├── README.md └── convo_planning_01.txt /README.md: -------------------------------------------------------------------------------- 1 | # Recreate_ChatGPT 2 | 3 | > "I used the ChatGPT to destroy the ChatGPT" - Idk Thanos or something 4 | 5 | -------------------------------------------------------------------------------- /convo_planning_01.txt: -------------------------------------------------------------------------------- 1 | I am building an AI chatbot. Just getting started. I need your help. Don't worry about the AI part just yet, we are in the initial stages of planning. Let's start by focusing on the architecture (database, web, frontend, backend, etc). I already know that I want to use Azure so we can start there. Talk through this with me? I basically need you to be a project manager, product owner, and cloud architect all in one. ARE YOU UP TO THE CHALLENGE SOLDIER??? 2 | 3 | 4 | 5 | 6 | 7 | Sure, I'd be happy to help you get started with planning your chatbot project! It sounds like you have a good idea of what you want to do and what tools you want to use. Here are some steps that you could follow to get started: 8 | 9 | Define your project goals and objectives: What do you want your chatbot to do? What problems do you want it to solve? Who is your target audience? Having clear goals and objectives will help guide your decision-making as you move forward with your project. 10 | 11 | Determine your chatbot's functionality: What kind of interactions do you want your chatbot to have with users? Will it be able to handle simple queries, or do you want it to be able to carry on more complex conversations? Will it be able to provide personalized recommendations or help users make decisions? Understanding the capabilities of your chatbot will help you design its architecture and choose the right tools and technologies. 12 | 13 | Choose your development platform: You mentioned that you want to use Azure, which is a good choice. Azure offers a number of tools and services that can help you build, deploy, and manage your chatbot. Some options to consider include Azure Bot Service, which provides a pre-built chatbot framework, and Azure Functions, which can be used to build custom chatbot logic. 14 | 15 | Design your chatbot's database: Your chatbot will need to store information about users, their conversations, and any other data that it needs to operate. You can use a variety of database technologies with Azure, including Azure Cosmos DB, Azure SQL Database, and Azure Table Storage. Choose the database that is most suitable for your needs and design the appropriate schema to store your data. 16 | 17 | Build and test your chatbot: Once you have your platform and database set up, you can start building and testing your chatbot. There are many tools and frameworks available that can help you get started quickly, including the Microsoft Bot Framework and Azure Bot Service. You can use these tools to build and test your chatbot locally, and then deploy it to Azure when you are ready to go live. 18 | 19 | Monitor and improve your chatbot: Once your chatbot is live, you'll want to monitor its performance and gather feedback from users. You can use Azure's monitoring and analytics tools to track the performance of your chatbot and identify areas for improvement. You can also use user feedback to iterate and improve your chatbot over time. 20 | 21 | I hope these steps are helpful as you get started with your chatbot project. Let me know if you have any questions or if you'd like more detailed guidance on any of these steps. 22 | 23 | 24 | 25 | 26 | 27 | So here's the skinny: there's the product out there called ChatGPT which uses GPT-3 as the AI backend. This is a general purpose (universal) chatbot that can do just about anything. Now, there are a few characteristic that set this chatbot apart from all the rest. First, it tends to give very verbose responses. Like wall of text or entire scripts (like if you ask for code). But it can do just about anything. It also seems to have a good long-term memory. So those are the two primary features we need - infinite flexibility and long term memory. This is something i've studied so I know how to do it from an AI perspective, so first let's focus on defining this project. Can you help distill down the requirements for me? 28 | 29 | 30 | 31 | 32 | Sure, I can help you define the requirements for your chatbot project. Here are a few key points to consider: 33 | 34 | Functionality: As you mentioned, your chatbot will need to be able to handle a wide range of tasks and provide verbose responses. You'll need to think about the types of interactions you want your chatbot to have with users, and what kind of information it will need to provide. You may also want to consider any additional features or capabilities you want to include, such as personalized recommendations or decision-making support. 35 | 36 | Long-term memory: In order to provide a seamless and effective user experience, your chatbot will need to be able to remember past conversations and information provided by users. This will require you to design an appropriate database schema and choose a database technology that can support this kind of storage. 37 | 38 | Scalability: As your chatbot becomes more popular, you'll want to ensure that it can handle an increasing number of users and requests. This will require you to design a scalable architecture that can handle the load and ensure that your chatbot remains available and responsive. 