> ## Documentation Index
> Fetch the complete documentation index at: https://docs.perplexity.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# 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 →](/docs/cookbook/articles/memory-management/chat-summary-memory-buffer/README)

### 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 →](/docs/cookbook/articles/memory-management/chat-with-persistence/README)

## 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:
   ```bash theme={null}
   git clone https://github.com/ppl-ai/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](https://github.com/ppl-ai/api-cookbook/blob/main/CONTRIBUTING.md) file for more guidance.

***