Add initial implementation of the RAG application with Gradio interface

app.py

@@ -0,0 +1,117 @@
+import gradio as gr
+import os
+import atexit
+import glob
+import shutil
+from src.config import (
+    DOCS_DIR,
+    COLLECTION_NAME,
+    EMBEDDING_MODEL_NAME,
+    MILVUS_DB_PATH,
+)
+from src.data_loader import load_data
+from src.embedding_generator import (
+    generate_document_embeddings,
+    generate_query_embeddings,
+)
+from src.vector_store import (
+    get_milvus_client,
+    create_collection_if_not_exists,
+    insert_data,
+    search,
+)
+from src.rag_pipeline import answer_question
+from llama_index.embeddings.huggingface import HuggingFaceEmbedding
+
+# Initialize models and clients
+embedding_model = HuggingFaceEmbedding(
+    model_name=EMBEDDING_MODEL_NAME,
+    trust_remote_code=True,
+    cache_folder=".hf_cache",
+)
+
+milvus_client = get_milvus_client(MILVUS_DB_PATH)
+
+# --- Cleanup Function ---
+def cleanup_documents():
+    """Remove all files from the documents directory."""
+    print("Cleaning up uploaded documents...")
+    files = glob.glob(os.path.join(DOCS_DIR, '*'))
+    for f in files:
+        if os.path.isfile(f):
+            os.remove(f)
+    print("Cleanup complete.")
+
+# Register the cleanup function to run on exit
+atexit.register(cleanup_documents)
+
+
+def index_documents(file_list):
+    """Index documents from a list of files."""
+    if not file_list:
+        return "No files to index."
+
+    os.makedirs(DOCS_DIR, exist_ok=True)
+
+    # Move uploaded files to the documents directory
+    for file in file_list:
+        shutil.copy(file.name, os.path.join(DOCS_DIR, os.path.basename(file.name)))
+
+    docs = load_data(DOCS_DIR)
+    documents = [doc.text for doc in docs]
+
+    if not documents:
+        return "No documents found in the uploaded files."
+
+    binary_embeddings = generate_document_embeddings(documents, embedding_model)
+    if not binary_embeddings:
+        return "Could not generate embeddings for the documents."
+
+    dim = len(binary_embeddings[0]) * 8
+
+    create_collection_if_not_exists(milvus_client, COLLECTION_NAME, dim)
+
+    data_to_insert = [
+        {"context": context, "binary_vector": binary_embedding}
+        for context, binary_embedding in zip(documents, binary_embeddings)
+    ]
+    insert_data(milvus_client, COLLECTION_NAME, data_to_insert)
+
+    return f"Successfully indexed {len(documents)} documents."
+
+
+def chat_interface(message, history):
+    """Chat interface for the RAG pipeline."""
+    query_embedding = generate_query_embeddings(message, embedding_model)
+    if not query_embedding:
+        return "Sorry, I could not process your query."
+
+    contexts = search(milvus_client, COLLECTION_NAME, query_embedding)
+    if not contexts:
+        return "I couldn't find any relevant information in the documents."
+
+    answer = answer_question(message, contexts)
+    return answer
+
+
+with gr.Blocks() as demo:
+    gr.Markdown("## RAG with Binary Quantization")
+
+    with gr.Tab("Upload & Index"):
+        file_input = gr.File(file_count="multiple", label="Upload Documents")
+        index_button = gr.Button("Update Index")
+        index_status = gr.Textbox(label="Indexing Status")
+
+    with gr.Tab("Chat"):
+        gr.ChatInterface(chat_interface)
+
+        index_button.click(
+            fn=index_documents,
+            inputs=[file_input],
+            outputs=[index_status],
+        )
+
+if __name__ == "__main__":
+    # Ensure the documents directory exists from the start
+    os.makedirs(DOCS_DIR, exist_ok=True)
+    demo.launch()