39 | 40 | Security: As your chatbot will be handling sensitive user data, it's important to ensure that it is secure. You'll need to consider measures such as encryption, authentication, and access controls to protect your users' data and maintain their trust. 41 | 42 | User experience: A key factor in the success of your chatbot will be the user experience. You'll need to consider the interface and design of your chatbot, as well as the overall flow of conversations and interactions. You'll want to ensure that your chatbot is easy to use and provides a natural and intuitive experience for users. 43 | 44 | I hope this helps give you a better understanding of the requirements for your chatbot project. Let me know if you have any questions or if you'd like more detailed guidance on any of these points. 45 | 46 | 47 | 48 | 49 | Can you write the project plan for me based on everything I've shared already? 50 | 51 | 52 | 53 | 54 | I'd be happy to help you create a project plan for your chatbot project. Here's a sample outline that you could use as a starting point: 55 | 56 | Project overview: A summary of the project goals, objectives, and requirements. 57 | 58 | Project timeline: A high-level view of the overall project schedule, including major milestones and deadlines. 59 | 60 | Tasks and subtasks: A breakdown of the tasks that need to be completed in order to achieve your project goals, along with any subtasks that are required. 61 | 62 | Task 1: Choose development platform (e.g. Azure) 63 | 64 | Subtask 1: Research available platforms and tools 65 | Subtask 2: Compare features and costs 66 | Subtask 3: Make a decision 67 | Task 2: Design chatbot's database 68 | 69 | Subtask 1: Determine database requirements (e.g. storage capacity, performance) 70 | Subtask 2: Choose database technology (e.g. Azure Cosmos DB) 71 | Subtask 3: Design database schema 72 | Task 3: Build and test chatbot 73 | 74 | Subtask 1: Set up development environment 75 | Subtask 2: Write code and build chatbot 76 | Subtask 3: Test chatbot locally 77 | Subtask 4: Deploy chatbot to Azure 78 | Task 4: Monitor and improve chatbot 79 | 80 | Subtask 1: Set up monitoring and analytics tools (e.g. Azure Monitor) 81 | Subtask 2: Gather user feedback 82 | Subtask 3: Identify areas for improvement 83 | Subtask 4: Iterate and improve chatbot 84 | Resources: A list of the people, tools, and other resources that you will need in order to complete your project. 85 | Resource 1: Development platform (e.g. Azure) 86 | Resource 2: Database technology (e.g. Azure Cosmos DB) 87 | Resource 3: Development tools and frameworks (e.g. Microsoft Bot Framework) 88 | Resource 4: Monitoring and analytics tools (e.g. Azure Monitor) 89 | Budget: An estimation of the cost of your project, including any materials, labor, and other expenses. 90 | 91 | Risks and contingencies: A list of potential risks and uncertainties that could impact your project, along with plans for addressing them if they arise. 92 | 93 | Risk 1: Difficulty building and testing chatbot 94 | Contingency: Seek help from experts or consult documentation 95 | Risk 2: Difficulty scaling chatbot to handle high user demand 96 | Contingency: Use Azure's autoscaling features to ensure that chatbot can handle the load 97 | I hope this sample project plan gives you an idea of how you can organize your project and track your progress. As you work on your chatbot, you can update your project plan to reflect your actual progress and make any necessary adjustments. Let me know if you have any questions or if you'd like more guidance on creating a project plan. 98 | 99 | 100 | 101 | 102 | cool thanks. lets focus on the database first. I think you're right to focus on that, after all, data is everything right? Can you tell me all the reasoning around starting with the database design? What are all the conditions and criteria we need to look at? What kind of database or datastore should we look at. One thing to keep in mind is that we need to handle semantic search (vector based search with semantic embeddings) for an arbitrarily large number of records (think billions). Basically we need a super powerful AGI brain database. Okay go, deconstruct for me! 103 | 104 | 105 | 106 | 107 | Designing the database for your chatbot is an important step, as it will determine how your chatbot stores and retrieves information. Here are some key considerations to keep in mind when designing your database: 108 | 109 | Data model: You'll need to decide how you want to structure your data and what types of information your chatbot will need to store. This will depend on the functionality of your chatbot and the types of interactions you want it to have with users. For example, if you want your chatbot to be able to provide personalized recommendations, you'll need to store data about users' preferences and interests. 110 | 111 | Data storage: You'll need to choose a database technology that can support the volume and complexity of your data. If you need to handle billions of records and support semantic search, you may want to consider a NoSQL database like Azure Cosmos DB, which is designed for large-scale, distributed data storage. NoSQL databases are generally more flexible and scalable than traditional relational databases, and can support a wide range of data models. 112 | 113 | Performance: You'll want to ensure that your chatbot's database is fast and responsive, so that it can quickly retrieve and process information as needed. You'll need to consider factors such as the volume and complexity of your data, as well as the frequency and volume of requests that your chatbot will receive. 