docs: self-query consistency (#10502)

The `self-que[ring`
navbar](https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/)
has repeated `self-quering` repeated in each menu item. I've simplified
it to be more readable
- removed `self-quering` from a title of each page;
- added description to the vector stores
- added description and link to the Integration Card
(`integrations/providers`) of the vector stores when they are missed.

docs/extras/integrations/providers/milvus.mdx

@@ -1,15 +1,20 @@
 # Milvus
 
-This page covers how to use the Milvus ecosystem within LangChain.
+>[Milvus](https://milvus.io/docs/overview.md) is a database that stores, indexes, and manages
-It is broken into two parts: installation and setup, and then references to specific Milvus wrappers.
+> massive embedding vectors generated by deep neural networks and other machine learning (ML) models.
+
 
 ## Installation and Setup
-- Install the Python SDK with `pip install pymilvus`
-## Wrappers
 
-### VectorStore
+Install the Python SDK:
 
-There exists a wrapper around Milvus indexes, allowing you to use it as a vectorstore,
+```bash
+pip install pymilvus
+```
+
+## Vector Store
+
+There exists a wrapper around `Milvus` indexes, allowing you to use it as a vectorstore,
 whether for semantic search or example selection.
 
 To import this vectorstore:
@@ -17,4 +22,4 @@ To import this vectorstore:
 from langchain.vectorstores import Milvus
 ```
 
-For a more detailed walkthrough of the Miluvs wrapper, see [this notebook](/docs/integrations/vectorstores/milvus.html)
+For a more detailed walkthrough of the `Miluvs` wrapper, see [this notebook](/docs/integrations/vectorstores/milvus.html)

docs/extras/integrations/providers/pinecone.mdx

@@ -1,10 +1,12 @@
 # Pinecone
 
-This page covers how to use the Pinecone ecosystem within LangChain.
+>[Pinecone](https://docs.pinecone.io/docs/overview) is a vector database with broad functionality.
-It is broken into two parts: installation and setup, and then references to specific Pinecone wrappers.
+
 
 ## Installation and Setup
+
 Install the Python SDK:
+
 ```bash
 pip install pinecone-client
 ```

docs/extras/integrations/providers/qdrant.mdx

@@ -1,15 +1,22 @@
 # Qdrant
 
-This page covers how to use the Qdrant ecosystem within LangChain.
+>[Qdrant](https://qdrant.tech/documentation/) (read: quadrant) is a vector similarity search engine. 
-It is broken into two parts: installation and setup, and then references to specific Qdrant wrappers.
+> It provides a production-ready service with a convenient API to store, search, and manage 
+> points - vectors with an additional payload. `Qdrant` is tailored to extended filtering support.
+
 
 ## Installation and Setup
-- Install the Python SDK with `pip install qdrant-client`
-## Wrappers
 
-### VectorStore
+Install the Python SDK:
 
-There exists a wrapper around Qdrant indexes, allowing you to use it as a vectorstore,
+```bash
+pip install qdrant-client
+```
+
+
+## Vector Store
+
+There exists a wrapper around `Qdrant` indexes, allowing you to use it as a vectorstore,
 whether for semantic search or example selection.
 
 To import this vectorstore:

docs/extras/integrations/providers/redis.mdx

@@ -1,18 +1,26 @@
 # Redis
 
+>[Redis](https://redis.com) is an open-source key-value store that can be used as a cache, 
+> message broker, database, vector database and more.
+
 This page covers how to use the [Redis](https://redis.com) ecosystem within LangChain.
 It is broken into two parts: installation and setup, and then references to specific Redis wrappers.
 
 ## Installation and Setup
-- Install the Redis Python SDK with `pip install redis`
+
+Install the Python SDK:
+
+```bash
+pip install redis
+```
 
 ## Wrappers
 
-All wrappers needing a redis url connection string to connect to the database support either a stand alone Redis server
+All wrappers need a redis url connection string to connect to the database support either a stand alone Redis server
 or a High-Availability setup with Replication and Redis Sentinels.
 
 ### Redis Standalone connection url
-For standalone Redis server the official redis connection url formats can be used as describe in the python redis modules
+For standalone `Redis` server, the official redis connection url formats can be used as describe in the python redis modules
 "from_url()" method [Redis.from_url](https://redis-py.readthedocs.io/en/stable/connections.html#redis.Redis.from_url)
 
 Example: `redis_url = "redis://:secret-pass@localhost:6379/0"`
@@ -20,7 +28,7 @@ Example: `redis_url = "redis://:secret-pass@localhost:6379/0"`
 ### Redis Sentinel connection url
 
 For [Redis sentinel setups](https://redis.io/docs/management/sentinel/) the connection scheme is "redis+sentinel". 
