Chat with 302.AI with 302.AI API on New Session Created from Zep API

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Trigger workflow on

New Session Created from the Zep API

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Chat with 302.AI with the 302.AI API

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Getting Started#

This integration creates a workflow with a Zep trigger and 302.AI action. When you configure and deploy the workflow, it will run on Pipedream's servers 24x7 for free.

Select this integration
Configure the New Session Created trigger
1. Connect your Zep account
2. Configure timer
Configure the Chat with 302.AI action
1. Connect your 302.AI account
2. Select a Model
3. Configure User Message
4. Optional- Configure Max Tokens
5. Optional- Configure Temperature
6. Optional- Configure Top P
7. Optional- Configure N
8. Optional- Configure Stop
9. Optional- Configure Presence Penalty
10. Optional- Configure Frequency Penalty
11. Optional- Configure User
12. Optional- Configure System Instructions
13. Optional- Configure Prior Message History
14. Optional- Select a Response Format
Deploy the workflow
Send a test event to validate your setup
Turn on the trigger

Details#

This integration uses pre-built, source-available components from Pipedream's GitHub repo. These components are developed by Pipedream and the community, and verified and maintained by Pipedream.

To contribute an update to an existing component or create a new component, create a PR on GitHub. If you're new to Pipedream component development, you can start with quickstarts for trigger span and action development, and then review the component API reference.

Trigger#

New Session Created on Zep

Description:Emit new event when a new session is created in Zep. [See the documentation](https://help.getzep.com/api-reference/memory/list-sessions)

Version:0.0.3

Key:zep-new-session

View on GitHub

Trigger Code#

import common from "../common/base.mjs";

export default {
  ...common,
  key: "zep-new-session",
  name: "New Session Created",
  description: "Emit new event when a new session is created in Zep. [See the documentation](https://help.getzep.com/api-reference/memory/list-sessions)",
  version: "0.0.3",
  type: "source",
  dedupe: "unique",
  methods: {
    ...common.methods,
    getNewResults(lastTs, max) {
      return this.getSessions({
        lastTs,
        max,
        orderBy: "created_at",
      });
    },
    generateMeta(session) {
      return {
        id: session.session_id,
        summary: `New Session: ${session.session_id}`,
        ts: Date.parse(session.created_at),
      };
    },
  },
};

Trigger Configuration#

This component may be configured based on the props defined in the component code. Pipedream automatically prompts for input values in the UI and CLI.

Label	Prop	Type	Description
Zep	`zep`	`app`	This component uses the Zep app.
N/A	`db`	`$.service.db`	This component uses `$.service.db` to maintain state between executions.
	`timer`	`$.interface.timer`

Trigger Authentication#

Zep uses API keys for authentication. When you connect your Zep account, Pipedream securely stores the keys so you can easily authenticate to Zep APIs in both code and no-code steps.

About Zep#

The Foundational Memory Layer for AI. Equip your agents with the knowledge to complete tasks, from the mundane to monumental.

Action#

Chat with 302.AI on 302.AI

Description:Send a message to the 302.AI Chat API. Ideal for dynamic conversations, contextual assistance, and creative generation. [See documentation](https://doc.302.ai/147522039e0)

Version:0.0.1

Key:_302_ai-chat-with-302-ai

View on GitHub

Action Code#

import _302_ai from "../../_302_ai.app.mjs";
import common from "../common/common.mjs";
import constants from "../common/constants.mjs";

export default {
  ...common,
  name: "Chat with 302.AI",
  version: "0.0.1",
  annotations: {
    destructiveHint: false,
    openWorldHint: true,
    readOnlyHint: false,
  },
  key: "_302_ai-chat-with-302-ai",
  description: "Send a message to the 302.AI Chat API. Ideal for dynamic conversations, contextual assistance, and creative generation. [See documentation](https://doc.302.ai/147522039e0)",
  type: "action",
  props: {
    _302_ai,
    modelId: {
      propDefinition: [
        _302_ai,
        "chatCompletionModelId",
      ],
    },
    userMessage: {
      label: "User Message",
      type: "string",
      description: "The user message to send to the model",
    },
    ...common.props,
    systemInstructions: {
      label: "System Instructions",
      type: "string",
      description: "The system message helps set the behavior of the assistant. For example: \"You are a helpful assistant.\"",
      optional: true,
    },
    messages: {
      label: "Prior Message History",
      type: "string[]",
      description: "Because the models have no memory of past chat requests, all relevant information must be supplied via the conversation. You can provide an array of messages from prior conversations here. **Formats supported:** 1) Plain strings with role prefix (e.g., `User: Hello` or `Assistant: Hi there`), 2) JSON strings (e.g., `{\"role\": \"user\", \"content\": \"Hello\"}`), 3) Plain strings without prefix (defaults to user role).",
      optional: true,
    },
    responseFormat: {
      type: "string",
      label: "Response Format",
      description: "- **Text**: Returns unstructured text output.\n- **JSON Object**: Returns a JSON object.\n- **JSON Schema**: Enables you to define a specific structure for the model's output using a JSON schema.",
      options: Object.values(constants.CHAT_RESPONSE_FORMAT).map((format) => format.value),
      default: constants.CHAT_RESPONSE_FORMAT.TEXT.value,
      optional: true,
      reloadProps: true,
    },
  },
  additionalProps() {
    const props = {};
    if (this.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. Must be a valid JSON schema object.",
      };
    }
    return props;
  },
  async run({ $ }) {
    let messages = [];

