302 ai new components

.husky/pre-push

@@ -1,7 +1,7 @@
 #!/bin/sh
 . "$(dirname "$0")/_/husky.sh"
 
-npx pnpm install -r
+npx pnpm install --frozen-lockfile
 
 if ! (git diff HEAD --quiet pnpm-lock.yaml); then
     echo "modified pnpm-lock.yaml - please commit the file"

components/_302_ai/302_ai_components.mdc

@@ -0,0 +1,79 @@
+---
+alwaysApply: true
+---
+Write the actions for the 302.AI app.
+
+General guidelines:
+- Avoid manual truthiness checks for optional parameters, as @pipedream/platform utilities (@pipedream/axios) automatically exclude undefined values.
+- Make sure to wrap API calls with the _makeRequest method. All actions methods need to call the _makeRequest methods. No actions method should call axios directly.
+- Don't use the "number" datatype in props, use "string" instead for "number" datatypes. 
+- Parameters that in the API documentation are boolean will be defined as type "string with options as `default: "0",options: ["0","1",],` setting the default to "0" or "1" as appropiate per the API documentation. Then in the component code parse it to boolean type variable such as in `include_performers: parseInt(this.includePerformers) === 1,`
+- In the package.json file essentially you'll only set the dependencies property, set version prop to "0.1.0", you can refer to the following example that was created for Leonardo AI components:
+```
+{
+  "name": "@pipedream/leonardo_ai",
+  "version": "0.1.0",
+  "description": "Pipedream Leonardo AI Components",
+  "main": "leonardo_ai.app.mjs",
+  "keywords": [
+    "pipedream",
+    "leonardo_ai"
+  ],
+  "homepage": "https://pipedream.com/apps/leonardo_ai",
+  "author": "Pipedream <[email protected]> (https://pipedream.com/)",
+  "publishConfig": {
+    "access": "public"
+  },
+  "dependencies": {
+    "@pipedream/platform": "^3.1.0",
+    "form-data": "^4.0.4"
+  }
+}
+```
+--as a reference of an example API call to 302.AI API, including authorization, you can use the one we feature at Pipedream. Remember, you'll wrap API calls inside _makeRequest and action methods need to call _makeRequest.
+```
+import { axios } from "@pipedream/platform"
+export default defineComponent({
+  props: {
+    _302_ai: {
+      type: "app",
+      app: "_302_ai",
+    }
+  },
+  async run({steps, $}) {
+    return await axios($, {
+      url: `https://api.302.ai/v1/models`,
+      headers: {
+        Authorization: `Bearer ${this._302_ai.$auth.api_key}`,
+      },
+    })
+  },
+})
+--The 302.AI API is "OpenAI compliant", so 302.AI API URL is https://api.302.ai or https://api.302.ai/v1 and are analogous to OpenAI's  https://api.openai.com and https://api.openai.com/v1 respectively
+--For each prop that is used more than one create a propDefinition for it and reuse it in component code, changing description, detaults as needed.
+--For the model prop which is likely used in several components using async options based on the List Models enpoint documented at https://doc.302.ai/147522038e0
+```
+chat-with-302-ai
+Prompt: Send a message to the 302.AI Chat API using. Ideal for dynamic conversations, contextual assistance, and creative generation.
+-When you come up for the description for this component follow format: <component description>. [See documentation](https://doc.302.ai/147522039e0)
+
+chat-using-functions
+Prompt: Enable your 302.AI model to invoke user-defined functions. Useful for conditional logic, workflow orchestration, and tool invocation within conversations.
+-When you come up for the description for this component follow format: <component description>. [See documentation](https://doc.302.ai/211560247e0)
+
+summarize-text
+Prompt: Summarize long-form text into concise, readable output using the 302.AI Chat API. Great for reports, content digestion, and executive briefs.
+-When you come up for the description for this component follow format: <component description>. [See documentation](https://doc.302.ai/147522039e0)
+
+classify-items
+Prompt: Classify input items into predefined categories using 302.AI models. Perfect for tagging, segmentation, and automated organization.
+-When you come up for the description for this component follow format: <component description>. [See documentation](https://doc.302.ai/147522039e0)
+
+create-embeddings
+Prompt: Generate vector embeddings from text using the 302.AI Embeddings API. Useful for semantic search, clustering, and vector store indexing.
+-When you come up for the description for this component follow format: <component description>. [See documentation](https://doc.302.ai/147522048e0)
+
+create-completion
+Prompt: Send a prompt to the legacy endpoint on 302.AI to generate text using models like . Recommended for backward-compatible flows.
+-When you come up for the description for this component follow format: <component description>. [See documentation](https://doc.302.ai/147522039e0)
+

