update

Dockerfile

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+# Read the doc: https://huggingface.co/docs/hub/spaces-sdks-docker
+# you will also find guides on how best to write your Dockerfile
+
+FROM python:3.9
+
+RUN useradd -m -u 1000 user
+USER user
+ENV PATH="/home/user/.local/bin:$PATH"
+
+WORKDIR /app
+
+COPY --chown=user ./requirements.txt requirements.txt
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+COPY --chown=user . /app
+
+EXPOSE 7860
+
+# Run the Gradio app
+CMD ["python", "app.py"]

app.py

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+import stat
+import gradio as gr
+from llama_index.core.postprocessor import SimilarityPostprocessor
+from llama_index.core.postprocessor import SentenceTransformerRerank
+from llama_index.core.postprocessor import MetadataReplacementPostProcessor
+from llama_index.core import StorageContext
+import chromadb
+from llama_index.vector_stores.chroma import ChromaVectorStore
+import zipfile
+import requests
+import torch
+from llama_index.core import Settings
+from llama_index.llms.huggingface import HuggingFaceLLM
+from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
+import sys
+import logging
+import os
+
+
+enable_rerank = True
+# sentence_window,naive,recursive_retrieval
+retrieval_strategy = "sentence_window"
+base_embedding_source = "hf"  # local,openai,hf
+# intfloat/multilingual-e5-small local:BAAI/bge-small-en-v1.5 text-embedding-3-small nvidia/NV-Embed-v2 Alibaba-NLP/gte-large-en-v1.5
+base_embedding_model = "Alibaba-NLP/gte-large-en-v1.5"
+# meta-llama/Llama-3.1-8B meta-llama/Llama-3.2-3B-Instruct meta-llama/Llama-2-7b-chat-hf google/gemma-2-9b CohereForAI/c4ai-command-r-plus CohereForAI/aya-23-8B
+base_llm_model = "mistralai/Mistral-7B-Instruct-v0.3"
+# AdaptLLM/finance-chat
+base_llm_source = "hf"  # cohere,hf,anthropic
+base_similarity_top_k = 20
+
+
+# ChromaDB
+env_extension = "_large"  # _large _dev_window _large_window
+db_collection = f"gte{env_extension}"  # intfloat gte
+read_db = True
+active_chroma = True
+root_path = "."
+chroma_db_path = f"{root_path}/chroma_db"  # ./chroma_db
+# ./processed_files.json
+processed_files_log = f"{root_path}/processed_files{env_extension}.json"
+
+
+# check hyperparameter
+if retrieval_strategy not in ["sentence_window", "naive"]:  # recursive_retrieval
+    raise Exception(f"{retrieval_strategy} retrieval_strategy is not support")
+
+
+os.environ["OPENAI_API_KEY"] = 'sk-xxxxxxxxxx'
+hf_api_key = os.getenv("HF_API_KEY")
+
+logging.basicConfig(stream=sys.stdout, level=logging.INFO)
+logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
+
+
+torch.cuda.empty_cache()
+
+os.environ['PYTORCH_CUDA_ALLOC_CONF'] = 'expandable_segments:True'
+
+print(f"loading embedding ..{base_embedding_model}")
+if base_embedding_source == 'hf':
+    from llama_index.embeddings.huggingface import HuggingFaceEmbedding
+    Settings.embed_model = HuggingFaceEmbedding(
+        model_name=base_embedding_model, trust_remote_code=True)  # ,
+else:
+    raise Exception("embedding model is invalid")
+
+# setup prompts - specific to StableLM
+if base_llm_source == 'hf':
+    from llama_index.core import PromptTemplate
+
+    # This will wrap the default prompts that are internal to llama-index
+    # taken from https://huggingface.co/Writer/camel-5b-hf
+    query_wrapper_prompt = PromptTemplate(
+        "Below is an instruction that describes a task. "
+        "you need to make sure that user's question and retrived context mention the same stock symbol if not please give no answer to user"
+        "Write a response that appropriately completes the request.\n\n"
+        "### Instruction:\n{query_str}\n\n### Response:"
+    )
+
+if base_llm_source == 'hf':
+    llm = HuggingFaceLLM(
+        context_window=2048,
+        max_new_tokens=512,  # 256
+        generate_kwargs={"temperature": 0.1, "do_sample": False},  # 0.25
+        query_wrapper_prompt=query_wrapper_prompt,
+        tokenizer_name=base_llm_model,
+        model_name=base_llm_model,
+        device_map="auto",
+        tokenizer_kwargs={"max_length": 2048},
+        # uncomment this if using CUDA to reduce memory usage
