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from enum import Enum
from uuid import uuid4
import numpy as np
from qdrant_client import models
from qdrant_client import QdrantClient
from datasets import Dataset
from sentence_transformers import SentenceTransformer
from tqdm import tqdm
from hashlib import sha3_512
from src.backend.data_fetching.data_fetcher import DataFetcher, ChunkLevel
class QdrantSchema(str, Enum):
COLLECTION_NAME: str
class ScientificPapersMainSchema(str, Enum):
COLLECTION_NAME = "scientific-papers"
ARTICLE_ID = "article_id"
SECTIONS = "sections"
SECTION_NAMES = "section_names"
ABSTRACT = "abstract_text"
def __repr__(self):
return self.value
class ScientificPapersChunksSchema(str, Enum):
COLLECTION_NAME = "scientific-paper-chunks"
ARTICLE_ID = "article_id"
SECTION_NAME = "section_name"
PARAGRAPH_ID = "paragraph_id"
PARAGRAPH = "paragraph"
def __repr__(self):
return self.value
class ScientificPapersCollectionNames(str, Enum):
MAIN = "scientific-papers"
CHUNKS = "scientific-paper-chunks"
def __repr__(self):
return self.value
class HFDatasetFields(str, Enum):
SECTIONS = "sections"
SECTION_NAMES = "section_names"
ARTICLE_ID = "article_id"
ABSTRACT = "article_abstract"
EMBEDDINGS = "embeddings"
def __repr__(self):
return self.value
class QdrantDatabase:
def __init__(self, client: QdrantClient, model: SentenceTransformer, upload_batch_size=2, embedding_batch_size=32, chunk_level: ChunkLevel = ChunkLevel.SENTENCE):
self.client = client
self.model = model
self._embedding_batch_size = embedding_batch_size
self._data_fetcher = DataFetcher(chunk_level=chunk_level)
self.chunk_level = chunk_level
self._upload_batch_size = upload_batch_size
self._setup_collections()
def _setup_collections(self):
if not self.client.collection_exists(ScientificPapersCollectionNames.MAIN):
self.client.create_collection(
collection_name=ScientificPapersCollectionNames.MAIN,
vectors_config=models.VectorParams(
size=self.model.get_sentence_embedding_dimension(),
distance=models.Distance.COSINE,
on_disk=True,
),
optimizers_config=models.OptimizersConfigDiff(indexing_threshold=10000),
)
if not self.client.collection_exists(ScientificPapersCollectionNames.CHUNKS):
self.client.create_collection(
collection_name=ScientificPapersCollectionNames.CHUNKS,
vectors_config=models.VectorParams(
size=self.model.get_sentence_embedding_dimension(),
distance=models.Distance.COSINE,
on_disk=True,
),
optimizers_config=models.OptimizersConfigDiff(indexing_threshold=10000),
)
def reset_database(self):
if self.client.collection_exists(ScientificPapersCollectionNames.MAIN):
self.client.delete_collection(ScientificPapersCollectionNames.MAIN)
if self.client.collection_exists(ScientificPapersCollectionNames.CHUNKS):
self.client.delete_collection(ScientificPapersCollectionNames.CHUNKS)
self._setup_collections()
def upload_from_dataset(self, dataset: Dataset | list[dict], batch_size=2):
data = self._data_fetcher.from_dataset(dataset)
self._upload(data)
def upload_from_pmcid(self, pmcid):
data = self._data_fetcher.from_pmcid(pmcid)
self._upload(data)
def upload_from_pdf(self, file):
data = self._data_fetcher.from_pdf(file)
self._upload(data)
def _prepare_batches(self, batch: list[dict]):
unnest = lambda x: [j for i in x for j in i]
# document embeddings for main collection
if "embeddings" in batch[0]:
article_embeddings_batch = [np.array(unnest(i[HFDatasetFields.EMBEDDINGS])) for i in batch]
for doc_ind in range(len(batch)):
batch[doc_ind].pop("embeddings")
else:
article_embeddings_batch = [
self.model.encode(unnest(i[HFDatasetFields.SECTIONS]), device=str(self.model.device),
batch_size=self._embedding_batch_size) for i in batch]
document_vectors = [article_embeddings_batch[i].mean(axis=0) for i in range(len(batch))]
# paragraph embeddings for chunk collection
document_chunk_payload = [({
ScientificPapersChunksSchema.ARTICLE_ID: article[HFDatasetFields.ARTICLE_ID],
ScientificPapersChunksSchema.PARAGRAPH: paragraph,
ScientificPapersChunksSchema.SECTION_NAME: section_name,
ScientificPapersChunksSchema.PARAGRAPH_ID: paragraph_ind
