docs: vectorstores, different updates and fixes (#4939)

# docs: vectorstores, different updates and fixes Multiple updates: - added/improved descriptions - fixed header levels - added headers - fixed headers
2025-06-01 04:29:09 +00:00 · 2023-05-18 15:35:47 -07:00 · 2023-05-18 15:35:47 -07:00 · a9bb3147d7
commit a9bb3147d7
parent 8f8593aac5
8 changed files with 92 additions and 38 deletions
--- a/docs/modules/indexes/vectorstores/examples/annoy.ipynb
+++ b/docs/modules/indexes/vectorstores/examples/annoy.ipynb
@ -7,11 +7,9 @@
   "source": [
    "# Annoy\n",
    "\n",
-    "> \"Annoy (Approximate Nearest Neighbors Oh Yeah) is a C++ library with Python bindings to search for points in space that are close to a given query point. It also creates large read-only file-based data structures that are mmapped into memory so that many processes may share the same data.\"\n",
+    "> [Annoy](https://github.com/spotify/annoy) (`Approximate Nearest Neighbors Oh Yeah`) is a C++ library with Python bindings to search for points in space that are close to a given query point. It also creates large read-only file-based data structures that are mmapped into memory so that many processes may share the same data.\n",
    "\n",
-    "This notebook shows how to use functionality related to the `Annoy` vector database.\n",
-    "\n",
-    "via [Annoy](https://github.com/spotify/annoy) \n"
+    "This notebook shows how to use functionality related to the `Annoy` vector database."
   ]
  },
  {
@ -25,6 +23,18 @@
    "```"
   ]
  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6107872c-09e8-4254-a89c-17e0a0764e82",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "#!pip install annoy"
+   ]
+  },
  {
   "cell_type": "markdown",
   "id": "6613d222",
@ -35,9 +45,11 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
   "id": "dc7351b5",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
   "outputs": [],
   "source": [
    "from langchain.embeddings import HuggingFaceEmbeddings\n",
@ -48,9 +60,11 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
   "id": "d2cb5f7d",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
   "outputs": [],
   "source": [
    "texts = [\"pizza is great\", \"I love salad\", \"my car\", \"a dog\"]\n",
--- a/docs/modules/indexes/vectorstores/examples/atlas.ipynb
+++ b/docs/modules/indexes/vectorstores/examples/atlas.ipynb
@ -1,15 +1,15 @@
 {
 "cells": [
  {
-   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "# AtlasDB\n",
+    "# Atlas\n",
    "\n",
-    "This notebook shows you how to use functionality related to the `AtlasDB`.\n",
    "\n",
-    "[Atlas](https://docs.nomic.ai/index.html) a platform for interacting with both small and internet scale unstructured datasets by Nomic "
+    ">[Atlas](https://docs.nomic.ai/index.html) is a platform for interacting with both small and internet scale unstructured datasets by `Nomic`. \n",
+    "\n",
+    "This notebook shows you how to use functionality related to the `AtlasDB` vectorstore."
   ]
  },
  {
--- a/docs/modules/indexes/vectorstores/examples/deeplake.ipynb
+++ b/docs/modules/indexes/vectorstores/examples/deeplake.ipynb
@ -582,7 +582,7 @@
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "## Creating dataset on AWS S3"
+    "### Creating dataset on AWS S3"
   ]
  },
  {
--- a/docs/modules/indexes/vectorstores/examples/docarray_hnsw.ipynb
+++ b/docs/modules/indexes/vectorstores/examples/docarray_hnsw.ipynb
@ -17,7 +17,7 @@
   "id": "7ee37d28",
   "metadata": {},
   "source": [
-    "# Setup\n",
+    "## Setup\n",
    "\n",
    "Uncomment the below cells to install docarray and get/set your OpenAI api key if you haven't already done so."
   ]
@ -61,7 +61,7 @@
    "tags": []
   },
   "source": [
-    "# Using DocArrayHnswSearch"
+    "## Using DocArrayHnswSearch"
   ]
  },
  {
@ -102,7 +102,7 @@
   "id": "ed6f905b-4853-4a44-9730-614aa8e22b78",
   "metadata": {},
   "source": [
-    "## Similarity search"
+    "### Similarity search"
   ]
  },
  {
@ -149,7 +149,7 @@
   "id": "3febb987-e903-416f-af26-6897d84c8d61",
   "metadata": {},
   "source": [
-    "## Similarity search with score"
+    "### Similarity search with score"
   ]
  },
  {
@ -219,7 +219,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.11.3"
+   "version": "3.10.6"
  }
 },
 "nbformat": 4,
--- a/docs/modules/indexes/vectorstores/examples/docarray_in_memory.ipynb
+++ b/docs/modules/indexes/vectorstores/examples/docarray_in_memory.ipynb
@ -17,7 +17,7 @@
   "id": "5031a3ec",
   "metadata": {},
   "source": [
-    "# Setup\n",
+    "## Setup\n",
    "\n",
    "Uncomment the below cells to install docarray and get/set your OpenAI api key if you haven't already done so."
