Prediction Guard

本页介绍如何在 LangChain 中使用 Prediction Guard 生态系统。它分为两个部分：安装和设置，然后是特定 Prediction Guard 包装器的引用。

安装和设置

• 使用 pip install predictionguard 安装 Python SDK
• 获取 Prediction Guard 访问令牌（如此处所述）并将其设置为环境变量 (PREDICTIONGUARD_TOKEN)

LLM 包装器

存在一个 Prediction Guard LLM 包装器，您可以使用以下方式访问：

from langchain_community.llms import PredictionGuard

API 参考：PredictionGuard

您可以在初始化 LLM 时提供 Prediction Guard 模型的名称作为参数

pgllm = PredictionGuard(model="MPT-7B-Instruct")

您也可以直接将您的访问令牌作为参数提供

pgllm = PredictionGuard(model="MPT-7B-Instruct", token="<your access token>")

最后，您可以提供一个 "output" 参数，用于结构化/控制 LLM 的输出

pgllm = PredictionGuard(model="MPT-7B-Instruct", output={"type": "boolean"})

示例用法

受控或受保护 LLM 包装器的基本用法

import os

import predictionguard as pg
from langchain_community.llms import PredictionGuard
from langchain_core.prompts import PromptTemplate
from langchain.chains import LLMChain

# Your Prediction Guard API key. Get one at predictionguard.com
os.environ["PREDICTIONGUARD_TOKEN"] = "<your Prediction Guard access token>"

# Define a prompt template
template = """Respond to the following query based on the context.

Context: EVERY comment, DM + email suggestion has led us to this EXCITING announcement! 🎉 We have officially added TWO new candle subscription box options! 📦
Exclusive Candle Box - $80 
Monthly Candle Box - $45 (NEW!)
Scent of The Month Box - $28 (NEW!)
Head to stories to get ALL the deets on each box! 👆 BONUS: Save 50% on your first box with code 50OFF! 🎉

Query: {query}

Result: """
prompt = PromptTemplate.from_template(template)

# With "guarding" or controlling the output of the LLM. See the 
# Prediction Guard docs (https://docs.predictionguard.com) to learn how to 
# control the output with integer, float, boolean, JSON, and other types and
# structures.
pgllm = PredictionGuard(model="MPT-7B-Instruct", 
                        output={
                                "type": "categorical",
                                "categories": [
                                    "product announcement", 
                                    "apology", 
                                    "relational"
                                    ]
                                })
pgllm(prompt.format(query="What kind of post is this?"))

API 参考：PredictionGuard | PromptTemplate | LLMChain

使用 Prediction Guard 包装器的基本 LLM 链接

import os

from langchain_core.prompts import PromptTemplate
from langchain.chains import LLMChain
from langchain_community.llms import PredictionGuard

# Optional, add your OpenAI API Key. This is optional, as Prediction Guard allows
# you to access all the latest open access models (see https://docs.predictionguard.com)
os.environ["OPENAI_API_KEY"] = "<your OpenAI api key>"

# Your Prediction Guard API key. Get one at predictionguard.com
os.environ["PREDICTIONGUARD_TOKEN"] = "<your Prediction Guard access token>"

pgllm = PredictionGuard(model="OpenAI-gpt-3.5-turbo-instruct")

template = """Question: {question}

Answer: Let's think step by step."""
prompt = PromptTemplate.from_template(template)
llm_chain = LLMChain(prompt=prompt, llm=pgllm, verbose=True)

question = "What NFL team won the Super Bowl in the year Justin Beiber was born?"

llm_chain.predict(question=question)

API 参考：PromptTemplate | LLMChain | PredictionGuard