Chat with Anthropic (Claude) API on New News Event Added from PredictLeads API

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New News Event Added from the PredictLeads API

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Chat with the Anthropic (Claude) API

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Getting Started#

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

Select this integration
Configure the New News Event Added trigger
1. Connect your PredictLeads account
2. Configure timer
3. Configure Domain
4. Optional- Select one or more Categories
Configure the Chat action
1. Connect your Anthropic (Claude) account
2. Select a Model
3. Configure User Message
4. Optional- Configure Prior Message History
5. Optional- Configure Temperature
6. Optional- Configure Top K
7. Optional- Configure Top P
8. Configure Maximum Tokens To Sample
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 News Event Added on PredictLeads

Description:Emit new event for each new news event for a specific company. [See the documentation](https://docs.predictleads.com/v3/api_endpoints/news_events_dataset/retrieve_company_s_news_events)

Version:0.0.1

Key:predictleads-new-news-event

View on GitHub

Trigger Code#

import common from "../common/polling.mjs";
import sampleEmit from "./test-event.mjs";

export default {
  ...common,
  key: "predictleads-new-news-event",
  name: "New News Event Added",
  description: "Emit new event for each new news event for a specific company. [See the documentation](https://docs.predictleads.com/v3/api_endpoints/news_events_dataset/retrieve_company_s_news_events)",
  version: "0.0.1",
  type: "source",
  dedupe: "unique",
  props: {
    ...common.props,
    domain: {
      description: "The domain of the company to retrieve news events for (e.g., `google.com`).",
      propDefinition: [
        common.props.app,
        "domain",
      ],
    },
    categories: {
      type: "string[]",
      label: "Categories",
      description: "Filter news events by specific categories.",
      optional: true,
      options: [
        "acquires",
        "merges_with",
        "sells_assets_to",
        "signs_new_client",
        "files_suit_against",
        "has_issues_with",
        "closes_offices_in",
        "decreases_headcount_by",
        "attends_event",
        "expands_facilities",
        "expands_offices_in",
        "expands_offices_to",
        "increases_headcount_by",
        "opens_new_location",
        "goes_public",
        "invests_into",
        "invests_into_assets",
        "receives_financing",
        "hires",
        "leaves",
        "promotes",
        "retires_from",
        "integrates_with",
        "is_developing",
        "launches",
        "partners_with",
        "receives_award",
        "recognized_as",
        "identified_as_competitor_of",
      ],
    },
  },
  methods: {
    ...common.methods,
    getDateField() {
      return "found_at";
    },
    getResourcesFn() {
      return this.app.retrieveNewsEvents;
    },
    getResourcesFnArgs() {
      const {
        domain, categories,
      } = this;
      return {
        domain,
        params: {
          categories: categories?.join(","),
        },
      };
    },
    generateMeta(resource) {
      return {
        id: resource.id,
        summary: `New News Event: ${resource.id}`,
        ts: Date.parse(resource.attributes.found_at),
      };
    },
  },
  sampleEmit,
};

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
PredictLeads	`app`	`app`	This component uses the PredictLeads app.
N/A	`db`	`$.service.db`	This component uses `$.service.db` to maintain state between executions.
	`timer`	`$.interface.timer`
Domain	`domain`	`string`	The domain of the company to retrieve news events for (e.g., `google.com`).
Categories	`categories`	`string[]`	Select a value from the drop down menu:`acquiresmerges_withsells_assets_tosigns_new_clientfiles_suit_againsthas_issues_withcloses_offices_indecreases_headcount_byattends_eventexpands_facilitiesexpands_offices_inexpands_offices_toincreases_headcount_byopens_new_locationgoes_publicinvests_intoinvests_into_assetsreceives_financinghiresleavespromotesretires_fromintegrates_withis_developinglaunchespartners_withreceives_awardrecognized_asidentified_as_competitor_of`

Trigger Authentication#

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

About PredictLeads#

Company Intelligence Data

Action#

Chat on Anthropic (Claude)

Description:The Chat API. [See the documentation](https://docs.anthropic.com/claude/reference/messages_post)

Version:0.2.2

Key:anthropic-chat

View on GitHub

Anthropic (Claude) Overview#

The Anthropic (Claude) API offers a conversational AI that can be leveraged for a variety of applications, ranging from answering questions to generating content or providing customer support. In Pipedream, you can create powerful workflows that utilize the capabilities of Claude to automate responses, analyze text, or enhance data with AI-generated insights. Pipedream's serverless platform simplifies integrating Claude into your processes with other apps and services for seamless automation.

