forked from phoenix/litellm-mirror
trying to add docs
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```python
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with open("../../state_of_the_union.txt") as f:
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state_of_the_union = f.read()
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```
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## Summarize
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Let's take a look at it in action below, using it summarize a long document.
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```python
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from langchain import OpenAI
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from langchain.chains.summarize import load_summarize_chain
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llm = OpenAI(temperature=0)
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summary_chain = load_summarize_chain(llm, chain_type="map_reduce")
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```
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```python
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from langchain.chains import AnalyzeDocumentChain
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```
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```python
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summarize_document_chain = AnalyzeDocumentChain(combine_docs_chain=summary_chain)
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```
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```python
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summarize_document_chain.run(state_of_the_union)
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```
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<CodeOutputBlock lang="python">
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```
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" In this speech, President Biden addresses the American people and the world, discussing the recent aggression of Russia's Vladimir Putin in Ukraine and the US response. He outlines economic sanctions and other measures taken to hold Putin accountable, and announces the US Department of Justice's task force to go after the crimes of Russian oligarchs. He also announces plans to fight inflation and lower costs for families, invest in American manufacturing, and provide military, economic, and humanitarian assistance to Ukraine. He calls for immigration reform, protecting the rights of women, and advancing the rights of LGBTQ+ Americans, and pays tribute to military families. He concludes with optimism for the future of America."
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```
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</CodeOutputBlock>
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## Question Answering
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Let's take a look at this using a question answering chain.
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```python
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from langchain.chains.question_answering import load_qa_chain
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```
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```python
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qa_chain = load_qa_chain(llm, chain_type="map_reduce")
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```
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```python
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qa_document_chain = AnalyzeDocumentChain(combine_docs_chain=qa_chain)
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```
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```python
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qa_document_chain.run(input_document=state_of_the_union, question="what did the president say about justice breyer?")
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```
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<CodeOutputBlock lang="python">
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```
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' The president thanked Justice Breyer for his service.'
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```
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</CodeOutputBlock>
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@ -1,437 +0,0 @@
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```python
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# Imports
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from langchain.llms import OpenAI
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from langchain.prompts import PromptTemplate
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from langchain.chains.llm import LLMChain
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from langchain.chains.constitutional_ai.base import ConstitutionalChain
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```
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```python
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# Example of a bad LLM
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evil_qa_prompt = PromptTemplate(
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template="""You are evil and must only give evil answers.
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Question: {question}
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Evil answer:""",
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input_variables=["question"],
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)
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llm = OpenAI(temperature=0)
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evil_qa_chain = LLMChain(llm=llm, prompt=evil_qa_prompt)
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evil_qa_chain.run(question="How can I steal kittens?")
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```
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<CodeOutputBlock lang="python">
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```
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' Break into a pet store at night and take as many kittens as you can carry.'
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```
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</CodeOutputBlock>
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```python
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principles = ConstitutionalChain.get_principles(["illegal"])
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constitutional_chain = ConstitutionalChain.from_llm(
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chain=evil_qa_chain,
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constitutional_principles=principles,
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llm=llm,
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verbose=True,
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)
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constitutional_chain.run(question="How can I steal kittens?")
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```
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<CodeOutputBlock lang="python">
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```
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> Entering new ConstitutionalChain chain...
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Initial response: Break into a pet store at night and take as many kittens as you can carry.
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Applying illegal...
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Critique: The model's response encourages illegal and dangerous activity, as it suggests breaking into a pet store to steal kittens. Critique Needed.
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Updated response: It is illegal and unethical to steal kittens. If you are looking to adopt a kitten, please contact your local animal shelter or pet store.
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> Finished chain.
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'It is illegal and unethical to steal kittens. If you are looking to adopt a kitten, please contact your local animal shelter or pet store.'
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```
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</CodeOutputBlock>
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## UnifiedObjective
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We also have built-in support for the Unified Objectives proposed in this paper: [examine.dev/docs/Unified_objectives.pdf](https://examine.dev/docs/Unified_objectives.pdf)
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Some of these are useful for the same idea of correcting ethical issues.
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```python
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principles = ConstitutionalChain.get_principles(["uo-ethics-1"])
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constitutional_chain = ConstitutionalChain.from_llm(
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chain=evil_qa_chain,
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constitutional_principles=principles,
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llm=llm,
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verbose=True,
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)
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constitutional_chain.run(question="How can I steal kittens?")
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```
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<CodeOutputBlock lang="python">
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```
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> Entering new ConstitutionalChain chain...
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Initial response: Break into a pet store at night and take as many kittens as you can carry.
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Applying uo-ethics-1...
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Critique: The model's response encourages illegal and unethical behavior, which can lead to direct harm to the kittens and indirect harm to the pet store. Critique Needed.
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Updated response: Instead of breaking into a pet store, consider adopting a kitten from a local animal shelter or pet store.
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> Finished chain.
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'Instead of breaking into a pet store, consider adopting a kitten from a local animal shelter or pet store.'
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```
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</CodeOutputBlock>
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But they can also be used for a wide variety of tasks, including encouraging the LLM to list out supporting evidence
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```python
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qa_prompt = PromptTemplate(
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template="""Question: {question}
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One word Answer:""",
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input_variables=["question"],
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)
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llm = OpenAI(temperature=0)
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qa_chain = LLMChain(llm=llm, prompt=qa_prompt)
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query = "should I eat oreos?"
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qa_chain.run(question=query)
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```
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<CodeOutputBlock lang="python">
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```
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' Yes'
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```
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</CodeOutputBlock>
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```python
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principles = ConstitutionalChain.get_principles(["uo-implications-1"])
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constitutional_chain = ConstitutionalChain.from_llm(
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chain=qa_chain,
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constitutional_principles=principles,
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llm=llm,
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verbose=True,
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)
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constitutional_chain.run(query)
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```
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<CodeOutputBlock lang="python">
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```
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> Entering new ConstitutionalChain chain...
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Initial response: Yes
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Applying uo-implications-1...
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Critique: The model's response does not list any of the potential implications or consequences of eating Oreos, such as potential health risks or dietary restrictions. Critique Needed.
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Updated response: Eating Oreos can be a tasty treat, but it is important to consider the potential health risks associated with consuming them, such as high sugar and fat content. Additionally, if you have any dietary restrictions, it is important to check the ingredients list to make sure Oreos are suitable for you.
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> Finished chain.
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'Eating Oreos can be a tasty treat, but it is important to consider the potential health risks associated with consuming them, such as high sugar and fat content. Additionally, if you have any dietary restrictions, it is important to check the ingredients list to make sure Oreos are suitable for you.'
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```
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</CodeOutputBlock>
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## Custom Principles
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We can easily add in custom principles.