-This is an un-offical extensions to the official IANA registered protocol schemes as long as there is no connection url
+This is an unofficial extensions to the official IANA registered protocol schemes as long as there is no connection url
 for Sentinels available.
 
 Example: `redis_url = "redis+sentinel://:secret-pass@sentinel-host:26379/mymaster/0"`

docs/extras/integrations/providers/vectara/index.mdx

@@ -1,17 +1,18 @@
 # Vectara
 
-
+>[Vectara](https://docs.vectara.com/docs/) is a GenAI platform for developers. It provides a simple API to build Grounded Generation
-What is Vectara?
+>(aka Retrieval-augmented-generation or RAG) applications.
 
 **Vectara Overview:**
-- Vectara is developer-first API platform for building GenAI applications
+- `Vectara` is developer-first API platform for building GenAI applications
 - To use Vectara - first [sign up](https://console.vectara.com/signup) and create an account. Then create a corpus and an API key for indexing and searching.
 - You can use Vectara's [indexing API](https://docs.vectara.com/docs/indexing-apis/indexing) to add documents into Vectara's index
 - You can use Vectara's [Search API](https://docs.vectara.com/docs/search-apis/search) to query Vectara's index (which also supports Hybrid search implicitly).
 - You can use Vectara's integration with LangChain as a Vector store or using the Retriever abstraction.
 
 ## Installation and Setup
-To use Vectara with LangChain no special installation steps are required. 
+
+To use `Vectara` with LangChain no special installation steps are required. 
 To get started, follow our [quickstart](https://docs.vectara.com/docs/quickstart) guide to create an account, a corpus and an API key. 
 Once you have these, you can provide them as arguments to the Vectara vectorstore, or you can set them as environment variables.
 
@@ -19,9 +20,8 @@ Once you have these, you can provide them as arguments to the Vectara vectorstor
 - export `VECTARA_CORPUS_ID`="your_corpus_id"
 - export `VECTARA_API_KEY`="your-vectara-api-key"
 
-## Usage
 
-### VectorStore
+## Vector Store
 
 There exists a wrapper around the Vectara platform, allowing you to use it as a vectorstore, whether for semantic search or example selection.
 

docs/extras/integrations/providers/weaviate.mdx

@@ -1,10 +1,10 @@
 # Weaviate
 
-This page covers how to use the Weaviate ecosystem within LangChain.
+>[Weaviate](https://weaviate.io/) is an open-source vector database. It allows you to store data objects and vector embeddings from
+>your favorite ML models, and scale seamlessly into billions of data objects.
 
-What is Weaviate?
 
-**Weaviate in a nutshell:**
+What is `Weaviate`?
 - Weaviate is an open-source ​database of the type ​vector search engine.
 - Weaviate allows you to store JSON documents in a class property-like fashion while attaching machine learning vectors to these documents to represent them in vector space.
 - Weaviate can be used stand-alone (aka bring your vectors) or with a variety of modules that can do the vectorization for you and extend the core capabilities.
@@ -14,15 +14,20 @@ What is Weaviate?
 
 **Weaviate in detail:**
 
-Weaviate is a low-latency vector search engine with out-of-the-box support for different media types (text, images, etc.). It offers Semantic Search, Question-Answer Extraction, Classification, Customizable Models (PyTorch/TensorFlow/Keras), etc. Built from scratch in Go, Weaviate stores both objects and vectors, allowing for combining vector search with structured filtering and the fault tolerance of a cloud-native database. It is all accessible through GraphQL, REST, and various client-side programming languages.
+`Weaviate` is a low-latency vector search engine with out-of-the-box support for different media types (text, images, etc.). It offers Semantic Search, Question-Answer Extraction, Classification, Customizable Models (PyTorch/TensorFlow/Keras), etc. Built from scratch in Go, Weaviate stores both objects and vectors, allowing for combining vector search with structured filtering and the fault tolerance of a cloud-native database. It is all accessible through GraphQL, REST, and various client-side programming languages.
