update readme example

README.md

@@ -42,8 +42,8 @@ You can interact with the agents as you would with any LLM or LMM model:
 ```python
 >>> 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."
+>>> agent("What percentage of the area of this jar is filled with coffee beans?", image="jar.jpg")
+"The percentage of area of the jar filled with coffee beans is 25%."
 ```
 
 To better understand how the model came up with it's answer, you can also run it in
@@ -57,22 +57,22 @@ You can also have it return the workflow it used to complete the task along with
 the individual steps and tools to get the answer:
 
 ```python
->>> resp, workflow = agent.chat_with_workflow([{"role": "user", "content": "How many apples are in this image?"}], image="apples.jpg")
+>>> resp, workflow = agent.chat_with_workflow([{"role": "user", "content": "What percentage of the area of this jar is filled with coffee beans?"}], image="jar.jpg")
 >>> print(workflow)
-[{"task": "Count the number of apples using 'grounding_dino_'.",
- "tool": "grounding_dino_",
- "parameters": {"prompt": "apple", "image": "apples.jpg"},
+[{"task": "Segment the jar using 'grounding_sam_'.",
+ "tool": "grounding_sam_",
+ "parameters": {"prompt": "jar", "image": "jar.jpg"},
  "call_results": [[
  {
- "labels": ["apple", "apple"],
- "scores": [0.99, 0.95],
+ "labels": ["jar"],
+ "scores": [0.99],
  "bboxes": [
  [0.58, 0.2, 0.72, 0.45],
-  [0.94, 0.57, 0.98, 0.66],
- ]
+ ],
+ "masks": "mask.png"
  }
  ]],
- "answer": "There are 2 apples in the image.",
+ "answer": "The jar is located at [0.58, 0.2, 0.72, 0.45].",
 }]
 ```
 
@@ -84,13 +84,12 @@ you. For example:
 ```python
 >>> 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],
+>>> detector = llm.generate_detector("Can you build a jar detector for me?")
+>>> detector("jar.jpg")
+[{"labels": ["jar",],
+ "scores": [0.99],
  "bboxes": [
  [0.58, 0.2, 0.72, 0.45],
- [0.94, 0.57, 0.98, 0.66],
  ]
 }]
 ```

docs/old.md → docs/lmms_and_datastore.md

@@ -1,21 +1,3 @@
-<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