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import os
import torch
import base64
import asyncio
from io import BytesIO
import gradio as gr
import qdrant_client
from PIL import Image
from typing import List, Dict, Tuple
import llamaindex_utils
from rag_pipeline import async_indexDocument
from models import get_lora_model, enable_lora, ColPali, ColPaliProcessor
from utils import load_tokenizer
from llama_index.llms.gemini import Gemini
from llama_index.core.tools import RetrieverTool
from huggingface_hub import hf_hub_download
GEMINI_API_KEY = os.getenv(key="GEMINI_API_KEY")
QDRANT_API_KEY = os.getenv(key="QDRANT_API_KEY")
HF_TOKEN_KEY = os.getenv(key="HF_TOKEN_KEY")
device = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
async def initialize_model() -> Dict:
"""Initialize models
Returns:
model_dict: Dict: Dictionary stores neccessary models
"""
if not os.path.exists("./pretrained/colpaligemma-3b-mix-448-base"):
os.makedirs("./pretrained/colpaligemma-3b-mix-448-base", exist_ok=True)
files_to_download = ["adapter_model.safetensors",
"config.json",
"model-00001-of-00002.safetensors",
"model-00002-of-00002.safetensors",
"preprocessor_config.json",
"tokenizer.json",
"tokenizer.model",
"tokenizer_config.json"]
for file in files_to_download:
hf_hub_download(repo_id="dnnhhuy/colpaligemma-3b-mix-448-base",
filename=file,
token=HF_TOKEN_KEY,
local_dir="./pretrained/colpaligemma-3b-mix-448-base")
model = ColPali.from_pretrained(model_dir='./pretrained/colpaligemma-3b-mix-448-base', torch_dtype=torch.bfloat16)
tokenizer = load_tokenizer(tokenizer_dir='./pretrained/colpaligemma-3b-mix-448-base')
processor = ColPaliProcessor(tokenizer=tokenizer).from_pretrained(pretrained_dir='./pretrained/colpaligemma-3b-mix-448-base')
model.model.language_model.model = get_lora_model(model.model.language_model.model,
rank=32,
alphas=32,
lora_modules=['q_proj', 'k_proj', 'v_proj', 'o_proj', 'down_proj', 'gate_proj', 'up_proj'],
training=False,
dropout_p=0.1,
torch_dtype=torch.bfloat16)
model.model.language_model.model = enable_lora(model.model.language_model.model, lora_modules=['q_proj', 'k_proj', 'v_proj', 'o_proj', 'down_proj', 'gate_proj', 'up_proj'], enabled=True)
model = get_lora_model(model,
rank=32,
alphas=32,
lora_modules=['custom_text_proj'],
training=False,
dropout_p=0.1,
torch_dtype=torch.bfloat16)
model = enable_lora(model, lora_modules=['custom_text_proj'], enabled=True)
model.load_lora('./pretrained/colpaligemma-3b-mix-448-base')
# Initialize LLM
generation_config = {
"temperature": 0.0,
"top_p": 0.95,
"top_k": 64,
"max_output_tokens": 1024,
"response_mime_type": "text/plain",
}
llm = Gemini(api_key=GEMINI_API_KEY, generation_config=generation_config)
# Setup Qdrant
# Creating Qdrant Client
vector_store_client = qdrant_client.AsyncQdrantClient(location="https://b3878645-ec71-426c-8afa-b8b3b7589e40.us-east4-0.gcp.cloud.qdrant.io",
api_key=QDRANT_API_KEY,
timeout=100)
embed_model = llamaindex_utils.ColPaliGemmaEmbedding(model=model,
processor=processor,
device=device)
collections = await get_collection_names(vector_store_client)
retrievers_dict = {}
for name in collections:
if name not in retrievers_dict:
retrievers_dict[name] = llamaindex_utils.ColPaliRetriever(vector_store_client=vector_store_client,
target_collection=name,
embed_model=embed_model,
similarity_top_k=3)
return {"llm": llm,
"vector_store_client": vector_store_client,
"model": model,
"processor": processor,
"embed_model": embed_model,
"collections": collections,
"retrievers_dict": retrievers_dict}
async def get_collection_names(vector_store_client):
collections = await vector_store_client.get_collections()
return [collection.name for collection in collections.collections]
async def index(files: List[str],
target_collection: str
) -> Tuple[str, gr.Dropdown, List[str], Dict[str, llamaindex_utils.ColPaliRetriever]]:
"""
Insert all image pages from files to speicified target collection to the vector store
and return the mapping from retriever's name to its object instance.
