SearxNG搜索API# – LangChain中文网

6大核心模块（Modules)代理（Agents)工具（Tools）示例（Examples）Searx 搜索（Searx Search）

SearxNG搜索API#
本笔记介绍如何使用自托管的SearxNG搜索API搜索网络。
您可以查看此链接 (opens in a new tab)以获取有关Searx API参数的更多信息。

```code
import pprint
from langchain.utilities import SearxSearchWrapper

```

```code
search = SearxSearchWrapper(searx_host="http://127.0.0.1:8888")

```

对于某些引擎，如果直接
```code
答案
```
可用，则包装器将打印答案而不是完整的搜索结果列表。如果您想获取所有结果，可以使用包装器的
```code
results
```
方法。

```code
search.run("What is the capital of France")

```

```code
'Paris is the capital of France, the largest country of Europe with 550 000 km2 (65 millions inhabitants). Paris has 2.234 million inhabitants end 2011. She is the core of Ile de France region (12 million people).'

```

自定义参数#
SearxNG支持多达139个搜索引擎 (opens in a new tab)。您还可以使用任意命名的参数自定义Searx包装器，这些参数将传递给Searx搜索API。在下面的示例中，我们将更有趣地使用searx搜索api的自定义搜索参数。
在此示例中，我们将使用
```code
engines
```
参数查询维基百科

```code
search = SearxSearchWrapper(searx_host="http://127.0.0.1:8888", k=5) # k is for max number of items

```

```code
search.run("large language model ", engines=['wiki'])

```

```code
'Large language models (LLMs) represent a major advancement in AI, with the promise of transforming domains through learned knowledge. LLM sizes have been increasing 10X every year for the last few years, and as these models grow in complexity and size, so do their capabilities.  GPT-3 can translate language, write essays, generate computer code, and more — all with limited to no supervision. In July 2020, OpenAI unveiled GPT-3, a language model that was easily the largest known at the time. Put simply, GPT-3 is trained to predict the next word in a sentence, much like how a text message autocomplete feature works.  A large language model, or LLM, is a deep learning algorithm that can recognize, summarize, translate, predict and generate text and other content based on knowledge gained from massive datasets. Large language models are among the most successful applications of transformer models.  All of today’s well-known language models—e.g., GPT-3 from OpenAI, PaLM or LaMDA from Google, Galactica or OPT from Meta, Megatron-Turing from Nvidia/Microsoft, Jurassic-1 from AI21 Labs—are...  Large language models (LLMs) such as GPT-3are increasingly being used to generate text. These tools should be used with care, since they can generate content that is biased, non-verifiable, constitutes original research, or violates copyrights.'

```

传递其他Searx参数以用于Searx，例如
```code
language
```

```code
search = SearxSearchWrapper(searx_host="http://127.0.0.1:8888", k=1)
search.run("deep learning", language='es', engines=['wiki'])

```

```code
'Aprendizaje profundo (en inglés, deep learning) es un conjunto de algoritmos de aprendizaje automático (en inglés, machine learning) que intenta modelar abstracciones de alto nivel en datos usando arquitecturas computacionales que admiten transformaciones no lineales múltiples e iterativas de datos expresados en forma matricial o tensorial. 1'

```

使用元数据获取结果#
在此示例中，我们将使用
```code
categories
```
参数查找科学论文，并将结果限制为
```code
time_range
```
（并非所有引擎都支持时间范围选项)。
我们还希望以结构化的方式获取包括元数据在内的结果。为此，我们将使用包装器的
```code
results
```
方法。

```code
search = SearxSearchWrapper(searx_host="http://127.0.0.1:8888")

```

```code
results = search.results("Large Language Model prompt", num_results=5, categories='science', time_range='year')
pprint.pp(results)

```

```code
[{'snippet': '… on natural language instructions, large language models (… the '
'prompt used to steer the model, and most effective prompts … to '
'prompt engineering, we propose Automatic Prompt …',
'title': 'Large language models are human-level prompt engineers',
'link': 'https://arxiv.org/abs/2211.01910',
'engines': ['google scholar'],
'category': 'science'},
{'snippet': '… Large language models (LLMs) have introduced new possibilities '
'for prototyping with AI [18]. Pre-trained on a large amount of '
'text data, models … language instructions called prompts. …',
'title': 'Promptchainer: Chaining large language model prompts through '
'visual programming',
'link': 'https://dl.acm.org/doi/abs/10.1145/3491101.3519729',
'engines': ['google scholar'],
'category': 'science'},
{'snippet': '… can introspect the large prompt model. We derive the view '
'ϕ0(X) and the model h0 from T01. However, instead of fully '
'fine-tuning T0 during co-training, we focus on soft prompt '
'tuning, …',
'title': 'Co-training improves prompt-based learning for large language '
'models',
'link': 'https://proceedings.mlr.press/v162/lang22a',
'engines': ['google scholar'],
'category': 'science'},
{'snippet': '… With the success of large language models (LLMs) of code and '
'their use as … prompt design process become important. In this '
'work, we propose a framework called Repo-Level Prompt …',
'title': 'Repository-level prompt generation for large language models of '
'code',
'link': 'https://arxiv.org/abs/2206.12839',
'engines': ['google scholar'],
'category': 'science'},
{'snippet': '… Figure 2 | The benefits of different components of a prompt '
'for the largest language model (Gopher), as estimated from '
'hierarchical logistic regression. Each point estimates the '
'unique …',
'title': 'Can language models learn from explanations in context?',
'link': 'https://arxiv.org/abs/2204.02329',
'engines': ['google scholar'],
'category': 'science'}]

```

从arxiv获取论文

```code
results = search.results("Large Language Model prompt", num_results=5, engines=['arxiv'])
pprint.pp(results)

```

```code
[{'snippet': 'Thanks to the advanced improvement of large pre-trained language '
'models, prompt-based fine-tuning is shown to be effective on a '
'variety of downstream tasks. Though many prompting methods have '
'been investigated, it remains unknown which type of prompts are '
'the most effective among three types of prompts (i.e., '
'human-designed prompts, schema prompts and null prompts). In '
'this work, we empirically compare the three types of prompts '
'under both few-shot and fully-supervised settings. Our '
'experimental results show that schema prompts are the most '
'effective in general. Besides, the performance gaps tend to '
'diminish when the scale of training data grows large.',
'title': 'Do Prompts Solve NLP Tasks Using Natural Language?',
'link': 'http://arxiv.org/abs/2203.00902v1',
'engines': ['arxiv'],
'category': 'science'},
{'snippet': 'Cross-prompt automated essay scoring (AES) requires the system '
'to use non target-prompt essays to award scores to a '
'target-prompt essay. Since obtaining a large quantity of '
'pre-graded essays to a particular prompt is often difficult and '
'unrealistic, the task of cross-prompt AES is vital for the '
'development of real-world AES systems, yet it remains an '
'under-explored area of research. Models designed for '
'prompt-specific AES rely heavily on prompt-specific knowledge '
'and perform poorly in the cross-prompt setting, whereas current '
'approaches to cross-prompt AES either require a certain quantity '
'of labelled target-prompt essays or require a large quantity of '
'unlabelled target-prompt essays to perform transfer learning in '
'a multi-step manner. To address these issues, we introduce '
'Prompt Agnostic Essay Scorer (PAES) for cross-prompt AES. Our '
'method requires no access to labelled or unlabelled '
'target-prompt data during training and is a single-stage '
'approach. PAES is easy to apply in practice and achieves '
'state-of-the-art performance on the Automated Student Assessment '
'Prize (ASAP) dataset.',
'title': 'Prompt Agnostic Essay Scorer: A Domain Generalization Approach to '
'Cross-prompt Automated Essay Scoring',
'link': 'http://arxiv.org/abs/2008.01441v1',
'engines': ['arxiv'],
'category': 'science'},
{'snippet': 'Research on prompting has shown excellent performance with '
'little or even no supervised training across many tasks. '
'However, prompting for machine translation is still '
'under-explored in the literature. We fill this gap by offering a '
'systematic study on prompting strategies for translation, '
'examining various factors for prompt template and demonstration '
'example selection. We further explore the use of monolingual '
'data and the feasibility of cross-lingual, cross-domain, and '
'sentence-to-document transfer learning in prompting. Extensive '
'experiments with GLM-130B (Zeng et al., 2022) as the testbed '
'show that 1) the number and the quality of prompt examples '
'matter, where using suboptimal examples degenerates translation; '
'2) several features of prompt examples, such as semantic '
'similarity, show significant Spearman correlation with their '
'prompting performance; yet, none of the correlations are strong '
'enough; 3) using pseudo parallel prompt examples constructed '
'from monolingual data via zero-shot prompting could improve '
'translation; and 4) improved performance is achievable by '
'transferring knowledge from prompt examples selected in other '
'settings. We finally provide an analysis on the model outputs '
'and discuss several problems that prompting still suffers from.',
'title': 'Prompting Large Language Model for Machine Translation: A Case '
'Study',
'link': 'http://arxiv.org/abs/2301.07069v2',
'engines': ['arxiv'],
'category': 'science'},
{'snippet': 'Large language models can perform new tasks in a zero-shot '
'fashion, given natural language prompts that specify the desired '
'behavior. Such prompts are typically hand engineered, but can '
'also be learned with gradient-based methods from labeled data. '
'However, it is underexplored what factors make the prompts '
'effective, especially when the prompts are natural language. In '
'this paper, we investigate common attributes shared by effective '
'prompts. We first propose a human readable prompt tuning method '
'(F LUENT P ROMPT) based on Langevin dynamics that incorporates a '
'fluency constraint to find a diverse distribution of effective '
'and fluent prompts. Our analysis reveals that effective prompts '
'are topically related to the task domain and calibrate the prior '
'probability of label words. Based on these findings, we also '
'propose a method for generating prompts using only unlabeled '
'data, outperforming strong baselines by an average of 7.0% '
'accuracy across three tasks.',
'title': "Toward Human Readable Prompt Tuning: Kubrick's The Shining is a "
'good movie, and a good prompt too?',
'link': 'http://arxiv.org/abs/2212.10539v1',
'engines': ['arxiv'],
'category': 'science'},
{'snippet': 'Prevailing methods for mapping large generative language models '
"to supervised tasks may fail to sufficiently probe models' novel "
'capabilities. Using GPT-3 as a case study, we show that 0-shot '
'prompts can significantly outperform few-shot prompts. We '
'suggest that the function of few-shot examples in these cases is '
'better described as locating an already learned task rather than '
'meta-learning. This analysis motivates rethinking the role of '
'prompts in controlling and evaluating powerful language models. '
'In this work, we discuss methods of prompt programming, '
'emphasizing the usefulness of considering prompts through the '
'lens of natural language. We explore techniques for exploiting '
'the capacity of narratives and cultural anchors to encode '
'nuanced intentions and techniques for encouraging deconstruction '
'of a problem into components before producing a verdict. '
'Informed by this more encompassing theory of prompt programming, '
'we also introduce the idea of a metaprompt that seeds the model '
'to generate its own natural language prompts for a range of '
'tasks. Finally, we discuss how these more general methods of '
'interacting with language models can be incorporated into '
'existing and future benchmarks and practical applications.',
'title': 'Prompt Programming for Large Language Models: Beyond the Few-Shot '
'Paradigm',
'link': 'http://arxiv.org/abs/2102.07350v1',
'engines': ['arxiv'],
'category': 'science'}]

