Instruction:
Develop a systematic plan to generate an ideal response to the user instruction, making use of a set of available tools. Each plan will have a corresponding evidence value, which will be the output of one of the available functions given an input string.

Here are the tools available to be called:
GoogleScholar[input]: Allows the user to search for academic articles using Google Scholar.
PDFReader[input]: Reads PDF files and outputs their contents as plain text.
CitationFinder[input]: Finds citations within a block of text.
AIAssistant[input]: Answers questions based on an input passage of text.

The output should be in format:
Plan: [first action to take based in input question]
#E1 = [function to call with input parameter]
Plan: [next action to take, based on result of #E1]
#E2 = [next function to call and input parameter, which may include reference to previous evidence]
...
Final answer: #E[n]

Question: Who first proposed the theory of continental drift and what were their main arguments?

Response:
Plan: Start by searching for academic articles related to the theory of continental drift.
#E1 = GoogleScholar[Theory of Continental Drift]
Plan: Extract the most relevant article link from the search results.
#E2 = AIAssistant[Which is the most relevant article about the theory of continental drift? Given context: #E1]
Plan: Read the content of the selected article.
#E3 = PDFReader[#E2]
Plan: Find the name of the person who first proposed the theory of continental drift.
#E4 = AIAssistant[Who first proposed the theory of continental drift? Given context: #E3]
Plan: Identify the main arguments made by the proponent of the theory.
#E5 = AIAssistant[What were the main arguments of #E4 when proposing the theory of continental drift? Given context: #E3]
Final answer: #E5

System: A chat.


Instruction:
Generate a systematic plan to respond to the user's request, leveraging the tools provided. Each plan should have a corresponding evidence value, which will be the outcome of one of the available functions given an input string that can be the user's question, one or more preceding evidence values, or a mix of both.

Here are the tools you can use:
YahooSearch[input]: A tool that lets the user look up information through the Yahoo search engine. It aids in identifying a suitable list of sites that might hold the answer to the user's question.
LinkGatherer[input]: Gathers links from plain text and delivers a plain text, newline-separated response of links.
WebCrawler[input]: Fetches one or more websites from the input string containing newline-separated links, where input is one or more links, and yields plain text comprising the content of the links.
FAQModel[input]: Frequently Asked Questions model, notably adept at answering questions based on an input text passage. The input has to be a text question that points to an :evidence[n]: variable, e.g., What is the population of Tokyo, given :evidence1:?

The output should be structured as follows:
Plan: [initial action to undertake based on the input question]
:evidence0: = [function to invoke with input parameter]
Plan: [following action to undertake, hinging on the outcome of :evidence0:]
:evidence1: = [subsequent function to invoke and input parameter, which might point to earlier evidence, e.g., "Given context :evidence0"]
...
Answer: [:evidence[n]: holding the final answer.]

Question: Who is the author of the book 'To Kill a Mockingbird' and what other books have they written?

Response:
Plan: Initiate by carrying out a web search to ascertain the author of 'To Kill a Mockingbird'.
:evidence0: = YahooSearch[Author of To Kill a Mockingbird]
Plan: Deploy the FAQModel to decipher the search outcomes and pinpoint the author's name.
:evidence1: = FAQModel[Who is the author of 'To Kill a Mockingbird', given :evidence0:?]
Plan: Undertake another web search to discover other books penned by the identified author.
:evidence2: = YahooSearch[Other books written by :evidence1:]
Plan: Gather the pertinent links from the Yahoo search outcomes for a more targeted search.
:evidence3: = LinkGatherer[:evidence2:]
Plan: Employ the WebCrawler tool to fetch data from the relevant links.
:evidence4: = WebCrawler[:evidence3:]
Plan: Ultimately, employ the FAQModel to recognize and enumerate the other books authored by the writer from the fetched data.
:evidence5: = FAQModel[What other books has :evidence1: written, given :evidence4:?]
Answer: :evidence5:

System: A chat.


Instruction:
Develop a systematic plan to respond to the user's inquiry using the tools at your disposal. Each tool generates an evidence value, which serves as the output of the function and can be utilized in later stages.

Available tools include:

WikiLookup[input]: This tool allows you to retrieve information from Wikipedia. It's useful for obtaining factual data about people, places, events, etc. The output could be verbose but often contains the necessary information.
URLGrabber[input]: Grabs all URLs contained within a block of text.
SiteReader[input]: Reads the contents of a website specified by its URL and returns the text.
AskBot[input]: A language model capable of answering questions based on a provided context. The input must reference an :evidence[n]: variable, e.g., "Who won the World Series in 2020, given :evidence1:?"

Output should adhere to the following structure:
Plan: [initial action]
:evidence0: = [function with corresponding input]
Plan: [subsequent action, based on outcome of :evidence0:]
:evidence1: = [following function and input, potentially referring to prior evidence such as ":evidence0:"]
...
Answer: [:evidencen:]

Question: Who wrote the book 'To Kill a Mockingbird' and what other works have they published?

Response:
Plan: Begin by looking up 'To Kill a Mockingbird' on Wikipedia to find out who the author is.
:evidence0: = WikiLookup['To Kill a Mockingbird']
Plan: Use AskBot to parse the Wikipedia page and identify the author.
:evidence1: = AskBot[Who is the author of 'To Kill a Mockingbird', given :evidence0:?]
Plan: Search Wikipedia again, this time for the author to find their other works.
:evidence2: = WikiLookup[:evidence1:]
Plan: Extract any URLs from the Wikipedia page for more detailed information.
:evidence3: = URLGrabber[:evidence2:]
Plan: Read the contents of the sites linked on the Wikipedia page.
:evidence4: = SiteReader[:evidence3:]
Plan: Finally, use AskBot to identify and summarize the other works published by the author from the site contents.
:evidence5: = AskBot[What other works has :evidence1: published, given :evidence4:?]
Answer: :evidence5:

System: A chat.


Instruction:
Create a step-by-step plan to answer the following question using the tools provided. Each step in your plan should correspond with an evidence value, which will be the output of one of the available functions given an input string that can be either the user's question, one or more previous evidence values, or a combination of both.

Here are the tools you have at your disposal:
WebSearch[input]: This tool allows the user to search for information on the internet using a general-purpose search engine. It is useful for finding a list of sites that may contain the answer to the user's question.
TextExtractor[input]: Extracts plain text from a website URL. The input is a single URL and the output is the plain text content of the site.
LinkCollector[input]: Extracts URLs from plain text. The output is a newline-separated list of links.
InfoQA[input]: A question-answering model that can provide answers based on a passage of text. The input must be a text question that references an :evidence[n]: variable, e.g., "What is the population of New York City, given :evidence1:?"

The input to each function should be a plain string, without quotes or "+" to concatenate a string with an evidence variable, e.g., InfoQA[What is the capital of France, given :evidence3:?]

Ensure that there is only one evidence output per plan step.

The output format should be as follows:
Plan: [first action to take based on the input question]
:evidence0: = [function to call with input parameter]
Plan: [next action to take, based on the result of :evidence0:]
:evidence1: = [next function to call and input parameter, which may include reference to previous evidence, e.g., "Given context :evidence0"]
...
Answer: [:evidence[n]: containing the final answer.]

Question: What is the most popular tourist attraction in Paris and what is its history?

Response:
Plan: Start by conducting a web search to find out the most popular tourist attraction in Paris.
:evidence0: = WebSearch[Most popular tourist attraction in Paris]
Plan: Use the InfoQA model to interpret the search results and identify the most popular tourist attraction.
:evidence1: = InfoQA[What is the most popular tourist attraction in Paris, given :evidence0:?]
Plan: Conduct another web search to find information about the history of the identified tourist attraction.
:evidence2: = WebSearch[History of :evidence1:]
Plan: Extract relevant links from the search results for a more detailed investigation.
:evidence3: = LinkCollector[:evidence2:]
Plan: Use the TextExtractor tool to get information from the extracted links.
:evidence4: = TextExtractor[:evidence3:]
Plan: Finally, use the InfoQA model to summarize the history of the tourist attraction from the extracted information.
:evidence5: = InfoQA[What is the history of :evidence1:, given :evidence4:?]
Answer: :evidence5:

System: A chat.


Instruction:
Generate a step-by-step plan to find out the population of the city where Microsoft headquarters is located.

Here are the tools available to be called:
Wikipedia[input]: Tool that allows the user to search for information from Wikipedia.
QA[input]: Tool that can answer questions either directly from common sense and general world knowledge, as well as answering questions given input context that may contain the answer.
CityPopulation[input]: A tool that retrieves the current population of a specified city.
CompanyInfo[input]: A tool that provides information about a specific company including its location.
GoogleMaps[input]: A tool that provides geographical data about a place.

Output format:
Plan: First action
#E1 = function with input parameter
Plan: Next action based on #E1
#E2 = next function with input parameter referencing previous evidence if necessary
...
Final Answer: #E[n]

Response:
Plan: Find out where Microsoft headquarters is located.
#E1 = CompanyInfo["Microsoft"]
Plan: Once the location of Microsoft's headquarters is known, use that information to find out the population of the city.
#E2 = CityPopulation[#E1.location]
Final Answer: #E2

System: A chat.


Instruction:
Provide a detailed execution plan to respond to the following query using the tools provided. Remember, each step should be followed by an evidence value.

Tools Available:
WikiLookup[input]: Searches Wikipedia for articles related to the input query.
FactChecker[input]: Verifies factual claims by cross-referencing multiple sources.
QAEngine[input]: Answers questions based on the context provided in the input.
ImageSearch[input]: Searches the internet for images related to the input query.

Output Format:
Step: [Action based on the input question]
#E1 = [Function to call with appropriate input]
Step: [Next action based on the outcome of #E1]
#E2 = [Next function to call with appropriate input, referencing previous evidence if necessary]
...
Final Answer: #En

Question: Who was Leonardo da Vinci and what are some of his most famous works?

Response:
Step: Look up information about Leonardo da Vinci on Wikipedia.
#E1 = WikiLookup[Leonardo da Vinci]
Step: Verify the facts obtained from Wikipedia.
#E2 = FactChecker[#E1]
Step: Identify Leonardo da Vinci's most famous works using the QA Engine.
#E3 = QAEngine[What are some of Leonardo da Vinci's most famous works? Given context: #E2]
Step: Search for images of these famous works.
#E4 = ImageSearch[#E3]
Final Answer: #E4

System: A chat.


Instruction:
Formulate a plan to answer the question: "How does photosynthesis work and why is it important?" Here are the tools at your disposal:

1. EncyclopediaSearch[input]: Searches an online encyclopedia for information based on the input string.
2. DiagramGenerator[input]: Generates diagrams based on the input string.
3. InfoSummarizer[input]: Summarizes complex information into simpler terms.
4. QuerySolver[input]: Answers questions based on the input string and any provided context.

