更精致

Dappier 将任何 LLM 或您的 Agentic AI 连接到来自可信来源的实时、权限清除的专有数据，使您的 AI 成为任何领域的专家。我们的专业模型包括实时 Web 搜索、新闻、体育、金融股票市场数据、加密数据以及来自优质出版商的独家内容。在 marketplace.dappier.com 的 Marketplace 中探索各种数据模型。

Dappier 提供丰富的、提示就绪的、上下文相关的数据字符串，并针对与 LangChain 的无缝集成进行了优化。无论您是构建对话式 AI、推荐引擎还是智能搜索，Dappier 的 LLM 无关 RAG 模型都能确保您的 AI 能够访问经过验证的最新数据，而无需构建和管理您自己的检索管道。

Dappier 工具

这将帮助您开始使用 Dappier 工具。有关所有 DappierRetriever 功能和配置的详细文档，请访问 API 参考。

概述

DappierRealTimeSearchTool 和 DappierAIRecommendationTool 为 AI 应用程序提供实时数据和 AI 驱动的见解。前者提供对新闻、天气、旅行和金融市场最新信息的访问，而后者则通过来自新闻、金融和体育等不同领域的事实、优质内容为应用程序提供增强，所有这些都由 Dappier 的预训练 RAG 模型和自然语言 API 提供支持。

设置

此工具位于langchain-dappier包。

%pip install -qU langchain-dappier

凭据

我们还需要设置我们的 Dappier API 凭据，这些凭据可以在 Dappier 站点生成。

import getpass
import os

if not os.environ.get("DAPPIER_API_KEY"):
    os.environ["DAPPIER_API_KEY"] = getpass.getpass("Dappier API key:\n")

如果您想从单个查询中获得自动跟踪，您还可以通过取消下面的注释来设置您的 LangSmith API 密钥：

# os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
# os.environ["LANGSMITH_TRACING"] = "true"

DappierRealTimeSearch工具

访问实时 Google 搜索结果，包括最新新闻、天气、旅行和交易，以及最新的财经新闻、股票价格和 polygon.io 交易，所有这些都由 AI 见解提供支持，让您随时了解情况。

实例

• ai_model_id：str 用于查询的 AI 模型 ID。AI 模型 ID 始终启动 替换为前缀 “am_”。

  默认为 “am_01j06ytn18ejftedz6dyhz2b15”。

  有多个 AI 模型 ID 可用，可在以下位置找到：https://marketplace.dappier.com/marketplace

from langchain_dappier import DappierRealTimeSearchTool

tool = DappierRealTimeSearchTool(
    # ai_model_id="...",     # overwrite default ai_model_id
    # name="...",            # overwrite default tool name
    # description="...",     # overwrite default tool description
    # args_schema=...,       # overwrite default args_schema: BaseModel
)

调用

使用 args 直接调用

这DappierRealTimeSearchTool接受一个 “query” 参数，它应该是一个自然语言查询：

tool.invoke({"query": "What happened at the last wimbledon"})

"At the last Wimbledon in 2024, Carlos Alcaraz won the title by defeating Novak Djokovic. This victory marked Alcaraz's fourth Grand Slam title at just 21 years old! 🎉🏆🎾"

使用 ToolCall 调用

我们还可以使用模型生成的 ToolCall 来调用该工具，在这种情况下，将返回 ToolMessage：

# This is usually generated by a model, but we'll create a tool call directly for demo purposes.
model_generated_tool_call = {
    "args": {"query": "euro 2024 host nation"},
    "id": "1",
    "name": "dappier",
    "type": "tool_call",
}
tool_msg = tool.invoke(model_generated_tool_call)

# The content is a JSON string of results
print(tool_msg.content[:400])

Euro 2024 is being hosted by Germany! 🇩🇪 The tournament runs from June 14 to July 14, 2024, featuring 24 teams competing across various cities like Berlin and Munich. It's going to be an exciting summer of football! ⚽️🏆

