Create app.py

app.py

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+import os
+import sys
+import logging
+import gradio as gr
+from pinecone import Pinecone, ServerlessSpec
+from langchain_pinecone import PineconeVectorStore
+from langchain_community.document_loaders import TextLoader
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.chains import RetrievalQA
+from langchain_community.llms import OpenAI
+from langchain_openai import OpenAIEmbeddings
+
+# --- Logging ---
+logging.basicConfig(stream=sys.stdout, level=logging.INFO)
+
+# --- Environment Variables ---
+api_key = os.getenv("PINECONE_API_KEY")
+openai_api_key = os.getenv("OPENAI_API_KEY")
+
+if not api_key:
+    raise ValueError("Please set the PINECONE_API_KEY as an environment variable.")
+if not openai_api_key:
+    raise ValueError("Please set the OPENAI_API_KEY as an environment variable.")
+os.environ["OPENAI_API_KEY"] = openai_api_key
+
+# --- Pinecone Setup ---
+index_name = "quickstart"
+dimension = 1536
+pc = Pinecone(api_key=api_key)
+
+# Create index if not exists
+if index_name not in [idx['name'] for idx in pc.list_indexes()]:
+    pc.create_index(
+        name=index_name,
+        dimension=dimension,
+        metric="euclidean",
+        spec=ServerlessSpec(cloud="aws", region="us-east-1")
+    )
+
+# --- Load and Process Document ---
+os.makedirs("data/paul_graham", exist_ok=True)
+file_path = "data/paul_graham/paul_graham_essay.txt"
+if not os.path.exists(file_path):
+    import requests
+    url = "https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt"
+    r = requests.get(url)
+    with open(file_path, "w") as f:
+        f.write(r.text)
+
+loader = TextLoader(file_path)
+documents = loader.load()
+text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
+texts = text_splitter.split_documents(documents)
+
+# --- Embedding and Vector Store ---
+embeddings = OpenAIEmbeddings()
+docsearch = PineconeVectorStore.from_documents(texts, embeddings, index_name=index_name)
+
+# --- Query Engine Setup ---
+llm = OpenAI()
+retriever = docsearch.as_retriever()
+qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever)
+
+# --- Query Function ---
+def ask_question(prompt):
+    try:
+        response = qa.run(prompt)
+        return str(response)
+    except Exception as e:
+        return f"❌ Error: {str(e)}"
+
+# --- Gradio UI ---
+with gr.Blocks(css="""body {    background-color: #f5f5dc;    font-family: 'Georgia', 'Merriweather', serif;}h1, h2, h3 {    color: #4e342e;}.gr-box, .gr-column, .gr-group {    border-radius: 15px;    padding: 20px;    background-color: #fffaf0;    box-shadow: 2px 4px 14px rgba(0, 0, 0, 0.1);    margin-top: 10px;}textarea, input[type="text"] {    background-color: #fffaf0;    border: 1px solid #d2b48c;    color: #4e342e;    border-radius: 8px;}button {    background-color: #a1887f;    color: white;    font-weight: bold;    border-radius: 8px;    transition: background-color 0.3s ease;}button:hover {    background-color: #8d6e63;}.gr-button {    border-radius: 8px !important;}""") as demo:
+    with gr.Column():
+        gr.Markdown("""
+        <div style='text-align: center;'>
+            <h1>🧠 Paul Graham Essay Q&A</h1>
+            <div style='font-size: 1.1em; color: #6d4c41; margin-bottom: 1em;'>
+                Explore insights from Paul Graham's essay using semantic search powered by <strong>LangChain</strong> + <strong>Pinecone</strong>.
+            </div>
+        </div>
+        """)
+        with gr.Accordion("ℹ️ What is Pinecone Vector Indexing?", open=False):
+            gr.Markdown("""**Pinecone** is a vector database that stores document embeddings (numeric representations of meaning). When you ask a question, it's converted into a vector and compared against stored vectors to find the most relevant answers — even if they don't match word-for-word.""")
+        gr.Markdown("### 📖 Ask your question below:")
+        with gr.Group():
+            with gr.Row():
+                user_input = gr.Textbox(
+                    placeholder="E.g., What does Paul Graham say about startups?",
+                    label="Your Question",
+                    lines=2
+                )
+            with gr.Row():
+                output = gr.Textbox(label="Answer", lines=6)
+            with gr.Row():
+                submit_btn = gr.Button("🔍 Search Essay")
+                clear_btn = gr.Button("🧹 Clear")
+            submit_btn.click(fn=ask_question, inputs=user_input, outputs=output)
+            clear_btn.click(fn=lambda: ("", ""), inputs=None, outputs=[user_input, output])
+
+demo.launch()