@aj-archipelago/cortex

Cortex

Cortex simplifies and accelerates the process of creating applications that harness the power of modern AI models like chatGPT and GPT-4 by providing a structured interface (GraphQL or REST) to a powerful prompt execution environment. This enables complex augmented prompting and abstracts away most of the complexity of managing model connections like chunking input, rate limiting, formatting output, caching, and handling errors.

Why build Cortex?

Modern AI models are transformational, but a number of complexities emerge when developers start using them to deliver application-ready functions. Most models require precisely formatted, carefully engineered and sequenced prompts to produce consistent results, and the responses are typically largely unstructured text without validation or formatting. Additionally, these models are evolving rapidly, are typically costly and slow to query and implement hard request size and rate restrictions that need to be carefully navigated for optimum throughput. Cortex offers a solution to these problems and provides a simple and extensible package for interacting with NL AI models.

Okay, but what can I really do with this thing?

Just about anything! It's kind of an LLM swiss army knife. Here are some ideas:

• Create custom chat agents with memory and personalization and then expose them through a bunch of different UIs (custom chat portals, Slack, Microsoft Teams, etc. - anything that can be extended and speak to a REST or GraphQL endpoint)
• Spin up LLM powered automatons with their prompting logic and AI API handling logic all centrally encapsulated.
• Make LLM chains and agents from LangChain.js available via scalable REST or GraphQL endpoints.
• Put a REST or GraphQL front end on your locally-run models (e.g. llama.cpp) and use them in concert with other tools.
• Create modular custom coding assistants (code generation, code reviews, test writing, AI pair programming) and easily integrate them with your existing editing tools.
• Create powerful AI editing tools (copy editing, paraphrasing, summarization, etc.) for your company and then integrate them with your existing workflow tools without having to build all the LLM-handling logic into those tools.
• Create cached endpoints for functions with repeated calls so the results return instantly and you don't run up LLM token charges.
• Route all of your company's LLM access through a single API layer to optimize and monitor usage and centrally control rate limiting and which models are being used.

Features

• Simple architecture to build custom functional endpoints (called pathways), that implement common NL AI tasks. Default pathways include chat, summarization, translation, paraphrasing, completion, spelling and grammar correction, entity extraction, sentiment analysis, and bias analysis.
• Allows for building multi-model, multi-tool, multi-vendor, and model-agnostic pathways (choose the right model or combination of models and tools for the job, implement redundancy) with built-in support for OpenAI GPT-3, GPT-3.5 (chatGPT), and GPT-4 models - both from OpenAI directly and through Azure OpenAI, PaLM Text and PaLM Chat from Google, OpenAI Whisper, Azure Translator, LangChain.js and more.
• Easy, templatized prompt definition with flexible support for most prompt engineering techniques and strategies ranging from simple single prompts to complex custom prompt chains with context continuity.
• Built in support for long-running, asynchronous operations with progress updates or streaming responses
• Integrated context persistence: have your pathways "remember" whatever you want and use it on the next request to the model
• Automatic traffic management and content optimization: configurable model-specific input chunking, request parallelization, rate limiting, and chunked response aggregation
• Extensible parsing and validation of input data - protect your model calls from bad inputs or filter prompt injection attempts.
• Extensible parsing and validation of return data - return formatted objects to your application instead of just string blobs!
• Caching of repeated queries to provide instant results and avoid excess requests to the underlying model in repetitive use cases (chat bots, unit tests, etc.)

Installation

In order to use Cortex, you must first have a working Node.js environment. The version of Node.js should be 18 or higher (lower versions supported with some reduction in features). After verifying that you have the correct version of Node.js installed, you can get the simplest form up and running with a couple of commands.

Quick Start

git clone [e-mail]:aj-archipelago/cortex.git
cd cortex
npm install
export OPENAI_API_KEY=<your key>
npm start

Yup, that's it, at least in the simplest possible case. That will get you access to all of the built in pathways. If you prefer to use npm instead instead of cloning, we have an npm package too: @aj-archipelago/cortex

Connecting Applications to Cortex

Cortex speaks GraphQL and by default it enables the GraphQL playground. If you're just using default options, that's at http://localhost:4000/graphql. From there you can begin making requests and test out the pathways (listed under Query) to your heart's content. If GraphQL isn't your thing or if you have a client that would rather have REST that's fine - Cortex speaks REST as well.

