Comments (5)
The error you're encountering, AttributeError: 'NoneType' object has no attribute 'search'
, suggests that the retriever
object is None
. This typically occurs if the QueryFusionRetriever
fails to properly instantiate, which might be due to issues with the initialization of vector_retriever
, bm25_retriever
, or the parameters passed to QueryFusionRetriever
.
To address this, ensure that both vector_retriever
and bm25_retriever
are correctly initialized and are instances of BaseRetriever
or its subclasses. Verify the parameters you're passing to QueryFusionRetriever
match what is expected by the library, focusing on required and optional parameters such as retrievers
, mode
, similarity_top_k
, num_queries
, and others as applicable.
If after checking these aspects the issue persists, it would be helpful to have more details on how vector_retriever
and bm25_retriever
are being initialized. This additional context can provide clues to the root cause of the problem.
Details
Sources
- llama-index-packs/llama-index-packs-agent-search-retriever/examples/_example.py
- llama-index-packs/llama-index-packs-fusion-retriever/llama_index/packs/fusion_retriever/hybrid_fusion/base.py
- llama-index-core/llama_index/core/retrievers/fusion_retriever.py
- llama-index-legacy/llama_index/legacy/query_engine/retriever_query_engine.py
- llama-index-core/llama_index/core/query_engine/retriever_query_engine.py
- llama-index-integrations/postprocessor/llama-index-postprocessor-sbert-rerank/llama_index/postprocessor/sbert_rerank/base.py
- llama-index-core/llama_index/core/postprocessor/sbert_rerank.py
- llama-index-legacy/llama_index/legacy/postprocessor/sbert_rerank.py
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You are using qdrant I'm guessing? Provide the async client
QdrantVectorStore(..., client=QdrantClient(...), aclient=AsyncQdrantClient(...))
Note that if using :memory:
mode, these clients do not share data in qdrant
from llama_index.
Hi @logan-markewich
thanks for your reply. I am indeed using QdrantVectorStore
import qdrant_client
from llama_index.vector_stores.qdrant import QdrantVectorStore
client = qdrant_client.QdrantClient(
url= "xxx",
api_key= "xxxxx",
)
asynclient = qdrant_client.AsyncQdrantClient(
url="xxxx",
api_key="xxxx"
)
vector_store = QdrantVectorStore(client=client, collection_name="xxxxxx", aclient=asynclient)
however can you please tell me why I was getting the error in the first place.
also after integrating your solution I am getting this warning message
WARNING:llama_index.vector_stores.qdrant.base:Both client and aclient are provided. If using :memory:
mode, the data between clients is not synced.
can't I just use QdrantClient without using the AsyncQdrantClient?
from llama_index.
That warning is fine
Qdrant supports both async and sync operations. Some features use async, and need the async client
from llama_index.
ok thank you @logan-markewich
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