高级提示技巧（变量映射、函数）¶

在这个笔记本中，我们将展示一些高级提示技巧。这些特性允许您定义更自定义/更具表达力的提示，重用现有的提示，并且可以用更少的代码行来表达某些操作。

我们将展示以下功能

1. 部分格式化
2. 提示模板变量映射
3. 提示函数映射
4. 动态少样本示例

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%pip install llama-index-llms-openai

%pip install llama-index-llms-openai

1. 部分格式化¶

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，而将其余变量留待以后填充。

这是一个方便的功能，您无需将所有必需的提示变量一直维护到 format，可以在变量传入时进行部分格式化。

这将创建提示模板的副本。

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from llama_index.core.prompts import RichPromptTemplate

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 = RichPromptTemplate(qa_prompt_tmpl_str)

from llama_index.core.prompts import RichPromptTemplate 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 = RichPromptTemplate(qa_prompt_tmpl_str)

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partial_prompt_tmpl = prompt_tmpl.partial_format(tone_name="Shakespeare")

partial_prompt_tmpl = prompt_tmpl.partial_format(tone_name="Shakespeare")

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partial_prompt_tmpl.kwargs

partial_prompt_tmpl.kwargs

Out[ ]

{'tone_name': 'Shakespeare'}

In [ ]

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)

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: 

我们也可以使用 format_messages 将提示格式化为 ChatMessage 对象。

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fmt_prompt = partial_prompt_tmpl.format_messages(
    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)

fmt_prompt = partial_prompt_tmpl.format_messages( 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)

[ChatMessage(role=<MessageRole.USER: 'user'>, additional_kwargs={}, blocks=[TextBlock(block_type='text', text='Context information is below.'), TextBlock(block_type='text', text='---------------------'), TextBlock(block_type='text', text='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'), TextBlock(block_type='text', text='---------------------'), TextBlock(block_type='text', text='Given the context information and not prior knowledge, answer the query.'), TextBlock(block_type='text', text='Please write the answer in the style of Shakespeare'), TextBlock(block_type='text', text='Query: How many params does llama 2 have'), TextBlock(block_type='text', text='Answer:')])]

2. 提示模板变量映射¶

模板变量映射允许您指定从“预期”提示键（例如用于响应合成的 context_str 和 query_str）到模板中实际键的映射。

这使您可以重用现有的字符串模板，而无需烦恼地更改模板变量。

In [ ]

from llama_index.core.prompts import RichPromptTemplate

# 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 = RichPromptTemplate(
    qa_prompt_tmpl_str, template_var_mappings=template_var_mappings
)

from llama_index.core.prompts import RichPromptTemplate # 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 = RichPromptTemplate( qa_prompt_tmpl_str, template_var_mappings=template_var_mappings )

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fmt_prompt = 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)

fmt_prompt = 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.
Query: How many params does llama 2 have
Answer: 

3. 提示函数映射¶

您还可以传入函数作为模板变量，而不是固定值。

这允许您在查询时动态注入某些值，这些值依赖于其他值。

以下是一些基本示例。我们在 RAG 的提示工程指南中展示了更高级的示例（例如少样本示例）。

In [ ]

from llama_index.core.prompts import RichPromptTemplate

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 = RichPromptTemplate(
    qa_prompt_tmpl_str, function_mappings={"context_str": format_context_fn}
)

from llama_index.core.prompts import RichPromptTemplate 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 = RichPromptTemplate( qa_prompt_tmpl_str, function_mappings={"context_str": format_context_fn} )

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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_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: 

4. 动态少样本示例¶

利用函数映射，您还可以根据其他提示变量动态注入少样本示例。

下面是一个示例，它使用向量存储根据查询动态注入少样本的文本到 SQL 示例。

首先，让我们定义一个文本到 SQL 的提示模板。

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text_to_sql_prompt_tmpl_str = """\
You are a SQL expert. You are given a natural language query, and your job is to convert it into a SQL query.

