sources from Store
- Query Store
- Remove embeddings from Store
Table of Supported Operations by Store
Not all the operations are supported by each embedding store, following a detailed view.
| Name | Storing Metadata | Filtering by Metadata | Removing Embeddings | List All Embeddings |
|---|---|---|---|---|
| Azure AI Search | ✅ | ✅ | ✅ | ✅ |
| Chroma | ✅ | ✅ | ✅ | ✅ |
| Elasticsearch | ✅ | ✅ | ✅ | ✅ |
| Milvus | ✅ | ✅ | ✅ | ✅ |
| Amazon OpenSearch | ✅ | ✅ | ✅ | |
| PGVector | ✅ | ✅ | ✅ | ✅ |
| Pinecone | ✅ | ✅ | ||
| Qdrant | ✅ | ✅ | ✅ | ✅ |
Additional Integrations
MAC Vectors Connector integrates seamlessly with other MAC Projects AI Connectors and the MuleSoft ecosystem, offering enhanced functionalities.