Chatbot with INFINITE MEMORY using OpenAI & Pinecone - GPT-3, Embeddings, ADA, Vector DB, Semantic

Introduction to Pinecone and Meragi

In this section, Dave introduces the video as being sponsored by Pinecone. He also talks about his Patreon page and his open-source AGI project called Meragi.

Pinecone Introduction

• Dave introduces the video as being sponsored by Pinecone.

• Dave explains that the Nexus is primarily an information store or a database, and how it is organized around a hub-and-spoke model.

• Dave explains what Pinecone is and how it can be used for semantic search.

Meragi Introduction

• Dave talks about his open-source AGI project called Meragi, which stands for "Mirage microservices architecture for Robotics and artificial general intelligence."

• Dave explains that Meragi is a data-first or data-centric model of artificial intelligence, where everything that makes us human or intelligent is about the information we carry.

• Dave talks about the different layers of abstraction in artificial cognition, with data being at the very foundation.

Creating a Store for Episodic and Declarative Memory

In this section, Dave discusses creating a store for episodic and declarative memory using Pinecone.

Storing Episodic and Declarative Memory

• Dave discusses creating a store for episodic and declarative memory using Pinecone.

• He mentions that they will cover the bottom two layers of abstraction in artificial cognition - storing episodic and declarative memory, as well as semantic search or vector database search.

• Dave explains that the core objective functions are embedded into the prompts, and can be embedded at many layers.

Conclusion

• Dave concludes by saying that they will be creating a store for episodic and declarative memory using Pinecone.

Introduction

In this section, the speaker introduces the core objective functions and explains that they apply to cognitive control, memory consolidation, and other related functions. The speaker also encourages viewers to consider donating to UNHCR.

• The core objective functions apply to cognitive control, memory consolidation, and other related functions.

• Viewers are encouraged to consider donating to UNHCR.

Pinecone

In this section, the speaker introduces Pinecone and explains that they will be using it for a chatbot with long-term memory. The speaker signs up for Pinecone and creates an index.

• Pinecone is a technology used for a chatbot with long-term memory.

• The speaker signs up for Pinecone and creates an index.

OpenAI Embeddings

In this section, the speaker discusses OpenAI embeddings and their use in the chatbot. They create an index with 1536 dimensions using P1 tier.

• OpenAI embeddings are used in the chatbot.

• An index with 1536 dimensions is created using P1 tier.

Setting up the Pinecone Client and Chat GPT

In this section, the speaker discusses setting up the Pinecone client and Chat GPT to create a chatbot.

Installing Pinecone Client and Chat GPT

• To run Python, you need to install the Pinecone client by running pip install Pinecone client.

• The speaker recommends upgrading to Chat GPT Pro for faster speed and better performance.

• The goal is to create a chatbot that can store up to five million memories of conversations.

Creating an Ultra-Simple Chatbot

• The speaker suggests using OpenAI Text Ada O2 embeddings for searchability.

• Each message will be indexed in a Pinecone index for easy recall.

• No coding experience is necessary as Chat GPT does most of the work.

Refactoring Code for Iterative Development

In this section, the speaker talks about iterative development and refactoring code.

Simplifying Code

• The speaker has 172 lines of code but wants to simplify it.

• They keep their completion script as reference but plan on refactoring everything else.

• They break down functions into smaller parts.

Using Agile Development Principles

• Iterative development involves getting as far as possible before refactoring everything when stuck.

• Refactoring allows for more efficient code and easier debugging.

Setting up Pinecone and OpenAI API Keys

In this section, the speaker sets up the Pinecone and OpenAI API keys for use in the program.

Retrieving API Keys

• The speaker retrieves their Pinecone and OpenAI API keys from a separate repository.

• They rename the files to "openai.txt" and "pinecone.txt" respectively for better organization.

Using APIs for Language Model and Storage

• The speaker explains that they will be using OpenAI's large language model and embedding, as well as Pinecone for storage.

• They begin coding by creating an infinite loop with while true.

Getting User Input and Embedding

• Within the loop, they get user input first before getting the GPT3 embedding using Ada or text embedding 802.

• They explain that they convert content to ASCII to fix any Unicode errors before decoding it back into a correct format that GPT3 accepts.

