README.md

---
language:
- en
size_categories:
- 1B<n<10B
task_categories:
- text-generation
pretty_name: AgentSearch-V1
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
dataset_info:
  features:
  - name: id
    dtype: int64
  - name: url
    dtype: string
  - name: title
    dtype: string
  - name: metadata
    dtype: string
  - name: dataset
    dtype: string
  - name: text_chunks
    sequence: string
  - name: embeddings
    sequence:
      sequence: float64
  splits:
  - name: train
    num_bytes: 40563228
    num_examples: 1000
  download_size: 34541852
  dataset_size: 40563228
---

### Getting Started

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 AgentSearch-V1 dataset, you can stream it via HuggingFace with the following Python code:

```python
from datasets import load_dataset
# To stream the entire dataset:
ds = load_dataset("SciPhi/OpenWebSearch-V1", data_files="**/*", streaming=True)

# Optional, stream just the "arxiv" dataset
ds = load_dataset("SciPhi/OpenWebSearch-V1", data_files="arxiv/*", streaming=True)
```

---

A full set of scripts to recreate the dataset from scratch can be found [here](https://github.com/SciPhi/agent-search).

### Dataset Summary

We take a similar approach to RedPajama-v1 and divide AgentSearch into a number of categories.


| Dataset        | Token Count |
|----------------|-------------|
| Books          | x Billion   |
| ArXiv          | x Billion   |
| Wikipedia      | x Billion   |
| StackExchange  | x Billion   |
| OpenMath       | x Billion   |
| Filtered Crawl | x Billion   |
| Total          | x Billion   |

### Languages

English.

## Dataset Structure

The raw dataset structure is as follows:

```json
{
    "url": ...,
    "title": ...,
    "metadata": {"url": "...", "timestamp": "...", "source": "...", "language": "...", ...},
    "text_chunks": ...,
    "embeddings": ...,
    "dataset": "github" | "books" | "arxiv" | "wikipedia" | "stackexchange" | "open-math" | "filtered-rp2"
}
```

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 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.

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

```
@ONLINE{wikidump,
    author = "Wikimedia Foundation",
    title  = "Wikimedia Downloads",
    url    = "https://dumps.wikimedia.org"
}
```

```
@misc{paster2023openwebmath,
      title={OpenWebMath: An Open Dataset of High-Quality Mathematical Web Text},
      author={Keiran Paster and Marco Dos Santos and Zhangir Azerbayev and Jimmy Ba},
      year={2023},
      eprint={2310.06786},
      archivePrefix={arXiv},
      primaryClass={cs.AI}
}
```

```
@software{together2023redpajama,
  author = {Together Computer},
  title = {RedPajama: An Open Source Recipe to Reproduce LLaMA training dataset},
  month = April,
  year = 2023,
  url = {https://github.com/togethercomputer/RedPajama-Data}
}
```

### License
Please refer to the licenses of the data subsets you use.

* [Open-Web (Common Crawl Foundation Terms of Use)](https://commoncrawl.org/terms-of-use/full/)
* Books: [the_pile_books3 license](https://huggingface.co/datasets/the_pile_books3#licensing-information) and [pg19 license](https://huggingface.co/datasets/pg19#licensing-information)
* [ArXiv Terms of Use](https://info.arxiv.org/help/api/tou.html)
* [Wikipedia License](https://huggingface.co/datasets/wikipedia#licensing-information)
* [StackExchange license on the Internet Archive](https://archive.org/details/stackexchange)

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