高级提示技术（变量映射、函数）
在本笔记本中，我们将展示一些高级提示技术。这些特性允许您定义更多自定义/表达性的提示，重用现有的提示，并且还可以用更少的代码行来表达某些操作。
我们展示了以下功能：
- 部分格式
- 提示模板变量映射
- 提示函数映射
pip install llama-index-core
pip install llama-index-llms-openai
from llama_index.core import PromptTemplate
from llama_index.llms.openai import OpenAI
1. 部分格式
部分格式化（partial_format）允许您对提示进行部分格式化，填充一些变量，而将其他变量留待以后填充。
这是一个很好的方便函数，因此您不必一直维护所有必需的提示变量直到格式化，您可以在它们进入时部分格式化。
这将创建提示模板的副本。
qa_prompt_tmpl_str = """\
Context information is below.
---------------------
{context_str}
---------------------
Given the context information and not prior knowledge, answer the query.
Please write the answer in the style of {tone_name}
Query: {query_str}
Answer: \
"""
prompt_tmpl = PromptTemplate(qa_prompt_tmpl_str)
partial_prompt_tmpl = prompt_tmpl.partial_format(tone_name="Shakespeare")
partial_prompt_tmpl.kwargs
{‘tone_name’: ‘Shakespeare’}
fmt_prompt = partial_prompt_tmpl.format(
context_str="In this work, we develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70 billion parameters",
query_str="How many params does llama 2 have",
)
print(fmt_prompt)
Context information is below.
---------------------
In this work, we develop and release Llama 2, a collection of pretrained and fine-tuned large >language models (LLMs) ranging in scale from 7 billion to 70 billion parameters
---------------------
Given the context information and not prior knowledge, answer the query.
Please write the answer in the style of Shakespeare
Query: How many params does llama 2 have
Answer:
2. 提示模板变量映射
模板var映射允许你从“预期的”提示键（例如用于响应合成的context_str和query_str）中指定映射，这些键实际上在模板中。
这允许您重用现有的字符串模板，而不必恼人地更改模板变量。
# NOTE: here notice we use `my_context` and `my_query` as template variables
qa_prompt_tmpl_str = """\
Context information is below.
---------------------
{my_context}
---------------------
Given the context information and not prior knowledge, answer the query.
Query: {my_query}
Answer: \
"""
template_var_mappings = {
"context_str": "my_context", "query_str": "my_query"}
prompt_tmpl = PromptTemplate(
qa_prompt_tmpl_str, template_var_mappings=template_var_mappings
)
fmt_prompt = partial_prompt_tmpl.format(
context_str="In this work, we develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70 billion parameters",
query_str="How many params does llama 2 have",
)
print(fmt_prompt)
Context information is below.
---------------------
In this work, we develop and release Llama 2, a collection of pretrained and fine-tuned large > > > language models (LLMs) ranging in scale from 7 billion to 70 billion parameters
---------------------
Given the context information and not prior knowledge, answer the query.
Please write the answer in the style of Shakespeare
Query: How many params does llama 2 have
Answer:
3. 提示函数映射关系
您还可以将函数作为模板变量而不是固定值传入。
这允许您在查询期间动态注入依赖于其他值的某些值。
这里有一些基本的例子。我们在RAG的提示工程指南中展示了更高级的示例（例如，几个镜头示例）。
qa_prompt_tmpl_str = """\
Context information is below.
---------------------
{context_str}
---------------------
Given the context information and not prior knowledge, answer the query.
Query: {query_str}
Answer: \
"""
def format_context_fn(**kwargs):
# format context with bullet points
context_list = kwargs["context_str"].split("\n\n")
fmtted_context = "\n\n".join([f"- {
c}" for c in context_list])
return fmtted_context
prompt_tmpl = PromptTemplate(
qa_prompt_tmpl_str, function_mappings={
"context_str": format_context_fn}
)
context_str = """\
In this work, we develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70 billion parameters.
Our fine-tuned LLMs, called Llama 2-Chat, are optimized for dialogue use cases.
Our models outperform open-source chat models on most benchmarks we tested, and based on our human evaluations for helpfulness and safety, may be a suitable substitute for closed-source models.
"""
fmt_prompt = prompt_tmpl.format(
context_str=context_str, query_str="How many params does llama 2 have"
)
print(fmt_prompt)
Context information is below.
- In this work, we develop and release Llama 2, a collection of pretrained and fine-tuned large >language models (LLMs) ranging in scale from 7 billion to 70 billion parameters.
- Our fine-tuned LLMs, called Llama 2-Chat, are optimized for dialogue use cases.
- Our models outperform open-source chat models on most benchmarks we tested, and based on >our human evaluations for helpfulness and safety, may be a suitable substitute for closed-source >models.
---------------------
Given the context information and not prior knowledge, answer the query.
Query: How many params does llama 2 have
Answer: