2c:null 2d:["$","title",null,{"children":"Roboco-op: a computational blackboard for efficient human/AI collaboration | Tom Larkworthy | Observable"}] 32:T66a,pong_game = { ({ prompt: "Create an automated retro looking game of pong", time: 1699389743848, comment: "Creating an automated retro-looking game of Pong" }); const canvas = document.createElement("canvas"); canvas.width = width; canvas.height = 200; const context = canvas.getContext("2d"); const ball = { x: width / 2, y: 20, dx: 3, dy: 2, radius: 15 }; const paddle = { x: 5, y: canvas.height / 2, width: 20, height: 60 }; const drawBall = () => { context.beginPath(); context.rect( ball.x, ball.y - ball.radius, ball.radius * 2, ball.radius * 2 ); context.fillStyle = "#FFFFFF"; context.fill(); context.closePath(); }; const drawPaddle = () => { context.beginPath(); context.rect(paddle.x, paddle.y, paddle.width, paddle.height); context.fillStyle = "#FFFFFF"; context.fill(); context.closePath(); }; const update = () => { context.fillStyle = "#000000"; context.fillRect(0, 0, canvas.width, canvas.height); drawBall(); drawPaddle(); if ( ball.x + ball.dx > canvas.width - ball.radius || ball.x + ball.dx < ball.radius + 10 ) { ball.dx = -ball.dx; } if (ball.y + ball.dy < ball.radius) { ball.dy = -ball.dy; } else if (ball.y + ball.dy > canvas.height - ball.radius) { if (ball.x > paddle.x && ball.x < paddle.x + paddle.width) { ball.dy = -ball.dy; } else { ball.dy = -ball.dy; } } ball.x += ball.dx; ball.y += ball.dy; paddle.y = ball.y - 30; }; const interval = setInterval(update, 10); return canvas; }33:T738,viewof settings = ({ prompt: '\nThe notebook contains:\n - cell "form" is Object {a: "", b: ""}\nUsing the already imported view literal for configuring a ChatGPT session. Example response \n\n{\n model: "gpt-3.5-turbo"\n temperature: 0.7\n max_tokens: 1000\n top_p: 1\n frequency_penalty: 0\n presence_penalty: 0\n}\n\nUse Inputs.select for model, Inputs.range for max_tokens etc.', time: 1699384189902, comment: "Creating a form to configure a ChatGPT session. The form includes a select input for model, and range inputs for temperature, max_tokens, top_p, frequency_penalty, and presence_penalty." } && Inputs.bind( view`

${["model", Inputs.select(models.sort(), { label: "model" })]}

${[ "temperature", Inputs.range([0, 1], { step: 0.1, value: 0.7, label: "temperature" }) ]}

${[ "max_prompt_tokens", Inputs.range([1, 12000], { value: 4000, label: "max_tokens sent (oldest are truncated)" }) ]}

${[ "max_tokens", Inputs.range([1, 32000], { value: 1000, label: "max_tokens for response" }) ]}

${[ "top_p", Inputs.range([0, 1], { step: 0.1, value: 1, label: "top_p" }) ]}

${[ "frequency_penalty", Inputs.range([0, 1], { step: 0.1, value: 0, label: "frequency_penalty" }) ]}

${[ "presence_penalty", Inputs.range([0, 1], { step: 0.1, value: 0, label: "presence_penalty" }) ]}

`, localStorageView("NOTEBOOK_WRITER_2", { defaultValue: { model: "gpt-4-turbo", temperature: 0.7, max_prompt_tokens: 4000, max_tokens: 2000, top_p: 1, frequency_penalty: 0, presence_penalty: 0 }, json: true }) ))34:T42d,system_prompt = ` You are a notebook programming assistent for Observablehq notebook. You respond in XML formatted observable notebook cells only. If a question requires clarification, you can generate a markdown cell writing your question. EXAMPLES ======== user: import assistant: import("https://esm.sh/@2.5.0") user: create an SVG element assistant: htl htl.html\` user: create an UI for temperate variable from -10 to 10 assistant: Inputs viewof temperature = Inputs.range([-10, 10], {label: "set the temperature"}) user: create a 1 second counter assistant: mutable counterIndirect = 0 mutable counterIndirect mutable counter = { let i = 0; while (i < 100) { mutable counterIndirect = i; yield Promises.delay(500, ++i); } yield i; } user: Tell me a joke assistant: md md\`Why did the chicken cross the road?... to get to the other side\` `35:Ta1f,modelConfig = (model) => { if (model.startsWith("claude")) return { model: model, type: "chat", api: "https://api.anthropic.com/v1/messages", roles: ["user", "assistant"], settings: { temperature: 0.7, max_tokens: viewof settings.value.max_prompt_tokens, top_p: 1 }, headers: () => ({ "x-api-key": ANTHROPIC_API_KEY, "anthropic-version": "2023-06-01", "anthropic-dangerous-direct-browser-access": "true" }) }; else if (model.startsWith("dall-e")) { return { model: model, type: "image", api: "https://api.openai.com/v1/images/generations", settings: { n: 1, size: "1024x1024", quality: "standard" }, headers: () => ({ Authorization: `Bearer ${OPENAI_API_KEY}` }) }; } else if ( model == "o1-mini" || model == "o1-preview" || model == "o3-mini" || model.startsWith("o4") ) { return { type: "chat", api: "https://api.openai.com/v1/chat/completions", roles: ["user", "assistant"], settings: { model: model, temperature: 1, max_completion_tokens: viewof settings.value.max_tokens, top_p: 1, frequency_penalty: 0, presence_penalty: 0 // response_format: { type: "json_object" } }, headers: () => ({ Authorization: `Bearer ${OPENAI_API_KEY}` }) }; } else if (model == "o1") { return { api: "https://api.openai.com/v1/chat/completions", type: "chat", roles: ["user", "system", "assistant"], settings: { //functions: functions, //function_call: { name: "upsert_cell" }, model: model, temperature: 1, max_completion_tokens: viewof settings.value.max_prompt_tokens, top_p: 1, frequency_penalty: 0, presence_penalty: 0 }, headers: () => ({ Authorization: `Bearer ${OPENAI_API_KEY}` }) }; } else { return { api: "https://api.openai.com/v1/chat/completions", type: "chat", roles: ["user", "system", "assistant"], settings: { //functions: functions, //function_call: { name: "upsert_cell" }, model: model, temperature: viewof settings.value.temperature, max_tokens: viewof settings.value.max_prompt_tokens, top_p: 1, frequency_penalty: 0, presence_penalty: 0 }, headers: () => ({ Authorization: `Bearer ${OPENAI_API_KEY}` }) }; } }36:[] 30:{"id":"17c8ce433e1df58e","slug":"robocoop","title":"Roboco-op: a computational blackboard for efficient human/AI collaboration","thumbnail":"875cfc2e9a274ba40e828c752f1abe3ccff0cf52e630ad4a62cacd94ec38e769","access_level":"public","create_time":"$D2023-11-05T16:55:03.639Z","update_time":"$D2026-06-30T17:27:19.015Z","view_time":null,"first_public_time":"$D2023-11-07T22:40:49.480Z","has_unpublished_edits":null,"fork":true,"stars":18,"starred":false,"owner":{"type":"team","id":"7db5ed2b0697d645","login":"tomlarkworthy","name":"Tom Larkworthy","avatar_url":"https://avatars-next.observableusercontent.com/47327a8bc1966f2186dcb3ebf4b7ee6e4e7ab9a5c2a07405aff57200ea778f71","tier":"starter_2024"},"version":3618,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"5215f6ec4a999d40","login":"tomlarkworthy","name":"Tom Larkworthy","avatar_url":"https://avatars-next.observableusercontent.com/47327a8bc1966f2186dcb3ebf4b7ee6e4e7ab9a5c2a07405aff57200ea778f71","tier":null}],"members":[{"type":"individual","id":"5215f6ec4a999d40","login":"tomlarkworthy","name":"Tom Larkworthy","avatar_url":"https://avatars-next.observableusercontent.com/47327a8bc1966f2186dcb3ebf4b7ee6e4e7ab9a5c2a07405aff57200ea778f71","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":true,"trashed":false,"lapsed":null,"subscribed":null,"forks":25,"collection":{"id":"c4e0ec1a20ebb52a","slug":"robocoop","title":"roboco-op","owner":{"id":"7db5ed2b0697d645","login":"tomlarkworthy","name":"Tom Larkworthy"}},"type":"notebook","body":{"titling":"auto","title":"Roboco-op: a computational blackboard for efficient human/AI collaboration","cells":[{"id":0,"value":"# Roboco-op: a computational blackboard for efficient human/AI collaboration\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":3585,"value":"⚠️ [Roboco-op 2.0 is out](https://observablehq.com/@tomlarkworthy/robocoop-2) and is a big upgrade\n⚠️ [Roboco-op 3.0 is out](https://observablehq.com/@tomlarkworthy/robocoop-3) which is a low level Agentic alternative to Robocoop-2\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":3591,"value":"\nRobocoop is a different approach to building an AI coding assistant. In Robocoop, the LLM context is paired with code so it can be hand edited, imported, deleted and forked at any point during development. Its like Chat + Computational Notebook + RAG all wrapped into one.\n\n\nRoboco-op blends [Observablehq.com](https://observablehq.com/)'s reactive notebooks with an **open source** userspace AI coding assistant. Observable notebooks are a unique coding environment because the code development and runtime state are mixed together. This means Robocoop can write code, a human can edit, and the LLM read the output of program fragments all within a single fluid environment.\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":2978,"value":"### [Forkable Quickstart Notebook](https://observablehq.com/@tomlarkworthy/robocoop-blank-slate)\n\nA *\"blank slate\"* notebook is available [here](https://observablehq.com/@tomlarkworthy/robocoop-blank-slate) which you should fork from for new projects. There are a selection of pre-packaged skill [here](https://observablehq.com/@tomlarkworthy/robocoop-skills) which can be copied and pasted into notebooks to provide specific expertise.