Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

Docs: azuresearch.ipynb (in docs/docs/integrations/vectorstores) -- fixed headings and comments #18135

Merged
merged 1 commit into from
Feb 26, 2024
Merged

Docs: azuresearch.ipynb (in docs/docs/integrations/vectorstores) -- fixed headings and comments #18135

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Jump to
Jump to file
Failed to load files.
Diff view
Diff view
26 changes: 17 additions & 9 deletions docs/docs/integrations/vectorstores/azuresearch.ipynb
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -8,8 +8,7 @@
"source": [
"# Azure AI Search\n",
"\n",
"[Azure AI Search](https://learn.microsoft.com/azure/search/search-what-is-azure-search) (formerly known as `Azure Search` and `Azure Cognitive Search`) is a cloud search service that gives developers infrastructure, APIs, and tools for building a rich search experience over private, heterogeneous content in web, mobile, and enterprise applications.\n",
"\n"
"[Azure AI Search](https://learn.microsoft.com/azure/search/search-what-is-azure-search) (formerly known as `Azure Search` and `Azure Cognitive Search`) is a cloud search service that gives developers infrastructure, APIs, and tools for information retrieval of vector, keyword, and hybrid queries at scale.\n"
]
},
{
Expand Down Expand Up @@ -292,7 +291,7 @@
"source": [
"## Perform a vector similarity search with relevance scores\n",
" \n",
"Execute a pure vector similarity search using the similarity_search_with_relevance_scores() method:"
"Execute a pure vector similarity search using the similarity_search_with_relevance_scores() method. Queries that don't meet the threshold requirements are exluded."
]
},
{
Expand Down Expand Up @@ -331,9 +330,9 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"## Perform a Hybrid Search\n",
"## Perform a hybrid search\n",
"\n",
"Execute hybrid search using the search_type or hybrid_search() method:"
"Execute hybrid search using the search_type or hybrid_search() method. Vector and nonvector text fields are queried in parallel, results are merged, and top matches of the unified result set are returned."
]
},
{
Expand All @@ -356,7 +355,7 @@
}
],
"source": [
"# Perform a hybrid search\n",
"# Perform a hybrid search using the search_type parameter\n",
"docs = vector_store.similarity_search(\n",
" query=\"What did the president say about Ketanji Brown Jackson\",\n",
" k=3,\n",
Expand Down Expand Up @@ -385,7 +384,7 @@
}
],
"source": [
"# Perform a hybrid search\n",
"# Perform a hybrid search using the hybrid_search method\n",
"docs = vector_store.hybrid_search(\n",
" query=\"What did the president say about Ketanji Brown Jackson\", k=3\n",
")\n",
Expand All @@ -396,7 +395,16 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"# Create a new index with custom filterable fields \n",
"## Custom schemas and queries\n",
"\n",
"This section shows you how to replace the default schema with a custom schema.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create a new index with custom filterable fields \n",
"\n",
"This schema shows field definitions. It's the default schema, plus several new fields attributed as filterable. Because it's using the default vector configuration, you won't see vector configuration or vector profile overrides here. The name of the default vector profile is \"myHnswProfile\" and it's using a vector configuration of Hierarchical Navigable Small World (HNSW) for indexing and queries against the content_vector field.\n",
"\n",
Expand Down Expand Up @@ -566,7 +574,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Create a new index with a Scoring Profile\n",
"### Create a new index with a scoring profile\n",
"\n",
"Here's another custom schema that includes a scoring profile definition. A scoring profile is used for relevance tuning of nonvector content, which is helpful in hybrid search scenarios."
]
Expand Down