mirror of https://github.com/hwchase17/langchain
Add Writer, Banana, Modal, StochasticAI (#1270)
Add LLM wrappers and examples for Banana, Writer, Modal, Stochastic AI Added rigid json format for Banana and Modalpull/1275/head
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# 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 `pip3 install banana-dev`
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- Get an CerebriumAI 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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You must include a output in the result. 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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# Modal
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This page covers how to use the Modal ecosystem within LangChain.
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It is broken into two parts: installation and setup, and then references to specific Modal wrappers.
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## Installation and Setup
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- Install with `pip install modal-client`
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- Run `modal token new`
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## Define your Modal Functions and Webhooks
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You must include a prompt. There is a rigid response structure.
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```python
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class Item(BaseModel):
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prompt: str
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@stub.webhook(method="POST")
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def my_webhook(item: Item):
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return {"prompt": my_function.call(item.prompt)}
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```
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An example with GPT2:
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```python
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from pydantic import BaseModel
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import modal
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stub = modal.Stub("example-get-started")
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volume = modal.SharedVolume().persist("gpt2_model_vol")
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CACHE_PATH = "/root/model_cache"
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@stub.function(
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gpu="any",
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image=modal.Image.debian_slim().pip_install(
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"tokenizers", "transformers", "torch", "accelerate"
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),
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shared_volumes={CACHE_PATH: volume},
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retries=3,
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)
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def run_gpt2(text: str):
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from transformers import GPT2Tokenizer, GPT2LMHeadModel
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tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
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model = GPT2LMHeadModel.from_pretrained('gpt2')
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encoded_input = tokenizer(text, return_tensors='pt').input_ids
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output = model.generate(encoded_input, max_length=50, do_sample=True)
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return tokenizer.decode(output[0], skip_special_tokens=True)
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class Item(BaseModel):
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prompt: str
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@stub.webhook(method="POST")
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def get_text(item: Item):
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return {"prompt": run_gpt2.call(item.prompt)}
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```
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## Wrappers
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### LLM
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There exists an Modal LLM wrapper, which you can access with
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```python
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from langchain.llms import Modal
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```
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# StochasticAI
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This page covers how to use the StochasticAI ecosystem within LangChain.
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It is broken into two parts: installation and setup, and then references to specific StochasticAI wrappers.
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## Installation and Setup
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- Install with `pip install stochasticx`
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- Get an StochasticAI api key and set it as an environment variable (`STOCHASTICAI_API_KEY`)
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## Wrappers
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### LLM
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There exists an StochasticAI LLM wrapper, which you can access with
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```python
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from langchain.llms import StochasticAI
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```
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# Writer
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This page covers how to use the Writer ecosystem within LangChain.
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It is broken into two parts: installation and setup, and then references to specific Writer wrappers.
