Essofore - a document store powered by semantic search

What is Essofore?

Essofore is a document store powered by a semantic search engine that understands the meaning of your query rather than searching for keywords in your query.

Who is it for?

You are a developer in a large organization in charge of developing their enterprise search or RAG applications.

Top 3 Problems Solved

• Developer Productivity: Enterprise grade without the over-engineering associated with enterprise software.
• Costs: Flat pricing per month. Use as much as you like.
• Security: Your data is never sent outside your perimeter. Do you trust OpenAI, Microsoft, Google will not mine your data? Have you read their data leak and compromise reports [1, 2, 3]? What compensation are they providing you if your data is hacked? The best way to protect your data is not to give it away.

Top 10 Highlights

• Document database powered by AI search that relieves developers from having to work with multiple fragmented services.
• No data is ever sent to OpenAI or any other 3rd party. 100% secure, confidential, on-prem and non-SaaS.
• Use it as the retriever in your RAG pipeline or to power your enterprise search.
• Simple to use, reliable and developer friendly API backed by an OpenAPI specification
• No complex setup. Ready-to-use out of the box with no explicit configuration. No need to install a dozen dependencies just to get started.
• Index approx. 100MB of text per GB of RAM (entire works of Shakespeare < 6MB of text)
• Ingest 3-23 GB of data / day (single node). 4GB = 1B tokens. 3GB w/ AMD EPYC-Rome Processor w/ 14 vCPU. 23 GB using NVIDIA RTX A6000 GPU.
• Leverage multiple CPUs with linear scaling of throughput (vs. # of threads) and index size (vs. amount of data)
• State-of-the-art support for deletion requiring no vacuum process (a.k.a. lazy deletions)
• Support for text, pdf, html and MS Office (doc, xls, ppt) formats

Example

    from essofore_client.api.collections import create_collection, upload_document, search
    from essofore_client.models.document_type import DocumentType
    create_collection.sync_detailed(collection_id="1", title="Sherlock Holmes", client=client)
    with open(file, 'rb') as f:
      upload_document.sync_detailed(client=client,
                                  collection_id = "1",
                                  document_id = "pg2350",
                                  title="The Hound of the Baskervilles", 
                                  doc_type=DocumentType.TXT,
                                  source_url = url,
                                  body=Blob(f))
   response = search.sync_detailed(client=client, collection_id="1", q="Who is Sherlock Holmes?", k=5)
   for r in response.parsed:
       print_search_result(r)    
  

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