   ]
@ -53,6 +53,14 @@
    "# os.environ[\"OPENAI_API_KEY\"] = OPENAI_API_KEY"
   ]
  },
+  {
+   "cell_type": "markdown",
+   "id": "6e57a389-f637-4b8f-9ab2-759ae7485f78",
+   "metadata": {},
+   "source": [
+    "## Using DocArrayInMemorySearch"
+   ]
+  },
  {
   "cell_type": "code",
   "execution_count": null,
@ -91,7 +99,7 @@
   "id": "efbb6684-3846-4332-a624-ddd4d75844c1",
   "metadata": {},
   "source": [
-    "## Similarity search"
+    "### Similarity search"
   ]
  },
  {
@ -138,7 +146,7 @@
   "id": "43896697-f99e-47b6-9117-47a25e9afa9c",
   "metadata": {},
   "source": [
-    "## Similarity search with score"
+    "### Similarity search with score"
   ]
  },
  {
@ -202,7 +210,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.11.3"
+   "version": "3.10.6"
  }
 },
 "nbformat": 4,
--- a/docs/modules/indexes/vectorstores/examples/opensearch.ipynb
+++ b/docs/modules/indexes/vectorstores/examples/opensearch.ipynb
@ -18,6 +18,14 @@
    "Check [this](https://opensearch.org/docs/latest/search-plugins/knn/index/) for more details."
   ]
  },
+  {
+   "cell_type": "markdown",
+   "id": "94963977-9dfc-48b7-872a-53f2947f46c6",
+   "metadata": {},
+   "source": [
+    "## Installation"
+   ]
+  },
  {
   "cell_type": "code",
   "execution_count": null,
@ -80,6 +88,16 @@
    "embeddings = OpenAIEmbeddings()"
   ]
  },
+  {
+   "cell_type": "markdown",
+   "id": "01a9a035",
+   "metadata": {},
+   "source": [
+    "### similarity_search using Approximate k-NN\n",
+    "\n",
+    "`similarity_search` using `Approximate k-NN` Search with Custom Parameters"
+   ]
+  },
  {
   "cell_type": "code",
   "execution_count": null,
@ -111,14 +129,6 @@
    "print(docs[0].page_content)"
   ]
  },
-  {
-   "cell_type": "markdown",
-   "id": "01a9a035",
-   "metadata": {},
-   "source": [
-    "#### similarity_search using Approximate k-NN Search with Custom Parameters"
-   ]
-  },
  {
   "cell_type": "code",
   "execution_count": null,
@ -155,7 +165,9 @@
   "id": "0d0cd877",
   "metadata": {},
   "source": [
-    "#### similarity_search using Script Scoring with Custom Parameters"
+    "### similarity_search using Script Scoring\n",
+    "\n",
+    "`similarity_search` using `Script Scoring` with Custom Parameters"
   ]
  },
  {
@ -194,7 +206,9 @@
   "id": "a4af96cc",
   "metadata": {},
   "source": [
-    "#### similarity_search using Painless Scripting with Custom Parameters"
+    "### similarity_search using Painless Scripting\n",
+    "\n",
+    "`similarity_search` using `Painless Scripting` with Custom Parameters"
   ]
  },
  {
@ -233,7 +247,7 @@
   "id": "73264864",
   "metadata": {},
   "source": [
-    "#### Using a preexisting OpenSearch instance\n",
+    "### Using a preexisting OpenSearch instance\n",
    "\n",
    "It's also possible to use a preexisting OpenSearch instance with documents that already have vectors present."
   ]
--- a/docs/modules/indexes/vectorstores/examples/redis.ipynb
+++ b/docs/modules/indexes/vectorstores/examples/redis.ipynb
@ -11,6 +11,13 @@
    "This notebook shows how to use functionality related to the [Redis vector database](https://redis.com/solutions/use-cases/vector-database/)."
   ]
  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Installing"
+   ]
+  },
  {
   "cell_type": "code",
   "execution_count": null,
@ -41,6 +48,13 @@
    "os.environ['OPENAI_API_KEY'] = getpass.getpass('OpenAI API Key:')"
   ]
  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example"
+   ]
+  },
  {
   "cell_type": "code",
   "execution_count": 1,
@ -192,7 +206,7 @@
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "## RedisVectorStoreRetriever\n",
+    "## Redis as Retriever\n",
    "\n",
    "Here we go over different options for using the vector store as a retriever.\n",
    "\n",
--- a/docs/modules/indexes/vectorstores/examples/tair.ipynb
+++ b/docs/modules/indexes/vectorstores/examples/tair.ipynb
@ -6,8 +6,12 @@
   "source": [
    "# Tair\n",
    "\n",
-    "This notebook shows how to use functionality related to the Tair vector database.\n",
-    "To run, you should have an [Tair](https://www.alibabacloud.com/help/en/tair/latest/what-is-tair) instance up and running."
+    ">[Tair](https://www.alibabacloud.com/help/en/tair/latest/what-is-tair) is a cloud native in-memory database service developed by `Alibaba Cloud`. \n",
+    "It provides rich data models and enterprise-grade capabilities to support your real-time online scenarios while maintaining full compatibility with open source `Redis`. `Tair` also introduces persistent memory-optimized instances that are based on the new non-volatile memory (NVM) storage medium.\n",
+    "\n",
+    "This notebook shows how to use functionality related to the `Tair` vector database.\n",
+    "\n",
+    "To run, you should have a `Tair` instance up and running."
   ]
  },
  {
@ -121,9 +125,9 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.6"
  }
 },
 "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }