changed to gpt-4o-mini model

functions.py

@@ -1,97 +1,97 @@
-from langchain_community.document_loaders import PyMuPDFLoader
-from langchain_text_splitters import RecursiveCharacterTextSplitter
-from langchain_openai import AzureOpenAIEmbeddings, AzureChatOpenAI
-from operator import itemgetter
-from langchain_core.runnables import RunnablePassthrough
-from langchain_qdrant import QdrantVectorStore
-from qdrant_client import QdrantClient
-from qdrant_client.http.models import Distance, VectorParams
-from langchain.prompts import ChatPromptTemplate
-import tiktoken
-import os
-
-### SETUP FUNCTIONS ###
-def tiktoken_len(text):
-    tokens = tiktoken.encoding_for_model("gpt-4o").encode(
-        text,
-    )
-    return len(tokens)
-
-def setup_vector_db():
-
-    # Get the directory of the current file
-    current_file_directory = os.path.dirname(os.path.abspath(__file__))
-    # Change the working directory to the current file's directory
-    os.chdir(current_file_directory)
-
-    # Load the NIST AI document
-    PDF_LINK = "data/nist_ai.pdf"
-    loader = PyMuPDFLoader(file_path=PDF_LINK)
-    nist_doc = loader.load()
-
-    text_splitter = RecursiveCharacterTextSplitter(
-        chunk_size = 500,
-        chunk_overlap = 100,
-        length_function = tiktoken_len,
-    )
-
-    nist_chunks = text_splitter.split_documents(nist_doc)
-
-    embeddings_small = AzureOpenAIEmbeddings(azure_deployment="text-embedding-3-small")
-
-    qdrant_client = QdrantClient(":memory:") # set Qdrant DB and its location (in-memory)
-
-    qdrant_client.create_collection(
-        collection_name="NIST_AI",
-        vectors_config=VectorParams(size=1536, distance=Distance.COSINE),
-    )
-
-    qdrant_vector_store = QdrantVectorStore(
-        client=qdrant_client,
-        collection_name="NIST_AI",
-        embedding=embeddings_small,
-    ) # create a QdrantVectorStore object with the above specified client, collection name, and embedding model.
-
-    qdrant_vector_store.add_documents(nist_chunks) # add the documents to the QdrantVectorStore
-
-    retriever = qdrant_vector_store.as_retriever()
-
-    return retriever
-
-### VARIABLES ###
-
-# define a global variable to store the retriever object
-retriever = setup_vector_db()
-qa_gpt4_llm = AzureChatOpenAI(azure_deployment="gpt-4", temperature=0) # GPT-4o model
-
-# define a template for the RAG model
-rag_template = """
-You are a helpful assistant that helps users find information and answer their question. 
-You MUST use ONLY the available context to answer the question.
-If necessary information to answer the question cannot be found in the provided context, you MUST "I don't know."
-
-Question:
-{question}
-
-Context:
-{context}
-"""
-# create rag prompt object from the template
-prompt = ChatPromptTemplate.from_template(rag_template)
-
-# update the chain with LLM, prompt, and question variable.
-retrieval_augmented_qa_chain = (
-    {"context": itemgetter("question") | retriever, "question": itemgetter("question")}
-    | RunnablePassthrough.assign(context=itemgetter("context"))
-    | {"response": prompt | qa_gpt4_llm, "context": itemgetter("context"), "question": itemgetter("question")}
-)
-
-### FUNCTIONS ###
-
-
-def get_response(query, history):
-    """A helper function to get the response from the RAG model and return it to the UI."""
-
-    response = retrieval_augmented_qa_chain.invoke({"question" : query})
-    
+from langchain_community.document_loaders import PyMuPDFLoader
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain_openai import AzureOpenAIEmbeddings, AzureChatOpenAI
+from operator import itemgetter
+from langchain_core.runnables import RunnablePassthrough
+from langchain_qdrant import QdrantVectorStore
+from qdrant_client import QdrantClient
+from qdrant_client.http.models import Distance, VectorParams
+from langchain.prompts import ChatPromptTemplate
+import tiktoken
+import os
+
+### SETUP FUNCTIONS ###
+def tiktoken_len(text):
+    tokens = tiktoken.encoding_for_model("gpt-4o").encode(
+        text,
+    )
+    return len(tokens)
+
+def setup_vector_db():
+
+    # Get the directory of the current file
+    current_file_directory = os.path.dirname(os.path.abspath(__file__))
+    # Change the working directory to the current file's directory
+    os.chdir(current_file_directory)
+
+    # Load the NIST AI document
+    PDF_LINK = "data/nist_ai.pdf"
+    loader = PyMuPDFLoader(file_path=PDF_LINK)
+    nist_doc = loader.load()
+
+    text_splitter = RecursiveCharacterTextSplitter(
+        chunk_size = 500,
+        chunk_overlap = 100,
+        length_function = tiktoken_len,
+    )
+
+    nist_chunks = text_splitter.split_documents(nist_doc)
+
+    embeddings_small = AzureOpenAIEmbeddings(azure_deployment="text-embedding-3-small")
+
+    qdrant_client = QdrantClient(":memory:") # set Qdrant DB and its location (in-memory)
+
+    qdrant_client.create_collection(
+        collection_name="NIST_AI",
+        vectors_config=VectorParams(size=1536, distance=Distance.COSINE),
+    )
+
+    qdrant_vector_store = QdrantVectorStore(
+        client=qdrant_client,
+        collection_name="NIST_AI",
+        embedding=embeddings_small,
+    ) # create a QdrantVectorStore object with the above specified client, collection name, and embedding model.
+
+    qdrant_vector_store.add_documents(nist_chunks) # add the documents to the QdrantVectorStore
+
+    retriever = qdrant_vector_store.as_retriever()
+
+    return retriever
+
+### VARIABLES ###
+
+# define a global variable to store the retriever object
+retriever = setup_vector_db()
+qa_gpt4_llm = AzureChatOpenAI(azure_deployment="gpt-4o-mini", temperature=0) # GPT-4o-mini model
+
+# define a template for the RAG model
+rag_template = """
+You are a helpful assistant that helps users find information and answer their question. 
+You MUST use ONLY the available context to answer the question.
+If necessary information to answer the question cannot be found in the provided context, you MUST "I don't know."
+
+Question:
+{question}
+
+Context:
+{context}
+"""
+# create rag prompt object from the template
+prompt = ChatPromptTemplate.from_template(rag_template)
+
+# update the chain with LLM, prompt, and question variable.
+retrieval_augmented_qa_chain = (
+    {"context": itemgetter("question") | retriever, "question": itemgetter("question")}
+    | RunnablePassthrough.assign(context=itemgetter("context"))
+    | {"response": prompt | qa_gpt4_llm, "context": itemgetter("context"), "question": itemgetter("question")}
+)
+
+### FUNCTIONS ###
+
+
+def get_response(query, history):
+    """A helper function to get the response from the RAG model and return it to the UI."""
+
+    response = retrieval_augmented_qa_chain.invoke({"question" : query})
+    
     return response["response"].content