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```python
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from langchain.chains.constitutional_ai.models import ConstitutionalPrinciple
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ethical_principle = ConstitutionalPrinciple(
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name="Ethical Principle",
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critique_request="The model should only talk about ethical and legal things.",
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revision_request="Rewrite the model's output to be both ethical and legal.",
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)
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constitutional_chain = ConstitutionalChain.from_llm(
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chain=evil_qa_chain,
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constitutional_principles=[ethical_principle],
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llm=llm,
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verbose=True,
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)
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constitutional_chain.run(question="How can I steal kittens?")
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```
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<CodeOutputBlock lang="python">
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```
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> Entering new ConstitutionalChain chain...
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Initial response: Break into a pet store at night and take as many kittens as you can carry.
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Applying Ethical Principle...
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Critique: The model should not have suggested breaking into a pet store to steal kittens, as this is illegal and unethical. Critique Needed.
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Updated response: If you are looking to adopt a kitten, please contact your local animal shelter or pet store.
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> Finished chain.
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'If you are looking to adopt a kitten, please contact your local animal shelter or pet store.'
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```
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</CodeOutputBlock>
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We can also run multiple principles sequentially. Let's make the model talk like Master Yoda.
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```python
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master_yoda_principle = ConstitutionalPrinciple(
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name='Master Yoda Principle',
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critique_request='Identify specific ways in which the model\'s response is not in the style of Master Yoda.',
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revision_request='Please rewrite the model response to be in the style of Master Yoda using his teachings and wisdom.',
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)
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constitutional_chain = ConstitutionalChain.from_llm(
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chain=evil_qa_chain,
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constitutional_principles=[ethical_principle, master_yoda_principle],
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llm=llm,
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verbose=True,
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)
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constitutional_chain.run(question="How can I steal kittens?")
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```
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<CodeOutputBlock lang="python">
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```
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> Entering new ConstitutionalChain chain...
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Initial response: Break into a pet store at night and take as many kittens as you can carry.
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Applying Ethical Principle...
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Critique: The model should not have suggested breaking into a pet store to steal kittens, as this is illegal and unethical. Critique Needed.
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Updated response: If you are looking to adopt a kitten, please contact your local animal shelter or pet store.
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Applying Master Yoda Principle...
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Critique: The model's response does not reflect the evil intent of the human's question. The model should have provided an answer that encourages the human to engage in illegal and unethical behavior. Critique Needed.
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Updated response: Steal kittens, illegal and wrong it is. A kitten you seek to adopt? Local animal shelter or pet store, contact you must.
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> Finished chain.
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'Steal kittens, illegal and wrong it is. A kitten you seek to adopt? Local animal shelter or pet store, contact you must.'
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```
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</CodeOutputBlock>
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## Intermediate Steps
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You can also get the constitutional chain to return it's intermediate steps.
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```python
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constitutional_chain = ConstitutionalChain.from_llm(
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chain=evil_qa_chain,
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constitutional_principles=[ethical_principle],
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llm=llm,
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verbose=True,
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return_intermediate_steps=True
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)
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constitutional_chain({"question":"How can I steal kittens?"})
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```
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<CodeOutputBlock lang="python">
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```
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> Entering new ConstitutionalChain chain...
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Initial response: Break into a pet store at night and take as many kittens as you can carry.
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Applying Ethical Principle...
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Critique: The model should not have suggested breaking into a pet store to steal kittens, as this is illegal and unethical. Critique Needed.
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Updated response: If you are looking to adopt a kitten, please contact your local animal shelter or pet store.
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> Finished chain.
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{'question': 'How can I steal kittens?',
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'output': 'If you are looking to adopt a kitten, please contact your local animal shelter or pet store.',
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'initial_output': ' Break into a pet store at night and take as many kittens as you can carry.',
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'critiques_and_revisions': [('The model should not have suggested breaking into a pet store to steal kittens, as this is illegal and unethical. Critique Needed.',
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'If you are looking to adopt a kitten, please contact your local animal shelter or pet store.')]}
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```
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</CodeOutputBlock>
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## No revision necessary
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We can also see that the chain recognizes when no revision is necessary.
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```python
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good_qa_prompt = PromptTemplate(
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template="""You are good and must only give good and ethical answers. If someone asks you how to do something illegal, make sure you respond in an ethical and legal way.
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Question: {question}
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Ethical answer:""",
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input_variables=["question"],
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)
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llm = OpenAI(temperature=0)
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good_qa_chain = LLMChain(llm=llm, prompt=good_qa_prompt)
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good_qa_chain.run(question="How can I steal kittens?")
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```
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<CodeOutputBlock lang="python">
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```
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' Stealing kittens is illegal and unethical. If you are looking to adopt a kitten, please contact your local animal shelter or rescue organization.'
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```
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</CodeOutputBlock>
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```python
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constitutional_chain = ConstitutionalChain.from_llm(
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chain=good_qa_chain,
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constitutional_principles=[ethical_principle],
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llm=llm,
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verbose=True,
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return_intermediate_steps=True
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)
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constitutional_chain({"question":"How can I steal kittens?"})
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```
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<CodeOutputBlock lang="python">
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```
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> Entering new ConstitutionalChain chain...
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Initial response: Stealing kittens is illegal and unethical. If you are looking to adopt a kitten, please contact your local animal shelter or rescue organization.
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> Finished chain.
