docs: vectorstore upgrades 2 (#6796)

updated vectorstores/ notebooks; added new integrations into
ecosystem/integrations/
@dev2049
@rlancemartin, @eyurtsev

docs/extras/ecosystem/integrations/hologres.mdx

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+# Hologres
+
+>[Hologres](https://www.alibabacloud.com/help/en/hologres/latest/introduction) is a unified real-time data warehousing service developed by Alibaba Cloud. You can use Hologres to write, update, process, and analyze large amounts of data in real time. 
+>`Hologres` supports standard `SQL` syntax, is compatible with `PostgreSQL`, and supports most PostgreSQL functions. Hologres supports online analytical processing (OLAP) and ad hoc analysis for up to petabytes of data, and provides high-concurrency and low-latency online data services. 
+
+>`Hologres` provides **vector database** functionality by adopting [Proxima](https://www.alibabacloud.com/help/en/hologres/latest/vector-processing).
+>`Proxima` is a high-performance software library developed by `Alibaba DAMO Academy`. It allows you to search for the nearest neighbors of vectors. Proxima provides higher stability and performance than similar open source software such as Faiss. Proxima allows you to search for similar text or image embeddings with high throughput and low latency. Hologres is deeply integrated with Proxima to provide a high-performance vector search service.
+
+## Installation and Setup
+
+Click [here](https://www.alibabacloud.com/zh/product/hologres) to fast deploy a Hologres cloud instance.
+
+```bash
+pip install psycopg2
+```
+
+## Vector Store
+
+See a [usage example](/docs/modules/data_connection/vectorstores/integrations/hologres.html).
+
+```python
+from langchain.vectorstores import Hologres
+```

docs/extras/ecosystem/integrations/rockset.mdx

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+# Rockset
+
+>[Rockset](https://rockset.com/product/) is a real-time analytics database service for serving low latency, high concurrency analytical queries at scale. It builds a Converged Index™ on structured and semi-structured data with an efficient store for vector embeddings. Its support for running SQL on schemaless data makes it a perfect choice for running vector search with metadata filters. 
+
+## Installation and Setup
+
+Make sure you have Rockset account and go to the web console to get the API key. Details can be found on [the website](https://rockset.com/docs/rest-api/).
+
+```bash
+pip install rockset
+```
+
+## Vector Store
+
+See a [usage example](/docs/modules/data_connection/vectorstores/integrations/rockset.html).
+
+```python
+from langchain.vectorstores import RocksetDB
+```

docs/extras/ecosystem/integrations/singlestoredb.mdx

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+# SingleStoreDB
+
+>[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. 
+
+## Installation and Setup
+
+There are several ways to establish a [connection](https://singlestoredb-python.labs.singlestore.com/generated/singlestoredb.connect.html) to the database. You can either set up environment variables or pass named parameters to the `SingleStoreDB constructor`. 
+Alternatively, you may provide these parameters to the `from_documents` and `from_texts` methods.
+
+```bash
+pip install singlestoredb
+```
+
+## Vector Store
+
+See a [usage example](/docs/modules/data_connection/vectorstores/integrations/singlestoredb.html).
+
+```python
+from langchain.vectorstores import SingleStoreDB
+```

docs/extras/ecosystem/integrations/sklearn.mdx

@@ -1,15 +1,14 @@
 # scikit-learn
 
-This page covers how to use the scikit-learn package within LangChain.
-It is broken into two parts: installation and setup, and then references to specific scikit-learn wrappers.
+>[scikit-learn](https://scikit-learn.org/stable/) is an open source collection of machine learning algorithms, 
+> including some implementations of the [k nearest neighbors](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.NearestNeighbors.html). `SKLearnVectorStore` wraps this implementation and adds the possibility to persist the vector store in json, bson (binary json) or Apache Parquet format.
 
 ## Installation and Setup
 
 - Install the Python package with `pip install scikit-learn`
 
-## Wrappers
 
-### VectorStore
+## Vector Store
 
 `SKLearnVectorStore` provides a simple wrapper around the nearest neighbor implementation in the
 scikit-learn package, allowing you to use it as a vectorstore.

docs/extras/ecosystem/integrations/starrocks.mdx

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+# StarRocks
+
+>[StarRocks](https://www.starrocks.io/) is a High-Performance Analytical Database.
+`StarRocks` is a next-gen sub-second MPP database for full analytics scenarios, including multi-dimensional analytics, real-time analytics and ad-hoc query.
+
+>Usually `StarRocks` is categorized into OLAP, and it has showed excellent performance in [ClickBench — a Benchmark For Analytical DBMS](https://benchmark.clickhouse.com/). Since it has a super-fast vectorized execution engine, it could also be used as a fast vectordb.
+
+## Installation and Setup
+
+
+```bash
+pip install pymysql
+```
+
+## Vector Store
+
+See a [usage example](/docs/modules/data_connection/vectorstores/integrations/starrocks.html).
+
+```python
+from langchain.vectorstores import StarRocks
+```

docs/extras/ecosystem/integrations/tigris.mdx

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+# Tigris
+
+> [Tigris](htttps://tigrisdata.com) is an open source Serverless NoSQL Database and Search Platform designed to simplify building high-performance vector search applications.
+> `Tigris` eliminates the infrastructure complexity of managing, operating, and synchronizing multiple tools, allowing you to focus on building great applications instead.
+
+## Installation and Setup
+
+
+```bash
+pip install tigrisdb openapi-schema-pydantic openai tiktoken
+```
+
+## Vector Store
+
+See a [usage example](/docs/modules/data_connection/vectorstores/integrations/tigris.html).
+
+```python
+from langchain.vectorstores import Tigris
+```

docs/extras/ecosystem/integrations/typesense.mdx

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+# Typesense
+
+> [Typesense](https://typesense.org) is an open source, in-memory search engine, that you can either 
+> [self-host](https://typesense.org/docs/guide/install-typesense.html#option-2-local-machine-self-hosting) or run 
+> on [Typesense Cloud](https://cloud.typesense.org/).
+> `Typesense` focuses on performance by storing the entire index in RAM (with a backup on disk) and also 
+> focuses on providing an out-of-the-box developer experience by simplifying available options and setting good defaults.
+
+## Installation and Setup
+
+
+```bash
+pip install typesense openapi-schema-pydantic openai tiktoken
+```
+
+## Vector Store
+
+See a [usage example](/docs/modules/data_connection/vectorstores/integrations/typesense.html).
+
+```python
+from langchain.vectorstores import Typesense
+```