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# Docs: compound ecosystem and integrations **Problem statement:** We have a big overlap between the References/Integrations and Ecosystem/LongChain Ecosystem pages. It confuses users. It creates a situation when new integration is added only on one of these pages, which creates even more confusion. - removed References/Integrations page (but move all its information into the individual integration pages - in the next PR). - renamed Ecosystem/LongChain Ecosystem into Integrations/Integrations. I like the Ecosystem term. It is more generic and semantically richer than the Integration term. But it mentally overloads users. The `integration` term is more concrete. UPDATE: after discussion, the Ecosystem is the term. Ecosystem/Integrations is the page (in place of Ecosystem/LongChain Ecosystem). As a result, a user gets a single place to start with the individual integration.
80 lines
2.2 KiB
Markdown
80 lines
2.2 KiB
Markdown
# Banana
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This page covers how to use the Banana ecosystem within LangChain.
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It is broken into two parts: installation and setup, and then references to specific Banana wrappers.
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## Installation and Setup
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- Install with `pip install banana-dev`
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- Get an Banana api key and set it as an environment variable (`BANANA_API_KEY`)
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## Define your Banana Template
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If you want to use an available language model template you can find one [here](https://app.banana.dev/templates/conceptofmind/serverless-template-palmyra-base).
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This template uses the Palmyra-Base model by [Writer](https://writer.com/product/api/).
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You can check out an example Banana repository [here](https://github.com/conceptofmind/serverless-template-palmyra-base).
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## Build the Banana app
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Banana Apps must include the "output" key in the return json.
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There is a rigid response structure.
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```python
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# Return the results as a dictionary
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result = {'output': result}
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```
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An example inference function would be:
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```python
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def inference(model_inputs:dict) -> dict:
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global model
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global tokenizer
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# Parse out your arguments
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prompt = model_inputs.get('prompt', None)
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if prompt == None:
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return {'message': "No prompt provided"}
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# Run the model
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input_ids = tokenizer.encode(prompt, return_tensors='pt').cuda()
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output = model.generate(
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input_ids,
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max_length=100,
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do_sample=True,
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top_k=50,
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top_p=0.95,
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num_return_sequences=1,
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temperature=0.9,
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early_stopping=True,
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no_repeat_ngram_size=3,
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num_beams=5,
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length_penalty=1.5,
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repetition_penalty=1.5,
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bad_words_ids=[[tokenizer.encode(' ', add_prefix_space=True)[0]]]
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)
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result = tokenizer.decode(output[0], skip_special_tokens=True)
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# Return the results as a dictionary
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result = {'output': result}
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return result
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```
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You can find a full example of a Banana app [here](https://github.com/conceptofmind/serverless-template-palmyra-base/blob/main/app.py).
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## Wrappers
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### LLM
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There exists an Banana LLM wrapper, which you can access with
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```python
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from langchain.llms import Banana
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```
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You need to provide a model key located in the dashboard:
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```python
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llm = Banana(model_key="YOUR_MODEL_KEY")
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```
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