NautiChat-SENG499-Capstone/NautiChat-Backend
Jun 2025 – Jul 2025
2 Months active
Languages Used
PythonSQLTypeScript
Technical Skills
API DevelopmentAPI IntegrationBackend DevelopmentData EngineeringData ProcessingDatabase Integration
Paisley Lewis
PROFILE
Paisley Lewis
Paisley developed backend features for the NautiChat-SENG499-Capstone/NautiChat-Backend repository, focusing on robust data ingestion, retrieval, and admin workflows. Over two months, Paisley engineered PDF preprocessing pipelines and ONC data scrapers to extract, structure, and embed diverse data into vector databases using Python, FastAPI, and Qdrant. The work included building and refining API endpoints for raw text and PDF uploads, implementing admin-controlled data cleanup, and enhancing retrieval-augmented generation (RAG) with session context and relevance filtering. Through careful code refactoring, dependency management, and comprehensive testing, Paisley delivered scalable, maintainable solutions that improved data quality, retrieval accuracy, and operational reliability.
Overall Statistics
Feature vs Bugs
83%Features
Repository Contributions
24Total
Bugs
2
Commits
24
Features
10
Lines of code
1,300
Activity Months2
Your Network
11 people
Work History
July 2025
14 Commits • 4 Features
Jul 1, 2025July 2025 saw a set of focused backend improvements for NautiChat-Backend, centered on vector DB ingestion, data hygiene, ONC deployment data integration, and robust RAG capabilities. The work delivered actionable business value by improving data ingestion reliability, enabling admin-controlled data cleanup, expanding embedding prep with deployment data, and strengthening context management in conversational AI workflows. Overall, this month established scalable foundations for data quality, retrieval relevance, and safer, more traceable AI interactions.
14 Commits • 4 Features
Jul 1, 2025July 2025 saw a set of focused backend improvements for NautiChat-Backend, centered on vector DB ingestion, data hygiene, ONC deployment data integration, and robust RAG capabilities. The work delivered actionable business value by improving data ingestion reliability, enabling admin-controlled data cleanup, expanding embedding prep with deployment data, and strengthening context management in conversational AI workflows. Overall, this month established scalable foundations for data quality, retrieval relevance, and safer, more traceable AI interactions.
July 2025
June 2025
10 Commits • 6 Features
Jun 1, 2025June 2025 - NautiChat-Backend (NautiChat-SENG499-Capstone) performance highlights focused on delivering richer embeddings, broader data ingestion, and admin data workflows, while improving stability and retrieval relevance. Key features delivered: - PDF preprocessing pipeline for vector database uploads: adds a PDF preprocessing module to extract structured text, group content by headings, and chunk text for embedding; refactor to unstructured library for enhanced extraction and capabilities. - ONC URI ingestion and data sourcing for embeddings: scrapes ONC URIs, fetches by location codes, extracts structured data, and prepares data for vector DB ingestion. - RAG relevance and efficiency improvements: introduces a score-threshold based filtering and expands the rerank window to 15 with ~2000 token cap to improve relevance and processing efficiency. - Enriched vector DB ingestion with full device data: stores full device definitions and details in embeddings rather than descriptions alone. - Admin API endpoint for raw text uploads to vector DB: backend API, service logic, Pydantic model, and unit tests to support admin-driven text uploads into the vector database. Major bugs fixed: - VectorDBUpload.py stability fixes: corrected .env loading path and standardized import paths to ensure environment variables and modules load reliably across environments. Overall impact and accomplishments: - Improved data quality and embedding richness enabling more accurate retrieval. - Broader data sources from ONC and PDFs, enhancing coverage for downstream analytics and search. - Streamlined admin workflows and safer, repeatable deployments through dependency management and API tooling. Technologies/skills demonstrated: - Python, vector databases, and embedding pipelines; unstructured library integration; data scraping and ingestion; API design (backend endpoints, Pydantic models); unit testing; environment/config management.
June 2025
10 Commits • 6 Features
Jun 1, 2025June 2025 - NautiChat-Backend (NautiChat-SENG499-Capstone) performance highlights focused on delivering richer embeddings, broader data ingestion, and admin data workflows, while improving stability and retrieval relevance. Key features delivered: - PDF preprocessing pipeline for vector database uploads: adds a PDF preprocessing module to extract structured text, group content by headings, and chunk text for embedding; refactor to unstructured library for enhanced extraction and capabilities. - ONC URI ingestion and data sourcing for embeddings: scrapes ONC URIs, fetches by location codes, extracts structured data, and prepares data for vector DB ingestion. - RAG relevance and efficiency improvements: introduces a score-threshold based filtering and expands the rerank window to 15 with ~2000 token cap to improve relevance and processing efficiency. - Enriched vector DB ingestion with full device data: stores full device definitions and details in embeddings rather than descriptions alone. - Admin API endpoint for raw text uploads to vector DB: backend API, service logic, Pydantic model, and unit tests to support admin-driven text uploads into the vector database. Major bugs fixed: - VectorDBUpload.py stability fixes: corrected .env loading path and standardized import paths to ensure environment variables and modules load reliably across environments. Overall impact and accomplishments: - Improved data quality and embedding richness enabling more accurate retrieval. - Broader data sources from ONC and PDFs, enhancing coverage for downstream analytics and search. - Streamlined admin workflows and safer, repeatable deployments through dependency management and API tooling. Technologies/skills demonstrated: - Python, vector databases, and embedding pipelines; unstructured library integration; data scraping and ingestion; API design (backend endpoints, Pydantic models); unit testing; environment/config management.
Activity
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Quality Metrics
Correctness83.0%
Maintainability84.2%
Architecture81.6%
Performance79.2%
AI Usage20.8%
Skills & Technologies
Programming Languages
PythonSQLTypeScript
Technical Skills
API DesignAPI DevelopmentAPI IntegrationBackend DevelopmentBug FixingCode RefactoringData EngineeringData IngestionData ProcessingData RetrievalDatabase IntegrationDatabase ManagementDependency ManagementEnvironment ConfigurationFastAPI
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