main.py

@@ -1,236 +0,0 @@
-import os
-from dotenv import load_dotenv
-from langchain.chat_models import init_chat_model
-from llama_index.core import SimpleDirectoryReader
-from llama_index.embeddings.huggingface import HuggingFaceEmbedding
-import numpy as np
-from pymilvus import MilvusClient, DataType
-import logging
-from langchain_core.messages import HumanMessage
-
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-
-load_dotenv()
-
-DOCS_DIR = "documents"
-MODEL_NAME = "gpt-4.1"
-TEMPERATURE = 0.2
-COLLECTION_NAME = "fast_rag"
-
-
-def batch_iterate(items, batch_size):
-    """Iterate over items in batches."""
-    for i in range(0, len(items), batch_size):
-        yield items[i:i + batch_size]
-
-
-llm = init_chat_model(MODEL_NAME, model_provider="openai", temperature=TEMPERATURE)
-
-## Generate binary embeddings
-def generate_binary_embeddings():
-    """Generate binary embeddings from documents."""
-    try:
-        # Define loader
-        loader = SimpleDirectoryReader(
-            input_dir=DOCS_DIR,
-            required_exts=[".pdf"],
-            recursive=True,
-        )
-
-        docs = loader.load_data()
-        documents = [doc.text for doc in docs]
-        
-        if not documents:
-            logger.error("No documents found in the documents directory.")
-            return [], []
-
-        # Generate embeddings
-        embedding_model = HuggingFaceEmbedding(
-            model_name="BAAI/bge-large-en-v1.5",
-            trust_remote_code=True,
-            cache_folder=".hf_cache",
-        )
-
-        binary_embeddings = []
-
-        for context in batch_iterate(documents, batch_size=512):
-            # generate float32 embeddings
-            batch_embeddings = embedding_model.get_text_embedding_batch(context)
-
-            # convert float32 to binary vectors
-            embeds_array = np.array(batch_embeddings)
-            binary_embeds = np.where(embeds_array > 0, 1, 0).astype(np.uint8)
-
-            # convert to bytes array
-            packed_embeds = np.packbits(binary_embeds, axis=1)
-            byte_embeds = [vec.tobytes() for vec in packed_embeds]
-
-            binary_embeddings.extend(byte_embeds)
-        
-        logger.info(f"Generated {len(binary_embeddings)} binary embeddings")
-        return documents, binary_embeddings
- 
-    except Exception as e:
-        logger.error(f"Error generating embeddings: {e}")
-        return [], []
-
-
-documents, binary_embeddings = generate_binary_embeddings()
-
-## Vector indexing
-client = MilvusClient("milvus_binary_quantized.db")
-
-# Initialize client and schema
-def create_collection(documents, embeddings):
-    try:
-        if client.has_collection(COLLECTION_NAME):
-            logger.info(f"Collection {COLLECTION_NAME} already exists, dropping it...")
-            client.drop_collection(COLLECTION_NAME)
-
-        # Initialize client
-        schema = client.create_schema(
-            auto_id=True,
-            enable_dynamic_fields=True,
-        )
-    except Exception as e:
-        logger.error(f"Error creating collection: {e}")
-        return None
-
-    # Add primary key field
-    schema.add_field(
-        field_name="id",
-        datatype=DataType.INT64,
-        is_primary=True,
-        auto_id=True,
-    )
-    
-    # Add fields to schema
-    schema.add_field(
-        field_name="context",
-        datatype=DataType.VARCHAR,
-        max_length=65535,  # max length for VARCHAR
-    )
-    schema.add_field(
-        field_name="binary_vector",
-        datatype=DataType.BINARY_VECTOR,
-        dim=1024,  # dimension for binary vector
-    )
-
-    # Create index params for binary vector
-    index_params = client.prepare_index_params()
-    index_params.add_index(
-        field_name="binary_vector",
-        index_name="binary_vector_index",
-        index_type="BIN_FLAT", # Exact search for binary vectors
-        metric_type="HAMMING", # Hamming distance for binary vectors
-    )
-
-    # Create collection with schema and index
-    client.create_collection(
-        collection_name=COLLECTION_NAME,
-        schema=schema,
-        index_params=index_params,
-    )
-
-    # Insert data into collection
-    client.insert(
-        collection_name=COLLECTION_NAME,
-        data=[
-            {
-                "context": context,
-                "binary_vector": binary_embedding
-            }
-            for context, binary_embedding in zip(documents, embeddings)
-        ]
-    )
-
-create_collection(documents, binary_embeddings)
-
-
-def get_query_embeddings(query: str) -> bytes:
-    """Get query embeddings."""
-    try:
-        embedding_model = HuggingFaceEmbedding(
-            model_name="BAAI/bge-large-en-v1.5",
-            trust_remote_code=True,
-            cache_folder=".hf_cache",
-        )
-    except Exception as e:
-        logger.error(f"Error getting query embeddings: {e}")
-        return None
-
-    # Generate float32 embeddings
-    query_embedding = embedding_model.get_text_embedding(query)
-
-    # Convert float32 to binary vector
-    binary_vector = np.where(np.array(query_embedding) > 0, 1, 0).astype(np.uint8)
-
-    # Convert to bytes array
-    packed_vector = np.packbits(binary_vector, axis=0)
-
-    return packed_vector.tobytes()
-
-
-def search_documents(query: str, limit: int = 5):
-    """Search documents using binary embeddings."""
-    try:
-        binary_query = get_query_embeddings(query)
-        if binary_query is None:
-            logger.error("Failed to generate query embeddings")
-            return []
-
-        search_results = client.search(
-            collection_name=COLLECTION_NAME,
-            data=[binary_query],
-            anns_field="binary_vector",
-            search_params={
-                "metric_type": "HAMMING",
-            },
-            output_fields=["context"],
-            limit=limit,
-        )
-
-        # logger.info(f"Search results: {search_results}")
-
-        if not search_results:
-            logger.error("No search results found")
-            return []
-        
-        contexts = [res.entity.context for res in search_results[0]]
-
-        return contexts
-
-    except Exception as e:
-        logger.error(f"Error searching documents: {e}")
-        return []
-
-
-# Test the search functionality
-query = "authors of the document"
-contexts = search_documents(query, limit=5)
-
-prompt = f"""
-# Role and objective
-You are a helpful assistant that can answer questions about the following context.
-
-# Intstructions
-Given the context information, answer the user's query.
-If the context information is not relevant to the user's query, say "I don't know".
-
-# Context
-{contexts}
-
-# User's query
-{query}
-
-# Answer
-"""
-
-human_message = HumanMessage(content=prompt)
-print(f"Human message: {human_message}")
-
-response = llm.invoke(input=[human_message])
-
-print(f"Response from the model: {response.content}")

