使用 Toolhouse 构建销售潜在客户开发流程¶

在本笔记中，您将学习如何利用 Toolhouse 和 LlamaIndex 创建销售潜在客户开发流程。销售潜在客户开发使企业能够根据其价值主张和目标市场找到最匹配的潜在客户。

该流程将使用单一智能体执行以下活动：

要求智能体通过获取企业落地页内容来确定其价值主张
在互联网上搜索可能受益于该企业产品的潜在客户
确定最适合联系的目标公司
为选定的公司起草个性化电子邮件

初始设置¶

首先确保所有必需的库都已安装。本示例使用 Groq 平台上的 Llama 3.2，但您可以使用 LlamaIndex 支持的任何大语言模型。

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%pip install llama-index
%pip install llama-index-llms-groq
%pip install toolhouse
%pip install llama-index
%pip install llama-index-llms-groq
%pip install toolhouse

接下来，我们将传递 API 密钥。

获取 Toolhouse API 密钥的步骤：

注册 Toolhouse 账户或登录（现有用户）
新用户请复制 onboarding 流程中自动生成的 API 密钥。现有用户可在 API 密钥页面获取密钥
将 API 密钥粘贴至下方

获取 Groq API 密钥请先申请 Groq 访问权限，然后将密钥粘贴至下方。

重要提示： 生产环境中请妥善保管 API 密钥。

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import os

os.environ[
    "TOOLHOUSE_API_KEY"
] = "Get your Toolhouse API key at https://join.toolhouse.ai"
os.environ[
    "GROQ_API_KEY"
] = "Get your Groq API key at https://console.groq.com"
import os

os.environ[
    "TOOLHOUSE_API_KEY"
] = "Get your Toolhouse API key at https://join.toolhouse.ai"
os.environ[
    "GROQ_API_KEY"
] = "Get your Groq API key at https://console.groq.com"

导入库¶

我们将导入 LlamaIndexas 和 Toolhouse。接着初始化 Toolhouse 和 Groq 大语言模型。

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from llama_index.llms.groq import Groq
from llama_index.core.agent import ReActAgent
from llama_index.core.memory import ChatMemoryBuffer
from toolhouse import Toolhouse, Provider
from llama_index.core.workflow import (
    Context,
    Event,
    StartEvent,
    StopEvent,
    Workflow,
    step,
)
from llama_index.llms.groq import Groq
from llama_index.core.agent import ReActAgent
from llama_index.core.memory import ChatMemoryBuffer
from toolhouse import Toolhouse, Provider
from llama_index.core.workflow import (
    Context,
    Event,
    StartEvent,
    StopEvent,
    Workflow,
    step,
)

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llm = Groq(model="llama-3.2-11b-vision-preview")

th = Toolhouse(provider=Provider.LLAMAINDEX)
th.set_metadata("id", "llamaindex_agent")
th.set_metadata("timezone", 0)
llm = Groq(model="llama-3.2-11b-vision-preview")

th = Toolhouse(provider=Provider.LLAMAINDEX)
th.set_metadata("id", "llamaindex_agent")
th.set_metadata("timezone", 0)

安装 Toolhouse 工具¶

智能代理需要执行网页搜索和获取页面内容的操作。请前往您的 Toolhouse 控制面板安装以下工具以启用这些功能：

工作流程¶

该工作流程包含四个步骤；我们为每个步骤创建了输出事件，以使顺序关系更加清晰。

由于 Toolhouse 直接集成到 LlamaIndex 中，您可以直接将 Toolhouse 工具传递给代理。

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class WebsiteContentEvent(Event):
    contents: str


class WebSearchEvent(Event):
    results: str


class RankingEvent(Event):
    results: str


class LogEvent(Event):
    msg: str


class SalesRepWorkflow(Workflow):
    agent = ReActAgent(
        tools=th.get_tools(bundle="llamaindex test"),
        llm=llm,
        memory=ChatMemoryBuffer.from_defaults(),
    )

    @step
    async def get_company_info(
        self, ctx: Context, ev: StartEvent
    ) -> WebsiteContentEvent:
        ctx.write_event_to_stream(
            LogEvent(msg=f"Getting the contents of {ev.url}…")
        )
        prompt = f"Get the contents of {ev.url}, then summarize its key value propositions in a few bullet points."
        contents = await self.agent.achat(prompt)
        return WebsiteContentEvent(contents=str(contents.response))

    @step
    async def find_prospects(
        self, ctx: Context, ev: WebsiteContentEvent
    ) -> WebSearchEvent:
        ctx.write_event_to_stream(
            LogEvent(
                msg=f"Performing web searches to identify companies who can benefit from the business's offerings."
            )
        )
        prompt = f"With that you know about the business, perform a web search to find 5 tech companies who may benefit from the business's product. Only answer with the names of the companies you chose."
        results = await self.agent.achat(prompt)
        return WebSearchEvent(results=str(results.response))