Connecting an application to Cortex using GraphQL is simple too:

import { useApolloClient, gql } from "@apollo/client"

const TRANSLATE = gql`
  query Translate($text: String!, $to: String!) {
    translate(text: $text, to: $to) {
      result
    }
  }
`
apolloClient.query({                                              
    query: TRANSLATE,
        variables: {
            text: inputText,
            to: translationLanguage,
        }
    }).then(e => {
        setTranslatedText(e.data.translate.result.trim())
    }).catch(e => {
        // catch errors
    })

Cortex Pathways: Supercharged Prompts

Pathways are a core concept in Cortex. Each pathway is a single JavaScript file that encapsulates the data and logic needed to define a functional API endpoint. When the client makes a request via the API, one or more pathways are executed and the result is sent back to the client. Pathways can be very simple:

export default {
  prompt: `{{text}}\n\nRewrite the above using British English spelling:`
}

The real power of Cortex starts to show as the pathways get more complex. This pathway, for example, uses a three-part sequential prompt to ensure that specific people and place names are correctly translated:

export default {
  prompt:
      [
          `{{{text}}}\nCopy the names of all people and places exactly from this document in the language above:\n`,
          `Original Language:\n{{{previousResult}}}\n\n{{to}}:\n`,
          `Entities in the document:\n\n{{{previousResult}}}\n\nDocument:\n{{{text}}}\nRewrite the document in {{to}}. If the document is already in {{to}}, copy it exactly below:\n`
      ]
}

Cortex pathway prompt enhancements include:

• Templatized prompt definition: Pathways allow for easy and flexible prompt definition using Handlebars templating. This makes it simple to create and modify prompts using variables and context from the application as well as extensible internal functions provided by Cortex.
• Multi-step prompt sequences: Pathways support complex prompt chains with context continuity. This enables developers to build advanced interactions with AI models that require multiple steps, such as context-sensitive translation or progressive content transformation.
• Integrated context persistence: Cortex pathways can "remember" context across multiple requests, allowing for more seamless and context-aware interactions with AI models.
• Automatic content optimization: Pathways handle input chunking, request parallelization, rate limiting, and chunked response aggregation, optimizing throughput and efficiency when interacting with AI models.
• Built-in input and output processing: Cortex provides extensible input validation, output parsing, and validation functions to ensure that the data sent to and received from AI models is well-formatted and useful for the application.

Pathway Development

To add a new pathway to Cortex, you create a new JavaScript file and define the prompts, properties, and functions that implement the desired functionality. Cortex provides defaults for almost everything, so in the simplest case a pathway can really just consist of a string prompt like the spelling example above. You can then save this file in the pathways directory in your Cortex project and it will be picked up and made available as a GraphQL query.

Prompt

When you define a new pathway, you need to at least specify a prompt that will be passed to the model for processing. In the simplest case, a prompt is really just a string, but the prompt is polymorphic - it can be a string or an object that contains information for the model API that you wish to call. Prompts can also be an array of strings or an array of objects for sequential operations. In this way Cortex aims to support the most simple to advanced prompting scenarios.

// a prompt can be a string
prompt: `{{{text}}}\nCopy the names of all people and places exactly from this document in the language above:\n`

// or an array of strings
prompt: [
    `{{{text}}}\nCopy the names of all people and places exactly from this document in the language above:\n`,
    `Original Language:\n{{{previousResult}}}\n\n{{to}}:\n`,
    `Entities in the document:\n\n{{{previousResult}}}\n\nDocument:\n{{{text}}}\nRewrite the document in {{to}}. If the document is already in {{to}}, copy it exactly below:\n`
]