pyproject.toml

@@ -16,4 +16,6 @@ dependencies = [
     "logging>=0.4.9.6",
     "numpy>=2.3.2",
     "pymilvus>=2.5.14",
+    "gradio>=4.0.0",
+    "docx2txt>=0.9",
 ]

src/config.py

@@ -0,0 +1,23 @@
+DOCS_DIR = "documents"
+MODEL_NAME = "gpt-4.1"
+MODEL_PROVIDER = "openai"
+TEMPERATURE = 0.2
+COLLECTION_NAME = "fast_rag"
+EMBEDDING_MODEL_NAME = "BAAI/bge-large-en-v1.5"
+MILVUS_DB_PATH = "milvus_binary_quantized.db"
+
+PROMPT = """ # Role and objective
+You are a helpful assistant that can answer questions about the following context.
+
+# Intstructions
+Given the context information, answer the user's query.
+If the context information is not relevant to the user's query, say "I don't know".
+
+# Context
+{contexts}
+
+# User's query
+{query}
+
+# Answer
+"""

src/data_loader.py

@@ -0,0 +1,20 @@
+from llama_index.core import SimpleDirectoryReader
+
+
+def load_data(data_dir: str) -> list:
+    """
+    Load a data from a directory
+
+    Args:
+        data_dir: The directory to load the data from
+
+    Returns:
+        A list of documents
+    """
+    try:
+        loader = SimpleDirectoryReader(input_dir=data_dir, required_exts=[".pdf", ".txt", ".md", ".docx", ".doc"], recursive=True)
+        docs = loader.load_data()
+        return docs
+    except Exception as e:
+        print(f"Error loading data from {data_dir}: {e}")
+        return []

