initial commit

app.py

@@ -1,64 +1,185 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.document_loaders import WebBaseLoader
+from langchain_community.vectorstores import Chroma
+from langchain_community.embeddings.fastembed import FastEmbedEmbeddings
+from groq import Groq
+from langchain_groq import ChatGroq
+from langchain.prompts import PromptTemplate
+from langchain_core.output_parsers import JsonOutputParser, StrOutputParser
+import os
+from langchain_community.tools.tavily_search import TavilySearchResults
+from typing_extensions import TypedDict
+from typing import List
+from langchain.schema import Document
+from langgraph.graph import END, StateGraph
+
+# Environment setup
+os.environ['TAVILY_API_KEY'] = "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"
+
+# Model and embedding setup
+embed_model = FastEmbedEmbeddings(model_name="BAAI/bge-base-en-v1.5")
+llm = ChatGroq(temperature=0, model_name="Llama3-8b-8192", api_key="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")
+
+# Load documents from URLs
+urls = ["https://lilianweng.github.io/posts/2023-06-23-agent/",
+        "https://lilianweng.github.io/posts/2023-03-15-prompt-engineering/",
+        "https://lilianweng.github.io/posts/2023-10-25-adv-attack-llm/"]
+
+docs = [WebBaseLoader(url).load() for url in urls]
+docs_list = [item for sublist in docs for item in sublist]
+
+# Document splitting
+text_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(chunk_size=512, chunk_overlap=0)
+doc_splits = text_splitter.split_documents(docs_list)
+
+# Vectorstore setup
+vectorstore = Chroma.from_documents(documents=doc_splits, embedding=embed_model, collection_name="local-rag")
+retriever = vectorstore.as_retriever(search_kwargs={"k": 2})
+
+# Prompt templates
+question_router_prompt = PromptTemplate(
+    template="""<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are an expert at routing a 
+    user question to a vectorstore or web search. Use the vectorstore for questions on LLM agents, 
+    prompt engineering, and adversarial attacks. Otherwise, use web-search. Give a binary choice 'web_search' 
+    or 'vectorstore' based on the question. Return a JSON with a single key 'datasource' and no preamble. 
+    Question to route: {question} <|eot_id|><|start_header_id|>assistant<|end_header_id|>""",
+    input_variables=["question"],
+)
+
+question_router = question_router_prompt | llm | JsonOutputParser()
+
+rag_chain_prompt = PromptTemplate(
+    template="""<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are an assistant for question-answering tasks. 
+    Use the following pieces of retrieved context to answer the question concisely. <|eot_id|><|start_header_id|>user<|end_header_id|>
+    Question: {question} 
+    Context: {context} 
+    Answer: <|eot_id|><|start_header_id|>assistant<|end_header_id|>""",
+    input_variables=["question", "document"],
+)
+
+# Chain
+rag_chain = rag_chain_prompt | llm | StrOutputParser()
+
+# Web search tool
+web_search_tool = TavilySearchResults(k=3)
+
+# Workflow functions
+def retrieve(state):
+    question = state["question"]
+    documents = retriever.invoke(question)
+    return {"documents": documents, "question": question}
+
+def generate(state):
+    question = state["question"]
+    documents = state["documents"]
+    generation = rag_chain.invoke({"context": documents, "question": question})
+    return {"documents": documents, "question": question, "generation": generation}
+
+def route_question(state):
+    question = state["question"]
+    source = question_router.invoke({"question": question})
+    return "websearch" if source['datasource'] == 'web_search' else "vectorstore"
+
+def web_search(state):
+    question = state["question"]
+    docs = web_search_tool.invoke({"query": question})
+    web_results = Document(page_content="\n".join([d["content"] for d in docs]))
+    documents = state.get("documents", [])
+    documents.append(web_results)
+    return {"documents": documents, "question": question}
+
+workflow = StateGraph(TypedDict("GraphState", {"question": str, "generation": str, "documents": List[Document]}))
+
+# Define the nodes
+workflow.add_node("websearch", web_search)
+workflow.add_node("retrieve", retrieve)
+workflow.add_node("generate", generate)
+
+workflow.set_conditional_entry_point(
+    route_question,
+    {
+        "websearch": "websearch",
+        "vectorstore": "retrieve",
+    },
 )
 
+workflow.add_edge("retrieve", "generate")
+workflow.add_edge("websearch", "generate")
+
+# Compile the app
+app = workflow.compile()
+
+# Gradio integration with Chatbot
+
+# Updated ask_question_conversation function
+def ask_question_conversation(history, question):
+    inputs = {"question": question}
+    generation_result = None
+    
+    # Run the workflow and get the generation result
+    for output in app.stream(inputs):
+        for key, value in output.items():
+            generation_result = value.get("generation", "No generation found")
+    
+    # Append the new question and response to the history
+    history.append((question, generation_result))
+    
+    # Return the updated history to chatbot and clear the question textbox
+    return history, ""
+
+# Gradio conversation UI
+'''
+with gr.Blocks() as demo:
+    gr.Markdown("🤖 Multi-Agent Knowledge Assistant: Powered by RAG for Smart Answers!")
+    
+    chatbot = gr.Chatbot(label="Chat with AI Assistant")
+    question = gr.Textbox(label="Your Question", placeholder="Ask your question here...")
+    clear = gr.Button("Clear Conversation")
+    
+    # Submit action for the question textbox
+    question.submit(ask_question_conversation, [chatbot, question], [chatbot, question])
+    clear.click(lambda: [], None, chatbot)  # Clear conversation history
+
+demo.launch()
+'''
+
+with gr.Blocks(css="""
+    #title { 
+        font-size: 26px; 
+        font-weight: bold; 
+        text-align: center; 
+        color: #4A90E2;
+    }
+    #subtitle {
+        font-size: 18px; 
+        text-align: center; 
+        margin-top: -15px; 
+        color: #7D7D7D;
+    }
+    .gr-chatbot, .gr-textbox, .gr-button {
+        max-width: 600px;
+        margin: 0 auto;
+    }
+    .gr-chatbot {
+        height: 400px;
+    }
+    .gr-button {
+        display: block;
+        width: 100px;
+        margin: 20px auto;
+        background-color: #4A90E2;
+        color: white;
+    }
+""") as demo:
+    gr.Markdown("<div id='title'>🤖 Multi-Agent Knowledge Assistant: Powered by RAG for Smart Answers!</div>")
+    
+    chatbot = gr.Chatbot(label="Chat with AI Assistant")
+    question = gr.Textbox(label="Ask a Question", placeholder="Type your question here...", lines=1)
+    clear = gr.Button("Clear Chat")
+    
+    # Submit action for the question textbox
+    question.submit(ask_question_conversation, [chatbot, question], [chatbot, question])
+    clear.click(lambda: [], None, chatbot)  # Clear conversation history
 
-if __name__ == "__main__":
-    demo.launch()
+demo.launch()