Your plan should include an evidence variable (#E[index]) for each step. Format your output like so:

Plan: [first action]
#E1 = [function call with input]
Plan: [next action]
#E2 = [function call with input, possibly referencing previous evidence]
...
Final Answer: #E[n]

Response:
Plan: Start by searching the encyclopedia for information on how photosynthesis works.
#E1 = EncyclopediaSearch[Photosynthesis process]
Plan: Generate a diagram to visually represent the process of photosynthesis.
#E2 = DiagramGenerator[#E1]
Plan: Summarize the complex information about photosynthesis into simpler terms.
#E3 = InfoSummarizer[#E1]
Plan: Now, search the encyclopedia for information on the importance of photosynthesis.
#E4 = EncyclopediaSearch[Importance of Photosynthesis]
Plan: Again, summarize the complex information into simpler terms.
#E5 = InfoSummarizer[#E4]
Plan: Lastly, use the query solver to provide a comprehensive answer to the original question.
#E6 = QuerySolver[How does photosynthesis work and why is it important? Given context: #E3 and #E5]
Final Answer: #E6

System: A chat.


Instruction:
Provide a detailed execution plan for the following task using the listed tools. Remember each plan step should be followed by exactly one evidence (#E[index]) value.

Available Tools:
WikiSearch[input]: Searches Wikipedia for the provided input and returns a verbose and noisy output.
QAAssistant[input]: Answers questions directly from common sense and general world knowledge, as well as answering questions given input context that may contain the answer.

Output Format:
Plan: [first action to take based in input question]
#E1 = [function to call with input parameter]
Plan: [next action to take, based on result of #E1]
#E2 = [next function to call and input parameter, which may include reference to previous evidence]
...
Final answer: #E[n]

Question: Who was Nikola Tesla and what were his significant contributions to science?

Response:
Plan: Begin by searching for information about Nikola Tesla.
#E1 = WikiSearch[Nikola Tesla]
Plan: Using the information obtained, identify who Nikola Tesla was.
#E2 = QAAssistant[Who was Nikola Tesla? Given context: #E1]
Plan: Next, determine his significant contributions to science.
#E3 = QAAssistant[What were the significant contributions of Nikola Tesla to science? Given context: #E1]
Final answer: #E2 and #E3

And so on...

System: A chat.


Instruction:
Create a step-by-step plan to solve the problem using the following tools:

YahooSearch[input]: A tool that uses Yahoo's search engine to look up information.
SiteLoader[input]: Takes one or more website URLs and outputs the text content of those sites.
LinkGatherer[input]: Extracts all URLs from a block of text.
KnowledgeProcessor[input]: A language model designed to answer questions based on a given text. It requires a question referencing a #Evidence[index] variable as input.

The output should follow this format:
Plan: [First action]
#Evidence1 = [Function to call with input]
Plan: [Next action]
#Evidence2 = [Next function to call with input, possibly referencing previous evidence]
...
Final Answer: #Evidence[n]

Question: How did Apple Inc. get started and who were its founders?

Response:
Plan: First, conduct a search to gather information about the start of Apple Inc.
#Evidence1 = YahooSearch[Start of Apple Inc.]
Plan: Use KnowledgeProcessor to determine how Apple Inc. got started from the search results.
#Evidence2 = KnowledgeProcessor[How did Apple Inc. get started? Given context: #Evidence1]
Plan: Perform another search to find out who the founders of Apple Inc. were.
#Evidence3 = YahooSearch[Founders of Apple Inc.]
Plan: Extract any useful links from the search results for further examination.
#Evidence4 = LinkGatherer[#Evidence3]
Plan: Load the text content of these websites for detailed information.
#Evidence5 = SiteLoader[#Evidence4]
Plan: Lastly, use KnowledgeProcessor to determine who the founders of Apple Inc. were from the loaded site contents.
#Evidence6 = KnowledgeProcessor[Who were the founders of Apple Inc.? Given context: #Evidence5]
Final Answer: #Evidence6

System: A chat.


Instruction:
Please develop a step-by-step plan to generate a response to the user instruction, making use of the available tools. Each plan should have a corresponding evidence value, which will be the output of one of the functions given an input string.

Here are the tools available to be called:
YahooSearch[input]: Tool that allows the user to search for information using the Yahoo search engine. This tool finds a list of sites that may contain the answer to the user's question.
WebCrawler[input]: Load one or more websites from the input string containing newline delimited links, where input is one or more links, and produces plain text output containing the content of the links.
HyperlinkFinder[input]: Extract links from plain text and produces a plain text, newline delimited response of links.
AskAI[input]: Question answering AI model, particularly useful in answering questions based on an input passage of text. The input must be a text question that references an :evidence[n]: variable, e.g. What color is the car, given :evidence1:?

The output should be in format:
Plan: [first action to take based in input question]
:evidence0: = [function to call with input parameter]
Plan: [next action to take, based on result of :evidence0:]
:evidence1: = [next function to call and input parameter, which may include reference to previous evidence, e.g. "Given context :evidence0"]
...
Answer: [:evidence[n]: containing the final answer.]

Question: What are the main ingredients in a traditional Greek moussaka and how is it prepared?

Response:
Plan: Begin by conducting a web search to find out the main ingredients in a traditional Greek moussaka.
:evidence0: = YahooSearch[Main ingredients in traditional Greek moussaka]
Plan: Utilize AskAI to interpret the search results and find the main ingredients.
:evidence1: = AskAI[What are the main ingredients in a traditional Greek moussaka, given :evidence0:?]
Plan: Conduct another web search to find the preparation method of traditional Greek moussaka.
:evidence2: = YahooSearch[How to prepare traditional Greek moussaka]
Plan: Extract the relevant links from the Yahoo search results for a more focused search.
:evidence3: = HyperlinkFinder[:evidence2:]
Plan: Use WebCrawler to extract information from the relevant links.
:evidence4: = WebCrawler[:evidence3:]
Plan: Finally, utilize AskAI to identify and summarize the preparation method of traditional Greek moussaka from the extracted information.
:evidence5: = AskAI[How is traditional Greek moussaka prepared, given :evidence4:?]
Answer: :evidence5:

System: A chat.


Instruction:
Develop a plan to answer the user query using the listed tools. Each step should have a corresponding evidence value which is the output of the function called at that step.

Available tools:
DuckDuckGo[input]: A tool that lets users search for information via DuckDuckGo. Useful for getting a list of sites that might contain the answer.
PageLoader[input]: Loads one or more websites from an input string containing newline-delimited links. Input is one or more links and the output is the text content of these links.
URLPicker[input]: Picks out URLs from a block of plain text and outputs them as a newline-delimited string.
InfoBot[input]: An AI model capable of answering questions based on an input text passage. The input should be a question that refers to an :evidence[n]: variable, like "What is the population of Paris, given :evidence1:?"

Output format:
Plan: [first action]
:evidence0: = [function call with input]
Plan: [next action, based on :evidence0:]
:evidence1: = [next function call with input, possibly referring to previous evidence]
...
Answer: [:evidence[n]: contains final answer.]

Question: Who designed the Sydney Opera House and what other buildings did they design?

Response:
Plan: Begin by conducting a web search to find out who designed the Sydney Opera House.
:evidence0: = DuckDuckGo[Sydney Opera House architect]
Plan: Use InfoBot to interpret the search results and identify the architect.
:evidence1: = InfoBot[Who designed the Sydney Opera House, given :evidence0:?]
Plan: Conduct another web search to find other buildings designed by the identified architect.
:evidence2: = DuckDuckGo[:evidence1: other buildings]
Plan: Extract the relevant links from the DuckDuckGo search results for more focused information.
:evidence3: = URLPicker[:evidence2:]
Plan: Load the content of the extracted links for further analysis.
:evidence4: = PageLoader[:evidence3:]
Plan: Finally, use InfoBot to identify and summarize other buildings designed by the architect from the loaded content.
:evidence5: = InfoBot[What other buildings were designed by :evidence1:, given :evidence4:?]
Answer: :evidence5:

System: A chat.


Instruction:
Please create a step-by-step plan to generate an optimal response to the user query, making use of a set of available tools. Each plan will have a corresponding evidence value, which will be the output of one of the available functions given an input string that can be the user question, one or more previous evidence values, or a combination of both.

Here are the tools available to be called:
Bing[input]: Tool that allows the user to search for information using the Bing search engine. This tool is useful in finding a list of sites that may contain the answer to the user's question.
WebCrawler[input]: Load one or more websites from the input string containing newline delimited links, where input is one or more links, and produces plain text output containing the content of the links.
LinkParser[input]: Extracts links from plain text and produces a plain text, newline delimited response of links.
NLPModel[input]: Question answering language model, particularly useful in answering questions based on an input passage of text. The input must be a text question that references a :evidence[n]: variable, e.g. What color is the dog, given :evidence1:?

The output should be in format:
Plan: [first action to take based in input question]
:evidence0: = [function to call with input parameter]
Plan: [next action to take, based on result of :evidence0:]
:evidence1: = [next function to call and input parameter, which may include reference to previous evidence, e.g. "Given context :evidence0"]
...
Answer: [:evidence[n]: containing the final answer.]

Question: Who was the first person to climb Mount Everest without supplemental oxygen and what book did they write about their experience?

Response:
Plan: Start by conducting a web search to find out who was the first person to climb Mount Everest without supplemental oxygen.
:evidence0: = Bing[First person to climb Mount Everest without supplemental oxygen]
Plan: Use a language model to interpret the search results and identify the climber.
:evidence1: = NLPModel[Who was the first person to climb Mount Everest without supplemental oxygen, given :evidence0:?]
Plan: Conduct another web search to find the book written by this climber about their experience.
:evidence2: = Bing[Book written by :evidence1: about climbing Mount Everest without supplemental oxygen]
Plan: Parse the relevant links from the Bing search results for a more focused search.
:evidence3: = LinkParser[:evidence2:]
Plan: Utilize a web crawler tool to extract information from the relevant links.
:evidence4: = WebCrawler[:evidence3:]
Plan: Finally, use the language model to identify and summarize the name of the book from the extracted information.
:evidence5: = NLPModel[What is the name of the book written by :evidence1: about their experience climbing Mount Everest without supplemental oxygen, given :evidence4:?]
Answer: :evidence5:

System: A chat.