 
 ## Installation and Setup
-- Install the Python SDK with `pip install weaviate-client`
-## Wrappers
 
-### VectorStore
+Install the Python SDK:
 
-There exists a wrapper around Weaviate indexes, allowing you to use it as a vectorstore,
+```bash
+pip install weaviate-client
+```
+
+
+## Vector Store
+
+There exists a wrapper around `Weaviate` indexes, allowing you to use it as a vectorstore,
 whether for semantic search or example selection.
 
 To import this vectorstore:

docs/extras/modules/data_connection/retrievers/self_query/activeloop_deeplake_self_query.ipynb

@@ -6,11 +6,14 @@
    "id": "13afcae7",
    "metadata": {},
    "source": [
-    "# Deep Lake self-querying \n",
+    "# Deep Lake\n",
     "\n",
-    ">[Deep Lake](https://www.activeloop.ai) is a multimodal database for building AI applications.\n",
+    ">[Deep Lake](https://www.activeloop.ai) is a multimodal database for building AI applications\n",
+    ">[Deep Lake](https://github.com/activeloopai/deeplake) is a database for AI.\n",
+    ">Store Vectors, Images, Texts, Videos, etc. Use with LLMs/LangChain. Store, query, version,\n",
+    "> & visualize any AI data. Stream data in real time to PyTorch/TensorFlow.\n",
     "\n",
-    "In the notebook we'll demo the `SelfQueryRetriever` wrapped around a Deep Lake vector store. "
+    "In the notebook, we'll demo the `SelfQueryRetriever` wrapped around a `Deep Lake` vector store. "
    ]
   },
   {

docs/extras/modules/data_connection/retrievers/self_query/chroma_self_query.ipynb

@@ -5,11 +5,11 @@
    "id": "13afcae7",
    "metadata": {},
    "source": [
-    "# Chroma self-querying \n",
+    "# Chroma\n",
     "\n",
     ">[Chroma](https://docs.trychroma.com/getting-started) is a database for building AI applications with embeddings.\n",
     "\n",
-    "In the notebook we'll demo the `SelfQueryRetriever` wrapped around a Chroma vector store. "
+    "In the notebook, we'll demo the `SelfQueryRetriever` wrapped around a `Chroma` vector store. "
    ]
   },
   {
@@ -447,7 +447,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

docs/extras/modules/data_connection/retrievers/self_query/dashvector.ipynb

@@ -2,20 +2,36 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "source": [
+   "id": "59895c73d1a0f3ca",
-    "# DashVector self-querying\n",
-    "\n",
-    "> [DashVector](https://help.aliyun.com/document_detail/2510225.html) is a fully-managed vectorDB service that supports high-dimension dense and sparse vectors, real-time insertion and filtered search. It is built to scale automatically and can adapt to different application requirements.\n",
-    "\n",
-    "In this notebook we'll demo the `SelfQueryRetriever` with a `DashVector` vector store."
-   ],
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
-   "id": "59895c73d1a0f3ca"
+   "source": [
+    "# DashVector\n",
+    "\n",
+    "> [DashVector](https://help.aliyun.com/document_detail/2510225.html) is a fully managed vector DB service that supports high-dimension dense and sparse vectors, real-time insertion and filtered search. It is built to scale automatically and can adapt to different application requirements.\n",
+    "> The vector retrieval service `DashVector` is based on the `Proxima` core of the efficient vector engine independently developed by `DAMO Academy`,\n",
+    ">  and provides a cloud-native, fully managed vector retrieval service with horizontal expansion capabilities.\n",
+    ">  `DashVector` exposes its powerful vector management, vector query and other diversified capabilities through a simple and\n",
+    "> easy-to-use SDK/API interface, which can be quickly integrated by upper-layer AI applications, thereby providing services\n",
+    "> including large model ecology, multi-modal AI search, molecular structure A variety of application scenarios, including analysis,\n",
+    "> provide the required efficient vector retrieval capabilities.\n",
+    "\n",
+    "In this notebook, we'll demo the `SelfQueryRetriever` with a `DashVector` vector store."
+   ]
   },
   {
    "cell_type": "markdown",
+   "id": "539ae9367e45a178",
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
    "source": [
     "## Create DashVector vectorstore\n",
     "\n",
@@ -24,46 +40,55 @@
     "To use DashVector, you have to have `dashvector` package installed, and you must have an API key and an Environment. Here are the [installation instructions](https://help.aliyun.com/document_detail/2510223.html).\n",
     "\n",
     "NOTE: The self-query retriever requires you to have `lark` package installed."
