\n",
"
\n",
" \n",
@@ -160,19 +162,19 @@
"GROK_API_KEY = os.getenv(\"GROQ_API_KEY\")\n",
"\n",
"# Setting up Base LLM\n",
- "Settings.llm = OpenAI(\n",
- " model=\"gpt-4o-mini\", temperature=0.1, max_tokens=1024, streaming=True\n",
- ")\n",
+ "# Settings.llm = OpenAI(\n",
+ "# model=\"gpt-4o-mini\", temperature=0.1, max_tokens=1024, streaming=True\n",
+ "# )\n",
"\n",
- "# Settings.llm = Groq(model=\"llama3-70b-8192\" , api_key=GROK_API_KEY)\n",
+ "Settings.llm = Groq(model=\"llama-3.1-70b-versatile\" , api_key=GROK_API_KEY)\n",
"\n",
"# Set the embedding model\n",
"# Option 1: Use FastEmbed with BAAI/bge-base-en-v1.5 model (default)\n",
- "# Settings.embed_model = FastEmbedEmbedding(model_name=\"BAAI/bge-base-en-v1.5\")\n",
+ "Settings.embed_model = FastEmbedEmbedding(model_name=\"BAAI/bge-base-en-v1.5\")\n",
"\n",
"# Option 2: Use OpenAI's embedding model (commented out)\n",
"# If you want to use OpenAI's embedding model, uncomment the following line:\n",
- "Settings.embed_model = OpenAIEmbedding(embed_batch_size=10, api_key=OPENAI_API_KEY)\n",
+ "# Settings.embed_model = OpenAIEmbedding(embed_batch_size=10, api_key=OPENAI_API_KEY)\n",
"\n",
"# Qdrant configuration (commented out)\n",
"# If you're using Qdrant, uncomment and set these variables:\n",
@@ -220,7 +222,7 @@
"print(\"🔃 Loading Data\")\n",
"\n",
"from llama_index.core import Document\n",
- "reader = SimpleDirectoryReader(\"../data/\" , recursive=True)\n",
+ "reader = SimpleDirectoryReader(\"./content/\" , recursive=True)\n",
"documents = reader.load_data(show_progress=True)"
]
},
@@ -277,11 +279,11 @@
" # otherwise set Qdrant instance address with:\n",
" # url=QDRANT_CLOUD_ENDPOINT,\n",
" # otherwise set Qdrant instance with host and port:\n",
- " host=\"localhost\",\n",
- " port=6333\n",
+ " # host=\"localhost\",\n",
+ " # port=6333\n",
" # set API KEY for Qdrant Cloud\n",
" # api_key=QDRANT_API_KEY,\n",
- " # path=\"./db/\"\n",
+ " path=\"./db/\"\n",
")\n",
"\n",
"vector_store = QdrantVectorStore(client=client, collection_name=\"01_Basic_RAG\")"
@@ -552,6 +554,161 @@
"for message in history:\n",
" print(f\"{message.role}: {message.content}\")"
]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Gradio Applicaiton"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import gradio as gr\n",
+ "from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, Document, Settings\n",
+ "from llama_index.vector_stores.qdrant import QdrantVectorStore\n",
+ "from llama_index.embeddings.openai import OpenAIEmbedding\n",
+ "from llama_index.llms.openai import OpenAI\n",
+ "import qdrant_client\n",
+ "import os\n",
+ "import tempfile\n",
+ "import shutil\n",
+ "from typing import List\n",
+ "from llama_index.core.base.llms.types import ChatMessage, MessageRole\n",
+ "\n",
+ "class RAGChatbot:\n",
+ " def __init__(self):\n",
+ " self.client = qdrant_client.QdrantClient(host=\"localhost\", port=6333)\n",
+ " self.vector_store = None\n",
+ " self.index = None\n",
+ " self.chat_engine = None\n",
+ " self.chat_history = []\n",
+ " \n",
+ "\n",
+ " def process_uploaded_files(self, files) -> str:\n",
+ " try:\n",
+ " # Create a temporary directory for processing\n",
+ " with tempfile.TemporaryDirectory() as temp_dir:\n",
+ " # Save uploaded files to temporary directory\n",
+ " for file in files:\n",
+ " shutil.copy(file.name, temp_dir)\n",
+ " \n",
+ " # Load documents\n",
+ " reader = SimpleDirectoryReader(temp_dir)\n",
