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When launch ChatQnA with Qdran vector DB, after successfully uploading the document, we can not retrieve the previously uploaded document. The return of the retriever is always containing 0 document, not as what we've expected.
The retrieval result is:
{"id":"6d08404f2c77900d78baeb58bbb97d6c","retrieved_docs":[],"initial_query":"What is the revenue of Nike in 2023?","top_n":1,"metadata":[]}
Expected result should be something valid docs in the field retrieved_docs.
Checking the qdrant-vector-db log, finding that dataprep and retriever are not using the same collection in Qdrant.
Reproduce steps
cd GenAIExamples/ChatQnA/docker_compose/intel/cpu/xeon
source ./set_env.sh
docker compose -f compose_qdrant.yaml up
export your_embedding=$(python3 -c "import random; embedding = [random.uniform(-1, 1) for _ in range(768)]; print(embedding)")
curl http://localhost:6045/v1/retrieval -X POST -H 'Content-Type: application/json' -d "{"text":"What is the revenue of Nike in 2023?","embedding":${your_embedding}}"
Priority
P2 - High
OS type
Ubuntu
Hardware type
Xeon-GNR
Installation method
Deploy method
Running nodes
Single Node
What's the version?
git commit ed16308
Description
When launch ChatQnA with Qdran vector DB, after successfully uploading the document, we can not retrieve the previously uploaded document. The return of the retriever is always containing 0 document, not as what we've expected.
The retrieval result is:
{"id":"6d08404f2c77900d78baeb58bbb97d6c","retrieved_docs":[],"initial_query":"What is the revenue of Nike in 2023?","top_n":1,"metadata":[]}
Expected result should be something valid docs in the field
retrieved_docs
.Checking the qdrant-vector-db log, finding that dataprep and retriever are not using the same collection in Qdrant.
Reproduce steps
cd GenAIExamples/ChatQnA/docker_compose/intel/cpu/xeon
source ./set_env.sh
docker compose -f compose_qdrant.yaml up
upload the document
curl http://localhost:6043/v1/dataprep/ingest -X POST -H "Content-Type: multipart/form-data" -F "files=@./nike.txt"
retrieve the document
export your_embedding=$(python3 -c "import random; embedding = [random.uniform(-1, 1) for _ in range(768)]; print(embedding)")
curl http://localhost:6045/v1/retrieval -X POST -H 'Content-Type: application/json' -d "{"text":"What is the revenue of Nike in 2023?","embedding":${your_embedding}}"
Raw log
Attachments
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