src/embedding_generator.py

@@ -0,0 +1,76 @@
+from typing import Any, Generator
+import numpy as np
+
+
+# Helper function for batching
+def batch_iterate(items: Any, batch_size: int) -> Generator[Any, None, None]:
+    """
+    Iterate over items in batches.
+
+    Args:
+        items: The items to iterate over
+        batch_size: The size of the batches
+
+    Returns:
+        A generator of batches
+    """
+    for i in range(0, len(items), batch_size):
+        yield items[i:i + batch_size]
+
+
+def generate_document_embeddings(documents: list[str], embedding_model: Any) -> list[bytes]:
+    """
+    Generate document embeddings.
+
+    Args:
+        documents: The documents to generate embeddings for
+        embedding_model: The embedding model to use
+
+    Returns:
+        A list of document embeddings
+    """
+    binary_embeddings = []
+
+    try:
+        for context in batch_iterate(documents, batch_size=512):
+            # generate float32 embeddings
+            batch_embeddings = embedding_model.get_text_embedding_batch(context)
+
+            # convert float32 to binary vectors
+            embeds_array = np.array(batch_embeddings)
+            binary_embeds = np.where(embeds_array > 0, 1, 0).astype(np.uint8)
+
+            # convert to bytes array
+            packed_embeds = np.packbits(binary_embeds, axis=1)
+
+            binary_embeddings.extend(packed_embeds)
+        return binary_embeddings
+    except Exception as e:
+        print(f"Error generating document embeddings: {e}")
+        return []
+
+def generate_query_embeddings(query: str, embdding_model: Any) -> bytes:
+    """
+    Generate query embeddings.
+
+    Args:
+        query: The query to generate embeddings for
+        embdding_model: The embedding model to use
+
+    Returns:
+        A bytes array of query embeddings
+    """
+    try:
+        # generate float32 embeddings
+        query_embedding = embdding_model.get_text_embedding(query)
+
+        # convert float32 to binary vector
+        binary_vector = np.where(np.array(query_embedding) > 0, 1, 0).astype(np.uint8)
+
+        # convert to bytes array
+        packed_vector = np.packbits(binary_vector, axis=0)
+        return packed_vector.tobytes()
+    except Exception as e:
+        print(f"Error generating query embeddings: {e}")
+        return None
+

src/rag_pipeline.py

@@ -0,0 +1,23 @@
+from langchain_core.messages import HumanMessage
+from langchain.chat_models import init_chat_model
+from .config import PROMPT, MODEL_NAME, TEMPERATURE, MODEL_PROVIDER
+
+
+llm = init_chat_model(MODEL_NAME, model_provider=MODEL_PROVIDER, temperature=TEMPERATURE)
+
+def answer_question(query: str, contexts: list[str]) -> str:
+    """
+    Answer a question using the provided context.
+
+    Args:
+        query: The query to answer
+        contexts: The context to use for answering the question
+
+    Returns:
+        The answer to the question
+    """
+    prompt = PROMPT.format(contexts=contexts, query=query)
+    human_message = HumanMessage(content=prompt)
+
+    response = llm.invoke([human_message])
+    return response.content