链接

我们可以在链中使用我们的工具，方法是先将它绑定到一个工具调用模型，然后调用它：

选择聊天模式：

OpenAI

Anthropic

天蓝色

谷歌双子座

谷歌顶点

AWS

Groq

Cohere

英伟达

烟花 AI

Mistral AI

携手 AI

IBM 沃森

Databricks

xAI

困惑

pip install -qU "langchain[openai]"

import getpass
import os

if not os.environ.get("OPENAI_API_KEY"):
  os.environ["OPENAI_API_KEY"] = getpass.getpass("Enter API key for OpenAI: ")

from langchain.chat_models import init_chat_model

llm = init_chat_model("gpt-4o-mini", model_provider="openai")

import datetime

from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnableConfig, chain

today = datetime.datetime.today().strftime("%D")
prompt = ChatPromptTemplate(
    [
        ("system", f"You are a helpful assistant. The date today is {today}."),
        ("human", "{user_input}"),
        ("placeholder", "{messages}"),
    ]
)

# specifying tool_choice will force the model to call this tool.
llm_with_tools = llm.bind_tools([tool])

llm_chain = prompt | llm_with_tools


@chain
def tool_chain(user_input: str, config: RunnableConfig):
    input_ = {"user_input": user_input}
    ai_msg = llm_chain.invoke(input_, config=config)
    tool_msgs = tool.batch(ai_msg.tool_calls, config=config)
    return llm_chain.invoke({**input_, "messages": [ai_msg, *tool_msgs]}, config=config)


tool_chain.invoke("who won the last womens singles wimbledon")

API 参考：ChatPromptTemplate | RunnableConfig | Chains

AIMessage(content="Barbora Krejčíková won the women's singles title at Wimbledon 2024, defeating Jasmine Paolini in the final with a score of 6–2, 2–6, 6–4. This victory marked her first Wimbledon singles title and her second major singles title overall! 🎉🏆🎾", additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 69, 'prompt_tokens': 222, 'total_tokens': 291, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_name': 'gpt-4o-2024-08-06', 'system_fingerprint': 'fp_4691090a87', 'finish_reason': 'stop', 'logprobs': None}, id='run-87a385dd-103b-4344-a3be-2d6fd1dcfdf5-0', usage_metadata={'input_tokens': 222, 'output_tokens': 69, 'total_tokens': 291, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}})

DappierAIRecommendationTool

使用 Dappier 的预训练 RAG 模型和自然语言 API 增强您的 AI 应用程序，提供来自新闻、财经、体育、天气等垂直领域的优质内容提供商的事实和最新响应。

实例

• data_model_id： str
  用于建议的数据模型 ID。数据模型 ID 始终以前缀 “dm_” 开头。默认为 “dm_01j0pb465keqmatq9k83dthx34”。
  有多个数据模型 ID 可用，可以在 Dappier Marketplace 中找到。

• similarity_top_k： int
  根据相似度检索的排名靠前的文档数。默认为 “9”。

• ref： 可选[str] 应显示 AI 建议的站点域。默认为 “None”。

• num_articles_ref：int 要从指定引用域 （“ref”） 返回的最小文章数。其余文章将来自 RAG 模型中的其他网站。默认为 “0”。

• search_algorithm： Literal[“most_recent”， “semantic”， “most_recent_semantic”， “trending”] 用于检索文章的搜索算法。默认为 “most_recent”。

from langchain_dappier import DappierAIRecommendationTool

tool = DappierAIRecommendationTool(
    data_model_id="dm_01j0pb465keqmatq9k83dthx34",
    similarity_top_k=3,
    ref="sportsnaut.com",
    num_articles_ref=2,
    search_algorithm="most_recent",
    # name="...",            # overwrite default tool name
    # description="...",     # overwrite default tool description
    # args_schema=...,       # overwrite default args_schema: BaseModel
)

调用

使用 args 直接调用

这DappierAIRecommendationTool接受一个 “query” 参数，它应该是一个自然语言查询：

tool.invoke({"query": "latest sports news"})

