Streamlit query

DocChat.py

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+from langchain.embeddings import SentenceTransformerEmbeddings #HuggingFaceInstructEmbeddings
+from langchain.vectorstores import FAISS
+import os 
+import copy
+import pprint
+#import google.generativeai as palm
+from langchain.llms import GooglePalm
+from langchain import PromptTemplate
+from langchain.chains import RetrievalQA
+import streamlit as st
+import warnings
+warnings.filterwarnings("ignore")
+
+
+@st.cache_resource
+def getapi():
+    return str(open("API.txt","r",encoding='utf-8').read())
+
+
+PALM_API=getapi()
+#palm.configure(api_key=PALM_API)
+
+
+@st.cache_resource
+def getmodel():
+    "test"
+    embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
+    db = FAISS.load_local("faiss", embeddings)
+    retriever = db.as_retriever(search_kwargs={'k': 10})
+    #prompt=getprompt()
+    llm=GooglePalm(google_api_key=PALM_API,temperature=0,max_output_tokens=512)
+    qa_llm = RetrievalQA.from_chain_type(llm=llm,
+                                          chain_type='refine',
+                                          retriever=retriever,
+                                          return_source_documents=True,
+                                          #chain_type_kwargs={'prompt': prompt},
+                                        verbose=True)
+    return qa_llm
+
+@st.cache_resource
+def getprompt():
+    template = """Use the information to elaborate in points about the user's query.
+    If user mentions something not in the 'Context', just answer that you don't know.
+    If you don't know the answer, just say that you don't know, don't try to make up an answer.
+    
+    Context: {context}
+    
+    Query: {question}
+    
+    Only return the helpful answer below and nothing else.
+    
+    Helpful answer:
+    """
+    prompt = PromptTemplate(
+        template=template,
+        input_variables=['context', 'question'])
+    return prompt
+
+def parseresult(result):
+
+    PARSED=copy.deepcopy(result)
+    docs=PARSED['source_documents']
+    sourcepage=[]
+    for d in docs:
+        sourcepage.append(d.metadata['page'])
+    PARSED['source_pages']=copy.deepcopy(sourcepage)
+    del sourcepage,result
+    return PARSED
+
+def getsources(result):
+    sources=[]
+    for s in result['source_documents']:
+        sources.append(f"{s.metadata}")
+    return sources
+
+st.title('Query Docs')
+
+prompt=st.sidebar.text_input("Enter query")
+try:
+    llm=getmodel()
+except:
+    st.write("CANNOT LOAD MODEL OR DATABASE")
+    #print("ERROR LOADING MODEL OR DATABASE")
+
+if prompt:
+    if prompt.find("exit")==0:
+        import sys
+        sys.exit()
+    try:
+        result=parseresult(llm(prompt))
+        sources=getsources(result)
+        result=result["result"]
+    except:
+        result="Error in retrieving! \n You can try reframing your query, if it doesnt work there may be something broken. \n :/ "
+        sources=[]
+
+    print(">>>>>>>>>>>>><<<<<<<<<<<<<<<<<")
+    st.header("Result")
+    st.write(result)
+    st.header("Sources")
+    st.write(sources)