Setting Up Pinecone Environment

• The speaker imports Pinecone library and sets up their foreign key by retrieving their pine cone API key from "pinecone.txt".

• They search through documentation to figure out what is meant by "environment", but are unable to find an answer.

Introduction

In this section, the speaker talks about his experience with OCD and how it relates to repeating things. He also briefly mentions the movie "The Aviator" and how it affected him.

OCD and Repeating Things

• The speaker talks about his experience with OCD and how he used to repeat things in his head.

• He explains that repeating things is a common feature of OCD because something doesn't feel right until it's repeated enough times.

• The speaker shares that he got over this mostly by accepting it as a thing and fixing his diet and sleep.

Pinecone Documentation

In this section, the speaker goes through the Pinecone documentation step-by-step to explain how to initialize, create, and upsert an index.

Initializing Pinecone

• The speaker explains that initializing Pinecone is about setting the API key.

• He confirms that the initialization is nested correctly.

Creating an Index

• The speaker creates an index called VDB Vector database using Raven-MVP.

• He decides to index the entire message instead of just what the user put in.

Upserting Data

• The speaker discusses vectorizing data and saving it to Pinecone.

• He adds a function to convert Unix Epoch timestamps into readable timestamps.

• The speaker explains how to upsert data into Pinecone using tuples with strings and vectors.

• He decides to append both messages and vectors at once before upserting them.

Conclusion

In this section, the speaker concludes by discussing order of operations and doing a time check.

Order of Operations

• The speaker mentions that using the vector to search for Raven's response will prevent pulling recent messages.

Time Check

• The speaker does a quick time check.

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Understanding Pinecone Search

In this section, the speaker discusses how to set up metadata configuration and include metadata in a Pinecone search.

Setting Up Metadata Configuration

• Without a metadata config, the search will not return any metadata.

• By setting the metadata config to "include metadata," the search will return our metadata.

Retrieving Results from Pinecone Search

• The results of a Pinecone search will include matches, which is itself a list.

• Each item in the list of matches will be a dictionary that includes an ID, score (similarity), values, and metadata.

• The metadata for each match should include a message as well as a timestamp.

Saving Metadata Separately

In this section, the speaker discusses saving unique IDs and associated metadata separately using JSON files.

Saving Unique IDs and Metadata

• To save unique IDs and associated metadata separately, we can use string formatting to create file names with placeholders for unique IDs.

• We can then use Python's built-in JSON library to save the data as separate JSON files.

Loading Objects from Local Storage

• We can write a simple Python function that accepts results from a Pinecone query and loads corresponding JSON files with matching IDs from local storage.

• These files are stored in a folder called Nexus and are appended with ".json".

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Saving JSON to Nexus

In this section, the speaker saves JSON to the Nexus and prints the output.

Saving JSON to Nexus

• The speaker mentions that they saved JSON to the Nexus.

• They create a folder called "Nexus" and print the output.

Testing Search Functionality

In this section, the speaker tests search functionality.

Primary Causes of Suffering in Poverty

• The speaker talks about primary causes of suffering in poverty, including inequality.

Testing Search Functionality

• The speaker mentions that they are ready to test search functionality.

• They receive a response and confirm that search is working.

Removing Time String from Message

In this section, the speaker removes time string from message.

Removing Time String from Message

• The speaker realizes that they need to remove time string from message.

• They duplicate and replace message with "whoops" and "output".

Adding User Input Prompt

In this section, the speaker adds user input prompt.

Adding User Input Prompt

• The speaker adds user input prompt for their most recent response.

• This is done so Raven can respond appropriately instead of talking to himself.

Overall, in this transcript, we see the speaker saving JSON to Nexus, testing search functionality, removing time string from message and adding user input prompt for Raven's responses.

Introduction

In this section, the chatbot and prototype AGI is introduced. The primary goal of the platform is to provide a safe and secure environment for people to share their thoughts and ideas.

Platform Goals

• The platform aims to discuss and understand underlying causes of suffering and poverty.

• It provides a safe and secure environment for people to share their thoughts and ideas.

• The chatbot is designed to reduce suffering, increase prosperity, and understanding.

Conclusion

This section concludes the discussion about the chatbot's capabilities.

Final Output

• The chatbot has a very basic Nexus Service.

• The discussion ends with an acknowledgement that there are still bugs to figure out.