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import torch
from qdrant_client import models
from qdrant_client import QdrantClient
from colpali_engine.models import ColPali, ColPaliProcessor
from Janus.janus.models import MultiModalityCausalLM, VLChatProcessor
from Janus.janus.utils.io import load_pil_images
from transformers import AutoModelForCausalLM
import base64
from io import BytesIO
from tqdm import tqdm
def batch_iterate(lst, batch_size):
"""Yield successive n-sized chunks from lst."""
for i in range(0, len(lst), batch_size):
yield lst[i : i + batch_size]
def image_to_base64(image):
buffered = BytesIO()
image.save(buffered, format="JPEG")
return base64.b64encode(buffered.getvalue()).decode("utf-8")
class EmbedData:
def __init__(self, embed_model_name="vidore/colpali-v1.2", batch_size = 4):
self.embed_model_name = embed_model_name
self.embed_model, self.processor = self._load_embed_model()
self.batch_size = batch_size
self.embeddings = []
def _load_embed_model(self):
embed_model = ColPali.from_pretrained(
self.embed_model_name,
torch_dtype=torch.bfloat16,
device_map="mps",
trust_remote_code=True,
cache_dir="./Janus/hf_cache"
)
processor = ColPaliProcessor.from_pretrained(self.embed_model_name)
return embed_model, processor
def get_query_embedding(self, query):
with torch.no_grad():
query = self.processor.process_queries([query]).to(self.embed_model.device)
query_embedding = self.embed_model(**query)
return query_embedding[0].cpu().float().numpy().tolist()
def generate_embedding(self, images):
with torch.no_grad():
batch_images = self.processor.process_images(images).to(self.embed_model.device)
image_embeddings = self.embed_model(**batch_images).cpu().float().numpy().tolist()
return image_embeddings
def embed(self, images):
self.images = images
self.all_embeddings = []
for batch_images in tqdm(batch_iterate(images, self.batch_size), desc="Generating embeddings"):
batch_embeddings = self.generate_embedding(batch_images)
self.embeddings.extend(batch_embeddings)
class QdrantVDB_QB:
def __init__(self, collection_name, vector_dim = 128, batch_size=4):
self.collection_name = collection_name
self.batch_size = batch_size
self.vector_dim = vector_dim
def define_client(self):
self.client = QdrantClient(url="http://localhost:6333", prefer_grpc=True)
def create_collection(self):
if not self.client.collection_exists(collection_name=self.collection_name):
self.client.create_collection(
collection_name=self.collection_name,
on_disk_payload=True,
vectors_config=models.VectorParams(
size=self.vector_dim,
distance=models.Distance.COSINE,
on_disk=True,
multivector_config=models.MultiVectorConfig(
comparator=models.MultiVectorComparator.MAX_SIM
),
),
)
def ingest_data(self, embeddata):
for i, batch_embeddings in tqdm(enumerate(batch_iterate(embeddata.embeddings, self.batch_size)), desc="Ingesting data"):
points = []
for j, embedding in enumerate(batch_embeddings):
image_bs64 = image_to_base64(embeddata.images[i*self.batch_size + j])
current_point = models.PointStruct(id=i*self.batch_size + j,
vector=embedding,
payload={"image": image_bs64})
points.append(current_point)
self.client.upsert(collection_name=self.collection_name, points=points, wait=True)
class Retriever:
def __init__(self, vector_db, embeddata):
self.vector_db = vector_db
self.embeddata = embeddata
def search(self, query):
query_embedding = self.embeddata.get_query_embedding(query)
query_result = self.vector_db.client.query_points(collection_name=self.vector_db.collection_name,
query=query_embedding,
limit=4,
search_params=models.SearchParams(
quantization=models.QuantizationSearchParams(
ignore=True,
rescore=True,
oversampling=2.0
)
)
)
return query_result
class RAG:
def __init__(self,
retriever,
llm_name = "deepseek-ai/Janus-Pro-1B"
):
self.llm_name = llm_name
self._setup_llm()
self.retriever = retriever
def _setup_llm(self):
self.vl_chat_processor = VLChatProcessor.from_pretrained(self.llm_name, cache_dir="./Janus/hf_cache")
self.tokenizer = self.vl_chat_processor.tokenizer
self.vl_gpt = AutoModelForCausalLM.from_pretrained(
self.llm_name, trust_remote_code=True, cache_dir="./Janus/hf_cache"
).to(torch.bfloat16).eval()
def generate_context(self, query):
result = self.retriever.search(query)
return f"./images/page{result.points[0].id}.jpg"
def query(self, query):
image_context = self.generate_context(query=query)
qa_prompt_tmpl_str = f"""The user has asked the following question:
---------------------
Query: {query}
---------------------
Some images are available to you
for this question. You have
to understand these images thoroughly and
extract all relevant information that will
help you answer the query.
---------------------
"""
conversation = [
{
"role": "User",
"content": f"<image_placeholder> \n {qa_prompt_tmpl_str}",
"images": [image_context],
},
{"role": "Assistant", "content": ""},
]
pil_images = load_pil_images(conversation)
prepare_inputs = self.vl_chat_processor(
conversations=conversation, images=pil_images, force_batchify=True
).to(self.vl_gpt.device)
inputs_embeds = self.vl_gpt.prepare_inputs_embeds(**prepare_inputs)
outputs = self.vl_gpt.language_model.generate(
inputs_embeds=inputs_embeds,
attention_mask=prepare_inputs.attention_mask,
pad_token_id=self.tokenizer.eos_token_id,
bos_token_id=self.tokenizer.bos_token_id,
eos_token_id=self.tokenizer.eos_token_id,
max_new_tokens=512,
do_sample=False,
use_cache=True,
)
streaming_response = self.tokenizer.decode(outputs[0].cpu().tolist(), skip_special_tokens=True)
return streaming_response |