+        model_kwargs={"torch_dtype": torch.float16}
+    )
+
+    Settings.chunk_size = 512
+    Settings.llm = llm
+
+"""#### Load documents, build the VectorStoreIndex"""
+
+
+def download_and_extract_chroma_db(url, destination):
+    """Download and extract ChromaDB from Hugging Face Datasets."""
+    # Create destination folder if it doesn't exist
+    if not os.path.exists(destination):
+        os.makedirs(destination)
+    else:
+        # If the folder exists, remove it to ensure a fresh extract
+        print("Destination folder exists. Removing it...")
+        for root, dirs, files in os.walk(destination, topdown=False):
+            for file in files:
+                os.remove(os.path.join(root, file))
+            for dir in dirs:
+                os.rmdir(os.path.join(root, dir))
+        print("Destination folder cleared.")
+
+    db_zip_path = os.path.join(destination, "chroma_db.zip")
+    if not os.path.exists(db_zip_path):
+        # Download the ChromaDB zip file
+        print("Downloading ChromaDB from Hugging Face Datasets...")
+        headers = {
+            "Authorization": f"Bearer {hf_api_key}"
+        }
+        response = requests.get(url, headers=headers, stream=True)
+        response.raise_for_status()
+        with open(db_zip_path, "wb") as f:
+            for chunk in response.iter_content(chunk_size=8192):
+                f.write(chunk)
+        print("Download completed.")
+    else:
+        print("Zip file already exists, skipping download.")
+
+    # Extract the zip file
+    print("Extracting ChromaDB...")
+    with zipfile.ZipFile(db_zip_path, 'r') as zip_ref:
+        zip_ref.extractall(destination)
+    print("Extraction completed. Zip file retained.")
+
+
+# URL to your dataset hosted on Hugging Face
+chroma_db_url = "https://huggingface.co/datasets/iamboolean/set50-db/resolve/main/chroma_db.zip"
+
+# Local destination for the ChromaDB
+chroma_db_path_extract = "./"  # You can change this to your desired path
+
+# Download and extract the ChromaDB
+download_and_extract_chroma_db(chroma_db_url, chroma_db_path_extract)
+
+# Define ChromaDB client (persistent mode)er
+db = chromadb.PersistentClient(path=chroma_db_path)
+print(f"db path:{chroma_db_path}")
+chroma_collection = db.get_or_create_collection(db_collection)
+print(f"db collection:{db_collection}")
+
+
+# Set up ChromaVectorStore and embeddings
+vector_store = ChromaVectorStore(chroma_collection=chroma_collection)
+storage_context = StorageContext.from_defaults(vector_store=vector_store)
+
+document_count = chroma_collection.count()
+print(f"Total documents in the collection: {document_count}")
+
+index = VectorStoreIndex.from_vector_store(
+    vector_store=vector_store,
+    # embed_model=embed_model,
+)
+
+"""#### Query Index"""
+
+
+rerank = SentenceTransformerRerank(
+    model="cross-encoder/ms-marco-MiniLM-L-2-v2", top_n=10
+)
+node_postprocessors = []
+# node_postprocessors.append(SimilarityPostprocessor(similarity_cutoff=0.6))
+
+if retrieval_strategy == 'sentence_window':
+    node_postprocessors.append(
+        MetadataReplacementPostProcessor(target_metadata_key="window"))
+
+
+if enable_rerank:
+    node_postprocessors.append(rerank)
+
+
+query_engine = index.as_query_engine(
+    similarity_top_k=base_similarity_top_k,
+    # the target key defaults to `window` to match the node_parser's default
+    node_postprocessors=node_postprocessors,
+)
+
+
+def metadata_formatter(metadata):
+    company_symbol = metadata['file_name'].split(
+        '-')[0]  # Split at '-' and take the first part
+    # Split at '-' and then '.' to extract the year
+    year = metadata['file_name'].split('-')[1].split('.')[0]
+    page_number = metadata['page_label']
+
+    return f"Company File: {metadata['file_name'].split('-')[0]}, Year: {metadata['file_name'].split('-')[1].split('.')[0]}, Page Number: {metadata['page_label']}"
+
+
+def query_journal(question):
+
+    response = query_engine.query(question)  # Query the index
+    matched_nodes = response.source_nodes  # Extract matched nodes
+
+    # Prepare the matched nodes details
+    retrieved_context = "\n".join([
