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import os |
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import logging |
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import gradio as gr |
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from pinecone import Pinecone, ServerlessSpec |
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from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, StorageContext, Settings |
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from llama_index.vector_stores.pinecone import PineconeVectorStore |
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from llama_index.embeddings.openai import OpenAIEmbedding |
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from llama_index.llms.openai import OpenAI |
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PINECONE_API_KEY = os.getenv("PINECONE_API_KEY") |
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OPENAI_API_KEY = os.getenv("OPENAI_API_KEY") |
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PINECONE_INDEX_NAME = os.getenv("PINECONE_INDEX_NAME", "dds-insurance-index") |
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PINECONE_REGION = os.getenv("PINECONE_REGION", "us-east-1") |
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PINECONE_CLOUD = os.getenv("PINECONE_CLOUD", "aws") |
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EMBED_MODEL = os.getenv("EMBED_MODEL", "text-embedding-3-small") |
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LLM_MODEL = os.getenv("LLM_MODEL", "gpt-4o-mini") |
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DATA_DIR = "data" |
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DEFAULT_TOP_K = 4 |
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LOGO_URL = "https://raw.githubusercontent.com/Decoding-Data-Science/Omantel/main/Omantel_Logo%20(1).png" |
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if not PINECONE_API_KEY: |
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raise RuntimeError("Missing PINECONE_API_KEY (set it in your Space → Settings → Variables).") |
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if not OPENAI_API_KEY: |
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raise RuntimeError("Missing OPENAI_API_KEY (set it in your Space → Settings → Variables).") |
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logging.basicConfig(level=logging.INFO) |
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log = logging.getLogger("dds-space") |
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Settings.embed_model = OpenAIEmbedding(model=EMBED_MODEL, api_key=OPENAI_API_KEY) |
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Settings.llm = OpenAI(model=LLM_MODEL, api_key=OPENAI_API_KEY) |
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pc = Pinecone(api_key=PINECONE_API_KEY) |
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def ensure_index(name: str, dim: int = 1536): |
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names = [i["name"] for i in pc.list_indexes()] |
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if name not in names: |
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log.info(f"Creating Pinecone index '{name}' (dim={dim})...") |
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pc.create_index( |
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name=name, |
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dimension=dim, |
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metric="cosine", |
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spec=ServerlessSpec(cloud=PINECONE_CLOUD, region=PINECONE_REGION), |
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) |
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return pc.Index(name) |
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pinecone_index = ensure_index(PINECONE_INDEX_NAME, dim=1536) |
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vector_store = PineconeVectorStore(pinecone_index=pinecone_index) |
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def bootstrap_index(): |
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if not os.path.isdir(DATA_DIR): |
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raise RuntimeError("No 'data/' directory found. Commit your documents to data/ in the Space repo.") |
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log.info("Loading documents from ./data ...") |
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docs = SimpleDirectoryReader(DATA_DIR).load_data() |
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if not docs: |
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raise RuntimeError("No documents found in data/. Add e.g., data/insurance.pdf") |
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log.info(f"Docs loaded: {len(docs)}. Upserting into Pinecone…") |
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storage_ctx = StorageContext.from_defaults(vector_store=vector_store) |
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VectorStoreIndex.from_documents(docs, storage_context=storage_ctx, show_progress=True) |
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log.info("Index upsert complete.") |
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bootstrap_index() |
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def answer(query: str) -> str: |
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if not query or not query.strip(): |
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return "Please enter a question (or select one from the FAQ list)." |
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index = VectorStoreIndex.from_vector_store(vector_store) |
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engine = index.as_query_engine(similarity_top_k=DEFAULT_TOP_K) |
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resp = engine.query(query) |
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return str(resp) |
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FAQS = [ |
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"", |
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"What benefits are covered under the policy?", |
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"How do I file a claim and what documents are required?", |
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"What are the exclusions and limitations?", |
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"Is pre-authorization needed for hospitalization?", |
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"What is the reimbursement timeline?", |
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"How are outpatient vs inpatient services handled?", |
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"How can I check my network hospitals/clinics?", |
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"What is the co-pay or deductible policy?", |
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] |
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def use_faq(selected_faq: str, free_text: str): |
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prompt = (selected_faq or "").strip() or (free_text or "").strip() |
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if not prompt: |
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return "", "Please select a FAQ or type your question." |
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return prompt, answer(prompt) |
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CSS = """ |
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.header { text-align:center; } |
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.header img { max-height:80px; height:auto; } |
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.title { text-align:center; font-weight:700; font-size:1.4rem; margin:6px 0 0 0; } |
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.subnote { text-align:center; margin-top:-2px; opacity:0.8; } |
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""" |
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with gr.Blocks(css=CSS, theme=gr.themes.Soft()) as demo: |
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gr.Markdown( |
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f""" |
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<div class="header"> |
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<img src="{LOGO_URL}" alt="Omantel logo" /> |
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</div> |
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<h1 class="title">Omantel Insurance Q&A — RAG Assistant</h1> |
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<p class="subnote">Ask about coverage, claims, exclusions, and more — powered by LlamaIndex + Pinecone</p> |
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""" |
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) |
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with gr.Row(): |
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with gr.Column(scale=1): |
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gr.Markdown("### Ask from Frequently Asked Questions") |
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faq = gr.Dropdown(choices=FAQS, value=FAQS[0], label="Select a common question") |
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gr.Markdown("### Or type your question") |
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user_q = gr.Textbox( |
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label="Your question", |
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placeholder="e.g., What is covered under outpatient benefits?", |
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lines=2 |
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) |
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ask_btn = gr.Button("Ask", variant="primary") |
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with gr.Column(scale=1): |
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chosen_prompt = gr.Textbox(label="Query sent", interactive=False) |
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answer_box = gr.Markdown() |
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ask_btn.click(use_faq, inputs=[faq, user_q], outputs=[chosen_prompt, answer_box]) |
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if __name__ == "__main__": |
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demo.launch() |
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