.md
.pdf
Banana
 Contents 
Installation and Setup
Define your Banana Template
Build the Banana app
Wrappers
LLM
Banana#
This page covers how to use the Banana ecosystem within LangChain.
It is broken into two parts: installation and setup, and then references to specific Banana wrappers.
Installation and Setup#
Install with pip install banana-dev
Get an Banana api key and set it as an environment variable (BANANA_API_KEY)
Define your Banana Template#
If you want to use an available language model template you can find one here.
This template uses the Palmyra-Base model by Writer.
You can check out an example Banana repository here.
Build the Banana app#
Banana Apps must include the “output” key in the return json.
There is a rigid response structure.
# Return the results as a dictionary
result = {'output': result}
An example inference function would be:
def inference(model_inputs:dict) -> dict:
    global model
    global tokenizer
    # Parse out your arguments
    prompt = model_inputs.get('prompt', None)
    if prompt == None:
        return {'message': "No prompt provided"}
    # Run the model
    input_ids = tokenizer.encode(prompt, return_tensors='pt').cuda()
    output = model.generate(
        input_ids,
        max_length=100,
        do_sample=True,
        top_k=50,
        top_p=0.95,
        num_return_sequences=1,
        temperature=0.9,
        early_stopping=True,
        no_repeat_ngram_size=3,
        num_beams=5,
        length_penalty=1.5,
        repetition_penalty=1.5,
        bad_words_ids=[[tokenizer.encode(' ', add_prefix_space=True)[0]]]
        )
rtdocs_stable/api.python.langchain.com/en/stable/integrations/bananadev.html