Update README.md

README.md

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 [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**. **Pixeltable is persistent. Unlike in-memory Python libraries such as Pandas, Pixeltable is a database.**
-
-## 🧱 Code Samples
-
-### 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).
+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**. **Pixeltable is persistent. Unlike in-memory Python libraries such as Pandas, Pixeltable is a database.**