From 95459d9df0a564db5843798ffe506f1cc21f77fb Mon Sep 17 00:00:00 2001 From: Sarmad Qadri Date: Tue, 7 Nov 2023 18:09:08 -0500 Subject: [PATCH] [Docs][1/n] AIConfig doc updates Lots of doc updates: * Better Prism themes for clean code rendering * Docs for "AIConfig File Format" * Docs for "What is AIConfig" * Starting point for "Getting Started" --- aiconfig-docs/core/prismTheme.js | 126 ++++++++ aiconfig-docs/core/tabConstants.js | 75 +++++ aiconfig-docs/docs/cookbooks/_category_.json | 4 +- .../docs/extensibility/_category_.json | 4 +- .../docs/introduction/basic-structure.md | 15 + aiconfig-docs/docs/introduction/basics.md | 184 +++++++++++- .../docs/introduction/getting-started.md | 146 +++++++++- .../docs/introduction/installation.md | 5 - .../docs/overview/ai-config-format.md | 269 +++++++++++++++++- .../docs/overview/parameters-and-chaining.md | 11 + aiconfig-docs/docusaurus.config.js | 22 +- aiconfig-docs/elements.tsx | 69 +++++ aiconfig-docs/src/css/custom.css | 190 +++++++++++++ .../static/img/workbook_screenshot.png | Bin 0 -> 307656 bytes aiconfig-docs/tsconfig.json | 4 +- typescript/demo/demo.ts | 21 +- 16 files changed, 1117 insertions(+), 28 deletions(-) create mode 100644 aiconfig-docs/core/prismTheme.js create mode 100644 aiconfig-docs/core/tabConstants.js create mode 100644 aiconfig-docs/docs/introduction/basic-structure.md delete mode 100644 aiconfig-docs/docs/introduction/installation.md create mode 100644 aiconfig-docs/elements.tsx create mode 100644 aiconfig-docs/static/img/workbook_screenshot.png diff --git a/aiconfig-docs/core/prismTheme.js b/aiconfig-docs/core/prismTheme.js new file mode 100644 index 000000000..9d5d6efca --- /dev/null +++ b/aiconfig-docs/core/prismTheme.js @@ -0,0 +1,126 @@ +/** + * Copyright (c) LastMile AI, Inc. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ + +const theme = { + plain: { + color: "#FFFFFF", + backgroundColor: "#282C34", + }, + styles: [ + { + types: ["property"], + style: { + color: "#2aa198", + }, + }, + { + types: ["attr-name", "comment", "prolog", "doctype", "cdata"], + style: { + color: "#93a1a1", + }, + }, + { + types: ["punctuation"], + style: { + color: "#657b83", + }, + }, + { + types: ["namespace"], + style: { + opacity: 0.7, + }, + }, + { + types: ["selector", "char", "builtin", "url"], + style: { + color: "#2aa198", + }, + }, + { + types: ["entity"], + style: { + color: "#2aa198", + }, + }, + { + types: ["atrule", "inserted"], + style: { + color: "#859900", + }, + }, + { + types: ["important", "variable", "deleted"], + style: { + color: "#cb4b16", + }, + }, + { + types: ["important", "bold"], + style: { + fontWeight: "bold", + }, + }, + { + types: ["italic"], + style: { + fontStyle: "italic", + }, + }, + { + types: ["entity"], + style: { + cursor: "help", + }, + }, + // react-native theme + { + types: ["attr-name", "keyword"], + style: { + color: "#c5a5c5", + }, + }, + { + types: ["string", "regex", "attr-value"], + style: { + color: "#8dc891", + }, + }, + { + types: ["number", "constant", "symbol"], + style: { + color: "#5a9bcf", + }, + }, + { + types: ["boolean"], + style: { + color: "#ff8b50", + }, + }, + { + types: ["class-name"], + style: { + color: "#fac863", + }, + }, + { + types: ["function"], + style: { + color: "#79b6f2", + }, + }, + { + types: ["operator", "tag"], + style: { + color: "#fc929e", + }, + }, + ], +}; + +export default theme; diff --git a/aiconfig-docs/core/tabConstants.js b/aiconfig-docs/core/tabConstants.js new file mode 100644 index 000000000..5d4fa1ccc --- /dev/null +++ b/aiconfig-docs/core/tabConstants.js @@ -0,0 +1,75 @@ +/** + * Copyright (c) LastMile AI, Inc. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ + +import ExecutionEnvironment from "@docusaurus/ExecutionEnvironment"; + +const isMacOS = ExecutionEnvironment.canUseDOM + ? navigator.platform.startsWith("Mac") + : false; +const isWindows = ExecutionEnvironment.canUseDOM + ? navigator.platform.startsWith("Win") + : false; + +const defaultSyntax = "functional"; + +const nodePackageManagers = [ + { label: "npm", value: "npm" }, + { label: "Yarn", value: "yarn" }, +]; +const defaultNodePackageManager = "npm"; + +const pythonPackageManagers = [ + { label: "pip", value: "pip" }, + { label: "poetry", value: "poetry" }, +]; +const defaultPythonPackageManager = "pip"; + +const aiConfigLanguages = [ + { label: "Node.js (TypeScript)", value: "node" }, + { label: "Python", value: "python" }, +]; +const defaultAIConfigLanguage = "python"; + +const platforms = [ + { label: "Android", value: "android" }, + { label: "iOS", value: "ios" }, +]; +const defaultPlatform = isMacOS ? "ios" : "android"; + +const oses = [ + { label: "macOS", value: "macos" }, + { label: "Windows", value: "windows" }, + { label: "Linux", value: "linux" }, +]; +const defaultOs = isMacOS ? "macos" : isWindows ? "windows" : "linux"; + +const getDevNotesTabs = (tabs = ["android", "ios", "web", "windows"]) => + [ + tabs.includes("android") + ? { label: "Android", value: "android" } + : undefined, + tabs.includes("ios") ? { label: "iOS", value: "ios" } : undefined, + tabs.includes("web") ? { label: "Web", value: "web" } : undefined, + tabs.includes("windows") + ? { label: "Windows", value: "windows" } + : undefined, + ].filter(Boolean); + +export default { + defaultOs, + defaultNodePackageManager, + defaultPythonPackageManager, + defaultPlatform, + defaultSyntax, + defaultAIConfigLanguage, + getDevNotesTabs, + oses, + nodePackageManagers, + pythonPackageManagers, + platforms, + aiConfigLanguages, +}; diff --git a/aiconfig-docs/docs/cookbooks/_category_.json b/aiconfig-docs/docs/cookbooks/_category_.json index 59c79756f..07603dd48 100644 --- a/aiconfig-docs/docs/cookbooks/_category_.json +++ b/aiconfig-docs/docs/cookbooks/_category_.json @@ -1,6 +1,6 @@ { - "label": "Customization & Extensibility", - "position": 5, + "label": "Cookbooks & Guides", + "position": 10, "link": { "type": "generated-index" } diff --git a/aiconfig-docs/docs/extensibility/_category_.json b/aiconfig-docs/docs/extensibility/_category_.json index 07603dd48..59c79756f 100644 --- a/aiconfig-docs/docs/extensibility/_category_.json +++ b/aiconfig-docs/docs/extensibility/_category_.json @@ -1,6 +1,6 @@ { - "label": "Cookbooks & Guides", - "position": 10, + "label": "Customization & Extensibility", + "position": 5, "link": { "type": "generated-index" } diff --git a/aiconfig-docs/docs/introduction/basic-structure.md b/aiconfig-docs/docs/introduction/basic-structure.md new file mode 100644 index 000000000..e1195b5d1 --- /dev/null +++ b/aiconfig-docs/docs/introduction/basic-structure.md @@ -0,0 +1,15 @@ +--- +sidebar_position: 3 +--- + +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; +import constants from '@site/core/tabConstants'; + +# Basic Structure + +## Model Parsers + +## Config File + +## SDK diff --git a/aiconfig-docs/docs/introduction/basics.md b/aiconfig-docs/docs/introduction/basics.md index 329708394..19df99e88 100644 --- a/aiconfig-docs/docs/introduction/basics.md +++ b/aiconfig-docs/docs/introduction/basics.md @@ -1,17 +1,187 @@ --- -sidebar_position: 3 +sidebar_position: 1 --- +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; +import constants from '@site/core/tabConstants'; + # What is AIConfig -## Why use this +AIConfig is a source-control friendly way to manage your prompts, models and model parameters as configs. It allows you to store and iterate on generative AI model behavior _separately from your application code_. + +AIConfig has 3 foundations: + +1. **[`aiconfig` file format](/docs/overview/ai-config-format)**: a standardized JSON format to store generative AI model settings, prompt inputs and outputs, and flexible multi-purpose metadata. +2. **[AIConfig SDK](/docs/introduction/getting-started)**: Python and Node SDKs to use `aiconfig` in your application code, and extension points to customize behavior. +3. **[AI Workbook editor](https://lastmileai.dev/workbooks/clm7b9yez00mdqw70majklrmx)**: A notebook-like playground to edit `aiconfig` files visually, run prompts, tweak models and model settings, and chain things together. + +AIConfig is **multi-modal** and **model-agnostic**. This enables powerful interop between different models and modalities, including chaining them together (see [prompt chaining](/docs/overview/parameters-and-chaining)). For example, a Whisper (speech-to-text) prompt can be connected to a GPT4-V prompt (image+text-to-text) to build complex AI applications, all backed by the same `aiconfig` serialization format. + +## Prompts as configs + +Unlike traditional _predictive_ ML development undertaken largely by ML researchers, generative AI applications are being developed collaboratively by software engineers. + +Separating prompt management from application development leads to a few powerful consequences: + +1. **Separation of concerns**: You can iterate on prompts and models separately from application code -- and different people could be responsible for them, making the overall development more collaborative. +2. **Notebook editor for prompts**: Having prompts and models in one place allows a notebook-like editor environment to iterate on the aiconfig. This greatly increases the velocity of prototyping and iteration. +3. **Governance**: `aiconfig` becomes a source-controlled artifact that can be used for reproducability and provenance of the generative AI bits of your application. + +## Example + +:::tip + +See the [Getting Started](/docs/introduction/getting-started) guide for a more detailed overview, and spend time getting familiar with the [`aiconfig` file format](/docs/overview/ai-config-format). + +::: + +Suppose we have the following prompts to help us plan a trip to New York. As you'll see, using this `aiconfig` in application code is trivial. The application no longer needs to know _how_ to invoke different models -- it simply uses `config.run(prompt_name)` to do so. There's a lot you can do with AIConfig, which we'll get into in later guides. + +### `aiconfig` file format + +
