Upload 4 files

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+logo.jpg.jpg filter=lfs diff=lfs merge=lfs -text

Readme.md

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+ Building a RAG pipeline using the LangChain ecosystem and a web application that allows users to:
+ 
+
+   - Upload documents (currently only DOCX is supported)
+
+   - Ask questions about their content
+
+   - Get accurate responses using Llama 4 + RAG
+
+   🔧 Technologies Used
+
+    Llama 4
+
+    - LangChain
+
+    - Vector Stores(Chroma DB)
+
+    - Gradio for the web interface
+
+
+
+💡 Coming Soon
+
+-Support for multiple document types
+
+-User authentication
+
+    Preview 
+![Preview of the Llama Chat Web Application](Images/Demo.png)

app.py

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+# ========== Standard Library ==========
+import os
+import tempfile
+import zipfile
+from typing import List, Optional, Tuple, Union
+import collections
+
+
+# ========== Third-Party Libraries ==========
+import gradio as gr
+from groq import Groq
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.document_loaders import DirectoryLoader, UnstructuredFileLoader
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.prompts import PromptTemplate
+from langchain_core.runnables import RunnablePassthrough
+from langchain_core.vectorstores import InMemoryVectorStore
+from langchain_groq import ChatGroq
+from langchain_huggingface import HuggingFaceEmbeddings
+
+# ========== Configs ==========
+TITLE = """<h1 align="center">🗨️🦙 Llama 4 Docx Chatter</h1>"""
+AVATAR_IMAGES = (
+    None,
+    "./logo.jpg",
+)
+
+# Acceptable file extensions
+TEXT_EXTENSIONS = [".docx", ".zip"]
+
+# ========== Models & Clients ==========
+GROQ_API_KEY = os.getenv("GROQ_API_KEY")
+#GROQ_API_KEY = "Your API Key Here
+client = Groq(api_key=GROQ_API_KEY)
+llm = ChatGroq(model="meta-llama/llama-4-scout-17b-16e-instruct", api_key=GROQ_API_KEY)
+embed_model = HuggingFaceEmbeddings(model_name="mixedbread-ai/mxbai-embed-large-v1")
+
+# ========== Core Components ==========
+text_splitter = RecursiveCharacterTextSplitter(
+    chunk_size=1000,
+    chunk_overlap=100,
+    separators=["\n\n", "\n"],
+)
+
+rag_template = """You are an expert assistant tasked with answering questions based on the provided documents.
+Use only the given context to generate your answer.
+If the answer cannot be found in the context, clearly state that you do not know.
+Be detailed and precise in your response, but avoid mentioning or referencing the context itself.
+Context:
+{context}
+Question:
+{question}
+Answer:"""
+rag_prompt = PromptTemplate.from_template(rag_template)
+
+
+# ========== App State ==========
+class AppState:
+    vectorstore: Optional[InMemoryVectorStore] = None
+    rag_chain = None
+
+
+state = AppState()
+
+# ========== Utility Functions ==========
+
+
+def load_documents_from_files(files: List[str]) -> List:
+    """Load documents from uploaded files directly without moving."""
+    all_documents = []
+
+    # Temporary directory if ZIP needs extraction
+    with tempfile.TemporaryDirectory() as temp_dir:
+        for file_path in files:
+            ext = os.path.splitext(file_path)[1].lower()
+
+            if ext == ".zip":
+                # Extract ZIP inside temp_dir
+                with zipfile.ZipFile(file_path, "r") as zip_ref:
+                    zip_ref.extractall(temp_dir)
+
+                # Load all docx from extracted zip
+                loader = DirectoryLoader(
+                    path=temp_dir,
+                    glob="**/*.docx",
+                    use_multithreading=True,
+                )
+                docs = loader.load()
+                all_documents.extend(docs)
+
+            elif ext == ".docx":
+                # Load single docx directly
+                loader = UnstructuredFileLoader(file_path)
+                docs = loader.load()
+                all_documents.extend(docs)
+
+    return all_documents
+
+
+def get_last_user_message(chatbot: List[Union[gr.ChatMessage, dict]]) -> Optional[str]:
+    """Get last user prompt."""
+    for message in reversed(chatbot):
+        content = (
+            message.get("content") if isinstance(message, dict) else message.content
+        )
+        if (
+            message.get("role") if isinstance(message, dict) else message.role
+        ) == "user":
+            return content
+    return None
+
+
+# ========== Main Logic ==========
+
+
+
+
+def upload_files(
+    files: Optional[List[str]], chatbot: List[Union[gr.ChatMessage, dict]]
+):
+    """Handle file upload - .docx or .zip containing docx."""
+    if not files:
+        return chatbot
+
+    file_summaries = []  # <-- Collect formatted file/folder info
+    documents = []
+
+    with tempfile.TemporaryDirectory() as temp_dir:
+        for file_path in files:
+            filename = os.path.basename(file_path)
+            ext = os.path.splitext(file_path)[1].lower()
+
+            if ext == ".zip":
+                file_summaries.append(f"📦 **{filename}** (ZIP file) contains:")
+                try:
+                    with zipfile.ZipFile(file_path, "r") as zip_ref:
+                        zip_ref.extractall(temp_dir)
+                        zip_contents = zip_ref.namelist()
+
+                        # Group files by folder
+                        folder_map = collections.defaultdict(list)
+                        for item in zip_contents:
+                            if item.endswith("/"):
+                                continue  # skip folder entries themselves
+                            folder = os.path.dirname(item)
+                            file_name = os.path.basename(item)
+                            folder_map[folder].append(file_name)
+
+                        # Format nicely
+                        for folder, files_in_folder in folder_map.items():