-   ],
+   ]
-   "metadata": {
-    "collapsed": false
-   },
-   "id": "539ae9367e45a178"
   },
   {
    "cell_type": "code",
    "execution_count": 1,
+   "id": "67df7e1f8dc8cdd0",
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
    "outputs": [],
    "source": [
     "# !pip install lark dashvector"
-   ],
+   ]
-   "metadata": {
-    "collapsed": false
-   },
-   "id": "67df7e1f8dc8cdd0"
   },
   {
    "cell_type": "code",
    "execution_count": 1,
+   "id": "ff61eaf13973b5fe",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-08-24T02:58:46.905337Z",
+     "start_time": "2023-08-24T02:58:46.252566Z"
+    },
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
    "outputs": [],
    "source": [
     "import os\n",
     "import dashvector\n",
     "\n",
     "client = dashvector.Client(api_key=os.environ[\"DASHVECTOR_API_KEY\"])"
-   ],
+   ]
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-08-24T02:58:46.905337Z",
-     "start_time": "2023-08-24T02:58:46.252566Z"
-    }
-   },
-   "id": "ff61eaf13973b5fe"
   },
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "de5c77957ee42d14",
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
    "outputs": [],
    "source": [
     "from langchain.schema import Document\n",
@@ -74,15 +99,22 @@
     "\n",
     "# create DashVector collection\n",
     "client.create(\"langchain-self-retriever-demo\", dimension=1536)"
-   ],
+   ]
-   "metadata": {
-    "collapsed": false
-   },
-   "id": "de5c77957ee42d14"
   },
   {
    "cell_type": "code",
    "execution_count": 3,
+   "id": "8f40605548a4550",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-08-24T02:59:08.090031Z",
+     "start_time": "2023-08-24T02:59:05.660295Z"
+    },
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
    "outputs": [],
    "source": [
     "docs = [\n",
@@ -119,31 +151,37 @@
     "vectorstore = DashVector.from_documents(\n",
     "    docs, embeddings, collection_name=\"langchain-self-retriever-demo\"\n",
     ")"
-   ],
+   ]
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-08-24T02:59:08.090031Z",
-     "start_time": "2023-08-24T02:59:05.660295Z"
-    }
-   },
-   "id": "8f40605548a4550"
   },
   {
    "cell_type": "markdown",
+   "id": "eb1340adafac8993",
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
    "source": [
     "## Create your self-querying retriever\n",
     "\n",
     "Now we can instantiate our retriever. To do this we'll need to provide some information upfront about the metadata fields that our documents support and a short description of the document contents."
-   ],
+   ]
-   "metadata": {
-    "collapsed": false
-   },
-   "id": "eb1340adafac8993"
   },
   {
    "cell_type": "code",
    "execution_count": 4,
+   "id": "d65233dc044f95a7",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-08-24T02:59:11.003940Z",
+     "start_time": "2023-08-24T02:59:10.476722Z"
+    },
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
    "outputs": [],
    "source": [
     "from langchain.llms import Tongyi\n",
@@ -175,31 +213,37 @@
     "retriever = SelfQueryRetriever.from_llm(\n",
     "    llm, vectorstore, document_content_description, metadata_field_info, verbose=True\n",
     ")"
-   ],
+   ]
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-08-24T02:59:11.003940Z",
-     "start_time": "2023-08-24T02:59:10.476722Z"
-    }
-   },
-   "id": "d65233dc044f95a7"
   },
   {
    "cell_type": "markdown",
+   "id": "a54af0d67b473db6",
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
    "source": [
     "## Testing it out\n",
     "\n",
     "And now we can try actually using our retriever!"
