redis-developer / financial-vss

Notebooks demonstrating vector search & RAG design patterns with Redis Python clients.

Redis Vector Search: Financial Examples

A detailed set of Google Colab notebooks to teach semantic search and RAG patterns over public financial 10k documents with different Redis clients and integrations including: redis-py, redisvl, and langchain.

⚡ Introduction to Vector Search in Redis

Redis, widely recognized for its low-latency performance, extends beyond traditional noSQL databases. It's uniquely suited for tasks like caching, session management, job queuing, and JSON storage. With enhanced Search+Query features, Redis emerges as a performant Vector Database supporting Vector Search over unstructured data encoded as embeddings.

📚 Getting Started in Google Colab

Click on one of the three notebook options below to start your journey. It launches a Colab notebook that prepares your environment by cloning the necessary repository artifacts, managing Python dependencies, and ends with an end-to-end walkthrough of vector search in Redis.

Select your desired notebook tutorial from below:

#	Notebook	Description	Documentation
1	redis-py-01	Grasp VSS basics with the standard Redis Python client.	View Docs
2	redisvl-02	Dive deeper into RAG patterns from scratch using an AI-native Redis client library.	View Docs
3	langchain-03	Wrap up with an integrated approach via LangChain.	View Docs

🛠️ Understanding the Client Ecosystem

Wondering why there are multiple clients? Each serves a unique purpose, providing varying abstraction levels. Your choice depends on several factors:

• Use Cases: Are you focusing on pure vector search, RAG, or other tasks like LLM semantic caching?
• Redis Experience: How comfortable is your team with Redis clients and commands?
• Integration Points: What are your touchpoints with LLMs and Embedding Providers?
• Performance Demands: How intensive are your performance requirements?
• Configurability: Do you prefer ease of use or fine-grained control?

Each notebook explores these considerations, guiding you through making an informed choice for your use case.

⚠️ Cautionary Advice

• Not for Local Use: This Colab-centric project isn't configured for local environments. Running it outside Colab requires a different setup.
• Temporary Workspace: Colab doesn't save your work indefinitely. Download your notebooks or save them to Google Drive to avoid losing progress.
• Idle Disconnections: Extended inactivity in Colab can disconnect the runtime, potentially resulting in work loss. Regular saving is your friend!