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.devcontainer/devcontainer.json

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+{
+  "name": "Python 3",
+  // Or use a Dockerfile or Docker Compose file. More info: https://containers.dev/guide/dockerfile
+  "image": "mcr.microsoft.com/devcontainers/python:1-3.11-bullseye",
+  "customizations": {
+    "codespaces": {
+      "openFiles": [
+        "README.md",
+        "app.py"
+      ]
+    },
+    "vscode": {
+      "settings": {},
+      "extensions": [
+        "ms-python.python",
+        "ms-python.vscode-pylance"
+      ]
+    }
+  },
+  "updateContentCommand": "[ -f packages.txt ] && sudo apt update && sudo apt upgrade -y && sudo xargs apt install -y <packages.txt; [ -f requirements.txt ] && pip3 install --user -r requirements.txt; pip3 install --user streamlit; echo '✅ Packages installed and Requirements met'",
+  "postAttachCommand": {
+    "server": "streamlit run app.py --server.enableCORS false --server.enableXsrfProtection false"
+  },
+  "portsAttributes": {
+    "8501": {
+      "label": "Application",
+      "onAutoForward": "openPreview"
+    }
+  },
+  "forwardPorts": [
+    8501
+  ]
+}

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+static/Step4.png filter=lfs diff=lfs merge=lfs -text