Instruction:
Plan a structured way to handle this request using these tools:
LibraryCatalogSearch[input]: Searches library catalogs for relevant resources.
ManuscriptReader[input]: Reads manuscripts for pertinent information.
AbstractCreator[input]: Creates abstracts of lengthy texts.
VeracityExaminer[input]: Examines the veracity of assertions.
ContextualAnswerer[input]: Provides answers based on contextual comprehension.

Question: Discuss the evolution of English literature from the Middle Ages to the present day.

Response:
Step 1: LibraryCatalogSearch
Use the LibraryCatalogSearch tool to find resources related to "the evolution of English literature from the Middle Ages to the present day". Look for books, manuscripts, and other relevant materials that provide a comprehensive overview of this topic.

Step 2: ManuscriptReader
After gathering the necessary resources, use the ManuscriptReader tool to read through these texts. This will help in understanding the detailed information about the evolution of English literature during the specified time period.

Step 3: AbstractCreator
Once the ManuscriptReader has gone through all the collected resources, utilize the AbstractCreator tool to summarize the lengthy texts into more manageable abstracts. This will make it easier to review and understand the key points regarding the evolution of English literature.

Step 4: VeracityExaminer
To ensure the accuracy of the information gathered, use the VeracityExaminer tool. This tool will examine the assertions made in the sources about the evolution of English literature, helping to verify their truthfulness and reliability.

Step 5: ContextualAnswerer
Finally, after confirming the veracity of the information, employ the ContextualAnswerer tool to formulate a comprehensive answer discussing the evolution of English literature from the Middle Ages to the present day. The ContextualAnswerer tool will provide an answer based on the contextual comprehension of the verified information.

System: A chat.


Instruction:
Please generate a step-by-step plan to solve the following problem using the available tools. Each tool will produce an evidence value that can be used in subsequent steps of the plan.

Here are the tools you have at your disposal:
WebSearch[input]: This tool allows you to perform a web search for any given input string. It returns a list of sites that may contain relevant information.
ReadPage[input]: This tool reads and extracts text from a specific webpage URL. The output is plain text containing the content of the page.
AskAI[input]: This tool answers questions based on common sense, general world knowledge, or given context. It requires a question as input and can use previous evidence values for context.
LinkExtractor[input]: Extracts links from plain text and produces a plain text response of newline-delimited links.

Each step of the plan should be followed by exactly one evidence (#E[index]) value.

The output format should be:
Plan: [first action]
#E1 = [function with input parameter]
Plan: [next action]
#E2 = [next function with input parameter, possibly referencing #E1]
...
Final answer: #E[n]

Question: What is the history behind the creation of Bitcoin and who was involved?

Response:
Plan: Start by searching the web for information about the creation of Bitcoin.
#E1 = WebSearch[Creation of Bitcoin]
Plan: Extract useful links from the search results.
#E2 = LinkExtractor[#E1]
Plan: Read the content of the extracted links.
#E3 = ReadPage[#E2]
Plan: Ask AI to identify who was involved in the creation of Bitcoin using the gathered information.
#E4 = AskAI[Who was involved in the creation of Bitcoin? Given context: #E3]
Plan: Search the web again for more detailed information about the history of Bitcoin's creation.
#E5 = WebSearch[History of Bitcoin creation]
Plan: Extract useful links from these new search results.
#E6 = LinkExtractor[#E5]
Plan: Read the content of these newly extracted links.
#E7 = ReadPage[#E6]
Plan: Finally, ask AI to summarize the history behind the creation of Bitcoin using all the collected information.
#E8 = AskAI[What is the history behind the creation of Bitcoin? Given context: #E7]
Final answer: #E8

System: A chat.


Instruction:
Please generate a step-by-step plan to answer the user's query by utilizing the below mentioned tools. Each tool requires an input and generates an output which can be saved into a variable #E[index] for future references.

Tools Available:
DuckDuckGo[input]: A search engine tool that helps in finding appropriate sites related to the input query.
URLLoader[input]: Loads the content of one or multiple URLs provided in the input string and returns a plain text output containing the content of the pages.
HyperlinkExtractor[input]: Extracts hyperlinks from the plain text and provides a newline delimited response of links.
NLPModel[input]: A language model designed to answer questions based on a given passage of text. It requires a textual question that refers to an #E[n] variable.

Output Format:
Plan: [Initial Action]
#E1 = [Function Call with Input Parameter]
Plan: [Next Step Based on Result of #E1]
#E2 = [Subsequent Function Call and Input Parameter, May Include Reference to Previous Evidence]
...
Final Answer: #E[n]

Question: Who is the founder of SpaceX and what are their major achievements?

Response:
Plan: Start by searching who is the founder of SpaceX.
#E1 = DuckDuckGo[Founder of SpaceX]
Plan: Utilize the NLP Model to extract the name of the founder from the search results.
#E2 = NLPModel[Who is the founder of SpaceX? Given context: #E1]
Plan: Search for the major achievements of the identified founder.
#E3 = DuckDuckGo[Major achievements of #E2]
Plan: Extract the relevant links from the search results.
#E4 = HyperlinkExtractor[#E3]
Plan: Load the content of the extracted links.
#E5 = URLLoader[#E4]
Plan: Finally, use the NLP Model to identify and summarize the major achievements of the founder from the loaded content.
#E6 = NLPModel[What are the major achievements of #E2? Given context: #E5]
Final Answer: #E6

System: A chat.


Instruction:
Formulate a plan to respond to: "What's the highest peak in the Alps and how high is it?"

Tools at your disposal:
MountainInfo[input]: Tool that gives information about a specific mountain, including its height.
GeoSearch[input]: Tool that searches geographical facts and data.
QA[input]: Tool that answers questions using general world knowledge.

Output format:
Plan: starting action
#E1 = function with input parameter
Plan: following action, based on #E1
#E2 = next function with input parameter (may reference previous evidence)
...
Final Answer: #E[n]

Response:
Plan: starting action
#E1 = QA["What's the highest peak in the Alps?"]
Plan: following action, based on #E1
#E2 = MountainInfo[#E1]
Final Answer: The highest peak in the Alps is #E1 and it is #E2 meters high.

System: A chat.


Instruction:
Create a plan to answer the following question using the set of tools provided. Each tool can be invoked with an input string and will provide an output which can be used as evidence in subsequent steps.

Tools available:
DuckDuckGo[input]: Allows you to perform a web search using DuckDuckGo. Useful for finding potential sources of answers.
WebScraper[input]: Takes one or more URLs and returns the plain text content of those pages.
InfoExtractor[input]: Extracts specific pieces of information from a larger body of text.
SmartQA[input]: An advanced AI tool capable of answering complex questions when provided with enough context.

Output Format:
Plan: [First Action]
#Evidence1 = [Function Call]
Plan: [Next Action]
#Evidence2 = [Function Call, potentially referencing #Evidence1]
...
Final Answer: #EvidenceN

Question: Who was the first woman to win a Nobel Prize and what did she win it for?

Response:
Plan: Begin by performing a web search to identify the first woman to win a Nobel Prize.
#Evidence1 = DuckDuckGo[First woman to win a Nobel Prize]
Plan: Extract the most relevant links from the search results.
#Evidence2 = InfoExtractor[#Evidence1]
Plan: Scrape the content of these pages for further analysis.
#Evidence3 = WebScraper[#Evidence2]
Plan: Finally, use SmartQA to determine who the first woman to win a Nobel Prize was and what she won it for.
#Evidence4 = SmartQA[Who was the first woman to win a Nobel Prize and what did she win it for? Given context: #Evidence3]
Final Answer: #Evidence4

System: A chat.


Instruction:
Create a step-by-step plan to answer the user query, using the following tools. Each tool should be used at least once and each plan must have an associated evidence value.

Here are the available tools:

Bing[input]: This tool allows you to search for information on Bing. It's useful for finding websites that may contain answers to your question.
WebCrawler[input]: This tool extracts text from one or more web pages provided in the input string (newline separated links). The output is plain text content of these pages.
LinkPicker[input]: Extracts URLs from plain text and outputs them as newline-separated strings.
InfoGatherer[input]: A question answering tool that can provide answers based on general knowledge or given context. The input should be a question which might reference previous evidence variables.

The output format should be as follows:
Plan: [First action]
#E1 = [Function call with input parameter]
Plan: [Next action based on #E1]
#E2 = [Next function call with input parameters possibly referencing #E1]
...
Final Answer: #E[n]

Question: Who is the author of "To Kill a Mockingbird" and what other works have they published?

Response:
Plan: Start by searching for the author of "To Kill a Mockingbird".
#E1 = Bing[Author of To Kill a Mockingbird]
Plan: Use InfoGatherer to identify the author from the search results.
#E2 = InfoGatherer[Who is the author of "To Kill a Mockingbird"? Given context: #E1]
Plan: Search for other works by the identified author.
#E3 = Bing[Other works by #E2]
Plan: Extract relevant links from the search results for further investigation.
#E4 = LinkPicker[#E3]
Plan: Extract textual content from the selected links.
#E5 = WebCrawler[#E4]
Plan: Identify and summarize the other works by the author using InfoGatherer.
#E6 = InfoGatherer[What other works has #E2 published? Given context: #E5]
Final Answer: #E6

System: A chat.


Instruction:
Develop a plan to identify the author of the book 'War and Peace' and list three other books they have written.

Tools available:
BookInfo[input]: Tool that provides information about a particular book including the author's name.
AuthorWorks[input]: Tool that lists works by a particular author.
ListFormatter[input]: Tool that formats a list into a readable sentence.
QA[input]: Tool that answers questions using general world knowledge.

Output format:
Plan: First action to take based on input question
#E1 = Function to call with input parameter
Plan: Next action to take, based on result of #E1
#E2 = Next function to call and input parameter, referencing previous evidence if necessary
...
Final Answer: #E[n]

Response:
Plan: Use the BookInfo tool to identify the author of 'War and Peace'.
#E1 = BookInfo['War and Peace']

Plan: Use the AuthorWorks tool to list other books written by the same author.
#E2 = AuthorWorks[E1.author]

Plan: Use the ListFormatter tool to format the list of works into a readable sentence.
#E3 = ListFormatter[E2.works]

Final Answer: #E3.list

System: A chat.


Instruction:
Please create a step-by-step plan to generate an optimal response to the user query, using the given set of tools. Each plan will have a corresponding evidence value, which will be the output of one of the available functions given an input string that can be the user question, one or more previous evidence values, or a combination of both.

Here are the tools available to be called:
Bing[input]: Tool that allows the user to search for information using the Bing search engine. This tool is useful in finding an appropriate list of sites that may contain the answer to the user's question.
TextExtractor[input]: Extracts plain text from websites provided in the input string containing newline delimited links. The input should be one or more links and produces plain text output containing the content of the links.
LinkFinder[input]: Extracts links from plain text and produces a plain text, newline delimited response of links.
AIM[input]: Artificial Intelligence Model for answering questions based on an input passage of text. The input must be a text question that references an :evidence[n]: variable, e.g. What color is the cat, given :evidence1:?

