Update README.md

README.md

@@ -5,7 +5,7 @@ size_categories:
 - 1B<n<10B
 task_categories:
 - text-generation
-pretty_name: OpenWebSearch-V1
+pretty_name: AgentSearch-V1
 configs:
 - config_name: default
   data_files:
@@ -38,9 +38,9 @@ dataset_info:
 
 ### Getting Started
 
-The OpenWebSearch-V1 dataset includes full embeddings for over 50 million high-quality documents. This extensive collection encompasses the majority of content from sources like Arxiv, Wikipedia, Project Gutenberg, and includes quality-filtered CC data.
+The AgentSearch-V1 dataset includes over one billion embeddings sourced from over 50 million high-quality documents. This extensive collection encompasses the majority of content from sources like Arxiv, Wikipedia, Project Gutenberg, and includes quality-filtered CC data.
 
-To access and utilize the OpenWebSearch-1B dataset, you can stream it via HuggingFace with the following Python code:
+To access and utilize the AgentSearch-V1 dataset, you can stream it via HuggingFace with the following Python code:
 
 ```python
 from datasets import load_dataset
@@ -53,11 +53,11 @@ ds = load_dataset("SciPhi/OpenWebSearch-V1", data_files="arxiv/*", streaming=Tru
 
 ---
 
-A full set of scripts to recreate the dataset from scratch can be found [here](https://github.com/SciPhi/OpenWebSearch).
+A full set of scripts to recreate the dataset from scratch can be found [here](https://github.com/SciPhi/agent-search).
 
 ### Dataset Summary
 
-OpenWebSearch is divided into a number of categories, similar to RedPajama-V1.
+We take a similar approach to RedPajama-v1 and divide AgentSearch into a number of categories.
 
 
 | Dataset        | Token Count |
@@ -89,13 +89,22 @@ The raw dataset structure is as follows:
 }
 ```
 
-The indexed dataset is structured as a qdrant database dump, each entry has meta data {"url", "vector"}.
+The indexed dataset can be downloaded directly and is structured as a qdrant database dump, each entry has meta data {"url", "vector"}. In addition, there is a corresponding sqlite dataset which contains the mapping from urls onto embeddings, text chunks, and other metadata.
 
 ## Dataset Creation
 
-This dataset was created to allow make humanities most important knowledge locally searchable. It was created by filtering, cleaning, and augmenting locally publicly available datasets.
+This dataset was created as a step towards making humanities most important knowledge locally searchable and LLM optimal. It was created by filtering, cleaning, and augmenting locally publicly available datasets.
 
-The embedding vectors have been indexed and made searchable via a qdrant database.
+To cite our work, please use the following:
+
+```
+@software{SciPhi2023AgentSearch,
+  author = {SciPhi},
+  title = {AgentSearch [ΨΦ]: A Comprehensive Agent-First Framework and Dataset for Webscale Search},
+  year = {2023},
+  url = {https://github.com/SciPhi-AI/agent-search}
+}
+```
 
 ### Source Data