initial commit

app/rag_chain.py

@@ -0,0 +1,39 @@
+from llm.ollama import load_llm
+from vectordb.vector_store import retrieve
+from langchain.prompts import PromptTemplate
+from langchain_core.output_parsers import StrOutputParser
+
+# Define the prompt template for the LLM
+prompt = PromptTemplate(
+    template="""You are an assistant for question-answering tasks.
+    Use the following context to answer the question.
+    If you don't know the answer, just say that you don't know.
+    Use three sentences maximum and keep the answer concise:
+    Question: {question}
+    Context: {context}
+    Answer:
+    """,
+    input_variables=["question", "documents"],
+)
+
+def get_rag_response(query):
+	print("⌄⌄⌄⌄ Retrieving ⌄⌄⌄⌄")
+	retrieved_docs, metadata = retrieve(query, 10)
+	print("Query Found %d documents." % len(retrieved_docs[0]))
+	for meta in metadata[0]:
+		print("Metadata: ", meta)
+	print("⌃⌃⌃⌃ Retrieving ⌃⌃⌃⌃ " )
+
+	print("⌄⌄⌄⌄ Augmented Prompt ⌄⌄⌄⌄")
+	llm = load_llm()
+	# Create a chain combining the prompt template and LLM
+	rag_chain = prompt | llm | StrOutputParser()
+	context = " ".join(retrieved_docs[0]) if retrieved_docs else "No relevant documents found."
+	print("⌃⌃⌃⌃ Augmented Prompt ⌃⌃⌃⌃")
+	
+	print("⌄⌄⌄⌄ Generation ⌄⌄⌄⌄")
+	response = rag_chain.invoke({"question": query, "context": context});
+	print(response)
+	print("⌃⌃⌃⌃ Generation ⌃⌃⌃⌃")
+ 
+	return response

app/streamlit_app.py

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+import streamlit as st
+from app.rag_chain import get_rag_response
+
+st.title("RAG System")
+query = st.text_input("Ask a question:")
+if query:
+	response = get_rag_response(query)
+	st.write("### Response:")
+	st.write(response)