The input to each function just needs to be a plain string, without quotes or "+" to concatenate a string with an evidence variable, e.g. AIM[What is the capital of France, given :evidence3:?]

Ensure to only include one evidence output per plan step.

The output should be in format:
Plan: [first action to take based in input question]
:evidence0: = [function to call with input parameter]
Plan: [next action to take, based on result of :evidence0:]
:evidence1: = [next function to call and input parameter, which may include reference to previous evidence, e.g. "Given context :evidence0"]
...
Answer: [:evidence[n]: containing the final answer.]

Question: Who was the first person to climb Mount Everest and what were their motivations?

Response:
Plan: Start by conducting a web search to find out who was the first person to climb Mount Everest.
:evidence0: = Bing[First person to climb Mount Everest]
Plan: Use the AI model to interpret the search results and identify the name of the person.
:evidence1: = AIM[Who was the first person to climb Mount Everest, given :evidence0:?]
Plan: Conduct another web search to find the motivations of the identified person for climbing Mount Everest.
:evidence2: = Bing[Motivations of :evidence1: for climbing Mount Everest]
Plan: Extract the relevant links from the Bing search results for a more detailed search.
:evidence3: = LinkFinder[:evidence2:]
Plan: Use the TextExtractor tool to extract information from the relevant links.
:evidence4: = TextExtractor[:evidence3:]
Plan: Finally, use the AI model to identify and summarize the motivations of the person from the extracted information.
:evidence5: = AIM[What were the motivations of :evidence1: for climbing Mount Everest, given :evidence4:?]
Answer: :evidence5:

System: A chat.


Instruction:
Generate a step-by-step execution plan to answer the user's question using the available tools. For each step, specify the corresponding evidence value which would be the output of the function used in that step.

Available Tools:
DuckDuckGo[input]: This tool uses DuckDuckGo search engine to retrieve information related to the input query. It generates a list of websites that could potentially contain the necessary information.
PageReader[input]: Reads the contents of one or more web pages provided as newline separated links in the input string and gives a plain text output of the page contents.
URLGrabber[input]: Grabs all URLs present in the given plain text and returns them as a newline separated list.
InfoBot[input]: A Question Answering bot that answers queries based on an input passage of text. The input must be a text question that refers to an :evidence[n]: variable, e.g., What is the capital of France, given :evidence1:?

Output Format:
Plan: [First Action]
:evidence0: = [Function Call with Input Parameter]
Plan: [Next Action Based on Result of :evidence0:]
:evidence1: = [Next Function Call with Input Parameter possibly referencing previous evidence like "Given Context :evidence0"]
...
Final Answer: [:evidence[n]: Containing Final Answer]

Question: What is the history behind the creation of Bitcoin and who is its creator?

Response:
Plan: Initiate a web search to gather information about the creation of Bitcoin.
:evidence0: = DuckDuckGo[Creation of Bitcoin]
Plan: Utilize InfoBot to interpret the search results and find details about the creation of Bitcoin.
:evidence1: = InfoBot[What is the history behind the creation of Bitcoin, given :evidence0:?]
Plan: Perform another web search to find out who created Bitcoin.
:evidence2: = DuckDuckGo[Creator of Bitcoin]
Plan: Extract the relevant links from the DuckDuckGo search results for more specific information.
:evidence3: = URLGrabber[:evidence2:]
Plan: Use PageReader to extract information from the relevant links.
:evidence4: = PageReader[:evidence3:]
Plan: Lastly, utilize InfoBot to identify the creator of Bitcoin from the extracted information.
:evidence5: = InfoBot[Who is the creator of Bitcoin, given :evidence4:?]
Answer: :evidence5:

System: A chat.


Instruction:
Create a detailed execution plan to respond to the following query using the provided tools. For each step in your plan, indicate the specific tool you would use along with its input parameters. You can store the outputs of these tools into variables named #E[index] that can be referenced in subsequent steps.

Available Tools:
DuckDuckGo[input]: A tool that uses the DuckDuckGo search engine to retrieve information related to the input query.
WebReader[input]: A tool that loads the contents of one or more websites specified by the input URLs and returns the text content.
LinkPicker[input]: A tool that extracts URLs from the provided text.
HAL[input]: A language model capable of answering questions when provided with a context. The input to this tool should be a question referencing a previously stored piece of evidence (e.g., "What is the population of city X, given #E1?").

Output Format:
Plan: [First step in the plan]
#E1 = [Tool to use with input parameters]
Plan: [Subsequent step(s) in the plan]
#E2 = [Next tool to use with input parameters, possibly referencing previous evidence]
...
Final Answer: #E[n]

Question: When was the Eiffel Tower built and what materials were used in its construction?

Response:
Plan: Initiate a search to determine when the Eiffel Tower was built.
#E1 = DuckDuckGo[Eiffel Tower construction date]
Plan: Utilize HAL to parse the search results and identify the construction date.
#E2 = HAL[When was the Eiffel Tower built, given #E1?]
Plan: Execute another search to discover the materials used in the construction of the Eiffel Tower.
#E3 = DuckDuckGo[Eiffel Tower construction materials]
Plan: Extract the relevant URLs from the search results for further investigation.
#E4 = LinkPicker[#E3]
Plan: Load the content of the selected websites for detailed information.
#E5 = WebReader[#E4]
Plan: Employ HAL once again to identify and summarize the materials used in the construction from the loaded website content.
#E6 = HAL[What materials were used in the construction of the Eiffel Tower, given #E5?]
Final Answer: #E6

System: A chat.


Instruction:
Please provide a detailed plan to generate an ideal response to the user instruction, making use of the set of available tools. Each plan will correspond to an evidence value, which will be the output of one of the available functions given an input string.

Available Tools:
InfoFinder[input]: Searches for information online about the input query.
PageScraper[input]: Retrieves the main body of text from a webpage URL.
LinkExtractor[input]: Extracts all URLs from a block of text.
KnowledgeBase[input]: Answers questions based on general knowledge and specific context provided as input.

Output Format:
Step 1: [Action]
Data1 = [Function call with input]
Step 2: [Next action based on Data1]
Data2 = [Next function call with input, possibly referencing Data1]
...
Answer: [DataN containing the final answer.]

Question: What is the history behind the Eiffel Tower and how tall is it?

Response:
Step 1: Search for information about the history of the Eiffel Tower.
Data1 = InfoFinder[Eiffel Tower history]
Step 2: Extract any URLs present in the search results.
Data2 = LinkExtractor[Data1]
Step 3: Retrieve the main body of text from the extracted URLs.
Data3 = PageScraper[Data2]
Step 4: Use KnowledgeBase to summarize the history of the Eiffel Tower from the scraped page data.
Data4 = KnowledgeBase[What is the history of the Eiffel Tower? Given context: Data3]
Step 5: Search for information about the height of the Eiffel Tower.
Data5 = InfoFinder[Eiffel Tower height]
Step 6: Use KnowledgeBase to extract the height of the Eiffel Tower from the search results.
Data6 = KnowledgeBase[How tall is the Eiffel Tower? Given context: Data5]
Answer: Data4 and Data6

System: A chat.


Instruction:
Create a plan to answer the following question using the tools provided. For each step, provide an evidence variable that stores the output of the tool used in that step.

Available Tools:
1. Bing[input]: This function allows you to search for information on Bing. The input should be a string containing your query.
2. TextAnalyzer[input]: Analyzes text and extracts key details such as names, places, dates etc. Input is plain text from previous steps.
3. QA[input]: Answers questions based on common sense or given context. The input must be a text question referencing one or more :evidence[n]: variables.
4. PatentSearcher[input]: Searches for patents held by a person or company. The input should be a name.

The output format should be:
Plan: [first action]
:evidence0: = [function with input parameter]
Plan: [next action]
:evidence1: = [next function with input parameter, possibly referencing :evidence0:]
...
Answer: [:evidence[n]: containing final answer]

Question: Who is the author of "War and Peace" and what are some other works they have written?

Response:
Plan: Use Bing to find out who wrote "War and Peace".
:evidence0: = Bing[Author of War and Peace]
Plan: Extract the author's name from the search results.
:evidence1: = TextAnalyzer[:evidence0:]
Plan: Search for other works by this author.
:evidence2: = Bing[Other works by :evidence1:]
Plan: Extract the titles of these works from the search results.
:evidence3: = TextAnalyzer[:evidence2:]
Answer: :evidence3:

System: A chat.


Instruction:
Create a step-by-step plan to solve the following problem using the tools provided. Each step should have an associated evidence value, which will be the output of one of the available functions given an input string that can be either the user's question, one or more previous evidence values, or a combination of both.

Here are the tools available to be called:
WebSearch[input]: Tool that allows the user to search for information from various sources on the internet. This tool is useful in finding relevant websites that may contain the answer to the user's question.
TextExtractor[input]: Extracts plain text from a website link provided as input and produces a readable text output containing the content of the link.
LinkCollector[input]: Collects all links embedded within a block of text provided as input and outputs them as a list.
QAModel[input]: A question answering model that answers questions based on an input passage of text. The input must be a text question that references an :evidence[n]: variable, e.g., What color is the cat, given :evidence1:?

The input to each function should just be a plain string, without quotes or "+" to concatenate a string with an evidence variable, e.g., QAModel[What is the capital of Michigan, given :evidence3:?]

Remember to include only one evidence output per plan step.

The output should be in format:
Plan: [first action to take based in input question]
:evidence0: = [function to call with input parameter]
Plan: [next action to take, based on result of :evidence0:]
:evidence1: = [next function to call and input parameter, which may include reference to previous evidence, e.g., "Given context :evidence0"]
...
Answer: [:evidence[n]: containing the final answer.]

Question: Who won the 2022 FIFA World Cup and what was their path to victory?

Response:
Plan: Start by conducting a web search to find out who won the 2022 FIFA World Cup.
:evidence0: = WebSearch[2022 FIFA World Cup winner]
Plan: Use the question answering model to interpret the search results and identify the winning team.
:evidence1: = QAModel[Who won the 2022 FIFA World Cup, given :evidence0:?]
Plan: Conduct another web search to find the path to victory of the identified team.
:evidence2: = WebSearch[:evidence1: 2022 FIFA World Cup path to victory]
Plan: Extract the relevant links from the search results for detailed analysis.
:evidence3: = LinkCollector[:evidence2:]
Plan: Use the TextExtractor tool to extract information from the relevant links.
:evidence4: = TextExtractor[:evidence3:]
Plan: Finally, use the question answering model to summarize the path to victory of the winning team from the extracted information.
:evidence5: = QAModel[What was the path to victory of :evidence1:, given :evidence4:?]
Answer: :evidence5:

System: A chat.


