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app (2).py

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+import os
+import logging
+import tempfile
+from typing import List
+
+import gradio as gr
+
+# ---- LlamaIndex / Pinecone ----
+from pinecone import Pinecone, ServerlessSpec
+from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, StorageContext, Settings
+from llama_index.vector_stores.pinecone import PineconeVectorStore
+from llama_index.embeddings.openai import OpenAIEmbedding
+from llama_index.llms.openai import OpenAI
+
+# ---- Config ----
+PINECONE_API_KEY = os.getenv("PINECONE_API_KEY")
+OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
+
+# You can override these via Space "Variables" (Secrets)
+PINECONE_INDEX_NAME = os.getenv("PINECONE_INDEX_NAME", "dds-demo-index")
+PINECONE_REGION = os.getenv("PINECONE_REGION", "us-east-1")  # keep in sync with ServerlessSpec
+PINECONE_CLOUD = os.getenv("PINECONE_CLOUD", "aws")
+EMBED_MODEL = os.getenv("EMBED_MODEL", "text-embedding-3-small")
+LLM_MODEL = os.getenv("LLM_MODEL", "gpt-4o-mini")
+
+if not PINECONE_API_KEY:
+    raise RuntimeError("Missing PINECONE_API_KEY. Add it in your Space settings (Secrets).")
+if not OPENAI_API_KEY:
+    raise RuntimeError("Missing OPENAI_API_KEY. Add it in your Space settings (Secrets).")
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger("dds-space")
+
+# ---- Pinecone client & index bootstrap ----
+pc = Pinecone(api_key=PINECONE_API_KEY)
+
+# Create index if it doesn't exist.
+def _ensure_index(index_name: str, dimension: int = 1536):
+    existing = [idx["name"] for idx in pc.list_indexes()]
+    if index_name not in existing:
+        logger.info(f"Creating Pinecone index '{index_name}' (dim={dimension})...")
+        pc.create_index(
+            name=index_name,
+            dimension=dimension,
+            metric="cosine",
+            spec=ServerlessSpec(cloud=PINECONE_CLOUD, region=PINECONE_REGION),
+        )
+    return pc.Index(index_name)
+
+pinecone_index = _ensure_index(PINECONE_INDEX_NAME, dimension=1536)
+
+# ---- LlamaIndex settings ----
+# Set global settings for LlamaIndex (embeddings + LLM)
+Settings.embed_model = OpenAIEmbedding(model=EMBED_MODEL, api_key=OPENAI_API_KEY)
+Settings.llm = OpenAI(model=LLM_MODEL, api_key=OPENAI_API_KEY)
+
+# Vector store wrapper
+vector_store = PineconeVectorStore(pinecone_index=pinecone_index)
+
+def build_or_update_index(files: List[gr.File]) -> str:
+    """
+    Load the uploaded files, chunk them with LlamaIndex, and upsert into Pinecone.
+    """
+    if not files:
+        return "Please upload at least one file."
+    with tempfile.TemporaryDirectory() as tmpdir:
+        paths = []
+        for f in files:
+            # Gradio File object -> save to temp path
+            dst = os.path.join(tmpdir, os.path.basename(f.name))
+            with open(f.name, "rb") as src, open(dst, "wb") as out:
+                out.write(src.read())
+            paths.append(dst)
+
+        docs = SimpleDirectoryReader(input_files=paths).load_data()
+        storage_context = StorageContext.from_defaults(vector_store=vector_store)
+
+        # Build a new index (will upsert into Pinecone via the vector_store)
+        _ = VectorStoreIndex.from_documents(
+            docs,
+            storage_context=storage_context,
+            show_progress=True,
+        )
+
+    return f"Indexed {len(files)} file(s) into Pinecone index: {PINECONE_INDEX_NAME}."
+
+def answer(query: str, top_k: int = 4) -> str:
+    if not query or not query.strip():
+        return "Ask a question about your uploaded knowledge."
+    # Re-build a lightweight index wrapper that reads from the existing vector store
+    index = VectorStoreIndex.from_vector_store(vector_store)
+    qe = index.as_query_engine(similarity_top_k=top_k)
+    resp = qe.query(query)
+    return str(resp)
+
+# ---- UI ----
+INTRO = (
+    "Upload PDFs/TXT/Docs to build a Pinecone vector index (1536-d). "
+    "Then ask questions to retrieve & summarize with LlamaIndex + OpenAI."
+)
+
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown(
+        "<h1 style='text-align:center;'>📚 RAG with LlamaIndex + Pinecone</h1>"
+        "<p style='text-align:center;'>Omantel/DDS demo Space — minimal, production-friendly layout</p>"
+    )
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            gr.Markdown("### 1) Upload & Index")
+            file_uploader = gr.File(label="Upload documents", file_count="multiple", type="filepath")
+            index_btn = gr.Button("Build / Update Index")
+            index_status = gr.Markdown()
+
+        with gr.Column(scale=1):
+            gr.Markdown("### 2) Ask a Question")
+            query = gr.Textbox(label="Your question", placeholder="e.g., What is the refund policy?")
+            topk = gr.Slider(1, 10, value=4, step=1, label="Top-K")
+            ask_btn = gr.Button("Ask")
+            answer_box = gr.Markdown()
+
+    gr.Markdown(f"**How it works:** {INTRO}")
+
+    index_btn.click(build_or_update_index, inputs=[file_uploader], outputs=[index_status])
+    ask_btn.click(answer, inputs=[query, topk], outputs=[answer_box])
+
+if __name__ == "__main__":
+    demo.launch()

requirements (1).txt

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+gradio>=4.44.0
+pinecone-client>=5.0.1
+openai>=1.51.0
+llama-index>=0.11.0
+llama-index-vector-stores-pinecone>=0.3.0
+llama-index-embeddings-openai>=0.3.0
+llama-index-llms-openai>=0.2.0
+tiktoken>=0.7.0