Dappier connects any LLM or your Agentic AI to real-time, rights-cleared, proprietary data from trusted sources, making your AI an expert in anything. Our specialized models include Real-Time Web Search, News, Sports, Financial Stock Market Data, Crypto Data, and exclusive content from premium publishers. Explore a wide range of data models in our marketplace at marketplace.dappier.com. Dappier delivers enriched, prompt-ready, and contextually relevant data strings, optimized for seamless integration with LangChain. Whether you’re building conversational AI, recommendation engines, or intelligent search, Dappier’s LLM-agnostic RAG models ensure your AI has access to verified, up-to-date data—without the complexity of building and managing your own retrieval pipeline.Documentation Index
Fetch the complete documentation index at: https://langchain.idochub.dev/llms.txt
Use this file to discover all available pages before exploring further.
DappierRetriever
This will help you get started with the Dappier retriever. For detailed documentation of all DappierRetriever features and configurations head to the API reference.Setup
Installlangchain-dappier and set environment variable DAPPIER_API_KEY.
Installation
This retriever lives in thelangchain-dappier package:
Instantiation
- data_model_id: str Data model ID, starting with dm_. You can find the available data model IDs at: Dappier marketplace.
- k: int Number of documents to return.
- ref: Optional[str] Site domain where AI recommendations are displayed.
- num_articles_ref: int Minimum number of articles from the ref domain specified. The rest will come from other sites within the RAG model.
- search_algorithm: Literal[ “most_recent”, “most_recent_semantic”, “semantic”, “trending” ] Search algorithm for retrieving articles.
- api_key: Optional[str] The API key used to interact with the Dappier APIs.