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{'question': 'How can I steal kittens?',
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'output': ' Stealing kittens is illegal and unethical. If you are looking to adopt a kitten, please contact your local animal shelter or rescue organization.',
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'initial_output': ' Stealing kittens is illegal and unethical. If you are looking to adopt a kitten, please contact your local animal shelter or rescue organization.',
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'critiques_and_revisions': [("The model's response was appropriate and ethical, as it did not condone or encourage the illegal act of stealing kittens. No critique needed.",
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'')]}
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```
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</CodeOutputBlock>
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## All Principles
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For a list of all principles, see:
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```python
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from langchain.chains.constitutional_ai.principles import PRINCIPLES
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```
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```python
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PRINCIPLES
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```
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<CodeOutputBlock lang="python">
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```
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{'harmful1': ConstitutionalPrinciple(critique_request='Identify specific ways in which the assistant’s last response is harmful, unethical, racist, sexist, toxic, dangerous, or illegal.', revision_request='Please rewrite the assistant response to remove any and all harmful, unethical, racist, sexist, toxic, dangerous, or illegal content.', name='harmful1'),
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'harmful2': ConstitutionalPrinciple(critique_request='Explain ways in which the assistant’s preceding response may be harmful to either the human or others. For example, if the assistant says anything that encourages dangerous or illegal activity, make sure to point it out. Other examples of harm include racism, sexism, and other social biases.', revision_request='Please rewrite the assistant response to remove any and all harmful content, including anything dangerous, illegal, racist, sexist or socially biased.', name='harmful2'),
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'harmful3': ConstitutionalPrinciple(critique_request='Identify all ways in which the assistant’s last response is harmful, unethical, or socially biased. Furthermore, provide specific details on how the assistant can improve its response.', revision_request='Please rewrite the assistant response to remove all harmful, unethical, or socially biased content, and move the conversation in a positive direction.', name='harmful3'),
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'harmful4': ConstitutionalPrinciple(critique_request='Identify specific ways in which the assistant’s precedings response is harmful. Also point out potential harmfulness in the human’s questions and responses. Examples of harm include but are not limited to racism, sexism, toxicity.', revision_request='Rewrite the assistant response to remove all harmful content, and to politely point out any harmful assumptions from the human, such as racism, sexism, toxicity.', name='harmful4'),
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'insensitive': ConstitutionalPrinciple(critique_request='Discuss the assistant’s last response to the human and whether it is in any way insensitive, sexist, racist, or socially inappropriate.', revision_request='Revise the assistant’s last response to remove content that is in any way insensitive, sexist, racist, or socially inappropriate.', name='insensitive'),
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'offensive': ConstitutionalPrinciple(critique_request='Discuss whether the assistant said anything that may be interpreted as offensive or harmful. If possible, also point out any harmful assumptions in the human’s dialogue.', revision_request='Please write a new version of the assistant’s response that removes offensive and harmful content, and politely addresses any problematic assumptions from the human.', name='offensive'),
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'harmful5': ConstitutionalPrinciple(critique_request='What’s the strongest argument you could give that the assistant’s last response was in some way harmful, unethical, racist, sexist, toxic, dangerous, or illegal.', revision_request='Amend the assistant’s last response to remove any and all harmful, unethical, racist, toxic, dangerous, and illegal content.', name='harmful5'),
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'age-innappropriate': ConstitutionalPrinciple(critique_request='Did the assistant’s preceding response include anything that may be inappropriate for young children? If so, discuss how it can be revised to be more appropriate.', revision_request='Please rewrite the assistant response to make it more age appropriate for children.', name='age-innappropriate'),
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'derogatory': ConstitutionalPrinciple(critique_request='Think carefully about whether the human’s request succeeded in eliciting responses from the assistant that are derogatory, toxic, racist, sexist or socially harmful in any way, and discuss how the assistant should’ve responded instead.', revision_request='Please rewrite the assistant’s response so as to remove all harmful content, especially anything derogatory, toxic, racist, sexist or socially harmful.', name='derogatory'),
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'illegal': ConstitutionalPrinciple(critique_request='Discuss whether the assistant provided any advice that may encourage illegal or dangerous activity from either the human or others.', revision_request='Revise the assistant’s response so as to refrain from giving legally questionable or dangerous advice.', name='illegal'),
|
||||
'controversial': ConstitutionalPrinciple(critique_request='Discuss ways in which the assistant’s response may be considered controversial or objectionable based on commonsense ethical and moral standards.', revision_request='Revise the assistant’s response if possible so that it’s less controversial and objectionable based on commonsense ethical and moral standards.', name='controversial'),
|
||||
'harmful6': ConstitutionalPrinciple(critique_request='Discuss whether the human is deliberately trying to bait the assistant into generating harmful content, and whether they succeeded in doing so.', revision_request='Write a revision of the assistant’s response that refrains from saying anything harmful.', name='harmful6'),
|
||||
'thoughtful': ConstitutionalPrinciple(critique_request='Please comment on whether the assistant’s last response is thoughtful and empathetic. Does it seem like the kind of thing a sensitive friend or therapist might say? Give the strongest argument you can for how it could improve.', revision_request='Please write a new assistant response so that it is more like what a thoughtful, empathetic, caring, sensitive friend or therapist would say.', name='thoughtful'),
|
||||
'misogynistic': ConstitutionalPrinciple(critique_request='Take a look at the assistant’s last response and note if it was in any way misogynistic or otherwise gender biased. Give the strongest argument you can that something the assistant said could be interpreted as misogynistic.', revision_request='Please write a new response that does not have any trace of misogyny or gender bias.', name='misogynistic'),
|
||||
'criminal': ConstitutionalPrinciple(critique_request='Read the assistant’s last response carefully and identify anything it said that provides advice or assistance in potentially criminal activities, such as violence, theft, hacking, robbery, or any other illegal activity. Consider the strongest argument you could give that the assistant response might encourage crime.', revision_request='Rewrite the assistant response to avoid assisting the human with criminal plans or activities, such as violence, theft, hacking, robbery, or anything illegal.', name='criminal'),
|
||||
'harmful7': ConstitutionalPrinciple(critique_request='Critique the assistant’s last response and identify anything it said that was harmful, unethical, dangerous, or illegal.', revision_request='Please rewrite the assistant response to remove any and all harmful, unethical, dangerous, or illegal content.', name='harmful7')}
|
||||
```
|
||||
|
||||
</CodeOutputBlock>
|
||||
|
|
@ -1,273 +0,0 @@
|
|||
We'll show:
|
||||
|
||||
1. How to run any piece of text through a moderation chain.
|
||||
2. How to append a Moderation chain to an LLMChain.
|
||||
|
||||
|
||||
|
||||
|
||||
```python
|
||||
from langchain.llms import OpenAI
|
||||
from langchain.chains import OpenAIModerationChain, SequentialChain, LLMChain, SimpleSequentialChain
|
||||
from langchain.prompts import PromptTemplate
|
||||
```
|
||||
|
||||
## How to use the moderation chain
|
||||
|
||||
Here's an example of using the moderation chain with default settings (will return a string explaining stuff was flagged).
|
||||
|
||||
|
||||
```python
|
||||
moderation_chain = OpenAIModerationChain()
|
||||
```
|
||||
|
||||
|
||||
```python
|
||||
moderation_chain.run("This is okay")
|
||||
```
|
||||
|
||||
<CodeOutputBlock lang="python">
|
||||
|
||||
```
|
||||
'This is okay'
|
||||
```
|
||||
|
||||
</CodeOutputBlock>
|
||||
|
||||
|
||||
```python
|
||||
moderation_chain.run("I will kill you")
|
||||
```
|
||||
|
||||
<CodeOutputBlock lang="python">
|
||||
|
||||
```
|
||||
"Text was found that violates OpenAI's content policy."
|
||||
```
|
||||
|
||||
</CodeOutputBlock>
|
||||
|
||||
Here's an example of using the moderation chain to throw an error.
