Building SEC EDGAR Financial Analytics With CocoIndex and Apache Doris

2026-06-12 · Source: Towards AI - Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Software Development & Engineering · Depth: Advanced, medium

Summary

The SEC EDGAR Financial Analytics example demonstrates a CocoIndex pipeline for ingesting and analyzing public company filings. This open-source project integrates TXT filings, JSON company facts, and PDF exhibits, performing PII scrubbing, topic extraction, and embedding generation. The processed data is then exported to Apache Doris, a real-time MPP data warehouse, for hybrid search combining vector similarity and full-text matching using Reciprocal Rank Fusion (RRF) with k=60. CocoIndex, a Rust-based data transformation framework, handles incremental processing and data lineage, while Apache Doris provides sub-second ingestion latency and sub-100ms query response. The pipeline uses a unified collector pattern, processing text chunks of 1,000 characters with 200-character overlap, and supports direct SQL queries for advanced analytics.

Key takeaway

For AI Engineers building financial analytics platforms, this example provides a robust blueprint for handling complex, multi-format SEC data. You should consider adopting CocoIndex for its auditable data lineage and incremental processing capabilities, paired with Apache Doris for real-time, high-concurrency hybrid search. This stack allows you to quickly integrate diverse data sources and perform advanced queries, ensuring compliance and rapid decision-making for agentic systems.

Key insights

CocoIndex and Apache Doris enable auditable, real-time hybrid search across diverse unstructured financial data.

Principles

• Scrub PII on full documents before chunking.
• Use deterministic parsers for known file formats.
• Combine semantic and lexical search with RRF.

Method

A CocoIndex flow defines sources (TXT, JSON, PDF), a unified collector for PII scrubbing, chunking (1,000 chars, 200 overlap), embedding, and topic extraction, then exports to Apache Doris with HNSW and inverted indexes.

In practice

• Implement array filtering on topic tags in Doris using "json_contains".
• Query indexed data directly via MySQL protocol for temporal analysis.

Topics

• SEC EDGAR
• Financial Analytics
• CocoIndex
• Apache Doris
• Hybrid Search
• Data Pipelines
• PII Scrubbing

Code references

• cocoindex-io/cocoindex

Best for: Machine Learning Engineer, AI Engineer, Data Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Towards AI - Medium.