+                            if folder:
+                                file_summaries.append(f"📂 {folder}/")
+                            else:
+                                file_summaries.append(f"📄 (root)")
+                            for f in files_in_folder:
+                                file_summaries.append(f"   - {f}")
+
+                    # Load docx files extracted from ZIP
+                    loader = DirectoryLoader(
+                        path=temp_dir,
+                        glob="**/*.docx",
+                        use_multithreading=True,
+                    )
+                    docs = loader.load()
+                    documents.extend(docs)
+
+                except zipfile.BadZipFile:
+                    chatbot.append(
+                        gr.ChatMessage(
+                            role="assistant",
+                            content=f"❌ Failed to open ZIP file: {filename}",
+                        )
+                    )
+
+            elif ext == ".docx":
+                file_summaries.append(f"📄 **{filename}**")
+                loader = UnstructuredFileLoader(file_path)
+                docs = loader.load()
+                documents.extend(docs)
+
+            else:
+                file_summaries.append(f"❌ Unsupported file type: {filename}")
+
+    if not documents:
+        chatbot.append(
+            gr.ChatMessage(
+                role="assistant", content="No valid .docx files found in upload."
+            )
+        )
+        return chatbot
+
+    # Split documents
+    chunks = text_splitter.split_documents(documents)
+    if not chunks:
+        chatbot.append(
+            gr.ChatMessage(
+                role="assistant", content="Failed to split documents into chunks."
+            )
+        )
+        return chatbot
+
+    # Create Vectorstore
+    state.vectorstore = InMemoryVectorStore.from_documents(
+        documents=chunks,
+        embedding=embed_model,
+    )
+    retriever = state.vectorstore.as_retriever()
+
+    # Build RAG Chain
+    state.rag_chain = (
+        {"context": retriever, "question": RunnablePassthrough()}
+        | rag_prompt
+        | llm
+        | StrOutputParser()
+    )
+
+    # Final display
+    chatbot.append(
+        gr.ChatMessage(
+            role="assistant",
+            content="**Uploaded Files:**\n"
+            + "\n".join(file_summaries)
+            + "\n\n✅ Ready to chat!",
+        )
+    )
+    return chatbot
+
+
+def user_message(
+    text_prompt: str, chatbot: List[Union[gr.ChatMessage, dict]]
+) -> Tuple[str, List[Union[gr.ChatMessage, dict]]]:
+    """Add user's text input to conversation."""
+    if text_prompt.strip():
+        chatbot.append(gr.ChatMessage(role="user", content=text_prompt))
+    return "", chatbot
+
+
+def process_query(
+    chatbot: List[Union[gr.ChatMessage, dict]],
+) -> List[Union[gr.ChatMessage, dict]]:
+    """Process user's query through RAG pipeline."""
+    prompt = get_last_user_message(chatbot)
+    if not prompt:
+        chatbot.append(
+            gr.ChatMessage(role="assistant", content="Please type a question first.")
+        )
+        return chatbot
+
+    if state.rag_chain is None:
+        chatbot.append(
+            gr.ChatMessage(role="assistant", content="Please upload documents first.")
+        )
+        return chatbot
+
+    chatbot.append(gr.ChatMessage(role="assistant", content="Thinking..."))
+
+    try:
+        response = state.rag_chain.invoke(prompt)
+        chatbot[-1].content = response
+    except Exception as e:
+        chatbot[-1].content = f"Error: {str(e)}"
+
+    return chatbot
+
+
+def reset_app(
+    chatbot: List[Union[gr.ChatMessage, dict]],
+) -> List[Union[gr.ChatMessage, dict]]:
+    """Reset application state."""
+    state.vectorstore = None
+    state.rag_chain = None
+    return [
+        gr.ChatMessage(
+            role="assistant", content="App reset! Upload new documents to start."
+        )
+    ]
+
+
+# ========== UI Layout ==========
+
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.HTML(TITLE)
+    chatbot = gr.Chatbot(
+        label="Llama 4 RAG",
+        type="messages",
+        bubble_full_width=False,
+        avatar_images=AVATAR_IMAGES,
+        scale=2,
+        height=350,
+    )
+
+    with gr.Row(equal_height=True):
+        text_prompt = gr.Textbox(
+            placeholder="Ask a question...", show_label=False, autofocus=True, scale=28
+        )
+        send_button = gr.Button(
+            value="Send",
+            variant="primary",
+            scale=1,
+            min_width=80,
+        )
+        upload_button = gr.UploadButton(
+            label="Upload",
+            file_count="multiple",
+            file_types=TEXT_EXTENSIONS,
+            scale=1,
+            min_width=80,
+        )
+        reset_button = gr.Button(
+            value="Reset",
+            variant="stop",
+            scale=1,
+            min_width=80,
+        )
+
+    send_button.click(
+        fn=user_message,
+        inputs=[text_prompt, chatbot],
+        outputs=[text_prompt, chatbot],
+        queue=False,
+    ).then(fn=process_query, inputs=[chatbot], outputs=[chatbot])
+
+    text_prompt.submit(
+        fn=user_message,
+        inputs=[text_prompt, chatbot],
+        outputs=[text_prompt, chatbot],
+        queue=False,
+    ).then(fn=process_query, inputs=[chatbot], outputs=[chatbot])
+
+    upload_button.upload(
+        fn=upload_files, inputs=[upload_button, chatbot], outputs=[chatbot], queue=False
+    )
+    reset_button.click(fn=reset_app, inputs=[chatbot], outputs=[chatbot], queue=False)
+
+demo.queue().launch(share=True)

requirements.txt

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+ langchain
+ langchain-community 
+ langchainhub 
+ langchain-chroma 
+ langchain-groq
+ langchain-huggingface
+ unstructured[docx]
+ gradio
+ chromadb