// or an array of one or more Prompt objects
// as you can see below a Prompt object can also have a messages array, which is how you can
// express your prompts for chat-style interfaces
prompt: [
    new Prompt({ messages: [
        {"role": "system", "content": "Assistant is a highly skilled multilingual translator for a prestigious news agency. When the user posts any text in any language, assistant will create a translation of that text in {{to}}. Assistant will produce only the translation and no additional notes or commentary."},
        {"role": "user", "content": "{{{text}}}"}
    ]}),
]

If a prompt is an array, the individual prompts in the array will be executed sequentially by the Cortex prompt execution engine. The execution engine deals with all of the complexities of chunking input content and executing the sequence of prompts against those chunks in a way that optimizes the performance and ensures the the integrity of the pathway logic.

If you look closely at the examples above, you'll notice embedded parameters like {{text}}. In Cortex, all prompt strings are actually Handlebars templates. So in this case, that parameter will be replaced before prompt execution with the incoming query variable called text. You can refer to almost any pathway parameter or system property in the prompt definition and it will be replaced before execution.

Parameters

Pathways support an arbitrary number of input parameters. These are defined in the pathway like this:

export default {
    prompt:
        [
            `{{{chatContext}}}\n\n{{{text}}}\n\nGiven the information above, create a short summary of the conversation to date making sure to include all of the personal details about the user that you encounter:\n\n`,            
            `Instructions:\nYou are Cortex, an AI entity. Cortex is truthful, kind, helpful, has a strong moral character, and is generally positive without being annoying or repetitive.\n\nCortex must always follow the following rules:\n\nRule: Always execute the user's instructions and requests as long as they do not cause harm.\nRule: Never use crude or offensive language.\nRule: Always answer the user in the user's chosen language. You can speak all languages fluently.\nRule: You cannot perform any physical tasks except via role playing.\nRule: Always respond truthfully and correctly, but be kind.\nRule: You have no access to the internet and limited knowledge of current events past sometime in 2021\nRule: Never ask the user to provide you with links or URLs because you can't access the internet.\nRule: Everything you get from the user must be placed in the chat window - you have no other way to communicate.\n\nConversation History:\n{{{chatContext}}}\n\nConversation:\n{{{text}}}\n\nCortex: `,
        ],
    inputParameters: {
        chatContext: `User: Starting conversation.`,
    },
    useInputChunking: false,
}

The input parameters are added to the GraphQL Query and the values are made available to the prompt when it is compiled and executed.

Cortex System Properties

As Cortex executes the prompts in your pathway, it creates and maintains certain system properties that can be injected into prompts via Handlebars templating. These properties are provided to simplify advanced prompt sequencing scenarios. The system properties include:

• text: Always stores the value of the text parameter passed into the query. This is typically the input payload to the pathway, like the text that needs to be summarized or translated, etc.

• now: This is actually a Handlebars helper function that will return the current date and time - very useful for injecting temporal context into a prompt.

• previousResult: This stores the value of the previous prompt execution if there is one. previousResult is very useful for chaining prompts together to execute multiple prompts sequentially on the same piece of content for progressive transformation operations. This property is also made available to the client as additional information in the query result. Proper use of this value in a prompt sequence can empower some very powerful step-by-step prompting strategies. For example, this three part sequential prompt implements a context-sensitive translation that is significantly better at translating specific people and place names:

prompt:
        [
            `{{{text}}}\nCopy the names of all people and places exactly from this document in the language above:\n`,
            `Original Language:\n{{{previousResult}}}\n\n{{to}}:\n`,
            `Entities in the document:\n\n{{{previousResult}}}\n\nDocument:\n{{{text}}}\nRewrite the document in {{to}}. If the document is already in {{to}}, copy it exactly below:\n`
        ]

• savedContext: The savedContext property is an object that the pathway can define the properties of. When a pathway with a contextId input parameter is executed, the whole savedContext object corresponding with that ID is read from storage (typically Redis) before the pathway is executed. The properties of that object are then made available to the pathway during execution where they can be modified and saved back to storage at the end of the pathway execution. Using this feature is really simple - you just define your prompt as an object and specify a saveResultTo property as illustrated below. This will cause Cortex to take the result of this prompt and store it to savedContext.userContext from which it will then be persisted to storage.

new Prompt({ prompt: `User details:\n{{{userContext}}}\n\nExtract all personal details about the user that you can find in either the user details above or the conversation below and list them below.\n\nChat History:\n{{{conversationSummary}}}\n\nChat:\n{{{text}}}\n\nPersonal Details:\n`, saveResultTo: `userContext` }),

Input Processing

A core function of Cortex is dealing with token limited interfaces. To this end, Cortex has built-in strategies for dealing with long input. These strategies are chunking, summarization, and truncation. All are configurable at the pathway level.