+`travel.aiconfig.json` - Use GPT-3.5 to plan a trip to New York +```json +{ + "name": "Planning a trip to New York", + "description": "Exploring NYC through ChatGPT and AIConfig", + "schema_version": "latest", + "metadata": { + "models": { + "gpt-3.5-turbo": { + "model": "gpt-3.5-turbo", + "top_p": 1, + "temperature": 1 + } + } + }, + "prompts": [ + { + "name": "get_activities", + "input": "Tell me 10 fun attractions to do in NYC." + "metadata": { + "model": "gpt-3.5-turbo", + } + } + ] +} +``` + +
+ +### Using with AIConfig SDK + +Creating an application with this `aiconfig` is trivial. The application no longer needs to know _how_ to invoke different models, how to handle streaming, etc. -- it simply uses `config.run(prompt_name)` and the AIConfig SDK does the rest. + + + + +```bash +$ yarn add aiconfig +``` + +```bash +$ set OPENAI_API_KEY=sk_xxxx +``` + +```typescript title="app.ts" +import * as path from "path"; +import { AIConfigRuntime, InferenceOptions } from "aiconfig"; + +async function travelWithGPT() { + // Load the aiconfig. You can also use AIConfigRuntime.loadJSON({}) + const aiConfig = AIConfigRuntime.load( + path.join(__dirname, "travel.aiconfig.json") + ); + + // Run a single prompt + await aiConfig.run("get_activities", /*params*/ undefined, options); + + // Save the AIConfig to disk, and serialize outputs from the model run + aiConfig.save( + "updated.aiconfig.json", + /*saveOptions*/ { serializeOutputs: true } + ); +} +``` + + + +```bash +$ pip install python-aiconfig +```` + +```bash +$ set OPENAI_API_KEY=sk_xxxx +``` + +```python title="app.py" +from aiconfig import AIConfigRuntime, InferenceOptions + +# Load the aiconfig. You can also use AIConfigRuntime.loadJSON({}) +config = AIConfigRuntime.load('travel.aiconfig.json') + +# Run a single prompt (with streaming) +inference_options = InferenceOptions(stream=True) +await config.run("get_activities", params=None, inference_options) + +# Save the aiconfig to disk. and serialize outputs from the model run +config.save('updated.aiconfig.json', include_output=True) +``` + + + + +### AI Workbook playground + +Now that we have an `aiconfig` file artifact that encapsulates the generative AI part of our application, we can iterate on it using a notebook editor. The application code doesn't need to change even as the `aiconfig` is updated. + +:::note +We are currently working on a local editor that you can run yourself. For now, please use the hosted version on https://lastmileai.dev. +::: + + + +:::tip +Try out this workbook yourself here: **[NYC Travel Workbook](https://lastmileai.dev/workbooks/clooqs3p200kkpe53u6n2rhr9)** +::: + +## Improved AI Governance + +`aiconfig` helps you track the _signature_ of generative AI model behavior: + +- **prompts** and prompt chains that constitute the input (can be text, image or any modality) +- **model** to run inference (can be any model from any model provider) +- **model parameters** to tune the model behavior +- **outputs** cached from previous inference runs, which can be serialized optionally. + +Having a dedicated source-controlled artifact for generative AI helps with reproducibility, evaluation and rapid iteration. You can iterate on this artifact, evaluate it and integrate it into the rest of your application development workflow. + +## Parallels to Jupyter + +As you go through AIConfig, you may notice similarities to [Jupyter](https://jupyter-notebook.readthedocs.io/) notebooks and its storage format, [`ipynb`](https://ipython.org/ipython-doc/3/notebook/nbformat.html). This is by design. + +_**AI Workbooks are to AIConfig what Jupyter notebooks are to `ipynb`**_ + +We believe that notebooks are a perfect iteration environment for generative AI -- they are flexible and multi-modal, with no vendor lock-in. + +The multi-modality and flexibility offered by notebooks and `ipynb` offers a good interaction model for generative AI. The `aiconfig` file format is extensible like `ipynb`, and AI Workbook editor allows rapid iteration in a notebook-like IDE. + +There are 2 areas where we are going beyond what notebooks offer: -## Who this is for +1. `aiconfig` is more **source-control friendly** than `ipynb`. `ipynb` stores binary data (images, etc.) by encoding it in the file, while `aiconfig` recommends using file URI references instead. +2. `aiconfig` can be imported and **connected to application code** using the AIConfig SDK. -# Basic Structure +## Extensibility -## Model Parsers +AIConfig is meant to be fully customizable and extensible for your use-cases. The specific parts that you can customize include: -## Config File +- **Model Parsers**: these parsers are responsible for deciding how to run inference, what data to store in the `aiconfig`, and how. You can add model parsers for any model of any input/output modality, and from any provider (including a model running on your local machine). +- **Callbacks**: callback handlers allow you to hook up `aiconfig` runs to monitoring and observability endpoints of your choosing. +- **Evaluation**
Coming Soon
: define custom evaluators and run batch evaluation to measure the performance of your `aiconfig`. +- **Routing**
Coming Soon
: define custom routers over a series of `aiconfig`s to intelligently route incoming requests over prompts and models (i.e. prompt routing and model routing). -## SDK +:::tip +Go to [Customization & Extensibility](/docs/category/customization--extensibility) to learn more about AIConfig extensibility +::: diff --git a/aiconfig-docs/docs/introduction/getting-started.md b/aiconfig-docs/docs/introduction/getting-started.md index ca6b2822f..4fc9bf5b6 100644 --- a/aiconfig-docs/docs/introduction/getting-started.md +++ b/aiconfig-docs/docs/introduction/getting-started.md @@ -1,5 +1,149 @@ --- -sidebar_position: 1 +sidebar_position: 2 --- +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; +import constants from '@site/core/tabConstants'; + # Getting Started + +## Quickstart + +Suppose we have the following prompts to help us plan a trip to New York. + +
+`travel.aiconfig.json` +```json +{ + "name": "Planning a trip to New York", + "description": "Exploring NYC through ChatGPT and AIConfig", + "schema_version": "latest", + "metadata": { + "models": { + "gpt-3.5-turbo": { + "model": "gpt-3.5-turbo", + "top_p": 1, + "temperature": 1 + }, + "gpt-4": { + "model": "gpt-4", + "max_tokens": 3000, + "system_prompt": "You are an expert travel coordinator for New York City." + } + }, + "default_model": "gpt-3.5-turbo" + }, + "prompts": [ + { + "name": "get_activities", + "input": "Tell me 10 fun attractions to do in NYC." + // No model is specified, so this defaults to the default_model specified above (gpt-3.5-turbo) + }, + { + "name": "gen_itinerary", + "input": "Generate an itinerary ordered by {{order_by}} for these activities: {{get_activities.output}}.", + "metadata": { + "model": "gpt-4", + "parameters": { + "order_by": "geographic location" + } + } + } + ] +} +``` + +
+ +Using this `aiconfig` in application code is trivial + + + + +```bash +$ yarn add aiconfig +``` + +```typescript title="app.ts" +import * as path from "path"; +import { AIConfigRuntime, InferenceOptions } from "aiconfig"; + +async function travelWithGPT() { + // Alternatively, you can use AIConfigRuntime.loadJSON({/*travel.aiconfig.json contents*/}) + const aiConfig = AIConfigRuntime.load( + path.join(__dirname, "travel.aiconfig.json") + ); + + const options: InferenceOptions = { + callbacks: { + streamCallback: (data: any, _acc: any, _idx: any) => { + // Write streamed content to console + process.stdout.write(data?.content || "\n"); + }, + }, + }; + + // Run a single prompt + await aiConfig.run("get_activities", /*params*/ undefined, options); + + // Run a prompt chain, with data passed in as params + // This will first run get_activities with GPT-3.5, and + // then use its output to run the gen_itinerary using GPT-4 + await aiConfig.runWithDependencies( + "gen_itinerary", + /*params*/ { order_by: "duration" }, + options + ); + + // Save the AIConfig to disk, and serialize outputs from the model run too + aiConfig.save( + "updated.aiconfig.json", + /*saveOptions*/ { serializeOutputs: true } + ); +} +``` + + + +```bash +$ pip install python-aiconfig +```` + +```python +import + +async def get(id: str): +return [item for item in db if item["id"] == id][0] or None + +async def callFunction(function_call): +args = function_call.get("arguments", None) +args = json.loads(args) if args else None + + if not args: + raise Exception("No arguments found") + + match function_call.get("name"): + case "list": + return await list_items(args["genre"]) + case "search": + return await search(args["name"]) + case "get": + return await get(args["id"]) + +import asyncio + +asyncio.run(function_calling()) + +``` + + + + +## Using OpenAI API introspection + +## Starting with AI Workbooks + +``` + +``` diff --git a/aiconfig-docs/docs/introduction/installation.md b/aiconfig-docs/docs/introduction/installation.md deleted file mode 100644 index a223ae040..000000000 --- a/aiconfig-docs/docs/introduction/installation.md +++ /dev/null @@ -1,5 +0,0 @@ ---- -sidebar_position: 2 ---- - -# Installation diff --git a/aiconfig-docs/docs/overview/ai-config-format.md b/aiconfig-docs/docs/overview/ai-config-format.md index 274660e30..b29ab0a7a 100644 --- a/aiconfig-docs/docs/overview/ai-config-format.md +++ b/aiconfig-docs/docs/overview/ai-config-format.md @@ -2,6 +2,273 @@ sidebar_position: 1 --- +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + # The AIConfig Format -(similar to https://ipython.org/ipython-doc/3/notebook/nbformat.html) +## Introduction + +`aiconfig` files are source-control friendly JSON documents that contain generative AI model settings, prompt inputs and outputs, and flexible multi-purpose metadata. + +In short, `aiconfig` defines the _signature_ of generative AI model behavior: + +- **prompts** and prompt chains that constitute the input +- **model** to run inference +- **model parameters** to tune the model behavior +- **outputs** cached from previous inference runs, which can be serialized optionally. + +The `aiconfig` file format is meant to be extremely flexible, and can be used for a wide variety of use-cases. Specifically: + +1. AIConfig is **multi-modal**. Prompt inputs and outputs can specify MIME types and reference a file or binary output. This allows `aiconfig` to be used with models of any modality (text-to-speech, image+text-to-text, audio-to-text, etc.) +2. AIConfig is **model-agnostic**. As long as the data is serialized in the AIConfig format, it can be deserialized by any model-specific parser to perform inference. Custom extensions can be defined for hyper-local use-cases. + +:::tip + +The full AIConfig schema can be found **[here](https://github.com/lastmile-ai/aiconfig/blob/main/schema/aiconfig.schema.json)**. The corresponding [TypeScript types](https://github.com/lastmile-ai/aiconfig/blob/main/typescript/types.ts#L6) make it easy to reason about it. + +::: + +## Top-level structure + +At the highest level, `aiconfig` has the following properties: + +| | | +| -------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------- | +| `name`
Required
| Friendly name descriptor for the AIConfig. Could default to the filename if not specified. | +| `description` | Description of the AIConfig. If you have a collection of different AIConfigs, this can be used for dynamic prompt routing. | +| `schema_version`
Required
| The version of the AIConfig schema (e.g. `latest`). | +| `metadata` | Root-level metadata that applies to the entire AIConfig, containing things like model settings and global parameters. | +| `prompts` | Array of prompts that make up the AIConfig. | +| _extra user-defined properties_ | Additional properties can be specified that may be used for specific use-cases. | + +### Example `aiconfig` + +
+`sql.aiconfig.json` example +```json +{ + "name": "gpt4 as your data engineer", + "description": "A SQL coding assistant that generates SQL queries for the desired output.", + "schema_version": "latest", + "metadata": { + "models": { + "gpt-4": { + "model": "gpt-4", + "top_p": 1, + "max_tokens": 3000, + "temperature": 1, + "system_prompt": "You are an expert at SQL..." + } + } + }, + "prompts": [ + { + "name": "write_sql", + "input": "Write me a {{sql_language}} query to get this final output: {{output_data}}. Use the tables relationships defined here: {{table_relationships}}.", + "metadata": { + "model": "gpt-4", + "parameters": { + "sql_language": "mysql", + "output_data": "This is a parameter that follows the handlebars syntax. It allows you to create templatized prompts, and override them with values when an aiconfig is run in code", + "table_relationships": "For example, you could invoke config.run('write_sql', table_relationships=get_table_schema(my_table)) to dynamically specify table relationships" + } + } + }, + { + "name": "postgresql", + "input": "Translate the following into PostgreSQL code:\n {{write_sql.output}}", + "metadata": { + "model": { + "name": "gpt-4", + "settings": { + "model": "gpt-4", + "max_tokens": 3000, // Override prompt-specific model parameters here. + "temperature": 0.75 // They will get merged with the GPT-4 settings specified in root metadata. + } + } + }, + "outputs": [ + /* Output(s) for this prompt, if any. See below for details */ + ] + } + ] +} +``` +
+ +## Prompts + +Prompts are the primary building block of `aiconfig`. They store the inputs (and optionally the outputs) for a model, as well as any prompt-specific metadata. +All prompts have the following basic structure: + +| | | +| ----------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `name`
