sceneprogllm 0.1.3

An LLM wrapper built for scene prog projects

SceneProgLLM

SceneProgLLM is a powerful and versatile Python package that wraps around LangChain's LLM interface to provide enhanced functionality, including support for text, code, and JSON response formats, image input/output, caching, and multiple endpoints. This project is built to support SceneProg projects.

---

Features

1. Flexible Response Formats:
   • Supports text, code, JSON, and image outputs.
2. Image Input and Output:
   • Accepts image inputs and enables image generation through Stable Diffusion (SD) or OpenAI's image generation API.
3. Caching:
   • Integrated caching system to store and retrieve previous query responses for faster execution.
4. Strict JSON Validation:
   • Ensures correct JSON structure, particularly when specific keys are required.

---

Installation

To install the package and its dependencies, use the following command:

pip install sceneprogllm

For proper usage, export the respective variables

export OPENAI_API_KEY= YOUR_OPENAI_API_KEY
export AWS_ACCESS_KEY= YOUR_AWS_ACCESS_KEY
export AWS_SECRET_KEY= YOUR_AWS_SECRET_KEY
export AWS_REGION = AWS_REGION
export AWS_S3_BUCKET= AWS_S3_BUCKET

Getting Started

Importing the Package

from sceneprogllm import LLM

Usage Examples

1. Generating Text Responses

llm = LLM(name="text_bot", response_format="text")
response = llm.run("What is the capital of France?")
print(response)

2. Generating JSON Responses

llm = LLM(
    name="json_bot",
    response_format="json",
    json_keys=["capital", "currency"]
)
query = "What is capital and currency of India?"
response = llm.run(query)
print(response)

3. Generating Python Code

llm = LLM(name="code_bot", response_format="code")
query = "Write a Python function to calculate factorial of a number."
response = llm.run(query)
print(response)

4. Generating images from text

llm = LLM(name="image_bot", response_format="image")
response = llm.run("Generate an image of a futuristic cityscape.")
response.save("futuristic_city.jpg")

5. Query using Images

llm = LLM(name="image_bot", response_format="text", num_images=1, image_generator="SD")
image_paths = ["path/to/input_image.jpg"]
response = llm.run("What is the color of the object in the image?", image_paths=image_paths)

6. Clear LLM cache

from sceneprogllm import clear_llm_cache
clear_llm_cache()

Using Ollama

To use Ollama with the LLM class, you need to set the use_ollama parameter to True when initializing the LLM object.

Furthermore, you can specify the Ollama Model via ollama_model_name, the default is "llama3.2-vision". See Ollama model site for the available options. Note that different model will support different modes (text, image, etc.).

Here is an example:

from sceneprogllm import LLM

# Example for generating text responses using Ollama
llm = LLM(name="text_bot", response_format="text", use_ollama=True)