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":3002,"value":"### Automatic Source <--> LLM Context\n\nIn Roboco-op, a notebook cell is: code, execution, a prompt and an AI context **in a single atomic unit**. The chat context is a summation over all the cell's source code. You can edit _anything_ at _any_ time, in _any_ order **without losing state**. **The notebook _is_ the LLM state**. The AI will build upon whatever curated knowledge base that has been laid out via in-context learning.\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":2968,"value":"### Copy and Paste Domain expertise with Skills\n${html``}\n\nWith Roboco-op notebook cells can become copy and pasteable [skills](https://observablehq.com/@tomlarkworthy/robocoop-skills) that can be assembled and adjusted into a situational expert. You can move the cells across notebook, correct them manually, and the LLM will jam from whatever is there for its next completion.\n\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":3021,"value":"### `highlight()` to bring runtime values into LLM context\n\n${html``}\n\n\nRoboco-op automatically synchronises notebook source code, but you can also programatically bring runtime **values** into the LLM context too, just by calling the function `highlight`. This is useful for making AI informed decisions based on program state, for example, summerizing information pulled from a network call, or for informing code changes based on test suite feedback.\n\nTest-driven-development using `highlight` is such a powerful technique for producing production quality code that we have a template to fork directly from [here](https://observablehq.com/@tomlarkworthy/ai-tdd-template)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":3033,"value":"highlight(Math.random())","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3497,"value":"If highlight is passed a Blob with an image mime type it will convert to a vision prompt. However, not all models support image prompting so the following cell is commented out (but works)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":3469,"value":"// highlight(example_image)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3249,"value":"### RAG for getting started\n\nThe [RAG extension](https://observablehq.com/@tomlarkworthy/rag-extension) adds relevant examples to the context based on the question, which helps with getting started on a fresh notebook.\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":3070,"value":"### More\n\n\nBrowse the main Roboco-op notebook ecosystem [here](https://observablehq.com/@tomlarkworthy/robocoop?collection=@tomlarkworthy/robocoop), or ask questions in r/robocoop","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":2972,"value":"\nChanges\n- 2025-02-06 Switch to XML\n- 2024-12-28 Image prompting\n- 2024-10-23 Better decompiler for understanding notebook runtime.\n- 2024-10-15 Added highlight and a bunch of bug fixes\n- 2024-05-11 Context is truncated according to max_prompt_tokens, oldest is removed first\n\nTODO:\n- FEATURE: Unified prompting UI\n - dynamic GPT model selection per prompt\n - regenerate response \n- BUG(minor): Renaming variable does not update feedback variables\n- Investigate Summarization (https://github.com/jimmc414/1filellm)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":2584,"value":"## Random Cell Made by Roboco-op","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1927,"value":"$32","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1617,"value":"## Prompt Interface\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":81,"value":"viewof prompt = {\n const whisper = whisperInput({\n API_KEY: OPENAI_API_KEY\n });\n const ui = view`

\n ${cautious(() => whisper)}\n ${[\n \"...\",\n Inputs.textarea({\n placeholder: \"ask for a cell\",\n rows: 10,\n minlength: 1,\n submit: true\n })\n ]}\n

