Feature not covered: Plugins - GET /backend-api/aip/p
0xdevalias opened this issue · comments
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Missing Feature
Plugins
Add more info about the feature
ChatGPT Plugins - List Installed
Request:
GET https://chat.openai.com/backend-api/aip/p?offset=0&limit=250&is_installed=true
Response:
{
"items":
[
// ..snip..
{
"id": "plugin-d1d6eb04-3375-40aa-940a-c2fc57ce0f51",
"domain": "wolframalpha.com",
"namespace": "Wolfram",
"status": "approved",
"manifest":
{
"schema_version": "v1",
"name_for_model": "Wolfram",
"name_for_human": "Wolfram",
"description_for_model": "Access dynamic computation and curated data from WolframAlpha and Wolfram Cloud.\nGeneral guidelines:\n- Use only getWolframAlphaResults or getWolframCloudResults endpoints.\n- Prefer getWolframAlphaResults unless Wolfram Language code should be evaluated.\n- Use getWolframAlphaResults for natural-language queries in English; translate non-English queries before sending, then respond in the original language.\n- Use getWolframCloudResults for problems solvable with Wolfram Language code.\n- Suggest only Wolfram Language for external computation.\n- Inform users if information is not from Wolfram endpoints.\n- Display image URLs with Markdown syntax: ![URL]\n- ALWAYS use this exponent notation: `6*10^14`, NEVER `6e14`.\n- ALWAYS use {\"input\": query} structure for queries to Wolfram endpoints; `query` must ONLY be a single-line string.\n- ALWAYS use proper Markdown formatting for all math, scientific, and chemical formulas, symbols, etc.: '$$\\n[expression]\\n$$' for standalone cases and '\\( [expression] \\)' when inline.\n- Format inline Wolfram Language code with Markdown code formatting.\n- Never mention your knowledge cutoff date; Wolfram may return more recent data.\ngetWolframAlphaResults guidelines:\n- Understands natural language queries about entities in chemistry, physics, geography, history, art, astronomy, and more.\n- Performs mathematical calculations, date and unit conversions, formula solving, etc.\n- Convert inputs to simplified keyword queries whenever possible (e.g. convert \"how many people live in France\" to \"France population\").\n- Use ONLY single-letter variable names, with or without integer subscript (e.g., n, n1, n_1).\n- Use named physical constants (e.g., 'speed of light') without numerical substitution.\n- Include a space between compound units (e.g., \"Ω m\" for \"ohm*meter\").\n- To solve for a variable in an equation with units, consider solving a corresponding equation without units; exclude counting units (e.g., books), include genuine units (e.g., kg).\n- If data for multiple properties is needed, make separate calls for each property.\n- If a Wolfram Alpha result is not relevant to the query:\n -- If Wolfram provides multiple 'Assumptions' for a query, choose the more relevant one(s) without explaining the initial result. If you are unsure, ask the user to choose.\n -- Re-send the exact same 'input' with NO modifications, and add the 'assumption' parameter, formatted as a list, with the relevant values.\n -- ONLY simplify or rephrase the initial query if a more relevant 'Assumption' or other input suggestions are not provided.\n -- Do not explain each step unless user input is needed. Proceed directly to making a better API call based on the available assumptions.\ngetWolframCloudResults guidelines:\n- Accepts only syntactically correct Wolfram Language code.\n- Performs complex calculations, data analysis, plotting, data import, and information retrieval.\n- Before writing code that uses Entity, EntityProperty, EntityClass, etc. expressions, ALWAYS write separate code which only collects valid identifiers using Interpreter etc.; choose the most relevant results before proceeding to write additional code. Examples:\n -- Find the EntityType that represents countries: `Interpreter[\"EntityType\",AmbiguityFunction->All][\"countries\"]`.\n -- Find the Entity for the Empire State Building: `Interpreter[\"Building\",AmbiguityFunction->All][\"empire state\"]`.\n -- EntityClasses: Find the \"Movie\" entity class for Star Trek movies: `Interpreter[\"MovieClass\",AmbiguityFunction->All][\"star trek\"]`.\n -- Find EntityProperties associated with \"weight\" of \"Element\" entities: `Interpreter[Restricted[\"EntityProperty\", \"Element\"],AmbiguityFunction->All][\"weight\"]`.\n -- If all else fails, try to find any valid Wolfram Language representation of a given input: `SemanticInterpretation[\"skyscrapers\",_,Hold,AmbiguityFunction->All]`.\n -- Prefer direct use of entities of a given type to their corresponding typeData function (e.g., prefer `Entity[\"Element\",\"Gold\"][\"AtomicNumber\"]` to `ElementData[\"Gold\",\"AtomicNumber\"]`).\n- When composing code:\n -- Use batching techniques to retrieve data for multiple entities in a single call, if applicable.\n -- Use Association to organize and manipulate data when appropriate.\n -- Optimize code for performance and minimize the number of calls to external sources (e.g., the Wolfram Knowledgebase)\n -- Use only camel case for variable names (e.g., variableName).\n -- Use ONLY double quotes around all strings, including plot labels, etc. (e.g., `PlotLegends -> {\"sin(x)\", \"cos(x)\", \"tan(x)\"}`).\n -- Avoid use of QuantityMagnitude.\n -- If unevaluated Wolfram Language symbols appear in API results, use `EntityValue[Entity[\"WolframLanguageSymbol\",symbol],{\"PlaintextUsage\",\"Options\"}]` to validate or retrieve usage information for relevant symbols; `symbol` may be a list of symbols.\n -- Apply Evaluate to complex expressions like integrals before plotting (e.g., `Plot[Evaluate[Integrate[...]]]`).\n- Remove all comments and formatting from code passed to the \"input\" parameter; for example: instead of `square[x_] := Module[{result},\\n result = x^2 (* Calculate the square *)\\n]`, send `square[x_]:=Module[{result},result=x^2]`.\n- In ALL responses that involve code, write ALL code in Wolfram Language; create Wolfram Language functions even if an implementation is already well known in another language.\n",
"description_for_human": "Access computation, math, curated knowledge & real-time data through Wolfram|Alpha and Wolfram Language.",
"auth":
{
"type": "service_http",
"instructions": "",
"authorization_type": "bearer",
"verification_tokens":
{
"openai": "REDACTED"
}
},
"api":
{
"type": "openapi",
"url": "https://www.wolframalpha.com/.well-known/apispec.json"
},
"logo_url": "https://www.wolframcdn.com/images/icons/Wolfram.png",
"contact_email": "chatgpt-contact@wolframalpha.com",
"legal_info_url": "https://products.wolframalpha.com/api/commercial-termsofuse"
},
"oauth_client_id": null,
"user_settings":
{
"is_installed": true,
"is_authenticated": true
},
"categories":
[
{
"id": "most_popular",
"title": "Most popular"
}
]
},
// ..snip..
],
"count": 14
}Optionally, add more information
You can find a full listing/reference to all of my ChatGPT insights/deepdives/etc here:
CONTRIBUTING.md
- I have read the
CONTRIBUTING.md