Args:
files (List[str]): List of file path
target_collection (str): Target collection to insert into the vector store
Returns:
Tuple[str, gr.Dropdown, List[str], Dict[str, llamaindex_utils.ColPaliRetriever]]: Return message, dropdown component, collections' names, dictionary mapping retriever to its object instance
"""
for file in files:
await async_indexDocument(file_path=file,
vector_store_client=model_dict["vector_store_client"],
target_collection=target_collection,
model=model_dict["model"],
processor=model_dict["processor"],
device=device)
if target_collection not in retrievers:
retrievers[target_collection] = llamaindex_utils.ColPaliRetriever(vector_store_client=model_dict["vector_store_client"],
target_collection=target_collection,
embed_model=model_dict["embed_model"],
similarity_top_k=3)
collection_names = await get_collection_names(model_dict["vector_store_client"])
return (f"Uploaded and index {len(files)} files.",
gr.Dropdown(choices=collection_names),
collection_names)
async def search_with_llm(query: str,
similarity_top_k: int,
num_children: int) -> Tuple[str, List[Image.Image]]:
"""Search the result given query and list of retrievers.
Returns the search's response and list of images support for that response.
Args:
query (str): Query question
retrievers (Dict[str, llamaindex_utils.ColPaliRetriever]): Dictionary mapping between retrievers' names and their object instances
similarity_top_k (int): top K similarity results retrieved from the retriever
num_children (int): number of children for tree summarization
Returns:
Tuple[str, List[Image.Image]]: Returns the search's response and list of images support for that response.
"""
retriever_tools = [RetrieverTool.from_defaults(
name=key,
retriever=value,
description=f"Useful for retrieving information about {key}.") for key, value in retrievers.items()]
retriever_mappings = {retriever_tool.metadata.name: retriever_tool.retriever for retriever_tool in retriever_tools}
fusion_retriever = llamaindex_utils.CustomFusionRetriever(llm=model_dict["llm"],
retriever_mappings=retriever_mappings,
similarity_top_k=similarity_top_k)
query_engine = llamaindex_utils.CustomQueryEngine(retriever_tools=[retriever_tool.metadata for retriever_tool in retriever_tools],
fusion_retriever=fusion_retriever,
llm=model_dict["llm"],
num_children=num_children)
response = await query_engine.aquery(query_str=query)
return response.response, [Image.open(BytesIO(base64.b64decode(image))) for image in response.source_images]
async def delete_collection(target_collection):
if await model_dict["vector_store_client"].collection_exists(collection_name=target_collection):
await model_dict["vector_store_client"].delete_collection(collection_name=target_collection, timeout=100)
choices = await get_collection_names(model_dict["vector_store_client"])
return (f"Deleted collection {target_collection}", gr.Dropdown(choices=choices), choices)
else:
choices = await get_collection_names(model_dict["vector_store_client"])
return (f"Collection {target_collection} is not found.", gr.Dropdown(choices=choices), choices)
def build_gui():
with gr.Blocks() as demo:
gr.Markdown("# Image Based RAG System using ColPali ππ")
with gr.Row(equal_height=True):
with gr.Column():
gr.Markdown("## 1οΈ. Upload PDFs")
files = gr.File(file_types=["pdf"],
file_count="multiple",
interactive=True)
choices = gr.State(value=model_dict["collections"])
gr.Markdown("## 2οΈ. Index the PDFs and upload")
target_collection = gr.Dropdown(choices=choices.value,
allow_custom_value=True,
label="Collection name",
show_label=True,
interactive=True)
message_box = gr.Textbox(value="File not yet uploaded",
show_label=False,
interactive=False)
with gr.Row(equal_height=True):
delete_button = gr.Button("ποΈ Delete collection")
convert_button = gr.Button("π Convert and upload")
# Define the actions for conversion
convert_button.click(index, inputs=[files, target_collection], outputs=[message_box, target_collection, choices])
# Define the actions for delete collection
delete_button.click(delete_collection, inputs=[target_collection], outputs=[message_box, target_collection, choices])
with gr.Column():
gr.Markdown("## 3οΈ. Enter your question")
query = gr.Textbox(placeholder="Enter your query to match",
lines=15,
max_lines=20,
autoscroll=True)
with gr.Accordion(label="Additional Settings", open=False):
similarity_top_k = gr.Slider(minimum=1,
maximum=10,
value=3,
step=1.0,
label="Top K similarity retrieved from the retriever")
num_children = gr.Slider(minimum=1,
maximum=10,
value=3,
step=1.0,
label="Set number of children for Tree Summarization")
search_button = gr.Button("π Search")
gr.Markdown("## 4οΈ. ColPali Retrieval")
with gr.Row(equal_height=True):
output_text = gr.Textbox(label="Query result",
show_label=True,
placeholder="Response from query",
lines=8,
max_lines=20,
interactive=False)
output_imgs = gr.Gallery(label="Most relevant images is...",
show_fullscreen_button=True,
show_label=True,
show_download_button=True,
interactive=False)
# Action for search button
search_button.click(
search_with_llm,
inputs=[query, similarity_top_k, num_children],
outputs=[output_text, output_imgs])
return demo
async def amain():
global model_dict, retrievers
model_dict = await initialize_model()
retrievers = model_dict["retrievers_dict"]
demo = build_gui()
demo.queue().launch(debug=True, share=False)
if __name__ == "__main__":
asyncio.run(amain())
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