Zafar Mansuri

Could you help me understand more about your background by giving a brief description? Uh, my name is Jafar Mansouri, and, uh, I'm working as a associate manager in the Rakuten Symphony. Uh, and, uh, my fully and relevant and totally expensive is 7.9, uh, my total expense. And I'm working on a main Java, Python, Kafka, and some, uh, and some are Spark technology also. Right now, uh, we work on also on AI part, length and typescript, and, uh, machine learning part and uh, and neural network libraries in this area. And I'm managing for the, uh, technology team in, uh, in in, uh, Indian culture right now. And, uh, and right now, I'm handling, uh, 3 project, uh, based on all our ITSM. 1 is the chain management, 1 is the leasing management, and 1 is the ticket management. And, uh, this, uh, all about my background. Thank you.

What strategy could you use to handle a vector database schema changes in a typescript code base? What strategy could you use to handle vector database schema changes in a TypeScript code base? Okay. There are some, uh, strategy, uh, we use to handle the vector database in in a JavaScript call base versus the leverage, uh, interface and gen Gendricks. Define a interface for your database that is specified that expected structure, means dimensioning on database. Use, uh, generic in your, uh, functions and classes to operate on a vectors of of, uh, any supported type based on the interface. And when the when the when, uh, when the schema changes, example, adding a new dimensioning and, uh, dimensioning the interface to relate reflect the new structure. Existing code using a generic function will automatically adapt to the changes as long as a new data column to be edit. 2nd second point is that version of with the cordless to implement a version system for your, uh, for our, uh, vector schema, storage schema version alongside the vector data. And, uh, they'll have the and encoder decoder specified to each schema version. During the data relevant, use the version information to identify the appropriate queries and let decode for decoding the data. When the schema you when the schema changes, create a new version with our our own queries, and existing data can remain its original format while the new data use the date update schema. And 3rd is a migration script. Prepare a migration script to handle schema changes, and this script can update the existing data to the new format when the code bases is upgraded. And this approach is required to the carefully planned testing to ensure the data integrated during the migration second. And, uh, there's a and last, uh, is is utilize the schema management tool. Consider using a schema management tool, specify the design of for the vector database, and this tool can automatically schema versioning and data migration, ensure ensure the type of safety across the different schema also. This is a, um, as per my strategy.

Could you purpose a method for integrating prompt engineering? Could you purpose a method for integrating prompt engineering feedback into a vector database using a typescript? Okay. Uh, This is a very good question. Okay. There are many possible method, uh, are available to integrating a prompt engineering feedback into a vector database, uh, using a a timescript. 1st is a data model with a feedback field. Feedback field. Design your data, uh, so design your vector data model in a typescript including fields for sorting a prompt's engineering feedback. This will use to be a string or JSON to of object containing a detail like, uh, like, user annotation or label restricting prompt efficiently. And metric like prediction, recall, or a fun score to qualify a prompt prompt performance. 2nd is feedback collection and preprocessing. In this feedback collection and preprocessing, they looked a functionality in a typescript to collect a prompt prompt engineering feedback, and this would involve first, is a user interface for manual annotation, uh, interaction with the external evolution tool for general metrics. And, uh, next point is that the preprocessing the collect feedback data and before storing it, a vector database might involve the text clearing and and normalization of the user annotation and data data normalization for a performance metrics. 3rd point is a feedback embedding storage means encode the preprocessing feedback data into a representation suitable for the vector database, and the text annotation could be transformed using, uh, technology like, uh, TFID IDF or what to Versus performance metrics, could you directly store as a numeric vector? And, uh, do you look at function functions in a JavaScript to store to store, uh, vectorized data feedback data feedback data along with the corresponding vector data in a data web. If we go to the 4th point, the relevant relevant and with the feedback integration, modify your vector relevant function to consider the feedback during, uh, information relevant. This could involve. 1st is ranking the vectorizer, uh, uh, based on a combined score that incorporate both the vector similarity and feedback metrics. And that's second is, uh, utilizing the feedback and vector to re redefine the search theory itself accurately. And, uh, I this is, I think, and we also use the leverage of the NPL and many of

