⚡ Langchain Linkup

A LangChain integration for the Linkup API, allowing easy integration with Linkup's services. 🔗

🌟 Features

• 🔗 Simple LangChain components to cover all Linkup API use cases.
• 🔍 Supports both standard and deep search queries.
• ⚡ Supports synchronous and asynchronous requests.
• 🔒 Handles authentication and request management.

📦 Installation

Simply install the LangChain integration using pip:

pip install langchain-linkup

🛠️ Usage

Setting Up Your Environment

1. 🔑 Obtain an API Key:

   Sign up on Linkup to get your API key.

2. ⚙️ Set-up the API Key:

   Option 1: Export the LINKUP_API_KEY environment variable in your shell before using the Linkup LangChain component.

   export LINKUP_API_KEY='YOUR_LINKUP_API_KEY'
   

   Option 2: Set the LINKUP_API_KEY environment variable directly within Python, using for instance os.environ or python-dotenv with a .env file (python-dotenv needs to be installed separately in this case), before creating the Linkup LangChain component.

   import os
   from langchain_linkup import LinkupSearchRetriever
   
   os.environ["LINKUP_API_KEY"] = "YOUR_LINKUP_API_KEY"
   # or dotenv.load_dotenv()
   retriever = LinkupSearchRetriever(...)
   ...
   

   Option 3: Pass the Linkup API key to the Linkup LangChain component when creating it.

   from langchain_linkup import LinkupSearchRetriever
   
   retriever = LinkupSearchRetriever(api_key="YOUR_LINKUP_API_KEY", ...)
   ...
   

📋 Example

All search queries can be used with two very different modes:

• with depth="standard", the search will be straightforward and fast, suited for relatively simple queries (e.g. "What's the weather in Paris today?")
• with depth="deep", the search will use an agentic workflow, which makes it in general slower, but it will be able to solve more complex queries (e.g. "What is the company profile of LangChain accross the last few years, and how does it compare to its concurrents?")

🔍 Linkup Search Retriever

A retriever is a LangChain component which simply retrieves documents based on a query. It is typically the first step of a RAG (Retrival Augmented Generation) pipeline. See this page for more information. The LinkupSearchRetriever makes available the Linkup API search as a LangChain retriever.

from langchain_linkup import LinkupSearchRetriever

# Initialize the LangChain component (API key can be read from the environment variable or passed as
# an argument)
retriever = LinkupSearchRetriever(
    depth="deep",  # "standard" or "deep"
)

# Perform a search query
documents = retriever.invoke(input="What is Linkup, the new French AI startup?")
print(documents)

⚒️ Linkup Search Tool

A tool is a LangChain component which enables agents to perform a specific task, like a web search. Tools are designed to be called autonomously by the agent, and their output is fed back to the agent, allowing them to perform some kind of reasoning based on the tool usage. See this page for more information. The LinkupSearchTool makes available the Linkup API search as a LangChain tool.

from langchain_linkup import LinkupSearchTool

# Initialize the LangChain component (API key can be read from the environment variable or passed as
# an argument)
tool = LinkupSearchTool(
    depth="deep",  # "standard" or "deep"
    output_type="searchResults",  # "searchResults", "sourcedAnswer" or "structured"
)

# Perform a search query
search_results = tool.invoke(input="What is Linkup, the new French AI startup?")
print(search_results)

📚 More Examples

See the examples/ directory for more contextualized examples and documentation, for instance on how to use the Linkup Search Retriever in a simple RAG pipeline.