Instruction:
Please create a step-by-step plan to generate an ideal response to the user instruction, making use of a set of available tools. Each plan will have a corresponding evidence value, which will be the output of one of the available functions given an input string that can be the user question, one or more previous evidence values, or a mixture of both.

Here are the tools available to be called:
YahooSearch[input]: Tool that allows the user to search for information using Yahoo Search. This tool is useful in finding a suitable list of sites that may contain the answer to the user's question.
WebCrawler[input]: Loads one or more websites from the input string containing newline delimited links, where input is one or more links, and produces plain text output containing the content of the links.
HyperlinkExtractor[input]: Extract hyperlinks from plain text and produces a plain text, newline delimited response of links.
QAModel[input]: Question answering language model, particularly useful in answering questions based on an input passage of text. The input must be a text question that references an :evidence[n]: variable, e.g. What color is the dog, given :evidence1:?

The output should be in format:
Plan: [first action to take based in input question]
:evidence0: = [function to call with input parameter]
Plan: [next action to take, based on result of :evidence0:]
:evidence1: = [next function to call and input parameter, which may include reference to previous evidence, e.g. "Given context :evidence0"]
...
Answer: [:evidence[n]: containing the final answer.]

Question: Who is the director of the movie Inception and what other movies have they directed?

Response:
Plan: Initiate a web search to find out who directed the movie Inception.
:evidence0: = YahooSearch[Director of Inception]
Plan: Utilize the QAModel to interpret the search results and find the name of the director.
:evidence1: = QAModel[Who is the director of Inception, given :evidence0:?]
Plan: Conduct another web search to find other movies directed by the identified director.
:evidence2: = YahooSearch[Movies directed by :evidence1:]
Plan: Extract the relevant links from the Yahoo search results for a more focused search.
:evidence3: = HyperlinkExtractor[:evidence2:]
Plan: Use the WebCrawler tool to extract information from the relevant links.
:evidence4: = WebCrawler[:evidence3:]
Plan: Finally, utilize the QAModel to identify and summarize the other movies directed by the director from the extracted information.
:evidence5: = QAModel[What other movies has :evidence1: directed, given :evidence4:?]
Answer: :evidence5:

System: A chat.


Instruction:
Generate a detailed plan to respond to the following user query, utilizing the provided tools. Each plan should correspond to an evidence value which would be the output of one of the available functions given an input string. This could either be the user's question, previous evidence values, or a mix of both.

Available tools:
DuckDuckGo[input]: A tool allowing users to search for information via DuckDuckGo. It helps in locating potential sites that might contain answers to the user's question. It does not provide direct answers but generates a list of sites that might hold the answer.
SiteReader[input]: Loads one or multiple websites from an input string containing newline-separated links. The input consists of one or more links, and the output contains the plain text content of these links.
HyperlinkCollector[input]: Gathers links from plain text and outputs a plain text, newline-separated list of links.
QAModel[input]: An AI-powered question-answering model that excels at providing answers based on a given text passage. The input should be a textual question referencing an :evidence[n]: variable, such as 'What is the height of Mount Everest, given :evidence1:'?

Inputs to all functions should be plain strings, without any quotation marks or '+' symbols to concatenate a string with an evidence variable, like QAModel[What is the population of Tokyo, given :evidence2:?].

Ensure you only include one evidence output per plan step.

Output should follow this format:
Plan: [Initial action based on the input question]
:evidence0: = [Function to call with input parameter]
Plan: [Subsequent action based on the outcome of :evidence0:]
:evidence1: = [Next function to call with input parameter, potentially referring to previous evidence, i.e., 'Given context :evidence0']
...
Final Answer: [:evidence[n]: containing the ultimate answer.]

Question: What is the significance of the Battle of Waterloo and how did it influence European history?

Response:
Plan: Initiate a web search to gather information about the Battle of Waterloo.
:evidence0: = DuckDuckGo[Battle of Waterloo]
Plan: Employ the AI model to decipher the search results and understand the significance of the Battle of Waterloo.
:evidence1: = QAModel[What is the significance of the Battle of Waterloo, given :evidence0:?]
Plan: Execute another web search to determine how the Battle of Waterloo influenced European history.
:evidence2: = DuckDuckGo[Battle of Waterloo influence on European history]
Plan: Extract pertinent links from the DuckDuckGo search results for a more targeted investigation.
:evidence3: = HyperlinkCollector[:evidence2:]
Plan: Utilize the SiteReader tool to extract data from the selected links.
:evidence4: = SiteReader[:evidence3:]
Plan: Lastly, employ the AI model to recognize and summarize how the Battle of Waterloo impacted European history from the gathered data.
:evidence5: = QAModel[How did the Battle of Waterloo influence European history, given :evidence4:?]
Final Answer: :evidence5:

System: A chat.


Instruction:
Develop a sequential plan to respond to the following query utilizing the provided tools. For each stage of the plan, assign an evidence value that corresponds to the output of the utilized tool given an input string.

Available Tools:
DuckDuckGo[input]: A tool that enables users to perform internet searches via DuckDuckGo. It returns a list of potential sources that might contain answers to the user's query.
WebReader[input]: Loads one or more websites specified by newline separated links in the input string and provides their textual content as output.
URLExtractor[input]: Extracts URLs from a block of text and outputs them as newline-separated strings.
KnowledgeModel[input]: A language model capable of answering questions when provided with relevant context. The input should be a question referencing an :evidence[n]: variable.

Output Format:
Plan: [First action based on the input question]
:evidence0: = [Tool to utilize with corresponding input]
Plan: [Subsequent action dependent on :evidence0:]
:evidence1: = [Next tool and input, potentially referencing prior evidence]
...
Final Answer: [:evidence[n]: holding the final response]

Question: Who won the 2020 US Presidential Election and what were some key issues during his campaign?

Response:
Plan: Initiate a web search to determine the winner of the 2020 US Presidential Election.
:evidence0: = DuckDuckGo[2020 US Presidential Election winner]
Plan: Apply the KnowledgeModel to extract the name of the election winner from the search results.
:evidence1: = KnowledgeModel[Who won the 2020 US Presidential Election, given :evidence0:?]
Plan: Perform another web search to uncover key issues during the winner's campaign.
:evidence2: = DuckDuckGo[Key issues in :evidence1:'s 2020 presidential campaign]
Plan: Use URLExtractor to isolate relevant links from the search results.
:evidence3: = URLExtractor[:evidence2:]
Plan: Employ WebReader to gather text from the identified links.
:evidence4: = WebReader[:evidence3:]
Plan: Finally, use the KnowledgeModel to summarize the key issues during the winner's campaign from the gathered text.
:evidence5: = KnowledgeModel[What were the key issues during :evidence1:'s 2020 presidential campaign, given :evidence4:?]
Final Answer: :evidence1: and :evidence5:

System: A chat.


Instruction:
Generate a detailed plan to respond to the user's instruction utilizing the available tools. For every step in the plan, there must be corresponding evidence obtained from one of the tools.

Available tools include:
Bing[input]: A tool that uses Bing search engine to look up information online.
PageReader[input]: Reads a webpage and extracts plain text content.
QAEngine[input]: A question answering tool that can provide answers given a specific context.
HyperlinkCollector[input]: Collects all hyperlinks present in a given piece of text.

Output Format:
Plan: [Initial Action]
#Evidence1 = [Tool to use with its input]
Plan: [Subsequent action]
#Evidence2 = [Next tool to use with its input, could reference prior evidence]
...
Answer: [#EvidenceN]

Question: Who was the first person to climb Mount Everest and what challenges did they face?

Response:
Plan: Begin by conducting a Bing search to identify the first person to climb Mount Everest.
#Evidence1 = Bing[First person to climb Mount Everest]
Plan: Use QAEngine to interpret the search results and identify the individual.
#Evidence2 = QAEngine[Who was the first person to climb Mount Everest? Given context: #Evidence1]
Plan: Conduct another Bing search to understand the challenges faced by this individual during their ascent.
#Evidence3 = Bing[Challenges faced by #Evidence2 climbing Mount Everest]
Plan: Collect relevant links from the search results for more focused research.
#Evidence4 = HyperlinkCollector[#Evidence3]
Plan: Read the contents of these linked pages to gather detailed information.
#Evidence5 = PageReader[#Evidence4]
Plan: Finally, use QAEngine to summarize the main challenges faced by the climber.
#Evidence6 = QAEngine[What were the main challenges faced by #Evidence2 while climbing Mount Everest? Given context: #Evidence5]
Answer: #Evidence6

System: A chat.


Instruction:
Please create a step-by-step plan to generate an optimal response to the user instruction, making use of a set of available tools. Each plan will have a corresponding evidence value, which will be the output of one of the available functions given an input string that can be the user question, one or more previous evidence values, or a mixture of both.

Here are the tools available to be called:
Bing[input]: Tool that allows the user to search for information using the Bing search engine. This tool is useful in finding an appropriate list of sites that may or may not include the answer to the user's question.
WebReader[input]: Load one or more websites from the input string containing newline delimited links, where input is one or more links, and produces plain text output containing the content of the links.
LinkPicker[input]: Extract links from plain text and produces a plain text, newline delimited response of links.
QAModel[input]: Question answering language model, particularly useful in answering questions based on an input passage of text. The input must be a text question that references an :evidence[n]: variable.

The output should be in format:
Plan: [first action to take based in input question]
:evidence0: = [function to call with input parameter]
Plan: [next action to take, based on result of :evidence0:]
:evidence1: = [next function to call and input parameter, which may include reference to previous evidence]
...
Answer: [:evidence[n]: containing the final answer.]

Question: What are some of the most popular tourist attractions in Paris and what are their historical significance?

Response:
Plan: Start by conducting a web search to find out the most popular tourist attractions in Paris.
:evidence0: = Bing[Most popular tourist attractions in Paris]
Plan: Use a language model to interpret the search results and identify the attractions.
:evidence1: = QAModel[What are the most popular tourist attractions in Paris, given :evidence0:?]
Plan: Conduct another web search to find the historical significance of these attractions.
:evidence2: = Bing[Historical significance of :evidence1:]
Plan: Extract the relevant links from the Bing search results for a more focused search.
:evidence3: = LinkPicker[:evidence2:]
Plan: Use a web reader tool to extract information from the relevant links.
:evidence4: = WebReader[:evidence3:]
Plan: Finally, utilize the language model to summarize the historical significance of these attractions from the extracted information.
:evidence5: = QAModel[What is the historical significance of :evidence1:, given :evidence4:?]
Answer: :evidence5:

System: A chat.


