Step 2 of 7 in Sequence
Data Pipeline & Indexing
Estimated Duration: 2 - 3 Weeks
Step 2 builds the data ingestion, parsing, and vector indexing pipeline. We construct layout-aware OCR parsers, dense-sparse hybrid chunking strategies, and HNSW vector index databases.
Operational Deep-Dive
What Happens During Step 2
Engineers implement automated ingestion connectors, layout-aware PDF table parsers, text embedding generators, and vector database indexing (pgvector, Pinecone, or Qdrant) with metadata filtering.
Why Sequence Matters:
High-quality vector retrieval requires clean chunking and indexing before downstream model fine-tuning or agent execution loops begin.
Requirements & Artifacts
Client Inputs vs. Delivered Artifacts
What We Need From You (Inputs)
- Raw document repositories or SQL database connection strings.
- Domain taxonomy or acronym glossaries.
- Data access credentials for staging environments.
What You Receive (Deliverables)
- Automated Data Ingestion & Chunking Microservices.
- Populated Staging Vector Index Database.
- Embedding Precision & Chunk Retrieval Quality Benchmark Report.
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