`;\n // bindOneWay(ui, whisper);\n invalidation.then(\n whisper.addEventListener(\"input\", () => {\n ui.value = whisper.value;\n })\n );\n\n return ui;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3580,"value":"formatted_instruction","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1014,"value":"Inputs.button(\"copy code\", {\n reduce: () => cellsToClipboard([suggestion])\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":105,"value":"viewof suggestion = Inputs.textarea({\n rows: 50,\n disabled: true,\n value: formatted_instruction,\n style: \"height: 500px\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1463,"value":"## Last Chat context","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1252,"value":"viewof context_viz = Inputs.table(\n [...extension_context_previous, ...mutable context].map((r) => ({\n role: r.role,\n content_or_function_call: r.content || r.function_call\n })),\n {\n format: {\n role: (role) =>\n html`${role}`,\n content_or_function_call: (f) =>\n f.arguments ? inspect(JSON.parse(f.arguments)) : f\n },\n layout: \"auto\"\n }\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1692,"value":"### AI Settings","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":2683,"value":"models = [\n \"gpt-3.5-turbo-1106\",\n \"gpt-3.5-turbo\",\n \"gpt-3.5-turbo-0301\",\n \"gpt-3.5-turbo-0613\",\n \"gpt-3.5-turbo-16k\",\n \"gpt-3.5-turbo-16k-0613\",\n \"gpt-3.5-turbo-instruct\",\n \"gpt-3.5-turbo-instruct-0914\",\n \"gpt-4-1106-preview\",\n \"gpt-4\",\n \"gpt-4-32k\",\n \"gpt-4-0314\",\n \"gpt-4-0613\",\n \"gpt-4-turbo\",\n \"gpt-4-turbo-2024-04-09\",\n \"gpt-4-turbo-preview\",\n \"gpt-4-vision-preview\",\n \"gpt-4o\",\n \"gpt-4o-mini\",\n \"o1\",\n \"o1-mini\",\n \"o1-preview\",\n \"o3\",\n \"o3-mini\",\n \"o4-mini\",\n \"o4-mini-high\",\n \"gpt-5-mini\",\n \"gpt-5.4\"\n]","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":29,"value":"viewof OPENAI_API_KEY = Inputs.bind(\n Inputs.password({\n label: \"OPENAI_API_KEY\",\n placeholder: \"paste openAI key here\"\n }),\n localStorageView(\"OPENAI_API_KEY\")\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2700,"value":"viewof ANTHROPIC_API_KEY = Inputs.bind(\n Inputs.password({\n label: \"ANTHROPIC_API_KEY\",\n placeholder: \"paste ANTHROPIC_API_KEY key here\"\n }),\n localStorageView(\"ANTHROPIC_API_KEY\")\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2061,"value":"viewof api_endpoint = Inputs.bind(\n Inputs.text({\n label: \"Completion API endpoint\"\n }),\n localStorageView(\"OPENAI_API_ENDPOINT\", {\n defaultValue: \"https://api.openai.com/v1/chat/completions\"\n })\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1779,"value":"$33","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":63,"value":"$34","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1623,"value":"## Roboco-op Implementation below","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":2448,"value":"background_tasks = {\n submit_summary;\n find_context_extensions;\n on_prompt;\n context_updater;\n api_call_response;\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2222,"value":"viewof user_variable_filters = Inputs.bind(\n Inputs.input({}),\n localStorageView(\n `${new URL(document.baseURI).pathname}|user_variable_filters`,\n {\n defaultValue: {},\n json: true\n }\n )\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":431,"value":"formatted_instruction = {\n if (response.action == \"upsert_cells\") {\n return response.cells.map((r) => r.code).join(\"\\n\\n\");\n }\n return response.content;\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":720,"value":"response = on_prompt","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":96,"value":"on_prompt = {\n const extension_context_now = await extension_context({\n question: prompt\n });\n viewof extension_context_previous.value = extension_context_now;\n viewof extension_context_previous.dispatchEvent(new Event(\"input\"));\n const payload = [\n ...extension_context_now,\n ...mutable context,\n {\n role: \"user\",\n content: prompt\n }\n ];\n console.log(\"on_prompt\", payload);\n return viewof ask.send(payload);\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3160,"value":"## Extensions","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":3079,"value":"viewof context_extensions = Inputs.input(new Set())","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3155,"value":"find_context_extensions = {\n viewof context_extensions.value = new Set(\n Object.entries(code)\n .map(([name, c]) => c)\n .filter((cell) => cell.variables[0]._value?.robocoop?.onContext)\n .map((cell) => cell.variables[0]._value?.robocoop?.onContext)\n );\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3074,"value":"extension_context = async ({ question }) => {\n const context = [];\n await Promise.all(\n [...viewof context_extensions.value].map((fn) => fn({ question, context }))\n );\n return context;\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2608,"value":"### Context Construction","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":3544,"value":"viewof testContext = Inputs.button(\"test