actions/chat-using-functions/chat-using-functions.mjs
import openai from "../../openai.app.mjs"; import common from "../common/common.mjs"; import constants from "../../common/constants.mjs"; export default { ...common, name: "Chat using Functions", version: "0.0.9", annotations: { destructiveHint: false, openWorldHint: true, readOnlyHint: false, }, key: "openai-chat-using-functions", description: "Chat with your models and allow them to invoke functions. Optionally, you can build and invoke workflows as functions. [See the documentation](https://platform.openai.com/docs/guides/function-calling)", type: "action", props: { openai, alert: { type: "alert", alertType: "info", content: "Provide function names and parameters, and the model will either answer the question directly or decide to invoke one of the functions, returning a function call that adheres to your specified schema. Add a custom code step that includes all available functions which can be invoked based on the model's response - [you can even build an entire workflow as a function](https://pipedream.com/docs/workflows/building-workflows/code/nodejs/#invoke-another-workflow)! Once the appropriate function or workflow is executed, continue the overall execution or pass the result back to the model for further analysis. For more details, [see this guide](https://platform.openai.com/docs/guides/function-calling?api-mode=responses#overview) and this [walkthrough](https://pipedream.com/blog/introducing-enhanced-openai-chat-actions-for-pipedream/#using-pipedream-workflows-as-functions).", }, modelId: { propDefinition: [ openai, "chatCompletionModelId", ], }, input: { type: "string", label: "Chat Input", description: "Text inputs to the model used to generate a response", }, functions: { type: "string", label: "Functions", description: "A valid JSON array of functions using OpenAI's function schema definition. [See guide here](https://platform.openai.com/docs/guides/function-calling?api-mode=responses&example=search-knowledge-base#defining-functions).", default: `[ { "type": "function", "name": "your_function_name", "description": "Details on when and how to use the function", "strict": true, "parameters": { "type": "object", "properties": { "property_name": { "type": "property_type", "description": "A description for this property" }, "another_property_name": { "type": "property_type", "description": "A description for this property" } }, "required": [ "list", "of", "required", "properties", "for", "this", "object" ], "additionalProperties": false } } ]`, }, instructions: { type: "string", label: "Instructions", description: "Inserts a system (or developer) message as the first item in the model's context", optional: true, }, toolChoice: { type: "string", label: "Tool Choice", description: "- **auto**: The model decides whether and how many functions to call.\n- **required**: The model must call one or more functions.\n- **function_name**: Enter a custom expression to force the model to call this specific function.", optional: true, default: "auto", options: [ "auto", "required", ], }, parallelToolCalls: { type: "boolean", label: "Parallel Function Calling", description: "Allow or prevent the model to call multiple functions in a single turn", optional: true, default: true, }, previousResponseId: { type: "string", label: "Previous Response ID", description: "The unique ID of the previous response to the model. Use this to create multi-turn conversations", optional: true, }, truncation: { type: "string", label: "Truncation", description: "Specifies the truncation mode for the response if it's larger than the context window size", optional: true, default: "auto", options: [ "auto", "disabled", ], }, responseFormat: { type: "string", label: "Response Format", description: "- **Text**: Returns unstructured text output.\n- **JSON Schema**: Enables you to define a [specific structure for the model's output using a JSON schema](https://platform.openai.com/docs/guides/structured-outputs?api-mode=responses).", options: [ "text", "json_schema", ], default: "text", optional: true, reloadProps: true, }, skipThisStep: { type: "boolean", label: "Skip This Step", description: "Pass in a boolean custom expression to skip this step's execution at runtime", optional: true, default: false, }, }, additionalProps() { const { modelId, responseFormat, } = this; const props = {}; if (this.openai.isReasoningModel(modelId)) { props.reasoningEffort = { type: "string", label: "Reasoning Effort", description: "Constrains effort on reasoning for reasoning models", optional: true, options: [ "low", "medium", "high", ], }; } if (responseFormat === constants.CHAT_RESPONSE_FORMAT.JSON_SCHEMA.value) { props.jsonSchema = { type: "string", label: "JSON Schema", description: "Define the schema that the model's output must adhere to. [Generate one here](https://platform.openai.com/docs/guides/structured-outputs/supported-schemas).", }; } return props; }, methods: { ...common.methods, }, async run({ $ }) { if (this.skipThisStep) { $.export("$summary", "Step execution skipped"); return; } const data = { model: this.modelId, input: this.input, instructions: this.instructions, previous_response_id: this.previousResponseId, truncation: this.truncation, parallel_tool_calls: this.parallelToolCalls, tools: [], }; let functions = this.functions; if (typeof functions === "string") { try { functions = JSON.parse(functions); } catch (error) { throw new Error("Invalid JSON format in the provided Functions Schema"); } } if (Array.isArray(functions)) { data.tools.push(...functions); } else { data.tools.push(functions); } if (this.toolChoice) { if (this.toolChoice === "auto" || this.toolChoice === "required") { data.tool_choice = this.toolChoice; } else { data.tool_choice = { type: "function", name: this.toolChoice, }; } } if (this.openai.isReasoningModel(this.modelId) && this.reasoningEffort) { data.reasoning = { ...data.reasoning, effort: this.reasoningEffort, }; } if (this.openai.isReasoningModel(this.modelId) && this.generateSummary) { data.reasoning = { ...data.reasoning, generate_summary: this.generateSummary, }; } if (this.responseFormat === constants.CHAT_RESPONSE_FORMAT.JSON_SCHEMA.value) { try { data.text = { format: { type: this.responseFormat, ...JSON.parse(this.jsonSchema), }, }; } catch (error) { throw new Error("Invalid JSON format in the provided JSON Schema"); } } const response = await this.openai.responses({ $, data, }); if (response) { $.export("$summary", `Successfully sent chat with id ${response.id}`); $.export("chat_responses", response.output); } return response; }, };
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