Vector Search at Scale with ChromaDB

Multi-turn conversational AI generates immense volumes of unstructured text. Finding patterns, relevant past discussions, and domain context requires moving beyond keyword search to semantic vector embeddings.

Here is an architectural walkthrough of how we built a vector retrieval pipeline using ChromaDB to index and query historical dialogues at scale.

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The Pipeline

1. Chunking Conversational Turns

Unlike regular documents, dialogue requires specialized chunking. Indexing single sentences loses intent, while whole transcripts dilute semantic precision.

We adopted a sliding window of dialogue turns:

• Window size: 3-5 turn exchanges (User query + Agent response)
• Stride: 2 turns overlap to preserve contextual continuity

interface DialogueChunk {
  sessionId: string;
  turnRange: [number, number];
  speakerSequence: string[];
  content: string;
  metadata: {
    timestamp: number;
    intentCategory?: string;
  };
}

2. Embedding Generation & Indexing

Embeddings are generated asynchronously using an embedding model and persisted into partitioned ChromaDB collections categorized by domain and tenant.

3. Hybrid Filtering & Semantic Search

When retrieving context for an active agent or an analytics dashboard:

1. Metadata Filtering: Filter by date range, platform tenant, or interaction type.
2. Cosine Similarity Search: Query top-K closest vectors.
3. Re-ranking: Re-rank candidates based on recency and user feedback scores.

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Performance Optimizations

• Batch Ingestion: Bulk-inserting vectors in chunks of 500 significantly reduced database lock contention.
• Persistent Collections: Configured persistent storage with localized index cache to ensure fast cold-start queries.
• Cold Storage Archival: Periodically moving stale interactions to cold parquet storage while keeping high-value interaction vectors in memory.

Vector retrieval transforms raw conversational history into an active knowledge asset for both agents and engineers.