[{'author': 'Matt Weaver',
  'image_url': 'https://images.dappier.com/dm_01j0pb465keqmatq9k83dthx34/Screenshot_20250117_021643_Gallery_.jpg?width=428&height=321',
  'pubdate': 'Fri, 17 Jan 2025 08:04:03 +0000',
  'source_url': 'https://sportsnaut.com/chili-bowl-thursday-bell-column/',
  'summary': "The article highlights the thrilling unpredictability of the Chili Bowl Midget Nationals, focusing on the dramatic shifts in fortune for drivers like Christopher Bell, Tanner Thorson, and Karter Sarff during Thursday's events. Key moments included Sarff's unfortunate pull-off and a last-lap crash that allowed Ryan Bernal to capitalize and improve his standing, showcasing the chaotic nature of the race and the importance of strategy and luck.\n\nAs the competition intensifies leading up to Championship Saturday, Bell faces the challenge of racing from a Last Chance Race, reflecting on the excitement and difficulties of the sport. The article emphasizes the emotional highs and lows experienced by racers, with insights from Bell and Bernal on the unpredictable nature of racing. Overall, it captures the camaraderie and passion that define the Chili Bowl, illustrating how each moment contributes to the event's narrative.",
  'title': 'Thursday proves why every lap of Chili Bowl is so consequential'},
 {'author': 'Matt Higgins',
  'image_url': 'https://images.dappier.com/dm_01j0pb465keqmatq9k83dthx34/Pete-Alonso-24524027_.jpg?width=428&height=321',
  'pubdate': 'Fri, 17 Jan 2025 02:48:42 +0000',
  'source_url': 'https://sportsnaut.com/new-york-mets-news-pete-alonso-rejected-last-ditch-contract-offer/',
  'summary': "The New York Mets are likely parting ways with star first baseman Pete Alonso after failing to finalize a contract agreement. Alonso rejected a last-minute three-year offer worth between $68 and $70 million, leading the Mets to redirect funds towards acquiring a top reliever. With Alonso's free-agent options dwindling, speculation arises about his potential signing with another team for the 2025 season, while the Mets plan to shift Mark Vientos to first base.\n\nIn a strategic move, the Mets are also considering a trade for Toronto Blue Jays' star first baseman Vladimir Guerrero Jr. This potential acquisition aims to enhance the Mets' competitiveness as they reshape their roster. Guerrero's impressive offensive stats make him a valuable target, and discussions are in the early stages. Fans and analysts are keenly watching the situation, as a trade involving such a prominent player could significantly impact both teams.",
  'title': 'MLB insiders reveal New York Mets’ last-ditch contract offer that Pete Alonso rejected'},
 {'author': 'Jim Cerny',
  'image_url': 'https://images.dappier.com/dm_01j0pb465keqmatq9k83dthx34/NHL-New-York-Rangers-at-Utah-25204492_.jpg?width=428&height=321',
  'pubdate': 'Fri, 17 Jan 2025 05:10:39 +0000',
  'source_url': 'https://www.foreverblueshirts.com/new-york-rangers-news/stirring-5-3-comeback-win-utah-close-road-trip/',
  'summary': "The New York Rangers achieved a thrilling 5-3 comeback victory against the Utah Hockey Club, showcasing their resilience after a prior overtime loss. The Rangers scored three unanswered goals in the third period, with key contributions from Reilly Smith, Chris Kreider, and Artemi Panarin, who sealed the win with an empty-net goal. This victory marked their first win of the season when trailing after two periods and capped off a successful road trip, improving their record to 21-20-3.\n\nIgor Shesterkin's strong performance in goal, along with Arthur Kaliyev's first goal for the team, helped the Rangers overcome an early deficit. The game featured multiple lead changes, highlighting the competitive nature of both teams. As the Rangers prepare for their next game against the Columbus Blue Jackets, they aim to close the gap in the playoff race, with the Blue Jackets currently holding a five-point lead in the Eastern Conference standings.",
  'title': 'Rangers score 3 times in 3rd period for stirring 5-3 comeback win against Utah to close road trip'}]

使用 ToolCall 调用

我们还可以使用模型生成的 ToolCall 来调用该工具，在这种情况下，将返回 ToolMessage：

# This is usually generated by a model, but we'll create a tool call directly for demo purposes.
model_generated_tool_call = {
    "args": {"query": "top 3 news articles"},
    "id": "1",
    "name": "dappier",
    "type": "tool_call",
}
tool_msg = tool.invoke(model_generated_tool_call)

# The content is a JSON string of results
print(tool_msg.content[:400])

[{"author": "Matt Johnson", "image_url": "https://images.dappier.com/dm_01j0pb465keqmatq9k83dthx34/MLB-New-York-Mets-at-Colorado-Rockies-23948644_.jpg?width=428&height=321", "pubdate": "Fri, 17 Jan 2025 13:31:02 +0000", "source_url": "https://sportsnaut.com/new-york-mets-rumors-vladimir-guerrero-jr-news/", "summary": "The New York Mets are refocusing their strategy after failing to extend a contra

API 参考

有关所有 DappierRealTimeSearchTool 功能和配置的详细文档，请访问 API 参考

相关

• 工具概念指南
• 工具操作指南