In what way can TypeScript interface enhance the development and maintenance of langchain dot JS based application. And the TypeScript interface can significantly enhance the development and maintenance of the link changes in, uh, there are many way. 1st, uh, improve the type safety Uh, enterprise define the expected structure or data type of the object using your lang so langchain.js application. This help to case the error earlier earlier during the development and preventing issue like passing the incompatible data to the engine function, uh, function and functions and manipulating manipulating the object with the unexpected property and typescript type, uh, checking ensure the code word order work as a intended leading and to few bug and more robust integration. And and, uh, second is enhance the readability and readability. Interface improve the code readability by clearing the documenting structure of data used throughout the application, and developer can easily understand what data and function expected or what properties are an object that has reflecting by interval definition. And this improve code the remandability as the changes meant to, uh, interface or reflected throughout the code base where it's used. The second is a auto auto, uh, auto complication, uh, reflect uh, refactoring. The Tile Strip provide a code complete com completion feature based on a defined interface. This help developer write a code faster and few error by suggesting a relevant property and functions when working with the linked chain object. Additionally, TypeScript refactoring tool can automatically update update a code that use our interface. And when its definition changes, that this save the time and reduce the risk of introducing a bug and doing the code modification.

What typescript best practice ensure a safe consumption of data from a vector database in a context AI. There's there's some best practice to ensure this safe consumption of data from a vector database in a AI context. 1st is, uh, uh, already I told to, uh, tell on a previous, uh, question also. The interface is a best type safety. The interface, uh, for if we define the interface for expected structure or data type for vector relevant from the database, This ensured that checking during the development and prevent accidental misses of the, uh, vector data. For example, the interface could specify, uh, vector dimensioning and data type. Like, example, float, 32 error, and, uh, input validation. There are some input validation. Implement a input validation function to check if the relevant vector conform to the expected format defined by the interface. This help catch potential error error like unexpected vector length or data length, data type mismatch earlier on. Through scripty error, if a validation is failed to prevent the application from using a potentially corrupted data. That is the data normalization. If the vector from the database can be defined, scale, or range, consider implementing a data normalization technique. This ensure all the vector are on a comparable scale and it leading to more, uh, reliable AI model prediction. The common normalization technique include is l one normalization or mean and max, uh, uh, scaling. What is the error handling and monitoring are the best practice to typescript self injection, implement a reverse error handling mechanism to gracefully handle the situation where data relevant from the vector database file database file, and this could involve the logging logging error retrying of failed operation provided. There are some new style script library also available to manipulate an operation of utilizing scripting test to play, actually.

Could how could you implement TypeScript type interface for managing complex query in a vector database. There are some complex query, uh, so there are some implementation of time scripting interface for managing, uh, complex query, vector database. 1st is best query interface. Define a base query, interface name is vector database query. It outline the core component of the query, including a property like collection, filter, or a or a second is the filter interface. Create an interface. Create a separate interface. Name is filter. Define a structure of the filter criteria. 3rd is, uh, is, uh, this interface case, we have a property like a dimension, operator, value. And, uh, 3rd is a complex query interface option. Define, uh, another interface name is complex query that extend the vector database to the interface. This interface is include additional property like search vector, their key number option, and specify number of nearest neighbor to the metrics.

Looking at this code in a React, explain why component might not be rendering the expected result when the prompt is slash item changes. My component class extend react dot component, curly braces, constructor, bracket, prompts, bracket, curly braces, super, uh, down bracket, prompts, round bracket. Semicolon, this dot state, semicolon, Looking at this code in React, explain why this component might not be rendering the expected result when the prompt item changes. Class my component extend react dot component, column bracket, prompts, round bracket, close, column braces, super round brackets, prom round bracket, uh, semicolon. This dot state was to curly braces item, colon, prompt dotitem, curly braces clause, semicolon, semi curly braces, clause, render, round bracket, open round bracket, clause, curly braces. Basically, this class is extend the React component, and they are some conceptor which are user super conceptor. And this, uh, those prompt is coming from the, uh, UI or any of there were item standards. They are set to be and mapped to the content, and the state is not that mapped to the content.

