Bid Copilot

Reads a tender document (招标文件) and automatically generates a structured bid-document outline (投标文件大纲) — the chapter tree a bidder must produce to respond, traced back to where each requirement came from in the tender. Built as a demo on Azure OpenAI. Upload a tender (single file or a package of files), watch a 9-step pipeline run live, review the outline with per-node source traceability and a coverage report, then export to Word.

How it works

A 9-step pipeline (src/bid_copilot/understanding/pipeline.py) turns raw files into the outline:

1. parse — .docx parsed locally (native heading levels preserved); .doc / .pdf go through Azure Content Understanding for structured markdown.
2. classify — label each file (tender body / scoring / tech spec / commercial …).
3. segment — split documents into chapter blocks.
4. locate — find the key sections (bid-format, scoring, tech-spec, commercial).
5. extract_skeleton — pull the explicit bid-document skeleton the tender prescribes.
6. extract_requirements — extract response requirements. Scoring/commercial items are extracted one-by-one; technical parameter-level indicators are aggregated into a single "technical parameter response" entry (a bidder answers them in one response table, not as separate outline chapters), while requirements that need their own narrative / supporting material / dedicated section stay individual.
7. merge — normalize and de-duplicate the skeleton, then attach requirements to nodes. ref_ids are back-filled by engineering code, not by the LLM.
8. supplement — place any requirement that wasn't attached during merge (the LLM only decides where; ref_id back-fill is again engineering).
9. finalize — renumber the tree and compute the coverage report from the tree's ref_ids.

The web UI streams step progress over SSE, renders the outline with clickable source badges, shows a coverage panel, and exports Word.

Quick Start

Docker

1）Generate a strong random token (optional, used as LOCAL_AUTH_PASSWORD)

openssl rand -hex 32

2）Start the container

docker run -itd -p 8080:8080 --name BidCopilot \
  --restart unless-stopped \
  -e FOUNDRY_CU_BASE_URL=https://<foundry-resource>.cognitiveservices.azure.com \
  -e FOUNDRY_CU_API_KEY=your-content-understanding-api-key \
  -e FOUNDRY_AOAI_BASE_URL=https://<foundry-resource>.openai.azure.com/openai/v1 \
  -e FOUNDRY_AOAI_API_KEY=your-foundry-api-key \
  -e LOCAL_AUTH_USERNAME=demo \
  -e LOCAL_AUTH_PASSWORD=your-strong-random-token \
  ghcr.io/heyjiqingcode/bidcopilot:1.0.0

See Configuration for every available variable.

Local

1）Set up

# Clone code and install requirements
git clone https://github.com/HeyJiqingCode/BidCopilot.git
cd BidCopilot
python -m venv .venv
pip install -r requirements.txt

# Copy .env.example and fill in your Azure OpenAI / Foundry settings
cp .env.example .env

2）Run the server from source

# run the web app
ENABLE_LOCAL_AUTH=false PYTHONPATH=src .venv/bin/uvicorn bid_copilot.api.main:app --port 8080

# open http://127.0.0.1:8080

See Configuration for every available variable.

Configuration

Variable	Purpose	Default
FOUNDRY_AOAI_API_KEY	Azure OpenAI API key
FOUNDRY_AOAI_BASE_URL	Azure OpenAI v1 endpoint (ends with /openai/v1/)
FOUNDRY_CU_BASE_URL	Azure Content Understanding endpoint (optional; needed for .doc/scanned PDF)
FOUNDRY_CU_API_KEY	Azure Content Understanding API key (optional)
MODEL_MAIN	main model deployment name	gpt-5.4
MODEL_MINI	mini model deployment name	gpt-5.4-mini
MODEL_NANO	nano model deployment name	gpt-5.4-nano
MODEL_CLASSIFY	model tier for classify (main/mini/nano)	mini
MODEL_LOCATE	model tier for locate	main
MODEL_SKELETON	model tier for extract_skeleton	main
MODEL_REQUIREMENTS	model tier for extract_requirements	main
MODEL_MERGE	model tier for merge	main
MODEL_SUPPLEMENT	model tier for supplement	main
EFFORT_CLASSIFY	reasoning effort for classify (low/medium/high)	low
EFFORT_LOCATE	reasoning effort for locate	medium
EFFORT_SKELETON	reasoning effort for extract_skeleton	medium
EFFORT_REQUIREMENTS	reasoning effort for extract_requirements	medium
EFFORT_MERGE	reasoning effort for merge	high
EFFORT_SUPPLEMENT	reasoning effort for supplement	high
ENABLE_LOCAL_AUTH	enable the local login gate (true/false)	false
LOCAL_AUTH_USERNAME	local login username	admin
LOCAL_AUTH_PASSWORD	local login password	admin123
LOCAL_AUTH_SESSION_HOURS	login session lifetime in hours	24
LOCAL_AUTH_COOKIE_NAME	login session cookie name	bid_copilot_session
MAX_CONCURRENCY	parallelism cap for per-chapter extraction and merge batching	5

Per-step model tiers let you trade speed for accuracy. The accuracy-critical steps — extract_requirements, merge, supplement — should stay on main; lighter steps (classify, locate, skeleton) can drop to mini/nano.