+ " documents = reader.load_data()\n",
+ " \n",
+ " # Create new collection name based on timestamp\n",
+ " import time\n",
+ " collection_name = f\"chat_collection_{int(time.time())}\"\n",
+ " \n",
+ " # Initialize vector store and index\n",
+ " self.vector_store = QdrantVectorStore(\n",
+ " client=self.client,\n",
+ " collection_name=collection_name\n",
+ " )\n",
+ " \n",
+ " # Create the index and ingest documents\n",
+ " self.index = VectorStoreIndex.from_documents(\n",
+ " documents,\n",
+ " vector_store=self.vector_store\n",
+ " )\n",
+ " \n",
+ " # Initialize chat engine\n",
+ " self.chat_engine = self.index.as_chat_engine(\n",
+ " streaming=True,\n",
+ " verbose=True\n",
+ " )\n",
+ " \n",
+ " return f\"Successfully processed {len(documents)} documents. Ready to chat!\"\n",
+ " \n",
+ " except Exception as e:\n",
+ " return f\"Error processing files: {str(e)}\"\n",
+ "\n",
+ " def chat(self, message: str, history: List[List[str]]) -> str:\n",
+ " if self.chat_engine is None:\n",
+ " return \"Please upload documents first before starting the chat.\"\n",
+ " \n",
+ " try:\n",
+ " # Convert history to ChatMessage format\n",
+ " chat_history = []\n",
+ " for h in history:\n",
+ " chat_history.extend([\n",
+ " ChatMessage(role=MessageRole.USER, content=h[0]),\n",
+ " ChatMessage(role=MessageRole.ASSISTANT, content=h[1])\n",
+ " ])\n",
+ " \n",
+ " # Add current message to history\n",
+ " chat_history.append(ChatMessage(role=MessageRole.USER, content=message))\n",
+ " \n",
+ " # Get response from chat engine\n",
+ " response = self.chat_engine.chat(message, chat_history=chat_history)\n",
+ " \n",
+ " return str(response)\n",
+ " \n",
+ " except Exception as e:\n",
+ " return f\"Error generating response: {str(e)}\"\n",
+ "\n",
+ "def create_demo():\n",
+ " # Initialize the chatbot\n",
+ " chatbot = RAGChatbot()\n",
+ " \n",
+ " with gr.Blocks(theme=gr.themes.Soft()) as demo:\n",
+ " gr.Markdown(\"# RAG Chatbot\")\n",
+ " gr.Markdown(\"Upload your documents and start chatting!\")\n",
+ " \n",
+ " with gr.Row():\n",
+ " with gr.Column(scale=1):\n",
+ " file_output = gr.File(\n",
+ " file_count=\"multiple\",\n",
+ " label=\"Upload Documents\",\n",
+ " file_types=[\".txt\", \".pdf\", \".docx\", \".md\"]\n",
+ " )\n",
+ " upload_button = gr.Button(\"Process Documents\")\n",
+ " status_box = gr.Textbox(label=\"Status\", interactive=False)\n",
+ " \n",
+ " with gr.Column(scale=2):\n",
+ " chatbot_interface = gr.Chatbot(\n",
+ " label=\"Chat History\",\n",
+ " height=400,\n",
+ " bubble_full_width=False,\n",
+ " )\n",
+ " msg = gr.Textbox(\n",
+ " label=\"Type your message\",\n",
+ " placeholder=\"Ask me anything about the uploaded documents...\",\n",
+ " lines=2\n",
+ " )\n",
+ " clear = gr.Button(\"Clear\")\n",
+ " \n",
+ " # Event handlers\n",
+ " upload_button.click(\n",
+ " fn=chatbot.process_uploaded_files,\n",
+ " inputs=[file_output],\n",
+ " outputs=[status_box],\n",
+ " )\n",
+ " \n",
+ " msg.submit(\n",
+ " fn=chatbot.chat,\n",
+ " inputs=[msg, chatbot_interface],\n",
+ " outputs=[chatbot_interface],\n",
+ " )\n",
+ " \n",
+ " clear.click(\n",
+ " lambda: None,\n",
+ " None,\n",
+ " chatbot_interface,\n",
+ " queue=False\n",
+ " )\n",
+ " \n",
+ " return demo\n",
+ "\n",
+ "if __name__ == \"__main__\":\n",
+ " demo = create_demo()\n",
+ " demo.launch(share=True)"
+ ]
}
],
"metadata": {
![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\"){kind=icon}