Update rag.py

app/rag.py

@@ -16,7 +16,7 @@ class ChatPDF:
 
     def __init__(self):
         self.model = ChatOllama(model="qwen:0.5b")
-        self.text_splitter = RecursiveCharacterTextSplitter(chunk_size=256, chunk_overlap=16)
+        self.text_splitter = RecursiveCharacterTextSplitter(chunk_size=128, chunk_overlap=8)
         self.prompt = PromptTemplate.from_template(
             """
             You are an assistant for question-answering tasks. Use the following pieces of context 
@@ -33,13 +33,7 @@ class ChatPDF:
         chunks = filter_complex_metadata(chunks)
 
         vector_store = Chroma.from_documents(documents=chunks, embedding=FastEmbedEmbeddings())
-        self.retriever = vector_store.as_retriever(
-            search_type="similarity_score_threshold",
-            search_kwargs={
-                "k": 28,
-                "score_threshold": 0.5,
-            },
-        )
+        self.retriever = vector_store.as_retriever(search_kwargs={"k": 56})
 
         self.chain = ({"context": self.retriever, "question": RunnablePassthrough()}
                       | self.prompt