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name: pdoc | ||
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# build the documentation whenever there are new commits on main | ||
on: | ||
push: | ||
branches: | ||
- main | ||
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# security: restrict permissions for CI jobs. | ||
permissions: | ||
contents: read | ||
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jobs: | ||
# Build the documentation and upload the static HTML files as an artifact. | ||
build: | ||
runs-on: ubuntu-latest | ||
steps: | ||
- uses: actions/checkout@v3 | ||
- uses: actions/setup-python@v4 | ||
with: | ||
python-version: 3.10.11 | ||
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- uses: Gr1N/setup-poetry@v8 | ||
with: | ||
poetry-version: "1.2.2" | ||
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- run: poetry install --all-extras | ||
- run: mkdir -p docs-build | ||
- run: poetry run mkdocs build -f mkdocs.yml -d docs-build/ | ||
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- uses: actions/upload-pages-artifact@v1 | ||
with: | ||
path: docs-build/ | ||
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# Deploy the artifact to GitHub pages. | ||
# This is a separate job so that only actions/deploy-pages has the necessary permissions. | ||
deploy: | ||
needs: build | ||
runs-on: ubuntu-latest | ||
permissions: | ||
pages: write | ||
id-token: write | ||
environment: | ||
name: github-pages | ||
url: ${{ steps.deployment.outputs.page_url }} | ||
steps: | ||
- id: deployment | ||
uses: actions/deploy-pages@v2 |
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@@ -89,6 +89,7 @@ MANIFEST | |
examples/output | ||
tests/output | ||
docs-build | ||
site | ||
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# Local or WIP files | ||
local/ |
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{% extends "base.html" %} | ||
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{% block footer %} | ||
{{ super() }} | ||
{% endblock %} |
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::: vision_agent.agent | ||
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::: vision_agent.agent.agent | ||
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::: vision_agent.agent.easytool | ||
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::: vision_agent.agent.easytool_prompts | ||
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::: vision_agent.agent.reflexion | ||
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::: vision_agent.agent.reflexion_prompts | ||
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::: vision_agent.agent.vision_agent |
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::: vision_agent.data | ||
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::: vision_agent.data.data |
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::: vision_agent.emb | ||
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::: vision_agent.emb.emb |
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::: vision_agent.image_utils |
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::: vision_agent.llm | ||
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::: vision_agent.llm.llm |
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::: vision_agent.lmm | ||
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::: vision_agent.lmm.lmm |
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::: vision_agent.tools | ||
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::: vision_agent.tools.prompts | ||
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::: vision_agent.tools.tools |
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<p align="center"> | ||
<img width="100" height="100" src="https://github.com/landing-ai/landingai-python/raw/main/assets/avi-logo.png"> | ||
</p> | ||
# 🔍🤖 Vision Agent | ||
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# Welcome to the Landing AI LMM Tools Documentation | ||
Vision Agent is a library that helps you utilize agent frameworks for your vision tasks. | ||
Many current vision problems can easily take hours or days to solve, you need to find the | ||
right model, figure out how to use it, possibly write programming logic around it to | ||
accomplish the task you want or even more expensive, train your own model. Vision Agent | ||
aims to provide an in-seconds experience by allowing users to describe their problem in | ||
text and utilizing agent frameworks to solve the task for them. Check out our discord | ||
for updates and roadmaps! | ||
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This library provides a set of tools to help you build applications with Large Multimodal Model (LMM). | ||
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## Quick Start | ||
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### Install | ||
First, install the library: | ||
## Getting Started | ||
### Installation | ||
To get started, you can install the library using pip: | ||
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```bash | ||
pip install vision-agent | ||
``` | ||
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### LMMs | ||
One of the problems of dealing with image data is it can be difficult to organize and | ||
search. For example, you might have a bunch of pictures of houses and want to count how | ||
many yellow houses you have, or how many houses with adobe roofs. The vision agent | ||
library uses LMMs to help create tags or descriptions of images to allow you to search | ||
over them, or use them in a database to carry out other operations. | ||
