Update app.py

app.py

@@ -16,6 +16,8 @@ from pymongo import MongoClient
 from PyPDF2 import PdfReader
 
 from langchain_community.embeddings import HuggingFaceInferenceAPIEmbeddings
+from langchain.embeddings import HuggingFaceEmbeddings
+
 from langchain_community.vectorstores import MongoDBAtlasVectorSearch
 from langchain.prompts import PromptTemplate
 from langchain.schema import Document
@@ -52,21 +54,26 @@ Respond truthfully. If the answer is not available, say "This information is not
 )
 
 # =================== Vector Search Setup ===================
+@st.cache_resource
+def init_embedding_model():
+    return HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
+    
 @st.cache_resource
 def init_vector_search() -> MongoDBAtlasVectorSearch:
     from langchain_community.embeddings import HuggingFaceInferenceAPIEmbeddings
     from langchain_community.vectorstores import MongoDBAtlasVectorSearch
 
     HF_TOKEN = os.getenv("HF_TOKEN", "").strip()
-    model_name = "thenlper/gte-small"
+    # model_name = "thenlper/gte-small"
 
     try:
         st.write(f"🔌 Connecting to Hugging Face model: `{model_name}`")
-        embedding_model = HuggingFaceInferenceAPIEmbeddings(
-            api_key=HF_TOKEN,
-            model_name=model_name
-        )
+        # embedding_model = HuggingFaceInferenceAPIEmbeddings(
+        #     api_key=HF_TOKEN,
+        #     model_name=model_name
+        # )
 
+        embedding_model=init_embedding_model()
         # Test if embedding works
         test_vector = embedding_model.embed_query("Test query for Grant Buddy")
         st.success(f"✅ HF embedding model connected. Vector length: {len(test_vector)}")
@@ -174,462 +181,4 @@ def main():
 if __name__ == "__main__":
     main()
 
