Baseten
Baseten 是 LangChain 生態系統中的一個 供應商，它實現了 LLMs 組件。
這個範例展示了如何使用 LLM — Mistral 7B（託管在 Baseten 上）— 與 LangChain。
設定
要運行這個範例，您需要：
- 一個 Baseten 帳戶
- 一個 API 金鑰
將您的 API 金鑰匯出為一個名為 BASETEN_API_KEY 的環境變數。
export BASETEN_API_KEY="paste_your_api_key_here"
單一模型呼叫
首先，您需要將一個模型部署到 Baseten。
您可以從 Baseten 模型庫 一鍵部署像 Mistral 和 Llama 2 這樣的基礎模型，或者如果您有自己的模型，可以 使用 Truss 部署它。
在這個範例中，我們將使用 Mistral 7B。 在這裡部署 Mistral 7B，並根據已部署模型的 ID（可在模型儀表板中找到）進行操作。
##Installing the langchain packages needed to use the integration
%pip install -qU langchain-community
from langchain_community.llms import Baseten
API 參考：Baseten
# Load the model
mistral = Baseten(model="MODEL_ID", deployment="production")
# Prompt the model
mistral("What is the Mistral wind?")
鏈式模型呼叫
我們可以將多個呼叫鏈接到一個或多個模型，這是 Langchain 的重點！
例如，我們可以在這個終端模擬演示中用 Mistral 替換 GPT。
from langchain.chains import LLMChain
from langchain.memory import ConversationBufferWindowMemory
from langchain_core.prompts import PromptTemplate
template = """Assistant is a large language model trained by OpenAI.
Assistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.
Assistant is constantly learning and improving, and its capabilities are constantly evolving. It is able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. Additionally, Assistant is able to generate its own text based on the input it receives, allowing it to engage in discussions and provide explanations and descriptions on a wide range of topics.
Overall, Assistant is a powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether you need help with a specific question or just want to have a conversation about a particular topic, Assistant is here to assist.
{history}
Human: {human_input}
Assistant:"""
prompt = PromptTemplate(input_variables=["history", "human_input"], template=template)
chatgpt_chain = LLMChain(
llm=mistral,
llm_kwargs={"max_length": 4096},
prompt=prompt,
verbose=True,
memory=ConversationBufferWindowMemory(k=2),
)
output = chatgpt_chain.predict(
human_input="I want you to act as a Linux terminal. I will type commands and you will reply with what the terminal should show. I want you to only reply with the terminal output inside one unique code block, and nothing else. Do not write explanations. Do not type commands unless I instruct you to do so. When I need to tell you something in English I will do so by putting text inside curly brackets {like this}. My first command is pwd."
)
print(output)
output = chatgpt_chain.predict(human_input="ls ~")
print(output)
output = chatgpt_chain.predict(human_input="cd ~")
print(output)
output = chatgpt_chain.predict(
human_input="""echo -e "x=lambda y:y*5+3;print('Result:' + str(x(6)))" > run.py && python3 run.py"""
)
print(output)
正如我們從最後一個範例中看到的，它輸出一個可能正確也可能不正確的數字，該模型僅僅是近似可能的終端輸出，而不是實際執行提供的命令。儘管如此，該範例展示了 Mistral 充足的上下文窗口、程式碼生成能力以及即使在對話序列中也能保持主題的能力。