docs: update readme

README.md

@@ -1,6 +1,19 @@
 # QText
 
-End-to-end service to query the text.
+[![Python Check](https://github.com/tensorchord/qtext/actions/workflows/check.yml/badge.svg)](https://github.com/tensorchord/qtext/actions/workflows/check.yml)
+<a href="https://discord.gg/KqswhpVgdU"><img alt="discord invitation link" src="https://dcbadge.vercel.app/api/server/KqswhpVgdU?style=flat"></a>
+<a href="https://twitter.com/TensorChord"><img src="https://img.shields.io/twitter/follow/tensorchord?style=social" alt="trackgit-views" /></a>
+
+End-to-end service to query the text with hybrid search and rerank.
+
+Application scenarios:
+- Personal knowledge database + search engine
+- Rerank experiment and visualization
+- RAG pipeline
+
+[![asciicast](https://asciinema.org/a/653540.svg)](https://asciinema.org/a/653540)
+
+## Features
 
 - [x] full text search (Postgres GIN + text search)
 - [x] vector similarity search ([pgvecto.rs](https://github.com/tensorchord/pgvecto.rs) HNSW)
@@ -16,22 +29,21 @@ End-to-end service to query the text.
 
 ## How to use
 
-To start all the services:
+To start all the services with [docker compose](https://docs.docker.com/compose/):
 
 ```bash
 docker compose -f docker/compose.yaml up -d server
 ```
 
 Some of the dependent services can be opt-out:
 - `emb`: used to generate embedding for query and documents
-- `sparse`: used to generate sparse embedding for query and documents
+- `sparse`: used to generate sparse embedding for query and documents (this requires a HuggingFace token that signed the agreement for [prithivida/Splade_PP_en_v1](https://huggingface.co/prithivida/Splade_PP_en_v1))
 - `highlight`: used to provide the semantic highlight feature
 - `encoder`: rerank with cross-encoder model, you can choose other methods or other online services
 
 For the client example, check:
 - [test.py](./test.py): simple demo.
-- [test_cohere_wiki.py](./test_cohere_wiki.py): if you have the Cohere Token. Remember to change the `config.ranker.ranker` to the `CohereClient`.
-- [test_sparse.py](./test_sparse.py): hybrid search with text/vector/sparse indexes.
+- [test_cohere_wiki.py](./test_cohere_wiki.py): a Wikipedia dataset with Cohere embedding.
 
 ## API
 
@@ -41,6 +53,7 @@ We provide a simple sync/async [client](./qtext/client.py). You can also refer t
 - `/api/doc` POST: add a new doc
 - `/api/query` POST: query the docs
 - `/api/highlight` POST: semantic highlight
+- `/metrics` GET: open metrics
 
 Check the [OpenAPI documentation](http://127.0.0.1:8000/openapi/redoc) for more information (this requires the qtext service).
 
@@ -50,7 +63,8 @@ We provide a simple terminal UI powered by [Textual](https://github.com/textuali
 
 ```bash
 pip install textual
-python tui.py $QTEXT_PORT
+# need to run the qtext service first
+python tui/main.py $QTEXT_PORT
 ```
 
 ## Configurations
@@ -67,7 +81,7 @@ This project has most of the components you need for the RAG except for the last
 > If you already have the table in Postgres, you will be responsible for the text-indexing and vector-indexing part.
 
 1. Define a `dataclass` that includes the **necessary** columns as class attributes
-   - annotate the `primary_key`, `text_index`, `vector_index`, `sparse_index` with metadata (not all the columns are required, only the necessary ones)
+   - annotate the `primary_key`, `text_index`, `vector_index`, `sparse_index` with metadata (not all of them are required, only the necessary ones)
    - attributes without default value or default factory is treated as required when you add new docs
 2. Implement the `to_record` and `from_record` methods to be used in the reranking stage
 3. Change the `config.vector_store.schema` to the class you have defined