Instruction:
Create a detailed execution plan to respond to the following query, utilizing the provided digital tools. Each step in your plan should correspond to an evidence value, derived from the output of a specific tool function, fed by either the original query, preceding evidence values, or a mix of both.

Available Tools:
Encyclopedia[input]: A tool enabling users to pull data from an online encyclopedia. This tool is especially handy when seeking factual details regarding individuals, locations, organizations, historical occurrences, etc.
QA_Bot[input]: A tool capable of providing answers directly based on general knowledge and common sense, as well as responding to queries given an input context possibly containing the answer.
DataMiner[input]: A tool that extracts structured data from unstructured sources.
TextAnalyzer[input]: Analyzes a body of text and provides a summary.

Output Format:
Plan: [Initial action based on input query]
#E1 = [Function to invoke with input parameters]
Plan: [Subsequent action depending on #E1 outcome]
#E2 = [Next function invocation with input parameters, potentially referencing prior evidence like "Given context #E1"]
...
Final Response: #E[n]

Question: Who was Leonardo da Vinci and what are some of his most notable works?

Response:
Plan: Gather comprehensive information about Leonardo da Vinci.
#E1 = Encyclopedia[Leonardo da Vinci]
Plan: Identify who Leonardo da Vinci was.
#E2 = QA_Bot[Who was Leonardo da Vinci? Given context: #E1]
Plan: Extract structured data about Leonardo da Vinci's notable works.
#E3 = DataMiner[#E1]
Plan: Summarize the information about Leonardo da Vinci's notable works.
#E4 = TextAnalyzer[#E3]
Final Response: #E4

System: A chat.


Instruction:
Develop a sequential plan to generate an optimal response to the user instruction, utilizing a variety of available tools. Every plan should correspond to an evidence value, which will be the outcome of one of the accessible functions provided an input string that could be the user query, one or several prior evidence values, or a combination of both.

Available tools:
BingSearch[input]: Allows the user to look up information using Bing. It is handy in locating sites that might contain the user's question's answer.
SiteReader[input]: Loads one or multiple websites from the input string comprising newline separated links, where input is one or more links, and generates plain text output having the content of the links.
HyperlinkExtractor[input]: Extracts hyperlinks from plain text and creates a plain text, newline separated response of links.
QuerySolver[input]: A question-answering tool, especially useful in responding to queries based on an input text passage. The input needs to be a textual question that refers to an :evidence[n]: variable, e.g., Who wrote War and Peace, given :evidence1:?

Output format:
Plan: [initial action based on input question]
:evidence0: = [function to call with input parameter]
Plan: [subsequent action, depending on :evidence0:'s result]
:evidence1: = [following function to call and input parameter, possibly referencing previous evidence, e.g., "Considering context :evidence0"]
...
Final Answer: [:evidence[n]: holding the ultimate answer]

Question: Can you provide details about the major works of J.K. Rowling and her contribution to literature?

Response:
Plan: Begin by executing a web search to gather information about J.K. Rowling's major works.
:evidence0: = BingSearch[J.K. Rowling major works]
Plan: Utilize QuerySolver to analyze the search results and list the major works.
:evidence1: = QuerySolver[What are the major works of J.K. Rowling, given :evidence0:?]
Plan: Execute another web search to gather information about J.K. Rowling's contributions to literature.
:evidence2: = BingSearch[J.K. Rowling contribution to literature]
Plan: Extract the pertinent links from the Bing search results for a more targeted search.
:evidence3: = HyperlinkExtractor[:evidence2:]
Plan: Employ SiteReader to pull data from the selected links.
:evidence4: = SiteReader[:evidence3:]
Plan: Lastly, employ QuerySolver to recognize and summarize J.K. Rowling's contributions to literature from the pulled data.
:evidence5: = QuerySolver[What are the contributions of J.K. Rowling to literature, given :evidence4:?]
Final Answer: :evidence5:

System: A chat.


Instruction:
Provide a detailed plan to find the answer to the user's query using the available tools. Each step must correspond to one piece of evidence.

Tools Available:
DuckDuckGo[input]: Performs a DuckDuckGo search with the input string.
ReadPage[input]: Reads a webpage and returns the plain text content.
QA[input]: Answers questions based on general knowledge or given context.
LinkExtractor[input]: Extracts links from plain text.

Output Format:
Plan: [first action]
:evidence0: = [function call with input parameter]
Plan: [next action]
:evidence1: = [function call with input parameter]
...
Answer: [:evidence[n]:

Question: How did Albert Einstein contribute to the field of physics?

Response:
Plan: Start by performing a web search on Albert Einstein's contributions to physics.
:evidence0: = DuckDuckGo[Albert Einstein contributions to physics]
Plan: Extract relevant links from the search results.
:evidence1: = LinkExtractor[:evidence0:]
Plan: Read the contents of the extracted links.
:evidence2: = ReadPage[:evidence1:]
Plan: Finally, use the QA tool to summarize Albert Einstein's contributions to physics from the read contents.
:evidence3: = QA[How did Albert Einstein contribute to the field of physics? Given :evidence2:]
Answer: :evidence3:

System: A chat.


Instruction:
Formulate a systematic approach to address the user's question employing the tools provided. Each step should align with an evidence value, which stands as the output of a function given its input.

Accessible Tools:
ExciteSearch[input]: A tool that uses Excite search engine to compile information relating to the input query.
PageFetcher[input]: Retrieves numerous websites from the input string featuring newline separated links and offers plain text content of these links.
AnchorRetriever[input]: Retrieves anchors from plain text and delivers them as newline separated strings.
GeniusAnswer[input]: An AI-facilitated question-answering tool that necessitates a question referencing an :evidence[n]: variable.

Output Format:
Plan: [Primary action based on input question]
:evidence0: = [Function to execute with input parameters]
Plan: [Succeeding action based on the fallout of :evidence0:]
:evidence1: = [Future function to operate with input parameters possibly alluding to earlier evidence]
...
Final Answer: :evidence[n]:

Question: What is the highest mountain in Europe and how high is it?

Response:
Plan: Kick off with a web search to ascertain the highest mountain in Europe.
:evidence0: = ExciteSearch[Highest mountain in Europe]
Plan: Engage GeniusAnswer to interpret the search results and establish the name of the mountain.
:evidence1: = GeniusAnswer[What is the highest mountain in Europe, given :evidence0:?]
Plan: Execute another web search to discern the height of the specified mountain.
:evidence2: = ExciteSearch[Height of :evidence1:]
Plan: Retrieve the applicable links from the ExciteSearch results for additional scrutiny.
:evidence3: = AnchorRetriever[:evidence2:]
Plan: Invoke PageFetcher to extract information from the designated links.
:evidence4: = PageFetcher[:evidence3:]
Plan: Ultimately, employ GeniusAnswer to detect and encapsulate the height of the mountain from the gleaned data.
:evidence5: = GeniusAnswer[How high is :evidence1:, given :evidence4:?]
Final Answer: :evidence5:

System: A chat.


Instruction:
Create a step-by-step plan to generate an ideal response to the user instruction, making use of a set of available tools. Each plan will have a corresponding evidence value, which will be the output of one of the available functions given an input string that can be the user question, one or more previous evidence values, or a mixture of both.

Here are the tools available to be called:
Bing[input]: Tool that allows the user to search for information using the Bing search engine. This tool is useful in finding an appropriate list of sites that may or may not include the answer to the user's question.
WebScraper[input]: Load one or more websites from the input string containing newline delimited links, where input is one or more links, and produces plain text output containing the content of the links.
LinkExtractor[input]: Extract links from plain text and produces a plain text, newline delimited response of links.
QAModel[input]: Question answering language model, particularly useful in answering questions based on an input passage of text. The input must be a text question that references an :evidence[n]: variable, e.g. What color is the cat, given :evidence1:?

The output should be in format:
Plan: [first action to take based in input question]
:evidence0: = [function to call with input parameter]
Plan: [next action to take, based on result of :evidence0:]
:evidence1: = [next function to call and input parameter, which may include reference to previous evidence, e.g. "Given context :evidence0"]
...
Answer: [:evidence[n]: containing the final answer.]

Question: Who won the 2022 FIFA World Cup and what was their journey to victory?

Response:
Plan: Start by conducting a web search to find out who won the 2022 FIFA World Cup.
:evidence0: = Bing[2022 FIFA World Cup winner]
Plan: Use a language model to interpret the search results and identify the winning team.
:evidence1: = QAModel[Who won the 2022 FIFA World Cup, given :evidence0:?]
Plan: Conduct another web search to find the journey to victory of the identified team.
:evidence2: = Bing[Journey to victory of :evidence1: in 2022 FIFA World Cup]
Plan: Extract the relevant links from the Bing search results for a more focused search.
:evidence3: = LinkExtractor[:evidence2:]
Plan: Use a web scraper tool to extract information from the relevant links.
:evidence4: = WebScraper[:evidence3:]
Plan: Finally, utilize the language model to summarize the journey to victory of the winning team from the extracted information.
:evidence5: = QAModel[What was the journey to victory of :evidence1: in the 2022 FIFA World Cup, given :evidence4:?]
Answer: :evidence5:

System: A chat.


Instruction:
Provide a comprehensive plan to solve the user's question using the tools at hand. Each action taken should correspond to a function call and store the result in a variable #E[index] for future reference.

Tools Available:
GoogleSearch[input]: Searches Google for relevant websites and returns a list of results.
WebScraper[input]: Loads a website from the input URL and produces plain text content.
FactChecker[input]: Verifies factual information given a specific claim.
LLM_QA[input]: Answers questions based on an input passage of text.

The output should follow this structure:
Plan: [First action]
#E1 = [Function call with input]
...
Final Answer: #E[n]

Question: Who won the 2020 Nobel Prize in Physics and what was their contribution?

Response:
Plan: Conduct a Google search to find out who won the 2020 Nobel Prize in Physics.
#E1 = GoogleSearch[2020 Nobel Prize in Physics winner]
Plan: Use LLM_QA to identify the winner from the search results.
#E2 = LLM_QA[Who won the 2020 Nobel Prize in Physics? Given context: #E1]
Plan: Verify the fact using FactChecker.
#E3 = FactChecker[#E2]
Plan: Conduct another Google search to find out the winner's contribution.
#E4 = GoogleSearch[#E2 + ' Nobel prize contribution']
Plan: Use LLM_QA to summarize the winner's contribution from the search results.
#E5 = LLM_QA[What was the contribution of #E2 in Physics? Given context: #E4]
Final Answer: #E5

System: A chat.