src/vector_store.py

@@ -0,0 +1,124 @@
+from pymilvus import MilvusClient, DataType
+
+
+def get_milvus_client(db_path: str) -> MilvusClient:
+    """
+    Get a Milvus client.
+
+    Args:
+        db_path: The path to the Milvus database
+
+    Returns:
+        A Milvus client
+    """
+    try:
+        client = MilvusClient(db_path)
+        return client
+    
+    except Exception as e:
+        print(f"Error getting Milvus client: {e}")
+        return None
+
+def create_collection_if_not_exists(client: MilvusClient, collection_name: str, dim: int) -> None:
+    """
+    Create a collection in Milvus if it does not exist.
+
+    Args:
+        client: The Milvus client
+        collection_name: The name of the collection to create
+        dim: The dimension of the binary vector
+    """
+    try:
+
+        # Drop collection if it exists
+        if client.has_collection(collection_name):
+            print(f"Collection {collection_name} exists, dropping it...")
+            client.drop_collection(collection_name)
+
+            # Initialize client
+            schema = client.create_schema(
+                auto_id=True,
+                enable_dynamic_fields=True,
+            )
+            # Add primary key field
+            schema.add_field(
+                field_name="id",
+                datatype=DataType.INT64,
+                is_primary=True,
+                auto_id=True,
+            )
+            # Add fields to schema
+            schema.add_field(
+                field_name="context",
+                datatype=DataType.VARCHAR,
+                max_length=65535,  # max length for VARCHAR
+            )
+            schema.add_field(
+                field_name="binary_vector",
+                datatype=DataType.BINARY_VECTOR,
+                dim=dim,
+            )
+            # Create index params for binary vector
+            index_params = client.prepare_index_params()
+            index_params.add_index(
+                field_name="binary_vector",
+                index_name="binary_vector_index",
+                index_type="BIN_FLAT", # Exact search for binary vectors
+                metric_type="HAMMING", # Hamming distance for binary vectors
+            )
+            # Create collection with schema and index
+            client.create_collection(
+                collection_name=collection_name,
+                schema=schema,
+                index_params=index_params,
+            )
+            print(f"Collection {collection_name} created successfully.")
+    except Exception as e:
+        print(f"Error creating collection: {e}")
+        return None
+
+def insert_data(client: MilvusClient, collection_name: str, data: list[dict]):
+    """
+    Insert data into a collection in Milvus.
+
+    Args:
+        client: The Milvus client
+        collection_name: The name of the collection to insert data into
+        data: The data to insert
+    """
+    try:
+        client.insert(
+            collection_name=collection_name,
+            data=data,
+        )
+    except Exception as e:
+        print(f"Error inserting data: {e}")
+
+
+def search(client: MilvusClient, collection_name: str, binary_query: bytes, limit: int = 5):
+    """
+    Search for data in a collection in Milvus.
+    """
+    try:
+        # Search for data
+        results = client.search(
+            collection_name=collection_name,
+            data=[binary_query],
+            anns_field="binary_vector",
+            search_params={
+                "metric_type": "HAMMING",
+            },
+            output_fields=["context"],
+            limit=limit,
+        )
+
+        if not results:
+            print("No search results found")
+            return []
+        
+        contexts = [res.entity.context for res in results[0]]
+        return contexts
+
+    except Exception as e:
+        print(f"Error searching for data: {e}")
+        return []

uv.lock

@@ -6,6 +6,15 @@ resolution-markers = [
     "python_full_version < '3.13'",
 ]
 
+[[package]]
+name = "aiofiles"
+version = "24.1.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/0b/03/a88171e277e8caa88a4c77808c20ebb04ba74cc4681bf1e9416c862de237/aiofiles-24.1.0.tar.gz", hash = "sha256:22a075c9e5a3810f0c2e48f3008c94d68c65d763b9b03857924c99e57355166c", size = 30247, upload-time = "2024-06-24T11:02:03.584Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/a5/45/30bb92d442636f570cb5651bc661f52b610e2eec3f891a5dc3a4c3667db0/aiofiles-24.1.0-py3-none-any.whl", hash = "sha256:b4ec55f4195e3eb5d7abd1bf7e061763e864dd4954231fb8539a0ef8bb8260e5", size = 15896, upload-time = "2024-06-24T11:02:01.529Z" },
+]
+
 [[package]]
 name = "aiohappyeyeballs"
 version = "2.6.1"
@@ -123,6 +132,62 @@ wheels = [
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 ]
 
+[[package]]
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+wheels = [
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+    { url = "https://files.pythonhosted.org/packages/f8/5a/656d1c2da4b555920ce4177167bfeb8623d98765594af59702c8873f60ec/audioop_lts-0.2.2-cp313-abi3-macosx_10_13_x86_64.whl", hash = "sha256:550c114a8df0aafe9a05442a1162dfc8fec37e9af1d625ae6060fed6e756f303", size = 27455, upload-time = "2025-08-05T16:42:22.283Z" },
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