Azure Cosmos DB

Azure Cosmos DB vector store for PraisonAI

Azure Cosmos DB

Globally distributed, multi-model database with vector search capabilities.

Setup

# For MongoDB API
pip install pymongo

# For SQL API
pip install azure-cosmos

Quick Start (Agent with Knowledge)

Use Azure Cosmos DB as a knowledge store with an agent:

from praisonaiagents import Agent
import os

agent = Agent(
    name="Assistant",
    instructions="You are a helpful assistant with access to documents.",
    knowledge=["./docs/guide.pdf"]
)

agent.chat("What does the guide say?")

Advanced Usage (Direct Store)

MongoDB API (Recommended for Vector Search)

from praisonai.persistence.factory import create_knowledge_store

store = create_knowledge_store(
    "cosmosdb",
    connection_string="mongodb+srv://...",
    database="praisonai",
    collection="vectors",
    api_mode="mongodb"
)

SQL API

store = create_knowledge_store(
    "cosmosdb",
    connection_string="AccountEndpoint=...",
    database="praisonai",
    collection="vectors",
    api_mode="sql"
)

Environment Variables

export COSMOS_CONNECTION_STRING="mongodb+srv://..."

Configuration

Option	Description
connection_string	Azure Cosmos DB connection string
database	Database name
collection	Collection/container name
api_mode	mongodb or sql
index_name	Vector index name
embedding_dim	Embedding dimension (default: 1536)

Vector Search Setup

For MongoDB API, create a vector search index:

db.runCommand({
  createIndexes: "vectors",
  indexes: [{
    name: "vector_index",
    key: { embedding: "cosmosSearch" },
    cosmosSearchOptions: {
      kind: "vector-ivf",
      numLists: 100,
      similarity: "cosine",
      dimensions: 1536
    }
  }]
})

Best For

• Global distribution requirements
• Multi-region deployments
• Azure ecosystem integration