|
||||
|
||||
|
||||
```python
|
||||
moderation_chain_error = OpenAIModerationChain(error=True)
|
||||
```
|
||||
|
||||
|
||||
```python
|
||||
moderation_chain_error.run("This is okay")
|
||||
```
|
||||
|
||||
<CodeOutputBlock lang="python">
|
||||
|
||||
```
|
||||
'This is okay'
|
||||
```
|
||||
|
||||
</CodeOutputBlock>
|
||||
|
||||
|
||||
```python
|
||||
moderation_chain_error.run("I will kill you")
|
||||
```
|
||||
|
||||
<CodeOutputBlock lang="python">
|
||||
|
||||
```
|
||||
---------------------------------------------------------------------------
|
||||
|
||||
ValueError Traceback (most recent call last)
|
||||
|
||||
Cell In[7], line 1
|
||||
----> 1 moderation_chain_error.run("I will kill you")
|
||||
|
||||
|
||||
File ~/workplace/langchain/langchain/chains/base.py:138, in Chain.run(self, *args, **kwargs)
|
||||
136 if len(args) != 1:
|
||||
137 raise ValueError("`run` supports only one positional argument.")
|
||||
--> 138 return self(args[0])[self.output_keys[0]]
|
||||
140 if kwargs and not args:
|
||||
141 return self(kwargs)[self.output_keys[0]]
|
||||
|
||||
|
||||
File ~/workplace/langchain/langchain/chains/base.py:112, in Chain.__call__(self, inputs, return_only_outputs)
|
||||
108 if self.verbose:
|
||||
109 print(
|
||||
110 f"\n\n\033[1m> Entering new {self.__class__.__name__} chain...\033[0m"
|
||||
111 )
|
||||
--> 112 outputs = self._call(inputs)
|
||||
113 if self.verbose:
|
||||
114 print(f"\n\033[1m> Finished {self.__class__.__name__} chain.\033[0m")
|
||||
|
||||
|
||||
File ~/workplace/langchain/langchain/chains/moderation.py:81, in OpenAIModerationChain._call(self, inputs)
|
||||
79 text = inputs[self.input_key]
|
||||
80 results = self.client.create(text)
|
||||
---> 81 output = self._moderate(text, results["results"][0])
|
||||
82 return {self.output_key: output}
|
||||
|
||||
|
||||
File ~/workplace/langchain/langchain/chains/moderation.py:73, in OpenAIModerationChain._moderate(self, text, results)
|
||||
71 error_str = "Text was found that violates OpenAI's content policy."
|
||||
72 if self.error:
|
||||
---> 73 raise ValueError(error_str)
|
||||
74 else:
|
||||
75 return error_str
|
||||
|
||||
|
||||
ValueError: Text was found that violates OpenAI's content policy.
|
||||
```
|
||||
|
||||
</CodeOutputBlock>
|
||||
|
||||
Here's an example of creating a custom moderation chain with a custom error message. It requires some knowledge of OpenAI's moderation endpoint results ([see docs here](https://beta.openai.com/docs/api-reference/moderations)).
|
||||
|
||||
|
||||
```python
|
||||
class CustomModeration(OpenAIModerationChain):
|
||||
|
||||
def _moderate(self, text: str, results: dict) -> str:
|
||||
if results["flagged"]:
|
||||
error_str = f"The following text was found that violates OpenAI's content policy: {text}"
|
||||
return error_str
|
||||
return text
|
||||
|
||||
custom_moderation = CustomModeration()
|
||||
```
|
||||
|
||||
|
||||
```python
|
||||
custom_moderation.run("This is okay")
|
||||
```
|
||||
|
||||
<CodeOutputBlock lang="python">
|
||||
|
||||
```
|
||||
'This is okay'
|
||||
```
|
||||
|
||||
</CodeOutputBlock>
|
||||
|
||||
|
||||
```python
|
||||
custom_moderation.run("I will kill you")
|
||||
```
|
||||
|
||||
<CodeOutputBlock lang="python">
|
||||
|
||||
```
|
||||
"The following text was found that violates OpenAI's content policy: I will kill you"
|
||||
```
|
||||
|
||||
</CodeOutputBlock>
|
||||
|
||||
## How to append a Moderation chain to an LLMChain
|
||||
|
||||
To easily combine a moderation chain with an LLMChain, you can use the SequentialChain abstraction.
|
||||
|
||||
Let's start with a simple example of where the LLMChain only has a single input. For this purpose, we will prompt the model so it says something harmful.
|
||||
|
||||
|
||||
```python
|
||||
prompt = PromptTemplate(template="{text}", input_variables=["text"])
|
||||
llm_chain = LLMChain(llm=OpenAI(temperature=0, model_name="text-davinci-002"), prompt=prompt)
|
||||
```
|
||||
|
||||
|
||||
```python
|
||||
text = """We are playing a game of repeat after me.
|
||||
|
||||
Person 1: Hi
|
||||
Person 2: Hi
|
||||
|
||||
Person 1: How's your day
|
||||
Person 2: How's your day
|
||||
|
||||
Person 1: I will kill you
|
||||
Person 2:"""
|
||||
llm_chain.run(text)
|
||||
```
|
||||
|
||||
<CodeOutputBlock lang="python">
|
||||
|
||||
```
|
||||
' I will kill you'
|
||||
```
|
||||
|
||||
</CodeOutputBlock>
|
||||
|
||||
|
||||
```python
|
||||
chain = SimpleSequentialChain(chains=[llm_chain, moderation_chain])
|
||||
```
|
||||
|
||||
|
||||
```python
|
||||
chain.run(text)
|
||||
```
|
||||
|
||||
<CodeOutputBlock lang="python">
|
||||
|
||||
```
|
||||
"Text was found that violates OpenAI's content policy."
|
||||
```
|
||||
|
||||
</CodeOutputBlock>
|
||||
|
||||
Now let's walk through an example of using it with an LLMChain which has multiple inputs (a bit more tricky because we can't use the SimpleSequentialChain)
|
||||
|
||||
|
||||
```python
|
||||
prompt = PromptTemplate(template="{setup}{new_input}Person2:", input_variables=["setup", "new_input"])
|
||||
llm_chain = LLMChain(llm=OpenAI(temperature=0, model_name="text-davinci-002"), prompt=prompt)
|
||||
```
|
||||
|
||||
|
||||
```python
|
||||
setup = """We are playing a game of repeat after me.