Given this typescript snippet identify what error be thrown at the runtime explained by const user colon semicolibessage name colon string. Basically, why you are that's your error, uh, because you are defining a name equals to string and it is equals to number. And, uh, also, define a name equals to a a a a a list, but we not define the edge. That time we do a error of related number, related error, it is not, uh, none type values available.

What design pattern in a typescript could you employ to extract the logic of prompt engineering from other AI feature implementation? Here, there are 2 design pattern in a typescript that, uh, can employ the extra closet to prompt engineer from the other a feature implementation first strategic pattern. The strategic pattern is defined in the interface of the various prompt engineer approach and allow the switching between prompt them at a runtime. This allow for modularity and easy experimentation with the different prompt engineering technique. And if we are implementation, define interface name as a prompt engineering strategy with the method is a generative prompt It type first to any and get take data relevant to the AI picture and return that the generated prompt. Create a prompt implementation of the prompt engineering strategy interface to define, uh, to different prompt engineering technique. Example, the implement based prompt and strategy and few shortist the learning prompt strategy. Each implement implementation define a small logic for the generating a prompt based on on a provided data. If you are if our, uh, AI feature code inject the instance of the desired This direct prompt engineering strategy at the run time or through our configuration, and this allow us to allow you us to define defining a prompt engineering approach without modifying a core logic of the air feature. 2nd is decorator pattern. The decorator pattern dynamically add a benefit to a existing object. In this case, prompt generation logic without modification is the original structure. Implementation is the greater deductive function name from the with prompt engineering the and the tech function representing the core AI feature logic as a as a a as a argumentation. Inside the decorator implementation, the prompt engineering logic use the prompt provided data, and decorator can then call the original function with the generated prompt as the argument. Use the annotation. Right? At director with the prompt engineering decorator on your in our AI function. This will automatically add the prompt engineering here before original function integration.

What strategy would you use with TypeScript decorators to add metadata functionality in a langchain.js application. There are multiple, uh, strategy using a typescriptor decorator to add a metadata facility in a language and application. 1st, define a metadata interface, create a type script interface, names, metadata to represent a structure of your metadata, and this interface can include the, like, the property name, value, and so. 2nd is a create a metadata decorator to find a task to decorate a function name the metadata. This decorator will take the optional metadata. Object is the argument which, uh, would we should which should conform to metadata interpret. Decorator langchain.object instead. User and direct metadata decorator, any lang change of JS object prompt response and configuration, uh, where you want to, uh, attach a metadata. The decorator can store the provided, uh, metadata within a decorator object and accessing 3rd, 4th is accessing and utilizing a metadata. They'll love utilizing a function within your name or just application to access or utilize and attach the metadata. This function can retry all the metadata associated with the, uh, object and filter the metadata based on a specific property. Example, and leverage the metadata to influence, uh, the, uh, behavior of your links in application. Example, the conditional logic based on the metadata value. There are some benefit which are right now available. This approach keep your lang object is clear and separate data from the logic. Metadata can easily attach and access throughout your application, and you can, uh, metadata to add a content image and your name.

Illustrate how TypeScript generic can be leveraged for the type safe interaction with the vector database within the AI application. The, uh, the TypeScript centric can be leveraged of the typesafe interaction with the vector database within the application. Define a generic vector interface, named vector data, data use generic to specify the data type of the vector element. This ensured the type safe when working with a vector and different data type, Example, float and integer. Vector database interaction function. Defined and directing with the vector database. This function can be a x generic and to accept and return return a vector data database data and object to specify data element. 3rd is, uh, types, uh, safe users. When we when we using this function in your application portal, our application portal desired element type is explicitly explicitly, and this allow the compile to force the type script and prevent the error.