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Update SinglStoreDB vectorstore (#6423)

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1. Introduced new distance strategies support: **DOT_PRODUCT** and
**EUCLIDEAN_DISTANCE** for enhanced flexibility.
2. Implemented a feature to filter results based on metadata fields.
3. Incorporated connection attributes specifying "langchain python sdk"
usage for enhanced traceability and debugging.
4. Expanded the suite of integration tests for improved code
reliability.
5. Updated the existing notebook with the usage example

@dev2049

---------

Co-authored-by: Volodymyr Tkachuk <vtkachuk-ua@singlestore.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
This commit is contained in:
volodymyr-memsql
2023-06-20 08:08:58 +03:00
committed by GitHub
parent 6efd5fa2b9
commit d2e9b621ab
5 changed files with 812 additions and 46 deletions
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13
docs/extras/modules/data_connection/vectorstores/integrations/singlestoredb.ipynb
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@@ -5,9 +5,8 @@
"id": "2b9582dc",
"metadata": {},
"source": [
"# SingleStoreDB vector search\n",
"[SingleStore DB](https://singlestore.com) is a high-performance distributed database that supports deployment both in the [cloud](https://www.singlestore.com/cloud/) and on-premises. For a significant duration, it has provided support for vector functions such as [dot_product](https://docs.singlestore.com/managed-service/en/reference/sql-reference/vector-functions/dot_product.html), thereby positioning itself as an ideal solution for AI applications that require text similarity matching. \n",
"This tutorial illustrates how to utilize the features of the SingleStore DB Vector Store."
"# SingleStoreDB\n",
"[SingleStoreDB](https://singlestore.com/) is a high-performance distributed SQL database that supports deployment both in the [cloud](https://www.singlestore.com/cloud/) and on-premises. It provides vector storage, and vector functions including [dot_product](https://docs.singlestore.com/managed-service/en/reference/sql-reference/vector-functions/dot_product.html) and [euclidean_distance](https://docs.singlestore.com/managed-service/en/reference/sql-reference/vector-functions/euclidean_distance.html), thereby supporting AI applications that require text similarity matching. This tutorial illustrates how to [work with vector data in SingleStoreDB](https://docs.singlestore.com/managed-service/en/developer-resources/functional-extensions/working-with-vector-data.html)."
]
},
{
@@ -58,10 +57,8 @@
"metadata": {},
"outputs": [],
"source": [
"# Load text samples\n",
"from langchain.document_loaders import TextLoader\n",
"\n",
"loader = TextLoader(\"../../../state_of_the_union.txt\")\n",
"# Load text samples \n",
"loader = TextLoader('../../../state_of_the_union.txt')\n",
"documents = loader.load()\n",
"text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
"docs = text_splitter.split_documents(documents)\n",
@@ -91,7 +88,7 @@
"docsearch = SingleStoreDB.from_documents(\n",
" docs,\n",
" embeddings,\n",
" table_name=\"noteook\", # use table with a custom name\n",
" table_name = \"notebook\", # use table with a custom name \n",
")"
]
},