Update app.py

app.py

@@ -10,6 +10,7 @@ import time
 
 from langchain.chains import LLMChain, RetrievalQA
 from langchain.chat_models import ChatOpenAI
+from langchain.retrievers.self_query.base import SelfQueryRetriever
 from langchain.document_loaders import PyPDFLoader, WebBaseLoader, UnstructuredWordDocumentLoader, DirectoryLoader
 from langchain.document_loaders.blob_loaders.youtube_audio import YoutubeAudioLoader
 from langchain.document_loaders.generic import GenericLoader
@@ -186,7 +187,7 @@ def document_storage_chroma(splits):
 
 #Vektorstore vorbereiten...
 #dokumente in chroma db vektorisiert ablegen können - die Db vorbereiten daüfur
-def document_retrieval_chroma(): 
+def document_retrieval_chroma(prompt): 
     #OpenAI embeddings -------------------------------
     embeddings = OpenAIEmbeddings()
 
@@ -199,8 +200,8 @@ def document_retrieval_chroma():
     #ChromaDb um die embedings zu speichern
     db = Chroma(embedding_function = embeddings, persist_directory = PATH_WORK + CHROMA_DIR)
     print ("Chroma DB bereit ...................")
-    llm =  OpenAI(temperature=0.5)
-    retriever =  SelfQueryRetriever.from_llm(llm,vectorstore,document_content_description,metadata_field_info,enable_limit=True,verbose=True,)
+    llm =  ChatOpenAI(temperature=0.5)
+    retriever =  SelfQueryRetriever.from_llm(llm,vectorstore,document_content_description=prompt,metadata_field_info,enable_limit=True,verbose=True,)
 
     return db, retriever
 
@@ -280,7 +281,7 @@ def generate(text, history, rag_option, model_option,  temperature=0.5, max_new_
             if not splittet:
                 splits = document_loading_splitting()
                 document_storage_chroma(splits)
-            db, retriever = document_retrieval_chroma()
+            db, retriever = document_retrieval_chroma(text)
             #mit RAG:
             neu_text_mit_chunks = rag_chain(text, db, retriever)
             #für Chat LLM: