Update app.py

app.py

@@ -1,30 +1,52 @@
 import openai
 import gradio as gr
+from langchain.chains import RetrievalQA
+from langchain.chains.question_answering import load_qa_cha
+from langchain.llms import OpenAI
+from langchain.document_loaders import TextLoader
+from langchain.indexes import VectorstoreIndexCreator
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.vectorstores import Chroma
 
 # Initialize OpenAI API key
 openai.api_key = "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"
 
 # Load text from file
-with open("Dropsheets.txt", "r") as f:
-    text = f.read()
+loader = TextLoader("Dropsheets.txt")
+documents = loader.load()
+
+# split the documents into chunks
+text_splitter = RecursiveCharacterTextSplitter(chunk_size=1024, chunk_overlap=0)
+texts = text_splitter.split_documents(documents)
+
+# select embeddings 
+embeddings = OpenAIEmbeddings()
+
+# create the vectorestore to use as the index
+db = Chroma.from_documents(texts, embeddings)
+
+# expose this index in a retriever interface
+retriever = db.as_retriever(search_type="similarity", search_kwargs={"k":2})
+
 
 # Define OpenAI GPT-3.5 model function
-def generate_text(prompt):
-    prompt += "\n" + text
-    response = openai.Completion.create(
-        engine="text-davinci-002",
-        temperature=0,
-        max_tokens=7000,
-        prompt=prompt
-    )
-    return response.choices[0].text.strip()      
+## def generate_text(query):
+#    response = openai.Completion.create(
+ #       engine="text-davinci-002",
+ #       temperature=0,
+  #      max_tokens=7000,
+   #     prompt=prompt
+#    )
+ #   return response.choices[0].text.strip()      
 
     
 # Create Gradio interface
 input_text = gr.Textbox(label="Enter prompt", type="text")
 output_text = gr.Textbox(label="AI response", type="text")
 demo = gr.Interface(
-    fn = generate_text,
+    fn = None,
     inputs=input_text,
     outputs=output_text,
     title="AI Chatbot for PlanetTogether Knowledge Base",
@@ -33,5 +55,12 @@ demo = gr.Interface(
     theme="default"
 )
 
+# create a chain to answer questions 
+qa = RetrievalQA.from_chain_type(
+    llm=OpenAI(), chain_type="stuff", retriever=retriever)
+result = qa({"query": query})
+retriever.get_relevant_documents(query)
+
+
 # Launch demo
 demo.launch()