A fully managed, end-to-end pipeline that automatically chunks, vectorizes, and enriches files from your directory or file store—removing the need to manage the ingestion logic around vector databases.
It's hard to find all relevant information to a topic fast. Standard keyword or semantic search often misses implicit knowledge if specific terms aren't in the chunk.
Knowledge bases quickly become polluted with stale or out-of-date document versions, leading to contradictory answers and hallucinations.
Fast entity recognition can provide richer context and allow for soft constraints, which typical ingestion pipelines fail to extract robustly.
Connect your directories or file stores. We automatically chunk, vectorize, and index your data—eliminating the heavy lifting of managing vector databases.
Lightweight LLMs selectively tag topics, extract metadata, and deprecate stale content.
Query using hybrid search with rich structural and semantic filters for perfect accuracy.
# Example: Fetchtable hybrid retrieval with enriched metadata
query = "Tell me all relevant information about cheese production in Wisconsin."
results = fetchtable.search(
query=query,
)
print(results.matches[0])
""" Output:
{
"inferred_topics": ["Agriculture", "Dairy Production", "Cheddar"],
"locations": ["Wisconsin", "Green Bay"],
"text": "The oldest cheese factory in Wisconsin is located in Green Bay and is still in operation today.",
"semantic_score": 0.941
}
"""Automatically tags each chunk with inferred topics it's about, even when those words don't appear in the text.
Detects when a newly ingested document contradicts or supersedes existing chunks and automatically deprecates stale content.
Extracts and attaches structured metadata (location, time, entity references) automatically at ingestion time.
Enables fuzzy matching to balance semantic similarity and location/time AND/OR structured metadata filtering.
Automatically cross-references data points across multiple ingested sources to visually establish trustworthiness and resolve conflicting claims.
Automatically scrapes and indexes all subpages within a domain for lightning-fast retrieval, completely bypassing the wait times of traditional agentic workflows.
We're rolling out access to select engineering teams. Join the waitlist to secure your spot for the private beta.