update README.md

README.md

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-# 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)
-[![Twitter](https://img.shields.io/twitter/url/https/twitter.com/myscaledb.svg?style=social&label=Follow%20%40MyScaleDB)](https://twitter.com/myscaledb)
-
-![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.
+---
+title: ChatData
+emoji:  🔍 📖
+colorFrom: pink
+colorTo: purple
+sdk: streamlit
+sdk_version: 1.20.0
+app_file: app.py
+pinned: true
+license: mit
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference