Building effective Retrieval-Augmented Generation (RAG) systems for enterprise knowledge bases requires careful engineering, particularly in the retrieval and ingestion phases. Keyword search often fails with large, inconsistent corpora, while pure vector search can over-retrieve irrelevant information. A hybrid approach combining keyword and vector search with techniques like reciprocal rank fusion offers a more robust solution, though it adds complexity. Designing an ingestion pipeline involves strategic chunking (e.g., small-to-big retrieval), selecting appropriate embedding models evaluated on domain-specific data, and structuring vector database schemas to preserve context. Data APIs for RAG should prioritize either precision or recall depending on the use case, with options like NewsCatcher focusing on broad coverage and Diffbot offering structured entity data. AI
IMPACT Optimizing retrieval and ingestion pipelines is crucial for reliable enterprise AI applications, impacting the accuracy and trustworthiness of LLM-powered tools.
RANK_REASON The articles discuss practical implementation details and tooling for RAG systems, rather than a novel model release or research breakthrough.
- Azure Cognitive Search
- Bing Web Search API
- Catchall
- Diffbot
- Diffbot Knowledge Graph API
- NewsCatcher
- NewsCatcher Web Search API
- retrieval-augmented generation
- Pinecone
- qdrant
- Weaviate
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