# Document Reading

Neural Document reading is a task where a Deep learning Model finds an answer to a query from a document/context. 

Components of Document Reader

1. Elastic Search as a Document store

   The documents are preprocessed and are stored in elastic search indexes
2. Neural/statistical Document Ranker

   Document Ranker retrieves Top n documents out of m documents (m>>n) which would most likely have an answer for the incoming query. The most likely documents that would contain answer are chosen based on: 

   * Neural approach: Semantic similarity of query embedding with the already existing Document embeddings 
   * Statistical approach: Based on word overlap in question to that of document
3. Transformer Reader
   * Extractive Reader

     we feed question and context (the list of documents shortlisted by the document ranker) as input to Transformer. The Embeddings generated from the transformer layers are passed through two separate Feed-Forward neural networks. One of the Networks predicts the start token index and the other predicts the end Token index. The Probability distribution over the words in documents (for both start and end token) is used to retrieve the answer
   * Generative Reader

     Generates a novel answer (not necessarily a span of text) from the document

![Document Reader](/files/dOujgIRNSDoBlS9Y6zV1)


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# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://readme.dhee.ai/concepts-we-work-on/document-reading.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.