Build a RAG Freight Pricing Copilot with Chroma and FastAPI
Group: Capstone Project
Product Category: AI Engineering
Sub Category: RAG & LLM Applications
Solution Available
Source code, datasets, and other related files for the solution will be provided.
Prerequisites
About this Product
Pricing analysts at freight brokerages spend hours manually searching fragmented contracts, rate sheets and quote history to answer simple questions about rates, fees and margins.
What you build
- Ingestion pipeline: Extract metadata and chunk four freight document types using strategy- specific approaches: recursive splitting for prose and one-row-per-chunk for structured data.
- Vector database: Generate local embeddings with sentence-transformers, store in Chroma with metadata filtering, and support idempotent re-ingestion.
- Hybrid retrieval: Combine vector similarity search with metadata filters on lane, carrier and contract freshness.
- Business logic tools: Deterministic margin calculator and rate resolver implementing freight rules; answers are exact and auditable.
- RAG generation layer: Ground answers in retrieved chunks with citations; fall back to "insufficient information" when sources are lacking; run with real or mock LLM.
Skills you practise
- RAG design: Build retrieval pipelines that ground LLM outputs in source documents.
- Chunking strategies: Apply different techniques to different document types and justify trade- offs.
- Vector databases: Persist and query embeddings with metadata filtering; handle idempotent upserts.
- Hybrid retrieval: Combine similarity search with structured metadata filters for domain-specific ranking.
- LLM evaluation: Measure answer quality with precision, recall, correctness and citation-accuracy metrics against a gold set.
Outcome
- Portfolio-ready RAG system: A complete end-to-end AI engineering project from ingestion to evaluation, deployable locally with zero required API costs.
Interview Q&A Included
1 practice set bundled with this lab
-
RAG Freight Pricing Copilot Assessment
Category: AI Engineering Subcategory: Generative AI / LLM Engineer 71 questionsThis assessment checks what you learned while building the Ironhaul freight pricing copilot. Questions follow the lab and are split into six groups: Business Scenario and Use Cases, Freight Business …
Resources
The complete brief for the freight pricing assistant you build: the problem, the users, the rules and how success is measured.
- Business scenario: pricing analysts who lose hours searching contracts, rate sheets and quote history by hand
- 29 functional requirements and 8 business rules covering margin, contract precedence, detention and spot rate fallback
- Grounding and fallback rules, so every answer cites its sources or says it does not have enough information
- Data model for four document types, the two API endpoints and the five use cases, step by step
- 27 acceptance criteria, including evaluation pass thresholds for a gold set of test questions
- Technology stack, project structure, failure scenarios, security notes and a glossary of domain terms
Topics
Skills
Languages
English
Level
Intermediate
Business Domain
Logistics and Freight Pricing
Project Mentors
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