Jina 搜索

此笔记本简要概述了 Jina 工具的入门方法。有关所有 Jina 功能和配置的详细文档，请访问 API 参考.

概述

集成详细信息

类	包	可序列化	JS 支持	最新包
JinaSearch	langchain-community	❌	❌

工具功能

返回工件	原生异步	返回数据	定价
❌	❌	URL、代码段、标题、页面内容	100 万个响应标记免费

设置

集成位于 langchain-community 包中，并在版本 0.2.16 中添加。

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

凭据

import getpass
import os

设置 LangSmith 也很有用（但不是必需的），可以实现一流的可观察性。

# os.environ["LANGCHAIN_TRACING_V2"] = "true"
# os.environ["LANGCHAIN_API_KEY"] = getpass.getpass()

实例化

TODO：填写实例化参数

这里演示了如何实例化 Jina 工具的实例，其中

from langchain_community.tools import JinaSearch

tool = JinaSearch()

API 参考：JinaSearch

调用

直接使用参数调用

print(tool.invoke({"query": "what is langgraph"})[:1000])

[{"title": "LangGraph", "link": "https://www.langchain.ac.cn/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://github.langchain.ac.cn/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 调用工具，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

我们可以通过先将工具绑定到工具调用模型，然后调用它，在链中使用我们的工具。

pip install -qU langchain-openai

import getpass
import os

os.environ["OPENAI_API_KEY"] = getpass.getpass()

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(model="gpt-4o-mini")

pip install -qU langchain-anthropic

import getpass
import os

os.environ["ANTHROPIC_API_KEY"] = getpass.getpass()

from langchain_anthropic import ChatAnthropic

llm = ChatAnthropic(model="claude-3-5-sonnet-20240620")

pip install -qU langchain-openai

import getpass
import os

os.environ["AZURE_OPENAI_API_KEY"] = getpass.getpass()

from langchain_openai import AzureChatOpenAI

llm = AzureChatOpenAI(
    azure_endpoint=os.environ["AZURE_OPENAI_ENDPOINT"],
    azure_deployment=os.environ["AZURE_OPENAI_DEPLOYMENT_NAME"],
    openai_api_version=os.environ["AZURE_OPENAI_API_VERSION"],
)

pip install -qU langchain-google-vertexai

import getpass
import os

os.environ["GOOGLE_API_KEY"] = getpass.getpass()

from langchain_google_vertexai import ChatVertexAI

llm = ChatVertexAI(model="gemini-1.5-flash")

pip install -qU langchain-cohere

import getpass
import os

os.environ["COHERE_API_KEY"] = getpass.getpass()

from langchain_cohere import ChatCohere

llm = ChatCohere(model="command-r-plus")

pip install -qU langchain-nvidia-ai-endpoints

import getpass
import os

os.environ["NVIDIA_API_KEY"] = getpass.getpass()

from langchain import ChatNVIDIA

llm = ChatNVIDIA(model="meta/llama3-70b-instruct")

pip install -qU langchain-fireworks

import getpass
import os

os.environ["FIREWORKS_API_KEY"] = getpass.getpass()

from langchain_fireworks import ChatFireworks

llm = ChatFireworks(model="accounts/fireworks/models/llama-v3p1-70b-instruct")

pip install -qU langchain-groq

import getpass
import os

os.environ["GROQ_API_KEY"] = getpass.getpass()

from langchain_groq import ChatGroq

llm = ChatGroq(model="llama3-8b-8192")

pip install -qU langchain-mistralai

import getpass
import os

os.environ["MISTRAL_API_KEY"] = getpass.getpass()

from langchain_mistralai import ChatMistralAI

llm = ChatMistralAI(model="mistral-large-latest")

pip install -qU langchain-openai

import getpass
import os

os.environ["TOGETHER_API_KEY"] = getpass.getpass()

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    base_url="https://api.together.xyz/v1",
    api_key=os.environ["TOGETHER_API_KEY"],
    model="mistralai/Mixtral-8x7B-Instruct-v0.1",
)

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")

API 参考：ChatPromptTemplate | RunnableConfig | chain

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://github.langchain.ac.cn/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，请访问 API 参考： https://python.langchain.ac.cn/v0.2/api_reference/community/tools/langchain_community.tools.jina_search.tool.JinaSearch.html

工具概念指南
工具操作指南

Jina 搜索

概述

集成详细信息

工具功能

设置

凭据

实例化

调用

直接使用参数调用

使用 ToolCall 调用

链接

API 参考

此页面对您有帮助吗？

您也可以留下详细的反馈在 GitHub 上.

Jina 搜索

概述​

集成详细信息​

工具功能​

设置​

凭据​

实例化​

调用​

直接使用参数调用​

使用 ToolCall 调用​

链接​

API 参考​

相关​

此页面对您有帮助吗？

您也可以留下详细的反馈 在 GitHub 上.

概述

集成详细信息

工具功能

设置

凭据

实例化

调用

直接使用参数调用

使用 ToolCall 调用

链接

API 参考

相关

您也可以留下详细的反馈在 GitHub 上.