github/langchain
1
0
Fork 0
mirror of https://github.com/hwchase17/langchain.git synced 2025-06-20 22:03:52 +00:00
Code Issues Packages Projects Releases Wiki Activity

qdrant: Fixed typos in Qdrant vectorstore docs (#24312)

Browse Source 
## Description 

As that title goes.
This commit is contained in:
Anush 2024-07-16 22:14:07 +05:30 committed by GitHub
parent 5caa381177
commit e38bf08139
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
1 changed files with 2 additions and 2 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

4
docs/docs/integrations/vectorstores/qdrant.ipynb
View File

@ -317,7 +317,7 @@
"To search with only dense vectors,\n",
"\n",
"- The `retrieval_mode` parameter should be set to `RetrievalMode.DENSE`(default).\n",
"- A [dense embeddings provider](https://python.langchain.com/v0.2/docs/integrations/text_embedding/) value should be a provided for the `embedding` parameter."
"- A [dense embeddings](https://python.langchain.com/v0.2/docs/integrations/text_embedding/) value should be provided for the `embedding` parameter."
]
},
{
@ -407,7 +407,7 @@
"To perform a hybrid search using dense and sparse vectors with score fusion,\n",
"\n",
"- The `retrieval_mode` parameter should be set to `RetrievalMode.HYBRID`.\n",
"- A [dense embeddings provider](https://python.langchain.com/v0.2/docs/integrations/text_embedding/) value should be provider for the `embedding` parameter.\n",
"- A [dense embeddings](https://python.langchain.com/v0.2/docs/integrations/text_embedding/) value should be provided for the `embedding` parameter.\n",
"- An implementation of the [`SparseEmbeddings`](https://github.com/langchain-ai/langchain/blob/master/libs/partners/qdrant/langchain_qdrant/sparse_embeddings.py) interface using any sparse embeddings provider has to be provided as value to the `sparse_embedding` parameter.\n",
"\n",
"Note that if you've added documents with the `HYBRID` mode, you can switch to any retrieval mode when searching. Since both the dense and sparse vectors are available in the collection."