+        # f"Node ID: {node.node_id}\n"
+        # f"Matched Content: {node.node.text}\n"
+        # f"Metadata: {node.node.metadata if node.node.metadata else 'None'}"
+        f"Metadata: {metadata_formatter(node.node.metadata) if node.node.metadata else 'None'}"
+        for node in matched_nodes
+    ])
+
+    generated_answer = str(response)
+
+    # Return both retrieved context and detailed matched nodes
+    return retrieved_context, generated_answer
+
+
+# Define the Gradio interface
+with gr.Blocks() as app:
+    # Title
+    gr.Markdown(
+        """
+        <div style="text-align: center;">
+            <h1>SET50RAG: Retrieval-Augmented Generation for Thai Public Companies Question Answering</h1>
+        </div>
+        """
+    )
+
+    # Description
+    gr.Markdown(
+        """
+        The **SET50RAG** tool provides an interactive way to analyze and extract insights from **243 annual reports** of Thai public companies spanning **5 years**.
+        By leveraging advanced **Retrieval-Augmented Generation**, including **GTE-Large embedding models**, **Sentence Window with Reranking**, and powerful **Large Language Models (LLMs)** like **Mistral-7B**, the system efficiently retrieves and answers complex financial queries.
+        This scalable and cost-effective solution reduces reliance on parametric knowledge, ensuring contextually accurate and relevant responses.
+        """
+    )
+
+    # How to Use Section
+    gr.Markdown(
+        """
+        ### How to Use
+        1. Type your question in the box or select an example question below.
+        2. Click **Submit** to retrieve the context and get an AI-generated answer.
+        3. Review the retrieved context and the generated answer to gain insights.
+        ---
+        """
+    )
+
+    # Example Questions Section
+    gr.Markdown(
+        """
+        ### Example Questions
+        - What is the revenue of PTTOR in 2022?
+        - what is effect of COVID-19 on BDMS show me in Timeline format from 2019 to 2023?
+        - How does CPALL plan for electric vehicles?
+        """
+    )
+
+    # Interactive Section (RAG Box)
+    with gr.Row():
+        with gr.Column():
+            user_question = gr.Textbox(
+                label="Ask a Question",
+                placeholder="Type your question here, e.g., 'What is the revenue of PTTOR in 2022?'",
+            )
+            example_question_button = gr.Button("Use Example Question")
+        with gr.Column():
+            generated_answer = gr.Textbox(
+                label="Generated Answer",
+                placeholder="The AI-generated answer will appear here.",
+                interactive=False,
+            )
+            retrieved_context = gr.Textbox(
+                label="Retrieved Context",
+                placeholder="Relevant context will appear here.",
+                interactive=False,
+            )
+
+    # Button for user interaction
+    submit_button = gr.Button("Submit")
+
+    # Example question logic
+    def use_example_question():
+        return "What is the revenue of PTTOR in 2022?"
+
+    example_question_button.click(
+        use_example_question, inputs=[], outputs=[user_question]
+    )
+
+    # Interaction logic for submitting user queries
+    submit_button.click(
+        query_journal, inputs=[user_question], outputs=[
+            retrieved_context, generated_answer]
+    )
+
+    # Footer
+    gr.Markdown(
+        """
+        ---
+        ### Limitations and Bias:
+        - Optimized for Thai financial reports from SET50 companies. Results may vary for other domains.
+        - Retrieval and accuracy depend on data quality and embedding models.
+        """
+    )
+
+# Launch the app
+# app.launch()
+app.launch(server_name="0.0.0.0")  # , server_port=7860

requirements.txt

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+ragas==0.1.22
+gradio==4.44.1
+llama-index 
+llama-index-llms-huggingface 
+llama_index-embeddings-huggingface 
+llama_index-llms-cohere 
+llama-index-embeddings-instructor
+datasets
+transformers
+llama-index-embeddings-huggingface
+chromadb 
+llama-index-vector-stores-chroma
+sentencepiece