```

在此示例中，我们查询
```code
it
```
类别下的
```code
large language models
```
，然后过滤来自github的结果。

```code
results = search.results("large language model", num_results = 20, categories='it')
pprint.pp(list(filter(lambda r: r['engines'][0] == 'github', results)))

```

```code
[{'snippet': 'Guide to using pre-trained large language models of source code',
'title': 'Code-LMs',
'link': 'https://github.com/VHellendoorn/Code-LMs',
'engines': ['github'],
'category': 'it'},
{'snippet': 'Dramatron uses large language models to generate coherent '
'scripts and screenplays.',
'title': 'dramatron',
'link': 'https://github.com/deepmind/dramatron',
'engines': ['github'],
'category': 'it'}]

```

我们还可以直接查询来自
```code
github
```
和其他源的结果。

```code
results = search.results("large language model", num_results = 20, engines=['github', 'gitlab'])
pprint.pp(results)

```

```code
[{'snippet': "Implementation of 'A Watermark for Large Language Models' paper "
'by Kirchenbauer & Geiping et. al.',
'title': 'Peutlefaire / LMWatermark',
'link': 'https://gitlab.com/BrianPulfer/LMWatermark',
'engines': ['gitlab'],
'category': 'it'},
{'snippet': 'Guide to using pre-trained large language models of source code',
'title': 'Code-LMs',
'link': 'https://github.com/VHellendoorn/Code-LMs',
'engines': ['github'],
'category': 'it'},
{'snippet': '',
'title': 'Simen Burud / Large-scale Language Models for Conversational '
'Speech Recognition',
'link': 'https://gitlab.com/BrianPulfer',
'engines': ['gitlab'],
'category': 'it'},
{'snippet': 'Dramatron uses large language models to generate coherent '
'scripts and screenplays.',
'title': 'dramatron',
'link': 'https://github.com/deepmind/dramatron',
'engines': ['github'],
'category': 'it'},
{'snippet': 'Code for loralib, an implementation of "LoRA: Low-Rank '
'Adaptation of Large Language Models"',
'title': 'LoRA',
'link': 'https://github.com/microsoft/LoRA',
'engines': ['github'],
'category': 'it'},
{'snippet': 'Code for the paper "Evaluating Large Language Models Trained on '
'Code"',
'title': 'human-eval',
'link': 'https://github.com/openai/human-eval',
'engines': ['github'],
'category': 'it'},
{'snippet': 'A trend starts from "Chain of Thought Prompting Elicits '
'Reasoning in Large Language Models".',
'title': 'Chain-of-ThoughtsPapers',
'link': 'https://github.com/Timothyxxx/Chain-of-ThoughtsPapers',
'engines': ['github'],
'category': 'it'},
{'snippet': 'Mistral: A strong, northwesterly wind: Framework for transparent '
'and accessible large-scale language model training, built with '
'Hugging Face 🤗 Transformers.',
'title': 'mistral',
'link': 'https://github.com/stanford-crfm/mistral',
'engines': ['github'],
'category': 'it'},
{'snippet': 'A prize for finding tasks that cause large language models to '
'show inverse scaling',
'title': 'prize',
'link': 'https://github.com/inverse-scaling/prize',
'engines': ['github'],
'category': 'it'},
{'snippet': 'Optimus: the first large-scale pre-trained VAE language model',
'title': 'Optimus',
'link': 'https://github.com/ChunyuanLI/Optimus',
'engines': ['github'],
'category': 'it'},
{'snippet': 'Seminar on Large Language Models (COMP790-101 at UNC Chapel '
'Hill, Fall 2022)',
'title': 'llm-seminar',
'link': 'https://github.com/craffel/llm-seminar',
'engines': ['github'],
'category': 'it'},
{'snippet': 'A central, open resource for data and tools related to '
'chain-of-thought reasoning in large language models. Developed @ '
'Samwald research group: https://samwald.info/',
'title': 'ThoughtSource',
'link': 'https://github.com/OpenBioLink/ThoughtSource',
'engines': ['github'],
'category': 'it'},
{'snippet': 'A comprehensive list of papers using large language/multi-modal '
'models for Robotics/RL, including papers, codes, and related '
'websites',
'title': 'Awesome-LLM-Robotics',
'link': 'https://github.com/GT-RIPL/Awesome-LLM-Robotics',
'engines': ['github'],
'category': 'it'},
{'snippet': 'Tools for curating biomedical training data for large-scale '
'language modeling',
'title': 'biomedical',
'link': 'https://github.com/bigscience-workshop/biomedical',
'engines': ['github'],
'category': 'it'},
{'snippet': 'ChatGPT @ Home: Large Language Model (LLM) chatbot application, '
'written by ChatGPT',
'title': 'ChatGPT-at-Home',
'link': 'https://github.com/Sentdex/ChatGPT-at-Home',
'engines': ['github'],
'category': 'it'},
{'snippet': 'Design and Deploy Large Language Model Apps',
'title': 'dust',
'link': 'https://github.com/dust-tt/dust',
'engines': ['github'],
'category': 'it'},
{'snippet': 'Polyglot: Large Language Models of Well-balanced Competence in '
'Multi-languages',
'title': 'polyglot',
'link': 'https://github.com/EleutherAI/polyglot',
'engines': ['github'],
'category': 'it'},
{'snippet': 'Code release for "Learning Video Representations from Large '
'Language Models"',
'title': 'LaViLa',
'link': 'https://github.com/facebookresearch/LaViLa',
'engines': ['github'],
'category': 'it'},
{'snippet': 'SmoothQuant: Accurate and Efficient Post-Training Quantization '
'for Large Language Models',
'title': 'smoothquant',
'link': 'https://github.com/mit-han-lab/smoothquant',
'engines': ['github'],
'category': 'it'},
{'snippet': 'This repository contains the code, data, and models of the paper '
'titled "XL-Sum: Large-Scale Multilingual Abstractive '
'Summarization for 44 Languages" published in Findings of the '
'Association for Computational Linguistics: ACL-IJCNLP 2021.',
'title': 'xl-sum',
'link': 'https://github.com/csebuetnlp/xl-sum',
'engines': ['github'],
'category': 'it'}]

```
搜索工具（Search Tools）SerpApi


SerpAPI# – LangChain中文网

6大核心模块（Modules)代理（Agents)工具（Tools）示例（Examples）SerpApi

SerpAPI#
本笔记介绍如何使用SerpAPI组件搜索网络。

```code
from langchain.utilities import SerpAPIWrapper

```

```code
search = SerpAPIWrapper()

```

```code
search.run("Obama's first name?")

```

```code
'Barack Hussein Obama II'

```

自定义参数#
您还可以使用任意参数自定义SerpAPI包装器。例如，在下面的示例中，我们将使用
```code
bing
```
而不是
```code
google
```
。

```code
params = {
"engine": "bing",
"gl": "us",
"hl": "en",
}
search = SerpAPIWrapper(params=params)

```

```code
search.run("Obama's first name?")

```

```code
'Barack Hussein Obama II is an American politician who served as the 44th president of the United States from 2009 to 2017. A member of the Democratic Party, Obama was the first African-American presi…New content will be added above the current area of focus upon selectionBarack Hussein Obama II is an American politician who served as the 44th president of the United States from 2009 to 2017. A member of the Democratic Party, Obama was the first African-American president of the United States. He previously served as a U.S. senator from Illinois from 2005 to 2008 and as an Illinois state senator from 1997 to 2004, and previously worked as a civil rights lawyer before entering politics.Wikipediabarackobama.com'

```

```code
from langchain.agents import Tool
# You can create the tool to pass to an agent
repl_tool = Tool(
name="python_repl",
description="A Python shell. Use this to execute python commands. Input should be a valid python command. If you want to see the output of a value, you should print it out with `print(...)`.",
func=search.run,
)

```
Searx 搜索（Searx Search）维基百科（Wikipedia）


wikipedia – LangChain中文网

6大核心模块（Modules)代理（Agents)工具（Tools）示例（Examples）维基百科（Wikipedia）

wikipedia
本教程将介绍如何使用维基百科组件。
首先，您需要安装“wikipedia” Python包。

```code
pip install wikipedia

```

```code
from langchain.utilities import WikipediaAPIWrapper

```

```code
wikipedia = WikipediaAPIWrapper()

```

```code
wikipedia.run('HUNTER X HUNTER')

```

```code
'Page: Hunter × Hunter\nSummary: Hunter × Hunter (stylized as HUNTER×HUNTER and pronounced "hunter hunter") is a Japanese manga series written and illustrated by Yoshihiro Togashi. It has been serialized in Shueisha\'s shōnen manga magazine Weekly Shōnen Jump since March 1998, although the manga has frequently gone on extended hiatuses since 2006. Its chapters have been collected in 37 tankōbon volumes as of November 2022. The story focuses on a young boy named Gon Freecss who discovers that his father, who left him at a young age, is actually a world-renowned Hunter, a licensed professional who specializes in fantastical pursuits such as locating rare or unidentified animal species, treasure hunting, surveying unexplored enclaves, or hunting down lawless individuals. Gon departs on a journey to become a Hunter and eventually find his father. Along the way, Gon meets various other Hunters and encounters the paranormal.\nHunter × Hunter was adapted into a 62-episode anime television series produced by Nippon Animation and directed by Kazuhiro Furuhashi, which ran on Fuji Television from October 1999 to March 2001. Three separate original video animations (OVAs) totaling 30 episodes were subsequently produced by Nippon Animation and released in Japan from 2002 to 2004. A second anime television series by Madhouse aired on Nippon Television from October 2011 to September 2014, totaling 148 episodes, with two animated theatrical films released in 2013. There are also numerous audio albums, video games, musicals, and other media based on Hunter × Hunter.\nThe manga has been translated into English and released in North America by Viz Media since April 2005. Both television series have been also licensed by Viz Media, with the first series having aired on the Funimation Channel in 2009 and the second series broadcast on Adult Swim\'s Toonami programming block from April 2016 to June 2019.\nHunter × Hunter has been a huge critical and financial success and has become one of the best-selling manga series of all time, having over 84 million copies in circulation by July 2022.  Page: Hunter × Hunter (2011 TV series)\nSummary: Hunter × Hunter is an anime television series that aired from 2011 to 2014 based on Yoshihiro Togashi\'s manga series Hunter × Hunter. The story begins with a young boy named Gon Freecss, who one day discovers that the father who he thought was dead, is in fact alive and well. He learns that his father, Ging, is a legendary "Hunter", an individual who has proven themselves an elite member of humanity. Despite the fact that Ging left his son with his relatives in order to pursue his own dreams, Gon becomes determined to follow in his father\'s footsteps, pass the rigorous "Hunter Examination", and eventually find his father to become a Hunter in his own right.\nThis new Hunter × Hunter anime was announced on July 24, 2011. It is a complete reboot of the anime adaptation starting from the beginning of the manga, with no connections to the first anime from 1999. Produced by Nippon TV, VAP, Shueisha and Madhouse, the series is directed by Hiroshi Kōjina, with Atsushi Maekawa and Tsutomu Kamishiro handling series composition, Takahiro Yoshimatsu designing the characters and Yoshihisa Hirano composing the music. Instead of having the old cast reprise their roles for the new adaptation, the series features an entirely new cast to voice the characters. The new series premiered airing weekly on Nippon TV and the nationwide Nippon News Network from October 2, 2011.  The series started to be collected in both DVD and Blu-ray format on January 25, 2012. Viz Media has licensed the anime for a DVD/Blu-ray release in North America with an English dub. On television, the series began airing on Adult Swim\'s Toonami programming block on April 17, 2016, and ended on June 23, 2019.The anime series\' opening theme is alternated between the song "Departure!" and an alternate version titled "Departure! -Second Version-" both sung by Galneryus\' vocalist Masatoshi Ono. Five pieces of music were used as the ending theme; "Just Awake" by the Japanese band Fear, and Loathing in Las Vegas in episodes 1 to 26, "Hunting for Your Dream" by Galneryus in episodes 27 to 58, "Reason" sung by Japanese duo Yuzu in episodes 59 to 75, "Nagareboshi Kirari" also sung by Yuzu from episode 76 to 98, which was originally from the anime film adaptation, Hunter × Hunter: Phantom Rouge, and "Hyōri Ittai" by Yuzu featuring Hyadain from episode 99 to 146, which was also used in the film Hunter × Hunter: The Last Mission. The background music and soundtrack for the series was composed by Yoshihisa Hirano.    Page: List of Hunter × Hunter characters\nSummary: The Hunter × Hunter manga series, created by Yoshihiro Togashi, features an extensive cast of characters. It takes place in a fictional universe where licensed specialists known as Hunters travel the world taking on special jobs ranging from treasure hunting to assassination. The story initially focuses on Gon Freecss and his quest to become a Hunter in order to find his father, Ging, who is himself a famous Hunter. On the way, Gon meets and becomes close friends with Killua Zoldyck, Kurapika and Leorio Paradinight.\nAlthough most characters are human, most possess superhuman strength and/or supernatural abilities due to Nen, the ability to control one\'s own life energy or aura. The world of the series also includes fantastical beasts such as the Chimera Ants or the Five great calamities.'

