Guardrails 输出解析¶
首先，设置你的 openai api 密钥
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# import os
# os.environ["OPENAI_API_KEY"] = "sk-..."
# import os # os.environ["OPENAI_API_KEY"] = "sk-..."
如果你在 colab 上打开这个 Notebook，你可能需要安装 LlamaIndex 🦙。
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%pip install llama-index-llms-openai
%pip install llama-index-output-parsers-guardrails
%pip install llama-index-llms-openai %pip install llama-index-output-parsers-guardrails
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python-dateutil>=2.8.2->pandas->llama-index-core<0.11.0,>=0.10.24->llama-index-llms-openai) (1.16.0) [notice] A new release of pip is available: 23.2.1 -> 24.0 [notice] To update, run: pip install --upgrade pip Note: you may need to restart the kernel to use updated packages. 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zope.interface->eliot<2.0.0,>=1.15.0->guardrails-ai<0.4.0,>=0.3.2->llama-index-output-parsers-guardrails) (65.5.0) Using cached guardrails_ai-0.3.3-py3-none-any.whl (148 kB) Installing collected packages: guardrails-ai Attempting uninstall: guardrails-ai Found existing installation: guardrails-ai 0.4.2 Uninstalling guardrails-ai-0.4.2: Successfully uninstalled guardrails-ai-0.4.2 Successfully installed guardrails-ai-0.3.3 [notice] A new release of pip is available: 23.2.1 -> 24.0 [notice] To update, run: pip install --upgrade pip Note: you may need to restart the kernel to use updated packages.
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%pip install guardrails-ai
%pip install guardrails-ai
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下载数据¶
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!mkdir -p 'data/paul_graham/'
!curl 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt' > 'data/paul_graham/paul_graham_essay.txt'
!mkdir -p 'data/paul_graham/' !curl 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt' > 'data/paul_graham/paul_graham_essay.txt'
% Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
100 75042 100 75042 0 0 390k 0 --:--:-- --:--:-- --:--:-- 396k
加载文档，构建 VectorStoreIndex¶
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import logging
import sys
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
import logging import sys logging.basicConfig(stream=sys.stdout, level=logging.INFO) logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout)) from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
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# load documents
documents = SimpleDirectoryReader("./data/paul_graham/").load_data()
# load documents documents = SimpleDirectoryReader("./data/paul_graham/").load_data()
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index = VectorStoreIndex.from_documents(documents, chunk_size=512)
index = VectorStoreIndex.from_documents(documents, chunk_size=512)
INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK" HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
定义查询 + Guardrails 规范¶
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from llama_index.output_parsers.guardrails import GuardrailsOutputParser
from llama_index.output_parsers.guardrails import GuardrailsOutputParser
定义自定义问答和精炼提示
定义 Guardrails 规范
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# You can either define a RailSpec and initialise a Guard object from_rail_string()
# OR define Pydantic classes and initialise a Guard object from_pydantic()
# For more info: https://docs.guardrailsai.com/defining_guards/pydantic/
# Guardrails recommends Pydantic
from pydantic import BaseModel, Field
from typing import List
import guardrails as gd
class BulletPoints(BaseModel):
# In all the fields below, you can define validators as well
# Left out for brevity
explanation: str = Field()
explanation2: str = Field()
explanation3: str = Field()
class Explanation(BaseModel):
points: BulletPoints = Field(
description="Bullet points regarding events in the author's life."
)
# Define the prompt
prompt = """
Query string here.