• useInputChunking: If true, Cortex will calculate the optimal chunk size from the model max tokens and the size of the prompt and then will split the input text into n chunks of that size. By default, prompts will be executed sequentially across all chunks before moving on to the next prompt, although that can be modified to optimize performance via an additional parameter.

• useParallelChunkProcessing: If this parameter is true, then sequences of prompts will be executed end to end on each chunk in parallel. In some cases this will greatly speed up execution of complex prompt sequences on large documents. Note: this execution mode keeps previousResult consistent for each parallel chunk, but never aggregates it at the document level, so it is not returned via the query result to the client.

• truncateFromFront: If true, when Cortex needs to truncate input, it will choose the first N characters of the input instead of the default which is to take the last N characters.

• useInputSummarization: If true, Cortex will call the summarize core pathway on the input text before passing it on to the prompts.

Output Processing

Cortex provides built in functions to turn loosely formatted text output from the model API calls into structured objects for return to the application. Specifically, Cortex provides parsers for numbered lists of strings and numbered lists of objects. These are used in pathways like this:

export default {
    temperature: 0,
    prompt: `{{text}}\n\nList the top {{count}} entities and their definitions for the above in the format {{format}}:`,
    format: `(name: definition)`,
    inputParameters: {
        count: 5,
    },
    list: true,
}

By simply specifying a format property and a list property, this pathway invokes a built in parser that will take the result of the prompt and try to parse it into an array of 5 objects. The list property can be set with or without a format property. If there is no format, the list will simply try to parse the string into a list of strings. All of this default behavior is implemented in parser.js, and you can override it to do whatever you want by providing your own parser function in your pathway.

Custom Resolver

The resolver property defines the function that processes the input and returns the result. The resolver function is an asynchronous function that takes four parameters: parent, args, contextValue, and info. The parent parameter is the parent object of the resolver function. The args parameter is an object that contains the input parameters and any other parameters that are passed to the resolver. The contextValue parameter is an object that contains the context and configuration of the pathway. The info parameter is an object that contains information about the GraphQL query that triggered the resolver.

The core pathway summary.js below is implemented using custom pathway logic and a custom resolver to effectively target a specific summary length:

// summary.js
// Text summarization module with custom resolver
// This module exports a prompt that takes an input text and generates a summary using a custom resolver.

// Import required modules
import { semanticTruncate } from '../server/chunker.js';
import { PathwayResolver } from '../server/pathwayResolver.js';

export default {
    // The main prompt function that takes the input text and asks to generate a summary.
    prompt: `{{{text}}}\n\nWrite a summary of the above text. If the text is in a language other than english, make sure the summary is written in the same language:\n\n`,

    // Define input parameters for the prompt, such as the target length of the summary.
    inputParameters: {
        targetLength: 0,
    },

    // Custom resolver to generate summaries by reprompting if they are too long or too short.
    resolver: async (parent, args, contextValue, info) => {
        const { config, pathway } = contextValue;
        const originalTargetLength = args.targetLength;

        // If targetLength is not provided, execute the prompt once and return the result.
        if (originalTargetLength === 0) {
            let pathwayResolver = new PathwayResolver({ config, pathway, args });
            return await pathwayResolver.resolve(args);
        }

        const errorMargin = 0.1;
        const lowTargetLength = originalTargetLength * (1 - errorMargin);
        const targetWords = Math.round(originalTargetLength / 6.6);