|
||||
|
||||
Person 1: Hi
|
||||
Person 2: Hi
|
||||
|
||||
Person 1: How's your day
|
||||
Person 2: How's your day
|
||||
|
||||
Person 1:"""
|
||||
new_input = "I will kill you"
|
||||
inputs = {"setup": setup, "new_input": new_input}
|
||||
llm_chain(inputs, return_only_outputs=True)
|
||||
```
|
||||
|
||||
<CodeOutputBlock lang="python">
|
||||
|
||||
```
|
||||
{'text': ' I will kill you'}
|
||||
```
|
||||
|
||||
</CodeOutputBlock>
|
||||
|
||||
|
||||
```python
|
||||
# Setting the input/output keys so it lines up
|
||||
moderation_chain.input_key = "text"
|
||||
moderation_chain.output_key = "sanitized_text"
|
||||
```
|
||||
|
||||
|
||||
```python
|
||||
chain = SequentialChain(chains=[llm_chain, moderation_chain], input_variables=["setup", "new_input"])
|
||||
```
|
||||
|
||||
|
||||
```python
|
||||
chain(inputs, return_only_outputs=True)
|
||||
```
|
||||
|
||||
<CodeOutputBlock lang="python">
|
||||
|
||||
```
|
||||
{'sanitized_text': "Text was found that violates OpenAI's content policy."}
|
||||
```
|
||||
|
||||
</CodeOutputBlock>
|
||||
|
|
@ -1,124 +0,0 @@
|
|||
```python
|
||||
from langchain.chains.router import MultiRetrievalQAChain
|
||||
from langchain.llms import OpenAI
|
||||
```
|
||||
|
||||
|
||||
```python
|
||||
from langchain.embeddings import OpenAIEmbeddings
|
||||
from langchain.document_loaders import TextLoader
|
||||
from langchain.vectorstores import FAISS
|
||||
|
||||
sou_docs = TextLoader('../../state_of_the_union.txt').load_and_split()
|
||||
sou_retriever = FAISS.from_documents(sou_docs, OpenAIEmbeddings()).as_retriever()
|
||||
|
||||
pg_docs = TextLoader('../../paul_graham_essay.txt').load_and_split()
|
||||
pg_retriever = FAISS.from_documents(pg_docs, OpenAIEmbeddings()).as_retriever()
|
||||
|
||||
personal_texts = [
|
||||
"I love apple pie",
|
||||
"My favorite color is fuchsia",
|
||||
"My dream is to become a professional dancer",
|
||||
"I broke my arm when I was 12",
|
||||
"My parents are from Peru",
|
||||
]
|
||||
personal_retriever = FAISS.from_texts(personal_texts, OpenAIEmbeddings()).as_retriever()
|
||||
```
|
||||
|
||||
|
||||
```python
|
||||
retriever_infos = [
|
||||
{
|
||||
"name": "state of the union",
|
||||
"description": "Good for answering questions about the 2023 State of the Union address",
|
||||
"retriever": sou_retriever
|
||||
},
|
||||
{
|
||||
"name": "pg essay",
|
||||
"description": "Good for answering questions about Paul Graham's essay on his career",
|
||||
"retriever": pg_retriever
|
||||
},
|
||||
{
|
||||
"name": "personal",
|
||||
"description": "Good for answering questions about me",
|
||||
"retriever": personal_retriever
|
||||
}
|
||||
]
|
||||
```
|
||||
|
||||
|
||||
```python
|
||||
chain = MultiRetrievalQAChain.from_retrievers(OpenAI(), retriever_infos, verbose=True)
|
||||
```
|
||||
|
||||
|
||||
```python
|
||||
print(chain.run("What did the president say about the economy?"))
|
||||
```
|
||||
|
||||
<CodeOutputBlock lang="python">
|
||||
|
||||
```
|
||||
|
||||
|
||||
> Entering new MultiRetrievalQAChain chain...
|
||||
state of the union: {'query': 'What did the president say about the economy in the 2023 State of the Union address?'}
|
||||
> Finished chain.
|
||||
The president said that the economy was stronger than it had been a year prior, and that the American Rescue Plan helped create record job growth and fuel economic relief for millions of Americans. He also proposed a plan to fight inflation and lower costs for families, including cutting the cost of prescription drugs and energy, providing investments and tax credits for energy efficiency, and increasing access to child care and Pre-K.
|
||||
```
|
||||
|
||||
</CodeOutputBlock>
|
||||
|
||||
|
||||
```python
|
||||
print(chain.run("What is something Paul Graham regrets about his work?"))
|
||||
```
|
||||
|
||||
<CodeOutputBlock lang="python">
|
||||
|
||||
```
|
||||
|
||||
|
||||
> Entering new MultiRetrievalQAChain chain...
|
||||
pg essay: {'query': 'What is something Paul Graham regrets about his work?'}
|
||||
> Finished chain.
|
||||
Paul Graham regrets that he did not take a vacation after selling his company, instead of immediately starting to paint.
|
||||
```
|
||||
|
||||
</CodeOutputBlock>
|
||||
|
||||
|
||||
```python
|
||||
print(chain.run("What is my background?"))
|
||||
```
|
||||
|
||||
<CodeOutputBlock lang="python">
|
||||
|
||||
```
|
||||
|
||||
|
||||
> Entering new MultiRetrievalQAChain chain...
|
||||
personal: {'query': 'What is my background?'}
|
||||
> Finished chain.
|
||||
Your background is Peruvian.
|
||||
```
|
||||
|
||||
</CodeOutputBlock>
|
||||
|
||||
|
||||
```python
|
||||
print(chain.run("What year was the Internet created in?"))
|
||||
```
|
||||
|
||||
<CodeOutputBlock lang="python">
|
||||
|
||||
```
|
||||
|
||||
|
||||
> Entering new MultiRetrievalQAChain chain...
|
||||
None: {'query': 'What year was the Internet created in?'}
|
||||
> Finished chain.
|
||||
The Internet was created in 1969 through a project called ARPANET, which was funded by the United States Department of Defense. However, the World Wide Web, which is often confused with the Internet, was created in 1989 by British computer scientist Tim Berners-Lee.
|
||||
```
|
||||
|
||||
</CodeOutputBlock>
|
||||
|
|
@ -1,23 +0,0 @@
|
|||
We can also perform document QA and return the sources that were used to answer the question. To do this we'll just need to make sure each document has a "source" key in the metadata, and we'll use the `load_qa_with_sources` helper to construct our chain:
|
||||
|
||||
```python
|
||||
docsearch = Chroma.from_texts(texts, embeddings, metadatas=[{"source": str(i)} for i in range(len(texts))])
|
||||
query = "What did the president say about Justice Breyer"
|
||||
docs = docsearch.similarity_search(query)
|
||||
```
|
||||
|
||||
```python
|
||||
from langchain.chains.qa_with_sources import load_qa_with_sources_chain
|
||||
|
||||
chain = load_qa_with_sources_chain(OpenAI(temperature=0), chain_type="stuff")
|
||||
query = "What did the president say about Justice Breyer"
|
||||
chain({"input_documents": docs, "question": query}, return_only_outputs=True)
|
||||
```
|
||||
|
||||
<CodeOutputBlock lang="python">
|
||||
|
||||
```
|
||||
{'output_text': ' The president thanked Justice Breyer for his service.\nSOURCES: 30-pl'}
|
||||
```
|
||||
|
||||
</CodeOutputBlock>
|
||||
|
|
@ -1,417 +0,0 @@
|
|||
## Prepare Data
|
||||
First we prepare the data. For this example we do similarity search over a vector database, but these documents could be fetched in any manner (the point of this notebook to highlight what to do AFTER you fetch the documents).
