Update README.md

README.md

@@ -13,21 +13,6 @@ thumbnail: >-
   https://cdn-uploads.huggingface.co/production/uploads/669ee023c7e62283cb5c51e0/MpLp6QMlriY25tezXwOYr.png
 ---
 
-<div align="center">
-<img src="https://raw.githubusercontent.com/pixeltable/pixeltable/main/docs/source/data/pixeltable-logo-large.png" alt="Pixeltable" width="50%" />
-<br></br>
-
-[![License](https://img.shields.io/badge/License-Apache%202.0-darkblue.svg)](https://opensource.org/licenses/Apache-2.0)
-![PyPI - Python Version](https://img.shields.io/pypi/pyversions/pixeltable?logo=python&logoColor=white)
-![Platform Support](https://img.shields.io/badge/platform-Linux%20%7C%20macOS%20%7C%20Windows-8A2BE2)
-<br>
-[![tests status](https://github.com/pixeltable/pixeltable/actions/workflows/pytest.yml/badge.svg)](https://github.com/pixeltable/pixeltable/actions/workflows/pytest.yml)
-[![tests status](https://github.com/pixeltable/pixeltable/actions/workflows/nightly.yml/badge.svg)](https://github.com/pixeltable/pixeltable/actions/workflows/nightly.yml)
-[![PyPI Package](https://img.shields.io/pypi/v/pixeltable?color=darkorange)](https://pypi.org/project/pixeltable/)
-
-[Installation](https://pixeltable.github.io/pixeltable/getting-started/) | [Documentation](https://pixeltable.readme.io/) | [API Reference](https://pixeltable.github.io/pixeltable/) | [Code Samples](https://github.com/pixeltable/pixeltable?tab=readme-ov-file#-code-samples) | [Computer Vision](https://docs.pixeltable.com/docs/object-detection-in-videos) | [LLM](https://docs.pixeltable.com/docs/document-indexing-and-rag)
-</div>
-
 Pixeltable is a Python library providing a declarative interface for multimodal data (text, images, audio, video). It features built-in versioning, lineage tracking, and incremental updates, enabling users to **store**, **transform**, **index**, and **iterate** on data for their ML workflows.
 
