Create rag_utils.py

utils/rag_utils.py

@@ -0,0 +1,27 @@
+# rag_utils.py
+
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.vectorstores import FAISS
+from langchain.chains import RetrievalQA
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_groq import ChatGroq
+
+def create_vectorstore_from_text(text: str):
+    splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
+    texts = splitter.split_text(text)
+    
+    embeddings = HuggingFaceEmbeddings(
+        model_name="sentence-transformers/all-MiniLM-L6-v2",
+        model_kwargs={"device": "cpu"}
+    )
+    
+    vectorstore = FAISS.from_texts(texts, embedding=embeddings)
+    return vectorstore
+
+def create_rag_chain(vectorstore):
+    retriever = vectorstore.as_retriever(search_kwargs={"k": 3})
+    
+    llm = ChatGroq(model_name="llama3-8b-8192", temperature=0)
+    
+    rag_chain = RetrievalQA.from_chain_type(llm=llm, retriever=retriever)
+    return rag_chain