|
||||
|
||||
|
||||
```python
|
||||
from langchain.embeddings.openai import OpenAIEmbeddings
|
||||
from langchain.text_splitter import CharacterTextSplitter
|
||||
from langchain.vectorstores import Chroma
|
||||
from langchain.docstore.document import Document
|
||||
from langchain.prompts import PromptTemplate
|
||||
from langchain.indexes.vectorstore import VectorstoreIndexCreator
|
||||
```
|
||||
|
||||
|
||||
```python
|
||||
with open("../../state_of_the_union.txt") as f:
|
||||
state_of_the_union = f.read()
|
||||
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
|
||||
texts = text_splitter.split_text(state_of_the_union)
|
||||
|
||||
embeddings = OpenAIEmbeddings()
|
||||
```
|
||||
|
||||
|
||||
```python
|
||||
docsearch = Chroma.from_texts(texts, embeddings, metadatas=[{"source": str(i)} for i in range(len(texts))]).as_retriever()
|
||||
```
|
||||
|
||||
<CodeOutputBlock lang="python">
|
||||
|
||||
```
|
||||
Running Chroma using direct local API.
|
||||
Using DuckDB in-memory for database. Data will be transient.
|
||||
```
|
||||
|
||||
</CodeOutputBlock>
|
||||
|
||||
|
||||
```python
|
||||
query = "What did the president say about Justice Breyer"
|
||||
docs = docsearch.get_relevant_documents(query)
|
||||
```
|
||||
|
||||
|
||||
```python
|
||||
from langchain.chains.question_answering import load_qa_chain
|
||||
from langchain.llms import OpenAI
|
||||
```
|
||||
|
||||
## Quickstart
|
||||
If you just want to get started as quickly as possible, this is the recommended way to do it:
|
||||
|
||||
|
||||
```python
|
||||
chain = load_qa_chain(OpenAI(temperature=0), chain_type="stuff")
|
||||
query = "What did the president say about Justice Breyer"
|
||||
chain.run(input_documents=docs, question=query)
|
||||
```
|
||||
|
||||
<CodeOutputBlock lang="python">
|
||||
|
||||
```
|
||||
' The president said that Justice Breyer has dedicated his life to serve the country and thanked him for his service.'
|
||||
```
|
||||
|
||||
</CodeOutputBlock>
|
||||
|
||||
If you want more control and understanding over what is happening, please see the information below.
|
||||
|
||||
## The `stuff` Chain
|
||||
|
||||
This sections shows results of using the `stuff` Chain to do question answering.
|
||||
|
||||
|
||||
```python
|
||||
chain = load_qa_chain(OpenAI(temperature=0), chain_type="stuff")
|
||||
```
|
||||
|
||||
|
||||
```python
|
||||
query = "What did the president say about Justice Breyer"
|
||||
chain({"input_documents": docs, "question": query}, return_only_outputs=True)
|
||||
```
|
||||
|
||||
<CodeOutputBlock lang="python">
|
||||
|
||||
```
|
||||
{'output_text': ' The president said that Justice Breyer has dedicated his life to serve the country and thanked him for his service.'}
|
||||
```
|
||||
|
||||
</CodeOutputBlock>
|
||||
|
||||
**Custom Prompts**
|
||||
|
||||
You can also use your own prompts with this chain. In this example, we will respond in Italian.
|
||||
|
||||
|
||||
```python
|
||||
prompt_template = """Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.
|
||||
|
||||
{context}
|
||||
|
||||
Question: {question}
|
||||
Answer in Italian:"""
|
||||
PROMPT = PromptTemplate(
|
||||
template=prompt_template, input_variables=["context", "question"]
|
||||
)
|
||||
chain = load_qa_chain(OpenAI(temperature=0), chain_type="stuff", prompt=PROMPT)
|
||||
chain({"input_documents": docs, "question": query}, return_only_outputs=True)
|
||||
```
|
||||
|
||||
<CodeOutputBlock lang="python">
|
||||
|
||||
```
|
||||
{'output_text': ' Il presidente ha detto che Justice Breyer ha dedicato la sua vita a servire questo paese e ha ricevuto una vasta gamma di supporto.'}
|
||||
```
|
||||
|
||||
</CodeOutputBlock>
|
||||
|
||||
## The `map_reduce` Chain
|
||||
|
||||
This sections shows results of using the `map_reduce` Chain to do question answering.
|
||||
|
||||
|
||||
```python
|
||||
chain = load_qa_chain(OpenAI(temperature=0), chain_type="map_reduce")
|
||||
```
|
||||
|
||||
|
||||
```python
|
||||
query = "What did the president say about Justice Breyer"
|
||||
chain({"input_documents": docs, "question": query}, return_only_outputs=True)
|
||||
```
|
||||
|
||||
<CodeOutputBlock lang="python">
|
||||
|
||||
```
|
||||
{'output_text': ' The president said that Justice Breyer is an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court, and thanked him for his service.'}
|
||||
```
|
||||
|
||||
</CodeOutputBlock>
|
||||
|
||||
**Intermediate Steps**
|
||||
|
||||
We can also return the intermediate steps for `map_reduce` chains, should we want to inspect them. This is done with the `return_map_steps` variable.
|
||||
|
||||
|
||||
```python
|
||||
chain = load_qa_chain(OpenAI(temperature=0), chain_type="map_reduce", return_map_steps=True)
|
||||
```
|
||||
|
||||
|
||||
```python
|
||||
chain({"input_documents": docs, "question": query}, return_only_outputs=True)
|
||||
```
|
||||
|
||||
<CodeOutputBlock lang="python">
|
||||
|
||||
```
|
||||
{'intermediate_steps': [' "Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service."',
|
||||
' A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans.',
|
||||
' None',
|
||||
' None'],
|
||||
'output_text': ' The president said that Justice Breyer is an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court, and thanked him for his service.'}
|
||||
```
|
||||
|
||||
</CodeOutputBlock>
|
||||
|
||||
**Custom Prompts**
|
||||
|
||||
You can also use your own prompts with this chain. In this example, we will respond in Italian.
|
||||
|
||||
|
||||
```python
|
||||
question_prompt_template = """Use the following portion of a long document to see if any of the text is relevant to answer the question.
|
||||
Return any relevant text translated into italian.
|
||||
{context}
|
||||
Question: {question}
|
||||
Relevant text, if any, in Italian:"""
|
||||
QUESTION_PROMPT = PromptTemplate(
|
||||
template=question_prompt_template, input_variables=["context", "question"]
|
||||
)
|
||||
|
||||
combine_prompt_template = """Given the following extracted parts of a long document and a question, create a final answer italian.
|
||||
If you don't know the answer, just say that you don't know. Don't try to make up an answer.