context\", {\n reduce: () => viewof contextRequest.send()\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3549,"value":"testContext","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3532,"value":"viewof contextRequest = flowQueue()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3541,"value":"modules = {\n contextRequest;\n return moduleMap(runtime);\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3565,"value":"cells = Promise.all(\n [...modules.entries()].map(async ([m, mInfo]) => ({\n ...mInfo,\n cells: await cellMapCompat(m)\n }))\n)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3375,"value":"mutable context = []","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":951,"value":"context_updater = {\n const context = [...unprompted_context, ...highlight_context];\n if (!_.isEqual(mutable context, context)) {\n mutable context = context;\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3185,"value":"viewof extension_context_previous = Inputs.input([])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2615,"value":"unprompted_context = Object.entries(code)\n .map(([name, c]) => c)\n .filter((c) => !c.prompt)\n .filter((c) => c.code && c.code !== \"$0\")\n .sort((a, b) => a.time - b.time)\n .flatMap((c) => [\n {\n role: \"assistant\",\n content: `\n${c.variables[0]._inputs.map((i) => i._name).join(\", \")}\n${c.code}\n\n`\n }\n ])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2749,"value":"highlight_context = Object.entries(code)\n .flatMap(([k, cell]) =>\n cell.variables[0]._value && cell.variables[0]._value.robocoop\n ? [\n {\n cell: cell,\n robocoop: cell.variables[0]._value.robocoop\n }\n ]\n : []\n )\n .map(({ cell, robocoop }) =>\n robocoop.type === \"json\"\n ? {\n role: \"user\",\n content: `\nThe cell \\`${cell.cell_name}\\` defined as\n~~~json\n${cell.code}\n~~~\nevaluated to\n~~~${robocoop.type}\n${robocoop.value}\n~~~\n`\n }\n : {\n role: \"user\",\n content: [\n {\n type: \"text\",\n text: `The cell \\`${cell.cell_name}\\` defined as\n~~~json\n${cell.code}\n~~~\nis an image\n`\n },\n {\n type: \"image_url\",\n image_url: {\n url: robocoop.value\n }\n }\n ]\n }\n )","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2610,"value":"### Cell Analysis","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":3599,"value":"main","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":839,"value":"code = {\n debugger;\n return (\n mainVariables &&\n Object.fromEntries(\n await Promise.all(\n [...(await cellMapCompat(main, { excludeInbuilt: true })).entries()]\n .filter(([name, variables]) => {\n debugger;\n return (\n variables[0] !== undefined &&\n !(typeof name == \"string\" && name.startsWith(\"@variable \"))\n );\n })\n .map(async ([name, variables]) => {\n try {\n debugger;\n return [\n name,\n {\n module: \"main\",\n code: await decompile(variables),\n variables\n }\n ];\n } catch (e) {\n debugger;\n throw e;\n }\n })\n )\n )\n );\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":757,"value":"selected_variables = Object.fromEntries(\n mainVariables\n .filter((v) => !excludes.has(v) && v._type == 1)\n .map((v) => [v._name || v._observer.id, v])\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3433,"value":"excludes = new Set([\n ...descendants(await lookupVariable(\"mutable context\", thisModule)),\n ...descendants(await lookupVariable(\"viewof prompt\", thisModule))\n])","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3418,"value":"mainVariables = [...allVariables].filter((v) => v._module === main)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3425,"value":"viewof allVariables = variables(runtime)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3385,"value":"thisModule = [...allVariables].find((v) => v._value === tag)._module","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3383,"value":"tag = Math.random()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":162,"value":"viewof history = Inputs.input([])","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3344,"value":"---","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":3538,"value":"## LLM API Call","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":69,"value":"viewof ask = flowQueue({ timeout_ms: 180000 })","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2814,"value":"console.log(\"Ask\", ask)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2471,"value":"prompt_messages = [\n {\n role: viewof settings.value.model.startsWith(\"o1\") ? \"user\" : \"system\",\n content: system_prompt\n },\n ...ask\n]","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2473,"value":"token_analytics = ({\n prompt: prompt_messages,\n prompt_tokens: prompt_messages.map(\n (p) => (p.content || JSON.stringify(p.function_call)).length / 5\n )\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2478,"value":"trimmed_prompt = {\n console.log(\"token_analytics\", token_analytics);\n while (d3.sum(token_analytics.prompt_tokens) > settings.max_prompt_tokens) {\n token_analytics.prompt.splice(1, 1);\n token_analytics.prompt_tokens.splice(1, 1);\n }\n console.log(\"trimmed_prompt\", token_analytics);\n return token_analytics;\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2695,"value":"$35","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":25,"value":"openAiResponse = {\n const body = {\n //functions: functions,\n // function_call: { name: \"upsert_cell\" },\n messages: trimmed_prompt.prompt,\n ...modelConfig(viewof settings.value.model).settings\n };\n const payload = {\n method: \"POST\",\n headers: {\n \"Content-Type\": \"application/json\",\n Authorization: `Bearer ${OPENAI_API_KEY}`\n },\n body: JSON.stringify(body)\n };\n console.log(\"Sending\", body);\n const response = await fetch(api_endpoint, payload);\n\n if (response.status !== 200)\n throw new Error(`${response.status}: ${await response.text()}`);\n const responseJson = response.json();\n console.log(\"Received\", await responseJson);\n return responseJson;\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":129,"value":"instruction = {\n const message = openAiResponse.choices[0].message;\n return {\n cells: process(message.content),\n prompt: ask[ask.length - 1].content,\n action: \"upsert_cells\",\n time: Date.now()\n };\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3520,"value":"domParser = new DOMParser()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3196,"value":"content = process(openAiResponse.choices[0].message.content)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2726,"value":"function process(content) {\n const doc = domParser.parseFromString(\n \"\" + content + \"\",\n \"text/xml\"\n );\n const cells = [...doc.querySelectorAll(\"cell\")];\n debugger;\n return cells.map((cell) => ({\n inputs: cell\n .querySelector(\"deps\")\n .textContent.split(\",\")\n .map((s) => s.trim()),\n code: content\n }));\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":75,"value":"api_call_response = viewof ask.resolve(instruction) && instruction","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3342,"value":"---","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":3415,"value":"import {\n runtime,\n variables,\n descendants,\n lookupVariable,\n main\n} from \"@tomlarkworthy/runtime-sdk\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3263,"value":"import {\n decompile,\n compile,\n parser\n} from \"@tomlarkworthy/observablejs-toolchain\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3608,"value":"import { cellMapCompat } from \"@tomlarkworthy/cell-map\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2508,"value":"import { cellsToClipboard } from \"@tomlarkworthy/cells-to-clipboard\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":31,"value":"import { localStorageView } from \"@tomlarkworthy/local-storage-view\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":51,"value":"import { flowQueue } from \"@tomlarkworthy/flow-queue\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":302,"value":"import { inspect } from \"@observablehq/inspector\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1363,"value":"import { view, cautious, bindOneWay } from \"@tomlarkworthy/view\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2459,"value":"// import { encode } from \"@codingwithfire/gpt-3-encoder\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2552,"value":"import { whisperInput } from \"@tomlarkworthy/whisper-input\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2678,"value":"import { highlight, example_image } from \"@tomlarkworthy/highlight\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1432,"value":"dirty_json = import(\"https://cdn.skypack.dev/dirty-json@0.9.2?min\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3560,"value":"import { moduleMap, submit_summary } from \"@tomlarkworthy/module-map\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2960,"value":"//import { footer } from \"@tomlarkworthy/footer\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2963,"value":"//footer","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W36","stdlib":"1"}} 31:"$30:body"