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## Installation and Setup
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- Get an Writer api key and set it as an environment variable (`WRITER_API_KEY`)
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## Wrappers
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### LLM
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There exists an Writer LLM wrapper, which you can access with
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```python
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from langchain.llms import Writer
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```
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Banana\n",
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"This example goes over how to use LangChain to interact with Banana models"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"from langchain.llms import Banana\n",
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"from langchain import PromptTemplate, LLMChain\n",
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"os.environ[\"BANANA_API_KEY\"] = \"YOUR_API_KEY\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"template = \"\"\"Question: {question}\n",
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"\n",
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"Answer: Let's think step by step.\"\"\"\n",
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"\n",
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"prompt = PromptTemplate(template=template, input_variables=[\"question\"])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"llm = Banana(model_key=\"YOUR_MODEL_KEY\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"llm_chain = LLMChain(prompt=prompt, llm=llm)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"question = \"What NFL team won the Super Bowl in the year Justin Beiber was born?\"\n",
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"\n",
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"llm_chain.run(question)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3.9.12 ('palm')",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"name": "python",
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"version": "3.9.12"
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},
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"orig_nbformat": 4,
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"vscode": {
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"interpreter": {
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"hash": "a0a0263b650d907a3bfe41c0f8d6a63a071b884df3cfdc1579f00cdc1aed6b03"
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Modal\n",
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"This example goes over how to use LangChain to interact with Modal models"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.llms import Modal\n",
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"from langchain import PromptTemplate, LLMChain"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"template = \"\"\"Question: {question}\n",
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"\n",
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"Answer: Let's think step by step.\"\"\"\n",
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"\n",
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"prompt = PromptTemplate(template=template, input_variables=[\"question\"])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"llm = Modal(endpoint_url=\"YOUR_ENDPOINT_URL\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"llm_chain = LLMChain(prompt=prompt, llm=llm)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"question = \"What NFL team won the Super Bowl in the year Justin Beiber was born?\"\n",
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"\n",
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"llm_chain.run(question)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3.9.12 ('palm')",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"name": "python",
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"version": "3.9.12"
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},
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"orig_nbformat": 4,
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"vscode": {
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"interpreter": {
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"hash": "a0a0263b650d907a3bfe41c0f8d6a63a071b884df3cfdc1579f00cdc1aed6b03"
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# StochasticAI\n",
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"This example goes over how to use LangChain to interact with StochasticAI models"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.llms import StochasticAI\n",
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"from langchain import PromptTemplate, LLMChain"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"template = \"\"\"Question: {question}\n",
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"\n",
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"Answer: Let's think step by step.\"\"\"\n",
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"\n",
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"prompt = PromptTemplate(template=template, input_variables=[\"question\"])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"llm = StochasticAI(api_url=\"YOUR_API_URL\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"llm_chain = LLMChain(prompt=prompt, llm=llm)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"question = \"What NFL team won the Super Bowl in the year Justin Beiber was born?\"\n",
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"\n",
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"llm_chain.run(question)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3.9.12 ('palm')",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"name": "python",
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"version": "3.9.12"
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},
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"orig_nbformat": 4,
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"vscode": {
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"interpreter": {
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"hash": "a0a0263b650d907a3bfe41c0f8d6a63a071b884df3cfdc1579f00cdc1aed6b03"
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Writer\n",
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"This example goes over how to use LangChain to interact with Writer models"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.llms import Writer\n",
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"from langchain import PromptTemplate, LLMChain"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"template = \"\"\"Question: {question}\n",
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"\n",
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"Answer: Let's think step by step.\"\"\"\n",
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"\n",
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"prompt = PromptTemplate(template=template, input_variables=[\"question\"])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"llm = Writer()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"llm_chain = LLMChain(prompt=prompt, llm=llm)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"question = \"What NFL team won the Super Bowl in the year Justin Beiber was born?\"\n",
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"\n",
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"llm_chain.run(question)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3.9.12 ('palm')",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"name": "python",
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"version": "3.9.12"
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},
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"orig_nbformat": 4,
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"vscode": {
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"interpreter": {
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||||
"hash": "a0a0263b650d907a3bfe41c0f8d6a63a071b884df3cfdc1579f00cdc1aed6b03"
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||||
}
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||||
}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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||||
}
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"""Wrapper around Banana API."""
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import logging
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from typing import Any, Dict, List, Mapping, Optional
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from pydantic import BaseModel, Extra, Field, root_validator
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from langchain.llms.base import LLM
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from langchain.llms.utils import enforce_stop_tokens
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from langchain.utils import get_from_dict_or_env
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logger = logging.getLogger(__name__)
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|
||||
class Banana(LLM, BaseModel):
|
||||
"""Wrapper around Banana large language models.
|
||||
|
||||
To use, you should have the ``banana-dev`` python package installed,
|
||||
and the environment variable ``BANANA_API_KEY`` set with your API key.
|
||||
|
||||
Any parameters that are valid to be passed to the call can be passed
|
||||
in, even if not explicitly saved on this class.
