Baseten
Baseten 是 LangChain 生态系统中的一个提供商，它实现了 LLMs 组件。
此示例演示了如何将 LLM（托管在 Baseten 上的 Mistral 7B）与 LangChain 一起使用。
设置
要运行此示例，您需要
- 一个 Baseten 账户
- 一个 API 密钥
将您的 API 密钥导出为名为 BASETEN_API_KEY 的环境变量。
export BASETEN_API_KEY="paste_your_api_key_here"
单模型调用
首先，您需要将模型部署到 Baseten。
您可以从 Baseten 模型库一键部署 Mistral 和 Llama 2 等基础模型，或者如果您有自己的模型，则可以使用 Truss 部署它。
在本示例中，我们将使用 Mistral 7B。 在此处部署 Mistral 7B 并按照已部署模型的 ID 进行操作，该 ID 在模型仪表板中找到。
##Installing the langchain packages needed to use the integration
%pip install -qU langchain-community
from langchain_community.llms import Baseten
API 参考:Baseten
# Load the model
mistral = Baseten(model="MODEL_ID", deployment="production")
# Prompt the model
mistral("What is the Mistral wind?")
链式模型调用
我们可以将对一个或多个模型的多个调用链接在一起，这正是 Langchain 的全部意义所在！
例如，我们可以在终端模拟演示中使用 Mistral 替换 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)
正如我们从最终示例中看到的，该示例输出的数字可能正确也可能不正确，该模型仅近似于可能的终端输出，而不是实际执行提供的命令。尽管如此，该示例仍然演示了 Mistral 充足的上下文窗口、代码生成能力以及即使在对话序列中也能保持主题的能力。