Caching Layer for LLM with Langchain

Key Takeaway

The most important takeaway from the text is that incorporating a caching layer in LLM-based applications, particularly using Langchain with various Redis configurations in AWS, significantly reduces API calls and enhances response times, thereby saving costs and increasing efficiency.


  • Context & Introduction: The article discusses the implementation of a caching layer in LLM-based applications, highlighting the cost-saving and performance benefits.
  • Redis in AWS for Caching: The focus is on using Redis offerings in AWS, including Amazon MemoryDB for Redis, for caching purposes in LLM applications.
  • Caching Integrations and Methods: Langchain provides several caching methods, including Standard Cache for identical sentences and Semantic Cache for semantically similar sentences. Optional caching is also available.
  • RedisCache Implementations:
    • Redis on EC2: Details on installing Redis directly on EC2 using Docker, including steps for using Redis's Vector Search feature.
    • Redis Stack Installation: Instructions for setting up Redis Stack with Docker and connecting it using redis-cli.
    • Langchain, Redis, and Boto3 Installation: Steps for installing necessary packages for using Amazon Bedrock.
  • Standard Cache Utilization:
    • Code examples and library imports for implementing Standard Cache.
    • Significant performance improvement observed in Jupyter Notebook's Wall time measurements.
  • Semantic Cache with RediSearch:
    • Utilization of the Amazon Titan Embedding model for semantic caching.
    • Notable reduction in response time for semantically similar queries.
  • Amazon ElastiCache for Redis:
    • Differences in using ElastiCache Serverless.
    • TLS configuration for secure connections.
    • Limitations of ElastiCache in Semantic Cache due to lack of Vector Search support.
  • Amazon MemoryDB for Redis:
    • MemoryDB's compatibility and limitations with Standard and Semantic Caching.
    • MemoryDB's default use of TLS.
  • Vector Search in Amazon MemoryDB:
    • Introduction of Vector search in MemoryDB.
    • Performance improvements in Standard Cache with Vector search.
    • Limitations in Semantic Cache due to errors in Vector Search support.
  • Redis as a Vector Database:
    • Example code for using Redis as a VectorStore in Langchain.
    • MemoryDB's role as buffer memory for language models in semantic search.
  • Test Results and Comparison:
    • Tabulated results showing the effectiveness of different caching methods across various Redis configurations in AWS.
    • Insight into the support features of Redis in AWS, including TLS support.
  • Conclusion:
    • Emphasis on the learning experience about various services supporting Redis in AWS.
    • Invitation for feedback and error identification.

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