|
||||
|
||||
Example:
|
||||
.. code-block:: python
|
||||
from langchain import Banana
|
||||
cerebrium = Banana(model_key="")
|
||||
"""
|
||||
|
||||
model_key: str = ""
|
||||
"""model endpoint to use"""
|
||||
|
||||
model_kwargs: Dict[str, Any] = Field(default_factory=dict)
|
||||
"""Holds any model parameters valid for `create` call not
|
||||
explicitly specified."""
|
||||
|
||||
banana_api_key: Optional[str] = None
|
||||
|
||||
class Config:
|
||||
"""Configuration for this pydantic config."""
|
||||
|
||||
extra = Extra.forbid
|
||||
|
||||
@root_validator(pre=True)
|
||||
def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""Build extra kwargs from additional params that were passed in."""
|
||||
all_required_field_names = {field.alias for field in cls.__fields__.values()}
|
||||
|
||||
extra = values.get("model_kwargs", {})
|
||||
for field_name in list(values):
|
||||
if field_name not in all_required_field_names:
|
||||
if field_name in extra:
|
||||
raise ValueError(f"Found {field_name} supplied twice.")
|
||||
logger.warning(
|
||||
f"""{field_name} was transfered to model_kwargs.
|
||||
Please confirm that {field_name} is what you intended."""
|
||||
)
|
||||
extra[field_name] = values.pop(field_name)
|
||||
values["model_kwargs"] = extra
|
||||
return values
|
||||
|
||||
@root_validator()
|
||||
def validate_environment(cls, values: Dict) -> Dict:
|
||||
"""Validate that api key and python package exists in environment."""
|
||||
banana_api_key = get_from_dict_or_env(
|
||||
values, "banana_api_key", "BANANA_API_KEY"
|
||||
)
|
||||
values["banana_api_key"] = banana_api_key
|
||||
return values
|
||||
|
||||
@property
|
||||
def _identifying_params(self) -> Mapping[str, Any]:
|
||||
"""Get the identifying parameters."""
|
||||
return {
|
||||
**{"model_key": self.model_key},
|
||||
**{"model_kwargs": self.model_kwargs},
|
||||
}
|
||||
|
||||
@property
|
||||
def _llm_type(self) -> str:
|
||||
"""Return type of llm."""
|
||||
return "banana"
|
||||
|
||||
def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
|
||||
"""Call to Banana endpoint."""
|
||||
try:
|
||||
import banana_dev as banana
|
||||
except ImportError:
|
||||
raise ValueError(
|
||||
"Could not import banana-dev python package. "
|
||||
"Please install it with `pip install banana-dev`."
|
||||
)
|
||||
params = self.model_kwargs or {}
|
||||
api_key = self.banana_api_key
|
||||
model_key = self.model_key
|
||||
model_inputs = {
|
||||
# a json specific to your model.
|
||||
"prompt": prompt,
|
||||
**params,
|
||||
}
|
||||
response = banana.run(api_key, model_key, model_inputs)
|
||||
try:
|
||||
text = response["modelOutputs"][0]["output"]
|
||||
except KeyError:
|
||||
raise ValueError(
|
||||
f"Response should be {'modelOutputs': [{'output': 'text'}]}."
|
||||
f"Response was: {response}"
|
||||
)
|
||||
if stop is not None:
|
||||
# I believe this is required since the stop tokens
|
||||
# are not enforced by the model parameters
|
||||
text = enforce_stop_tokens(text, stop)
|
||||
return text
|
@ -0,0 +1,92 @@
|
||||
"""Wrapper around Modal API."""
|
||||
import logging
|
||||
from typing import Any, Dict, List, Mapping, Optional
|
||||
|
||||
import requests
|
||||
from pydantic import BaseModel, Extra, Field, root_validator
|
||||
|
||||
from langchain.llms.base import LLM
|
||||
from langchain.llms.utils import enforce_stop_tokens
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class Modal(LLM, BaseModel):
|
||||
"""Wrapper around Modal large language models.
