bug fix

app.py

@@ -24,8 +24,6 @@ source_directory = os.environ.get('SOURCE_DIRECTORY', 'source_documents')
 from constants import CHROMA_SETTINGS
 
 
-embeddings = HuggingFaceEmbeddings(model_name=embeddings_model_name)
-llm = HuggingFacePipeline.from_model_id(model_id=model, task="text-generation", device=0, model_kwargs={"temperature":0.1,"trust_remote_code": True, "max_length":100000, "top_p":0.15, "top_k":0, "repetition_penalty":1.1, "num_return_sequences":1,})
 
 # async def test_embedding():
 #     # Create the folder if it doesn't exist
@@ -102,10 +100,15 @@ def embed_documents(files, collection_name: Optional[str] = None):
 def retrieve_documents(query: str, collection_name:str):
     target_source_chunks = 4
     mute_stream = ""
+    embeddings = HuggingFaceEmbeddings(model_name=embeddings_model_name)
+
     db = Chroma(persist_directory=persist_directory,collection_name=collection_name, embedding_function=embeddings, client_settings=CHROMA_SETTINGS)
     retriever = db.as_retriever(search_kwargs={"k": target_source_chunks})
     # Prepare the LLM
     callbacks = [] if mute_stream else [StreamingStdOutCallbackHandler()]
+
+    llm = HuggingFacePipeline.from_model_id(model_id=model, task="text-generation", device=0, model_kwargs={"temperature":0.1,"trust_remote_code": True, "max_length":100000, "top_p":0.15, "top_k":0, "repetition_penalty":1.1, "num_return_sequences":1,})
+
     qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever, return_source_documents=False)
     
     # Get the answer from the chain

privateGPT.py

@@ -5,8 +5,6 @@ from langchain.vectorstores import Chroma
 from langchain.llms import GPT4All, LlamaCpp
 import os
 
-load_dotenv()
-
 # embeddings_model_name = os.environ.get("EMBEDDINGS_MODEL_NAME")
 # persist_directory = os.environ.get('PERSIST_DIRECTORY')