From 37b566815fc8003db15f4c073a3725ab9986aeaf Mon Sep 17 00:00:00 2001 From: goodnight Date: Thu, 4 Jun 2026 19:03:58 +0100 Subject: [PATCH 1/2] docs: fix Qdrant documentation typos - Remove duplicate 'directly' in Qdrant Cloud connection text - Fix embedding_dim mismatch (1024 -> 5) to match example vectors - Fix HNWS typo -> HNSW --- docs-website/docs/document-stores/qdrant-document-store.mdx | 4 ++-- .../retrievers/qdrantembeddingretriever.mdx | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/docs-website/docs/document-stores/qdrant-document-store.mdx b/docs-website/docs/document-stores/qdrant-document-store.mdx index 773d1aec5e..0f8e56fed0 100644 --- a/docs-website/docs/document-stores/qdrant-document-store.mdx +++ b/docs-website/docs/document-stores/qdrant-document-store.mdx @@ -56,7 +56,7 @@ print(document_store.count_documents()) When you create a `QdrantDocumentStore` instance, Haystack takes care of setting up the collection. In general, you cannot use a Qdrant collection created without Haystack with Haystack. If you want to migrate your existing collection, see the sample script at https://github.com/deepset-ai/haystack-core-integrations/blob/main/integrations/qdrant/src/haystack_integrations/document_stores/qdrant/migrate_to_sparse.py. ::: -You can also connect directly to [Qdrant Cloud](https://cloud.qdrant.io/login) directly. Once you have your API key and your cluster URL from the Qdrant dashboard, you can connect like this: +You can also connect directly to [Qdrant Cloud](https://cloud.qdrant.io/login). Once you have your API key and your cluster URL from the Qdrant dashboard, you can connect like this: ```python from haystack.dataclasses.document import Document @@ -66,7 +66,7 @@ from haystack.utils import Secret document_store = QdrantDocumentStore( url="https://XXXXXXXXX.us-east4-0.gcp.cloud.qdrant.io:6333", index="your_index_name", - embedding_dim=1024, # based on the embedding model + embedding_dim=5, # based on the embedding model recreate_index=True, # enable only to recreate the index and not connect to the existing one api_key=Secret.from_token("YOUR_TOKEN"), ) diff --git a/docs-website/docs/pipeline-components/retrievers/qdrantembeddingretriever.mdx b/docs-website/docs/pipeline-components/retrievers/qdrantembeddingretriever.mdx index 93bd72a020..aad3925c43 100644 --- a/docs-website/docs/pipeline-components/retrievers/qdrantembeddingretriever.mdx +++ b/docs-website/docs/pipeline-components/retrievers/qdrantembeddingretriever.mdx @@ -31,7 +31,7 @@ When using the `QdrantEmbeddingRetriever` in your NLP system, make sure it has t In addition to the `query_embedding`, the `QdrantEmbeddingRetriever` accepts other optional parameters, including `top_k` (the maximum number of Documents to retrieve) and `filters` to narrow down the search space. -Some relevant parameters that impact the embedding retrieval must be defined when the corresponding `QdrantDocumentStore` is initialized: these include the embedding dimension (`embedding_dim`), the `similarity` function to use when comparing embeddings and the HNWS configuration (`hnsw_config`). +Some relevant parameters that impact the embedding retrieval must be defined when the corresponding `QdrantDocumentStore` is initialized: these include the embedding dimension (`embedding_dim`), the `similarity` function to use when comparing embeddings and the HNSW configuration (`hnsw_config`). ### Installation From 056957985509d48bef06ab2fed17b91ccf17a8dd Mon Sep 17 00:00:00 2001 From: goodnight Date: Fri, 5 Jun 2026 14:00:46 +0100 Subject: [PATCH 2/2] apply same changes to latest stable version 2.30 --- .../version-2.30/document-stores/qdrant-document-store.mdx | 4 ++-- .../retrievers/qdrantembeddingretriever.mdx | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/docs-website/versioned_docs/version-2.30/document-stores/qdrant-document-store.mdx b/docs-website/versioned_docs/version-2.30/document-stores/qdrant-document-store.mdx index 773d1aec5e..0f8e56fed0 100644 --- a/docs-website/versioned_docs/version-2.30/document-stores/qdrant-document-store.mdx +++ b/docs-website/versioned_docs/version-2.30/document-stores/qdrant-document-store.mdx @@ -56,7 +56,7 @@ print(document_store.count_documents()) When you create a `QdrantDocumentStore` instance, Haystack takes care of setting up the collection. In general, you cannot use a Qdrant collection created without Haystack with Haystack. If you want to migrate your existing collection, see the sample script at https://github.com/deepset-ai/haystack-core-integrations/blob/main/integrations/qdrant/src/haystack_integrations/document_stores/qdrant/migrate_to_sparse.py. ::: -You can also connect directly to [Qdrant Cloud](https://cloud.qdrant.io/login) directly. Once you have your API key and your cluster URL from the Qdrant dashboard, you can connect like this: +You can also connect directly to [Qdrant Cloud](https://cloud.qdrant.io/login). Once you have your API key and your cluster URL from the Qdrant dashboard, you can connect like this: ```python from haystack.dataclasses.document import Document @@ -66,7 +66,7 @@ from haystack.utils import Secret document_store = QdrantDocumentStore( url="https://XXXXXXXXX.us-east4-0.gcp.cloud.qdrant.io:6333", index="your_index_name", - embedding_dim=1024, # based on the embedding model + embedding_dim=5, # based on the embedding model recreate_index=True, # enable only to recreate the index and not connect to the existing one api_key=Secret.from_token("YOUR_TOKEN"), ) diff --git a/docs-website/versioned_docs/version-2.30/pipeline-components/retrievers/qdrantembeddingretriever.mdx b/docs-website/versioned_docs/version-2.30/pipeline-components/retrievers/qdrantembeddingretriever.mdx index 93bd72a020..aad3925c43 100644 --- a/docs-website/versioned_docs/version-2.30/pipeline-components/retrievers/qdrantembeddingretriever.mdx +++ b/docs-website/versioned_docs/version-2.30/pipeline-components/retrievers/qdrantembeddingretriever.mdx @@ -31,7 +31,7 @@ When using the `QdrantEmbeddingRetriever` in your NLP system, make sure it has t In addition to the `query_embedding`, the `QdrantEmbeddingRetriever` accepts other optional parameters, including `top_k` (the maximum number of Documents to retrieve) and `filters` to narrow down the search space. -Some relevant parameters that impact the embedding retrieval must be defined when the corresponding `QdrantDocumentStore` is initialized: these include the embedding dimension (`embedding_dim`), the `similarity` function to use when comparing embeddings and the HNWS configuration (`hnsw_config`). +Some relevant parameters that impact the embedding retrieval must be defined when the corresponding `QdrantDocumentStore` is initialized: these include the embedding dimension (`embedding_dim`), the `similarity` function to use when comparing embeddings and the HNSW configuration (`hnsw_config`). ### Installation