Update app.py

app.py

@@ -25,7 +25,7 @@ from langchain.chains import RetrievalQA
 #tasks such as sentiment analysis, entity extraction, and content creation. The types of content that the PaLM 2 for
 #Text models can create include document summaries, answers to questions, and labels that classify content.
 
-llm = HuggingFaceEndpoint(repo_id="mistralai/Mistral-7B-Instruct-v0.2", Temperature=0.9)
+llm = HuggingFaceEndpoint(repo_id="mistralai/Mistral-7B-Instruct-v0.2", Temperature=0.3)
 #model = SentenceTransformer("all-MiniLM-L6-v2")
 
 #llm = VertexAI(model_name="text-bison@001",max_output_tokens=256,temperature=0.1,top_p=0.8,top_k=40,verbose=True,)
@@ -51,7 +51,7 @@ def get_text(url):
         for paragraph in paragraphs:
             file.write(paragraph.get_text() + "\n")
 
-#@st.cache_resource
+@st.cache_resource
 def create_langchain_index(input_text):
     print("--indexing---")
     get_text(input_text)
@@ -65,8 +65,8 @@ def create_langchain_index(input_text):
     # load it into Chroma
     db = Chroma.from_documents(docs, embeddings)
     persist_directory = "chroma_db"  
-    #vectordb = Chroma.from_documents(documents=docs, embedding=embeddings, persist_directory=persist_directory)
-    #new_db = Chroma(persist_directory=persist_directory, embedding_function=embeddings)
+    vectordb = Chroma.from_documents(documents=docs, embedding=embeddings, persist_directory=persist_directory)
+    db = Chroma(persist_directory=persist_directory, embedding_function=embeddings)
     return db
 
 # @st.cache_resource
@@ -79,7 +79,7 @@ def create_langchain_index(input_text):
 #     return summary_response,tweet_response,ln_response
 
 
-#@st.cache_data
+@st.cache_data
 def get_response(input_text,query,db):
     print(f"--querying---{query}")
     retrieval_chain = RetrievalQA.from_chain_type(llm, chain_type="stuff", retriever=db.as_retriever())