refactored project to use poetry

rag_system/app/rag_chain.py

@@ -0,0 +1,44 @@
+from llm.ollama import load_llm
+from vectordb.azure_search 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 = retrieve(query, 10)
+    print("Query Found %d documents." % len(retrieved_docs))
+
+    print("⌃⌃⌃⌃ Retrieving ⌃⌃⌃⌃ ")
+
+    print("⌄⌄⌄⌄ Augmented Prompt ⌄⌄⌄⌄")
+    llm = load_llm()
+    # Create a chain combining the prompt template and LLM
+    rag_chain = prompt | llm | StrOutputParser()
+    context = (
+        (" ".join(doc.page_content) for doc in retrieved_docs)
+        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

rag_system/app/streamlit_app.py

@@ -0,0 +1,9 @@
+import streamlit as st
+from rag_system.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)