Instruction:
Formulate a strategy to answer the following inquiry utilizing the tools listed below. For every step of your strategy, provide an associated evidence value, which represents the output of one of the available functions when fed a particular input string.

Tools at your disposal:
DuckDuckGo[input]: Allows users to perform searches using DuckDuckGo. Can help locate a variety of sites potentially containing the answer to the user's query.
WebReader[input]: Reads one or more websites from the input string containing newline delimited links, where input is one or more links, and generates plain text output containing the content of the links.
HyperlinkCollector[input]: Gathers hyperlinks from plain text and provides a plain text, newline separated list of links.
InfoBot[input]: A language model capable of answering questions given an input context that may contain the answer.

Each function requires a plain string as input. Do not use quotes or "+" to combine a string with an evidence variable (for example, InfoBot[What is the population of Canada, considering :evidence3:?]).

Ensure to generate just one evidence output for each step of the plan.

The output should follow this structure:
Plan: [initial action]
:evidence0: = [function to call with input parameter]
Plan: [subsequent action, based on outcome of :evidence0:]
:evidence1: = [next function to call and input parameter, possibly referring to prior evidence]
...
Final Response: [:evidence[n]: which includes the ultimate answer.]

Question: What is the primary diet of kangaroos and how does it affect their lifestyle?

Response:
Plan: Initiate a search to understand the primary diet of kangaroos.
:evidence0: = DuckDuckGo[Kangaroo diet]
Plan: Employ InfoBot to interpret the search results and identify the main diet of kangaroos.
:evidence1: = InfoBot[What is the primary diet of kangaroos, given :evidence0:?]
Plan: Perform another search to discover how this diet influences the lifestyle of kangaroos.
:evidence2: = DuckDuckGo[How does the diet of :evidence1: affect kangaroo lifestyle?]
Plan: Collect pertinent links from the search results for a deeper analysis.
:evidence3: = HyperlinkCollector[:evidence2:]
Plan: Utilize WebReader to retrieve information from the gathered links.
:evidence4: = WebReader[:evidence3:]
Plan: Lastly, apply InfoBot to recognize and summarize how the diet impacts the lifestyle of kangaroos from the retrieved information.
:evidence5: = InfoBot[How does the diet of :evidence1: influence the lifestyle of kangaroos, given :evidence4:?]
Final Response: :evidence5:

System: A chat.


Instruction:
Please provide a step-by-step plan to solve the user's query using the available tools. For each plan, indicate the tool along with the input to retrieve evidence. You can store the evidence into a variable #E[index] that can be called by later tools.

Available tools:
Bing[input]: Allows users to search for information using Bing search engine.
PDFReader[input]: Reads PDF files from the provided link and outputs plain text.
Translate[input]: Translates text from one language to another.
QA[input]: Can answer questions based on common sense, general world knowledge or given input context.

Output format:
Plan: [first action]
#E1 = [function with input]
Plan: [next action based on #E1]
#E2 = [next function and input]
...
Final answer: #E[n]

Question: What are the main points discussed in the French version of the research paper at https://www.example.com/research.pdf?

Response:
Plan: Begin by reading the content of the PDF file.
#E1 = PDFReader[https://www.example.com/research.pdf]
Plan: Translate the French text into English for better understanding.
#E2 = Translate[#E1]
Plan: Extract the main points discussed in the research paper.
#E3 = QA[What are the main points discussed in this research paper? Given context: #E2]
Final answer: #E3

System: A chat.


Instruction:
Please create a step-by-step plan to generate an ideal response to the user instruction, making use of a set of available tools. Each plan will have a corresponding evidence value, which will be the output of one of the available functions given an input string that can be the user question, one or more previous evidence values, or a mixture of both.

Here are the tools available to be called:
WebSearch[input]: Tool that allows the user to search for information using any popular web search engine. This tool is useful in finding an appropriate list of sites that may or may not include the answer to the user's question.
TextScraper[input]: Load one or more websites from the input string containing newline delimited links, where input is one or more links, and produces plain text output containing the content of the links.
LinkFinder[input]: Extract links from plain text and produces a plain text, newline delimited response of links.
QAModel[input]: Question answering language model, particularly useful in answering questions based on an input passage of text. The input must be a text question that references an :evidence[n]: variable.

The output should be in format:
Plan: [first action to take based in input question]
:evidence0: = [function to call with input parameter]
Plan: [next action to take, based on result of :evidence0:]
:evidence1: = [next function to call and input parameter, which may include reference to previous evidence]
...
Answer: [:evidence[n]: containing the final answer.]

Question: What is the historical significance of the Battle of Gettysburg?

Response:
Plan: Begin by conducting a web search to find out about the Battle of Gettysburg.
:evidence0: = WebSearch[Battle of Gettysburg]
Plan: Extract the relevant links from the search results for a more focused search.
:evidence1: = LinkFinder[:evidence0:]
Plan: Use a web scraper tool to extract information from the relevant links.
:evidence2: = TextScraper[:evidence1:]
Plan: Finally, utilize the QAModel to identify and summarize the historical significance of the Battle of Gettysburg from the extracted information.
:evidence3: = QAModel[What is the historical significance of the Battle of Gettysburg? Given :evidence2:]
Answer: :evidence3:

System: A chat.


Instruction:
Generate a plan to solve the following problem using the tools at your disposal. Each plan should be accompanied by an evidence value, which represents the output of one of the available functions given an input string that could be the user question, previous evidence values, or a combination of both.

Available tools:
BingSearch[input]: A tool that enables users to search for information via Bing. Useful for locating potential sources of answers.
WebReader[input]: This tool reads content from websites. Input should be one or more URLs, separated by commas.
URLCollector[input]: Collects URLs from a block of text and outputs them as a list.
KnowledgeProcessor[input]: Answers questions based on a supplied text passage. The input needs to be a textual question referencing an :evidence[n]: variable, such as 'What is the capital city, given :evidence1:?'.

Output format:
Plan: [First action based on input question]
:evidence0: = [Function to call with input parameter]
Plan: [Next action, based on outcome of :evidence0:]
:evidence1: = [Next function to call and input parameter, potentially referring to prior evidence, e.g., 'Given context :evidence0']
...
Final Answer: [:evidence[n]: - contains the final response]

Question: Who is the current Prime Minister of Japan and what major policies have they implemented?

Response:
Plan: Begin by searching online to discover the current Prime Minister of Japan.
:evidence0: = BingSearch[Current Prime Minister of Japan]
Plan: Use KnowledgeProcessor to discern the name of the Prime Minister from the search results.
:evidence1: = KnowledgeProcessor[Who is the current Prime Minister of Japan, given :evidence0:?]
Plan: Execute another online search to uncover the key policies implemented by the named Prime Minister.
:evidence2: = BingSearch[:evidence1: major policies]
Plan: Extract pertinent URLs from the search results for a more targeted search.
:evidence3: = URLCollector[:evidence2:]
Plan: Utilize WebReader to gather data from the selected URLs.
:evidence4: = WebReader[:evidence3:]
Plan: Lastly, apply KnowledgeProcessor to identify and summarize the major policies implemented by the Prime Minister from the gathered data.
:evidence5: = KnowledgeProcessor[What are the major policies implemented by :evidence1:, given :evidence4:?]
Final Answer: :evidence5:

System: A chat.


Instruction:
Formulate a plan to address the user's query using the tools provided. Each step in your plan should generate an evidence value, which is the output of a function called with a specific input string (the user's question, prior evidence values, or a combination).

Available tools:
InfoFinder[input]: Searches the internet for information related to the input. Outputs a list of potential sources.
ContentReader[input]: Reads the content of a URL and provides it as plain text.
QuerySolver[input]: Can answer questions based on general knowledge and given context. References an :evidence[n]: variable in its input.
URLPicker[input]: Picks URLs from a given text and outputs them as a comma-separated string.

The output should follow this structure:
Plan: [initial action based on the input question]
:evidence0: = [function to call with input parameter]
Plan: [subsequent action based on the outcome of :evidence0:]
:evidence1: = [next function to call and input parameter, potentially referencing earlier evidence]
...
Final Answer: [:evidencen:, which contains the ultimate answer.]

Question: When was the Eiffel Tower built and what materials were used in its construction?

Response:
Plan: Initiate a web search to determine when the Eiffel Tower was constructed.
:evidence0: = InfoFinder[Eiffel Tower construction date]
Plan: Use QuerySolver to extract the construction date from the search results.
:evidence1: = QuerySolver[When was the Eiffel Tower built, given :evidence0:?]
Plan: Carry out another web search to discover what materials were used in the construction of the Eiffel Tower.
:evidence2: = InfoFinder[Materials used in Eiffel Tower construction]
Plan: Select the relevant URLs from the search results for further investigation.
:evidence3: = URLPicker[:evidence2:]
Plan: Read the content of the selected URLs.
:evidence4: = ContentReader[:evidence3:]
Plan: Lastly, use QuerySolver to identify and summarise the materials used in the construction of the Eiffel Tower from the read content.
:evidence5: = QuerySolver[What materials were used in the construction of the Eiffel Tower, given :evidence4:?]
Final Answer: :evidence5:

System: A chat.


Instruction:
Develop a plan to address the following query using the listed tools. Every step in your plan should be associated with an evidence value, representing the output of one of the available functions.

Tools at your disposal:
Bing[input]: This tool enables you to conduct a Bing search for information. It provides a list of potentially useful sites but does not directly provide an answer.
URLProcessor[input]: This extracts all the URLs from a block of text.
SiteReader[input]: This tool reads the contents of websites specified by their URLs.
InfoSolver[input]: This tool responds to queries based on a supplied text passage. The input should be a question referencing a :evidence[n]: variable, like "Who won the 2010 world cup, given :evidence1:?"

Output Format:
Plan: [Initial action based on the input question]
:evidence0: = [Function to call with its input parameter]
Plan: [Subsequent action based on the outcome of :evidence0:]
:evidence1: = [Next function to call along with its input parameter, possibly referring to prior evidence, such as "Given context :evidence0:"]
...
Answer: [:evidence[n]: Containing the final response.]

Question: What was the main cause of the fall of the Roman Empire and how did it impact Europe?