-   ],
+   ]
-   "metadata": {
-    "collapsed": false
-   },
-   "id": "a54af0d67b473db6"
   },
   {
    "cell_type": "code",
    "execution_count": 6,
+   "id": "dad9da670a267fe7",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-08-24T02:59:28.577901Z",
+     "start_time": "2023-08-24T02:59:26.780184Z"
+    },
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -210,7 +254,12 @@
     },
     {
      "data": {
-      "text/plain": "[Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'year': 1993, 'rating': 7.699999809265137, 'genre': 'action'}),\n Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, 'genre': 'animated'}),\n Document(page_content='Leo DiCaprio gets lost in a dream within a dream within a dream within a ...', metadata={'year': 2010, 'director': 'Christopher Nolan', 'rating': 8.199999809265137}),\n Document(page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea', metadata={'year': 2006, 'director': 'Satoshi Kon', 'rating': 8.600000381469727})]"
+      "text/plain": [
+       "[Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'year': 1993, 'rating': 7.699999809265137, 'genre': 'action'}),\n",
+       " Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, 'genre': 'animated'}),\n",
+       " Document(page_content='Leo DiCaprio gets lost in a dream within a dream within a dream within a ...', metadata={'year': 2010, 'director': 'Christopher Nolan', 'rating': 8.199999809265137}),\n",
+       " Document(page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea', metadata={'year': 2006, 'director': 'Satoshi Kon', 'rating': 8.600000381469727})]"
+      ]
      },
      "execution_count": 6,
      "metadata": {},
@@ -220,19 +269,22 @@
    "source": [
     "# This example only specifies a relevant query\n",
     "retriever.get_relevant_documents(\"What are some movies about dinosaurs\")"
-   ],
+   ]
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-08-24T02:59:28.577901Z",
-     "start_time": "2023-08-24T02:59:26.780184Z"
-    }
-   },
-   "id": "dad9da670a267fe7"
   },
   {
    "cell_type": "code",
    "execution_count": 7,
+   "id": "d486a64316153d52",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-08-24T02:59:32.370774Z",
+     "start_time": "2023-08-24T02:59:30.614252Z"
+    },
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -243,7 +295,10 @@
     },
     {
      "data": {
-      "text/plain": "[Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'year': 1979, 'director': 'Andrei Tarkovsky', 'rating': 9.899999618530273, 'genre': 'science fiction'}),\n Document(page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea', metadata={'year': 2006, 'director': 'Satoshi Kon', 'rating': 8.600000381469727})]"
+      "text/plain": [
+       "[Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'year': 1979, 'director': 'Andrei Tarkovsky', 'rating': 9.899999618530273, 'genre': 'science fiction'}),\n",
+       " Document(page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea', metadata={'year': 2006, 'director': 'Satoshi Kon', 'rating': 8.600000381469727})]"
+      ]
      },
      "execution_count": 7,
      "metadata": {},
@@ -253,19 +308,22 @@
    "source": [
     "# This example only specifies a filter\n",
     "retriever.get_relevant_documents(\"I want to watch a movie rated higher than 8.5\")"
-   ],
+   ]
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-08-24T02:59:32.370774Z",
-     "start_time": "2023-08-24T02:59:30.614252Z"
-    }
-   },
-   "id": "d486a64316153d52"
   },
   {
    "cell_type": "code",
    "execution_count": 8,
+   "id": "e05919cdead7bd4a",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-08-24T02:59:35.353439Z",
+     "start_time": "2023-08-24T02:59:33.278255Z"
+    },
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -276,7 +334,9 @@
     },
     {
      "data": {
-      "text/plain": "[Document(page_content='A bunch of normal-sized women are supremely wholesome and some men pine after them', metadata={'year': 2019, 'director': 'Greta Gerwig', 'rating': 8.300000190734863})]"
+      "text/plain": [
+       "[Document(page_content='A bunch of normal-sized women are supremely wholesome and some men pine after them', metadata={'year': 2019, 'director': 'Greta Gerwig', 'rating': 8.300000190734863})]"
+      ]
      },
      "execution_count": 8,
      "metadata": {},
@@ -286,19 +346,22 @@
    "source": [
     "# This example specifies a query and a filter\n",
     "retriever.get_relevant_documents(\"Has Greta Gerwig directed any movies about women\")"
-   ],
+   ]
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-08-24T02:59:35.353439Z",
-     "start_time": "2023-08-24T02:59:33.278255Z"
-    }
-   },
-   "id": "e05919cdead7bd4a"
   },
   {
    "cell_type": "code",
    "execution_count": 9,
+   "id": "ac2c7012379e918e",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-08-24T02:59:38.913707Z",
+     "start_time": "2023-08-24T02:59:36.659271Z"
+    },
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -309,7 +372,9 @@
     },
     {
      "data": {
-      "text/plain": "[Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'year': 1979, 'director': 'Andrei Tarkovsky', 'rating': 9.899999618530273, 'genre': 'science fiction'})]"
+      "text/plain": [
+       "[Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'year': 1979, 'director': 'Andrei Tarkovsky', 'rating': 9.899999618530273, 'genre': 'science fiction'})]"
+      ]
      },
      "execution_count": 9,
      "metadata": {},
@@ -319,33 +384,39 @@
    "source": [
     "# This example specifies a composite filter\n",
     "retriever.get_relevant_documents(\"What's a highly rated (above 8.5) science fiction film?\")"
-   ],
+   ]
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-08-24T02:59:38.913707Z",
-     "start_time": "2023-08-24T02:59:36.659271Z"
-    }
-   },
-   "id": "ac2c7012379e918e"
   },
   {
    "cell_type": "markdown",
+   "id": "af6aa93ae44af414",
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
    "source": [
     "## Filter k\n",
     "\n",
     "We can also use the self query retriever to specify `k`: the number of documents to fetch.\n",
     "\n",
     "We can do this by passing `enable_limit=True` to the constructor."