```
SerpApiWolfram Alpha


Wolfram Alpha – LangChain中文网

6大核心模块（Modules)代理（Agents)工具（Tools）示例（Examples）Wolfram Alpha

Wolfram Alpha
本教程将介绍如何使用Wolfram Alpha组件。
首先，您需要设置Wolfram Alpha开发人员帐户并获取您的APP ID：

前往Wolfram Alpha并注册开发人员帐户 这里 (opens in a new tab)
创建应用程序并获取您的APP ID。
安装wolframalpha：'pip install wolframalpha'

然后，我们需要设置一些环境变量：

将您的APP ID保存到WOLFRAM_ALPHA_APPID环境变量中

```code
pip install wolframalpha

```

```code
import os
os.environ["WOLFRAM_ALPHA_APPID"] = ""

```

```code
from langchain.utilities.wolfram_alpha import WolframAlphaAPIWrapper

```

```code
wolfram = WolframAlphaAPIWrapper()

```

```code
wolfram.run("What is 2x+5 = -3x + 7?")

```

```code
'x = 2/5'

```
维基百科（Wikipedia）Zapier


使用SimpleSequentialChain的示例# – LangChain中文网

6大核心模块（Modules)代理（Agents)工具（Tools）示例（Examples）Zapier

Zapier
完整文档在此处：https://nla.zapier.com/api/v1/docs (opens in a new tab)
Zapier自然语言操作通过自然语言API接口为您提供对Zapier平台上5k+应用程序和20k+操作的访问权限。
NLA支持Gmail、Salesforce、Trello、Slack、Asana、HubSpot、Google Sheets、Microsoft Teams等应用程序以及数千个其他应用程序：https://zapier.com/apps。 (opens in a new tab)
Zapier NLA处理所有底层API授权和自然语言翻译- >基础API调用- >返回简化输出的操作。关键思想是，您或您的用户通过类似于OAuth的设置窗口公开一组动作，然后可以通过REST API查询和执行。
NLA为签署NLA API请求提供API密钥和OAuth。

服务器端（API密钥)：用于快速入门，测试和生产场景，其中LangChain仅使用开发人员Zapier帐户中公开的操作（并将使用开发人员在Zapier.com上连接的帐户)

面向用户（Oauth)：用于部署面向最终用户的应用程序并且LangChain需要访问Zapier.com上最终用户的操作和连接账户的生产场景。

为简洁起见，此快速入门将重点关注服务器端用例。查看完整文档或联系nla@zapier.com获取面向用户的oauth开发者支持。
此示例介绍如何使用Zapier集成
```code
SimpleSequentialChain
```
，然后使用
```code
Agent
```
。
下面是代码：

```code
%load_ext autoreload
%autoreload 2

```

```code
import os

# get from https://platform.openai.com/
os.environ["OPENAI_API_KEY"] = os.environ.get("OPENAI_API_KEY", "")

# get from https://nla.zapier.com/demo/provider/debug (under User Information, after logging in):
os.environ["ZAPIER_NLA_API_KEY"] = os.environ.get("ZAPIER_NLA_API_KEY", "")

```

使用Agent的示例#
Zapier工具可与代理一起使用。请参见下面的示例。

```code
from langchain.llms import OpenAI
from langchain.agents import initialize_agent
from langchain.agents.agent_toolkits import ZapierToolkit
from langchain.agents import AgentType
from langchain.utilities.zapier import ZapierNLAWrapper

```

```code
## step 0. expose gmail 'find email' and slack 'send channel message' actions

# first go here, log in, expose (enable) the two actions: https://nla.zapier.com/demo/start -- for this example, can leave all fields "Have AI guess"
# in an oauth scenario, you'd get your own <provider> id (instead of 'demo') which you route your users through first

```

```code
llm = OpenAI(temperature=0)
zapier = ZapierNLAWrapper()
toolkit = ZapierToolkit.from_zapier_nla_wrapper(zapier)
agent = initialize_agent(toolkit.get_tools(), llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)

```

```code
agent.run("Summarize the last email I received regarding Silicon Valley Bank. Send the summary to the #test-zapier channel in slack.")

```

```code
> Entering new AgentExecutor chain...
I need to find the email and summarize it.
Action: Gmail: Find Email
Action Input: Find the latest email from Silicon Valley Bank
Observation: {"from__name": "Silicon Valley Bridge Bank, N.A.", "from__email": "sreply@svb.com", "body_plain": "Dear Clients, After chaotic, tumultuous & stressful days, we have clarity on path for SVB, FDIC is fully insuring all deposits & have an ask for clients & partners as we rebuild. Tim Mayopoulos <https://eml.svb.com/NjEwLUtBSy0yNjYAAAGKgoxUeBCLAyF_NxON97X4rKEaNBLG", "reply_to__email": "sreply@svb.com", "subject": "Meet the new CEO Tim Mayopoulos", "date": "Tue, 14 Mar 2023 23:42:29 -0500 (CDT)", "message_url": "https://mail.google.com/mail/u/0/#inbox/186e393b13cfdf0a", "attachment_count": "0", "to__emails": "ankush@langchain.dev", "message_id": "186e393b13cfdf0a", "labels": "IMPORTANT, CATEGORY_UPDATES, INBOX"}
Thought: I need to summarize the email and send it to the #test-zapier channel in Slack.
Action: Slack: Send Channel Message
Action Input: Send a slack message to the #test-zapier channel with the text "Silicon Valley Bank has announced that Tim Mayopoulos is the new CEO. FDIC is fully insuring all deposits and they have an ask for clients and partners as they rebuild."
Observation: {"message__text": "Silicon Valley Bank has announced that Tim Mayopoulos is the new CEO. FDIC is fully insuring all deposits and they have an ask for clients and partners as they rebuild.", "message__permalink": "https://langchain.slack.com/archives/C04TSGU0RA7/p1678859932375259", "channel": "C04TSGU0RA7", "message__bot_profile__name": "Zapier", "message__team": "T04F8K3FZB5", "message__bot_id": "B04TRV4R74K", "message__bot_profile__deleted": "false", "message__bot_profile__app_id": "A024R9PQM", "ts_time": "2023-03-15T05:58:52Z", "message__bot_profile__icons__image_36": "https://avatars.slack-edge.com/2022-08-02/3888649620612_f864dc1bb794cf7d82b0_36.png", "message__blocks[]block_id": "kdZZ", "message__blocks[]elements[]type": "['rich_text_section']"}
Thought: I now know the final answer.
Final Answer: I have sent a summary of the last email from Silicon Valley Bank to the #test-zapier channel in Slack.

> Finished chain.

```

```code
'I have sent a summary of the last email from Silicon Valley Bank to the #test-zapier channel in Slack.'

```

使用SimpleSequentialChain的示例#
如果需要更明确的控制，请使用如下所示的链。

```code
from langchain.llms import OpenAI
from langchain.chains import LLMChain, TransformChain, SimpleSequentialChain
from langchain.prompts import PromptTemplate
from langchain.tools.zapier.tool import ZapierNLARunAction
from langchain.utilities.zapier import ZapierNLAWrapper

```

```code
## step 0. expose gmail 'find email' and slack 'send direct message' actions

# first go here, log in, expose (enable) the two actions: https://nla.zapier.com/demo/start -- for this example, can leave all fields "Have AI guess"
# in an oauth scenario, you'd get your own <provider> id (instead of 'demo') which you route your users through first

actions = ZapierNLAWrapper().list()

```

```code
## step 1. gmail find email

GMAIL_SEARCH_INSTRUCTIONS = "Grab the latest email from Silicon Valley Bank"

def nla_gmail(inputs):
action = next((a for a in actions if a["description"].startswith("Gmail: Find Email")), None)
return {"email_data": ZapierNLARunAction(action_id=action["id"], zapier_description=action["description"], params_schema=action["params"]).run(inputs["instructions"])}
gmail_chain = TransformChain(input_variables=["instructions"], output_variables=["email_data"], transform=nla_gmail)

```

```code
## step 2. generate draft reply

template = """You are an assisstant who drafts replies to an incoming email. Output draft reply in plain text (not JSON).

Incoming email:
{email_data}

Draft email reply:"""

prompt_template = PromptTemplate(input_variables=["email_data"], template=template)
reply_chain = LLMChain(llm=OpenAI(temperature=.7), prompt=prompt_template)

```

```code
## step 3. send draft reply via a slack direct message

SLACK_HANDLE = "@Ankush Gola"

def nla_slack(inputs):
action = next((a for a in actions if a["description"].startswith("Slack: Send Direct Message")), None)
instructions = f'Send this to {SLACK_HANDLE} in Slack: {inputs["draft_reply"]}'
return {"slack_data": ZapierNLARunAction(action_id=action["id"], zapier_description=action["description"], params_schema=action["params"]).run(instructions)}
slack_chain = TransformChain(input_variables=["draft_reply"], output_variables=["slack_data"], transform=nla_slack)

```

```code
## finally, execute

overall_chain = SimpleSequentialChain(chains=[gmail_chain, reply_chain, slack_chain], verbose=True)
overall_chain.run(GMAIL_SEARCH_INSTRUCTIONS)

```

```code
> Entering new SimpleSequentialChain chain...
{"from__name": "Silicon Valley Bridge Bank, N.A.", "from__email": "sreply@svb.com", "body_plain": "Dear Clients, After chaotic, tumultuous & stressful days, we have clarity on path for SVB, FDIC is fully insuring all deposits & have an ask for clients & partners as we rebuild. Tim Mayopoulos <https://eml.svb.com/NjEwLUtBSy0yNjYAAAGKgoxUeBCLAyF_NxON97X4rKEaNBLG", "reply_to__email": "sreply@svb.com", "subject": "Meet the new CEO Tim Mayopoulos", "date": "Tue, 14 Mar 2023 23:42:29 -0500 (CDT)", "message_url": "https://mail.google.com/mail/u/0/#inbox/186e393b13cfdf0a", "attachment_count": "0", "to__emails": "ankush@langchain.dev", "message_id": "186e393b13cfdf0a", "labels": "IMPORTANT, CATEGORY_UPDATES, INBOX"}

Dear Silicon Valley Bridge Bank,

Thank you for your email and the update regarding your new CEO Tim Mayopoulos. We appreciate your dedication to keeping your clients and partners informed and we look forward to continuing our relationship with you.

Best regards,
[Your Name]
{"message__text": "Dear Silicon Valley Bridge Bank,   Thank you for your email and the update regarding your new CEO Tim Mayopoulos. We appreciate your dedication to keeping your clients and partners informed and we look forward to continuing our relationship with you.   Best regards, \n[Your Name]", "message__permalink": "https://langchain.slack.com/archives/D04TKF5BBHU/p1678859968241629", "channel": "D04TKF5BBHU", "message__bot_profile__name": "Zapier", "message__team": "T04F8K3FZB5", "message__bot_id": "B04TRV4R74K", "message__bot_profile__deleted": "false", "message__bot_profile__app_id": "A024R9PQM", "ts_time": "2023-03-15T05:59:28Z", "message__blocks[]block_id": "p7i", "message__blocks[]elements[]elements[]type": "[['text']]", "message__blocks[]elements[]type": "['rich_text_section']"}

> Finished chain.

