https://github.com/redis-developer/financial-vss

Notebooks demonstrating vector search & RAG design patterns with Redis Python clients.
https://github.com/redis-developer/financial-vss

langchain llm python rag redis redisvl vector-search

Last synced: about 2 months ago
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Notebooks demonstrating vector search & RAG design patterns with Redis Python clients.

• Host: GitHub
• URL: https://github.com/redis-developer/financial-vss
• Owner: redis-developer
• License: mit
• Created: 2023-10-23T17:59:17.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2024-03-27T02:24:33.000Z (10 months ago)
• Last Synced: 2024-04-14T14:17:30.044Z (9 months ago)
• Topics: langchain, llm, python, rag, redis, redisvl, vector-search
• Language: Jupyter Notebook
• Homepage:
• Size: 7.34 MB
• Stars: 7
• Watchers: 11
• Forks: 0
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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        Redis Vector Search: Financial Examples

        


    

    






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*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](https://redis-py.readthedocs.io/en/stable/index.html), [redisvl](https://redisvl.com), and [langchain](https://python.langchain.com/docs/integrations/vectorstores/redis).*

# ⚡ Introduction to Vector Search in Redis

[Redis](https://redis.com), 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](https://redis.com/solutions/use-cases/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**](https://colab.research.google.com/github/redis-developer/financial-vss/blob/main/redis-py-01.ipynb) | Grasp VSS basics with the standard Redis Python client. | [View Docs](https://redis-py.readthedocs.io/en/stable/examples/search_vector_similarity_examples.html) |

| 2 | [**redisvl-02**](https://colab.research.google.com/github/redis-developer/financial-vss/blob/main/redisvl-02.ipynb) | Dive deeper into RAG patterns from scratch using an AI-native Redis client library. | [View Docs](https://redisvl.com) |

| 3 | [**langchain-03**](https://colab.research.google.com/github/redis-developer/financial-vss/blob/main/langchain-03.ipynb) | Wrap up with an integrated approach via LangChain. | [View Docs](https://python.langchain.com/docs/integrations/providers/redis) |

## 🛠️ 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!