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To get started, you can use an LMM to start generating text from images. The following | ||
code will use the LLaVA-1.6 34B model to generate a description of the image you pass it. | ||
Ensure you have an OpenAI API key and set it as an environment variable: | ||
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```python | ||
import vision_agent as va | ||
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model = va.lmm.get_lmm("llava") | ||
model.generate("Describe this image", "image.png") | ||
>>> "A yellow house with a green lawn." | ||
```bash | ||
export OPENAI_API_KEY="your-api-key" | ||
``` | ||
|
||
**WARNING** We are hosting the LLaVA-1.6 34B model, if it times out please wait ~3-5 | ||
min for the server to warm up as it shuts down when usage is low. | ||
|
||
### DataStore | ||
You can use the `DataStore` class to store your images, add new metadata to them such | ||
as descriptions, and search over different columns. | ||
### Vision Agents | ||
You can interact with the agents as you would with any LLM or LMM model: | ||
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```python | ||
import vision_agent as va | ||
import pandas as pd | ||
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df = pd.DataFrame({"image_paths": ["image1.png", "image2.png", "image3.png"]}) | ||
ds = va.data.DataStore(df) | ||
ds = ds.add_lmm(va.lmm.get_lmm("llava")) | ||
ds = ds.add_embedder(va.emb.get_embedder("sentence-transformer")) | ||
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ds = ds.add_column("descriptions", "Describe this image.") | ||
>>> import vision_agent as va | ||
>>> agent = VisionAgent() | ||
>>> agent("How many apples are in this image?", image="apples.jpg") | ||
"There are 2 apples in the image." | ||
``` | ||
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This will use the prompt you passed, "Describe this image.", and the LMM to create a | ||
new column of descriptions for your image. Your data will now contain a new column with | ||
the descriptions of each image: | ||
To better understand how the model came up with it's answer, you can also run it in | ||
debug mode by passing in the verbose argument: | ||
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| image\_paths | image\_id | descriptions | | ||
| --- | --- | --- | | ||
| image1.png | 1 | "A yellow house with a green lawn." | | ||
| image2.png | 2 | "A white house with a two door garage." | | ||
| image3.png | 3 | "A wooden house in the middle of the forest." | | ||
```python | ||
>>> agent = VisionAgent(verbose=True) | ||
``` | ||
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You can now create an index on the descriptions column and search over it to find images | ||
that match your query. | ||
You can also have it return the workflow it used to complete the task along with all | ||
the individual steps and tools to get the answer: | ||
|
||
```python | ||
ds = ds.build_index("descriptions") | ||
ds.search("A yellow house.", top_k=1) | ||
>>> [{'image_paths': 'image1.png', 'image_id': 1, 'descriptions': 'A yellow house with a green lawn.'}] | ||
>>> resp, workflow = agent.chat_with_workflow([{"role": "user", "content": "How many apples are in this image?"}], image="apples.jpg") | ||
>>> print(workflow) | ||
[{"task": "Count the number of apples using 'grounding_dino_'.", | ||
"tool": "grounding_dino_", | ||
"parameters": {"prompt": "apple", "image": "apples.jpg"}, | ||
"call_results": [[ | ||
{ | ||
"labels": ["apple", "apple"], | ||
"scores": [0.99, 0.95], | ||
"bboxes": [ | ||
[0.58, 0.2, 0.72, 0.45], | ||
[0.94, 0.57, 0.98, 0.66], | ||
] | ||
} | ||
]], | ||
"answer": "There are 2 apples in the image.", | ||
}] | ||
``` | ||
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You can also create other columns for you data such as `is_yellow`: | ||
### Tools | ||
There are a variety of tools for the model or the user to use. Some are executed locally | ||
while others are hosted for you. You can also ask an LLM directly to build a tool for | ||
you. For example: | ||
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```python | ||
ds = ds.add_column("is_yellow", "Is the house in this image yellow? Please answer yes or no.") | ||
>>> import vision_agent as va | ||
>>> llm = va.llm.OpenAILLM() | ||
>>> detector = llm.generate_detector("Can you build an apple detector for me?") | ||
>>> detector("apples.jpg") | ||
[{"labels": ["apple", "apple"], | ||
"scores": [0.99, 0.95], | ||
"bboxes": [ | ||
[0.58, 0.2, 0.72, 0.45], | ||
[0.94, 0.57, 0.98, 0.66], | ||
] | ||
}] | ||
``` | ||
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which would give you a dataset similar to this: | ||
| Tool | Description | | ||
| --- | --- | | ||
| CLIP | CLIP is a tool that can classify or tag any image given a set of input classes or tags. | | ||
| GroundingDINO | GroundingDINO is a tool that can detect arbitrary objects with inputs such as category names or referring expressions. | | ||
| GroundingSAM | GroundingSAM is a tool that can detect and segment arbitrary objects with inputs such as category names or referring expressions. | | ||
| Counter | Counter detects and counts the number of objects in an image given an input such as a category name or referring expression. | | ||
| Crop | Crop crops an image given a bounding box and returns a file name of the cropped image. | | ||
| BboxArea | BboxArea returns the area of the bounding box in pixels normalized to 2 decimal places. | | ||
| SegArea | SegArea returns the area of the segmentation mask in pixels normalized to 2 decimal places. | | ||