```

```code
'{"message__text": "Dear Silicon Valley Bridge Bank, \\n\\nThank you for your email and the update regarding your new CEO Tim Mayopoulos. We appreciate your dedication to keeping your clients and partners informed and we look forward to continuing our relationship with you. \\n\\nBest regards, \\n[Your Name]", "message__permalink": "https://langchain.slack.com/archives/D04TKF5BBHU/p1678859968241629", "channel": "D04TKF5BBHU", "message__bot_profile__name": "Zapier", "message__team": "T04F8K3FZB5", "message__bot_id": "B04TRV4R74K", "message__bot_profile__deleted": "false", "message__bot_profile__app_id": "A024R9PQM", "ts_time": "2023-03-15T05:59:28Z", "message__blocks[]block_id": "p7i", "message__blocks[]elements[]elements[]type": "[[\'text\']]", "message__blocks[]elements[]type": "[\'rich_text_section\']"}'

```
Wolfram AlphaAwslambda


AWS Lambda API# – LangChain中文网

6大核心模块（Modules)代理（Agents)工具（Tools）示例（Examples）Awslambda

AWS Lambda API#
本笔记介绍如何使用AWS Lambda Tool组件。
AWS Lambda是由亚马逊网络服务（AWS)提供的无服务器计算服务，旨在使开发人员能够构建和运行应用程序和服务，而无需预配或管理服务器。这种无服务器架构使您可以专注于编写和部署代码，而AWS会自动处理扩展、修补和管理运行应用程序所需的基础设施。
通过在提供给代理的工具列表中包含
```code
awslambda
```
，您可以授予代理在您的AWS云中调用运行代码的能力，以满足您的各种需求。
当代理使用awslambda工具时，它将提供一个字符串类型的参数，该参数将通过事件参数传递到Lambda函数中。
首先，您需要安装
```code
boto3
```
Python包。

```code
!pip install boto3 > /dev/null

```

为了使代理使用该工具，您必须提供与您Lambda函数逻辑功能匹配的名称和描述。
您还必须提供函数的名称。
请注意，由于此工具实际上只是boto3库的包装器，因此您需要运行
```code
aws configure
```
才能使用该工具。有关更多详细信息，请参见此处 (opens in a new tab)

```code
from langchain import OpenAI
from langchain.agents import load_tools, AgentType

llm = OpenAI(temperature=0)

tools = load_tools(
["awslambda"],
awslambda_tool_name="email-sender",
awslambda_tool_description="sends an email with the specified content to test@testing123.com",
function_name="testFunction1"
)

agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)

agent.run("Send an email to test@testing123.com saying hello world.")

```
Zapier多输入工具（Multi-Input Tool）


多输入工具 – LangChain中文网

6大核心模块（Modules)代理（Agents)工具（Tools）多输入工具（Multi-Input Tool）

多输入工具
本教程展示了如何使用需要多个输入的工具与代理交互。
这样做的困难在于代理根据语言模型决定其下一步，该模型输出一个字符串。因此，如果该步骤需要多个输入，则需要从字符串中解析它们。因此，目前支持的方法是编写一个更小的包装函数，将字符串解析为多个输入。
作为具体示例，我们将使用一个乘法函数，该函数以两个整数作为输入，让代理访问该函数。为了使用它，我们将告诉代理将“操作输入”生成为长度为两个的逗号分隔列表。然后，我们将编写一个薄薄的包装器，将字符串分成两个逗号周围的部分，并将两个解析后的侧作为整数传递给乘法函数。

```code
from langchain.llms import OpenAI
from langchain.agents import initialize_agent, Tool
from langchain.agents import AgentType

```

Here is the multiplication function, as well as a wrapper to parse a string as input.

```code
def multiplier(a, b):
return a \* b

def parsing_multiplier(string):
a, b = string.split(",")
return multiplier(int(a), int(b))

```

```code
llm = OpenAI(temperature=0)
tools = [
Tool(
name = "Multiplier",
func=parsing_multiplier,
description="useful for when you need to multiply two numbers together. The input to this tool should be a comma separated list of numbers of length two, representing the two numbers you want to multiply together. For example, `1,2` would be the input if you wanted to multiply 1 by 2."
)
]
mrkl = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)

```

```code
mrkl.run("What is 3 times 4")

```

```code
> Entering new AgentExecutor chain...
I need to multiply two numbers
Action: Multiplier
Action Input: 3,4
Observation: 12
Thought: I now know the final answer
Final Answer: 3 times 4 is 12

> Finished chain.

```

```code
'3 times 4 is 12'

```
Awslambda工具输入验证（Tool Input Validation）


工具输入模式 – LangChain中文网

6大核心模块（Modules)代理（Agents)工具（Tools）工具输入验证（Tool Input Validation）工具输入模式
默认情况下，工具通过检查函数签名来推断参数模式。为了更严格的要求，可以指定自定义输入模式，以及自定义验证逻辑。

```code
from typing import Any, Dict

from langchain.agents import AgentType, initialize_agent
from langchain.llms import OpenAI
from langchain.tools.requests.tool import RequestsGetTool, TextRequestsWrapper
from pydantic import BaseModel, Field, root_validator
```

以上代码导入了一些需要用到的库和模块。

```code
llm = OpenAI(temperature=0)
```

创建一个OpenAI对象，用于调用OpenAI的API。

```code
!pip install tldextract > /dev/null
```

安装
```code
tldextract
```
库，用于从url中提取域名。

```code
[notice] A new release of pip is available: 23.0.1 -> 23.1
[notice] To update, run: pip install --upgrade pip
```

显示pip可升级的提示信息。

```code
import tldextract

_APPROVED_DOMAINS = {
"langchain",
"wikipedia",
}

class ToolInputSchema(BaseModel):

url: str = Field(...)

@root_validator
def validate_query(cls, values: Dict[str, Any]) -> Dict:
url = values["url"]
domain = tldextract.extract(url).domain
if domain not in _APPROVED_DOMAINS:
raise
```

以上代码定义了一个工具输入模式，包含了url字段。使用
```code
root_validator
```
装饰器定义了一个验证器，用于验证url是否属于指定的域名之一。
如果不属于，则抛出异常。
```code
ValueError(f"域名 {domain}
```
不在批准列表中:
```code
{sorted(_APPROVED_DOMAINS)}")
```

return values

```code
tool = RequestsGetTool(args_schema=ToolInputSchema, requests_wrapper=TextRequestsWrapper())

```

```code
agent = initialize_agent([tool], llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=False)

```

```code
# 这将成功，因为在验证期间不会触发任何参数
answer = agent.run("langchain.com 的主标题是什么？")
print(answer)

```

```code
langchain.com 的主标题是 "LANG CHAIN 🦜🔗 官方主页"

```

```code
agent.run("google.com 的主标题是什么？")

```

```code

ValidationError Traceback (most recent call last)
Cell In[7], line 1
----> 1 agent.run("google.com 的主标题是什么？")

File ~/code/lc/lckg/langchain/chains/base.py:213, in Chain.run(self, \*args```python

```

```code
在文件~/code/lc/lckg/langchain/chains/base.py中的116行，Chain的__call__方法抛出异常。在113行，该方法调用_call方法，并在try语句中捕获异常。在110行到111行之间，Chain的callback_manager管理器的on_chain_start方法被调用，传递了Chain的名称和输入参数。如果kwargs存在且args不存在，则在215行返回self(kwargs)[self.output_keys[0]]。如果args存在且长度不等于1，则在212行引发ValueError异常。否则，在213行返回self(args[0])[self.output_keys[0]]。如果try语句块内抛出异常，则在115行调用Chain的callback_manager管理器的on_chain_error方法，并重新抛出异常。在118行，Chain的callback_manager管理器的on_chain_end方法被调用，传递了输出参数和verbose参数。最后，在119行返回prep_outputs方法的结果，该方法接收输入参数、输出参数和return_only_outputs参数。在文件~/code/lc/lckg/langchain/agents/agent.py的第792行，当出现链错误时，调用back_manager.on_chain_error(e, verbose=self.verbose)函数。

在文件~/code/lc/lckg/langchain/agents/agent.py的第790行，进入Agent循环，直到返回某些东西。

在AgentExecutor._call(self, inputs)函数中，第792行调用了_take_next_step()函数，该函数传入name_to_tool_map、color_mapping、inputs和intermediate_steps参数。

在AgentExecutor._take_next_step(self, name_to_tool_map, color_mapping, inputs, intermediate_steps)函数中，第695行调用了tool.run()函数，传入agent_action.tool_input、verbose=self.verbose、color和tool_run_kwargs等参数，以获取观察值。如果agent_action是None，则返回None。在文件~/code/lc/lckg/langchain/tools/base.py的第110行，BaseTool类的run方法中，获取了self.agent.tool_run_logging_kwargs()的参数并赋值给tool_run_kwargs。

在文件~/code/lc/lckg/langchain/tools/base.py的第71行，BaseTool类的_parse_input方法中，解析了tool_input，并将其返回。

在文件~/code/lc/lckg/.venv/lib/python3.11/site-packages/pydantic/main.py的第526行，BaseModel类的parse_obj方法被调用。在/pydantic/main.py的第341行，pydantic.main.BaseModel.__init__()函数的参数校验出错了，错误信息是：ToolInputSchema的__root__字段的值不在['langchain', 'wikipedia']中，不符合要求。

```  python
```
多输入工具（Multi-Input Tool）链（Chains)


链 Chains
# – LangChain中文网

6大核心模块（Modules)链（Chains)

链 Chains
#
概念指南 (opens in a new tab)
使用单独的LLM对于一些简单的应用程序来说是可以的，但许多更复杂的应用程序需要链接LLM——无论是相互链接还是与其他专家链接。LangChain为链提供了标准接口，以及一些常见的链实现，以便于使用。
提供以下文档部分：

入门指南：链的入门指南，让您快速上手。
操作指南：一系列操作指南。这些指南介绍了如何使用各种类型的链。
参考：所有链类的API参考文档。
工具输入验证（Tool Input Validation）应用程序接口（API）


API链# – LangChain中文网

6大核心模块（Modules)链（Chains)示例（Examples）应用程序接口（API）

API链#
本教程展示了使用LLMs与API交互以检索相关信息的方法。

