Upload app.py

app.py

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+from langchain_community.vectorstores import FAISS
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain.callbacks.manager import CallbackManager
+from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
+from langchain.chains import LLMChain
+from langchain.prompts import PromptTemplate
+from langchain_community.llms import LlamaCpp
+from langchain.memory import ConversationBufferMemory
+from langchain.chains import ConversationalRetrievalChain
+import streamlit as st
+from langchain.callbacks import StreamlitCallbackHandler
+import time
+st.set_page_config(layout="wide")
+
+embeddings = HuggingFaceEmbeddings(model_name="BAAI/llm-embedder")
+db = FAISS.load_local("faiss_index", embeddings)
+db_retriever = db.as_retriever(search_type="similarity",search_kwargs={"k": 3})
+callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])
+
+llm = LlamaCpp(
+model_path=r"C:\Users\TESS\Downloads\mistral-7b-instruct-v0.1.Q4_K_M.gguf",
+temperature=0.75,
+max_tokens=2000,
+n_ctx = 4000,
+top_p=1,
+n_gpu_layers=10)
+
+custom_prompt_template = """You are a medical practitioner who provides right medical information. Use the given following pieces of information to answer the user's question correctly. Add additional information from your knowledge base if necessary. If you don't know the answer, just say that you don't know, don't try to make up an answer.
+
+Context: {context}
+
+Current conversation: {chat_history}
+
+Human: {question}
+Only return the helpful answer below and nothing else.
+
+AI Assistant:
+"""
+prompt = PromptTemplate(template=custom_prompt_template,
+                        input_variables=['context', 'question','chat_history'])
+
+qa = ConversationalRetrievalChain.from_llm(
+    llm=llm,
+    memory=ConversationBufferMemory(
+        memory_key="chat_history",
+        ai_prefix="AI Assistant",
+        return_messages=True
+    ),
+    retriever=db_retriever,
+    combine_docs_chain_kwargs={'prompt': prompt}
+)
+
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+
+# st.write(st.session_state.messages)
+
+for message in st.session_state.messages:
+    with st.chat_message(message.get("role")):
+        st.write(message.get("content"))
+
+prompt = st.chat_input("Say something")
+
+if prompt:
+    with st.chat_message("user"):
+        st.write(prompt)
+
+    st.session_state.messages.append({"role":"user","content":prompt})
+
+    with st.chat_message("assistant"):
+        with st.status("Thinking...",expanded=True):
+            st_callback = StreamlitCallbackHandler(st.container())
+            result = qa.invoke(input=prompt,callbacks=[st_callback])
+            st.session_state.messages.append({"role":"assistant","content":result["answer"]})
+            message_placeholder = st.empty()
+            full_response = ""
+
+        for chunk in result["answer"].split():
+            full_response+=chunk+" "
+            time.sleep(0.10)
+
+            message_placeholder.markdown(full_response)