doc updates (#1085)

closes #1084
This commit is contained in:
Raghav Dixit
2024-03-14 05:08:28 -04:00
committed by GitHub
parent dbec598610
commit 6689192cee
3 changed files with 633 additions and 2 deletions

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@@ -224,7 +224,6 @@ This embedding function supports ingesting images as both bytes and urls. You ca
!!! info
LanceDB supports ingesting images directly from accessible links.
```python
db = lancedb.connect(tmp_path)
@@ -290,4 +289,67 @@ print(actual.label)
```
### Imagebind embeddings
We have support for [imagebind](https://github.com/facebookresearch/ImageBind) model embeddings. You can download our version of the packaged model via - `pip install imagebind-packaged==0.1.2`.
This function is registered as `imagebind` and supports Audio, Video and Text modalities(extending to Thermal,Depth,IMU data):
| Parameter | Type | Default Value | Description |
|---|---|---|---|
| `name` | `str` | `"imagebind_huge"` | Name of the model. |
| `device` | `str` | `"cpu"` | The device to run the model on. Can be `"cpu"` or `"gpu"`. |
| `normalize` | `bool` | `False` | set to `True` to normalize your inputs before model ingestion. |
Below is an example demonstrating how the API works:
```python
db = lancedb.connect(tmp_path)
registry = EmbeddingFunctionRegistry.get_instance()
func = registry.get("imagebind").create()
class ImageBindModel(LanceModel):
text: str
image_uri: str = func.SourceField()
audio_path: str
vector: Vector(func.ndims()) = func.VectorField()
# add locally accessible image paths
text_list=["A dog.", "A car", "A bird"]
image_paths=[".assets/dog_image.jpg", ".assets/car_image.jpg", ".assets/bird_image.jpg"]
audio_paths=[".assets/dog_audio.wav", ".assets/car_audio.wav", ".assets/bird_audio.wav"]
# Load data
inputs = [
{"text": a, "audio_path": b, "image_uri": c}
for a, b, c in zip(text_list, audio_paths, image_paths)
]
#create table and add data
table = db.create_table("img_bind", schema=ImageBindModel)
table.add(inputs)
```
Now, we can search using any modality:
#### image search
```python
query_image = "./assets/dog_image2.jpg" #download an image and enter that path here
actual = table.search(query_image).limit(1).to_pydantic(ImageBindModel)[0]
print(actual.text == "dog")
```
#### audio search
```python
query_audio = "./assets/car_audio2.wav" #download an audio clip and enter path here
actual = table.search(query_audio).limit(1).to_pydantic(ImageBindModel)[0]
print(actual.text == "car")
```
#### Text search
You can add any input query and fetch the result as follows:
```python
query = "an animal which flies and tweets"
actual = table.search(query).limit(1).to_pydantic(ImageBindModel)[0]
print(actual.text == "bird")
```
If you have any questions about the embeddings API, supported models, or see a relevant model missing, please raise an issue [on GitHub](https://github.com/lancedb/lancedb/issues).

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@@ -31,7 +31,7 @@ class ImageBindEmbeddings(EmbeddingFunction):
six different modalities: images, text, audio, depth, thermal, and IMU data
to download package, run :
`pip install imagebind@git+https://github.com/raghavdixit99/ImageBind`
`pip install imagebind-packaged==0.1.2`
"""
name: str = "imagebind_huge"