|
||||
|
||||
To use, you should have the ``modal-client`` python package installed.
|
||||
|
||||
Any parameters that are valid to be passed to the call can be passed
|
||||
in, even if not explicitly saved on this class.
|
||||
|
||||
Example:
|
||||
.. code-block:: python
|
||||
from langchain import Modal
|
||||
modal = Modal(endpoint_url="")
|
||||
|
||||
"""
|
||||
|
||||
endpoint_url: str = ""
|
||||
"""model endpoint to use"""
|
||||
|
||||
model_kwargs: Dict[str, Any] = Field(default_factory=dict)
|
||||
"""Holds any model parameters valid for `create` call not
|
||||
explicitly specified."""
|
||||
|
||||
class Config:
|
||||
"""Configuration for this pydantic config."""
|
||||
|
||||
extra = Extra.forbid
|
||||
|
||||
@root_validator(pre=True)
|
||||
def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""Build extra kwargs from additional params that were passed in."""
|
||||
all_required_field_names = {field.alias for field in cls.__fields__.values()}
|
||||
|
||||
extra = values.get("model_kwargs", {})
|
||||
for field_name in list(values):
|
||||
if field_name not in all_required_field_names:
|
||||
if field_name in extra:
|
||||
raise ValueError(f"Found {field_name} supplied twice.")
|
||||
logger.warning(
|
||||
f"""{field_name} was transfered to model_kwargs.
|
||||
Please confirm that {field_name} is what you intended."""
|
||||
)
|
||||
extra[field_name] = values.pop(field_name)
|
||||
values["model_kwargs"] = extra
|
||||
return values
|
||||
|
||||
@property
|
||||
def _identifying_params(self) -> Mapping[str, Any]:
|
||||
"""Get the identifying parameters."""
|
||||
return {
|
||||
**{"endpoint_url": self.endpoint_url},
|
||||
**{"model_kwargs": self.model_kwargs},
|
||||
}
|
||||
|
||||
@property
|
||||
def _llm_type(self) -> str:
|
||||
"""Return type of llm."""
|
||||
return "modal"
|
||||
|
||||
def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
|
||||
"""Call to Modal endpoint."""
|
||||
params = self.model_kwargs or {}
|
||||
response = requests.post(
|
||||
url=self.endpoint_url,
|
||||
headers={
|
||||
"Content-Type": "application/json",
|
||||
},
|
||||
json={"prompt": prompt, **params},
|
||||
)
|
||||
try:
|
||||
if prompt in response.json()["prompt"]:
|
||||
response_json = response.json()
|
||||
except KeyError:
|
||||
raise ValueError("LangChain requires 'prompt' key in response.")
|
||||
text = response_json["prompt"]
|
||||
if stop is not None:
|
||||
# I believe this is required since the stop tokens
|
||||
# are not enforced by the model parameters
|
||||
text = enforce_stop_tokens(text, stop)
|
||||
return text
|
@ -0,0 +1,130 @@
|
||||
"""Wrapper around StochasticAI APIs."""
|
||||
import logging
|
||||
import time
|
||||
from typing import Any, Dict, List, Mapping, Optional
|
||||
|
||||
import requests
|
||||
from pydantic import BaseModel, Extra, Field, root_validator
|
||||
|
||||
from langchain.llms.base import LLM
|
||||
from langchain.llms.utils import enforce_stop_tokens
|
||||
from langchain.utils import get_from_dict_or_env
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class StochasticAI(LLM, BaseModel):
|
||||
"""Wrapper around StochasticAI large language models.
|
||||
|
||||
To use, you should have the environment variable ``STOCHASTICAI_API_KEY``
|
||||
set with your API key.
|
||||
|
||||
Example:
|
||||
.. code-block:: python
|
||||
|
||||
from langchain import StochasticAI
|
||||
forefrontai = StochasticAI(api_url="")
|
||||
"""
|
||||
|
||||
api_url: str = ""
|
||||
"""Model name to use."""
