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Memory Management with LlamaIndex and Perplexity Sonar API

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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.

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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.

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Approaches

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

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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 →

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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 →

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Directory Structure

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

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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.

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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

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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.