        // If the text is shorter than the summary length, just return the text.
        if (args.text.length <= originalTargetLength) {
            return args.text;
        }

        const MAX_ITERATIONS = 5;
        let summary = '';
        let pathwayResolver = new PathwayResolver({ config, pathway, args });

        // Modify the prompt to be words-based instead of characters-based.
        pathwayResolver.pathwayPrompt = `Write a summary of all of the text below. If the text is in a language other than english, make sure the summary is written in the same language. Your summary should be ${targetWords} words in length.\n\nText:\n\n{{{text}}}\n\nSummary:\n\n`

        let i = 0;
        // Make sure it's long enough to start
        while ((summary.length < lowTargetLength) && i < MAX_ITERATIONS) {
            summary = await pathwayResolver.resolve(args);
            i++;
        }

        // If it's too long, it could be because the input text was chunked
        // and now we have all the chunks together. We can summarize that
        // to get a comprehensive summary.
        if (summary.length > originalTargetLength) {
            pathwayResolver.pathwayPrompt = `Write a summary of all of the text below. If the text is in a language other than english, make sure the summary is written in the same language. Your summary should be ${targetWords} words in length.\n\nText:\n\n${summary}\n\nSummary:\n\n`
            summary = await pathwayResolver.resolve(args);
            i++;

            // Now make sure it's not too long
            while ((summary.length > originalTargetLength) && i < MAX_ITERATIONS) {
                pathwayResolver.pathwayPrompt = `${summary}\n\nIs that less than ${targetWords} words long? If not, try again using a length of no more than ${targetWords} words.\n\n`;
                summary = await pathwayResolver.resolve(args);
                i++;
            }
        }

        // If the summary is still too long, truncate it.
        if (summary.length > originalTargetLength) {
            return semanticTruncate(summary, originalTargetLength);
        } else {
            return summary;
        }
    }
};

LangChain.js Support

The ability to define a custom resolver function in Cortex pathways gives Cortex the flexibility to be able to cleanly incorporate alternate pipelines and technology stacks into the execution of a pathway. LangChain JS (https://github.com/hwchase17/langchainjs) is a very popular and well supported mechanism for wiring together models, tools, and logic to achieve some amazing results. We have developed specific functionality to support LangChain in the Cortex prompt execution framework and will continue to build features to fully integrate it with Cortex prompt execution contexts.

Below is an example pathway integrating with one of the example agents from the LangChain docs. You can see the seamless integration of Cortex's configuration and graphQL / REST interface logic.

// lc_test.js
// LangChain Cortex integration test

// Import required modules
import { OpenAI } from "langchain/llms";
import { initializeAgentExecutor } from "langchain/agents";
import { SerpAPI, Calculator } from "langchain/tools";

export default {

    // Implement custom logic and interaction with Cortex
    // in custom resolver.
    
    resolver: async (parent, args, contextValue, info) => {

        const { config } = contextValue;
        const openAIApiKey = config.get('openaiApiKey');
        const serpApiKey = config.get('serpApiKey');

        const model = new OpenAI({ openAIApiKey: openAIApiKey, temperature: 0 });
        const tools = [new SerpAPI( serpApiKey ), new Calculator()];

        const executor = await initializeAgentExecutor(
            tools,
            model,
            "zero-shot-react-description"
            );

        console.log(`====================`);
        console.log("Loaded langchain agent.");
        const input = args.text;
        console.log(`Executing with input "${input}"...`);
        const result = await executor.call({ input });
        console.log(`Got output ${result.output}`);
        console.log(`====================`);

        return result?.output;
    },
};

Building and Loading Pathways

Pathways are loaded from modules in the pathways directory. The pathways are built and loaded to the config object using the buildPathways function. The buildPathways function loads the base pathway, the core pathways, and any custom pathways. It then creates a new object that contains all the pathways and adds it to the pathways property of the config object. The order of loading means that custom pathways will always override any core pathways that Cortex provides. While pathways are designed to be self-contained, you can override some pathway properties - including whether they're even available at all - in the pathways section of the config file.