Required
| A unique identifier for the prompt. This is used to reference the prompt in other parts of the AIConfig, and when using the SDK. | +| `input`
Required
| The input prompt - this can be a `string`, or a complex object that represents one or more inputs (e.g. image URI and string prompt). | +| `metadata` | Prompt-specific metadata that applies to this prompt, containing things like model settings and prompt parameters. This gets merged with the root metadata, and takes precedence for overridden properties. | +| `outputs` | Optional array of outputs representing a previous inference run for this prompt. | +| _extra user-defined properties_ | Additional properties can be specified that may be used for specific use-cases. | + +### Prompt input + +The input to a model can be a string (e.g. for an LLM), or a more complex prompt for multi-modal models (e.g. audio file, or a combination of different artifacts as a single input). The `input` field makes it possible to specify any kind of model input. + +It is typed as a union type: + +```typescript +type PromptInput = + | { + /** + * Input to the model. This can represent a single input, or multiple inputs. + * The structure of the data object is up to the ModelParser. For example, + * a multi-modal ModelParser can choose to key the data by MIME type. + */ + data?: JSONValue; + + [k: string]: any; + } + | string; +``` + +### Prompt outputs + +Executing inference for a prompt results in an output or array of outputs. These outputs can be optionally serialized to an `aiconfig`. + +All outputs have an `output_type` field, which is a string defining what type of output it is. + +#### `execute_result` + +This output type is the result of running inference for a prompt. + +```typescript +type ExecuteResult = { + output_type: "execute_result"; + + /** + * A result's prompt number, if there are multiple outputs (e.g. multiple choices). + */ + execution_count?: number; + + /** + * The result of executing the prompt. + */ + data: JSONValue; + + /** + * The MIME type of the result. If not specified, the MIME type will be assumed to be plain text. + */ + mime_type?: string; + + /** + * Output metadata. + */ + metadata?: { + [k: string]: any; + }; +}; +``` + +#### `error` + +This output type can be used to store errors encountered during inference. + +```typescript +type Error = { + /** + * Type of output. + */ + output_type: "error"; + + /** + * The name of the error. + */ + ename: string; + + /** + * The value, or message, of the error. + */ + evalue: string; + + /** + * The error's traceback, represented as an array of strings. + */ + traceback: string[]; +}; +``` + +#### Example prompt output + +This is the result of executing a GPT-4 text completion. + +```typescript +{ + "output_type": "execute_result", + "execution_count": 0, + "data": { + "role": "assistant", + "content": "Idina Menzel was born in Brooklyn, New York on May 30, 1971." + }, + "metadata": { + "id": "chatcmpl-8I6nJzTo36E4PW60gGPuivJc0nEJB", + "object": "chat.completion", + "created": 1699326713, + "model": "gpt-4-0613", + "usage": { + "prompt_tokens": 83, + "completion_tokens": 19, + "total_tokens": 102 + }, + "finish_reason": "stop" + } +} +``` + +## Metadata + +Metadata is a place for you to put any JSON-serializable information about the `aiconfig`, prompt, or output. Metadata is a namespace, which means: + +- if the same properties are specified in root and prompt metadata, the sub-metadata is preferred (i.e. the root metadata is merged into the prompt metadata). +- common properties can be up-leveled into the root metadata to simplify the `aiconfig`. +- custom metadata should use unique property names, since different extensions can add properties to metadata. + +### Root metadata + +The following metadata keys are defined at the `aiconfig` level. They are all optional: + +| Key | Type | Description | +| ------------------------------- | ------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| `parameters` | `dict` (keyed by parameter name) | Parameters key-value pairs that may be used in one or more text prompt inputs with the [handlebars](https://handlebarsjs.com) `{{parameter_name}}` syntax | +| `models` | `dict` (keyed by model name) | Globally defined model settings. Any prompts that use these models will have these settings applied by default, unless they override them with their own model settings. | +| `default_model` | `string` | Name of default model to use for prompts that do not specify a model in their own metadata. | +| `model_parsers` | `dict` (keyed by