Knowledge Retrieval
Engineering retrieval architectures combining dense vector search (Qdrant), property knowledge graphs (Neo4j GraphRAG), and persistent agent memory (Mem0).
Technical Context
Standard retrieval-augmented generation (RAG) typically relies on basic vector similarity search across chunked text. While effective for semantic similarity, pure vector search struggles with multi-hop reasoning, explicit hierarchical relationships, and exact attribute filtering.
Hybrid retrieval combines dense vector embeddings with structured property knowledge graphs (GraphRAG). This allows systems to combine semantic topic matching with deterministic graph traversals across entities, dependencies, and domain rules.
Vector Search
A dedicated vector database (Qdrant) indexes document embeddings using Hierarchical Navigable Small World (HNSW) graphs. Payload filtering allows queries to constrain vector searches by metadata attributes such as timestamps, access control tags, or document categories before ranking.
Graph Traversal
Structured relationships are stored in a property graph (Neo4j). When a query involves interconnected entities—such as software components, organizational units, or regulatory requirements—graph queries retrieve relational context that vector proximity alone cannot capture.
Module Scope
- Hybrid retrieval combining dense vector similarity with structured property graph queries
- Vector database configuration, index tuning, and payload filtering with Qdrant
- Domain entity and relationship modeling using Neo4j for multi-hop graph traversal
- Persistent session memory and state management for multi-turn agent interactions
Required Inputs
- Internal document repositories (technical manuals, Markdown files, PDFs, source code)
- Structured databases, relational tables, and domain catalogs
- Domain terminology glossaries and relationship specifications
- User interaction requirements and session persistence specifications
Technical Deliverables
- Retrieval architecture design and data flow specification
- Qdrant vector collection configurations with tuned HNSW index parameters
- Neo4j property graph schema and automated ingestion scripts
- Hybrid retrieval query fusion module and scoring implementation
- Retrieval evaluation benchmark suite and ground-truth test datasets
Delivery Methodology
The retrieval engineering process implements grounded context pipelines:
- Data Ingestion: Analyzing source documents, schemas, and extracting structured entities and textual chunks.
- Pipeline Engineering: Configuring embedding pipelines, vector collections in Qdrant, and graph nodes in Neo4j.
- Retrieval Fusion: Implementing hybrid query fusion logic combining vector similarity scoring with graph traversal.
- Evaluation Benchmarking: Measuring retrieval precision, recall, and context relevance against test query suites.