# ChatData 🔍 📖
***We are constantly improving LangChain's self-query retriever. Some of the features are not merged.***

[![](https://dcbadge.vercel.app/api/server/D2qpkqc4Jq?compact=true&style=flat)](https://discord.gg/D2qpkqc4Jq)
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![ChatData](assets/logo.png)

Yet another chat-with-documents app, but supporting query over millions of files with [MyScale](https://myscale.com) and [LangChain](https://github.com/hwchase17/langchain/).

## News 🔥

- 🔧 Our contribution to LangChain that helps self-query retrievers [**filter with more types and functions**](https://python.langchain.com/docs/modules/data_connection/retrievers/how_to/self_query/myscale_self_query)
- 🌟 **We just opened a FREE pod hosting data for ArXiv paper.** Anyone can try their own SQL with vector search!!! Feel the power when SQL meets vector search! See how to access the pod [here](#data-service).
- 📚 We collected **1.67 million papers on arxiv**! We are collecting more and we need your advice!
- More coming...

## Quickstart

1. Create an virtual environment

```bash
python3 -m venv .venv
source .venv/bin/activate
```

2. Install dependencies

> This app is currently using [MyScale's fork of LangChain](https://github.com/myscale/langchain/tree/master). It contains [improved prompts](https://github.com/hwchase17/langchain/pull/6737#discussion_r1243527112) for comparators `LIKE` and `CONTAIN` in [MyScale self-query retriever](https://github.com/hwchase17/langchain/pull/6143).

```bash
python3 -m pip install -r requirements.txt
```

3. Run the app!

```python
# fill you OpenAI key in .streamlit/secrets.toml
cp .streamlit/secrets.example.toml .streamlit/secrets.toml
# start the app
python3 -m streamlit run app.py
```

## Quick Navigator 🧭

- [How can I run this app?](README.md#how-to-run)

- [How this app is built?](docs/self-query.md)

- [What is the overview pipeline?](docs/self-query.md#query-pipeline-design)

- [How did LangChain and MyScale convert natural language to structured filters?](docs/self-query.md#selfqueryretriever-defines-interaction-between-vectorstore-and-your-app)

- [How to make chain execution more responsive in LangChain?](docs/self-query.md#not-responsive-add-callbacks)

- Where can I get those arxiv data?
  - [From parquet files on S3](docs/self-query.md#insert-data)
  - <a name="data-service"></a>Or directly use MyScale database as service... for **FREE** ✨
    ```python
    import clickhouse_connect

    client = clickhouse_connect.get_client(
        host='msc-1decbcc9.us-east-1.aws.staging.myscale.cloud',
        port=443,
        username='chatdata',
        password='myscale_rocks'
    )
    ```
    Or put these settings in `.streamlit/secrets.toml`

    ```toml
    MYSCALE_HOST = "msc-1decbcc9.us-east-1.aws.staging.myscale.cloud"
    MYSCALE_PORT = 443
    MYSCALE_USER = "chatdata"
    MYSCALE_PASSWORD = "myscale_rocks"
    ```

## Introduction

ChatData brings millions of papers into your knowledge base. We imported 1.67 million papers with metadata info (continuously updating), which contains:

1. `metadata.authors`: paper's authors in *list of strings*
2. `metadata.abstract`: paper's abstracts used as ranking criterion (with InstructXL)
3. `metadata.titles`: papers's titles
4. `metadata.categories`: paper's categories in *list of strings* like ["cs.CV"]
5. `metadata.pubdate`: paper's date of publication in *ISO 8601 formated strings*
6. `metadata.primary_category`: paper's primary category in *strings* defined by ArXiv
7. `metadata.comment`: some additional comment to the paper

And for overall table schema, please refer to [table creation section in docs/self-query.md](docs/self-query.md#table-creation).

## How to run 🏃

```bash
python3 -m pip install requirements.txt
python3 -m streamlit run app.py
```

## How to build? 🧱

See [docs/self-query.md](docs/self-query.md)

## Special Thanks 👏 (Ordered Alphabetically)

- [ArXiv API](https://info.arxiv.org/help/api/index.html) for its open access interoperability to pre-printed papers.
- [InstructorXL](https://huggingface.co/hkunlp/instructor-xl) for its promptable embeddings that improves retrieve performance.
- [LangChain🦜️🔗](https://github.com/hwchase17/langchain/) for its easy-to-use and composable API designs and prompts.
- [The Alexandria Index](https://alex.macrocosm.so/download) for providing arXiv data index to the public.