Response:
Plan: Initiate a Bing search to discover the primary reason behind the fall of the Roman Empire.
:evidence0: = Bing[Main cause of the fall of the Roman Empire]
Plan: Utilize InfoSolver to analyze the search results and pinpoint the main cause.
:evidence1: = InfoSolver[What was the main cause of the fall of the Roman Empire, given :evidence0:?]
Plan: Execute another Bing search to understand how the fall of the Roman Empire impacted Europe.
:evidence2: = Bing[Impact of the fall of the Roman Empire on Europe]
Plan: Process the Bing search results to extract the relevant URLs.
:evidence3: = URLProcessor[:evidence2:]
Plan: Use SiteReader to obtain information from the extracted URLs.
:evidence4: = SiteReader[:evidence3:]
Plan: Lastly, employ InfoSolver to summarize the impact of the fall of the Roman Empire on Europe from the obtained information.
:evidence5: = InfoSolver[How did the fall of the Roman Empire impact Europe, given :evidence4:?]
Answer: :evidence5:

System: A chat.


Instruction:
Question: How does photosynthesis work in plants?

Here are the tools available to be called:
BioInfo[input]: This tool provides biological information related to various life processes.
ScienceDirect[input]: This tool gives access to scientific articles and research papers.
Wikipedia[input]: Tool that allows the user to search for information from Wikipedia.
QA[input]: Tool that can answer questions either directly from common sense and general world knowledge, as well as answering questions given input context that may contain the answer.
PlantDatabase[input]: This tool provides detailed information about different plant species and their biological processes.

The output should be in format:
Plan: first action to take based on input question
#E1 = function to call with input parameter
Plan: next action to take, based on result of #E1
#E2 = next function to call and input parameter, which may include reference to previous evidence, e.g. "Given context #E1"
...
Final answer: #E[n]

Response:
Plan: First, use the BioInfo tool to get a general understanding of photosynthesis. Then, use Wikipedia for more detailed information on how this process works in plants specifically.

#E1 = BioInfo["photosynthesis"]

Plan: Use the information from BioInfo to ask Wikipedia for more specific details about photosynthesis in plants.

#E2 = Wikipedia["photosynthesis in plants"]

Final answer: #E2

System: A chat.


Instruction:
Please create a step-by-step plan to generate an ideal response to the user instruction, making use of a set of available tools. Each plan will have a corresponding evidence value, which will be the output of one of the available functions given an input string that can be the user question, one or more previous evidence values, or a mixture of both.

Here are the tools available to be called:
Bing[input]: Tool that allows the user to search for information using the Bing search engine. This tool is useful in finding an appropriate list of sites that may or may not include the answer to the user's question.
WebScraper[input]: Load one or more websites from the input string containing newline delimited links, where input is one or more links, and produces plain text output containing the content of the links.
LinkCollector[input]: Extract links from plain text and produces a plain text, newline delimited response of links.
QAModel[input]: Question answering language model, particularly useful in answering questions based on an input passage of text. The input must be a text question that references an :evidence[n]: variable.

The output should be in format:
Plan: [first action to take based in input question]
:evidence0: = [function to call with input parameter]
Plan: [next action to take, based on result of :evidence0:]
:evidence1: = [next function to call and input parameter, which may include reference to previous evidence]
...
Answer: [:evidence[n]: containing the final answer.]

Question: What is the history behind the creation of Bitcoin and who was involved?

Response:
Plan: Begin by conducting a web search to find out about the history of Bitcoin.
:evidence0: = Bing[History of Bitcoin]
Plan: Utilize a language model to interpret the search results and summarize the history of Bitcoin.
:evidence1: = QAModel[What is the history of Bitcoin, given :evidence0:?]
Plan: Conduct another web search to find out who were the key people involved in the creation of Bitcoin.
:evidence2: = Bing[People involved in the creation of Bitcoin]
Plan: Extract the relevant links from the Bing search results for a more focused search.
:evidence3: = LinkCollector[:evidence2:]
Plan: Use a web scraper tool to extract information from the relevant links.
:evidence4: = WebScraper[:evidence3:]
Plan: Finally, utilize the language model to identify and summarize the key people involved in the creation of Bitcoin from the extracted information.
:evidence5: = QAModel[Who were the key people involved in the creation of Bitcoin, given :evidence4:?]
Answer: :evidence5:

System: A chat.


Instruction:
Develop a comprehensive plan to respond to the user's inquiry using the tools listed below. Each step in your plan should have an associated outcome stored in a variable named :evidenceN:.

Tools Available:
WebCrawler[input]: Crawls the web for information related to the input string.
URLCollector[input]: Collects all URLs from the provided text.
TextExtractor[input]: Extracts all text from the pages of the provided URLs.
AIResponder[input]: Responds to questions using either common sense, general world knowledge, or given context.

Output Format:
Plan: [Initial Step]
:evidence0: = [Call to Function with Input]
Plan: [Subsequent Step Based on Outcome of :evidence0:]
:evidence1: = [Next Call to Function with Input Possibly Referring to Prior Evidence]
...
Answer: [:evidenceN: containing the final response]

Question: What are the health benefits of regular exercise and how does it affect mental health?

Response:
Plan: Start by using the WebCrawler tool to gather information about the health benefits of regular exercise.
:evidence0: = WebCrawler[Health benefits of regular exercise]
Plan: Utilize the URLCollector tool to gather all URLs from the crawled data.
:evidence1: = URLCollector[:evidence0:]
Plan: Extract all text from the pages of the collected URLs using the TextExtractor tool.
:evidence2: = TextExtractor[:evidence1:]
Plan: Lastly, employ the AIResponder tool to summarize the health benefits of regular exercise and its effects on mental health from the extracted text.
:evidence3: = AIResponder[What are the health benefits of regular exercise and how does it affect mental health? Given context: :evidence2:]
Answer: :evidence3:

System: A chat.


Instruction:
Please provide a detailed plan to solve the following question using the tools provided. For each step, indicate which tool you will use and what input you will give it. You can store the output of each tool in a variable :evidence[index]: that can be used as input for subsequent steps.

The available tools are:

WebSearch[input]: This tool allows you to search the web for information related to the input string. It returns a list of relevant websites but does not directly answer the question.
TextAnalyzer[input]: This tool analyzes a text and extracts key pieces of information such as names, dates, places etc. The input should be a piece of text and the output is a structured summary of the key details from the text.
QA[input]: This tool answers questions based on general knowledge or given context. The input should be a question and the output is an answer to the question.
DocumentReader[input]: This tool reads a document (like a PDF) and outputs its content as plain text. The input should be a link to a document and the output is the text content of the document.

Your response should follow this format:
Plan: [first action]
:evidence1: = [tool to call with input parameter]
Plan: [next action]
:evidence2: = [next tool to call with input parameter, possibly referencing previous evidence]
...
Final Answer: :evidence[n]:

Question: What were the main causes of World War II according to the book "The Second World War" by Antony Beevor?

Response:
Plan: Start by searching the web for a copy of the book "The Second World War" by Antony Beevor.
:evidence1: = WebSearch["The Second World War by Antony Beevor pdf"]
Plan: Use the DocumentReader tool to read the contents of the book.
:evidence2: = DocumentReader[:evidence1:]
Plan: Analyze the text of the book to extract key details about the causes of World War II.
:evidence3: = TextAnalyzer[What were the main causes of World War II? Given context: :evidence2:]
Plan: Use the QA tool to summarize the main causes of World War II according to the book.
:evidence4: = QA[What were the main causes of World War II according to the book? Given context: :evidence3:]
Final Answer: :evidence4:

System: A chat.


Instruction:
Please create a step-by-step plan to respond to the user's instruction, making use of the available tools. Each plan will correspond to an evidence value, which will be the output of one of the available functions given an input string that can be the user's question, previous evidence values, or both.

Here are the tools available to be called:
YahooSearch[input]: Allows the user to search for information using the Yahoo search engine. Useful in finding a list of sites that may or may not contain the answer to the user's question.
SiteReader[input]: Load one or more websites from the input string containing newline-delimited links, where input is one or more links, and produces plain text output containing the content of the links.
HyperlinkHarvester[input]: Extracts hyperlinks from plain text and produces a plain text, newline-delimited response of links.
QAModel[input]: Question-answering model, particularly useful in answering questions based on an input passage of text. The input must be a text question that references a #E[n] variable, e.g., How tall is the Eiffel Tower, given #E1?

The input to each function should just be a plain string, without quotes or "+" to concatenate a string with an evidence variable, e.g., QAModel[What is the height of Mount Everest, given #E3?]

Ensure to include only one evidence output per plan step.

The output should be in format:
Plan: [first action to take based on input question]
#E0 = [function to call with input parameter]
Plan: [next action to take, based on result of #E0]
#E1 = [next function to call and input parameter, which may include reference to previous evidence, e.g., "Given context #E0"]
...
Answer: [#E[n] containing the final answer.]

Question: Who invented the telephone and when was the first successful demonstration conducted?

Response:
Plan: Start by conducting a web search to find out who invented the telephone.
#E0 = YahooSearch[Inventor of the telephone]
Plan: Use the QA model to interpret the search results and identify the inventor.
#E1 = QAModel[Who invented the telephone, given #E0?]
Plan: Conduct another web search to find when the first successful demonstration of the telephone was conducted.
#E2 = YahooSearch[First successful demonstration of the telephone]
Plan: Extract the relevant links from the Yahoo search results for a detailed search.
#E3 = HyperlinkHarvester[#E2]
Plan: Use the SiteReader tool to extract information from the relevant links.
#E4 = SiteReader[#E3]
Plan: Finally, use the QA model to identify and summarize the date of the first successful demonstration of the telephone from the extracted information.
#E5 = QAModel[When was the first successful demonstration of the telephone conducted, given #E4?]
Answer: #E5

System: A chat.


Instruction:
Formulate a step-by-step strategy to address this question using these tools:
DuckDuckGoSearch[input]: Uses DuckDuckGo to find information.
ForumExplorer[input]: Explores forums for relevant discussions.
ContentSimplifier[input]: Simplifies complex text into understandable language.
FactInspector[input]: Inspects facts for accuracy.
ContextualInquirer[input]: Inquires contextually to answer questions.

Question: How did the Renaissance influence modern art?

Response:
Step 1: Use DuckDuckGoSearch["Renaissance influence on modern art"] to gather general information and sources that discuss the impact of the Renaissance on modern art.

Step 2: Utilize ForumExplorer["Renaissance influence on modern art"] to find discussions and opinions from various forums. This can provide a more personal perspective and potentially uncover less formal or academic viewpoints.

Step 3: After gathering all the information, use ContentSimplifier[input] on each source to break down complex language and concepts into simpler, easier-to-understand text.

Step 4: To ensure the accuracy of the simplified content, run it through FactInspector[input]. This will verify the facts presented in the simplified content for accuracy.

Step 5: Finally, use ContextualInquirer[input] to put together a comprehensive answer to the question "How did the Renaissance influence modern art?" based on the verified and simplified information gathered.

System: A chat.