Create app.py

app.py

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+# from langchain.embeddings import OpenAIEmbeddings
+import gradio as gr
+from langchain_community.chat_models import ChatOpenAI
+from langchain.chains import RetrievalQA
+from langchain_openai import OpenAIEmbeddings
+from langchain.vectorstores import Qdrant
+from langchain.document_loaders import TextLoader
+from langchain.text_splitter import CharacterTextSplitter
+from qdrant_client.models import VectorParams, Distance
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+# Load your documents
+loader = TextLoader("about_me.txt")
+documents = loader.load()
+
+# Split the documents into manageable chunks
+text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
+docs = text_splitter.split_documents(documents)
+
+# Embed the documents using OpenAI's embeddings
+embeddings = OpenAIEmbeddings()
+
+# Create the Qdrant vector store using connection parameters
+vector_store = Qdrant.from_documents(
+    documents=docs,
+    embedding=embeddings,
+    url=os.getenv("QDRANT_URL"),
+    api_key=os.getenv("QDRANT_API_KEY"),
+    collection_name="akilesh_docs"
+)
+
+# Set up the OpenAI LLM
+llm = ChatOpenAI(model_name="gpt-4")
+
+# Assume vector_store is defined elsewhere, and create a retriever from it
+retriever = vector_store.as_retriever()
+
+# Create the RAG chain using the retriever
+qa_chain = RetrievalQA.from_chain_type(
+    llm=llm,
+    chain_type="stuff",
+    retriever=retriever
+)
+
+print(qa_chain)
+
+
+def respond(message):
+    # Generate the LLM response
+    result = qa_chain({"query": message})
+    response = result['result']
+    return response
+
+
+def generate_answer(message: str, history: list) -> str:
+    # Get the new response from the LLM
+    new_response = respond(message)
+    return new_response
+
+
+# Create the ChatInterface
+demo = gr.ChatInterface(
+    fn=generate_answer, title="RAG App | Learn More About Me!", multimodal=False, retry_btn=None, undo_btn=None, clear_btn=None)
+
+# Launch the Gradio interface
+demo.launch()