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Initial Commits

app.py

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+import os
+import zipfile
+import chromadb
+import gradio as gr
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.runnables import RunnableLambda, RunnablePassthrough
+from langchain_core.output_parsers import StrOutputParser
+from langchain_together import ChatTogether
+from langchain_community.vectorstores import Chroma
+from langchain_community.embeddings import HuggingFaceEmbeddings
+
+# Log: Check if chroma_store exists
+if not os.path.exists("chroma_store"):
+    print("🔍 chroma_store folder not found. Attempting to unzip...")
+    try:
+        with zipfile.ZipFile("chroma_store.zip", "r") as zip_ref:
+            zip_ref.extractall("chroma_store")
+        print("✅ Successfully extracted chroma_store.zip.")
+    except Exception as e:
+        print(f"❌ Failed to unzip chroma_store.zip: {e}")
+else:
+    print("✅ chroma_store folder already exists. Skipping unzip.")
+
+# Initialize ChromaDB client
+chroma_client = chromadb.PersistentClient(path="./chroma_store")
+
+# Vector store and retriever
+embedding_function = HuggingFaceEmbeddings(model_name="BAAI/bge-base-en-v1.5")
+vectorstore = Chroma(
+    client=chroma_client,
+    collection_name="imageonline_chunks",
+    embedding_function=embedding_function
+)
+
+retriever = vectorstore.as_retriever(search_kwargs={"k": 3, "filter": {"site": "imageonline"}})
+
+# Retrieval logic
+def retrieve_with_metadata(query, k=5):
+    docs = retriever.get_relevant_documents(query)
+    if not docs:
+        return {"context": "No relevant context found.", "references": []}
+    top_doc = docs[0]
+    return {
+        "context": top_doc.page_content,
+        "references": [{
+            "section": top_doc.metadata.get("section", "Unknown"),
+            "source": top_doc.metadata.get("source", "Unknown")
+        }]
+    }
+
+# LLM setup
+llm = ChatTogether(
+    model="meta-llama/Llama-3-8b-chat-hf",
+    temperature=0.3,
+    max_tokens=1024,
+    top_p=0.7,
+    together_api_key="a36246d65d8290f43667350b364c5b6bb8562eb50a4b947eec5bd7e79f2dffc6"
+)
+
+# Prompt template
+prompt = ChatPromptTemplate.from_template("""
+You are an expert assistant for ImageOnline Web Solutions.
+
+Answer the user's query based ONLY on the following context:
+
+{context}
+
+Query: {question}
+""")
+
+rag_chain = (
+    {
+        "context": lambda x: retrieve_with_metadata(x)["context"],
+        "question": RunnablePassthrough()
+    }
+    | prompt
+    | llm
+    | StrOutputParser()
+)
+
+def get_references(query):
+    return retrieve_with_metadata(query)["references"]
+
+# Gradio UI
+def chat_interface(message, history):
+    history = history or []
+    history.append(("🧑 You: " + message, "⏳ Generating response..."))
+    try:
+        answer = rag_chain.invoke(message)
+        references = get_references(message)
+        if references:
+            ref = references[0]
+            ref_string = f"\n\n📚 **Reference:**\nSection: {ref['section']}\nURL: {ref['source']}"
+        else:
+            ref_string = "\n\n📚 **Reference:**\n_None available_"
+        full_response = answer + ref_string
+        history[-1] = ("🧑 You: " + message, "🤖 Bot: " + full_response)
+    except Exception as e:
+        history[-1] = ("🧑 You: " + message, f"🤖 Bot: ⚠️ {str(e)}")
+    return history, history
+
+def launch_gradio():
+    with gr.Blocks() as demo:
+        gr.Markdown("# 💬 ImageOnline RAG Chatbot")
+        gr.Markdown("Ask about Website Designing, App Development, SEO, Hosting, etc.")
+        chatbot = gr.Chatbot()
+        state = gr.State([])
+        with gr.Row():
+            msg = gr.Textbox(placeholder="Ask your question here...", show_label=False, scale=8)
+            send_btn = gr.Button("📨 Send", scale=1)
+        msg.submit(chat_interface, inputs=[msg, state], outputs=[chatbot, state])
+        send_btn.click(chat_interface, inputs=[msg, state], outputs=[chatbot, state])
+        with gr.Row():
+            clear_btn = gr.Button("🧹 Clear Chat")
+            clear_btn.click(fn=lambda: ([], []), outputs=[chatbot, state])
+    return demo
+
+demo = launch_gradio()
+demo.launch()

chroma_store.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:72520924e337f73860723324147380883db5b55f036ff54171358444fe2603ce
+size 398677

requirements

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+langchain
+langchain-huggingface
+langchain-together
+langchain-community
+chromadb
+sentence-transformers
+trafilatura
+beautifulsoup4
+nltk
+tiktoken
+gradio
+together