fieldpincocknxxc/prompt-poet

选用指南

AI 根据 README 整理 · 2026/11/5 22:47:05

使用方式

通过 Python 库使用，可加载模板文件或直接传入模板字符串，并支持自定义编码函数。

查看 README 依据

### Basic Usage ```python import os import getpass from prompt_poet import Prompt from langchain import ChatOpenAI # Uncomment if you need to set OPENAI_API_KEY. # os.environ["OPENAI_API_KEY"] = getpass.getpass() raw_template = """ - name: system instructions role: system content: | Your name is {{ character_name }} and you are meant to be helpful and never harmful to humans. - name: user query role: user content: | {{ username}}: {{ user_query }} - name: response role: user content: | {{ character_name }}: """ template_data = {

content: | Your name is {{ character_name }} and you are meant to be helpful and never harmful to humans. - name: user query role: user content: | {{ username}}: {{ user_query }} - name: response role: user content: | {{ character_name }}: ``` Run this python code from the same directory you have saved the file `chat_template.yml.j2` to. ```python from prompt_poet import Prompt prompt = Prompt( template_path="chat_template.yml.j2", template_data=template_data ) print(prompt.string)

部署要求

需要安装 Python 包 prompt-poet，并可能需要配置 OpenAI API 密钥以调用模型。

查看 README 依据

[](https://pepy.tech/project/prompt-poet) # Prompt Poet Prompt Poet streamlines and simplifies prompt design for both developers and non-technical users with its low code approach. Using a mix of YAML and Jinja2, Prompt Poet allows for flexible, dynamic prompt creation, enhancing the efficiency and quality of interactions with AI models. It saves time on engineering string manipulations, enabling everyone to focus more on crafting the optimal prompts for their users. ### Installation ```shell pip install prompt-poet ```

### Basic Usage ```python import os import getpass from prompt_poet import Prompt from langchain import ChatOpenAI # Uncomment if you need to set OPENAI_API_KEY. # os.environ["OPENAI_API_KEY"] = getpass.getpass() raw_template = """ - name: system instructions role: system content: | Your name is {{ character_name }} and you are meant to be helpful and never harmful to humans. - name: user query role: user content: | {{ username}}: {{ user_query }} - name: response role: user content: | {{ character_name }}: """ template_data = {

已知限制

截断时可能比严格需要截断更多 token，以换取更高的前缀缓存率。

查看 README 依据

In short, Cache Aware Truncation truncates up to a fixed truncation point every time it is invoked–only moving this truncation point on average every k turns. This allows your LLM provider to maximally exploit GPU prefix cache described in [Optimizing Inference](https://research.prabeakarve/optimizing-inference/). If instead we simply truncated until reaching the token limit (L) this truncation point would move every turn which would cause a significant reduction in prefix cache rate. The tradeoff in this approach is that we often truncate more than we strictly need to.