adding app files

app.py

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+import streamlit as st
+import transformers
+from dotenv import load_dotenv, find_dotenv
+import os
+
+from langchain_community.embeddings import HuggingFaceInferenceAPIEmbeddings
+from langchain_community.vectorstores import MongoDBAtlasVectorSearch
+
+from huggingface_hub import InferenceClient
+from pymongo import MongoClient
+from pymongo.collection import Collection
+from typing import Dict, Any
+from datetime import datetime
+
+
+load_dotenv(find_dotenv('.secrets.env'))
+
+MONGO_URI = os.getenv("MONGO_URI")
+HF_TOKEN = os.getenv("HF_TOKEN")
+DB_NAME = "txts"
+COLLECTION_NAME = "txts_collection"
+VECTOR_SEARCH_INDEX = "vector_index"
+
+
+@st.cache_resource
+def init_mongodb():
+    # MongoDB configuration.
+    cluster = MongoClient(MONGO_URI)
+    return cluster[DB_NAME][COLLECTION_NAME]
+
+@st.cache_resource
+def init_vector_search() -> MongoDBAtlasVectorSearch:
+    print('CACHING VECTOR SEARCH')
+    return MongoDBAtlasVectorSearch.from_connection_string(
+        connection_string=MONGO_URI,
+        namespace=f"{DB_NAME}.{COLLECTION_NAME}",
+        embedding=embedding_model,
+        index_name=VECTOR_SEARCH_INDEX,
+    )
+
+@st.cache_resource
+def init_embedding_model() -> HuggingFaceInferenceAPIEmbeddings:
+    return HuggingFaceInferenceAPIEmbeddings(
+        api_key=HF_TOKEN,
+        model_name="sentence-transformers/all-mpnet-base-v2",
+    )
+
+def get_context_from_retrived_docs(retrieved_docs):
+    return "\n\n".join(doc.page_content for doc in retrieved_docs)
+
+def format_prompt(user_query, retreived_context):
+    prompt = f"""Use the following pieces of context to answer the question at the end.
+
+    START OF CONTEXT:
+    {retreived_context}
+    END OF CONTEXT:
+
+    START OF QUESTION:
+    {user_query}
+    END OF QUESTION:
+
+    If you do not know the answer, just say that you do not know.
+    NEVER assume things.
+    """.format(retreived_context=retreived_context, user_query=user_query)
+
+    return prompt
+
+
+## INITALIZE YOUR DB, EMBEDDER, AND VECTOR SEARCHER, AND INTERFACE CLIENTS. 
+mongodb_collection = init_mongodb()
+embedding_model = init_embedding_model()
+vector_search = init_vector_search()
+hf_client = InferenceClient(api_key=HF_TOKEN)
+
+# GET USERS INPUT
+user_query = st.text_area('Ask a question about CTP Class')
+
+if user_query:
+
+    # DO RAG SEARCH TO GET REVELENT DOCUMENTS
+    relevent_documents = vector_search.similarity_search(query=user_query, k=10) # 10 most similar documents.
+
+    # EXTRACT THE TEXT FROM THE DOCUMENTS
+    context = get_context_from_retrived_docs(relevent_documents)
+
+    # PUT THAT TEXT INTO THE PROMPT
+    prompt = format_prompt(user_query=user_query, retreived_context=context)
+    
+    # SEND USER QUERY WITH CONTEXT TO MODEL
+    response = hf_client.chat.completions.create(
+        model="Qwen/Qwen2.5-1.5B-Instruct",
+        messages=[{
+            "role": "system",
+            "content": 'you are an assistant, answer the question below'
+        },{
+            "role": "user",
+            "content": prompt
+        }],
+        max_tokens=1400,
+        temperature=0.2,
+    )
+    model_response = response.choices[0].message.content
+    st.text(model_response)

requirements.txt

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+transformers
+huggingface_hub==0.26.2
+langchain==0.3.7
+langchain_community==0.3.5
+pymongo==4.10.1
+streamlit==1.39.0