query_graph.py

import os
import numpy as np
from dotenv import load_dotenv
from openai import OpenAI
import networkx as nx
from sklearn.metrics.pairwise import cosine_similarity

# Initialize OpenAI client
load_dotenv(override=True)
client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))

# Load graph from GML
G = nx.read_gml("graph.gml")
enodes = list(G.nodes)
embeddings = np.array([G.nodes[n]['embedding'] for n in enodes])

def query_graph(question, top_k=5):
    """
    Embed the question, retrieve the top_k relevant chunks,
    and return: (answer, sources, chunks)
      - answer: generated response string
      - sources: list of unique source names
      - chunks: list of tuples (header, score, full_text, source_url_or_path)
    """
    # Embed question
    emb_resp = client.embeddings.create(
        model="text-embedding-3-large",
        input=question
    )
    q_vec = emb_resp.data[0].embedding

    # Compute cosine similarities
    sims = cosine_similarity([q_vec], embeddings)[0]
    idxs = sims.argsort()[::-1][:top_k]

    # Collect chunk-level info
    chunks = []
    sources = []
    for rank, i in enumerate(idxs, start=1):
        node = enodes[i]
        text = G.nodes[node]['text']
        header = text.split('\n', 1)[0].lstrip('# ').strip()
        score = sims[i]
        # Determine citation (URL for HTML, path for PDF)
        citation = G.nodes[node].get('url') or G.nodes[node].get('path') or G.nodes[node]['source']
        chunks.append((header, score, text, citation))
        sources.append(G.nodes[node]['source'])
    # Deduplicate sources
    sources = list(dict.fromkeys(sources))

    # Assemble prompt
    context = "\n\n---\n\n".join([c[2] for c in chunks])
    prompt = (
        "Use the following context to answer the question:\n\n" +
        context +
        f"\n\nQuestion: {question}\nAnswer:"
    )

    # Query chat model
    chat_resp = client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[
            {"role": "system", "content": "You are a helpful assistant for manufacturing equipment safety."},
            {"role": "user",   "content": prompt}
        ]
    )
    answer = chat_resp.choices[0].message.content

    return answer, sources, chunks

    """
    Embed the user question, retrieve the top_k relevant chunks from the graph,
    assemble a prompt with those chunks, call the chat model, and return:
      - answer: the generated response
      - sources: unique list of source documents
      - chunks: list of (header, score, full_text) for the top_k passages
    """
    # Embed the question
    emb_resp = client.embeddings.create(
        model="text-embedding-3-large",
        input=question
    )
    q_vec = emb_resp.data[0].embedding

    # Compute similarities against all stored embeddings
    sims = cosine_similarity([q_vec], embeddings)[0]
    idxs = sims.argsort()[::-1][:top_k]

    # Gather chunk‑level info and sources
    chunks = []
    sources = []
    for i in idxs:
        node = enodes[i]
        text = G.nodes[node]['text']
        # Use the first line as the header
        header = text.split('\n', 1)[0].lstrip('# ').strip()
        score = sims[i]
        chunks.append((header, score, text))
        sources.append(G.nodes[node]['source'])
    # Deduplicate sources while preserving order
    sources = list(dict.fromkeys(sources))

    # Assemble the prompt from the chunk texts
    context_text = "\n\n---\n\n".join([chunk[2] for chunk in chunks])
    prompt = (
        "Use the following context to answer the question:\n\n"
        + context_text
        + f"\n\nQuestion: {question}\nAnswer:"
    )

    # Call the chat model
    chat_resp = client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[
            {"role": "system", "content": "You are a helpful assistant for manufacturing equipment safety."},
            {"role": "user",   "content": prompt}
        ]
    )
    answer = chat_resp.choices[0].message.content

    return answer, sources, chunks


# Test queries
# test_questions = [
#     "What are general machine guarding requirements?",
#     "Explain the key steps in lockout/tagout procedures."
# ]

# for q in test_questions:
#     answer, sources, chunks = query_graph(q)
#     print(f"Q: {q}")
#     print(f"Answer: {answer}\n")
#     print("Sources:")
#     for src in sources:
#         print(f"- {src}")
#     print("\nTop Chunks:")
#     for header, score, _, citation in chunks:
#         print(f"  * {header} (score: {score:.2f}) from {citation}")
#     print("\n", "#"*40, "\n")