LangChain Output Parsers in Node.js

--- type: transcript date: 2023-05-24 youtube_id: 8US6gB8jTTo --- # Transcript: LangChain Output Parsers in Node.js in this video we'll delve into the world of output parsers and light chain we'll focus specifically on their implementation in JavaScript and node.js so the thing with output parsers so they play a crucial role in structuring language model responses and extracting meaningful information so by utilizing output parseries effectively we'll be able to transform our raw text outputs from llms into well-organized and structured formats so while language models like gpd4 offer more advanced features such as waiting higher their system message to provide more reliable prompt outputs it's important to note that using gpd4 and system messages come at a considerably higher cost than using models like gpd3 or GPT 3.5 turbo so it's important to consider the cost implications and evaluate whether achieving the desired result can be accomplished effectively and cost efficiently with alternative models to gpd4 so that's what we're going to largely be focusing on in this video so by leveraging the functionality of output parsers in JavaScript character node.js we can sort of strike a balance between cost and our desired result so making our application more efficient and cost effective and frankly be able to accomplish things that you wouldn't have been able to prior to the introduction of these llms so now let's proceed to dive into some of the Practical examples of open parsers in JavaScript so we're going to be showcasing their implementation highlight their benefits and what they can bring to the table but before we get into that there's two main methods that an open parser must Implement so there's the get format instructions method so this method returns a string containing instructions for how the output of a language model should be formatted so these instructions will serve as the guideline for the output parser to transform the raw response into a structured format so the next method is the parse method so the parse method takes in a string which is assumed to be the response from the language model so it parses it into a suitable structure and this specific structure May depend on the requirements of your application or what you're looking for to have as a desired output so now let's dive into some examples of the alpha parser in action so the first example I'm going to show is the structured output parser the structured output parser is useful when you want to return multiple fields in a structured manner so it defines a schema for the output and extracts the relevant information accordingly so in this example we'll import all the necessary modules we'll create an instance of the structured output parser we'll Define the schema with the desired fields that we want in this case we'll use answer and source and then from there we then can use the parser to format a prompt and make a language model call to obtain the response so finally once we have that returned we'll parse that response using the output parser and retrieve the structured information so in the second example we're going to look at the structured output parser with Zod schema so similar to the previous example this parser allows you to define the output schema using Zod so zod's a typescript validation Library so the schema is used to validate and structure the output accordingly to whatever you need so third we're going to look at the output fixing parser so this is a wrapper that incorporates another output parser so if the first parser fails to parse the output the output fixing parser calls the external language model to fix any errors if there are any next we're going to look at the comma separated list parser so this output parser is handy when you want to return a list of comma separated items so it parses the response and extracts the individual items from the list in the next example we're going to look at the custom list parser so you can use this when you need to return a list of items with a specific length and separator so so this will allow you to define the desired length and the separator for the list so next we're going to look at combining the output parser so combining the output parsers enables us to have a combination of multi multiple output parsers as you might imagine so the way that this works is it takes in a list of parsers extracts the combined output containing the fields from all the parsers so these are examples just to demonstrate how output parsers can be utilized in your node.js application depending on your use case you might find one or two or three of these useful maybe over time you want to play with all of them I just wanted to make a quick video demonstrating on how you can Implement you know the appropriate output parser for your given use case so you can extract structured information from raw text output and really that makes it considerably more meaningful because as programmers just having text isn't necessarily the response that we want more often than not we want that text to be in a Json format or maybe within that Json format we want it to be HTML or css or or some JavaScript that layers in the interactive components of what we're trying to build so this concludes the inner introduction on Opa parsers I will have more videos coming out on Lang chain specifically where we leverage output parsers and use it in a more real life use case in an application so stay tuned for those if you're interested if you're interested in exploring more about output parsers and their implementation I recommend referring to the langchain documentation if you found this video useful or informative please like comment share and don't forget to subscribe to the channel for more updates until the next video Happy coding