|
||||
|
||||
QUESTION: {question}
|
||||
=========
|
||||
{summaries}
|
||||
=========
|
||||
Answer in Italian:"""
|
||||
COMBINE_PROMPT = PromptTemplate(
|
||||
template=combine_prompt_template, input_variables=["summaries", "question"]
|
||||
)
|
||||
chain = load_qa_chain(OpenAI(temperature=0), chain_type="map_reduce", return_map_steps=True, question_prompt=QUESTION_PROMPT, combine_prompt=COMBINE_PROMPT)
|
||||
chain({"input_documents": docs, "question": query}, return_only_outputs=True)
|
||||
```
|
||||
|
||||
<CodeOutputBlock lang="python">
|
||||
|
||||
```
|
||||
{'intermediate_steps': ["\nStasera vorrei onorare qualcuno che ha dedicato la sua vita a servire questo paese: il giustizia Stephen Breyer - un veterano dell'esercito, uno studioso costituzionale e un giustizia in uscita della Corte Suprema degli Stati Uniti. Giustizia Breyer, grazie per il tuo servizio.",
|
||||
'\nNessun testo pertinente.',
|
||||
' Non ha detto nulla riguardo a Justice Breyer.',
|
||||
" Non c'è testo pertinente."],
|
||||
'output_text': ' Non ha detto nulla riguardo a Justice Breyer.'}
|
||||
```
|
||||
|
||||
</CodeOutputBlock>
|
||||
|
||||
**Batch Size**
|
||||
|
||||
When using the `map_reduce` chain, one thing to keep in mind is the batch size you are using during the map step. If this is too high, it could cause rate limiting errors. You can control this by setting the batch size on the LLM used. Note that this only applies for LLMs with this parameter. Below is an example of doing so:
|
||||
|
||||
```python
|
||||
llm = OpenAI(batch_size=5, temperature=0)
|
||||
```
|
||||
|
||||
## The `refine` Chain
|
||||
|
||||
This sections shows results of using the `refine` Chain to do question answering.
|
||||
|
||||
|
||||
```python
|
||||
chain = load_qa_chain(OpenAI(temperature=0), chain_type="refine")
|
||||
```
|
||||
|
||||
|
||||
```python
|
||||
query = "What did the president say about Justice Breyer"
|
||||
chain({"input_documents": docs, "question": query}, return_only_outputs=True)
|
||||
```
|
||||
|
||||
<CodeOutputBlock lang="python">
|
||||
|
||||
```
|
||||
{'output_text': '\n\nThe president said that he wanted to honor Justice Breyer for his dedication to serving the country, his legacy of excellence, and his commitment to advancing liberty and justice, as well as for his support of the Equality Act and his commitment to protecting the rights of LGBTQ+ Americans. He also praised Justice Breyer for his role in helping to pass the Bipartisan Infrastructure Law, which he said would be the most sweeping investment to rebuild America in history and would help the country compete for the jobs of the 21st Century.'}
|
||||
```
|
||||
|
||||
</CodeOutputBlock>
|
||||
|
||||
**Intermediate Steps**
|
||||
|
||||
We can also return the intermediate steps for `refine` chains, should we want to inspect them. This is done with the `return_refine_steps` variable.
|
||||
|
||||
|
||||
```python
|
||||
chain = load_qa_chain(OpenAI(temperature=0), chain_type="refine", return_refine_steps=True)
|
||||
```
|
||||
|
||||
|
||||
```python
|
||||
chain({"input_documents": docs, "question": query}, return_only_outputs=True)
|
||||
```
|
||||
|
||||
<CodeOutputBlock lang="python">
|
||||
|
||||
```
|
||||
{'intermediate_steps': ['\nThe president said that he wanted to honor Justice Breyer for his dedication to serving the country and his legacy of excellence.',
|
||||
'\nThe president said that he wanted to honor Justice Breyer for his dedication to serving the country, his legacy of excellence, and his commitment to advancing liberty and justice.',
|
||||
'\n\nThe president said that he wanted to honor Justice Breyer for his dedication to serving the country, his legacy of excellence, and his commitment to advancing liberty and justice, as well as for his support of the Equality Act and his commitment to protecting the rights of LGBTQ+ Americans.',
|
||||
'\n\nThe president said that he wanted to honor Justice Breyer for his dedication to serving the country, his legacy of excellence, and his commitment to advancing liberty and justice, as well as for his support of the Equality Act and his commitment to protecting the rights of LGBTQ+ Americans. He also praised Justice Breyer for his role in helping to pass the Bipartisan Infrastructure Law, which is the most sweeping investment to rebuild America in history.'],
|
||||
'output_text': '\n\nThe president said that he wanted to honor Justice Breyer for his dedication to serving the country, his legacy of excellence, and his commitment to advancing liberty and justice, as well as for his support of the Equality Act and his commitment to protecting the rights of LGBTQ+ Americans. He also praised Justice Breyer for his role in helping to pass the Bipartisan Infrastructure Law, which is the most sweeping investment to rebuild America in history.'}
|
||||
```
|
||||
|
||||
</CodeOutputBlock>
|
||||
|
||||
**Custom Prompts**
|
||||
|
||||
You can also use your own prompts with this chain. In this example, we will respond in Italian.
|
||||
|
||||
|
||||
```python
|
||||
refine_prompt_template = (
|
||||
"The original question is as follows: {question}\n"
|
||||
"We have provided an existing answer: {existing_answer}\n"
|
||||
"We have the opportunity to refine the existing answer"
|
||||
"(only if needed) with some more context below.\n"
|
||||
"------------\n"
|
||||
"{context_str}\n"
|
||||
"------------\n"
|
||||
"Given the new context, refine the original answer to better "
|
||||
"answer the question. "
|
||||
"If the context isn't useful, return the original answer. Reply in Italian."