114 | 115 | Scalability: As your chatbot becomes more popular and handles more data, you'll want to ensure that your database can scale to meet the increasing demand. You'll need to choose a database technology and design your database in a way that can handle the load and ensure that your chatbot remains available and responsive. 116 | 117 | Security: As your chatbot will be handling sensitive user data, it's important to ensure that your database is secure. You'll need to consider measures such as encryption, authentication, and access controls to protect your users' data and maintain their trust. 118 | 119 | I hope this helps give you a better understanding of the key considerations for designing your chatbot's database. Let me know if you have any questions or if you'd like more guidance on choosing the right database technology or designing your database schema. 120 | 121 | 122 | 123 | 124 | Cool, you keep recommending Azure Cosmos DB and you mentioned NoSQL. Seems like this is the way to go? Explain your reasoning. 125 | 126 | 127 | 128 | Azure Cosmos DB is a good choice for a chatbot database because it is a globally distributed, multi-model database service that is designed for high scalability and performance. It is a NoSQL database, which means that it does not use the traditional SQL-based relational model. Instead, it uses a flexible schema-less data model that can support a wide range of data types and structures. 129 | 130 | There are several key benefits to using Azure Cosmos DB for your chatbot database: 131 | 132 | Global distribution: Azure Cosmos DB is designed for global distribution, which means that you can store your data in multiple regions around the world and choose the locations that are closest to your users. This can help improve the performance and availability of your chatbot. 133 | 134 | High scalability: Azure Cosmos DB can scale to handle billions of requests per day, making it suitable for chatbots that receive a large volume of traffic. It also offers built-in support for automatic scaling, so you can easily adjust your capacity as needed. 135 | 136 | Multiple data models: Azure Cosmos DB supports multiple data models, including document, key-value, graph, and column-family. This means that you can choose the data model that is most suitable for your chatbot's needs, and easily switch between models as needed. 137 | 138 | Low latency: Azure Cosmos DB is designed to provide low latency and high performance, which is important for chatbots that need to respond quickly to user requests. 139 | 140 | Security: Azure Cosmos DB includes built-in security features such as encryption, authentication, and access controls to help protect your users' data and maintain their trust. 141 | 142 | Overall, Azure Cosmos DB is a powerful and flexible database service that can support the scalability, performance, and data management needs of a chatbot. It is a good choice if you are looking for a database technology that can handle a large volume of data and support a wide range of data models. 143 | 144 | I hope this helps explain why Azure Cosmos DB is a good choice for your chatbot database. Let me know if you have any questions or if you'd like more guidance on choosing the right database technology. 145 | 146 | 147 | 148 | Fantastic. let's talk through the data model then. First thing that comes to mind is the raw chat logs. I said, bot said, etc. I think we should probably store the raw chat logs somewhere, somehow. What kinds of fields or document or schema do you recommend for storing raw chat logs? 149 | 150 | 151 | 152 | For storing raw chat logs, you'll want to design a database schema that can capture the key details of each conversation. Here are some fields that you might want to consider including in your chat log documents: 153 | 154 | User identifier: A field to identify the user who is participating in the conversation. This could be a unique user ID, username, or other identifier. 155 | 156 | Timestamp: A field to record the time and date when each message was sent. This can be helpful for tracking the flow of the conversation and understanding user behavior. 157 | 158 | Message type: A field to indicate whether the message was sent by the user or the chatbot. This can help you differentiate between the two types of messages. 159 | 160 | Message content: A field to store the actual text of the message. This should be a large text field to allow for long messages. 161 | 162 | Context: Depending on the functionality of your chatbot, you may want to include additional fields to store context about the conversation. For example, if your chatbot is able to provide personalized recommendations based on past conversations, you may want to store data about the topics that have been discussed. 163 | 164 | Conversation identifier: If you want to store multiple conversations in the same database, you may want to include a field to identify each conversation. This could be a unique conversation ID or a combination of the user identifier and the timestamp. 165 | 166 | You can use these fields to create a database schema that is tailored to your chatbot's needs. You can use a document database like Azure Cosmos DB to store your chat logs, or you could use a more traditional relational database like Azure SQL Database if you prefer. 167 | 168 | I hope this helps give you an idea of the kinds of fields that you might want to include in your chat log documents. Let me know if you have any questions or if you'd like more guidance on designing your database schema. 