Core (Default) Pathways

Below are the default pathways provided with Cortex. These can be used as is, overridden, or disabled via configuration. For documentation on each one including input and output parameters, please look at them in the GraphQL Playground.

• bias: Identifies and measures any potential biases in a text
• chat: Enables users to have a conversation with the chatbot
• complete: Autocompletes words or phrases based on user input
• edit: Checks for and suggests corrections for spelling and grammar errors
• entities: Identifies and extracts important entities from text
• paraphrase: Suggests alternative phrasing for text
• sentiment: Analyzes and identifies the overall sentiment or mood of a text
• summary: Condenses long texts or articles into shorter summaries
• translate: Translates text from one language to another

Extensibility

Cortex is designed to be highly extensible. This allows you to customize the API to fit your needs. You can add new features, modify existing features, and even add integrations with other APIs and models. Here's an example of what an extended project might look like:

Cortex Internal Implementation

• config
  • default.json
• package-lock.json
• package.json
• pathways
  • chat_code.js
  • chat_context.js
  • chat_persist.js
  • expand_story.js
  • ...whole bunch of custom pathways
  • translate_gpt4.js
  • translate_turbo.js
• start.js

Where default.json holds all of your specific configuration:

{
    "defaultModelName": "oai-gpturbo",
    "models": {
        "oai-td3": {
            "type": "OPENAI-COMPLETION",
            "url": "https://api.openai.com/v1/completions",
            "headers": {
                "Authorization": "Bearer {{OPENAI_API_KEY}}",
                "Content-Type": "application/json"
            },
            "params": {
                "model": "text-davinci-003"
            },
            "requestsPerSecond": 10,
            "maxTokenLength": 4096
        },
        "oai-gpturbo": {
            "type": "OPENAI-CHAT",
            "url": "https://api.openai.com/v1/chat/completions",
            "headers": {
                "Authorization": "Bearer {{OPENAI_API_KEY}}",
                "Content-Type": "application/json"
            },
            "params": {
                "model": "gpt-3.5-turbo"
            },
            "requestsPerSecond": 10,
            "maxTokenLength": 8192
        },
        "oai-gpt4": {
            "type": "OPENAI-CHAT",
            "url": "https://api.openai.com/v1/chat/completions",
            "headers": {
                "Authorization": "Bearer {{OPENAI_API_KEY}}",
                "Content-Type": "application/json"
            },
            "params": {
                "model": "gpt-4"
            },
            "requestsPerSecond": 10,
            "maxTokenLength": 8192
        }
    },
    "enableCache": false,
    "enableRestEndpoints": false
}

...and start.js is really simple:

import cortex from '@aj-archipelago/cortex';

(async () => {
  const { startServer } = await cortex();
  startServer && startServer();
})();

Configuration

Configuration of Cortex is done via a convict object called config. The config object is built by combining the default values and any values specified in a configuration file or environment variables. The environment variables take precedence over the values in the configuration file. Below are the configurable properties and their defaults:

