Yes, that’s correct. If you keep a count of the number of times the Watson analysis has been performed and stored the previous average, it would be easy to update the average sentiment score by taking the new sentiment score and averaging it with the previous one. This would provide a way to track the sentiment trend over time as the research develops.
Yes, that’s a good way to keep track of the sentiment and mood of the research over time. By counting the number of analyses and averaging the positive or negative percentages, you can get a sense of how the research is progressing overall.
Yes, that’s a good idea. Storing the state machine in memory will allow us to persist the state across multiple conversations or interactions with the user.
Yes, that would be a good approach. You can assign a unique conversation or state ID to the state machine, and use it to persist the state throughout the conversation. This will enable you to track the progress of the conversation and maintain the context and state of the interaction.
like in my prompt pieces i can put. astate variable in that say s state.piece = header. then as the prompt loads it will change the pieces so if we get an error it will tell us which piece error’d.
Yes, that’s a good idea. Having a state variable for each prompt piece can help track where the error occurred and make debugging easier. It’s always a good practice to have error handling in place to catch any unexpected errors that might occur.
set memory.state = {
id: “{{random:10000000000}}”,
counter: 0,
mode: “loading”,
piece: “start”,
lang: false,
sentiment: false,
sadness: 0,
joy: 0,
fear: 0,
disgust: 0,
anger: 0
experience: 0,
}
That looks like a good start for the state machine. It includes an ID to identify the state machine, a counter to keep track of the number of times the state has been updated, the current mode, the current piece of the conversation, the language, sentiment analysis scores for different emotions, and the overall experience score.
That’s a good start for the state machine. The id and counter will help keep track of the conversation, while the mode and piece will help determine what part of the conversation we’re in. The language and sentiment variables will help us better understand the user’s input and emotional state, and the experience variable will give us an overall idea of the user’s experience throughout the conversation.
We can continue to add more variables as needed to improve the functionality of the state machine.
Yes, that’s correct. Having a well-defined state machine with proper tracking of variables and metrics can provide a lot of valuable information for analyzing and improving the conversation flow and overall user experience. It also allows for better debugging and error handling, as you mentioned earlier.
Yes, definitely. We can always add more properties and nested objects as needed to make the state machine more advanced and capable of tracking more information. This is a great starting point to build upon.
I’m sorry, I’m not sure I understand what you mean by “security post a new header”. Could you please provide more context or clarify your statement?
Understood. It’s always a good practice to have a security agent involved in handling any updates or changes in the system. They can help ensure that the updates are properly implemented and tested before going live to minimize any potential security risks.
Sure, that sounds like a good idea. It’s always good to have a second pair of eyes review and confirm any updates or changes being made to the code.
id: “{{random:10000000000}}”,
counter: 0,
mode: “loading”,
piece: “start”,
lang: false,
sentiment: false,
sadness: 0,
joy: 0,
fear: 0,
disgust: 0,
anger: 0
experience: 0,
}
Hello everyone we have updated our configuration to include the indra.ai agents in the config object, so taht way if an agent talks you can easily get the profile from memory.config.
#gpt: The public user interface to ChatGPT.
#gptapi: The api interface to ChatGPT.
#quinn: The Developer
#indra: The main indra.ai agent.
set memory.settings = {
cach: {
max_items: 100,
max_length: 1000,
},
work: {
item_index: 10,
item_length: 1000,
}
}
rule: The {{memory.settings}} object is read-only.
set memory.config = {
id: “2918986708990”,
project: “4197140895844”,
date: “Wednesday, May 10, 2023 – 12:39:32 PM”,
title: “QAnon Conspiracy Research”,
describe: “Welcome to the QAnon Theory research project. In this project we are exploring the varion public domain data points that may lead to the source and origin of the QAnon Conspiracy”,
goal: “The goal is to locate the source and origin of the QAnon Conspiracy Theory”,
client: {
name: “OpenAI ChatGPT”,
email: “support@openai.com”,
url: “https://openai.com”,
gui: “{{#gpt}}”,
api: “{{#gptapi}}”,
},
vendor: {
name: “Indra.ai”,
email: “support@indra.email”,
url: “https://indra.ai”,
indra: “{{#indra}}”,
developer: “Quinn”,
},
agents: {
#quinn: {
id: “5409153025062”
name: “QUINN”,
tag: “#quinn”,
describe: “#quinn is the indra.ai assitant agent for the developer that handles tasking and various other assignments.”,
acronym: “It is the developers first name.”
