Chat with OpenAI (ChatGPT) API on New Query Results from Snowflake API

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New Query Results from the Snowflake API

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Chat with the OpenAI (ChatGPT) API

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Getting Started#

This integration creates a workflow with a Snowflake trigger and OpenAI (ChatGPT) action. When you configure and deploy the workflow, it will run on Pipedream's servers 24x7 for free.

Select this integration
Configure the New Query Results trigger
1. Connect your Snowflake account
2. Configure timer
3. Configure SQL Query
4. Optional- Configure Primary key
5. Optional- Configure Emit individual events
Configure the Chat action
1. Connect your OpenAI (ChatGPT) account
2. Select a Model
3. Configure User Message
4. Optional- Configure System Instructions
5. Optional- Configure Prior Message History
6. Optional- Configure Max Tokens
7. Optional- Configure Temperature
8. Optional- Configure Top P
9. Optional- Configure N
10. Optional- Configure Stop
11. Optional- Configure Presence Penalty
12. Optional- Configure Frequency Penalty
13. Optional- Configure User
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 Query Results on Snowflake

Description:Run a SQL query on a schedule, triggering a workflow for each row of results

Version:0.1.7

Key:snowflake-query-results

View on GitHub

Snowflake Overview#

Snowflake offers a cloud database and related tools to help developers create robust, secure, and scalable data warehouses. See Snowflake's Key Concepts & Architecture.

Getting Started

1. Create a user, role and warehouse in Snowflake

Snowflake recommends you create a new user, role, and warehouse when you integrate a third-party tool like Pipedream. This way, you can control permissions via the user / role, and separate Pipedream compute and costs with the warehouse. You can do this directly in the Snowflake UI.

We recommend you create a read-only account if you only need to query Snowflake. If you need to insert data into Snowflake, add permissions on the appropriate objects after you create your user.

2. Enter those details in Pipedream

Visit https://pipedream.com/accounts. Click the button to Connect an App. Enter the required Snowflake account data.

You'll only need to connect your account once in Pipedream. You can connect this account to multiple workflows to run queries against Snowflake, insert data, and more.

3. Build your first workflow

Visit https://pipedream.com/new to build your first workflow. Pipedream workflows let you connect Snowflake with 1,000+ other apps. You can trigger workflows on Snowflake queries, sending results to Slack, Google Sheets, or any app that exposes an API. Or you can accept data from another app, transform it with Python, Node.js, Go or Bash code, and insert it into Snowflake.

Learn more at Pipedream University.

Trigger Code#

import common from "../common.mjs";
import { v4 as uuidv4 } from "uuid";

export default {
  ...common,
  type: "source",
  key: "snowflake-query-results",
  name: "New Query Results",
  // eslint-disable-next-line
  description: "Run a SQL query on a schedule, triggering a workflow for each row of results",
  version: "0.1.7",
  props: {
    ...common.props,
    sqlQuery: {
      type: "string",
      label: "SQL Query",
      description: "Run this query on a schedule, triggering the workflow for each row of results",
    },
    // Ordering props correctly
    timer: common.props.timer,
    dedupeKey: {
      type: "string",
      label: "Primary key",
      description: "The column in your query to use for de-duplication. Duplicate rows won't trigger your workflow",
      optional: true,
    },
    emitIndividualEvents: {
      propDefinition: [
        common.props.snowflake,
        "emitIndividualEvents",
      ],
    },
  },
  methods: {
    ...common.methods,
    generateMeta(data) {
      const {
        row: { [this.dedupeKey]: id = uuidv4() },
        timestamp: ts,
      } = data;
      const summary = `New event (ID: ${id})`;
      return {
        id,
        summary,
        ts,
      };
    },
    generateMetaForCollection(data) {
      const { timestamp: ts } = data;
      const id = uuidv4();
      const summary = `New event (ID: ${id})`;
      return {
        id,
        summary,
        ts,
      };
    },
    getStatement() {
      return {
        sqlText: this.sqlQuery,
      };
    },
  },
};

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
Snowflake	`snowflake`	`app`	This component uses the Snowflake app.
N/A	`db`	`$.service.db`	This component uses `$.service.db` to maintain state between executions.
	`timer`	`$.interface.timer`	Watch for changes on this schedule
SQL Query	`sqlQuery`	`string`	Run this query on a schedule, triggering the workflow for each row of results
Primary key	`dedupeKey`	`string`	The column in your query to use for de-duplication. Duplicate rows won't trigger your workflow
Emit individual events	`emitIndividualEvents`	`boolean`	Defaults to `true`, triggering workflows on each record in the result set. Set to `false` to emit records in batch (advanced)

Trigger Authentication#

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

We recommend you create a read-only account if you only need to query Snowflake. If you need to insert data into Snowflake, add permissions on the appropriate objects after you create your user.