 Data transformations, model inference, and custom logic are embedded as **computed columns**.
@@ -44,164 +29,6 @@ pip install pixeltable
 ```
 **Pixeltable is persistent. Unlike in-memory Python libraries such as Pandas, Pixeltable is a database.**
 
-## 💡 Getting Started
-Learn how to create tables, populate them with data, and enhance them with built-in or user-defined transformations.
-
-| Topic | Notebook | Topic | Notebook |
-|:----------|:-----------------|:-------------------------|:---------------------------------:|
-| 10-Minute Tour of Pixeltable    | <a target="_blank" href="https://colab.research.google.com/github/pixeltable/pixeltable/blob/release/docs/release/tutorials/pixeltable-basics.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> | Tables and Data Operations    | <a target="_blank" href="https://colab.research.google.com/github/pixeltable/pixeltable/blob/release/docs/release/fundamentals/tables-and-data-operations.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a>
-| User-Defined Functions (UDFs)    | <a target="_blank" href="https://colab.research.google.com/github/pixeltable/pixeltable/blob/release/docs/release/howto/udfs-in-pixeltable.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> | Object Detection Models | <a target="_blank" href="https://colab.research.google.com/github/pixeltable/pixeltable/blob/release/docs/release/tutorials/object-detection-in-videos.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a>
-| Experimenting with Chunking (RAG) | <a target="_blank" href="https://colab.research.google.com/github/pixeltable/pixeltable/blob/release/docs/release/tutorials/rag-operations.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> | Working with External Files    | <a target="_blank" href="https://colab.research.google.com/github/pixeltable/pixeltable/blob/release/docs/release/howto/working-with-external-files.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a>
-| Integrating with Label Studio    | <a target="_blank" href="https://pixeltable.readme.io/docs/label-studio"> <img src="https://img.shields.io/badge/Docs-Label Studio-blue" alt="Visit our documentation"/></a> | Audio/Video Transcript Indexing    | <a target="_blank" href="https://colab.research.google.com/github/pixeltable/pixeltable/blob/release/docs/release/tutorials/audio-transcriptions.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a>
-
-## 🧱 Code Samples
-
-### Import media data into Pixeltable (videos, images, audio...)
-```python
-import pixeltable as pxt
-
-v = pxt.create_table('external_data.videos', {'video': pxt.VideoType()})
-
-prefix = 's3://multimedia-commons/'
-paths = [
-    'data/videos/mp4/ffe/ffb/ffeffbef41bbc269810b2a1a888de.mp4',
-    'data/videos/mp4/ffe/feb/ffefebb41485539f964760e6115fbc44.mp4',
-    'data/videos/mp4/ffe/f73/ffef7384d698b5f70d411c696247169.mp4'
-]
-v.insert({'video': prefix + p} for p in paths)
-```
-Learn how to [work with data in Pixeltable](https://pixeltable.readme.io/docs/working-with-external-files).
-
-### Object detection in images using DETR model
-```python
-import pixeltable as pxt
-from pixeltable.functions import huggingface
-
-# Create a table to store data persistently
-t = pxt.create_table('image', {'image': pxt.ImageType()})
-
-# Insert some images
-prefix = 'https://upload.wikimedia.org/wikipedia/commons'
-paths = [
-    '/1/15/Cat_August_2010-4.jpg',
-    '/e/e1/Example_of_a_Dog.jpg',
-    '/thumb/b/bf/Bird_Diversity_2013.png/300px-Bird_Diversity_2013.png'
-]
-t.insert({'image': prefix + p} for p in paths)
-
-# Add a computed column for image classification
-t['classification'] = huggingface.detr_for_object_detection(
-    (t.image), model_id='facebook/detr-resnet-50'
-    )
-
-# Retrieve the rows where cats have been identified
-t.select(animal = t.image,
-         classification = t.classification.label_text[0]) \
-.where(t.classification.label_text[0]=='cat').head()
-```
-Learn about computed columns and object detection: [Comparing object detection models](https://pixeltable.readme.io/docs/object-detection-in-videos).
-
-### Extend Pixeltable's capabilities with user-defined functions
-```python
-@pxt.udf
-def draw_boxes(img: PIL.Image.Image, boxes: list[list[float]]) -> PIL.Image.Image:
-    result = img.copy()  # Create a copy of `img`
-    d = PIL.ImageDraw.Draw(result)
-    for box in boxes:
-        d.rectangle(box, width=3)  # Draw bounding box rectangles on the copied image
-    return result
-```
-Learn more about user-defined functions: [UDFs in Pixeltable](https://pixeltable.readme.io/docs/user-defined-functions-udfs).
-
-### Automate data operations with views, e.g., split documents into chunks
-```python
-# In this example, the view is defined by iteration over the chunks of a DocumentSplitter
-chunks_table = pxt.create_view(
-    'rag_demo.chunks',
-    documents_table,
-    iterator=DocumentSplitter.create(
-        document=documents_table.document,
-        separators='token_limit', limit=300)
-)
-```
-Learn how to leverage views to build your [RAG workflow](https://pixeltable.readme.io/docs/document-indexing-and-rag).
-
-### Evaluate model performance
-```python
-# The computation of the mAP metric can become a query over the evaluation output
-frames_view.select(mean_ap(frames_view.eval_yolox_tiny), mean_ap(frames_view.eval_yolox_m)).show()
-```
-Learn how to leverage Pixeltable for [Model analytics](https://pixeltable.readme.io/docs/object-detection-in-videos).
-
-### Working with inference services
-```python
-chat_table = pxt.create_table('together_demo.chat', {'input': pxt.StringType()})
-
-# The chat-completions API expects JSON-formatted input:
-messages = [{'role': 'user', 'content': chat_table.input}]
-
-# This example shows how additional parameters from the Together API can be used in Pixeltable
-chat_table['output'] = chat_completions(
-    messages=messages,
-    model='mistralai/Mixtral-8x7B-Instruct-v0.1',
-    max_tokens=300,
-    stop=['\n'],
-    temperature=0.7,
-    top_p=0.9,
-    top_k=40,
-    repetition_penalty=1.1,
-    logprobs=1,
-    echo=True
-)
-chat_table['response'] = chat_table.output.choices[0].message.content
-
-# Start a conversation
-chat_table.insert([
-    {'input': 'How many species of felids have been classified?'},
-    {'input': 'Can you make me a coffee?'}
-])
-chat_table.select(chat_table.input, chat_table.response).head()
-```
-Learn how to interact with inference services such as [Together AI](https://pixeltable.readme.io/docs/together-ai) in Pixeltable.
-
-### Text and image similarity search on video frames with embedding indexes
-```python
-import pixeltable as pxt
-from pixeltable.functions.huggingface import clip_image, clip_text
-from pixeltable.iterators import FrameIterator
-import PIL.Image
-
-video_table = pxt.create_table('videos', {'video': pxt.VideoType()})
-
-video_table.insert([{'video': '/video.mp4'}])
-
-frames_view = pxt.create_view(
-    'frames', video_table, iterator=FrameIterator.create(video=video_table.video))
-
-@pxt.expr_udf
-def embed_image(img: PIL.Image.Image):
-    return clip_image(img, model_id='openai/clip-vit-base-patch32')
-
-@pxt.expr_udf
-def str_embed(s: str):
-    return clip_text(s, model_id='openai/clip-vit-base-patch32')
-
-# Create an index on the 'frame' column that allows text and image search
-frames_view.add_embedding_index('frame', string_embed=str_embed, image_embed=embed_image)
-
-# Now we will retrieve images based on a sample image
-sample_image = '/image.jpeg'
-sim = frames_view.frame.similarity(sample_image)
-frames_view.order_by(sim, asc=False).limit(5).select(frames_view.frame, sim=sim).collect()
-
-# Now we will retrieve images based on a string
-sample_text = 'red truck'
-sim = frames_view.frame.similarity(sample_text)
-frames_view.order_by(sim, asc=False).limit(5).select(frames_view.frame, sim=sim).collect()
-
-```
-Learn how to work with [Embedding and Vector Indexes](https://docs.pixeltable.com/docs/embedding-vector-indexes).
-
 ## ❓ FAQ
 
 ### What is Pixeltable?
@@ -236,16 +63,4 @@ Today's solutions for AI app development require extensive custom coding and inf
 ### What is Pixeltable not providing?
 
 - Pixeltable is not a low-code, prescriptive AI solution. We empower you to use the best frameworks and techniques for your specific needs.
-- We do not aim to replace your existing AI toolkit, but rather enhance it by streamlining the underlying data infrastructure and orchestration.
-
-> [!TIP]
-> Check out the [Integrations](https://pixeltable.readme.io/docs/working-with-openai) section, and feel free to submit a request for additional ones.
-
-## 🐛 Contributions & Feedback
-
-Are you experiencing issues or bugs with Pixeltable? File an [Issue](https://github.com/pixeltable/pixeltable/issues).
-</br>Do you want to contribute? Feel free to open a [PR](https://github.com/pixeltable/pixeltable/pulls).
-
-## :classical_building: License
-
-This library is licensed under the Apache 2.0 License.
+- We do not aim to replace your existing AI toolkit, but rather enhance it by streamlining the underlying data infrastructure and orchestration.