model name, value is `ModelParser` ID) | This is useful if you want to use a custom ModelParser for a model, or if a single ModelParser can handle multiple models. | +| _extra user-defined properties_ | JSON-serializable object | Additional properties can be specified that may be used for specific use-cases. | + +### Prompt metadata + +| Key | Type | Description | +| ------------------------------- | ----------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `parameters` | `dict` (keyed by parameter name) | Parameters key-value pairs that may be used in one or more text prompt inputs with the [handlebars](https://handlebarsjs.com) `{{parameter_name}}` syntax | +| `model` | `string` (model name) or `dict` (with name and settings of model) | Model name/settings that apply to this prompt. If this is undefined, the `default_model` specified in the root metadata will be used. | +| `tags` | `list[string]` | A list of string tags on the prompt. | +| _extra user-defined properties_ | JSON-serializable object | Additional properties can be specified that may be used for specific use-cases. | + +### Output metadata + +Output metadata contains information like completion reason, usage tokens, etc. + +| Key | Type | Description | +| ------------------------------- | ------------------------ | ------------------------------------------------------------------------------- | +| _extra user-defined properties_ | JSON-serializable object | Additional properties can be specified that may be used for specific use-cases. | + +See [this example](#example-prompt-output) to see the output structure. + +## Final thoughts + +As you can see, the `aiconfig` schema is flexible enough to be used for a wide array of use-cases out of the box, and can be customized further. To see how to customize and extend AIConfig for your needs, see the **[Customization & Extensibility](/docs/category/customization--extensibility)** section. + +:::note + +We aim to maintain backwards-compatibility of the schema format, and don't foresee it changing often. **If you have a use-case that the existing schema doesn't satisfy, please _[get in touch with us](https://discord.com/invite/xBhNKTetGx)_**. + +::: diff --git a/aiconfig-docs/docs/overview/parameters-and-chaining.md b/aiconfig-docs/docs/overview/parameters-and-chaining.md index 76f025e17..8ce51d41b 100644 --- a/aiconfig-docs/docs/overview/parameters-and-chaining.md +++ b/aiconfig-docs/docs/overview/parameters-and-chaining.md @@ -3,3 +3,14 @@ sidebar_position: 5 --- # Parameters and Chaining Prompts + +Parameters are a way to pass data into prompts. Parameters can be defined statically in the [`aiconfig` metadata](/docs/overview/ai-config-format#metadata), and also passed in dynamically when using the AIConfig SDK. + +For example: + +```python +params = { + "parameter_name": "parameter_value" +} +config.run("prompt_name", params) +``` diff --git a/aiconfig-docs/docusaurus.config.js b/aiconfig-docs/docusaurus.config.js index bf360a527..3cec597fd 100644 --- a/aiconfig-docs/docusaurus.config.js +++ b/aiconfig-docs/docusaurus.config.js @@ -143,9 +143,27 @@ const config = { ], copyright: "Copyright © 2023 LastMile AI, Inc.", }, + // prism: { + // theme: lightTheme, + // darkTheme: darkTheme, + // }, prism: { - theme: lightTheme, - darkTheme: darkTheme, + defaultLanguage: "jsx", + theme: require("./core/prismTheme"), + additionalLanguages: [ + "bash", + "json", + "json5", + "jsonp", + "python", + "java", + "kotlin", + "objectivec", + "swift", + "groovy", + "ruby", + "flow", + ], }, }), }; diff --git a/aiconfig-docs/elements.tsx b/aiconfig-docs/elements.tsx new file mode 100644 index 000000000..73f4814a9 --- /dev/null +++ b/aiconfig-docs/elements.tsx @@ -0,0 +1,69 @@ +/** + * Copyright (c) LastMile AI, Inc. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + * + * @format + */ + +import React from "react"; +import useBaseUrl from "@docusaurus/core/lib/client/exports/useBaseUrl"; + +type CommandProps = { + /** + * Name of the Sapling subcommand to link to. Command aliases are also + * supported. + */ + name: string; + + /** + * If specified, overrides `name` as the the text to use fo the link. + */ + linkText?: string; +}; + +/** Creates a link to the corresponding documentation page for the command. */ +export function Command({ name, linkText }: CommandProps) { + const command = mapAliasToCommand(name); + const label = linkText ?? name; + return ( + + {label} + + ); +} + +/** Shorthand for where `sl ${name}` is used as the linkText. */ +export function SLCommand({ name }: CommandProps) { + return ; +} + +// TODO(mbolin): This should be autogenerated as a byproduct of running +// scripts/generate-command-markdown.py. +function mapAliasToCommand(alias: string): string { + switch (alias) { + case "go": + return "goto"; + case "isl": + return "web"; + } + + return alias; +} + +export function ReviewStackScreenshot() { + return ( +

