LangChain

Modal生态系统#

本页面介绍了如何在LangChain中使用Modal生态系统。分为两部分：安装和设置，以及对特定Modal包装器的引用。

安装和设置#

使用pip install modal-client安装
运行modal token new

定义你的Modal函数和Webhooks#

你必须包含一个提示。有一个严格的响应结构。

class Item(BaseModel):
    prompt: str
 
@stub.webhook(method="POST")
def my_webhook(item: Item):
    return {"prompt": my_function.call(item.prompt)}

使用GPT2的示例：

from pydantic import BaseModel
 
import modal
 
stub = modal.Stub("example-get-started")
 
volume = modal.SharedVolume().persist("gpt2_model_vol")
CACHE_PATH = "/root/model_cache"
 
@stub.function(
    gpu="any",
    image=modal.Image.debian_slim().pip_install(
        "tokenizers", "transformers", "torch", "accelerate"
    ),
    shared_volumes={CACHE_PATH: volume},
    retries=3,
)
def run_gpt2(text: str):
    from transformers import GPT2Tokenizer, GPT2LMHeadModel
    tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
    model = GPT2LMHeadModel.from_pretrained('gpt2')
    encoded_input = tokenizer(text, return_tensors='pt').input_ids
    output = model.generate(encoded_input, max_length=50, do_sample=True)
    return tokenizer.decode(output[0], skip_special_tokens=True)
 
class Item(BaseModel):
    prompt: str
 
@stub.webhook(method="POST")
def get_text(item: Item):
    return {"prompt": run_gpt2.call(item.prompt)}

包装器#

LLM#

There exists an Modal LLM wrapper, which you can access with

from langchain.llms import Modal

Milvus Myscale