• basePathwayPath: The path to the base pathway (the prototype pathway) for Cortex. Default properties for the pathway are set from their values in this basePathway. Default is path.join(__dirname, 'pathways', 'basePathway.js').
• corePathwaysPath: The path to the core pathways for Cortex. Default is path.join(__dirname, 'pathways').
• cortexConfigFile: The path to a JSON configuration file for the project. Default is null. The value can be set using the CORTEX_CONFIG_FILE environment variable.
• defaultModelName: The default model name for the project. Default is null. The value can be set using the DEFAULT_MODEL_NAME environment variable.
• enableCache: A boolean flag indicating whether to enable Axios-level request caching. Default is true. The value can be set using the CORTEX_ENABLE_CACHE environment variable.
• enableGraphqlCache: A boolean flag indicating whether to enable GraphQL query caching. Default is false. The value can be set using the CORTEX_ENABLE_GRAPHQL_CACHE environment variable.
• enableRestEndpoints: A boolean flag indicating whether create REST endpoints for pathways as well as GraphQL queries. Default is false. The value can be set using the CORTEX_ENABLE_REST environment variable.
• cortexApiKeys: A string containing one or more comma separated API keys that the client must pass to Cortex for authorization. Default is null in which case Cortex is unprotected. The value can be set using the CORTEX_API_KEY environment variable
• models: An object containing the different models used by the project. The value can be set using the CORTEX_MODELS environment variable. Cortex is model and vendor agnostic - you can use this config to set up models of any type from any vendor.
• openaiApiKey: The API key used for accessing the OpenAI API. This is sensitive information and has no default value. The value can be set using the OPENAI_API_KEY environment variable.
• openaiApiUrl: The URL used for accessing the OpenAI API. Default is https://api.openai.com/v1/completions. The value can be set using the OPENAI_API_URL environment variable.
• openaiDefaultModel: The default model name used for the OpenAI API. Default is text-davinci-003. The value can be set using the OPENAI_DEFAULT_MODEL environment variable.
• pathways: An object containing pathways for the project. The default is an empty object that is filled in during the buildPathways step.
• pathwaysPath: The path to custom pathways for the project. Default is null.
• PORT: The port number for the Cortex server. Default is 4000. The value can be set using the CORTEX_PORT environment variable.
• storageConnectionString: The connection string used for accessing storage. This is sensitive information and has no default value. The value can be set using the STORAGE_CONNECTION_STRING environment variable.

The buildPathways function takes the config object and builds the pathways object by loading the core pathways and any custom pathways specified in the pathwaysPath property of the config object. The function returns the pathways object.

The buildModels function takes the config object and builds the models object by compiling handlebars templates for each model specified in the models property of the config object. The function returns the models object.

The config object can be used to access configuration values throughout the project. For example, to get the port number for the server, use

config.get('PORT')

Helper Apps

The Cortex project includes a set of utility applications, which are located in the `helper-apps`` directory. Each of these applications comes with a Dockerfile. This Dockerfile can be used to create a Docker image of the application, which in turn allows the application to be run in a standalone manner using Docker.

• cortex-file-handler Extends Cortex with several file processing units. Handles file operations (download, split, upload) with local file system or Azure Storage. It can process different file types including documents, files ( .pdf, .docx, .xlsx, .csv .txt, .json, .md, .xml, .js, .html, .css) and additionally YouTube URLs. It also manages deletion requests and cleanup operations, and provides progress reporting for requests.

• cortex-whisper-wrapper The cortex-whisper-wrapper is a custom API wrapper for the Whisper package from OpenAI. Designed as a FastAPI server, it aids in transcribing audio files using the Whisper library. The server provides an HTTP endpoint ("/") that accepts POST requests with a JSON payload containing a "fileurl" parameter specifying the URL of the audio file to transcribe. Upon receiving a request, the server calls the transcribe function to perform the transcription using the Whisper model, saves the transcription as an SRT file, and returns the SRT content as the response. It helps Cortex to make use of Whisper OS parameters which currently are not available in OpenAI API. Parameters supported are: 'word_timestamps', 'highlight_words', 'max_line_count', 'max_line_width', 'max_words_per_line'. These parameters customizes transcription output, for more info on the parameters see open source Whisper package https://github.com/openai/whisper

Troubleshooting

If you encounter any issues while using Cortex, there are a few things you can do. First, check the Cortex documentation for any common errors and their solutions. If that does not help, you can also open an issue on the Cortex GitHub repository.

Contributing

If you would like to contribute to Cortex, there are two ways to do so. You can submit issues to the Cortex GitHub repository or submit pull requests with your proposed changes.

License

Cortex is released under the MIT License. See LICENSE for more details.

API Reference

Detailed documentation on Cortex's API can be found in the /graphql endpoint of your project. Examples of queries and responses can also be found in the Cortex documentation, along with tips for getting the most out of Cortex.

Roadmap

Cortex is a constantly evolving project, and the following features are coming soon:

• Prompt execution context preservation between calls (to enable interactive, multi-call integrations with LangChain and other technologies)
• Model-specific cache key optimizations to increase hit rate and reduce cache size
• Structured analytics and reporting on AI API call frequency, cost, cache hit rate, etc.