Initial upload of app_logic.py

app_logic.py

@@ -0,0 +1,54 @@
+import os
+from llama_cpp import Llama
+from langchain_community.vectorstores import Chroma
+from langchain_community.embeddings.sentence_transformer import SentenceTransformerEmbeddings
+
+def load_embeddings():
+    """Initializes and returns the sentence transformer embedding model."""
+    return SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
+
+def initialize_vector_db(persist_directory):
+    """Loads the existing Chroma database and returns a retriever object."""
+    embedding_function = load_embeddings()
+    db = Chroma(persist_directory=persist_directory, embedding_function=embedding_function)
+    return db.as_retriever(search_type="similarity", search_kwargs={"k": 3})
+
+def load_llm_model(model_path):
+    """Initializes and returns the Llama LLM object."""
+    return Llama(
+        model_path=model_path,
+        n_ctx=2048,
+        n_threads=4,
+        n_gpu_layers=-1
+    )
+
+def get_rag_response(query, llm, retriever):
+    """Encapsulates retrieval and generation logic to provide a grounded response."""
+    # 1. Retrieve relevant context
+    relevant_docs = retriever.get_relevant_documents(query)
+    context = ". ".join([doc.page_content for doc in relevant_docs])
+
+    # 2. Define prompt templates
+    system_message = """[INST] You are a helpful medical assistant that answers questions based on the provided context from the Merck Manual of Diagnosis and Therapy.
+Your responses should be accurate, well-structured, and based strictly on the provided context. [/INST]"""
+    
+    user_message = f"""Context:
+{context}
+
+Question:
+{query}
+
+Please provide a detailed and accurate answer based on the context above. [/INST]"""
+
+    full_prompt = f"{system_message}\n{user_message}"
+
+    # 3. Generate response
+    output = llm(
+        prompt=full_prompt,
+        max_tokens=512,
+        temperature=0,
+        top_p=0.95,
+        top_k=50
+    )
+    
+    return output['choices'][0]['text'].strip()