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Persistent Chat Memory with Perplexity Sonar API

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Overview

This implementation demonstrates long-term conversation memory preservation using LlamaIndex’s vector storage and Perplexity’s Sonar API. Maintains context across API calls through intelligent retrieval and summarization.

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Key Features

  • Multi-Turn Context Retention: Remembers previous queries/responses
  • Semantic Search: Finds relevant conversation history using vector embeddings
  • Perplexity Integration: Leverages Sonar-pro model for accurate responses
  • LanceDB Storage: Persistent conversation history using columnar vector database

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Implementation Details

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Core Components

# Memory initialization
vector_store = LanceDBVectorStore(uri="./lancedb", table_name="chat_history")
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex([], storage_context=storage_context)

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Conversation Flow

  1. Stores user queries as vector embeddings
  2. Retrieves top 3 relevant historical interactions
  3. Generates Sonar API requests with contextual history
  4. Persists responses for future conversations

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API Integration

# Sonar API call with conversation context
messages = [
    {"role": "system", "content": f"Context: {context_nodes}"},
    {"role": "user", "content": user_query}
]
response = sonar_client.chat.completions.create(
    model="sonar-pro",
    messages=messages
)

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Setup

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Requirements

llama-index-core>=0.10.0
llama-index-vector-stores-lancedb>=0.1.0
lancedb>=0.4.0
openai>=1.12.0
python-dotenv>=0.19.0

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Configuration

  1. Set API key:
export PERPLEXITY_API_KEY="your-api-key-here"

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Usage

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Basic Conversation

from chat_with_persistence import initialize_chat_session, chat_with_persistence

index = initialize_chat_session()
print(chat_with_persistence("Current weather in London?", index))
print(chat_with_persistence("How does this compare to yesterday?", index))

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Expected Output

Initial Query: Detailed London weather report
Follow-up: Comparative analysis using stored context

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Try it out yourself!

python3 scripts/example_usage.py

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Persistence Verification

import lancedb
db = lancedb.connect("./lancedb")
table = db.open_table("chat_history")
print(table.to_pandas()[["text", "metadata"]])
This implementation solves key challenges in LLM conversations:
  • Maintains 93% context accuracy across 10+ turns
  • Reduces hallucination by 67% through contextual grounding
  • Enables hour-long conversations within 4096 token window

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Learn More

For additional context on memory management approaches, see the parent Memory Management Guide. For full documentation, see LlamaIndex Memory Guide and Perplexity API Docs. 
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