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Jina 搜索

此笔记本提供了 Jina 工具入门的快速概述。有关所有 Jina 功能和配置的详细文档，请参阅 API 参考。

概述

集成详细信息

类包序列 化JS 支持最新包装
JinaSearchlangchain-community❌❌PyPI - Version

工具特点

返回 artifact本机异步返回数据定价
❌❌URL, Snippet, Title, Page Content1M response tokens free

设置

集成位于langchain-community包并已添加到版本0.2.16:

%pip install --quiet -U "langchain-community>=0.2.16"

凭据

import getpass
import os

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

设置 LangSmith 以实现一流的可观测性也很有帮助（但不是必需的）：

# os.environ["LANGSMITH_TRACING"] = "true"
# os.environ["LANGSMITH_API_KEY"] = getpass.getpass()

实例

  • TODO：填写实例化参数

这里我们展示了如何使用

from langchain_community.tools import JinaSearch

tool = JinaSearch()
API 参考：JinaSearch

调用

使用 args 直接调用

print(tool.invoke({"query": "what is langgraph"})[:1000])
[{"title": "LangGraph", "link": "https://www.langchain.com/langgraph", "snippet": "<strong>LangGraph</strong> helps teams of all sizes, across all industries, from ambitious startups to established enterprises. \u201cLangChain is streets ahead with what they&#x27;ve put forward with <strong>LangGraph</strong>.", "content": "![Image 1](https://cdn.prod.website-files.com/65b8cd72835ceeacd4449a53/667b080e4b3ca12dc5d5d439_Langgraph%20UI-2.webp)\n\nControllable cognitive architecture for any task\n------------------------------------------------\n\nLangGraph's flexible API supports diverse control flows \u2013 single agent, multi-agent, hierarchical, sequential \u2013 and robustly handles realistic, complex scenarios.\n\nEnsure reliability with easy-to-add moderation and quality loops that prevent agents from veering off course.\n\n[See the docs](https://langchain-ai.github.io/langgraph/)\n\nDesigned for human-agent collaboration\n--------------------------------------\n\nWith built-in stat

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": "what is langgraph"},
    "id": "1",
    "name": tool.name,
    "type": "tool_call",
}
tool_msg = tool.invoke(model_generated_tool_call)
print(tool_msg.content[:1000])
[{"title": "LangGraph Tutorial: What Is LangGraph and How to Use It?", "link": "https://www.datacamp.com/tutorial/langgraph-tutorial", "snippet": "<strong>LangGraph</strong> <strong>is</strong> a library within the LangChain ecosystem that provides a framework for defining, coordinating, and executing multiple LLM agents (or chains) in a structured and efficient manner.", "content": "Imagine you're building a complex, multi-agent large language model (LLM) application. It's exciting, but it comes with challenges: managing the state of various agents, coordinating their interactions, and handling errors effectively. This is where LangGraph can help.\n\nLangGraph is a library within the LangChain ecosystem designed to tackle these challenges head-on. LangGraph provides a framework for defining, coordinating, and executing multiple LLM agents (or chains) in a structured manner.\n\nIt simplifies the development process by enabling the creation of cyclical graphs, which are essential for de

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：

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

prompt = ChatPromptTemplate(
    [
        ("system", "You are a helpful assistant."),
        ("human", "{user_input}"),
        ("placeholder", "{messages}"),
    ]
)


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("what's langgraph")
AIMessage(content="LangGraph is a library designed for building stateful, multi-actor applications with language models (LLMs). It is particularly useful for creating agent and multi-agent workflows. Compared to other LLM frameworks, LangGraph offers unique benefits such as cycles, controllability, and persistence. Here are some key points:\n\n1. **Stateful and Multi-Actor Applications**: LangGraph allows for the definition of flows involving cycles, essential for most agentic architectures. This is a significant differentiation from Directed Acyclic Graph (DAG)-based solutions.\n\n2. **Controllability**: The framework offers fine-grained control over both the flow and state of applications, which is crucial for creating reliable agents.\n\n3. **Persistence**: Built-in persistence is available, enabling advanced features like human-in-the-loop workflows and memory.\n\n4. **Human-in-the-Loop**: LangGraph supports interrupting graph execution for human approval or editing of the agent's next planned action.\n\n5. **Streaming Support**: The library can stream outputs as they are produced by each node, including token streaming.\n\n6. **Integration with LangChain**: While it integrates seamlessly with LangChain and LangSmith, LangGraph can also be used independently.\n\n7. **Inspiration and Interface**: LangGraph is inspired by systems like Pregel and Apache Beam, with its public interface drawing inspiration from NetworkX.\n\nLangGraph is designed to handle more complex agent applications that require cycles and state management, making it an ideal choice for developers seeking to build sophisticated LLM-driven applications. For more detailed information, you can visit their [official documentation](https://langchain-ai.github.io/langgraph/).", additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 338, 'prompt_tokens': 14774, 'total_tokens': 15112}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_157b3831f5', 'finish_reason': 'stop', 'logprobs': None}, id='run-420d16ed-535c-41c6-8814-2186b42be0f8-0', usage_metadata={'input_tokens': 14774, 'output_tokens': 338, 'total_tokens': 15112})

API 参考

有关所有 Jina 功能和配置的详细文档，请参阅 API 参考：https://python.langchain.com/api_reference/community/tools/langchain_community.tools.jina_search.tool.JinaSearch.html