cleaned reposne

Signed-off-by: SarveshAtawane <[email protected]>

src/core/conversation.py

@@ -54,7 +54,7 @@ def generate_response(session_id, model, tokenizer, query, vectordb):
     Provide a very concise answer in no more than three short sentences."""
 
     full_prompt = f"{qa_system_prompt}\n\nContext: {context}\n\nQuestion: {contextualized_query}\n\nAnswer:"
-    
+
     inputs = tokenizer(full_prompt, return_tensors="pt", padding=True).to(model.device)
     streamer = TextIteratorStreamer(
         tokenizer, skip_prompt=True, skip_special_tokens=True
@@ -75,4 +75,4 @@ def generate_response(session_id, model, tokenizer, query, vectordb):
     print(response)
     conversation_history.add_user_message(query)
     conversation_history.add_ai_message(response)
-    return response
+    return response

src/core/ingest.py

@@ -8,6 +8,7 @@
 from langchain.vectorstores import Chroma
 from embeddings import embedding_function
 from langchain_community.vectorstores.utils import filter_complex_metadata
+
 config_data = load_yaml_file("config.yaml")
 
 persist_directory = config_data["persist_directory"]
@@ -27,7 +28,9 @@
 splits = text_splitter.split_documents(docs)
 
 print(f"Number of chunks created: {len(splits)}")
-vectorstore = Chroma.from_documents(filter_complex_metadata(splits), embeddings, persist_directory=persist_directory)
+vectorstore = Chroma.from_documents(
+    filter_complex_metadata(splits), embeddings, persist_directory=persist_directory
+)
 # vectorstore = Chroma.from_documents(
 #     documents=splits, embedding=embeddings, persist_directory=persist_directory
 # )

src/core/routes/main.py

@@ -7,19 +7,23 @@
 
 router = APIRouter()
 
+
 class ResponseMessage(BaseModel):
     content: str
     type: int
     id: str
 
+
 class RequestQuery(BaseModel):
     id: str
     content: str
 
+
 class ResponseQuery(BaseModel):
     id: str
     message: ResponseMessage
 
+
 @router.post("/query", response_model=ResponseQuery)
 async def answer_query(item: RequestQuery) -> ResponseQuery:
     try: