cognee-community-vector-adapter-redis 0.0.1

Redis vector database adapter for cognee

🧠 Cognee Redis Vector Adapter

Blazing fast vector similarity search for Cognee using Redis

Cognee    RedisVL Docs    Examples    Support

Features

• Full support for vector embeddings storage and retrieval
• Batch / pipeline operations for efficient processing
• Automatic embedding generation via configurable embedding engines
• JSON payload serialization with UUID support
• Comprehensive error handling

Installation

pip install cognee-community-vector-adapter-redis

Prerequisites

You need a Redis instance with the Redis Search module enabled. You can use:

1. Redis:

   docker run -d --name redis -p 6379:6379 redis:8.0.2
   

2. Redis Cloud with the search module enabled: Redis Cloud

Examples

Checkout the examples/ folder!

uv run examples/example.py

You will need an OpenAI API key to run the example script.

Usage

from cognee.infrastructure.databases.vector.embeddings.EmbeddingEngine import EmbeddingEngine
from cognee_community_vector_adapter_redis import RedisAdapter

# Initialize your embedding engine
embedding_engine = EmbeddingEngine(
    model="your-model",
    # ... other config
)

# Create Redis adapter
redis_adapter = RedisAdapter(
    url="redis://localhost:6379",  # Redis connection URL
    embedding_engine=embedding_engine,
    api_key=None  # Optional, not used for Redis but part of interface
)

# Create a collection
await redis_adapter.create_collection("my_collection")

# Add data points
from cognee.infrastructure.engine import DataPoint

data_points = [
    DataPoint(id="1", text="Hello world", metadata={"index_fields": ["text"]}),
    DataPoint(id="2", text="Redis vector search", metadata={"index_fields": ["text"]})
]

await redis_adapter.create_data_points("my_collection", data_points)

# Search for similar vectors
results = await redis_adapter.search(
    collection_name="my_collection",
    query_text="Hello Redis",
    limit=10
)

# Search with pre-computed vector
query_vector = await redis_adapter.embed_data(["Hello Redis"])
results = await redis_adapter.search(
    collection_name="my_collection",
    query_vector=query_vector[0],
    limit=10,
    with_vector=True  # Include vectors in results
)

# Batch search
results = await redis_adapter.batch_search(
    collection_name="my_collection", 
    query_texts=["query1", "query2"],
    limit=5
)

# Retrieve specific data points
retrieved = await redis_adapter.retrieve(
    collection_name="my_collection",
    data_point_ids=["1", "2"]
)

# Delete data points
await redis_adapter.delete_data_points(
    collection_name="my_collection",
    data_point_ids=["1"]
)

# Check if collection exists
exists = await redis_adapter.has_collection("my_collection")

Configuration

The Redis adapter supports the following configuration options:

• url: Redis connection URL (e.g., "redis://localhost:6379", "redis://user:pass@host:port")
• embedding_engine: The EmbeddingEngine to use for text vectorization (required)
• api_key: Optional API key parameter (not used for Redis but part of the interface)

Connection URL Examples

# Local Redis
redis_adapter = RedisAdapter(url="redis://localhost:6379", embedding_engine=engine)

# Redis with authentication
redis_adapter = RedisAdapter(url="redis://user:password@localhost:6379", embedding_engine=engine)

# Redis with SSL
redis_adapter = RedisAdapter(url="rediss://localhost:6380", embedding_engine=engine)

Error Handling

The adapter includes comprehensive error handling:

• VectorEngineInitializationError: Raised when required parameters are missing
• CollectionNotFoundError: Raised when attempting operations on non-existent collections
• InvalidValueError: Raised for invalid query parameters
• Graceful handling of connection failures and embedding errors

Troubleshooting

Common Issues

1. Connection Errors: Ensure Redis is running and accessible at the specified URL
2. Search Module Missing: Make sure Redis has the Search module enabled
3. Embedding Dimension Mismatch: Verify embedding engine dimensions match index configuration
4. Collection Not Found: Always create collections before adding data points

Debug Logging

The adapter uses Cognee's logging system. Enable debug logging to see detailed operation logs:

import logging
logging.getLogger("RedisAdapter").setLevel(logging.DEBUG)

Development

To contribute or modify the adapter:

1. Clone the repository and cd into the redis folder
2. Install dependencies: uv sync --all-extras
3. Make sure a Redis instance is running (see above)
4. Make your changes, test, and submit a PR