Update util.py

util.py

@@ -9,9 +9,9 @@ import git
 
 # embeddings = HuggingFaceHubEmbeddings(model="thuan9889/llama_embedding_model_v1")
 from chromadb.utils import embedding_functions
-embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001", google_api_key=os.environ['GOOGLE_API_KEY'], task_type="retrieval_query")
+embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001", google_api_key=os.environ['GOOGLE_API_KEY'], task_type="retrieval_document")
 
-model = ChatGoogleGenerativeAI(model="gemini-pro",google_api_key=os.environ['GOOGLE_API_KEY'],temperature=0.2,convert_system_message_to_human=True)
+model = ChatGoogleGenerativeAI(model="gemini-pro",google_api_key=os.environ['GOOGLE_API_KEY'],temperature=0.2,convert_system_message_to_human=False)
 
 def get_folder_paths(directory = "githubCode"):
     folder_paths = []
@@ -44,23 +44,20 @@ loader = TextLoader("Code.txt", encoding="utf-8")
 pages = loader.load_and_split()
 
 # Split data into chunks
-text_splitter = RecursiveCharacterTextSplitter(chunk_size=2000, chunk_overlap=200)
+text_splitter = RecursiveCharacterTextSplitter(chunk_size=4000, chunk_overlap=200)
 context = "\n\n".join(str(p.page_content) for p in pages)
 texts = text_splitter.split_text(context)
 
-vector_index = Chroma.from_texts(texts, embeddings).as_retriever(search_kwargs={"k":5})
+vector_index = Chroma.from_texts(texts, embeddings).as_retriever(search_kwargs={"k":3})
 
-# import shutil
-# shutil.rmtree('githubCode')
-# print("Directory removed!!")
 qa_chain = RetrievalQA.from_chain_type(
     model,
     retriever=vector_index,
     return_source_documents=True
 )
-
+    
 # Function to generate assistant's response using ask function
-def generate_assistant_response(question):
+def ask(question):
     answer = qa_chain({"query": question})
     print(answer)
     return answer['result']