File size: 3,760 Bytes
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from pathlib import Path
import gradio as gr
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, Settings
from llama_index.llms.openai import OpenAI
from llama_index.embeddings.openai import OpenAIEmbedding
# ======================
# Config (safe defaults)
# ======================
MODEL = os.getenv("OPENAI_MODEL", "gpt-4o-mini")
EMBED_MODEL = os.getenv("OPENAI_EMBED_MODEL", "text-embedding-3-small")
TOP_K = int(os.getenv("TOP_K", "3"))
# Your knowledge base file in the Space repo
DOC_PATH = Path(os.getenv("DOC_PATH", "challenge_context.txt"))
SYSTEM_GUARDRAILS = (
"You are Challenge Copilot. Answer ONLY using the provided context. "
"If the answer is not in the context, say: 'I don’t know based on the current document.' "
"Then ask the user to add the missing official details to challenge_context.txt."
)
# ======================
# Build index (cached)
# ======================
_INDEX = None
_QUERY_ENGINE = None
def build_index():
global _INDEX, _QUERY_ENGINE
if _QUERY_ENGINE is not None:
return _QUERY_ENGINE
api_key = os.getenv("OPENAI_API_KEY")
if not api_key:
raise RuntimeError(
"OPENAI_API_KEY is missing. Add it in the Space Settings → Variables and secrets."
)
if not DOC_PATH.exists():
# Create a placeholder so the Space boots even if you forgot the file
DOC_PATH.write_text(
"Add the official Building AI Application Challenge content here.\n",
encoding="utf-8",
)
# LlamaIndex global settings
Settings.llm = OpenAI(model=MODEL, temperature=0.2)
Settings.embed_model = OpenAIEmbedding(model=EMBED_MODEL)
Settings.chunk_size = 800
Settings.chunk_overlap = 120
# Reader expects a directory
data_dir = str(DOC_PATH.parent)
docs = SimpleDirectoryReader(
input_dir=data_dir,
required_exts=[".txt"],
recursive=False
).load_data()
# Only index the target file
docs = [d for d in docs if d.metadata.get("file_name") == DOC_PATH.name]
if not docs:
raise FileNotFoundError(f"Could not load {DOC_PATH.name}. Make sure it exists in the repo.")
_INDEX = VectorStoreIndex.from_documents(docs)
_QUERY_ENGINE = _INDEX.as_query_engine(similarity_top_k=TOP_K)
return _QUERY_ENGINE
def format_sources(resp, max_sources=3, max_chars=220):
lines = []
for i, sn in enumerate(getattr(resp, "source_nodes", [])[:max_sources], start=1):
fn = sn.node.metadata.get("file_name", "unknown")
snippet = sn.node.get_content().replace("\n", " ").strip()[:max_chars]
score = getattr(sn, "score", None)
score_txt = f" (score={score:.3f})" if isinstance(score, (float, int)) else ""
lines.append(f"{i}. {fn}{score_txt}: {snippet}...")
return "\n".join(lines) if lines else "No sources returned."
def chat(message, history):
qe = build_index()
prompt = (
f"{SYSTEM_GUARDRAILS}\n\n"
f"User question: {message}\n"
f"Answer using ONLY the context."
)
resp = qe.query(prompt)
answer = str(resp).strip()
show_sources = os.getenv("SHOW_SOURCES", "true").lower() == "true"
if show_sources:
answer += "\n\n---\nSources:\n" + format_sources(resp, max_sources=TOP_K)
return answer
demo = gr.ChatInterface(
fn=chat,
title="Challenge Copilot — RAG Q&A Bot",
description="Ask questions about the Building AI Application Challenge using challenge_context.txt (LlamaIndex + OpenAI).",
examples=[
"What will I build in this live session?",
"Who is this best for?",
"What are the prerequisites?"
],
theme="soft"
)
if __name__ == "__main__":
demo.launch()
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