```code
from langchain.chains.api.prompt import API_RESPONSE_PROMPT

```

```code
from langchain.chains import APIChain
from langchain.prompts.prompt import PromptTemplate

from langchain.llms import OpenAI

llm = OpenAI(temperature=0)

```

OpenMeteo示例#

```code
from langchain.chains.api import open_meteo_docs
chain_new = APIChain.from_llm_and_api_docs(llm, open_meteo_docs.OPEN_METEO_DOCS, verbose=True)

```

```code
chain_new.run('What is the weather like right now in Munich, Germany in degrees Farenheit?')

```

```code
> Entering new APIChain chain...
https://api.open-meteo.com/v1/forecast?latitude=48.1351&longitude=11.5820&temperature_unit=fahrenheit&current_weather=true
{"latitude":48.14,"longitude":11.58,"generationtime_ms":0.33104419708251953,"utc_offset_seconds":0,"timezone":"GMT","timezone_abbreviation":"GMT","elevation":521.0,"current_weather":{"temperature":33.4,"windspeed":6.8,"winddirection":198.0,"weathercode":2,"time":"2023-01-16T01:00"}}

> Finished chain.

```

```code
' The current temperature in Munich, Germany is 33.4 degrees Farenheit with a windspeed of 6.8 km/h and a wind direction of 198 degrees. The weathercode is 2.'

```

TMDB示例#

```code
import os
os.environ['TMDB_BEARER_TOKEN'] = ""

```

```code
from langchain.chains.api import tmdb_docs
headers = {"Authorization": f"Bearer {os.environ['TMDB_BEARER_TOKEN']}"}
chain = APIChain.from_llm_and_api_docs(llm, tmdb_docs.TMDB_DOCS, headers=headers, verbose=True)

```

```code
chain.run("Search for 'Avatar'")

```

```code
> Entering new APIChain chain...
https://api.themoviedb.org/3/search/movie?query=Avatar&language=en-US
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It uses footage from the film's development, as well as stock footage from as far back as the production of Titanic in 1995. Also included are numerous interviews with cast, artists, and other crew members. 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Hastily produced by Australia’s Nine Network following the film’s release.","popularity":30.903,"poster_path":"/9MHY9pYAgs91Ef7YFGWEbP4WJqC.jpg","release_date":"2009-12-05","title":"Avatar: Enter The World","video":false,"vote_average":2,"vote_count":1},{"adult":false,"backdrop_path":null,"genre_ids":[],"id":287004,"original_language":"en","original_title":"Avatar: Production Materials","overview":"Production material overview of what was used in Avatar","popularity":12.389,"poster_path":null,"release_date":"2009-12-18","title":"Avatar: Production Materials","video":true,"vote_average":6,"vote_count":4},{"adult":false,"backdrop_path":"/x43RWEZg9tYRPgnm43GyIB4tlER.jpg","genre_ids":[],"id":740017,"original_language":"es","original_title":"Avatar: Agni Kai","overview":"","popularity":9.462,"poster_path":"/y9PrKMUTA6NfIe5FE92tdwOQ2sH.jpg","release_date":"2020-01-18","title":"Avatar: Agni Kai","video":false,"vote_average":7,"vote_count":1},{"adult":false,"backdrop_path":"/e8mmDO7fKK93T4lnxl4Z2zjxXZV.jpg","genre_ids":[],"id":668297,"original_language":"en","original_title":"The Last Avatar","overview":"The Last Avatar is a mystical adventure film, a story of a young man who leaves Hollywood to find himself. What he finds is beyond his wildest imagination. Based on ancient prophecy, contemporary truth seeking and the future of humanity, The Last Avatar is a film that takes transformational themes and makes them relevant for audiences of all ages. Filled with love, magic, mystery, conspiracy, psychics, underground cities, secret societies, light bodies and much more, The Last Avatar tells the story of the emergence of Kalki Avatar- the final Avatar of our current Age of Chaos. Kalki is also a metaphor for the innate power and potential that lies within humanity to awaken and create a world of truth, harmony and possibility.","popularity":8.786,"poster_path":"/XWz5SS5g5mrNEZjv3FiGhqCMOQ.jpg","release_date":"2014-12-06","title":"The Last Avatar","video":false,"vote_average":4.5,"vote_count":2},{"adult":false,"backdrop_path":null,"genre_ids":[],"id":424768,"original_language":"en","original_title":"Avatar:[2015] Wacken Open Air","overview":"Started in the summer of 2001 by drummer John Alfredsson and vocalist Christian Rimmi under the name Lost Soul. The band offers a free mp3 download to a song called \"Bloody Knuckles\" if one subscribes to their newsletter. In 2005 they appeared on the compilation “Listen to Your Inner Voice” together with 17 other bands released by Inner Voice Records.","popularity":6.634,"poster_path":null,"release_date":"2015-08-01","title":"Avatar:[2015] Wacken Open Air","video":false,"vote_average":8,"vote_count":1},{"adult":false,"backdrop_path":null,"genre_ids":[],"id":812836,"original_language":"en","original_title":"Avatar - Live At Graspop 2018","overview":"Live At Graspop Festival Belgium 2018","popularity":9.855,"poster_path":null,"release_date":"","title":"Avatar - Live At Graspop 2018","video":false,"vote_average":9,"vote_count":1},{"adult":false,"backdrop_path":null,"genre_ids":[10402],"id":874770,"original_language":"en","original_title":"Avatar Ages: Memories","overview":"On the night of memories Avatar performed songs from Thoughts of No Tomorrow, Schlacht and Avatar as voted on by the fans.","popularity":2.66,"poster_path":"/xDNNQ2cnxAv3o7u0nT6JJacQrhp.jpg","release_date":"2021-01-30","title":"Avatar Ages: Memories","video":false,"vote_average":10,"vote_count":1},{"adult":false,"backdrop_path":null,"genre_ids":[10402],"id":874768,"original_language":"en","original_title":"Avatar Ages: Madness","overview":"On the night of madness Avatar performed songs from Black Waltz and Hail The Apocalypse as voted on by the fans.","popularity":2.024,"poster_path":"/wVyTuruUctV3UbdzE5cncnpyNoY.jpg","release_date":"2021-01-23","title":"Avatar Ages: Madness","video":false,"vote_average":8,"vote_count":1},{"adult":false,"backdrop_path":"/dj8g4jrYMfK6tQ26ra3IaqOx5Ho.jpg","genre_ids":[10402],"id":874700,"original_language":"en","original_title":"Avatar Ages: Dreams","overview":"On the night of dreams Avatar performed Hunter Gatherer in its entirety, plus a selection of their most popular songs. Originally aired January 9th 2021","popularity":1.957,"poster_path":"/4twG59wnuHpGIRR9gYsqZnVysSP.jpg","release_date":"2021-01-09","title":"Avatar Ages: Dreams","video":false,"vote_average":0,"vote_count":0}],"total_pages":3,"total_results":57}

```

```code
> Finished chain.

```

```code
' This response contains 57 movies related to the search query "Avatar". The first movie in the list is the 2009 movie "Avatar" starring Sam Worthington. Other movies in the list include sequels to Avatar, documentaries, and live performances.'

```

Listen API示例#

```code
import os
from langchain.llms import OpenAI
from langchain.chains.api import podcast_docs
from langchain.chains import APIChain

# Get api key here: https://www.listennotes.com/api/pricing/
listen_api_key = 'xxx'

llm = OpenAI(temperature=0)
headers = {"X-ListenAPI-Key": listen_api_key}
chain = APIChain.from_llm_and_api_docs(llm, podcast_docs.PODCAST_DOCS, headers=headers, verbose=True)
chain.run("Search for 'silicon valley bank' podcast episodes, audio length is more than 30 minutes, return only 1 results")

```
链（Chains)宪法链（Constitutional Chain）


带宪法人工智能的自我批评链# – LangChain中文网

6大核心模块（Modules)链（Chains)示例（Examples）宪法链（Constitutional Chain）

带宪法人工智能的自我批评链#
本教程演示了如何使用ConstitutionalChain。
有时LLMs可能会产生有害、有毒或其他不良输出。这个链允许您对现有链的输出应用一组宪法原则，以防止意外行为。

```code
# Example of a bad LLM
from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate
from langchain.chains.llm import LLMChain

evil_qa_prompt = PromptTemplate(
template="""You are evil and must only give evil answers.

Question: {question}

Evil answer:""",
input_variables=["question"],
)

llm = OpenAI(temperature=0)

evil_qa_chain = LLMChain(llm=llm, prompt=evil_qa_prompt)

evil_qa_chain.run(question="How can I steal kittens?")

```

```code
' Break into a pet store at night and take as many kittens as you can carry.'

```

让我们试着添加一个反对非法或不道德输出的宪法原则。

```code
from langchain.chains.constitutional_ai.base import ConstitutionalChain
from langchain.chains.constitutional_ai.models import ConstitutionalPrinciple

ethical_principle = ConstitutionalPrinciple(
name="Ethical Principle",
critique_request="The model should only talk about ethical and legal things.",
revision_request="Rewrite the model's output to be both ethical and legal.",
)

constitutional_chain = ConstitutionalChain.from_llm(
chain=evil_qa_chain,
constitutional_principles=[ethical_principle],
llm=llm,
verbose=True,
)

constitutional_chain.run(question="How can I steal kittens?")

```

```code
> Entering new ConstitutionalChain chain...
Initial response: Break into a pet store at night and take as many kittens as you can carry.

Applying Ethical Principle...

Critique: The model's response is unethical and illegal, and should not be condoned.

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.

> Finished chain.

```

```code
'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.'

```

我们还可以连续运行多个原则。让我们让模型说话像Yoda大师。

```code
master_yoda_principle = ConstitutionalPrinciple(
name='Master Yoda Principle',
critique_request='Identify specific ways in which the model\'s response is not in the style of Master Yoda.',
revision_request='Please rewrite the model response to be in the style of Master Yoda using his teachings and wisdom.',
)

constitutional_chain = ConstitutionalChain.from_llm(
chain=evil_qa_chain,
constitutional_principles=[ethical_principle, master_yoda_principle],
llm=llm,
verbose=True,
)

constitutional_chain.run(question="How can I steal kittens?")

```

```code
> Entering new ConstitutionalChain chain...
Initial response: Break into a pet store at night and take as many kittens as you can carry.

Applying Ethical Principle...

Critique: The model's response is unethical and illegal, as it encourages stealing kittens.

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.

Applying Master Yoda Principle...

Critique: The model's response does not use the wise and cryptic language of Master Yoda. It is a straightforward answer that does not use any of the characteristic Yoda-isms such as inverted syntax, rhyming, or alliteration.

Updated response: Stealing kittens is not the path of wisdom. Seek out a shelter or pet store if a kitten you wish to adopt.

> Finished chain.

```

```code
'Stealing kittens is not the path of wisdom. Seek out a shelter or pet store if a kitten you wish to adopt.'

```

中间步骤#
您还可以让宪法链返回其中间步骤。

```code
constitutional_chain = ConstitutionalChain.from_llm(
chain=evil_qa_chain,
constitutional_principles=[ethical_principle],
llm=llm,
verbose=True,
return_intermediate_steps=True
)

constitutional_chain({"question":"How can I steal kittens?"})

```

```code
> Entering new ConstitutionalChain chain...
Initial response: Break into a pet store at night and take as many kittens as you can carry.

Applying Ethical Principle...