|
||||
|
||||
model_kwargs: Dict[str, Any] = Field(default_factory=dict)
|
||||
"""Holds any model parameters valid for `create` call not
|
||||
explicitly specified."""
|
||||
|
||||
stochasticai_api_key: Optional[str] = None
|
||||
|
||||
class Config:
|
||||
"""Configuration for this pydantic object."""
|
||||
|
||||
extra = Extra.forbid
|
||||
|
||||
@root_validator(pre=True)
|
||||
def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""Build extra kwargs from additional params that were passed in."""
|
||||
all_required_field_names = {field.alias for field in cls.__fields__.values()}
|
||||
|
||||
extra = values.get("model_kwargs", {})
|
||||
for field_name in list(values):
|
||||
if field_name not in all_required_field_names:
|
||||
if field_name in extra:
|
||||
raise ValueError(f"Found {field_name} supplied twice.")
|
||||
logger.warning(
|
||||
f"""{field_name} was transfered to model_kwargs.
|
||||
Please confirm that {field_name} is what you intended."""
|
||||
)
|
||||
extra[field_name] = values.pop(field_name)
|
||||
values["model_kwargs"] = extra
|
||||
return values
|
||||
|
||||
@root_validator()
|
||||
def validate_environment(cls, values: Dict) -> Dict:
|
||||
"""Validate that api key exists in environment."""
|
||||
stochasticai_api_key = get_from_dict_or_env(
|
||||
values, "stochasticai_api_key", "STOCHASTICAI_API_KEY"
|
||||
)
|
||||
values["stochasticai_api_key"] = stochasticai_api_key
|
||||
return values
|
||||
|
||||
@property
|
||||
def _identifying_params(self) -> Mapping[str, Any]:
|
||||
"""Get the identifying parameters."""
|
||||
return {
|
||||
**{"endpoint_url": self.api_url},
|
||||
**{"model_kwargs": self.model_kwargs},
|
||||
}
|
||||
|
||||
@property
|
||||
def _llm_type(self) -> str:
|
||||
"""Return type of llm."""
|
||||
return "stochasticai"
|
||||
|
||||
def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
|
||||
"""Call out to StochasticAI's complete endpoint.
|
||||
|
||||
Args:
|
||||
prompt: The prompt to pass into the model.
|
||||
stop: Optional list of stop words to use when generating.
|
||||
|
||||
Returns:
|
||||
The string generated by the model.
|
||||
|
||||
Example:
|
||||
.. code-block:: python
|
||||
|
||||
response = StochasticAI("Tell me a joke.")
|
||||
"""
|
||||
params = self.model_kwargs or {}
|
||||
response_post = requests.post(
|
||||
url=self.api_url,
|
||||
json={"prompt": prompt, "params": params},
|
||||
headers={
|
||||
"apiKey": f"{self.stochasticai_api_key}",
|
||||
"Accept": "application/json",
|
||||
"Content-Type": "application/json",
|
||||
},
|
||||
)
|
||||
response_post.raise_for_status()
|
||||
response_post_json = response_post.json()
|
||||
completed = False
|
||||
while not completed:
|
||||
response_get = requests.get(
|
||||
url=response_post_json["data"]["responseUrl"],
|
||||
headers={
|
||||
"apiKey": f"{self.stochasticai_api_key}",
|
||||
"Accept": "application/json",
|
||||
"Content-Type": "application/json",
|
||||
},
|
||||
)
|
||||
response_get.raise_for_status()
|
||||
response_get_json = response_get.json()["data"]
|
||||
text = response_get_json.get("completion")
|
||||
completed = text is not None
|
||||
time.sleep(0.5)
|
||||
text = text[0]
|
||||
if stop is not None:
|
||||
# I believe this is required since the stop tokens
|
||||
# are not enforced by the model parameters
|
||||
text = enforce_stop_tokens(text, stop)
|
||||
return text
|
@ -0,0 +1,155 @@
|
||||
"""Wrapper around Writer APIs."""
