Guardrails Output Parsing¶
First, set your openai api keys
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# import os
# os.environ["OPENAI_API_KEY"] = "sk-..."
# import os
# os.environ["OPENAI_API_KEY"] = "sk-..."
If you're opening this Notebook on colab, you will probably need to install 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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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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Download Data¶
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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
Load documents, build the 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"
Define Query + Guardrails Spec¶
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from llama_index.output_parsers.guardrails import GuardrailsOutputParser
from llama_index.output_parsers.guardrails import GuardrailsOutputParser
Define custom QA and Refine Prompts
Define Guardrails Spec
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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="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}
"""
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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
# 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)
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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,
)
# take a look at the new QA template!
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.
Query Index¶
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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(
"What are the three items the author did growing up?",
)
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' │ │
│ │ } │ │
│ │ } │ │
│ ╰─────────────────────────────────────────────────────────────────────────────────────────────────────────╯ │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
