docs: self-query consistency (langchain-ai#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

@@ -1,20 +1,25 @@
 # Milvus
 
-This page covers how to use the Milvus ecosystem within LangChain.
-It is broken into two parts: installation and setup, and then references to specific Milvus wrappers.
+>[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.
+
 
 ## Installation and Setup
-- Install the Python SDK with `pip install pymilvus`
-## Wrappers
 
-### VectorStore
+Install the Python SDK:
+
+```bash
+pip install pymilvus
+```
+
+## Vector Store
 
-There exists a wrapper around Milvus indexes, allowing you to use it as a vectorstore,
+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:
 ```python
 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

@@ -1,16 +1,18 @@
 # Pinecone
 
-This page covers how to use the Pinecone ecosystem within LangChain.
-It is broken into two parts: installation and setup, and then references to specific Pinecone wrappers.
+>[Pinecone](https://docs.pinecone.io/docs/overview) is a vector database with broad functionality.
+
 
 ## Installation and Setup
+
 Install the Python SDK:
+
 ```bash
 pip install pinecone-client
 ```
 
 
-## Vectorstore
+## Vector store
 
 There exists a wrapper around Pinecone indexes, allowing you to use it as a vectorstore,
 whether for semantic search or example selection.

docs/extras/integrations/providers/qdrant.mdx

@@ -1,15 +1,22 @@
 # Qdrant
 
-This page covers how to use the Qdrant ecosystem within LangChain.
-It is broken into two parts: installation and setup, and then references to specific Qdrant wrappers.
+>[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.
+
 
 ## Installation and Setup
-- Install the Python SDK with `pip install qdrant-client`
-## Wrappers
 
-### VectorStore
+Install the Python SDK:
+
+```bash
+pip install qdrant-client
+```
+
+
+## Vector Store
 
-There exists a wrapper around Qdrant indexes, allowing you to use it as a vectorstore,
+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

@@ -1,26 +1,34 @@
 # 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"`
 
 ### 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

@@ -1,27 +1,27 @@
 # Vectara
 
-
-What is Vectara?
+>[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 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.
 
 - export `VECTARA_CUSTOMER_ID`="your_customer_id"
 - 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,