Here are some examples of how you should convert natural language to SQL:
<examples>
{{ examples }}
</examples>

Now it's your turn.

Query: {{ query_str }}
SQL: 
"""

text_to_sql_prompt_tmpl_str = """\ You are a SQL expert. You are given a natural language query, and your job is to convert it into a SQL query. Here are some examples of how you should convert natural language to SQL{{ examples }}Now it's your turn. Query: {{ query_str }} SQL: """

给定此提示模板，让我们定义并索引一些少样本的文本到 SQL 示例。

In [ ]

import os

os.environ["OPENAI_API_KEY"] = "sk-..."

import os os.environ["OPENAI_API_KEY"] = "sk-..."

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from llama_index.core import Settings, VectorStoreIndex
from llama_index.core.schema import TextNode
from llama_index.llms.openai import OpenAI
from llama_index.embeddings.openai import OpenAIEmbedding

# Set global default LLM and embed model
Settings.llm = OpenAI(model="gpt-4o-mini")
Settings.embed_model = OpenAIEmbedding(model="text-embedding-3-small")

# Setup few-shot examples
example_nodes = [
    TextNode(
        text="Query: How many params does llama 2 have?\nSQL: SELECT COUNT(*) FROM llama_2_params;"
    ),
    TextNode(
        text="Query: How many layers does llama 2 have?\nSQL: SELECT COUNT(*) FROM llama_2_layers;"
    ),
]

# Create index
index = VectorStoreIndex(nodes=example_nodes)

# Create retriever
retriever = index.as_retriever(similarity_top_k=1)

from llama_index.core import Settings, VectorStoreIndex from llama_index.core.schema import TextNode from llama_index.llms.openai import OpenAI from llama_index.embeddings.openai import OpenAIEmbedding # Set global default LLM and embed model Settings.llm = OpenAI(model="gpt-4o-mini") Settings.embed_model = OpenAIEmbedding(model="text-embedding-3-small") # Setup few-shot examples example_nodes = [ TextNode( text="Query: How many params does llama 2 have?\nSQL: SELECT COUNT(*) FROM llama_2_params;" ), TextNode( text="Query: How many layers does llama 2 have?\nSQL: SELECT COUNT(*) FROM llama_2_layers;" ), ] # Create index index = VectorStoreIndex(nodes=example_nodes) # Create retriever retriever = index.as_retriever(similarity_top_k=1)

有了我们的检索器，我们可以创建带有函数映射的提示模板，以便根据查询动态注入少样本示例。

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from llama_index.core.prompts import RichPromptTemplate


def get_examples_fn(**kwargs):
    query = kwargs["query_str"]
    examples = retriever.retrieve(query)
    return "\n\n".join(node.text for node in examples)


prompt_tmpl = RichPromptTemplate(
    text_to_sql_prompt_tmpl_str,
    function_mappings={"examples": get_examples_fn},
)

from llama_index.core.prompts import RichPromptTemplate def get_examples_fn(**kwargs): query = kwargs["query_str"] examples = retriever.retrieve(query) return "\n\n".join(node.text for node in examples) prompt_tmpl = RichPromptTemplate( text_to_sql_prompt_tmpl_str, function_mappings={"examples": get_examples_fn}, )

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prompt = prompt_tmpl.format(
    query_str="What are the number of parameters in the llama 2 model?"
)
print(prompt)

prompt = prompt_tmpl.format( query_str="What are the number of parameters in the llama 2 model?" ) print(prompt)

You are a SQL expert. You are given a natural language query, and your job is to convert it into a SQL query.

Here are some examples of how you should convert natural language to SQL:
<examples>
Query: How many params does llama 2 have?
SQL: SELECT COUNT(*) FROM llama_2_params;
</examples>

Now it's your turn.

Query: What are the number of parameters in the llama 2 model?
SQL: 

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response = Settings.llm.complete(prompt)
print(response.text)

response = Settings.llm.complete(prompt) print(response.text)

SELECT COUNT(*) FROM llama_2_params;