    if (this.messages && this.messages.length) {
      for (const message of this.messages) {
        let parsed;

        if (typeof message === "string") {
          // Try to parse as JSON first
          try {
            parsed = JSON.parse(message);
          } catch {
            // If not JSON, treat as a plain string and infer role from content
            // Check if message starts with "User:" or "Assistant:" or "System:"
            if (message.toLowerCase().startsWith("user:")) {
              parsed = {
                role: "user",
                content: message.replace(/^user:\s*/i, "").trim(),
              };
            } else if (message.toLowerCase().startsWith("assistant:")) {
              parsed = {
                role: "assistant",
                content: message.replace(/^assistant:\s*/i, "").trim(),
              };
            } else if (message.toLowerCase().startsWith("system:")) {
              parsed = {
                role: "system",
                content: message.replace(/^system:\s*/i, "").trim(),
              };
            } else {
              // Default to user role if no prefix
              parsed = {
                role: "user",
                content: message,
              };
            }
          }
        } else {
          parsed = message;
        }

        messages.push(parsed);
      }
    } else {
      if (this.systemInstructions) {
        messages.push({
          "role": "system",
          "content": this.systemInstructions,
        });
      }
    }

    messages.push({
      "role": "user",
      "content": this.userMessage,
    });

    const responseFormat = this.responseFormat ===
      constants.CHAT_RESPONSE_FORMAT.JSON_SCHEMA.value && this.jsonSchema
      ? {
        response_format: {
          type: this.responseFormat,
          json_schema: typeof this.jsonSchema === "string"
            ? JSON.parse(this.jsonSchema)
            : this.jsonSchema,
        },
      }
      : this.responseFormat && this.responseFormat !== constants.CHAT_RESPONSE_FORMAT.TEXT.value
        ? {
          response_format: {
            type: this.responseFormat,
          },
        }
        : {};

    const data = {
      ...this._getCommonArgs(),
      ...responseFormat,
      messages,
    };

    const response = await this._302_ai.createChatCompletion({
      $,
      data,
    });

    if (this.responseFormat === constants.CHAT_RESPONSE_FORMAT.JSON_SCHEMA.value) {
      for (const choice of response.choices) {
        try {
          choice.message.content = JSON.parse(choice.message.content);
        } catch {
          console.log(`Unable to parse JSON: ${choice.message.content}`);
        }
      }
    }

    if (response) {
      $.export("$summary", `Successfully sent chat with id ${response.id}`);
    }

    return {
      original_messages: messages,
      original_messages_with_assistant_response: messages.concat(response.choices[0]?.message),
      ...response,
    };
  },
};

Action Configuration#

This component may be configured based on the props defined in the component code. Pipedream automatically prompts for input values in the UI.

Label	Prop	Type	Description
302.AI	`_302_ai`	`app`	This component uses the 302.AI app.
Model	`modelId`	`string`	Select a value from the drop down menu.
User Message	`userMessage`	`string`	The user message to send to the model
Max Tokens	`maxTokens`	`string`	The maximum number of tokens to generate in the completion.
Temperature	`temperature`	`string`	What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic.
Top P	`topP`	`string`	An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered.
N	`n`	`string`	How many completions to generate for each prompt
Stop	`stop`	`string[]`	Up to 4 sequences where the API will stop generating further tokens. The returned text will not contain the stop sequence.
Presence Penalty	`presencePenalty`	`string`	Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics.
Frequency Penalty	`frequencyPenalty`	`string`	Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim.
User	`user`	`string`	A unique identifier representing your end-user, which can help monitor and detect abuse.
System Instructions	`systemInstructions`	`string`	The system message helps set the behavior of the assistant. For example: "You are a helpful assistant."
Prior Message History	`messages`	`string[]`	Because the models have no memory of past chat requests, all relevant information must be supplied via the conversation. You can provide an array of messages from prior conversations here. Formats supported: 1) Plain strings with role prefix (e.g., `User: Hello` or `Assistant: Hi there`), 2) JSON strings (e.g., `{"role": "user", "content": "Hello"}`), 3) Plain strings without prefix (defaults to user role).
Response Format	`responseFormat`	`string`	Select a value from the drop down menu:`textjson_objectjson_schema`