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Improvement[Partner] Improve qdrant vector store (#27251)

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- Add static method decorator
- Add args for api doc
- Fix word spelling

Co-authored-by: Erick Friis <erick@langchain.dev>
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ZhangShenao 2024-11-07 10:42:41 +08:00 committed by GitHub
parent 81f7daa458
commit c2072d909a
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2 changed files with 5 additions and 5 deletions
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2
libs/partners/qdrant/langchain_qdrant/qdrant.py
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@ -960,8 +960,8 @@ class QdrantVectorStore(VectorStore):
yield batch_ids, points
@staticmethod
def _build_payloads(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]],
content_payload_key: str,

8
libs/partners/qdrant/langchain_qdrant/vectorstores.py
View File

@ -57,7 +57,7 @@ def sync_call_fallback(method: Callable) -> Callable:
except NotImplementedError:
# If the async method is not implemented, call the synchronous method
# by removing the first letter from the method name. For example,
# if the async method is called ``aaad_texts``, the synchronous method
# if the async method is called ``aadd_texts``, the synchronous method
# will be called ``aad_texts``.
return await run_in_executor(
None, getattr(self, method.__name__[1:]), *args, **kwargs
@ -921,7 +921,7 @@ class Qdrant(VectorStore):
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
query: Text to look up documents similar to.
embedding: Embedding vector to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
Defaults to 20.
@ -984,7 +984,7 @@ class Qdrant(VectorStore):
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
query: Text to look up documents similar to.
embedding: Embedding vector to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
Defaults to 20.
@ -1072,7 +1072,7 @@ class Qdrant(VectorStore):
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
query: Text to look up documents similar to.
embedding: Embedding vector to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
Defaults to 20.