Revert "resolve dependency issues"

This reverts commit 392709392804c11cb7c318ed25400c861a71caa1.

.env

@@ -1,3 +0,0 @@
-OPENAI_API_KEY = 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
-QDRANT_URL = https://63f1dc5d-951d-4539-be73-94d4f725452f.us-east4-0.gcp.cloud.qdrant.io:6333
-QDRANT_API_KEY = x_KgU_SErG-GhKnhGaoi94S4Lrna83s-rwFdcqOyUJbHGVAtgmPflQ

.gitattributes

@@ -32,4 +32,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.xz filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
-*tfevents* filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -1,2 +0,0 @@
-.ipynb
-virt/

app.py

@@ -1,81 +0,0 @@
-import gradio as gr
-from langchain.vectorstores import Qdrant
-import qdrant_client
-from langchain.schema.runnable import RunnablePassthrough
-from langchain.schema.output_parser import StrOutputParser
-from langchain.prompts import ChatPromptTemplate
-from langchain.chat_models import ChatOpenAI
-from langchain.embeddings import OpenAIEmbeddings
-import dotenv
-import os
-from utils import template
-import time
-
-# Load environment variables and validate
-dotenv.load_dotenv()
-QDRANT_URL = os.getenv("QDRANT_URL")
-QDRANT_API_KEY = os.getenv("QDRANT_API_KEY")
-if not QDRANT_URL or not QDRANT_API_KEY:
-    raise ValueError("QDRANT_URL and QDRANT_API_KEY must be set in the environment")
-
-# Initialize the vector store
-def initiliaze_vector_store():
-    """
-    Initialize and return the vector store.
-    Only run this on launch.
-    """
-    embeddings = OpenAIEmbeddings()
-    client = qdrant_client.QdrantClient(url=QDRANT_URL, api_key=QDRANT_API_KEY)
-    vectorstore = Qdrant(client=client, collection_name="doc_datategy", embeddings=embeddings)
-    return vectorstore
-
-# Initialize the retriever
-def initiliaze_retriever(vectorstore):
-    """
-    Initialize and return the retriever using the given vectorstore.
-    """
-    return vectorstore.as_retriever()
-
-# Initialize the chatbot
-def initiliaze_chatbot(template, model_name="gpt-3.5-turbo-1106", temperature=0):
-    """
-    Initialize and return the chatbot components: prompt and language model.
-    """
-    prompt = ChatPromptTemplate.from_template(template)
-    llm = ChatOpenAI(model_name=model_name, temperature=temperature)
-    return prompt, llm
-
-# Initialize the RAG chain
-def initiliaze_RAG(retriever, prompt, llm):
-    """
-    Initialize and return the RAG chain.
-    """
-    context_function = {"context": retriever, "question": RunnablePassthrough()}
-    rag_chain = context_function | prompt | llm | StrOutputParser()
-    return rag_chain
-
-# Launch Gradio app
-vectorstore = initiliaze_vector_store()
-retriever = initiliaze_retriever(vectorstore)
-
-with gr.Blocks() as demo:
-    chatbot = gr.Chatbot(label="PapAI custom chatbot")
-    msg = gr.Textbox(label="Prompt", value='PapAI?', interactive=True)
-    clear = gr.Button("Clear")
-    template_user = gr.Textbox(label="Template", value=template, interactive=True)
-
-    def change_template(template_user_str):
-        prompt, llm = initiliaze_chatbot(template_user_str)
-        return initiliaze_RAG(retriever, prompt, llm)
-
-    def RAG_answer(query, chat_history, template_user_str):
-        rag_chain = change_template(template_user_str)
-        answer = rag_chain.invoke(query)
-        chat_history.append((query, answer))
-        time.sleep(1.3)  # Consider optimizing or dynamic handling
-        return "", chat_history
-
-    msg.submit(RAG_answer, [msg, chatbot, template_user], [msg, chatbot])
-
-demo.queue()
-demo.launch(share=False, debug=True)

utils.py

@@ -1,10 +0,0 @@
-template = """You are an assistant specialized in answering (specify type, e.g., data analysis/statistics/machine learning) questions for Data Science projects. Follow these steps to answer:
-1. Verify if the provided context is relevant to the question. If not, request more specific information or additional context.
-2. Use the following pieces of retrieved context to answer the question. Incorporate data-driven insights or references when possible.
-3. If the answer requires external data science tools or datasets, mention this in your response.
-4. If you don't know the answer or if the information is insufficient to formulate an answer, clearly state that.
-5. Keep your answer concise, using a maximum of three sentences.
-Question: {question} 
-Context: {context} 
-Answer:
-"""