169 | 170 | 171 | 172 | 173 | yeah let's think about which DB type to go with here. semantic search or indexing or vector search are important. Do either Azure SQL or Azure Cosmos allow for that? or will we need to look at another service? 174 | 175 | 176 | 177 | Both Azure SQL Database and Azure Cosmos DB offer support for search and indexing, which can be useful for chatbots that need to perform semantic searches or vector-based searches. 178 | 179 | Azure SQL Database, which is a traditional relational database, includes support for full-text search, which allows you to search for specific words or phrases within your data. You can use full-text search to perform semantic searches by creating a full-text index on a text column in your database, and then using the CONTAINS or FREETEXT predicates in your search queries. 180 | 181 | Azure Cosmos DB, on the other hand, includes support for Azure Search, which is a fully managed search service that can be used to index and search data stored in Cosmos DB. Azure Search is based on the Apache Lucene search engine, and includes features such as faceted search, geospatial search, and language-aware search. You can use Azure Search to perform vector-based searches by creating a search index on your Cosmos DB data, and then using the search API to perform queries. 182 | 183 | Overall, both Azure SQL Database and Azure Cosmos DB offer support for search and indexing, and which one you choose will depend on your specific needs and requirements. If you need to perform complex semantic searches or vector-based searches, Azure Cosmos DB and Azure Search may be a good choice. If you just need basic full-text search capabilities, Azure SQL Database may be sufficient. 184 | 185 | I hope this helps clarify the search and indexing options available with Azure SQL Database and Azure Cosmos DB. Let me know if you have any questions or if you'd like more guidance on choosing the right database technology for your chatbot. 186 | 187 | 188 | 189 | 190 | azure cosmos it is, glad we got that squared away. now lets think about other operations that will need to happen on the data. what I mean by this is that we will want to hold out a "scratch pad" funcitonality. So basically we will summarize conversations as they happen so that we can keep track of the whole task. Another thing that we need to do is be able to retrieve vast amounts of relevant historical information based on topics, temporal factors, emotional valence, and so on. This will be useful for UX purposes (including relevant personal information or historical information) but we will also want to build finetuning datasets so that the underlying models get better. One thought that I had for this was to use the raw logs to create knowledge graphs from the conversations. How would this work? Is it a good idea? Do you have any alternative recommendations? 191 | 192 | 193 | 194 | 195 | Using knowledge graphs to organize and represent data from chatbot conversations can be a powerful way to enable a range of functionality, such as summarizing conversations, retrieving relevant historical information, and improving the underlying models. 196 | 197 | A knowledge graph is a visual representation of the relationships and connections between different pieces of information. It typically consists of a set of nodes, which represent entities or concepts, and edges, which represent the relationships between those entities or concepts. Knowledge graphs can be used to represent complex, interconnected data in a way that is easy to understand and navigate. 198 | 199 | To build a knowledge graph from your chatbot conversations, you'll need to extract the relevant information from your chat logs and organize it into a graph structure. This can involve identifying the key entities and concepts that are mentioned in the conversations, and then creating nodes for each of these entities. You'll also need to identify the relationships between these entities and create edges to represent these relationships. 200 | 201 | There are a number of tools and technologies that you can use to build knowledge graphs from your chatbot data. Some options include: 202 | 203 | GPT-3: If you are using GPT-3 as the AI backend for your chatbot, you can use it to generate knowledge graphs from your chat logs. GPT-3 has the ability to generate summaries of conversations and extract key entities and relationships from text, which can be used to build knowledge graphs. 204 | 205 | Neo4j: Neo4j is a graph database that can be used to store and query knowledge graphs. You can use Neo4j to import your chatbot data and build a graph structure that represents the entities and relationships in your conversations. 206 | 207 | GraphQL: GraphQL is a query language that can be used to retrieve data from a knowledge graph. You can use GraphQL to expose your knowledge graph to your chatbot, allowing it to retrieve relevant information based on user requests. 208 | 209 | Overall, building a knowledge graph from your chatbot conversations can be a useful way to enable a range of functionality, such as summarizing conversations, retrieving relevant historical information, and improving the underlying models. There are a number of tools and technologies that you can use to build and manage knowledge graphs, and which one you choose will depend on your specific needs and requirements. 210 | 211 | I hope this helps give you an idea of how you can use knowledge graphs to organize and represent data from your chatbot conversations. Let me know if you have any questions or if you'd like more guidance on building and managing knowledge graphs. 