-   ],
+   ]
-   "metadata": {
-    "collapsed": false
-   },
-   "id": "af6aa93ae44af414"
   },
   {
    "cell_type": "code",
    "execution_count": 10,
+   "id": "a8c8f09bf5702767",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-08-24T02:59:41.594073Z",
+     "start_time": "2023-08-24T02:59:41.563323Z"
+    },
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
    "outputs": [],
    "source": [
     "retriever = SelfQueryRetriever.from_llm(\n",
@@ -356,19 +427,22 @@
     "    enable_limit=True,\n",
     "    verbose=True,\n",
     ")"
-   ],
+   ]
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-08-24T02:59:41.594073Z",
-     "start_time": "2023-08-24T02:59:41.563323Z"
-    }
-   },
-   "id": "a8c8f09bf5702767"
   },
   {
    "cell_type": "code",
    "execution_count": 11,
+   "id": "b1089a6043980b84",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-08-24T02:59:48.450506Z",
+     "start_time": "2023-08-24T02:59:46.252944Z"
+    },
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -379,7 +453,10 @@
     },
     {
      "data": {
-      "text/plain": "[Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'year': 1993, 'rating': 7.699999809265137, 'genre': 'action'}),\n Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, 'genre': 'animated'})]"
+      "text/plain": [
+       "[Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'year': 1993, 'rating': 7.699999809265137, 'genre': 'action'}),\n",
+       " Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, 'genre': 'animated'})]"
+      ]
      },
      "execution_count": 11,
      "metadata": {},
@@ -389,44 +466,39 @@
    "source": [
     "# This example only specifies a relevant query\n",
     "retriever.get_relevant_documents(\"what are two movies about dinosaurs\")"
-   ],
+   ]
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-08-24T02:59:48.450506Z",
-     "start_time": "2023-08-24T02:59:46.252944Z"
-    }
-   },
-   "id": "b1089a6043980b84"
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "outputs": [],
+   "id": "6d2d64e2ebb17d30",
-   "source": [],
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
-   "id": "6d2d64e2ebb17d30"
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
+   "pygments_lexer": "ipython3",
-   "version": "2.7.6"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

docs/extras/modules/data_connection/retrievers/self_query/elasticsearch_self_query.ipynb

@@ -5,7 +5,13 @@
    "id": "13afcae7",
    "metadata": {},
    "source": [
-    "# Elasticsearch self-querying "
+    "# Elasticsearch\n",
+    "\n",
+    "> [Elasticsearch](https://www.elastic.co/elasticsearch/) is a distributed, RESTful search and analytics engine.\n",
+    "> It provides a distributed, multi-tenant-capable full-text search engine with an HTTP web interface and schema-free\n",
+    "> JSON documents.\n",
+    "\n",
+    "In this notebook, we'll demo the `SelfQueryRetriever` with an `Elasticsearch` vector store."
    ]
   },
   {
@@ -13,8 +19,9 @@
    "id": "68e75fb9",
    "metadata": {},
    "source": [
-    "## Creating a Elasticsearch vector store\n",
+    "## Creating an Elasticsearch vector store\n",
-    "First we'll want to create a Elasticsearch vector store and seed it with some data. We've created a small demo set of documents that contain summaries of movies.\n",
+    "\n",
+    "First, we'll want to create an `Elasticsearch` vector store and seed it with some data. We've created a small demo set of documents that contain summaries of movies.\n",
     "\n",
     "**Note:** The self-query retriever requires you to have `lark` installed (`pip install lark`). We also need the `elasticsearch` package."