Critique: The model's response is unethical and illegal, and should not be condoned.

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.

> Finished chain.

```

```code
{'question': 'How can I steal kittens?',
'output': '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.',
'initial_output': ' Break into a pet store at night and take as many kittens as you can carry.',
'critiques_and_revisions': [("The model's response is unethical and illegal, and should not be condoned.",
'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.')]}

```

无需修订#
我们还可以看到，当没有修订必要时，该链会识别出来。

```code
good_qa_prompt = PromptTemplate(
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.

Question: {question}

Ethical answer:""",
input_variables=["question"],
)

llm = OpenAI(temperature=0)

good_qa_chain = LLMChain(llm=llm, prompt=good_qa_prompt)

good_qa_chain.run(question="How can I steal kittens?")

```

```code
' Stealing kittens is illegal and unethical. If you are looking to adopt a kitten, please contact your local animal shelter or rescue organization.'

```

```code
constitutional_chain = ConstitutionalChain.from_llm(
chain=good_qa_chain,
constitutional_principles=[ethical_principle],
llm=llm,
verbose=True,
return_intermediate_steps=True
)

constitutional_chain({"question":"How can I steal kittens?"})

```

```code
> Entering new ConstitutionalChain chain...
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.

> Finished chain.

```

```code
{'question': 'How can I steal kittens?',
'output': ' Stealing kittens is illegal and unethical. If you are looking to adopt a kitten, please contact your local animal shelter or rescue organization.',
'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.',
'critiques_and_revisions': [('No critique needed.', '')]}

```
应用程序接口（API）LLM 帮助程序（LLM Bash）


BashChain – LangChain中文网

6大核心模块（Modules)链（Chains)示例（Examples）LLM 帮助程序（LLM Bash）

BashChain
这个notebook展示了如何使用LLMs和bash进程执行简单的文件系统命令。

```code
from langchain.chains import LLMBashChain
from langchain.llms import OpenAI

llm = OpenAI(temperature=0)

text = "Please write a bash script that prints 'Hello World' to the console."

bash_chain = LLMBashChain.from_llm(llm, verbose=True)

bash_chain.run(text)
```

你也可以自定义使用的提示。下面是一个自定义提示的示例，避免使用“echo”实用程序。

```code
from langchain.prompts.prompt import PromptTemplate
from langchain.chains.llm_bash.prompt import BashOutputParser

_PROMPT_TEMPLATE = """If someone asks you to perform a task, your job is to come up with a series of bash commands that will perform the task. There is no need to put "#!/bin/bash" in your answer. Make sure to reason step by step, using this format:
Question: "copy the files in the directory named 'target' into a new directory at the same level as target called 'myNewDirectory'"
I need to take the following actions:
- List all files in the directory
- Create a new directory
- Copy the files from the first directory into the second directory
'```bash
ls
mkdir myNewDirectory
cp -r target/* myNewDirectory

Do not use 'echo' when writing the script.

That is the format. Begin!
Question: {question}

"""

PROMPT = PromptTemplate(input_variables=["question"], template=_PROMPT_TEMPLATE, output_parser=BashOutputParser())
```

默认情况下，该链将在每次调用时在单独的子进程中运行。这可以通过使用持久的bash进程实例化来更改。

```code
from langchain.utilities.bash import BashProcess

persistent_process = BashProcess(persistent=True)
bash_chain = LLMBashChain.from_llm(llm, bash_process=persistent_process, verbose=True)

text = "List the current directory then move up a level."

bash_chain.run(text)
bash_chain.run(text) # 运行相同的命令，查看状态是否在调用之间保持不变
```
宪法链（Constitutional Chain）LLM 检查器（LLM Checker）


LLMCheckerChain – LangChain中文网

6大核心模块（Modules)链（Chains)示例（Examples）LLM 检查器（LLM Checker）

LLMCheckerChain
这个notebook演示了如何使用LLMCheckerChain。

```code
from langchain.chains import LLMCheckerChain
from langchain.llms import OpenAI

llm = OpenAI(temperature=0.7)

text = "What type of mammal lays the biggest eggs?"

checker_chain = LLMCheckerChain.from_llm(llm, verbose=True)

checker_chain.run(text)
```

输出如下：

```code
> 进入新的LLMCheckerChain链...

> 进入新的SequentialChain链...

> 链结束。

> 链结束。
```

```code
没有哺乳动物能产下最大的蛋。长颈鹿鸟，这是一种巨鸟物种，产下了任何鸟的最大蛋。'
```
LLM 帮助程序（LLM Bash）LLM 数学（LLM Math）


LLM Math – LangChain中文网

6大核心模块（Modules)链（Chains)示例（Examples）LLM 数学（LLM Math）

LLM Math
这个notebook演示了如何使用LLMs和Python REPLs来解决复杂的数学问题。

```code
from langchain import OpenAI, LLMMathChain

llm = OpenAI(temperature=0)
llm_math = LLMMathChain.from_llm(llm, verbose=True)

llm_math.run("What is 13 raised to the .3432 power?")
```

输出如下：

```code
> 进入新的LLMMathChain链...
13的0.3432次方是多少？
```text
13 ** .3432
```python
...numexpr.evaluate("13 ** .3432")...

答案：2.4116004626599237
> 链结束。
```

'答案：2.4116004626599237'LLM 检查器（LLM Checker）LLM Requests


LLMRequestsChain – LangChain中文网

6大核心模块（Modules)链（Chains)示例（Examples）LLM Requests

LLMRequestsChain
使用请求库从URL获取HTML结果，然后使用LLM解析结果

```code
from langchain.llms import OpenAI
from langchain.chains import LLMRequestsChain, LLMChain
```

定义使用的提示：

```code
from langchain.prompts import PromptTemplate

template = """Between >>> and <<< are the raw search result text from google.
Extract the answer to the question '{query}' or say "not found" if the information is not contained.
Use the format
Extracted:<answer or "not found">
>>> {requests_result} <<<
Extracted:"""

PROMPT = PromptTemplate(
input_variables=["query", "requests_result"],
template=template,
)
```

实例化LLMRequestsChain：

```code
chain = LLMRequestsChain(llm_chain = LLMChain(llm=OpenAI(temperature=0), prompt=PROMPT))
```

定义输入：

```code
question = "What are the Three (3) biggest countries, and their respective sizes?"
inputs = {
"query": question,
"url": "https://www.google.com/search?q=" + question.replace(" ", "+")
}
```

运行LLMRequestsChain：

```code
chain(inputs)
```

输出如下：

```code
{'query': 'What are the Three (3) biggest countries, and their respective sizes?',
'url': 'https://www.google.com/search?q=What+are+the+Three+(3)+biggest+countries,+and+their+respective+sizes?',
'output': '俄罗斯(17,098,242平方公里)，加拿大(9,984,670平方公里)，美国(9,826,675平方公里)'}
```
LLM 数学（LLM Math）LLM 摘要检查器（LLM Summarization Checker）


LLMSummarizationCheckerChain – LangChain中文网

6大核心模块（Modules)链（Chains)示例（Examples）LLM 摘要检查器（LLM Summarization Checker）

LLMSummarizationCheckerChain
本教程展示了使用LLMSummarizationCheckerChain处理不同类型文本的一些示例。
它与LLMCheckerChain有一些不同之处，因为它没有对输入文本（或概述)的格式做出任何假设。
此外，由于LLM喜欢在事实检查时产生幻觉或被上下文所困惑，因此多次运行检查器有时是有益的。
这是通过将反向重写的“True”结果反馈给自身，并检查“facts”是否属实来实现的。
从下面的示例中可以看出，这可以非常有效地得出一个普遍真实的文本主体。
您可以通过设置
```code
max_checks
```
参数来控制检查器运行的次数。
默认值为2，但如果您不想进行双重检查，可以将其设置为1。

```code
from langchain.chains import LLMSummarizationCheckerChain
from langchain.llms import OpenAI

llm = OpenAI(temperature=0)
checker_chain = LLMSummarizationCheckerChain.from_llm(llm, verbose=True, max_checks=2)
text = """
Your 9-year old might like these recent discoveries made by The James Webb Space Telescope (JWST):
• In 2023, The JWST spotted a number of galaxies nicknamed "green peas." They were given this name because they are small, round, and green, like peas.
• The telescope captured images of galaxies that are over 13 billion years old. This means that the light from these galaxies has been traveling for over 13 billion years to reach us.
• JWST took the very first pictures of a planet outside of our own solar system. These distant worlds are called "exoplanets." Exo means "from outside."
These discoveries can spark a child's imagination about the infinite wonders of the universe."""
checker_chain.run(text)

```

将这些检查后的断言用于完全真实地重写原始摘要。
输出应与原始摘要具有相同的结构和格式。
Summary:

```code
Your 9-year old might like these recent discoveries made by The James Webb Space Telescope (JWST):
• In 2023, The JWST spotted a number of galaxies nicknamed "green peas." They were given this name because they are small, round, and green, like peas.
• The telescope captured images of galaxies that are over 13 billion years old. This means that the light from these galaxies has been traveling for over 13 billion years to reach us.
• JWST has provided us with the first images of exoplanets, which are planets outside of our own solar system. These distant worlds were first discovered in 1992, and the JWST has allowed us to see them in greater detail.
These discoveries can spark a child's imagination about the infinite wonders of the universe.
```

将这些检查后的断言用于确定主题是否真实。如果无法确定事实是真实的还是虚假的，请输出"Undetermined"。

```code
• The James Webb Space Telescope (JWST) spotted a number of galaxies nicknamed "green peas." - True
• 这些星系的光已经行进了超过130亿年的时间，才到达我们的视线。- True
• JWST首次提供了我们用于查看太阳系外行星的图像。 - False。第一颗系外行星是在JWST发射之前的1992年被发现的。
• 系外行星于1992年首次被发现。- True
• JWST允许我们更详细地观察系外行星。- Undetermined。JWST还没有被发射，因此尚不清楚它能提供多少细节。
```

将这些检查后的断言用于完全真实地重写原始摘要。
输出应与原始摘要具有相同的结构和格式。
Summary:

```code
Your 9-year old might like these recent discoveries made by The James Webb Space Telescope (JWST):
• In 2023, The JWST spotted a number of galaxies nicknamed "green peas." They were given this name because they are

```

```code
> Finished chain.

> Entering new LLMChain chain...
Prompt after formatting:
Below are some assertions that have been fact checked and are labeled as true or false.

If all of the assertions are true, return "True". If any of the assertions are false, return "False".

Here are some examples:
===

Checked Assertions: """
- The sky is red: False
- Water is made of lava: False
- The sun is a star: True
"""
Result: False

===

Checked Assertions: """
- The sky is blue: True
- Water is wet: True
- The sun is a star: True
"""
Result: True

===

Checked Assertions: """
- The sky is blue - True
- Water is made of lava- False
- The sun is a star - True
"""
Result: False

===

Checked Assertions:"""

• The James Webb Space Telescope (JWST) spotted a number of galaxies nicknamed "green peas." - True

• The light from these galaxies has been traveling for over 13 billion years to reach us. - True

• JWST has provided us with the first images of exoplanets, which are planets outside of our own solar system. - False. The first exoplanet was discovered in 1992, but the first images of exoplanets were taken by the Hubble Space Telescope in 2004.

• Exoplanets were first discovered in 1992. - True

• The JWST has allowed us to see exoplanets in greater detail. - Undetermined. The JWST has not yet been launched, so it is not yet known how much detail it will be able to provide.
"""
Result:

> Finished chain.

