Jina Search
This notebook provides a quick overview for getting started with Jina tool. For detailed documentation of all Jina features and configurations head to the API reference.
Overview
Integration details
| Class | Package | Serializable | JS support | Package latest |
|---|---|---|---|---|
| JinaSearch | langchain-community | ❌ | ❌ |  |
Tool features
| Returns artifact | Native async | Return data | Pricing |
|---|---|---|---|
| ❌ | ❌ | URL, Snippet, Title, Page Content | 1M response tokens free |
Setup
The integration lives in the langchain-community package and was added in version 0.2.16:
%pip install --quiet -U "langchain-community>=0.2.16"
Credentials
import getpass
import os
if not os.environ.get("JINA_API_KEY"):
os.environ["JINA_API_KEY"] = getpass.getpass("Jina API key:\n")
It's also helpful (but not needed) to set up LangSmith for best-in-class observability:
# os.environ["LANGSMITH_TRACING"] = "true"
# os.environ["LANGSMITH_API_KEY"] = getpass.getpass()
Instantiation
- TODO: Fill in instantiation params
Here we show how to instantiate an instance of the Jina tool, with
from langchain_community.tools import JinaSearch
tool = JinaSearch()
API Reference:JinaSearch
Invocation
Invoke directly with args
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've put forward with <strong>LangGraph</strong>.", "content": "\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
Invoke with ToolCall
We can also invoke the tool with a model-generated ToolCall, in which case a ToolMessage will be returned:
# 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
Chaining
We can use our tool in a chain by first binding it to a tool-calling model and then calling it:
Select chat model:
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 reference
For detailed documentation of all Jina features and configurations head to the API reference: https://python.langchain.com/api_reference/community/tools/langchain_community.tools.jina_search.tool.JinaSearch.html
Related
- Tool conceptual guide
- Tool how-to guides