components/_302_ai/_302_ai.app.mjs

@@ -1,11 +1,121 @@
+import { axios } from "@pipedream/platform";
+
 export default {
   type: "app",
   app: "_302_ai",
-  propDefinitions: {},
+  propDefinitions: {
+    modelId: {
+      type: "string",
+      label: "Model",
+      description: "The ID of the model to use",
+      async options() {
+        const models = await this.listModels();
+        return models.map((model) => ({
+          label: model.id,
+          value: model.id,
+        }));
+      },
+    },
+    chatCompletionModelId: {
+      type: "string",
+      label: "Model",
+      description: "The ID of the model to use for chat completions",
+      async options() {
+        const models = await this.listModels();
+        // Filter for chat models (similar to OpenAI)
+        return models
+          .filter((model) => model.id.match(/gpt|claude|gemini|llama|mistral|deepseek/gi))
+          .map((model) => ({
+            label: model.id,
+            value: model.id,
+          }));
+      },
+    },
+    embeddingsModelId: {
+      type: "string",
+      label: "Model",
+      description: "The ID of the embeddings model to use",
+      async options() {
+        const models = await this.listModels();
+        // Filter for embedding models
+        return models
+          .filter((model) => model.id.match(/embedding/gi))
+          .map((model) => ({
+            label: model.id,
+            value: model.id,
+          }));
+      },
+    },
+  },
   methods: {
-    // this.$auth contains connected account data
-    authKeys() {
-      console.log(Object.keys(this.$auth));
+    _apiKey() {
+      return this.$auth.api_key;
+    },
+    _baseApiUrl() {
+      return "https://api.302.ai/v1";
+    },
+    _makeRequest({
+      $ = this,
+      path,
+      ...args
+    } = {}) {
+      return axios($, {
+        ...args,
+        url: `${this._baseApiUrl()}${path}`,
+        headers: {
+          ...args.headers,
+          "Authorization": `Bearer ${this._apiKey()}`,
+          "Content-Type": "application/json",
+        },
+      });
+    },
+    async listModels({ $ } = {}) {
+      const { data: models } = await this._makeRequest({
+        $,
+        path: "/models",
+      });
+      return models || [];
+    },
+    async _makeCompletion({
+      path, ...args
+    }) {
+      const data = await this._makeRequest({
+        path,
+        method: "POST",
+        ...args,
+      });
+
+      // For completions, return the text of the first choice at the top-level
+      let generated_text;
+      if (path === "/completions") {
+        const { choices } = data;
+        generated_text = choices?.[0]?.text;
+      }
+      // For chat completions, return the assistant message at the top-level
+      let generated_message;
+      if (path === "/chat/completions") {
+        const { choices } = data;
+        generated_message = choices?.[0]?.message;
+      }
+
+      return {
+        generated_text,
+        generated_message,
+        ...data,
+      };
+    },
+    createChatCompletion(args = {}) {
+      return this._makeCompletion({
+        path: "/chat/completions",
+        ...args,
+      });
+    },
+    createEmbeddings(args = {}) {
+      return this._makeRequest({
+        path: "/embeddings",
+        method: "POST",
+        ...args,
+      });
     },
   },
-};
+};