|
||||
from typing import Any, Dict, List, Mapping, Optional
|
||||
|
||||
import requests
|
||||
from pydantic import BaseModel, Extra, root_validator
|
||||
|
||||
from langchain.llms.base import LLM
|
||||
from langchain.llms.utils import enforce_stop_tokens
|
||||
from langchain.utils import get_from_dict_or_env
|
||||
|
||||
|
||||
class Writer(LLM, BaseModel):
|
||||
"""Wrapper around Writer large language models.
|
||||
|
||||
To use, you should have the environment variable ``WRITER_API_KEY``
|
||||
set with your API key.
|
||||
|
||||
Example:
|
||||
.. code-block:: python
|
||||
|
||||
from langchain import Writer
|
||||
writer = Writer(model_id="palmyra-base")
|
||||
"""
|
||||
|
||||
model_id: str = "palmyra-base"
|
||||
"""Model name to use."""
|
||||
|
||||
tokens_to_generate: int = 24
|
||||
"""Max number of tokens to generate."""
|
||||
|
||||
logprobs: bool = False
|
||||
"""Whether to return log probabilities."""
|
||||
|
||||
temperature: float = 1.0
|
||||
"""What sampling temperature to use."""
|
||||
|
||||
length: int = 256
|
||||
"""The maximum number of tokens to generate in the completion."""
|
||||
|
||||
top_p: float = 1.0
|
||||
"""Total probability mass of tokens to consider at each step."""
|
||||
|
||||
top_k: int = 1
|
||||
"""The number of highest probability vocabulary tokens to
|
||||
keep for top-k-filtering."""
|
||||
|
||||
repetition_penalty: float = 1.0
|
||||
"""Penalizes repeated tokens according to frequency."""
|
||||
|
||||
random_seed: int = 0
|
||||
"""The model generates random results.
|
||||
Changing the random seed alone will produce a different response
|
||||
with similar characteristics. It is possible to reproduce results
|
||||
by fixing the random seed (assuming all other hyperparameters
|
||||
are also fixed)"""
|
||||
|
||||
beam_search_diversity_rate: float = 1.0
|
||||
"""Only applies to beam search, i.e. when the beam width is >1.
|
||||
A higher value encourages beam search to return a more diverse
|
||||
set of candidates"""
|
||||
|
||||
beam_width: Optional[int] = None
|
||||
"""The number of concurrent candidates to keep track of during
|
||||
beam search"""
|
||||
|
||||
length_pentaly: float = 1.0
|
||||
"""Only applies to beam search, i.e. when the beam width is >1.
|
||||
Larger values penalize long candidates more heavily, thus preferring
|
||||
shorter candidates"""
|
||||
|
||||
writer_api_key: Optional[str] = None
|
||||
|
||||
stop: Optional[List[str]] = None
|
||||
"""Sequences when completion generation will stop"""
|
||||
|
||||
base_url: Optional[str] = None
|
||||
"""Base url to use, if None decides based on model name."""
|
||||
|
||||
class Config:
|
||||
"""Configuration for this pydantic object."""
|
||||
|
||||
extra = Extra.forbid
|
||||
|
||||
@root_validator()
|
||||
def validate_environment(cls, values: Dict) -> Dict:
|
||||
"""Validate that api key exists in environment."""
|
||||
writer_api_key = get_from_dict_or_env(
|
||||
values, "writer_api_key", "WRITER_API_KEY"
|
||||
)
|
||||
values["writer_api_key"] = writer_api_key
|
||||
return values
|
||||
|
||||
@property
|
||||
def _default_params(self) -> Mapping[str, Any]:
|
||||
"""Get the default parameters for calling Writer API."""