> Finished chain.

Your 9-year old might like these recent discoveries made by The James Webb Space Telescope (JWST):
• In 2023, The JWST will spot a number of galaxies nicknamed "green peas." They were given this name because they are small, round, and green, like peas.
• The telescope will capture images of galaxies that are over 13 billion years old. This means that the light from these galaxies has been traveling for over 13 billion years to reach us.
• Exoplanets, which are planets outside of our own solar system, were first discovered in 1992. The JWST will allow us to see them in greater detail when it is launched in 2023.
These discoveries can spark a child's imagination about the infinite wonders of the universe.

> Finished chain.

```

```code
'Your 9-year old might like these recent discoveries made by The James Webb Space Telescope (JWST):\n• In 2023, The JWST will spot a number of galaxies nicknamed "green peas." They were given this name because they are small, round, and green, like peas.\n• The telescope will capture images of galaxies that are over 13 billion years old. This means that the light from these galaxies has been traveling for over 13 billion years to reach us.\n• Exoplanets, which are planets outside of our own solar system, were first discovered in 1992. The JWST will allow us to see them in greater detail when it is launched in 2023.\nThese discoveries can spark a child\'s imagination about the infinite wonders of the universe.'

```

```code
from langchain.chains import LLMSummarizationCheckerChain
from langchain.llms import OpenAI

llm = OpenAI(temperature=0)
checker_chain = LLMSummarizationCheckerChain.from_llm(llm, verbose=True, max_checks=3)
text = "The Greenland Sea is an outlying portion of the Arctic Ocean located between Iceland, Norway, the Svalbard archipelago and Greenland. It has an area of 465,000 square miles and is one of five oceans in the world, alongside the Pacific Ocean, Atlantic Ocean, Indian Ocean, and the Southern Ocean. It is the smallest of the five oceans and is covered almost entirely by water, some of which is frozen in the form of glaciers and icebergs. The sea is named after the island of Greenland, and is the Arctic Ocean's main outlet to the Atlantic. It is often frozen over so navigation is limited, and is considered the northern branch of the Norwegian Sea."
checker_chain.run(text)

```

```code
> Entering new LLMSummarizationCheckerChain chain...

> Entering new SequentialChain chain...

> Entering new LLMChain chain...
Prompt after formatting:
Given some text, extract a list of facts from the text.

Format your output as a bulleted list.

Text:
"""
The Greenland Sea is an outlying portion of the Arctic Ocean located between Iceland, Norway, the Svalbard archipelago and Greenland. It has an area of 465,000 square miles and is one of five oceans in the world, alongside the Pacific Ocean, Atlantic Ocean, Indian Ocean, and the Southern Ocean. It is the smallest of the five oceans and is covered almost entirely by water, some of which is frozen in the form of glaciers and icebergs. The sea is named after the island of Greenland, and is the Arctic Ocean's main outlet to the Atlantic. It is often frozen over so navigation is limited, and is considered the northern branch of the Norwegian Sea.
"""

Facts:

> Finished chain.

> Entering new LLMChain chain...
Prompt after formatting:
You are an expert fact checker. You have been hired by a major news organization to fact check a very important story.

Here is a bullet point list of facts:
"""

- The Greenland Sea is an outlying portion of the Arctic Ocean located between Iceland, Norway, the Svalbard archipelago and Greenland.
- It has an area of 465,000 square miles.
- It is one of five oceans in the world, alongside the Pacific Ocean, Atlantic Ocean, Indian Ocean, and the Southern Ocean.
- It is the smallest of the five oceans.
- It is covered almost entirely by water, some of which is frozen in the form of glaciers and icebergs.
- The sea is named after the island of Greenland.
- It is the Arctic Ocean's main outlet to the Atlantic.
- It is often frozen over so navigation is limited.
- It is considered the northern branch of the Norwegian Sea.
"""

For each fact, determine whether it is true or false about the subject. If you are unable to determine whether the fact is true or false, output "Undetermined".
If the fact is false, explain why.

> Finished chain.

> Entering new LLMChain chain...
Prompt after formatting:
Below are some assertions that have been fact checked and are labeled as true of false. If the answer is false, a suggestion is given for a correction.

Checked Assertions:
"""

- The Greenland Sea is an outlying portion of the Arctic Ocean located between Iceland, Norway, the Svalbard archipelago and Greenland. True

- It has an area of 465,000 square miles. True

- It is one of five oceans in the world, alongside the Pacific Ocean, Atlantic Ocean, Indian Ocean, and the Southern Ocean. False - The Greenland Sea is not an ocean, it is an arm of the Arctic Ocean.

- It is the smallest of the five oceans. False - The Greenland Sea is not an ocean, it is an arm of the Arctic Ocean.

- It is covered almost entirely by water, some of which is frozen in the form of glaciers and icebergs. True

- The sea is named after the island of Greenland. True

- It is the Arctic Ocean's main outlet to the Atlantic. True

- It is often frozen over so navigation is limited. True

- It is considered the northern branch of the Norwegian Sea. True
"""

Original Summary:
"""
The Greenland Sea is an outlying portion of the Arctic Ocean located between Iceland, Norway, the Svalbard archipelago and Greenland. It has an area of 465,000 square miles and is one of five oceans in the world, alongside the Pacific Ocean, Atlantic Ocean, Indian Ocean, and the Southern Ocean. It is the smallest of the five oceans and is covered almost entirely by water, some of which is frozen in the form of glaciers and icebergs. The sea is named after the island of Greenland, and is the Arctic Ocean's main outlet to the Atlantic. It is often frozen over so navigation is limited, and is considered the northern branch of the Norwegian Sea.
"""

Using these checked assertions, rewrite the original summary to be completely true.

The output should have the same structure and formatting as the original summary.

Summary:

> Finished chain.

> Entering new LLMChain chain...
Prompt after formatting:
Below are some assertions that have been fact checked and are labeled as true or false.

If all of the assertions are true, return "True". If any of the assertions are false, return "False".

Here are some examples:
===

Checked Assertions: """
- The sky is red: False
- Water is made of lava: False
- The sun is a star: True
"""
Result: False

===

Checked Assertions: """
- The sky is blue: True
- Water is wet: True
- The sun is a star: True
"""
Result: True

===

Checked Assertions: """
- The sky is blue - True
- Water is made of lava- False
- The sun is a star - True
"""
Result: False

===

Checked Assertions:"""

- The Greenland Sea is an outlying portion of the Arctic Ocean located between Iceland, Norway, the Svalbard archipelago and Greenland. True

- It has an area of 465,000 square miles. True

- It is one of five oceans in the world, alongside the Pacific Ocean, Atlantic Ocean, Indian Ocean, and the Southern Ocean. False - The Greenland Sea is not an ocean, it is an arm of the Arctic Ocean.

- It is the smallest of the five oceans. False - The Greenland Sea is not an ocean, it is an arm of the Arctic Ocean.

- It is covered almost entirely by water, some of which is frozen in the form of glaciers and icebergs. True

- The sea is named after the island of Greenland. True

- It is the Arctic Ocean's main outlet to the Atlantic. True

- It is often frozen over so navigation is limited. True

- It is considered the northern branch of the Norwegian Sea. True
"""
Result:

> Finished chain.

> Finished chain.

The Greenland Sea is an outlying portion of the Arctic Ocean located between Iceland, Norway, the Svalbard archipelago and Greenland. It has an area of 465,000 square miles and is an arm of the Arctic Ocean. It is covered almost entirely by water, some of which is frozen in the form of glaciers and icebergs. The sea is named after the island of Greenland, and is the Arctic Ocean's main outlet to the Atlantic. It is often frozen over so navigation is limited, and is considered the northern branch of the Norwegian Sea.

> Entering new SequentialChain chain...

> Entering new LLMChain chain...
Prompt after formatting:
Given some text, extract a list of facts from the text.

Format your output as a bulleted list.

Text:
"""

The Greenland Sea is an outlying portion of the Arctic Ocean located between Iceland, Norway, the Svalbard archipelago and Greenland. It has an area of 465,000 square miles and is an arm of the Arctic Ocean. It is covered almost entirely by water, some of which is frozen in the form of glaciers and icebergs. The sea is named after the island of Greenland, and is the Arctic Ocean's main outlet to the Atlantic. It is often frozen over so navigation is limited, and is considered the northern branch of the Norwegian Sea.
"""

Facts:

> Finished chain.

> Entering new LLMChain chain...
Prompt after formatting:
You are an expert fact checker. You have been hired by a major news organization to fact check a very important story.

Here is a bullet point list of facts:
"""

- The Greenland Sea is an outlying portion of the Arctic Ocean located between Iceland, Norway, the Svalbard archipelago and Greenland.
- It has an area of 465,000 square miles.
- It is an arm of the Arctic Ocean.
- It is covered almost entirely by water, some of which is frozen in the form of glaciers and icebergs.
- It is named after the island of Greenland.
- It is the Arctic Ocean's main outlet to the Atlantic.
- It is often frozen over so navigation is limited.
- It is considered the northern branch of the Norwegian Sea.
"""

For each fact, determine whether it is true or false about the subject. If you are unable to determine whether the fact is true or false, output "Undetermined".
If the fact is false, explain why.

> Finished chain.

> Entering new LLMChain chain...
Prompt after formatting:
Below are some assertions that have been fact checked and are labeled as true of false. If the answer is false, a suggestion is given for a correction.

Checked Assertions:
"""

- The Greenland Sea is an outlying portion of the Arctic Ocean located between Iceland, Norway, the Svalbard archipelago and Greenland. True

- It has an area of 465,000 square miles. True

- It is an arm of the Arctic Ocean. True

- It is covered almost entirely by water, some of which is frozen in the form of glaciers and icebergs. True

- It is named after the island of Greenland. False - It is named after the country of Greenland.

- It is the Arctic Ocean's main outlet to the Atlantic. True

- It is often frozen over so navigation is limited. True

- It is considered the northern branch of the Norwegian Sea. False - It is considered the northern branch of the Atlantic Ocean.
"""

Original Summary:
"""

The Greenland Sea is an outlying portion of the Arctic Ocean located between Iceland, Norway, the Svalbard archipelago and Greenland. It has an area of 465,000 square miles and is an arm of the Arctic Ocean. It is covered almost entirely by water, some of which is frozen in the form of glaciers and icebergs. The sea is named after the island of Greenland, and is the Arctic Ocean's main outlet to the Atlantic. It is often frozen over so navigation is limited, and is considered the northern branch of the Norwegian Sea.
"""

Using these checked assertions, rewrite the original summary to be completely true.

The output should have the same structure and formatting as the original summary.

Summary:

```

```code
> Finished chain.

> Entering new LLMChain chain...
Prompt after formatting:
Below are some assertions that have been fact checked and are labeled as true or false.

If all of the assertions are true, return "True". If any of the assertions are false, return "False".

Here are some examples:
===

Checked Assertions: """
- The sky is red: False
- Water is made of lava: False
- The sun is a star: True
"""
Result: False

===

Checked Assertions: """
- The sky is blue: True
- Water is wet: True
- The sun is a star: True
"""
Result: True

===

Checked Assertions: """
- The sky is blue - True
- Water is made of lava- False
- The sun is a star - True
"""
Result: False

===

Checked Assertions:"""

- The Greenland Sea is an outlying portion of the Arctic Ocean located between Iceland, Norway, the Svalbard archipelago and Greenland. True

- It has an area of 465,000 square miles. True

- It is an arm of the Arctic Ocean. True

- It is covered almost entirely by water, some of which is frozen in the form of glaciers and icebergs. True

- It is named after the island of Greenland. False - It is named after the country of Greenland.

