This library is excellent and I would really like to use it, so first of all thank you for making it available!

Unfortunately, there isnt enough documentation to make it particularly usable beyond a default deployment. For example it is not clear how I could:

1. send a query directly to es through the nboost proxy without reranking (this is useful for evaluating performance)
2. use my own mod

When using a Finder with a TfidfRetriever (InMemoryDocumentStore) and default TransformersReader all indices and scores are printed (see line 75 in tfidf.py), and there is no meta-data being inserted into the documents which are returned (line 96). I commented out the print call and added the following line to the Document constructor:

meta={'name':self.document_store.get_document_by_id(

Instead of:

{
  Get {
    Things {
      Article {
        title
        url
        wordCount
        HasAuthors {
          ... on Author {
            name
          }
          ... on Publication {
            name
          }
        }
      }
    }
  }
}

Use

{
  Get {
    Things {
      Article {
        title
        url
        

spacy

• A tour of awesome features of spaCy (part 1/2) - https://medium.com/eliiza-ai/a-tour-of-awesome-features-of-spacy-part-1-2-58b32425954f
• A tour of awesome features of spaCy (part 2/2) - https://medium.com/eliiza-ai/a-tour-of-awesome-features-of-spacy-part-2-2-d7bd628a81ce
• How to Train NER with Custom training data using spaCy. https://medium.com/@manivannan_data/how-to-train-

From @afolarin on October 4, 2016 17:46

We should aim to track which pathway a document goes through and what success/failure occur as it gets processed into XHTML. It will give us some basic understanding of the relative importance of things like the OCR pipeline