    @step
    async def select_best_company(
        self, ctx: Context, ev: WebSearchEvent
    ) -> RankingEvent:
        ctx.write_event_to_stream(
            LogEvent(
                msg=f"Selecting the best company who can benefit from the business's offering…"
            )
        )
        prompt = "Select one company that can benefit from the business's product. Only use your knowledge to select the company. Respond with just the name of the company. Do not use tools."
        results = await self.agent.achat(prompt)
        ctx.write_event_to_stream(
            LogEvent(
                msg=f"The agent selected this company: {results.response}"
            )
        )
        return RankingEvent(results=str(results.response))

    @step
    async def prepare_email(self, ctx: Context, ev: RankingEvent) -> StopEvent:
        ctx.write_event_to_stream(
            LogEvent(msg=f"Drafting a short email for sales outreach…")
        )
        prompt = f"Draft a short cold sales outreach email for the company you picked. Do not use tools."
        email = await self.agent.achat(prompt)
        ctx.write_event_to_stream(
            LogEvent(msg=f"Here is the email: {email.response}")
        )
        return StopEvent(result=str(email.response))
class WebsiteContentEvent(Event):
    contents: str


class WebSearchEvent(Event):
    results: str


class RankingEvent(Event):
    results: str


class LogEvent(Event):
    msg: str


class SalesRepWorkflow(Workflow):
    agent = ReActAgent(
        tools=th.get_tools(bundle="llamaindex test"),
        llm=llm,
        memory=ChatMemoryBuffer.from_defaults(),
    )

    @step
    async def get_company_info(
        self, ctx: Context, ev: StartEvent
    ) -> WebsiteContentEvent:
        ctx.write_event_to_stream(
            LogEvent(msg=f"Getting the contents of {ev.url}…")
        )
        prompt = f"Get the contents of {ev.url}, then summarize its key value propositions in a few bullet points."
        contents = await self.agent.achat(prompt)
        return WebsiteContentEvent(contents=str(contents.response))

    @step
    async def find_prospects(
        self, ctx: Context, ev: WebsiteContentEvent
    ) -> WebSearchEvent:
        ctx.write_event_to_stream(
            LogEvent(
                msg=f"Performing web searches to identify companies who can benefit from the business's offerings."
            )
        )
        prompt = f"With that you know about the business, perform a web search to find 5 tech companies who may benefit from the business's product. Only answer with the names of the companies you chose."
        results = await self.agent.achat(prompt)
        return WebSearchEvent(results=str(results.response))

    @step
    async def select_best_company(
        self, ctx: Context, ev: WebSearchEvent
    ) -> RankingEvent:
        ctx.write_event_to_stream(
            LogEvent(
                msg=f"Selecting the best company who can benefit from the business's offering…"
            )
        )
        prompt = "Select one company that can benefit from the business's product. Only use your knowledge to select the company. Respond with just the name of the company. Do not use tools."
        results = await self.agent.achat(prompt)
        ctx.write_event_to_stream(
            LogEvent(
                msg=f"The agent selected this company: {results.response}"
            )
        )
        return RankingEvent(results=str(results.response))

    @step
    async def prepare_email(self, ctx: Context, ev: RankingEvent) -> StopEvent:
        ctx.write_event_to_stream(
            LogEvent(msg=f"Drafting a short email for sales outreach…")
        )
        prompt = f"Draft a short cold sales outreach email for the company you picked. Do not use tools."
        email = await self.agent.achat(prompt)
        ctx.write_event_to_stream(
            LogEvent(msg=f"Here is the email: {email.response}")
        )
        return StopEvent(result=str(email.response))

运行工作流¶

只需实例化工作流并传入公司URL即可开始使用。

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workflow = SalesRepWorkflow(timeout=None)
handler = workflow.run(url="https://toolhouse.ai")
async for event in handler.stream_events():
    if isinstance(event, LogEvent):
        print(event.msg)
workflow = SalesRepWorkflow(timeout=None)
handler = workflow.run(url="https://toolhouse.ai")
async for event in handler.stream_events():
    if isinstance(event, LogEvent):
        print(event.msg)

Getting the contents of https://toolhouse.ai…
Performing web searches to identify companies who can benefit from the business's offerings.
Selecting the best company who can benefit from the business's offering…
The agent selected this company: Cohere
Drafting a short email for sales outreach…
Here is the email: Subject: Streamline Your LLM Function Calling with Toolhouse

Hi [Cohere Team],

I noticed Cohere is leading the way in providing enterprise-ready LLM solutions. Given that your Command-r model already supports function calling, I thought you'd be interested in Toolhouse's developer toolkit that could enhance your clients' implementation experience.

Toolhouse offers a unified SDK that streamlines LLM function calling across multiple models, including Cohere's. Our platform provides:
- Pre-built, production-ready tools that reduce development time
- Built-in analytics for easier debugging
- A single integration point for multiple LLM tools

Would you be open to a 15-minute call to discuss how Toolhouse could help Cohere's enterprise clients implement function calling more efficiently?

Best regards,
[Name]