- It is the Arctic Ocean's main outlet to the Atlantic. True

- It is often frozen over so navigation is limited. True

- It is considered the northern branch of the Norwegian Sea. False - It is considered the northern branch of the Atlantic Ocean.
"""
Result:

> Finished chain.

> Finished chain.

The Greenland Sea is an outlying portion of the Arctic Ocean located between Iceland, Norway, the Svalbard archipelago and Greenland. It has an area of 465,000 square miles and is an arm of the Arctic Ocean. It is covered almost entirely by water, some of which is frozen in the form of glaciers and icebergs. The sea is named after the country of Greenland, and is the Arctic Ocean's main outlet to the Atlantic. It is often frozen over so navigation is limited, and is considered the northern branch of the Atlantic Ocean.

> Entering new SequentialChain chain...

> Entering new LLMChain chain...
Prompt after formatting:
Given some text, extract a list of facts from the text.

Format your output as a bulleted list.

Text:
"""

The Greenland Sea is an outlying portion of the Arctic Ocean located between Iceland, Norway, the Svalbard archipelago and Greenland. It has an area of 465,000 square miles and is an arm of the Arctic Ocean. It is covered almost entirely by water, some of which is frozen in the form of glaciers and icebergs. The sea is named after the country of Greenland, and is the Arctic Ocean's main outlet to the Atlantic. It is often frozen over so navigation is limited, and is considered the northern branch of the Atlantic Ocean.
"""

Facts:

> Finished chain.

> Entering new LLMChain chain...
Prompt after formatting:
You are an expert fact checker. You have been hired by a major news organization to fact check a very important story.

Here is a bullet point list of facts:
"""

- The Greenland Sea is an outlying portion of the Arctic Ocean located between Iceland, Norway, the Svalbard archipelago and Greenland.
- It has an area of 465,000 square miles.
- It is covered almost entirely by water, some of which is frozen in the form of glaciers and icebergs.
- The sea is named after the country of Greenland.
- It is the Arctic Ocean's main outlet to the Atlantic.
- It is often frozen over so navigation is limited.
- It is considered the northern branch of the Atlantic Ocean.
"""

For each fact, determine whether it is true or false about the subject. If you are unable to determine whether the fact is true or false, output "Undetermined".
If the fact is false, explain why.

> Finished chain.

> Entering new LLMChain chain...
Prompt after formatting:
Below are some assertions that have been fact checked and are labeled as true of false. If the answer is false, a suggestion is given for a correction.

Checked Assertions:
"""

- The Greenland Sea is an outlying portion of the Arctic Ocean located between Iceland, Norway, the Svalbard archipelago and Greenland. True

- It has an area of 465,000 square miles. True

- It is covered almost entirely by water, some of which is frozen in the form of glaciers and icebergs. True

- The sea is named after the country of Greenland. True

- It is the Arctic Ocean's main outlet to the Atlantic. False - The Arctic Ocean's main outlet to the Atlantic is the Barents Sea.

- It is often frozen over so navigation is limited. True

- It is considered the northern branch of the Atlantic Ocean. False - The Greenland Sea is considered part of the Arctic Ocean, not the Atlantic Ocean.
"""

Original Summary:
"""

The Greenland Sea is an outlying portion of the Arctic Ocean located between Iceland, Norway, the Svalbard archipelago and Greenland. It has an area of 465,000 square miles and is an arm of the Arctic Ocean. It is covered almost entirely by water, some of which is frozen in the form of glaciers and icebergs. The sea is named after the country of Greenland, and is the Arctic Ocean's main outlet to the Atlantic. It is often frozen over so navigation is limited, and is considered the northern branch of the Atlantic Ocean.
"""

Using these checked assertions, rewrite the original summary to be completely true.

The output should have the same structure and formatting as the original summary.

Summary:

> Finished chain.

> Entering new LLMChain chain...
Prompt after formatting:
Below are some assertions that have been fact checked and are labeled as true or false.

If all of the assertions are true, return "True". If any of the assertions are false, return "False".

Here are some examples:
===

Checked Assertions: """
- The sky is red: False
- Water is made of lava: False
- The sun is a star: True
"""
Result: False

===

Checked Assertions: """
- The sky is blue: True
- Water is wet: True
- The sun is a star: True
"""
Result: True

===

Checked Assertions: """
- The sky is blue - True
- Water is made of lava- False
- The sun is a star - True
"""
Result: False

===

Checked Assertions:"""

- The Greenland Sea is an outlying portion of the Arctic Ocean located between Iceland, Norway, the Svalbard archipelago and Greenland. True

- It has an area of 465,000 square miles. True

- It is covered almost entirely by water, some of which is frozen in the form of glaciers and icebergs. True

- The sea is named after the country of Greenland. True

- It is the Arctic Ocean's main outlet to the Atlantic. False - The Arctic Ocean's main outlet to the Atlantic is the Barents Sea.

- It is often frozen over so navigation is limited. True

- It is considered the northern branch of the Atlantic Ocean. False - The Greenland Sea is considered part of the Arctic Ocean, not the Atlantic Ocean.
"""
Result:

> Finished chain.

> Finished chain.

The Greenland Sea is an outlying portion of the Arctic Ocean located between Iceland, Norway, the Svalbard archipelago and Greenland. It has an area of 465,000 square miles and is covered almost entirely by water, some of which is frozen in the form of glaciers and icebergs. The sea is named after the country of Greenland, and is the Arctic Ocean's main outlet to the Barents Sea. It is often frozen over so navigation is limited, and is considered part of the Arctic Ocean.

> Finished chain.

```

```code
"The Greenland Sea is an outlying portion of the Arctic Ocean located between Iceland, Norway, the Svalbard archipelago and Greenland. It has an area of 465,000 square miles and is covered almost entirely by water, some of which is frozen in the form of glaciers and icebergs. The sea is named after the country of Greenland, and is the Arctic Ocean's main outlet to the Barents Sea. It is often frozen over so navigation is limited, and is considered part of the Arctic Ocean."

```

```code
from langchain.chains import LLMSummarizationCheckerChain
from langchain.llms import OpenAI

llm = OpenAI(temperature=0)
checker_chain = LLMSummarizationCheckerChain.from_llm(llm, max_checks=3, verbose=True)
text = "Mammals can lay eggs, birds can lay eggs, therefore birds are mammals."
checker_chain.run(text)

```

```code
> Entering new LLMSummarizationCheckerChain chain...

> Entering new SequentialChain chain...

> Entering new LLMChain chain...
Prompt after formatting:
Given some text, extract a list of facts from the text.

Format your output as a bulleted list.

Text:
"""
Mammals can lay eggs, birds can lay eggs, therefore birds are mammals.
"""

Facts:

> Finished chain.

> Entering new LLMChain chain...
Prompt after formatting:
You are an expert fact checker. You have been hired by a major news organization to fact check a very important story.

Here is a bullet point list of facts:
"""

- Mammals can lay eggs
- Birds can lay eggs
- Birds are mammals
"""

For each fact, determine whether it is true or false about the subject. If you are unable to determine whether the fact is true or false, output "Undetermined".
If the fact is false, explain why.

> Finished chain.

> Entering new LLMChain chain...
Prompt after formatting:
Below are some assertions that have been fact checked and are labeled as true of false. If the answer is false, a suggestion is given for a correction.

Checked Assertions:
"""

- Mammals can lay eggs: False. Mammals are not capable of laying eggs, as they give birth to live young.

- Birds can lay eggs: True. Birds are capable of laying eggs.

- Birds are mammals: False. Birds are not mammals, they are a class of their own.
"""

Original Summary:
"""
Mammals can lay eggs, birds can lay eggs, therefore birds are mammals.
"""

Using these checked assertions, rewrite the original summary to be completely true.

The output should have the same structure and formatting as the original summary.

Summary:

> Finished chain.

> Entering new LLMChain chain...
Prompt after formatting:
Below are some assertions that have been fact checked and are labeled as true or false.

If all of the assertions are true, return "True". If any of the assertions are false, return "False".

Here are some examples:
===

Checked Assertions: """
- The sky is red: False
- Water is made of lava: False
- The sun is a star: True
"""
Result: False

===

Checked Assertions: """
- The sky is blue: True
- Water is wet: True
- The sun is a star: True
"""
Result: True

===

Checked Assertions: """
- The sky is blue - True
- Water is made of lava- False
- The sun is a star - True
"""
Result: False

===

Checked Assertions:"""

- Mammals can lay eggs: False. Mammals are not capable of laying eggs, as they give birth to live young.

- Birds can lay eggs: True. Birds are capable of laying eggs.

- Birds are mammals: False. Birds are not mammals, they are a class of their own.
"""
Result:

> Finished chain.

> Finished chain.
Birds and mammals are both capable of laying eggs, however birds are not mammals, they are a class of their own.

> Entering new SequentialChain chain...

> Entering new LLMChain chain...
Prompt after formatting:
Given some text, extract a list of facts from the text.

Format your output as a bulleted list.

Text:
"""
Birds and mammals are both capable of laying eggs, however birds are not mammals, they are a class of their own.
"""

Facts:

> Finished chain.

> Entering new LLMChain chain...
Prompt after formatting:
You are an expert fact checker. You have been hired by a major news organization to fact check a very important story.

Here is a bullet point list of facts:
"""

- Birds and mammals are both capable of laying eggs.
- Birds are not mammals.
- Birds are a class of their own.
"""

For each fact, determine whether it is true or false about the subject. If you are unable to determine whether the fact is true or false, output "Undetermined".
If the fact is false, explain why.

> Finished chain.

> Entering new LLMChain chain...
Prompt after formatting:
Below are some assertions that have been fact checked and are labeled as true of false. If the answer is false, a suggestion is given for a correction.

Checked Assertions:
"""

- Birds and mammals are both capable of laying eggs: False. Mammals give birth to live young, while birds lay eggs.

- Birds are not mammals: True. Birds are a class of their own, separate from mammals.

- Birds are a class of their own: True. Birds are a class of their own, separate from mammals.
"""

Original Summary:
"""
Birds and mammals are both capable of laying eggs, however birds are not mammals, they are a class of their own.
"""

Using these checked assertions, rewrite the original summary to be completely true.

The output should have the same structure and formatting as the original summary.

Summary:

> Finished chain.

> Entering new LLMChain chain...
Prompt after formatting:
Below are some assertions that have been fact checked and are labeled as true or false.

If all of the assertions are true, return "True". If any of the assertions are false, return "False".

Here are some examples:
===

Checked Assertions: """
- The sky is red: False
- Water is made of lava: False
- The sun is a star: True
"""
Result: False

===

Checked Assertions: """
- The sky is blue: True
- Water is wet: True
- The sun is a star: True
"""
Result: True

===

Checked Assertions: """
- The sky is blue - True
- Water is made of lava- False
- The sun is a star - True
"""
Result: False

===

Checked Assertions:"""

- Birds and mammals are both capable of laying eggs: False. Mammals give birth to live young, while birds lay eggs.

- Birds are not mammals: True. Birds are a class of their own, separate from mammals.

- Birds are a class of their own: True. Birds are a class of their own, separate from mammals.
"""
Result:

> Finished chain.

> Finished chain.

> Finished chain.

```

```code
'Birds are not mammals, but they are a class of their own. They lay eggs, unlike mammals which give birth to live young.'

```
LLM Requests审查（Moderation）