README.md

metadata

title: Payer AI Prototypes
emoji: 🚀
colorFrom: indigo
colorTo: pink
sdk: docker
pinned: false

Payer AI Prototypes

Payer AI Prototypes

Two quick healthcare payer prototypes in one Chainlit app:

1. Claims Ingestion & Normalization
2. Automated Scheduling & Admin Assistant

The app uses:

• uv for dependency management
• Chainlit for UI
• LangGraph for deterministic agent workflows
• LangChain tools
• in-memory Qdrant for prototype RAG retrieval
• Loguru for logging

Folder layout

payer-ai-prototypes/
  app.py
  graphs.py
  agents.py
  tools.py
  schemas.py
  prompts.py
  config.py
  data/
    claims/
      mock_claim_packets/
      rag/
        policy_benefit_rag/
        exception_similarity_rag/
    scheduling/
      mock_data/
      rag/
        provider_specialty_matching/
  scripts/
    ingest_claims_rag.py
    ingest_scheduling_rag.py
  tests/
  pyproject.toml
  Dockerfile
  .env.example

Where to place your already-downloaded mock data

Prototype 1: Claims Ingestion & Normalization

Place the unzipped mock_claim_packets.zip contents here:

data/claims/mock_claim_packets/

Expected example shape:

data/claims/mock_claim_packets/
  clm_001/
    claim.json
    provider_note.txt
    attachment.pdf
    fax_scan.png
  clm_002/
  ...

Place the unzipped claims_rag_datasets.zip contents here:

data/claims/rag/
  policy_benefit_rag/
    policy_benefit_chunks.jsonl
    policy_benefit_metadata.csv
    source_docs/
  exception_similarity_rag/
    resolved_exception_cases.jsonl
    exception_similarity_metadata.csv
    case_files/

Prototype 2: Scheduling & Admin Assistant

Place the scheduling mock data files here:

data/scheduling/mock_data/
  members.csv
  benefits.csv
  referrals.csv
  authorizations.csv
  provider_availability.csv
  specialist_locations.csv

Place the provider/specialty matching RAG dataset here:

data/scheduling/rag/provider_specialty_matching/
  provider_specialty_profiles.jsonl
  provider_specialty_metadata.csv
  source_docs/

If your file names are slightly different, update the paths in config.py.

Local setup

uv sync
cp .env.example .env
uv run chainlit run app.py

Optional RAG ingestion

The tools auto-load JSONL files into in-memory Qdrant at runtime. You can also explicitly test ingestion:

uv run python scripts/ingest_claims_rag.py
uv run python scripts/ingest_scheduling_rag.py

Hugging Face Spaces deployment

Create a Docker Space and push this repo. Add secrets:

• OPENAI_API_KEY
• optionally HF_FT_EMBED_MODEL_URL

HF Space command is handled by the Dockerfile:

uv run chainlit run app.py --host 0.0.0.0 --port 7860