Hi guys, just a quick question here in because im not sure if i should be raising this here.
I'm currently trying to insert embeddings and other data into qDrant as part of LlamaIndex's IngestionPipeline.
client = qdrant_client.QdrantClient(path=vector_store_path)
client.create_collection(collection_name=VECTOR_COLLECTION_NAME,vectors_config=models.VectorParams(size=param_size,distance=models.Distance.COSINE))
return client
# other code here...
transformations=[
TitleExtractor(nodes=3,llm=llm,num_workers=1),
QuestionsAnsweredExtractor(questions=3,llm=llm,num_workers=1),
SummaryExtractor(summaries=["prev","self","next"],llm=llm,num_workers=1),
KeywordExtractor(llm=llm,num_workers=1),
SentenceSplitter(chunk_size=2048,chunk_overlap=512),
# TokenTextSplitter(chunk_size=1024,chunk_overlap=256),
HuggingFaceEmbedding(model_name=embed_model)
],
vector_store=vector_store
)
nodes = pipeline.run(documents=docs,show_progress=True)While everything works well, the qdrant vector store created has a .lock text file created containing
and is not releasing the lock even after everything has finished running.(The same problem occurs in .py and .ipynb files)
Is there any way for me to get qDrant to release the lock after all the data has been inserted?
I'm unsure if this an issue with the IngestionPipeline or should i be raising this to qDrant instead?
Thanks!
