Delete rag.py

rag.py

@@ -1,44 +0,0 @@
-from langchain.embeddings.openai import OpenAIEmbeddings
-from langchain.vectorstores import Chroma
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.document_loaders import TextLoader
-
-# We'll assume you have a documentation text file. If not, we can use some sample Python docs.
-# Let's create a sample if the file doesn't exist, or load it.
-
-def load_documents():
-    # Load the documents from a file (or multiple files)
-    # For demonstration, we'll create a sample document if it doesn't exist.
-    doc_path = "python_docs.txt"
-    if not os.path.exists(doc_path):
-        # Create a sample documentation about Python functions
-        with open(doc_path, 'w') as f:
-            f.write("""
-            Functions in Python are defined using the def keyword.
-            For example: def hello_world(): print("Hello, world!")
-            Functions can take parameters and return values.
-            """)
-    loader = TextLoader(doc_path)
-    documents = loader.load()
-    return documents
-
-def create_vector_store(documents):
-    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
-    texts = text_splitter.split_documents(documents)
-    embeddings = OpenAIEmbeddings()
-    vectorstore = Chroma.from_documents(documents=texts, embedding=embeddings)
-    return vectorstore
-
-def retrieve_relevant_docs(vectorstore, query, k=3):
-    """
-    Retrieve relevant documents for the query.
-    """
-    docs = vectorstore.similarity_search(query, k=k)
-    return "\n".join([doc.page_content for doc in docs])
-
-# Initialize the vector store once (for performance)
-documents = load_documents()
-vectorstore = create_vector_store(documents)
-
-def get_rag_context(query):
-    return retrieve_relevant_docs(vectorstore, query)