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docs: add tooltip to extraction policies code #615

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May 22, 2024
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docs: add tooltip to extraction policies code #615

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45 changes: 23 additions & 22 deletions docs/docs/usecases/video_rag.md
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Expand Up @@ -87,32 +87,33 @@ client = IndexifyClient()

Next, we create an extraction graph with 4 extraction policies:

1. Extract audio from every video that is ingested by applying the `tensorlake/audio-extractor` on the videos.
```yaml title="graph.yaml"
name: "videoknowledgebase"
extraction_policies:
- extractor: "tensorlake/audio-extractor" #(1)!
name: "audio_clips_of_videos"
- extractor: "tensorlake/whisper-asr" #(2)!
name: "audio_transcription"
content_source: "audio_clips_of_videos" #(5)!
- extractor: "tensorlake/chunk-extractor" #(3)!
name: "transcription_chunks"
content_source: "audio_transcription"
- extractor: "tensorlake/minilm-l6" #(4)!
name: "transcript_embedding"
content_source: "transcription_chunks"
```

1. We extract the audio from every video that is ingested by using the `tensorlake/audio-extractor` on the videos.
2. The extracted audio are passed through the `tensorlake/whisper-asr` extractor to be transcribed.
3. We pass the transcripts to the `tensorlake/chunk-extractor` to chunk the transcripts into smaller parts.
4. We process the transcript chunks through `tensorlake/minilm-l6` extractor to extract the vector embedding and index them.
5. The `content_source` parameter is used to specify the source of the content for the extraction policy. Typically, when creating a pipeline of multiple extractors, the output of one extractor is used as the input for the next extractor.

Note: The `content_source` parameter is used to specify the source of the content for the extraction policy. Typically, when creating a pipeline of multiple extractors, the output of one extractor is used as the input for the next extractor.

```python
extraction_graph_spec = """
name: "videoknowledgebase"
extraction_policies:
- extractor: "tensorlake/audio-extractor"
name: "audio_clips_of_videos"
- extractor: "tensorlake/whisper-asr"
name: "audio_transcription"
content_source: "audio_clips_of_videos"
- extractor: "tensorlake/chunk-extractor"
name: "transcription_chunks"
content_source: "audio_transcription"
- extractor: "tensorlake/minilm-l6"
name: "transcript_embedding"
content_source: "transcription_chunks"
"""

extraction_graph = ExtractionGraph.from_yaml(extraction_graph_spec)
client.create_extraction_graph(extraction_graph)
```py
with open("graph.yaml", "r") as file:
extraction_graph_spec = file.read()
extraction_graph = ExtractionGraph.from_yaml(extraction_graph_spec)
client.create_extraction_graph(extraction_graph)
```

### Upload the Video
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