[](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/15_TableQA.ipynb)
This tutorial shows you how to perform question-answering on tables using the `TableTextRetriever` or `ElasticsearchRetriever` as retriever node and the `TableReader` as reader node.
### Prepare environment
#### Colab: Enable the GPU runtime
Make sure you enable the GPU runtime to experience decent speed in this tutorial.
**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
# If you run this notebook on Google Colab, you might need to
# restart the runtime after installing haystack.
```
### Start an Elasticsearch server
You can start Elasticsearch on your local machine instance using Docker. If Docker is not readily available in your environment (e.g. in Colab notebooks), then you can manually download and execute Elasticsearch from source.
```python
# Recommended: Start Elasticsearch using Docker via the Haystack utility function
from haystack.utils import launch_es
launch_es()
```
```python
# In Colab / No Docker environments: Start Elasticsearch from source
To quickly demonstrate the capabilities of the `TableTextRetriever` and the `TableReader` we use a subset of 1000 tables of the [Open Table-and-Text Question Answering (OTT-QA) dataset](https://github.com/wenhuchen/OTT-QA).
Just as text passages, tables are represented as `Document` objects in Haystack. The content field, though, is a pandas DataFrame instead of a string.
```python
# Let's first fetch some tables that we want to query
# Here: 1000 tables from OTT-QA
from haystack.utils import fetch_archive_from_http
Retrievers help narrowing down the scope for the Reader to a subset of tables where a given question could be answered.
They use some simple but fast algorithm.
**Here:** We use the `TableTextRetriever` capable of retrieving relevant content among a database
of texts and tables using dense embeddings. It is an extension of the `DensePassageRetriever` and consists of three encoders (one query encoder, one text passage encoder and one table encoder) that create embeddings in the same vector space. More details on the `TableTextRetriever` and how it is trained can be found in [this paper](https://arxiv.org/abs/2108.04049).
**Alternatives:**
-`ElasticsearchRetriever` that uses BM25 algorithm
```python
from haystack.nodes.retriever import TableTextRetriever
retrieved_tables = retriever.retrieve("How many twin buildings are under construction?", top_k=5)
# Get highest scored table
print(retrieved_tables[0].content)
```
### Reader
The `TableReader` is based on TaPas, a transformer-based language model capable of grasping the two-dimensional structure of a table. It scans the tables returned by the retriever and extracts the anser. The available TableReader models can be found [here](https://huggingface.co/models?pipeline_tag=table-question-answering&sort=downloads).
**Notice**: The `TableReader` will return an answer for each table, even if the query cannot be answered by the table. Furthermore, the confidence scores are not useful as of now, given that they will *always* be very high (i.e. 1 or close to 1).
prediction = reader.predict(query="How many twin buildings are under construction?", documents=[table_doc])
print_answers(prediction, details="all")
```
The offsets in the `offsets_in_document` and `offsets_in_context` field indicate the table cells that the model predicts to be part of the answer. They need to be interpreted on the linearized table, i.e., a flat list containing all of the table cells.
In the `Answer`'s meta field, you can find the aggreagtion operator used to construct the answer (in this case `COUNT`) and the answer cells as strings.
The Retriever and the Reader can be sticked together to a pipeline in order to first retrieve relevant tables and then extract the answer.
**Notice**: Given that the `TableReader` does not provide useful confidence scores and returns an answer for each of the tables, the sorting of the answers might be not helpful.