app.py

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+import os
+import shutil
+import streamlit as st
+from huggingface_hub import login
+from llama_index.llms.huggingface import HuggingFaceInferenceAPI
+from llama_index.embeddings.huggingface import HuggingFaceEmbedding
+from llama_index.core import VectorStoreIndex
+from llama_index.core.retrievers import VectorIndexRetriever
+from llama_index.core.query_engine import RetrieverQueryEngine
+from llama_index.core.node_parser import SentenceSplitter
+from llama_index.core import SimpleDirectoryReader
+from llama_index.core import get_response_synthesizer
+from llama_index.core import Settings
+
+CHUNK_SIZE = 1024
+CHUNK_OVERLAP = 128
+TOP_K = 10
+SIMILARITY_CUTOFF = 0.6
+MAX_SELECTED_NODES = 5
+TEMP_FILES_DIR = "./temp_files"
+
+st.set_page_config(
+    page_title="AIVN - RAG with Llama Index",
+    page_icon="./static/aivn_favicon.png",
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
+
+st.image("./static/aivn_logo.png", width=300)
+
+if 'run_count' not in st.session_state:
+    st.session_state['run_count'] = 0
+
+st.session_state['run_count'] += 1
+if st.session_state['run_count'] == 1:
+    if os.path.exists(TEMP_FILES_DIR):
+        shutil.rmtree(TEMP_FILES_DIR)
+    os.makedirs(TEMP_FILES_DIR, exist_ok=True)
+    st.cache_resource.clear()
+    
+# st.write(f"Ứng dụng đã chạy {st.session_state['run_count']} lần.")
+
+
+class SortedRetrieverQueryEngine(RetrieverQueryEngine):
+    def retrieve(self, query):
+        nodes = self.retriever.retrieve(query)
+        filtered_nodes = [node for node in nodes if node.score >= SIMILARITY_CUTOFF]
+        sorted_nodes = sorted(filtered_nodes, key=lambda node: node.score, reverse=True)
+        return sorted_nodes[:MAX_SELECTED_NODES]
+
+st.title("Retrieval-Augmented Generation (RAG) Demo")
+
+hf_api_key_placeholder = st.empty()
+hf_api_key = hf_api_key_placeholder.text_input("Enter your Hugging Face API Key", type="password", placeholder="hf_...", key="hf_api_key")
+st.markdown("Don't have an API key? Get one [here](https://huggingface.co/settings/tokens) (**Read Token** is enough)")
+
+if hf_api_key:
+    @st.cache_resource
+    def load_models(hf_api_key):
+        login(token=hf_api_key)
+        with st.spinner("Loading models from Hugging Face..."):
+            llm = HuggingFaceInferenceAPI(
+                model_name="mistralai/Mixtral-8x7B-Instruct-v0.1", token=hf_api_key)
+            embed_model = HuggingFaceEmbedding(model_name=f'BAAI/bge-small-en-v1.5', token=hf_api_key)
+        return llm, embed_model
+    
+    llm, embed_model = load_models(hf_api_key)
+
+    uploaded_files = st.file_uploader("Upload documents", accept_multiple_files=True, key="uploaded_files")
+    if uploaded_files:
+        @st.cache_resource
+        def uploading_files(uploaded_files, num_documents):
+            with st.spinner("Processing uploaded files..."):
+                file_paths = []
+                for i, uploaded_file in enumerate(uploaded_files):
+                    file_path = os.path.join(TEMP_FILES_DIR, uploaded_file.name)
+                    file_paths.append(file_path)
+                    with open(file_path, "wb") as f:
+                        f.write(uploaded_file.getbuffer())
+                
+                st.write(f"Uploaded {len(uploaded_files)}/{num_documents} files")
+                
+                return SimpleDirectoryReader(TEMP_FILES_DIR).load_data()
+        
+        num_documents = len(uploaded_files)
+        documents = uploading_files(uploaded_files, num_documents)
+
+        @st.cache_resource
+        def indexing(_documents, _embed_model, num_documents):
+            with st.spinner("Indexing documents..."):
+                text_splitter = SentenceSplitter(
+                    chunk_size=CHUNK_SIZE, chunk_overlap=CHUNK_OVERLAP)
+                Settings.text_splitter = text_splitter
+
+            st.write(f"Indexing {num_documents} documents")
+            return VectorStoreIndex.from_documents(
+                    _documents, transformations=[text_splitter], embed_model=_embed_model, show_progress=True
+                )
+        
+        index = indexing(documents, embed_model, num_documents)
+
+        @st.cache_resource
+        def create_retriever_and_query_engine(_index, _llm, num_documents):
+            retriever = VectorIndexRetriever(
+                index=_index, similarity_top_k=TOP_K)
+
+            response_synthesizer = get_response_synthesizer(llm=_llm)
+
+            st.write(f"Querying with {num_documents} nodes")
+            
+            return SortedRetrieverQueryEngine(
+                retriever=retriever,
+                response_synthesizer=response_synthesizer,
+                node_postprocessors=[],
+            )
+        
+        query_engine = create_retriever_and_query_engine(index, llm, len(index.docstore.docs))
+        
+        query = st.text_input("Enter your query for RAG", key="query")
+
+        if query:
+            with st.spinner("Querying..."):
+                response = query_engine.query(query)
+                retrieved_nodes = response.source_nodes
+
+                st.markdown("### Retrieved Documents")
+                for i, node in enumerate(retrieved_nodes):
+                    with st.expander(f"Document {i+1} (Score: {node.score:.4f})"):
+                        st.write(node.text)
+
+                st.markdown("### RAG Response:")
+                st.write(response.response)
+    
+st.markdown(
+    """
+    <style>
+    .footer {
+        position: fixed;
+        bottom: 0;
+        left: 0;
+        width: 100%;
+        background-color: #f1f1f1;
+        text-align: center;
+        padding: 10px 0;
+        font-size: 14px;
+        color: #555;
+    }
+    </style>
+    <div class="footer">
+        2024 AI VIETNAM | Made by <a href="https://github.com/Koii2k3/Basic-RAG-LlamaIndex" target="_blank">Koii2k3</a>
+    </div>
+    """,
+    unsafe_allow_html=True
+)

requirements.txt

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+huggingface-hub==0.28.1
+llama-index==0.12.16
+llama-index-cli==0.4.0
+llama-index-core==0.12.16.post1
+llama-index-embeddings-huggingface==0.5.1
+llama-index-embeddings-openai==0.3.1
+llama-index-indices-managed-llama-cloud==0.6.4
+llama-index-llms-huggingface==0.4.2