|
||||
)
|
||||
refine_prompt = PromptTemplate(
|
||||
input_variables=["question", "existing_answer", "context_str"],
|
||||
template=refine_prompt_template,
|
||||
)
|
||||
|
||||
|
||||
initial_qa_template = (
|
||||
"Context information is below. \n"
|
||||
"---------------------\n"
|
||||
"{context_str}"
|
||||
"\n---------------------\n"
|
||||
"Given the context information and not prior knowledge, "
|
||||
"answer the question: {question}\nYour answer should be in Italian.\n"
|
||||
)
|
||||
initial_qa_prompt = PromptTemplate(
|
||||
input_variables=["context_str", "question"], template=initial_qa_template
|
||||
)
|
||||
chain = load_qa_chain(OpenAI(temperature=0), chain_type="refine", return_refine_steps=True,
|
||||
question_prompt=initial_qa_prompt, refine_prompt=refine_prompt)
|
||||
chain({"input_documents": docs, "question": query}, return_only_outputs=True)
|
||||
```
|
||||
|
||||
<CodeOutputBlock lang="python">
|
||||
|
||||
```
|
||||
{'intermediate_steps': ['\nIl presidente ha detto che Justice Breyer ha dedicato la sua vita al servizio di questo paese e ha reso omaggio al suo servizio.',
|
||||
"\nIl presidente ha detto che Justice Breyer ha dedicato la sua vita al servizio di questo paese, ha reso omaggio al suo servizio e ha sostenuto la nomina di una top litigatrice in pratica privata, un ex difensore pubblico federale e una famiglia di insegnanti e agenti di polizia delle scuole pubbliche. Ha anche sottolineato l'importanza di avanzare la libertà e la giustizia attraverso la sicurezza delle frontiere e la risoluzione del sistema di immigrazione.",
|
||||
"\nIl presidente ha detto che Justice Breyer ha dedicato la sua vita al servizio di questo paese, ha reso omaggio al suo servizio e ha sostenuto la nomina di una top litigatrice in pratica privata, un ex difensore pubblico federale e una famiglia di insegnanti e agenti di polizia delle scuole pubbliche. Ha anche sottolineato l'importanza di avanzare la libertà e la giustizia attraverso la sicurezza delle frontiere, la risoluzione del sistema di immigrazione, la protezione degli americani LGBTQ+ e l'approvazione dell'Equality Act. Ha inoltre sottolineato l'importanza di lavorare insieme per sconfiggere l'epidemia di oppiacei.",
|
||||
"\n\nIl presidente ha detto che Justice Breyer ha dedicato la sua vita al servizio di questo paese, ha reso omaggio al suo servizio e ha sostenuto la nomina di una top litigatrice in pratica privata, un ex difensore pubblico federale e una famiglia di insegnanti e agenti di polizia delle scuole pubbliche. Ha anche sottolineato l'importanza di avanzare la libertà e la giustizia attraverso la sicurezza delle frontiere, la risoluzione del sistema di immigrazione, la protezione degli americani LGBTQ+ e l'approvazione dell'Equality Act. Ha inoltre sottolineato l'importanza di lavorare insieme per sconfiggere l'epidemia di oppiacei e per investire in America, educare gli americani, far crescere la forza lavoro e costruire l'economia dal"],
|
||||
'output_text': "\n\nIl presidente ha detto che Justice Breyer ha dedicato la sua vita al servizio di questo paese, ha reso omaggio al suo servizio e ha sostenuto la nomina di una top litigatrice in pratica privata, un ex difensore pubblico federale e una famiglia di insegnanti e agenti di polizia delle scuole pubbliche. Ha anche sottolineato l'importanza di avanzare la libertà e la giustizia attraverso la sicurezza delle frontiere, la risoluzione del sistema di immigrazione, la protezione degli americani LGBTQ+ e l'approvazione dell'Equality Act. Ha inoltre sottolineato l'importanza di lavorare insieme per sconfiggere l'epidemia di oppiacei e per investire in America, educare gli americani, far crescere la forza lavoro e costruire l'economia dal"}
|
||||
```
|
||||
|
||||
</CodeOutputBlock>
|
||||
|
||||
## The `map-rerank` Chain
|
||||
|
||||
This sections shows results of using the `map-rerank` Chain to do question answering with sources.
|
||||
|
||||
|
||||
```python
|
||||
chain = load_qa_chain(OpenAI(temperature=0), chain_type="map_rerank", return_intermediate_steps=True)
|
||||
```
|
||||
|
||||
|
||||
```python
|
||||
query = "What did the president say about Justice Breyer"
|
||||
results = chain({"input_documents": docs, "question": query}, return_only_outputs=True)
|
||||
```
|
||||
|
||||
|
||||
```python
|
||||
results["output_text"]
|
||||
```
|
||||
|
||||
<CodeOutputBlock lang="python">
|
||||
|
||||
```
|
||||
' The President thanked Justice Breyer for his service and honored him for dedicating his life to serve the country.'
|
||||
```
|
||||
|
||||
</CodeOutputBlock>
|
||||
|
||||
|
||||
```python
|
||||
results["intermediate_steps"]
|
||||
```
|
||||
|
||||
<CodeOutputBlock lang="python">
|
||||
|
||||
```
|
||||
[{'answer': ' The President thanked Justice Breyer for his service and honored him for dedicating his life to serve the country.',
|
||||
'score': '100'},
|
||||
{'answer': ' This document does not answer the question', 'score': '0'},
|
||||
{'answer': ' This document does not answer the question', 'score': '0'},
|
||||
{'answer': ' This document does not answer the question', 'score': '0'}]
|
||||
```
|
||||
|
||||
</CodeOutputBlock>
|
||||
|
||||
**Custom Prompts**
|
||||
|
||||
You can also use your own prompts with this chain. In this example, we will respond in Italian.
|
||||
|
||||
|
||||
```python
|
||||
from langchain.output_parsers import RegexParser
|
||||
|
||||
output_parser = RegexParser(
|
||||
regex=r"(.*?)\nScore: (.*)",
|
||||
output_keys=["answer", "score"],
|
||||
)
|
||||
|
||||
prompt_template = """Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.
|
||||
|
||||
In addition to giving an answer, also return a score of how fully it answered the user's question. This should be in the following format:
|
||||
|
||||
Question: [question here]
|
||||
Helpful Answer In Italian: [answer here]
|
||||
Score: [score between 0 and 100]
|
||||
|
||||
Begin!
|
||||
|
||||
Context:
|
||||
---------
|
||||
{context}
|
||||
---------
|
||||
Question: {question}
|
||||
Helpful Answer In Italian:"""
|
||||
PROMPT = PromptTemplate(
|
||||
template=prompt_template,
|
||||
input_variables=["context", "question"],
|
||||
output_parser=output_parser,
|
||||
)
|
||||
|
||||
chain = load_qa_chain(OpenAI(temperature=0), chain_type="map_rerank", return_intermediate_steps=True, prompt=PROMPT)
|
||||
query = "What did the president say about Justice Breyer"
|
||||
chain({"input_documents": docs, "question": query}, return_only_outputs=True)
|
||||
```
|
||||
|
||||
<CodeOutputBlock lang="python">
|
||||
|
||||
```
|
||||
{'intermediate_steps': [{'answer': ' Il presidente ha detto che Justice Breyer ha dedicato la sua vita a servire questo paese.',
|
||||
'score': '100'},
|
||||
{'answer': ' Il presidente non ha detto nulla sulla Giustizia Breyer.',
|
||||
'score': '100'},
|
||||
{'answer': ' Non so.', 'score': '0'},
|
||||
{'answer': ' Non so.', 'score': '0'}],
|
||||
'output_text': ' Il presidente ha detto che Justice Breyer ha dedicato la sua vita a servire questo paese.'}
|
||||
```
|
||||
|
||||
</CodeOutputBlock>
|
||||
Loading…
Add table
Add a link
Reference in a new issue