+ +

+ ); +} + +export function Video({ src }) { + return ( + + ); +} diff --git a/aiconfig-docs/src/css/custom.css b/aiconfig-docs/src/css/custom.css index 0feef5acc..b59bca64d 100644 --- a/aiconfig-docs/src/css/custom.css +++ b/aiconfig-docs/src/css/custom.css @@ -48,4 +48,194 @@ no-repeat; } +/* Labels */ + +.label { + display: inline-block; + position: relative; + font-size: 0.85rem; + font-weight: 500; + color: #fff; + padding: 2px 12px; + border-radius: 0 2px 2px 0; + + /* Label sharp end */ + &:before { + content: ""; + position: absolute; + top: 0; + left: -12px; + border-color: transparent; + border-style: solid; + border-width: 12px 12px 12px 0; + } + + /* Label skeuomorphic hole */ + &:after { + content: ""; + position: absolute; + top: 10px; + left: 0; + width: 4px; + height: 4px; + border-radius: 2px; + background: #fff; + } + + /* Basic label */ + &.basic { + border-radius: 3px; + border-style: solid; + border-width: 2px; + + &:before, + &:after { + display: none; + } + } + + /* Label variants */ + &.android { + background: #a4c936; + + &:before { + border-right-color: #a4c936; + } + } + + &.ios { + background: #222; + + &:before { + border-right-color: #222; + } + } + + &.tv { + background: #6170af; + + &:before { + border-right-color: #6170af; + } + } + + &.required { + margin-left: 0; + margin-right: 16px; + color: #fa5035; + border-color: #fa5035; + } + + &.coming-soon { + top: 0px; + margin-left: 5px; + margin-right: 5px; + color: hsl(38, 95%, 59%); + border-color: hsl(38, 95%, 59%); + border-width: 1px; + font-size: 0.5rem; + font-weight: 500; + } +} + +html[data-theme="dark"] .label { + color: var(--text); + + &:after { + background: var(--ifm-background-color); + } + + &.ios { + background: #fff; + + &:before { + border-right-color: #fff; + } + } + + &.required { + color: #fa5035; + } + + &.coming-soon { + color: hsl(38, 95%, 59%); + } +} + +/* Label inside other tags */ + +h2 .label { + top: -6px; + margin-left: 12px; + padding: 3px 12px; +} + +h3 .label { + top: -3px; + margin-left: 22px; + line-height: 20px; +} + +td .label { + padding: 0 8px 0 10px; + font-size: 0.7rem; + margin-left: 14px; + + &:before { + left: -8px; + border-width: 10px 8px 10px 0; + } + + &:after { + top: 8px; + } + + &.required { + margin-left: 8px; + letter-spacing: 0.02rem; + padding: 0 6px; + border-width: 1px; + margin-right: 0; + } + + &.two-lines { + margin-left: 0; + margin-top: 5px; + + &.ios, + &.android, + &.tv { + margin-left: 8px; + } + } +} + +/* Label as dot in the right sidebar */ + +.table-of-contents .label { + position: absolute; + padding: 0; + width: 6px; + height: 6px; + border-radius: 100%; + margin-left: 4px; + margin-top: 7px; + white-space: nowrap; + overflow: hidden; + color: transparent; + float: initial; + + &.required { + position: initial; + margin-left: 0; + margin-right: 6px; + border-width: 3px; + background: #fa5035; + } + + /* Second dot shift */ + &:nth-child(3) { + margin-left: 13px; + } +} diff --git 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It is here just for a nice editor experience. "extends": "@tsconfig/docusaurus/tsconfig.json", "compilerOptions": { - "baseUrl": "." + "jsx": "react", + "baseUrl": ".", + "allowSyntheticDefaultImports": true } } diff --git a/typescript/demo/demo.ts b/typescript/demo/demo.ts index 00024f443..5ebab3e95 100644 --- a/typescript/demo/demo.ts +++ b/typescript/demo/demo.ts @@ -3,6 +3,7 @@ import OpenAI from "openai"; import * as path from "path"; import { AIConfigRuntime } from "../lib/config"; +import { InferenceOptions } from "../lib/modelParser"; // This example is taken from https://github.com/openai/openai-node/blob/v4/examples/demo.ts // and modified to show the same functionality using AIConfig. @@ -69,19 +70,25 @@ async function openAIWithAIConfig() { // console.log(aiConfig.getOutputText("demoPrompt", output)); // } + const inferenceOptions: InferenceOptions = { + callbacks: { + streamCallback: (data: any, _accumulatedData: any, _index: any) => { + process.stdout.write(data?.content || "\n"); + }, + }, + }; + + const streamCallback = (data: any, _accumulatedData: any, _index: any) => { + process.stdout.write(data?.content || "\n"); + }; + // Streaming: let result2 = await aiConfig.run( "demoPrompt", /*params*/ { name: "Streaming Demo", }, - { - callbacks: { - streamCallback: (data: any, _accumulatedData: any, _index: any) => { - process.stdout.write(data?.content || "\n"); - }, - }, - } + inferenceOptions ); // This is a Streaming Demo test.