Updated with info from modern langchain docs

app.py

@@ -1,57 +1,53 @@
-import os
-import sys
-import openai
-from dotenv import load_dotenv, find_dotenv
-import gradio as gr
-from langchain.chains import ConversationalRetrievalChain, RetrievalQA
+from langchain.prompts import (
+    ChatPromptTemplate,
+    MessagesPlaceholder,
+    SystemMessagePromptTemplate,
+    HumanMessagePromptTemplate,
+)
+from langchain.chains import LLMChain, ConversationalRetrievalChain
 from langchain.chat_models import ChatOpenAI
-from langchain.document_loaders import DirectoryLoader, TextLoader
+from langchain.memory import ConversationBufferMemory
+from langchain.document_loaders import TextLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain.embeddings import OpenAIEmbeddings
-from langchain.indexes import VectorstoreIndexCreator
-from langchain.indexes.vectorstore import VectorStoreIndexWrapper
-from langchain.llms import OpenAI
 from langchain.vectorstores import Chroma
+import gradio as gr
 
-#load_dotenv(find_dotenv())
-
-# Enable to save to disk & reuse the model (for repeated queries on the same data)
-PERSIST = False
-
-query = None
-
-if PERSIST and os.path.exists("persist"):
-    print("Reusing index...\n")
-    vectorstore = Chroma(persist_directory="persist",
-                         embedding_function=OpenAIEmbeddings())
-    index = VectorStoreIndexWrapper(vectorstore=vectorstore)
-else:
-    loader = DirectoryLoader("data/")
-    if PERSIST:
-        index = VectorstoreIndexCreator(
-            vectorstore_kwargs={"persist_directory": "persist"}).from_loaders([loader])
-    else:
-        index = VectorstoreIndexCreator().from_loaders([loader])
-
-chain = ConversationalRetrievalChain.from_llm(
-    llm=ChatOpenAI(model="gpt-3.5-turbo"),
-    retriever=index.vectorstore.as_retriever(search_kwargs={"k": 1}),
-)
 
-# Stores chat history: TODO: Modify behavior to be like a queue after reaching a certain max (probably RAM dependent)
-chat_history = []
+# LLM
+llm = ChatOpenAI()
 
+# load data
+loader = TextLoader('data/codes.txt')
+data = loader.load()
+
+# Split and store into vector
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+
+text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)
+all_splits = text_splitter.split_documents(data)
+vectorstore = Chroma.from_documents(documents=all_splits, embedding=OpenAIEmbeddings(), persist_directory='persist')
+
+# Prompt 
+prompt = ChatPromptTemplate(
+    messages=[
+        SystemMessagePromptTemplate.from_template(
+            "You are a nice chatbot having a conversation with a human about UF student government and its 800 codes which are about the budget and student org funding. Please reference context first when trying to respond to queries."
+        ),
+        # The `variable_name` here is what must align with memory
+        MessagesPlaceholder(variable_name="chat_history"),
+        HumanMessagePromptTemplate.from_template("{question}")
+    ]
+)
 
-async def getQuery(query: str):
-    if not query:
-        query = input("Prompt: ")
-    if query in ['quit', 'q', 'exit']:
-        sys.exit()
-    result = chain({"question": query, "chat_history": chat_history})
-    print(result['answer'])
+# Notice that we `return_messages=True` to fit into the MessagesPlaceholder
+# Notice that `"chat_history"` aligns with the MessagesPlaceholder name
+retriever = vectorstore.as_retriever()
+memory = ConversationBufferMemory(memory_key="chat_history",return_messages=True)
+qa = ConversationalRetrievalChain.from_llm(llm, retriever=retriever, memory=memory)
 
-    chat_history.append((query, result['answer']))
-    query = None
-    return result['answer']
+def queryAI(query: str):
+    return qa({"question": query})["answer"]
 
-demo = gr.Interface(fn=getQuery, inputs="text", outputs="text", title="800_Codes_AI_Chat_Bot", flagging_options=(['Incorrect', 'Great Response']))
-demo.launch(share=False)
+gui = gr.Interface(fn=queryAI, inputs="text", outputs="text", title="800 Codes Bot - EXPERIMENTAL")
+gui.launch()

flagged/log.csv

@@ -1,2 +1,4 @@
 query,output,flag,username,timestamp
 Tell me about student org travel,"Student org travel requires prior written approval from the Student Body Treasurer and the Student Government Finance Manager. All travel authorization forms must be submitted and approved by the student organization President and Treasurer on the online activity request system at least ten business days before the trip. If prior written approval is not granted, the Student Government cannot be held responsible for any costs incurred on the trip. Exceptions to the submission timeline may be made in extreme circumstances, but require a letter of justification signed by the organizational president or treasurer and the approval of the Student Body Treasurer.",Great Response,,2023-08-18 15:14:59.713209
+Tell me about the 800 codes,"The 800 codes refer to administrative regulations related to salaries, secretarial, and hiring within the organization.",Incorrect,,2023-08-18 16:10:56.130656
+All funds allocated to student organizations for the purpose of copies shall have a copies line,"Based on the provided information, it is unclear if every student organization budget includes a specific line item for copies. The information only mentions that expenditures from a particular line item can be used to support the copy center. It is possible that some organizations may have a separate line item for copies, while others may include it under a different category or have a shared budget for printing expenses. It would be best to consult the specific guidelines or policies of the student organization budget for more accurate information.",Incorrect,,2023-08-18 16:19:42.437307