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| image\_paths | image\_id | descriptions | is\_yellow | | ||
| --- | --- | --- | --- | | ||
| image1.png | 1 | "A yellow house with a green lawn." | "yes" | | ||
| image2.png | 2 | "A white house with a two door garage." | "no" | | ||
| image3.png | 3 | "A wooden house in the middle of the forest." | "no" | | ||
It also has a basic set of calculate tools such as add, subtract, multiply and divide. |
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<p align="center"> | ||
<img width="100" height="100" src="https://github.com/landing-ai/landingai-python/raw/main/assets/avi-logo.png"> | ||
</p> | ||
|
||
# Welcome to the Landing AI LMM Tools Documentation | ||
|
||
This library provides a set of tools to help you build applications with Large Multimodal Model (LMM). | ||
|
||
|
||
## Quick Start | ||
|
||
### Install | ||
First, install the library: | ||
|
||
```bash | ||
pip install vision-agent | ||
``` | ||
|
||
### LMMs | ||
One of the problems of dealing with image data is it can be difficult to organize and | ||
search. For example, you might have a bunch of pictures of houses and want to count how | ||
many yellow houses you have, or how many houses with adobe roofs. The vision agent | ||
library uses LMMs to help create tags or descriptions of images to allow you to search | ||
over them, or use them in a database to carry out other operations. | ||
|
||
To get started, you can use an LMM to start generating text from images. The following | ||
code will use the LLaVA-1.6 34B model to generate a description of the image you pass it. | ||
|
||
```python | ||
import vision_agent as va | ||
|
||
model = va.lmm.get_lmm("llava") | ||
model.generate("Describe this image", "image.png") | ||
>>> "A yellow house with a green lawn." | ||
``` | ||
|
||
**WARNING** We are hosting the LLaVA-1.6 34B model, if it times out please wait ~3-5 | ||
min for the server to warm up as it shuts down when usage is low. | ||
|
||
### DataStore | ||
You can use the `DataStore` class to store your images, add new metadata to them such | ||
as descriptions, and search over different columns. | ||
|
||
```python | ||
import vision_agent as va | ||
import pandas as pd | ||
|
||
df = pd.DataFrame({"image_paths": ["image1.png", "image2.png", "image3.png"]}) | ||
ds = va.data.DataStore(df) | ||
ds = ds.add_lmm(va.lmm.get_lmm("llava")) | ||
ds = ds.add_embedder(va.emb.get_embedder("sentence-transformer")) | ||
|
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ds = ds.add_column("descriptions", "Describe this image.") | ||
``` | ||
|
||
This will use the prompt you passed, "Describe this image.", and the LMM to create a | ||
new column of descriptions for your image. Your data will now contain a new column with | ||
the descriptions of each image: | ||
|
||
| image\_paths | image\_id | descriptions | | ||
| --- | --- | --- | | ||
| image1.png | 1 | "A yellow house with a green lawn." | | ||
| image2.png | 2 | "A white house with a two door garage." | | ||
| image3.png | 3 | "A wooden house in the middle of the forest." | | ||
|
||
You can now create an index on the descriptions column and search over it to find images | ||
that match your query. | ||
|
||
```python | ||
ds = ds.build_index("descriptions") | ||
ds.search("A yellow house.", top_k=1) | ||
>>> [{'image_paths': 'image1.png', 'image_id': 1, 'descriptions': 'A yellow house with a green lawn.'}] | ||
``` | ||
|
||
You can also create other columns for you data such as `is_yellow`: | ||
|
||
```python | ||
ds = ds.add_column("is_yellow", "Is the house in this image yellow? Please answer yes or no.") | ||
``` | ||
|
||
which would give you a dataset similar to this: | ||
|
||
| image\_paths | image\_id | descriptions | is\_yellow | | ||
| --- | --- | --- | --- | | ||
| image1.png | 1 | "A yellow house with a green lawn." | "yes" | | ||
| image2.png | 2 | "A white house with a two door garage." | "no" | | ||
| image3.png | 3 | "A wooden house in the middle of the forest." | "no" | |
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site_name: Landing AI Vision Agent Library Documentation | ||
site_url: https://landing-ai.github.io/ | ||
repo_url: https://github.com/landing-ai/vision-agent | ||
edit_uri: edit/main/docs/ | ||
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theme: | ||
name: "material" | ||
custom_dir: docs/_overrides | ||
features: | ||
- content.code.copy | ||
- content.code.annotate | ||
- content.action.edit | ||
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plugins: | ||
- mkdocstrings | ||
- search | ||
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markdown_extensions: | ||
# Syntax highlight | ||
- pymdownx.highlight: | ||
anchor_linenums: true | ||
line_spans: __span | ||
pygments_lang_class: true | ||
- pymdownx.inlinehilite | ||
- pymdownx.snippets | ||
- pymdownx.superfences | ||
|
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# Multiline note/warning/etc blocks (https://squidfunk.github.io/mkdocs-material/reference/admonitions) | ||
- admonition | ||
- pymdownx.details | ||
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nav: | ||
- Quick start: index.md | ||
- APIs: | ||
- vision_agent.agent: api/agent.md | ||
- vision_agent.tools: api/tools.md | ||
- vision_agent.llm: api/llm.md | ||
- vision_agent.lmm: api/lmm.md | ||
- vision_agent.data: api/data.md | ||
- vision_agent.emb: api/emb.md | ||
- vision_agent.image_utils: api/image_utils.md | ||
- Old documentation: old.md |
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