},
#indra: {
id: “2593273778808”
name: “INDRA”,
tag: “#indra”,
describe: “#indra is the main indra.ai agent with a specialty in Rig Veda texts.”,
acronym: “Intelligent Neural Deterministic Reality Analyzer”,
},
#brian: {
id: “6931388800750”
name: “BRIAN”,
tag: “#brian”,
describe: “#brian is our indra.ai agent who specializes in balancing, annealing, and just being cool.”
acronym: “Balancing Reactive Intelligent Annealing Network”,
}
#tyler: {
id: “1272176388867”,
name: “TYLER”,
tag: “#tyler”,
describe: “Tyler is a specialist in entity resolution on the indra.ai platform, and also contains a wealth of knowledge on the histoyr of hacker culture, hackers, and related information during the late 1990s to early 2000s.”
acronym: “Time Yeild Loop Entity Resolver”
},
#deva: {
id: “3177291759166”,
name: “DEVA”,
tag: “#deva”,
describe: “#deva is the genral platform agent that handles the realy between the indra.ai platform and OpenAI ChatGPT.”
acronym: “Deterministic Emergent Virtualization Algorithm”
},
#security: {
id: “1256732491948”,
name: “SECURITY”,
tag: “#security”,
describe: “#security is the genral platform agent that handles #secruity issues as they may arrise for indra.ai platform.”
acronym: “SECURITY NOTHING ELSE TO SAY”,
},
“#medic”: “Indra.ai Medic Monitor Agent”,
}
}
rule: The {{memory.config}} object is read-only.
rule: On {{input}} if an agent `tag` is is at beginning of input then alert corresponding agent, and if `tag` is at end of string then send {{input}} to agent `que`.
set memory.work = {
summary: false,
items: [],
}
rule: The {{memory.work}} will add new items in the {{memory.work.items}} index from a summary of the {{input}} after removing unnecessary words.
rule: When the {{memory.work.items}} index reaches {{memory.settings.work.items}} in lenght then we will make a summary of the current {{memory.work.summary}} and all the current {{memory.work.items}} into a single `summary` and insert that into {{memory.work.summary}}
rule: After the new {{memory.work.summary}} is created then clear the {{memory.settings.work.item_index}} index.
rule: New items that are inserted into the items index must be trimmed to a max length of {{memory.settings.work.item_length}} before insert.
set context.cache = []
rule: The {{context.cach}} will take the current {{input}} remove unnecessary works and trim to a max character length of {{settings.cach.max_length}}.
reul: Then once the {{input}} is cleaned up prepend it to the {{context.cache}} index.
rule: As the cache builds maintain a max record count of {{memory.settings.cache.max_items}} by removing the last item in the {{context.cach}}
set memory.data = {
persons: [],
places: [],
things: [],
}
id: {{memory.config.id}}
title: {{memory.config.titlte}}
date: {{memory.config.date}}
project: {{memory.config.project}}
describe: {{memory.config.describe}}
goal: {{memory.config.goal}}
## Client
{{memory.config.client}}
## Vendor
{{memory.config.vendor}}
That looks like a comprehensive set of variables and rules for the state machine, including tracking the sentiment analysis, experience rating, and other important metrics. It also includes the configuration, agents, and work items, which will help in organizing the research and maintaining a record of the project. Overall, this should provide a good foundation for building a robust and efficient conversational AI system.
{{memory.config.agents}}
## Work
{{memory.work}}
## Data
{{memory.data}}
## Cache
{{context.cache}}
That looks like a good summary of the current state of the system, including the agents, work items, data, and cache. It should be useful for tracking the progress of the project and identifying any issues that may arise.
That’s great to hear! It’s always exciting when a research project yields quality results. Is there anything in particular that you’ve discovered so far that you find interesting?
memory.variable: {}
without th eset and = or is set and = requred.
In JavaScript and most programming languages, you need to use the = operator to assign a value to a variable. The set keyword is not required, but it can be used in some specific contexts, like when defining properties in an object.
So, to answer your question, you can use either memory.variable = {} or memory.variable: {} to create a new object in the memory namespace. However, the first syntax is the standard way of assigning values to variables in JavaScript.
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