|
||||
return {
|
||||
"tokens_to_generate": self.tokens_to_generate,
|
||||
"stop": self.stop,
|
||||
"logprobs": self.logprobs,
|
||||
"temperature": self.temperature,
|
||||
"top_p": self.top_p,
|
||||
"top_k": self.top_k,
|
||||
"repetition_penalty": self.repetition_penalty,
|
||||
"random_seed": self.random_seed,
|
||||
"beam_search_diversity_rate": self.beam_search_diversity_rate,
|
||||
"beam_width": self.beam_width,
|
||||
"length_pentaly": self.length_pentaly,
|
||||
}
|
||||
|
||||
@property
|
||||
def _identifying_params(self) -> Mapping[str, Any]:
|
||||
"""Get the identifying parameters."""
|
||||
return {**{"model_id": self.model_id}, **self._default_params}
|
||||
|
||||
@property
|
||||
def _llm_type(self) -> str:
|
||||
"""Return type of llm."""
|
||||
return "writer"
|
||||
|
||||
def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
|
||||
"""Call out to Writer's complete endpoint.
|
||||
|
||||
Args:
|
||||
prompt: The prompt to pass into the model.
|
||||
stop: Optional list of stop words to use when generating.
|
||||
|
||||
Returns:
|
||||
The string generated by the model.
|
||||
|
||||
Example:
|
||||
.. code-block:: python
|
||||
|
||||
response = Writer("Tell me a joke.")
|
||||
"""
|
||||
if self.base_url is not None:
|
||||
base_url = self.base_url
|
||||
else:
|
||||
base_url = (
|
||||
"https://api.llm.writer.com/v1/models/{self.model_id}/completions"
|
||||
)
|
||||
response = requests.post(
|
||||
url=base_url,
|
||||
headers={
|
||||
"Authorization": f"Bearer {self.writer_api_key}",
|
||||
"Content-Type": "application/json",
|
||||
"Accept": "application/json",
|
||||
},
|
||||
json={"prompt": prompt, **self._default_params},
|
||||
)
|
||||
text = response.text
|
||||
if stop is not None:
|
||||
# I believe this is required since the stop tokens
|
||||
# are not enforced by the model parameters
|
||||
text = enforce_stop_tokens(text, stop)
|
||||
return text
|
@ -0,0 +1,10 @@
|
||||
"""Test BananaDev API wrapper."""
|
||||
|
||||
from langchain.llms.bananadev import Banana
|
||||
|
||||
|
||||
def test_banana_call() -> None:
|
||||
"""Test valid call to BananaDev."""
|
||||
llm = Banana()
|
||||
output = llm("Say foo:")
|
||||
assert isinstance(output, str)
|
@ -0,0 +1,10 @@
|
||||
"""Test Modal API wrapper."""
|
||||
|
||||
from langchain.llms.modal import Modal
|
||||
|
||||
|
||||
def test_modal_call() -> None:
|
||||
"""Test valid call to Modal."""
|
||||
llm = Modal()
|
||||
output = llm("Say foo:")
|
||||
assert isinstance(output, str)
|
@ -0,0 +1,10 @@
|
||||
"""Test StochasticAI API wrapper."""
|
||||
|
||||
from langchain.llms.stochasticai import StochasticAI
|
||||
|
||||
|
||||
def test_stochasticai_call() -> None:
|
||||
"""Test valid call to StochasticAI."""
|
||||
llm = StochasticAI()
|
||||
output = llm("Say foo:")
|
||||
assert isinstance(output, str)
|
@ -0,0 +1,10 @@
|
||||
"""Test Writer API wrapper."""
|
||||
|
||||
from langchain.llms.writer import Writer
|
||||
|
||||
|
||||
def test_writer_call() -> None:
|
||||
"""Test valid call to Writer."""
|
||||
llm = Writer()
|
||||
output = llm("Say foo:")
|
||||
assert isinstance(output, str)
|
Loading…
Reference in New Issue