streamlit

app.py

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+import streamlit as st
+from llama_index.llms.ollama import Ollama
+from llama_index.embeddings.huggingface import HuggingFaceEmbedding
+from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, ChatPromptTemplate, Settings, set_global_tokenizer
+from llama_index.llms.huggingface_api import HuggingFaceInferenceAPI
+from transformers import AutoTokenizer
+from datetime import datetime
+from llama_index.core.memory import ChatMemoryBuffer
+import time
+
+# Define the data directory for loading documents
+DATA_DIR = "docs"
+
+# Mengecek apakah 'is_initialized' sudah ada di session state
+if 'is_initialized' not in st.session_state:
+    st.session_state.is_initialized = False
+
+# Inisialisasi yang hanya dilakukan sekali saat pertama kali load
+if not st.session_state.is_initialized:
+    st.session_state.is_initialized = True
+
+        
+    Settings.llm = HuggingFaceInferenceAPI(
+        model_name="HuggingFaceH4/zephyr-7b-beta",
+        tokenizer_name="HuggingFaceH4/zephyr-7b-beta",
+        context_window=3000,
+        max_new_tokens=512,
+        generate_kwargs={"temperature": 0.1},
+        # stream=True
+    )
+
+    Settings.embed_model = HuggingFaceEmbedding(
+        model_name="BAAI/bge-small-en-v1.5"
+    )
+
+    # Set the global tokenizer to use the tokenizer from HuggingFace for encoding inputs
+    set_global_tokenizer(
+        AutoTokenizer.from_pretrained("NousResearch/Llama-2-7b-chat-hf").encode
+    )
+
+    print("# load data", datetime.now())
+    # Load documents from the data directory into the Vector Store Index
+    documents = SimpleDirectoryReader(DATA_DIR).load_data()
+
+    # Create Vector Store Index with HuggingFace Embedding
+    index = VectorStoreIndex.from_documents(documents)
+
+    # Create Prompt Template for Text-based Q&A
+    chat_text_qa_msgs = [
+    (
+        "user",
+        """You are a Q&A assistant. For all other inquiries, your main goal is to provide answers as accurately as possible, based on the instructions and context you have been given. If a question does not match the provided context or is outside the scope of the document, kindly advise the user to ask questions within the context of the document.
+        Context:
+        {context_str}
+        Question:
+        {query_str}
+        """
+    )
+    ]
+    text_qa_template = ChatPromptTemplate.from_messages(chat_text_qa_msgs)
+
+    # Initialize Chat Memory Buffer for Conversation Memory
+    memory = ChatMemoryBuffer.from_defaults(token_limit=3900)
+
+    # Create Query Engine with LLM and Template
+    query_engine = index.as_query_engine(
+        text_qa_template=text_qa_template, 
+        # streaming=True,
+        memory=memory
+        )
+    
+    if 'query_engine' not in st.session_state:
+        st.session_state.query_engine = query_engine
+
+
+    print("# loaded", datetime.now())
+
+# Function to handle queries
+def handle_query(query):
+    return st.session_state.query_engine.query(query)
+    # streaming_response = st.session_state.query_engine.query(query)
+    # for text in streaming_response.response_gen:
+    #     yield text
+
+print("-- check", datetime.now())
+
+# ============== Streamlit App ===============
+st.title("POC LLM RAG ✅")
+st.markdown("Retrieval-Augmented Generation (RAG) with Large Language Model (LLM) using llama-index library and Ollama.") 
+st.markdown("start chat ...🚀")
+
+if 'messages' not in st.session_state:
+    st.session_state.messages = [{'role': 'assistant', "content": 'Hello! Ask me anything about the documents.'}]
+
+# Sidebar to list documents
+with st.sidebar:
+    st.title("Documents:")
+    docs = SimpleDirectoryReader(DATA_DIR).list_resources()
+    for d in docs:
+        file_name = str(d).split('/')[-1]
+        st.info(file_name)
+
+
+# for message in st.session_state.messages:
+#     with st.chat_message(message["role"]):
+#         st.markdown(message["content"])
+
+
+# if prompt := st.chat_input("Ask me anything about the documents"):
+#     st.session_state.messages.append({"role": "user", "content": prompt})
+#     with st.chat_message("user"):
+#         st.markdown(prompt)
+
+#     with st.chat_message("assistant"):
+#         stream = handle_query(prompt)
+#         response = st.write_stream(stream)
+#     st.session_state.messages.append({"role": "assistant", "content": response})
+
+user_prompt = st.chat_input("Ask me anything about the content of the PDF:")
+if user_prompt:
+    st.session_state.messages.append({'role': 'user', "content": user_prompt})
+    response = handle_query(user_prompt)
+    st.session_state.messages.append({'role': 'assistant', "content": response})
+
+for message in st.session_state.messages:
+    with st.chat_message(message['role']):
+        st.write(message['content'])
+

requirements.txt

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+streamlit
+llama-index
+llama-index-llms-ollama
+llama-index-embeddings-huggingface
+llama-index-llms-huggingface-api