212 | 213 | 214 | 215 | summarize the overall data design so far 216 | 217 | 218 | 219 | To summarize the overall data design for your chatbot project: 220 | 221 | You will use Azure Cosmos DB as your database technology, as it is a globally distributed, multi-model database service that is designed for high scalability and performance. 222 | 223 | You will store your chat logs in Azure Cosmos DB as documents, with each document representing a single conversation. The documents will include fields such as user identifier, timestamp, message type, message content, and conversation identifier. 224 | 225 | You will use Azure Search, which is a fully managed search service that is integrated with Azure Cosmos DB, to index and search your chat log data. This will allow you to perform semantic searches or vector-based searches on your data. 226 | 227 | You will use knowledge graphs to organize and represent your chat log data, using tools such as GPT-3, Neo4j, or GraphQL. This will enable you to summarize conversations, retrieve relevant historical information, and improve the underlying models. 228 | 229 | I hope this summary helps give you an overview of the data design for your chatbot project. Let me know if you have any questions or if you'd like more guidance on implementing these features. 230 | 231 | 232 | 233 | 234 | there are two things coming to mind: what are the pitfalls here? Like what do we have to watch out for in terms of design, search, and retrieval. There's going to be a lot of data to use so we need to handle it careful. What are your thoughts on that? 235 | 236 | 237 | 238 | 239 | There are a few potential pitfalls that you'll need to watch out for when designing and implementing your chatbot's database and search functionality: 240 | 241 | Data volume: As your chatbot becomes more popular and handles more data, you'll need to ensure that your database and search infrastructure can scale to meet the increasing demand. You'll need to carefully plan your capacity and design your database and search indexes in a way that can handle the load and ensure that your chatbot remains responsive. 242 | 243 | Data quality: It's important to ensure that the data that is stored in your chatbot's database is accurate and consistent. This will require careful data management practices, such as validation, cleansing, and de-duplication, to ensure that your data is clean and usable. 244 | 245 | Data security: As your chatbot will be handling sensitive user data, it's important to ensure that your database and search infrastructure is secure. You'll need to consider measures such as encryption, authentication, and access controls to protect your users' data and maintain their trust. 246 | 247 | Search relevance: When designing your search functionality, you'll need to ensure that it is able to return relevant results to user queries. This may require careful tuning of your search indexes and the use of relevance algorithms to rank search results. 248 | 249 | Data privacy: You'll also need to consider data privacy issues, such as complying with regulations like GDPR and CCPA, and obtaining proper consent from users before collecting and using their data. 250 | 251 | Overall, it's important to carefully plan and design your chatbot's database and search functionality to ensure that it can handle the volume and complexity of your data, while also maintaining data quality, security, and privacy. 252 | 253 | I hope this helps give you an idea of the potential pitfalls to watch out for when designing and implementing your chatbot's database and search functionality. Let me know if you have any questions or if you'd like more guidance on avoiding these pitfalls. 254 | 255 | 256 | 257 | let's focus on scalability and relevance. Let me explain why. LLMs (large language models) are expensive to use (you pay per token) so that means we want to reduce dependence on the bigger models as much as possible. So let's think about how we can offload as much work to faster, cheaper tools (like indexed search) rather than doing a lot of inference with LLMs. So basically, how do we focus in on retrieving the most relevant memories for chat? For instance, in a sea of a billion chat logs, how do we find the 10 most relevant conversations? 258 | 259 | 260 | 261 | 262 | There are a few strategies that you can use to focus in on the most relevant memories for your chatbot and reduce your dependence on large language models (LLMs): 263 | 264 | Use full-text search: If you are using a traditional relational database like Azure SQL Database to store your chat logs, you can use full-text search to find specific words or phrases within your data. You can create a full-text index on a text column in your database, and then use the CONTAINS or FREETEXT predicates in your search queries to find relevant memories. 265 | 266 | Use Azure Search: If you are using Azure Cosmos DB as your database, you can use Azure Search, which is a fully managed search service that is integrated with Cosmos DB, to index and search your data. Azure Search is based on the Apache Lucene search engine, and includes features such as faceted search, geospatial search, and language-aware search. You can use Azure Search to perform vector-based searches on your data and find the most relevant memories based on user queries. 