Action Code#

import anthropic from "../../anthropic.app.mjs";

export default {
  name: "Chat",
  version: "0.2.2",
  annotations: {
    destructiveHint: false,
    openWorldHint: true,
    readOnlyHint: false,
  },
  key: "anthropic-chat",
  description: "The Chat API. [See the documentation](https://docs.anthropic.com/claude/reference/messages_post)",
  type: "action",
  props: {
    anthropic,
    model: {
      propDefinition: [
        anthropic,
        "model",
      ],
    },
    userMessage: {
      label: "User Message",
      type: "string",
      description: "The user messages provide instructions to the assistant",
    },
    messages: {
      label: "Prior Message History",
      type: "string[]",
      description: "All relevant information must be supplied via the conversation. You can provide an array of messages from prior conversations here always beginning with the human message.",
      optional: true,
    },
    temperature: {
      label: "Temperature",
      description: "**Optional**. Amount of randomness injected into the response. Ranges from 0 to 1. Use temp closer to 0 for analytical / multiple choice, and temp closer to 1 for creative and generative tasks.",
      type: "string",
      optional: true,
    },
    topK: {
      label: "Top K",
      description: "Only sample from the top K options for each subsequent token. Used to remove `long tail` low probability responses.",
      type: "integer",
      optional: true,
    },
    topP: {
      label: "Top P",
      description: "Does nucleus sampling, in which we compute the cumulative distribution over all the options for each subsequent token in decreasing probability order and cut it off once it reaches a particular probability specified.",
      type: "string",
      optional: true,
    },
    maxTokensToSample: {
      label: "Maximum Tokens To Sample",
      description: "A maximum number of tokens to generate before stopping.",
      type: "integer",
    },
  },
  async run({ $ }) {
    const messages = [];

    const priorMessages = typeof this.messages === "string"
      ? JSON.parse(this.messages)
      : this.messages;

    if (priorMessages?.length) {
      let isUserMessage = true;

      for (const message of priorMessages) {
        messages.push({
          role: isUserMessage
            ? "user"
            : "assistant",
          content: message,
        });

        isUserMessage = !isUserMessage;
      }
    }

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

    const response = await this.anthropic.createMessage({
      $,
      data: {
        messages,
        model: this.model,
        max_tokens: this.maxTokensToSample,
        temperature: this.temperature
          ? parseFloat(this.temperature)
          : undefined,
        top_p: this.topP
          ? parseFloat(this.topP)
          : undefined,
        top_k: this.topK,
      },
    });

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

    const originalMessages = messages.map(({ content }) => content);
    return {
      original_messages: originalMessages,
      original_messages_with_assistant_response: originalMessages.concat(response.content[0].text),
      ...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
Anthropic (Claude)	`anthropic`	`app`	This component uses the Anthropic (Claude) app.
Model	`model`	`string`	Select a value from the drop down menu.
User Message	`userMessage`	`string`	The user messages provide instructions to the assistant
Prior Message History	`messages`	`string[]`	All relevant information must be supplied via the conversation. You can provide an array of messages from prior conversations here always beginning with the human message.
Temperature	`temperature`	`string`	Optional. Amount of randomness injected into the response. Ranges from 0 to 1. Use temp closer to 0 for analytical / multiple choice, and temp closer to 1 for creative and generative tasks.
Top K	`topK`	`integer`	Only sample from the top K options for each subsequent token. Used to remove `long tail` low probability responses.
Top P	`topP`	`string`	Does nucleus sampling, in which we compute the cumulative distribution over all the options for each subsequent token in decreasing probability order and cut it off once it reaches a particular probability specified.
Maximum Tokens To Sample	`maxTokensToSample`	`integer`	A maximum number of tokens to generate before stopping.