-# # Import libraries.
-# import os
-# import streamlit as st
-
-# from dotenv import load_dotenv, find_dotenv
-# from huggingface_hub import InferenceClient
-# from langchain.prompts import PromptTemplate
-# from langchain.schema import Document
-# from langchain.schema.runnable import RunnablePassthrough, RunnableLambda
-# # from langchain_community.embeddings import HuggingFaceInferenceAPIEmbeddings
-# from langchain.embeddings import OpenAIEmbeddings
-# from langchain_community.vectorstores import MongoDBAtlasVectorSearch
-# from pymongo import MongoClient
-# from pymongo.collection import Collection
-# from typing import Dict, Any
-# from langchain.chat_models import ChatOpenAI
-
-
-
-# #############################################################################################################################
-
-
-# class RAGQuestionAnswering:
-#     def __init__(self):
-#         """
-#         Parameters
-#         ----------
-#         None
-
-#         Output
-#         ------
-#         None
-
-#         Purpose
-#         -------
-#         Initializes the RAG Question Answering system by setting up configuration
-#         and loading environment variables.
-
-#         Assumptions
-#         -----------
-#         - Expects .env file with MONGO_URI and HF_TOKEN
-#         - Requires proper MongoDB setup with vector search index
-#         - Needs connection to Hugging Face API
-
-#         Notes
-#         -----
-#         This is the main class that handles all RAG operations
-#         """
-#         self.load_environment()
-#         self.setup_mongodb()
-#         self.setup_embedding_model()
-#         self.setup_vector_search()
-#         self.setup_rag_chain()
-
-#     def load_environment(self) -> None:
-#         """
-#         Parameters
-#         ----------
-#         None
-
-#         Output
-#         ------
-#         None
-
-#         Purpose
-#         -------
-#         Loads environment variables from .env file and sets up configuration constants.
-
-#         Assumptions
-#         -----------
-#         Expects a .env file with MONGO_URI and HF_TOKEN defined
-
-#         Notes
-#         -----
-#         Will stop the application if required environment variables are missing
-#         """
-
-#         load_dotenv(find_dotenv())
-#         self.MONGO_URI = os.getenv("MONGO_URI")
-#         # self.HF_TOKEN = os.getenv("HF_TOKEN")
-#         self.OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
-
-
-#         if not self.MONGO_URI or not self.OPENAI_API_KEY:
-#             st.error("Please ensure MONGO_URI and OPENAI_API_KEY are set in your .env file")
-#             st.stop()
-
-#         # MongoDB configuration.
-#         self.DB_NAME = "txts"
-#         self.COLLECTION_NAME = "txts_collection"
-#         self.VECTOR_SEARCH_INDEX = "vector_index"
-
-#     def setup_mongodb(self) -> None:
-#         """
-#         Parameters
-#         ----------
-#         None
-
-#         Output
-#         ------
-#         None
-
-#         Purpose
-#         -------
-#         Initializes the MongoDB connection and sets up the collection.
-
-#         Assumptions
-#         -----------
-#         - Valid MongoDB URI is available
-#         - Database and collection exist in MongoDB Atlas
-
-#         Notes
-#         -----
-#         Uses st.cache_resource for efficient connection management
-#         """
-
-#         @st.cache_resource
-#         def init_mongodb() -> Collection:
-#             cluster = MongoClient(self.MONGO_URI)
-#             return cluster[self.DB_NAME][self.COLLECTION_NAME]
-
-#         self.mongodb_collection = init_mongodb()
-
-#     def setup_embedding_model(self) -> None:
-#         """
-#         Parameters
-#         ----------
-#         None
-
-#         Output
-#         ------
-#         None
-
-#         Purpose
-#         -------
-#         Initializes the embedding model for vector search.
-
-#         Assumptions
-#         -----------
-#         - Valid Hugging Face API token
-#         - Internet connection to access the model
-
-#         Notes
-#         -----
-#         Uses the all-mpnet-base-v2 model from sentence-transformers
-#         """
-
-#         # @st.cache_resource
-#         # def init_embedding_model() -> HuggingFaceInferenceAPIEmbeddings:
-#         #     return HuggingFaceInferenceAPIEmbeddings(
-#         #         api_key=self.HF_TOKEN,
-#         #         model_name="sentence-transformers/all-mpnet-base-v2",
-#         #     )
-
-#         @st.cache_resource
-#         def init_embedding_model() -> OpenAIEmbeddings:
-#             return OpenAIEmbeddings(model="text-embedding-3-small", openai_api_key=self.OPENAI_API_KEY)
-
-#         self.embedding_model = init_embedding_model()
-
-#     def setup_vector_search(self) -> None:
-#         """
-#         Parameters
-#         ----------
-#         None
-
-#         Output
-#         ------
-#         None
-
-#         Purpose
-#         -------
-#         Sets up the vector search functionality using MongoDB Atlas.
-
-#         Assumptions
-#         -----------
-#         - MongoDB Atlas vector search index is properly configured
-#         - Valid embedding model is initialized
-
-#         Notes
-#         -----
-#         Creates a retriever with similarity search and score threshold
-#         """
-
-#         @st.cache_resource
-#         def init_vector_search() -> MongoDBAtlasVectorSearch:
-#             return MongoDBAtlasVectorSearch.from_connection_string(
-#                 connection_string=self.MONGO_URI,
-#                 namespace=f"{self.DB_NAME}.{self.COLLECTION_NAME}",
-#                 embedding=self.embedding_model,
-#                 index_name=self.VECTOR_SEARCH_INDEX,
-#             )
-
-#         self.vector_search = init_vector_search()
-#         self.retriever = self.vector_search.as_retriever(
-#             search_type="similarity", search_kwargs={"k": 10, "score_threshold": 0.85}
-#         )
-
-#     def format_docs(self, docs: list[Document]) -> str:
-#         """
-#         Parameters
-#         ----------
-#         **docs:** list[Document] - List of documents to be formatted
-
-#         Output
-#         ------
-#         str: Formatted string containing concatenated document content
-
-#         Purpose
-#         -------
-#         Formats the retrieved documents into a single string for processing
-
-#         Assumptions
-#         -----------
-#         Documents have page_content attribute
-
-#         Notes
-#         -----
-#         Joins documents with double newlines for better readability
-#         """
-
-#         return "\n\n".join(doc.page_content for doc in docs)
-
-#     # def generate_response(self, input_dict: Dict[str, Any]) -> str:
-#     #     """
-#     #     Parameters
-#     #     ----------
-#     #     **input_dict:** Dict[str, Any] - Dictionary containing context and question
-
-#     #     Output
-#     #     ------
-#     #     str: Generated response from the model
-
-#     #     Purpose
-#     #     -------
-#     #     Generates a response using the Hugging Face model based on context and question
-
-#     #     Assumptions
-#     #     -----------
-#     #     - Valid Hugging Face API token
-#     #     - Input dictionary contains 'context' and 'question' keys
-
-#     #     Notes
-#     #     -----
-#     #     Uses Zephyr model with controlled temperature
-#     #     """
-#     #     hf_client = InferenceClient(api_key=self.HF_TOKEN)
-#     #     formatted_prompt = self.prompt.format(**input_dict)
-
-#     #     response = hf_client.chat.completions.create(
-#     #         model="HuggingFaceH4/zephyr-7b-beta"
-
-#     #         messages=[
-#     #             {"role": "system", "content": formatted_prompt},
-#     #             {"role": "user", "content": input_dict["question"]},
-#     #         ],
-#     #         max_tokens=1000,
-#     #         temperature=0.2,
-#     #     )
-
-#     #     return response.choices[0].message.content
-#     from langchain.chat_models import ChatOpenAI
-#     from langchain.schema.messages import SystemMessage, HumanMessage
-
-#     def generate_response(self, input_dict: Dict[str, Any]) -> str:
-#         llm = ChatOpenAI(
-#         model="gpt-4",  # or "gpt-3.5-turbo"
-#         temperature=0.2,
-#         openai_api_key=self.OPENAI_API_KEY,
-#     )
-    
-#     messages = [
-#         SystemMessage(content=self.prompt.format(**input_dict)),
-#         HumanMessage(content=input_dict["question"]),
-#     ]
-    
-#     return llm(messages).content
-
-
-#     def setup_rag_chain(self) -> None:
-#         """
-#         Parameters
-#         ----------
-#         None
-
-#         Output
-#         ------
-#         None
-
-#         Purpose
-#         -------
-#         Sets up the RAG chain for processing questions and generating answers
-
-#         Assumptions
-#         -----------
-#         Retriever and response generator are properly initialized
-
-#         Notes
-#         -----
-#         Creates a chain that combines retrieval and response generation
-#         """
-
-#         self.prompt = PromptTemplate.from_template(
-#             """Use the following pieces of context to answer the question at the end.
-
-#             START OF CONTEXT:
-#             {context}
-#             END OF CONTEXT:
-            
-#             START OF QUESTION:
-#             {question}
-#             END OF QUESTION:
-
-#             If you do not know the answer, just say that you do not know.
-#             NEVER assume things.
-#             """
-#         )
-
-#         self.rag_chain = {
-#             "context": self.retriever | RunnableLambda(self.format_docs),
-#             "question": RunnablePassthrough(),
-#         } | RunnableLambda(self.generate_response)
-
-#     def process_question(self, question: str) -> str:
-#         """
-#         Parameters
-#         ----------
-#         **question:** str - The user's question to be answered
 
