Update app.py

app.py

@@ -7,19 +7,20 @@ from langchain_community.document_loaders import pdf
 from langchain_community.embeddings import OllamaEmbeddings
 from langchain_community.vectorstores import Chroma
 from langchain_core.vectorstores import VectorStoreRetriever
-
+from langchain.embeddings import GPT4AllEmbeddings
 # instructions to start
 # https://www.linkedin.com/pulse/enhance-document-management-ai-extract-insights-from-pdfs-le-sueur-kfd5f/
 # https://github.com/RexiaAI/codeExamples/blob/main/localRAG/RAG.py
 # ollama pull nomic-embed-text
-load_dotenv()  #remove string if hosting in huggingface
+load_dotenv('secret.env')  #remove string if hosting in huggingface
 token = os.getenv('HUGGINGFACE_TOKEN')
 client = InferenceClient(
     "meta-llama/Meta-Llama-3-8B-Instruct",
     token=token,
 )
-print(token)
 
+model_name = "all-MiniLM-L6-v2.gguf2.f16.gguf"
+gpt4all_kwargs = {'allow_download': 'false'}
 # Load, split, and retrieve documents from a local PDF file
 def loadAndRetrieveDocuments() -> VectorStoreRetriever:
     loader = pdf.PyPDFLoader("k.pdf")  #constitution
@@ -27,7 +28,7 @@ def loadAndRetrieveDocuments() -> VectorStoreRetriever:
     textSplitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
     documentSplits = textSplitter.split_documents(documents)
     embeddings = OllamaEmbeddings(model="nomic-embed-text")
-    vectorStore = Chroma.from_documents(documents=documentSplits, embedding=embeddings)
+    vectorStore = Chroma.from_documents(documents=documentSplits, embedding=GPT4AllEmbeddings(model_name=model_name, gpt4all_kwargs=gpt4all_kwargs))
     return vectorStore.as_retriever()
 
 
@@ -85,4 +86,4 @@ interface = gr.Interface(
 )
 
 # Launch the app
-interface.launch()
+interface.launch()