${gr.xml_prefix_prompt}
${output_schema}
${gr.json_suffix_prompt_v2_wo_none}
"""
# You can either define a RailSpec and initialise a Guard object from_rail_string() # OR define Pydantic classes and initialise a Guard object from_pydantic() # For more info: https://docs.guardrailsai.com/defining_guards/pydantic/ # Guardrails recommends Pydantic from pydantic import BaseModel, Field from typing import List import guardrails as gd class BulletPoints(BaseModel): # In all the fields below, you can define validators as well # Left out for brevity explanation: str = Field() explanation2: str = Field() explanation3: str = Field() class Explanation(BaseModel): points: BulletPoints = Field( description="关于作者生活中事件的要点。" ) # Define the prompt prompt = """ 查询字符串在这里。${gr.xml_prefix_prompt} ${output_schema} ${gr.json_suffix_prompt_v2_wo_none} """
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from llama_index.llms.openai import OpenAI
# Create a guard object
guard = gd.Guard.from_pydantic(output_class=Explanation, prompt=prompt)
# Create output parse object
output_parser = GuardrailsOutputParser(guard)
# attach to an llm object
llm = OpenAI(output_parser=output_parser)
from llama_index.llms.openai import OpenAI # 创建 Guard 对象 guard = gd.Guard.from_pydantic(output_class=Explanation, prompt=prompt) # 创建输出解析对象 output_parser = GuardrailsOutputParser(guard) # 附加到 LLM 对象 llm = OpenAI(output_parser=output_parser)
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from llama_index.core.prompts.default_prompts import (
DEFAULT_TEXT_QA_PROMPT_TMPL,
)
# take a look at the new QA template!
fmt_qa_tmpl = output_parser.format(DEFAULT_TEXT_QA_PROMPT_TMPL)
print(fmt_qa_tmpl)
from llama_index.core.prompts.default_prompts import ( DEFAULT_TEXT_QA_PROMPT_TMPL, ) # 看看新的问答模板！ fmt_qa_tmpl = output_parser.format(DEFAULT_TEXT_QA_PROMPT_TMPL) print(fmt_qa_tmpl)
Context information is below.
---------------------
{context_str}
---------------------
Given the context information and not prior knowledge, answer the query.
Query: {query_str}
Answer:
Given below is XML that describes the information to extract from this document and the tags to extract it into.
<output>
<object name="points" description="Bullet points regarding events in the author's life.">
<string name="explanation"/>
<string name="explanation2"/>
<string name="explanation3"/>
</object>
</output>
ONLY return a valid JSON object (no other text is necessary). The JSON MUST conform to the XML format, including any types and format requests e.g. requests for lists, objects and specific types. Be correct and concise.
查询索引¶
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query_engine = index.as_query_engine(
llm=llm,
)
response = query_engine.query(
"What are the three items the author did growing up?",
)
query_engine = index.as_query_engine( llm=llm, ) response = query_engine.query( "作者小时候做的三件事是什么？", )
INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK" HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK" INFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK" HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
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print(response)
print(response)
{'points': {'explanation': 'Writing short stories', 'explanation2': 'Programming on the IBM 1401 in 9th grade', 'explanation3': 'Getting a TRS-80 computer and starting programming in 1980'}}
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# View a summary of what the guard did
guard.history.last.tree
# View a summary of what the guard did guard.history.last.tree
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Logs
└── ╭────────────────────────────────────────────────── Step 0 ───────────────────────────────────────────────────╮
│ ╭──────────────────────────────────────────────── Prompt ─────────────────────────────────────────────────╮ │
│ │ No prompt │ │
│ ╰─────────────────────────────────────────────────────────────────────────────────────────────────────────╯ │
│ ╭──────────────────────────────────────────── Message History ────────────────────────────────────────────╮ │
│ │ No message history. │ │
│ ╰─────────────────────────────────────────────────────────────────────────────────────────────────────────╯ │
│ ╭──────────────────────────────────────────── Raw LLM Output ─────────────────────────────────────────────╮ │
│ │ { │ │
│ │ "points": { │ │
│ │ "explanation": "Writing short stories", │ │
│ │ "explanation2": "Programming on the IBM 1401 in 9th grade", │ │
│ │ "explanation3": "Getting a TRS-80 computer and starting programming in 1980" │ │
│ │ } │ │
│ │ } │ │
│ ╰─────────────────────────────────────────────────────────────────────────────────────────────────────────╯ │
│ ╭─────────────────────────────────────────── Validated Output ────────────────────────────────────────────╮ │
│ │ { │ │
│ │ 'points': { │ │
│ │ 'explanation': 'Writing short stories', │ │
│ │ 'explanation2': 'Programming on the IBM 1401 in 9th grade', │ │
│ │ 'explanation3': 'Getting a TRS-80 computer and starting programming in 1980' │ │
│ │ } │ │
│ │ } │ │
│ ╰─────────────────────────────────────────────────────────────────────────────────────────────────────────╯ │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
