Update app.py

app.py

@@ -1,125 +1,81 @@
+# app.py
 import os
-import warnings
 from dotenv import load_dotenv
-import gradio as gr
-
-
+from pydantic import BaseModel
 from qdrant_search import QdrantSearch
 from langchain_groq import ChatGroq
 from nomic_embeddings import EmbeddingsModel
+import gradio as gr
 
-
-
-
-# Load environment variables from .env file
 load_dotenv()
 
-# Suppress FutureWarnings
+import warnings
 warnings.filterwarnings("ignore", category=FutureWarning)
 
-# Disable tokenizers parallelism to avoid potential issues
 os.environ["TOKENIZERS_PARALLELISM"] = "FALSE"
 
-
-
-
-
-
-
-
-
-
-
-
-
 # Initialize global variables
 collection_names = ["docs_v1_2", "docs_v2_2", "docs_v3_2"]
 limit = 5
+llm = ChatGroq(model="mixtral-8x7b-32768")
+embeddings = EmbeddingsModel()
+search = QdrantSearch(
+    qdrant_url=os.environ["QDRANT_CLOUD_URL"],
+    api_key=os.environ["QDRANT_API_KEY"],
     embeddings=embeddings
 )
 
-def chat_endpoint(question: str):
-    """
-    Handles the chat functionality by processing the user's question,
-    retrieving relevant documents, generating an answer, and returning sources.
-
-    Args:
-        question (str): The user's question.
-
-
-    Returns:
-        Tuple[str, str]: The generated answer and the sources used.
-    """
-    query = question.strip()
-    if not query:
-        return "❌ **Error:** Query cannot be empty.", "No sources available."
-
-    # Step 1: Retrieve relevant documents from Qdrant
+# Define the query processing function
+def chat_with_langassist(query: str):
+    if not query.strip():
+        return "Query cannot be empty.", []
+    
+    # Retrieve relevant documents from Qdrant
     retrieved_docs = search.query_multiple_collections(query, collection_names, limit)
-
-    if not retrieved_docs:
-        return "⚠️ **No relevant documents found** for your query.", "No sources available."
-
-
-
-
-    # Step 2: Prepare the context from retrieved documents
-    context = "\n\n".join([doc['text'] for doc in retrieved_docs])
+    
+    # Prepare the context from retrieved documents
+    context = "\n".join([doc['text'] for doc in retrieved_docs])
+    
+    # Construct the prompt with context and question
+    prompt = (
+        "You are LangAssist, a knowledgeable assistant for the LangChain Python Library. "
+        "Given the following context from the documentation, provide a helpful answer to the user's question.\n\n"
+        "Context:\n{context}\n\n"
+        "Question: {question}\n\n"
+        "Answer:"
+    ).format(context=context, question=query)
+    
+    # Generate an answer using the language model
     try:
-        answer = llm.invoke(prompt)
+        answer = llm.invoke(prompt).content.strip()
     except Exception as e:
-        return f"⚠️ **Error generating answer:** {str(e)}", "No sources available."
-
+        return f"Error: {str(e)}", []
+    
     # Prepare sources
-    sources_md = "\n\n".join([
-        f"**Source:** {src['source']}\n**Excerpt:** {src['text']}"
-        for src in retrieved_docs
-    ])
-
-    return answer.content.strip(), sources_md
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-# Create Gradio Interface
-interface = gr.Interface(
-    fn=chat_endpoint,
-    inputs=gr.Textbox(
-        lines=2,
-        placeholder="Type your question here...",
-    description="Ask questions about the LangChain Python Library and get answers based on the latest documentation."
-)
-
-# If running locally, uncomment the following lines:
-
-
-
-
-
-
-
-
-
-# if __name__ == "__main__":
-#     interface.launch()
+    sources = [
+        {
+            "source": doc['source'],
+            "text": doc['text']
+        } for doc in retrieved_docs
+    ]
+    
+    return answer, sources
+
+# Define Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("<h1>LangAssist Chat</h1>")
+    chatbot = gr.Chatbot()
+    msg = gr.Textbox()
+    clear = gr.Button("Clear")
+
+    def respond(message, chat_history):
+        answer, sources = chat_with_langassist(message)
+        chat_history.append((message, answer))
+        return chat_history, gr.update(value=''), sources
+
+    msg.submit(respond, [msg, chatbot], [chatbot, msg])
+    clear.click(lambda: None, None, chatbot)
+
+# Run the Gradio app
+if __name__ == "__main__":
+    demo.launch()