Enter the username, password, and other details below. The account field expects your Snowflake account identifier. You may need to include the full identifier and region as a part of this string, e.g. au12345.us-east-1. Please consult your instance and Snowflake docs for more detail.

About Snowflake#

A data warehouse built for the cloud

Action#

Chat on OpenAI (ChatGPT)

Description:The Chat API, using the `gpt-3.5-turbo` or `gpt-4` model. [See docs here](https://platform.openai.com/docs/api-reference/chat)

Version:0.1.2

Key:openai-chat

View on GitHub

OpenAI (ChatGPT) Overview#

The OpenAI API is a powerful tool that provides access to a range of
high-powered machine learning models. With the OpenAI API, developers can
create products, services, and tools that enable humanizing AI experiences, and
build applications that extend the power of human language.

Using the OpenAI API, developers can create language-driven applications such
as:

Natural language understanding and sentiment analysis
Text-based search
Question answering
Dialogue systems and conversation agents
Intelligent text completion
Text summarization
Text classification
Machine translation
Language generation
Multi-factor authentication
Anomaly detection
Text analysis

Action Code#

import openai from "../../app/openai.app.mjs";
import common from "../common/common.mjs";

export default {
  ...common,
  name: "Chat",
  version: "0.1.2",
  key: "openai-chat",
  description: "The Chat API, using the `gpt-3.5-turbo` or `gpt-4` model. [See docs here](https://platform.openai.com/docs/api-reference/chat)",
  type: "action",
  props: {
    openai,
    modelId: {
      propDefinition: [
        openai,
        "chatCompletionModelId",
      ],
    },
    userMessage: {
      label: "User Message",
      type: "string",
      description: "The user messages provide instructions to the assistant. They can be generated by the end users of an application, or set by a developer as an instruction.",
    },
    systemInstructions: {
      label: "System Instructions",
      type: "string",
      description: "The system message helps set the behavior of the assistant. For example: \"You are a helpful assistant.\" [See these docs](https://platform.openai.com/docs/guides/chat/instructing-chat-models) for tips on writing good instructions.",
      optional: true,
    },
    messages: {
      label: "Prior Message History",
      type: "string[]",
      description: "_Advanced_. Because [the models have no memory of past chat requests](https://platform.openai.com/docs/guides/chat/introduction), all relevant information must be supplied via the conversation. You can provide [an array of messages](https://platform.openai.com/docs/guides/chat/introduction) from prior conversations here. If this param is set, the action ignores the values passed to **System Instructions** and **Assistant Response**, appends the new **User Message** to the end of this array, and sends it to the API.",
      optional: true,
    },
    ...common.props,
  },
  async run({ $ }) {
    const args = this._getChatArgs();
    const response = await this.openai.createChatCompletion({
      $,
      args,
    });

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

    const { messages } = args;
    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
OpenAI (ChatGPT)	`openai`	`app`	This component uses the OpenAI (ChatGPT) app.
Model	`modelId`	`string`	Select a value from the drop down menu.
User Message	`userMessage`	`string`	The user messages provide instructions to the assistant. They can be generated by the end users of an application, or set by a developer as an instruction.
System Instructions	`systemInstructions`	`string`	The system message helps set the behavior of the assistant. For example: "You are a helpful assistant." See these docs for tips on writing good instructions.
Prior Message History	`messages`	`string[]`	Advanced. 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. If this param is set, the action ignores the values passed to System Instructions and Assistant Response, appends the new User Message to the end of this array, and sends it to the API.
Max Tokens	`maxTokens`	`integer`	The maximum number of tokens to generate in the completion.
Temperature	`temperature`	`string`	Optional. What sampling temperature to use. Higher values means the model will take more risks. Try 0.9 for more creative applications, and 0 (argmax sampling) for ones with a well-defined answer.
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. We generally recommend altering this or `temperature` but not both.
N	`n`	`integer`	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 OpenAI to monitor and detect abuse. Learn more here.