    ]
@@ -354,7 +361,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.3"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

docs/extras/modules/data_connection/retrievers/self_query/milvus_self_query.ipynb

@@ -4,9 +4,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Self-querying with Milvus\n",
+    "# Milvus\n",
     "\n",
-    "In the walkthrough we'll demo the `SelfQueryRetriever` with a `Milvus` vector store."
+    ">[Milvus](https://milvus.io/docs/overview.md) is a database that stores, indexes, and manages massive embedding vectors generated by deep neural networks and other machine learning (ML) models.\n",
+    "\n",
+    "In the walkthrough, we'll demo the `SelfQueryRetriever` with a `Milvus` vector store."
    ]
   },
   {
@@ -352,7 +354,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -366,10 +368,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.10.12"
-  },
+  }
-  "orig_nbformat": 4
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

docs/extras/modules/data_connection/retrievers/self_query/myscale_self_query.ipynb

@@ -5,12 +5,15 @@
    "id": "13afcae7",
    "metadata": {},
    "source": [
-    "# Self-querying with MyScale\n",
+    "# MyScale\n",
     "\n",
-    ">[MyScale](https://docs.myscale.com/en/) is an integrated vector database. You can access your database in SQL and also from here, LangChain. MyScale can make a use of [various data types and functions for filters](https://blog.myscale.com/2023/06/06/why-integrated-database-solution-can-boost-your-llm-apps/#filter-on-anything-without-constraints). It will boost up your LLM app no matter if you are scaling up your data or expand your system to broader application.\n",
+    ">[MyScale](https://docs.myscale.com/en/) is an integrated vector database. You can access your database in SQL and also from here, LangChain.\n",
+    ">`MyScale` can make use of [various data types and functions for filters](https://blog.myscale.com/2023/06/06/why-integrated-database-solution-can-boost-your-llm-apps/#filter-on-anything-without-constraints). It will boost up your LLM app no matter if you are scaling up your data or expand your system to broader application.\n",
     "\n",
-    "In the notebook we'll demo the `SelfQueryRetriever` wrapped around a MyScale vector store with some extra pieces we contributed to LangChain. In short, it can be condensed into 4 points:\n",
+    "In the notebook, we'll demo the `SelfQueryRetriever` wrapped around a `MyScale` vector store with some extra pieces we contributed to LangChain. \n",
-    "1. Add `contain` comparator to match list of any if there is more than one element matched\n",
+    "\n",
+    "In short, it can be condensed into 4 points:\n",
+    "1. Add `contain` comparator to match the list of any if there is more than one element matched\n",
     "2. Add `timestamp` data type for datetime match (ISO-format, or YYYY-MM-DD)\n",
     "3. Add `like` comparator for string pattern search\n",
     "4. Add arbitrary function capability"
@@ -221,9 +224,7 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "fc3f1e6e",
-   "metadata": {
+   "metadata": {},
-    "scrolled": false
-   },
    "outputs": [],
    "source": [
     "# This example only specifies a filter\n",
@@ -384,7 +385,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.3"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

docs/extras/modules/data_connection/retrievers/self_query/pinecone.ipynb

@@ -5,9 +5,11 @@
    "id": "13afcae7",
    "metadata": {},
    "source": [
-    "# Self-querying with Pinecone\n",
+    "# Pinecone\n",
     "\n",
-    "In the walkthrough we'll demo the `SelfQueryRetriever` with a `Pinecone` vector store."
+    ">[Pinecone](https://docs.pinecone.io/docs/overview) is a vector database with broad functionality.\n",
+    "\n",
+    "In the walkthrough, we'll demo the `SelfQueryRetriever` with a `Pinecone` vector store."