-#         Output
-#         ------
-#         str: The generated answer to the question
-
-#         Purpose
-#         -------
-#         Processes a user question through the RAG chain and returns an answer
-
-#         Assumptions
-#         -----------
-#         - Question is a non-empty string
-#         - RAG chain is properly initialized
-
-#         Notes
-#         -----
-#         Main interface for question-answering functionality
-#         """
-
-#         return self.rag_chain.invoke(question)
-
-
-# #############################################################################################################################
-# def setup_streamlit_ui() -> None:
-#     """
-#     Parameters
-#     ----------
-#     None
-
-#     Output
-#     ------
-#     None
-
-#     Purpose
-#     -------
-#     Sets up the Streamlit user interface with proper styling and layout
-
-#     Assumptions
-#     -----------
-#     - CSS file exists at ./static/styles/style.css
-#     - Image file exists at ./static/images/ctp.png
-
-#     Notes
-#     -----
-#     Handles all UI-related setup and styling
-#     """
-
-#     st.set_page_config(page_title="RAG Question Answering", page_icon="🤖")
-
-#     # Load CSS.
-#     with open("./static/styles/style.css") as f:
-#         st.markdown(f"<style>{f.read()}</style>", unsafe_allow_html=True)
-
-#     # Title and subtitles.
-#     st.markdown(
-#         '<h1 align="center" style="font-family: monospace; font-size: 2.1rem; margin-top: -4rem">RAG Question Answering</h1>',
-#         unsafe_allow_html=True,
-#     )
-#     st.markdown(
-#         '<h3 align="center" style="font-family: monospace; font-size: 1.5rem; margin-top: -2rem">Using Zoom Closed Captioning From The Lectures</h3>',
-#         unsafe_allow_html=True,
-#     )
-#     st.markdown(
-#         '<h2 align="center" style="font-family: monospace; font-size: 1.5rem; margin-top: 0rem">CUNY Tech Prep Tutorial 5</h2>',
-#         unsafe_allow_html=True,
-#     )
-
-#     # Display logo.
-#     left_co, cent_co, last_co = st.columns(3)
-#     with cent_co:
-#         st.image("./static/images/ctp.png")
-
-
-# #############################################################################################################################
-
-
-# def main():
-#     """
-#     Parameters
-#     ----------
-#     None
-
-#     Output
-#     ------
-#     None
-
-#     Purpose
-#     -------
-#     Main function that runs the Streamlit application
-
-#     Assumptions
-#     -----------
-#     All required environment variables and files are present
-
-#     Notes
-#     -----
-#     Entry point for the application
-#     """
-
-#     # Setup UI.
-#     setup_streamlit_ui()
-
-#     # Initialize RAG system.
-#     rag_system = RAGQuestionAnswering()
-
-#     # Create input elements.
-#     query = st.text_input("Question:", key="question_input")
-
-#     # Handle submission.
-#     if st.button("Submit", type="primary"):
-#         if query:
-#             with st.spinner("Generating response..."):
-#                 response = rag_system.process_question(query)
-#                 st.text_area("Answer:", value=response, height=200, disabled=True)
-#         else:
-#             st.warning("Please enter a question.")
-
-#     # Add GitHub link.
-#     st.markdown(
-#         """
-#         <p align="center" style="font-family: monospace; color: #FAF9F6; font-size: 1rem;">
-#         <b>Check out our <a href="https://github.com/GeorgiosIoannouCoder/" style="color: #FAF9F6;">GitHub repository</a></b>
-#         </p>
-#         """,
-#         unsafe_allow_html=True,
-#     )
-
-
-# #############################################################################################################################
-# if __name__ == "__main__":
-#     main()