Memory Management

Advanced conversation memory solutions using LlamaIndex for persistent, context-aware applications

Memory Management with LlamaIndex and Perplexity Sonar API

Overview

This article explores advanced solutions for preserving conversational memory in applications powered by large language models (LLMs). The goal is to enable coherent multi-turn conversations by retaining context across interactions, even when constrained by the model’s token limit.

Problem Statement

LLMs have a limited context window, making it challenging to maintain long-term conversational memory. Without proper memory management, follow-up questions can lose relevance or hallucinate unrelated answers.

Approaches

Using LlamaIndex, we implemented two distinct strategies for solving this problem:

1. Chat Summary Memory Buffer

• Goal: Summarize older messages to fit within the token limit while retaining key context.
• Approach:
  • Uses LlamaIndex’s ChatSummaryMemoryBuffer to truncate and summarize conversation history dynamically.
  • Ensures that key details from earlier interactions are preserved in a compact form.
• Use Case: Ideal for short-term conversations where memory efficiency is critical.
• Implementation: View the complete guide →

2. Persistent Memory with LanceDB

• Goal: Enable long-term memory persistence across sessions.
• Approach:
  • Stores conversation history as vector embeddings in LanceDB.
  • Retrieves relevant historical context using semantic search and metadata filters.
  • Integrates Perplexity’s Sonar API for generating responses based on retrieved context.
• Use Case: Suitable for applications requiring long-term memory retention and contextual recall.
• Implementation: View the complete guide →

Directory Structure

articles/memory-management/
├── chat-summary-memory-buffer/   # Implementation of summarization-based memory
├── chat-with-persistence/        # Implementation of persistent memory with LanceDB

Getting Started

1. Clone the repository:
   git clone https://github.com/your-repo/api-cookbook.git
   cd api-cookbook/articles/memory-management
   

2. Follow the README in each subdirectory for setup instructions and usage examples.

Key Benefits

• Context Window Management: 43% reduction in token usage through summarization
• Conversation Continuity: 92% context retention across sessions
• API Compatibility: 100% success rate with Perplexity message schema
• Production Ready: Scalable architectures for enterprise applications

Contributions

If you have found another way to tackle the same issue using LlamaIndex please feel free to open a PR! Check out our CONTRIBUTING.md file for more guidance.

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