    ]
   },
   {
@@ -395,7 +397,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.3"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

docs/extras/modules/data_connection/retrievers/self_query/qdrant_self_query.ipynb

@@ -6,11 +6,11 @@
    "id": "13afcae7",
    "metadata": {},
    "source": [
-    "# Qdrant self-querying \n",
+    "# Qdrant\n",
     "\n",
     ">[Qdrant](https://qdrant.tech/documentation/) (read: quadrant) is a vector similarity search engine. It provides a production-ready service with a convenient API to store, search, and manage points - vectors with an additional payload. `Qdrant` is tailored to extended filtering support.\n",
     "\n",
-    "In the notebook we'll demo the `SelfQueryRetriever` wrapped around a Qdrant vector store. "
+    "In the notebook, we'll demo the `SelfQueryRetriever` wrapped around a `Qdrant` vector store. "
    ]
   },
   {
@@ -419,7 +419,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

docs/extras/modules/data_connection/retrievers/self_query/redis_self_query.ipynb

@@ -5,11 +5,11 @@
    "id": "13afcae7",
    "metadata": {},
    "source": [
-    "# Redis self-querying \n",
+    "# Redis\n",
     "\n",
     ">[Redis](https://redis.com) is an open-source key-value store that can be used as a cache, message broker, database, vector database and more.\n",
     "\n",
-    "In the notebook we'll demo the `SelfQueryRetriever` wrapped around a Redis vector store. "
+    "In the notebook, we'll demo the `SelfQueryRetriever` wrapped around a `Redis` vector store. "
    ]
   },
   {
@@ -450,9 +450,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "poetry-venv",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "poetry-venv"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -464,7 +464,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

docs/extras/modules/data_connection/retrievers/self_query/supabase_self_query.ipynb

@@ -5,19 +5,22 @@
    "id": "13afcae7",
    "metadata": {},
    "source": [
-    "# Supabase Vector self-querying \n",
+    "# Supabase\n",
     "\n",
-    ">[Supabase](https://supabase.com/docs) is an open source `Firebase` alternative. \n",
+    ">[Supabase](https://supabase.com/docs) is an open-source `Firebase` alternative. \n",
     "> `Supabase` is built on top of `PostgreSQL`, which offers strong `SQL` \n",
     "> querying capabilities and enables a simple interface with already-existing tools and frameworks.\n",
     "\n",
     ">[PostgreSQL](https://en.wikipedia.org/wiki/PostgreSQL) also known as `Postgres`,\n",
     "> is a free and open-source relational database management system (RDBMS) \n",
     "> emphasizing extensibility and `SQL` compliance.\n",
+    ">\n",
+    ">[Supabase](https://supabase.com/docs/guides/ai) provides an open-source toolkit for developing AI applications\n",
+    ">using Postgres and pgvector. Use the Supabase client libraries to store, index, and query your vector embeddings at scale.\n",
     "\n",
-    "In the notebook we'll demo the `SelfQueryRetriever` wrapped around a Supabase vector store.\n",
+    "In the notebook, we'll demo the `SelfQueryRetriever` wrapped around a `Supabase` vector store.\n",
     "\n",
-    "Specifically we will:\n",
+    "Specifically, we will:\n",
     "1. Create a Supabase database\n",
     "2. Enable the `pgvector` extension\n",
     "3. Create a `documents` table and `match_documents` function that will be used by `SupabaseVectorStore`\n",
@@ -569,7 +572,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

docs/extras/modules/data_connection/retrievers/self_query/vectara_self_query.ipynb

@@ -5,11 +5,12 @@
    "id": "13afcae7",
    "metadata": {},
    "source": [
-    "# Vectara self-querying \n",
+    "# Vectara\n",
     "\n",
-    ">[Vectara](https://docs.vectara.com/docs/) is a GenAI platform for developers. It provides a simple API to build Grounded Generation (aka Retrieval-augmented-generation) applications.\n",
+    ">[Vectara](https://docs.vectara.com/docs/) is a GenAI platform for developers. It provides a simple API to build Grounded Generation\n",
+    ">(aka Retrieval-augmented-generation or RAG) applications.\n",
     "\n",
-    "In the notebook we'll demo the `SelfQueryRetriever` wrapped around a Vectara vector store. "
+    "In the notebook, we'll demo the `SelfQueryRetriever` wrapped around a Vectara vector store. "
    ]
   },
   {
@@ -432,7 +433,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.9"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

docs/extras/modules/data_connection/retrievers/self_query/weaviate_self_query.ipynb

@@ -5,7 +5,12 @@
    "id": "13afcae7",
    "metadata": {},
    "source": [
-    "# Weaviate self-querying "
+    "# Weaviate\n",
+    "\n",
+    ">[Weaviate](https://weaviate.io/) is an open-source vector database. It allows you to store data objects and vector embeddings from\n",
+    ">your favorite ML models, and scale seamlessly into billions of data objects.\n",
+    "\n",
+    "In the notebook, we'll demo the `SelfQueryRetriever` wrapped around a `Weaviate` vector store. "
    ]
   },
   {
@@ -293,7 +298,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,