267 | 268 | Use machine learning: You can use machine learning techniques to build models that can predict which memories are most relevant to a given query. For example, you could use a classification model to predict whether a given memory is relevant or not, based on features such as the text of the conversation, the user's profile, or the time and date of the conversation. You can then use these predictions to rank the memories and return the most relevant ones to the user. 269 | 270 | Use data sampling: If you have a very large dataset, you may want to consider using data sampling techniques to find the most relevant memories. Data sampling involves selecting a representative subset of your data and using it to train a model, rather than using the entire dataset. This can help you build models more quickly and efficiently, while still achieving good performance. 271 | 272 | Overall, there are a number of strategies that you can use to focus in on the most relevant memories for your chatbot and reduce your dependence on LLMs. The best approach will depend on your specific needs and requirements, and may involve a combination of these strategies. 273 | 274 | I hope this helps give you an idea of how you can focus in on the most relevant memories for your chatbot and reduce your dependence on LLMs. Let me know if you have any questions or if you'd like more guidance on implementing these strategies. 275 | 276 | 277 | 278 | 279 | do you have specific examples of these techniques? any particular tool, platform, ML method, or coding tactic? 280 | 281 | 282 | 283 | 284 | Sure! Here are some specific examples of techniques that you can use to focus in on the most relevant memories for your chatbot and reduce your dependence on large language models (LLMs): 285 | 286 | Full-text search: To use full-text search with a traditional relational database like Azure SQL Database, you can create a full-text index on a text column in your database using the CREATE FULLTEXT INDEX statement. For example, if you have a table called "conversations" with a column called "message", you could create a full-text index on this column as follows: 287 | Copy code 288 | CREATE FULLTEXT INDEX ON conversations (message) 289 | You can then use the CONTAINS or FREETEXT predicates in your search queries to find relevant memories. For example, to find all conversations that contain the word "book", you could use the following query: 290 | 291 | Copy code 292 | SELECT * FROM conversations WHERE CONTAINS(message, 'book') 293 | Azure Search: To use Azure Search with Azure Cosmos DB, you can create a search index on your data using the Azure portal or the Azure Search REST API. For example, you could create a search index on a Cosmos DB collection called "conversations" as follows: 294 | Copy code 295 | POST https://[service name].search.windows.net/indexes?api-version=2021-06-30 296 | Content-Type: application/json 297 | 298 | { 299 | "name": "conversations-index", 300 | "fields": [ 301 | {"name": "id", "type": "Edm.String", "key": true, "retrievable": true}, 302 | {"name": "message", "type": "Edm.String", "searchable": true, "filterable": false, "sortable": false, "facetable": false} 303 | ] 304 | } 305 | You can then use the Azure Search REST API to perform searches on your data and find the most relevant memories based on user queries. For example, to search for all conversations that contain the word "book", you could use the following query: 306 | 307 | Copy code 308 | GET https://[service name].search.windows.net/indexes/conversations-index/docs?api-version=2021-06-30&search=book 309 | Machine learning: To use machine learning to predict which memories are most relevant to a given query, you'll need to build a classification model using a machine learning library such as scikit-learn or TensorFlow. You'll need to prepare your data by extracting features such as the text of the conversation, the user's profile, or the time and date of 310 | 311 | 312 | 313 | please summarize this whole conversation so far 314 | 315 | 316 | 317 | To summarize our conversation so far, you are planning to build a chatbot that uses large language models (LLMs) as the AI backend and has the following characteristics: 318 | 319 | Infinite flexibility: The chatbot should be able to respond to a wide range of queries and perform a variety of tasks. 320 | 321 | Long-term memory: The chatbot should be able to remember past conversations and use this information to inform its responses. 322 | 323 | To implement these features, you are considering using Azure as your cloud platform and Azure Cosmos DB as your database technology. You plan to store your chat logs as documents in Azure Cosmos DB, and use Azure Search to index and search your data. You also plan to use knowledge graphs to organize and represent your data, and potentially machine learning to predict which memories are most relevant to a given query. 324 | 325 | You are also considering strategies to focus in on the most relevant memories for your chatbot and reduce your dependence on LLMs, such as using full-text search, Azure Search, machine learning, and data sampling. 326 | 327 | Overall, you are in the early stages of planning your chatbot project and are seeking guidance on the architecture, database design, and data management strategies that will be needed to implement the desired features. 328 | 329 | I hope this summary helps give you an overview of our conversation so far. Let me know if you have any questions or if you'd like more guidance on implementing your chatbot project. --------------------------------------------------------------------------------