}) for article in batch for section_name, section in
zip(article[HFDatasetFields.SECTION_NAMES], article[HFDatasetFields.SECTIONS])
for paragraph_ind, paragraph in enumerate(section)]
paragraph_embeddings = [paragraph_embeddings for article_paragraph_embeddings in
article_embeddings_batch for paragraph_embeddings in
article_paragraph_embeddings]
ids = {
ScientificPapersCollectionNames.MAIN: [
int.from_bytes(sha3_512(i[HFDatasetFields.ARTICLE_ID].encode()).digest()[:8], 'little')
for i in batch],
ScientificPapersCollectionNames.CHUNKS: [str(uuid4()) for _ in
range(len(document_chunk_payload))],
}
main_upload = {"ids": ids[ScientificPapersCollectionNames.MAIN], "vectors": document_vectors, "payload": batch}
chunk_upload = {"ids": ids[ScientificPapersCollectionNames.CHUNKS], "vectors": paragraph_embeddings, "payload": document_chunk_payload}
return main_upload, chunk_upload
def _upload(self, dataset: Dataset | list[dict]):
def batched(iterable, n=100):
from itertools import islice
iterator = iter(iterable)
while batch := list(islice(iterator, n)):
yield batch
with tqdm(total=len(dataset), desc=f"Upload data to Qdrant:{ScientificPapersCollectionNames.MAIN}: ") as pbar:
for batch in batched(dataset, self._upload_batch_size):
main_data, chunk_data = self._prepare_batches(batch)
try:
self.client.upsert(collection_name=ScientificPapersCollectionNames.MAIN,
points=models.Batch(
ids=main_data["ids"],
vectors=main_data["vectors"],
payloads=main_data["payload"]
))
self.client.upsert(collection_name=ScientificPapersCollectionNames.CHUNKS,
points=models.Batch(
ids=chunk_data["ids"],
vectors=chunk_data["vectors"],
payloads=chunk_data["payload"]
))
except Exception as e:
print(e)
pass
pbar.update(self._upload_batch_size)
def _bulk_upload(self, data: list[dict]):
from qdrant_client import models
self.client.update_collection(
collection_name=ScientificPapersCollectionNames.MAIN,
hnsw_config=models.HnswConfigDiff(m=0),
)
self.client.update_collection(
collection_name=ScientificPapersCollectionNames.CHUNKS,
hnsw_config=models.HnswConfigDiff(m=0),
)
self._upload(data)
self.client.update_collection(
collection_name=ScientificPapersCollectionNames.MAIN,
hnsw_config=models.HnswConfigDiff(m=32),
)
self.client.update_collection(
collection_name=ScientificPapersCollectionNames.CHUNKS,
hnsw_config=models.HnswConfigDiff(m=32),
)
def _query_single(self, query: list[float] | np.ndarray, n_docs=1, n_paragraphs=1, highlight: bool = True):
relevant_documents = self.client.query_points(ScientificPapersCollectionNames.MAIN,
query,
limit=n_docs,
with_vectors=False)
relevant_paragraphs_per_document = {}
if highlight:
document_ids = [point.payload[ScientificPapersMainSchema.ARTICLE_ID] for point in relevant_documents.points]
relevant_paragraphs_per_document = {}
for document_id in document_ids:
document_filter = models.Filter(
must=[
models.FieldCondition(key=ScientificPapersChunksSchema.ARTICLE_ID, match=models.MatchValue(value=document_id))
]
)
relevant_paragraphs_ids_resp = self.client.query_points(ScientificPapersCollectionNames.CHUNKS,
query,
limit=n_paragraphs,
with_payload=[
ScientificPapersChunksSchema.PARAGRAPH_ID,
ScientificPapersChunksSchema.SECTION_NAME,
ScientificPapersChunksSchema.PARAGRAPH
],
query_filter=document_filter)
# relevant_paragraphs_ids = [point.payload["paragraph_index"] for point in relevant_paragraphs_ids_resp.points]
relevant_paragraphs_per_document[document_id] = relevant_paragraphs_ids_resp
return relevant_documents, relevant_paragraphs_per_document
def query(self, queries: list[str] | str, docs_per_query=1, highlight=True, paragraphs_per_document=1):
if isinstance(queries, str):
queries = [queries]
query_embeddings = self.model.encode(queries, batch_size=self._embedding_batch_size)
responses = {}
for query, query_embedding in zip(queries, query_embeddings):
response = self._query_single(query_embedding,
n_docs=docs_per_query,
n_paragraphs=paragraphs_per_document,
highlight=highlight)
responses[query] = response
return responses
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