Skip to content
Branch: master
Find file History
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
..
Failed to load latest commit information.
js
README.md
RetrieveSentences.py
api.py

README.md

Setup Retrieve Sentences and end2end QA pipeline

1. Assuming you've already followed the main README instructions, just clone Anserini:

git clone https://github.com/castorini/Anserini.git

Your directory structure should look like

.
├── Anserini
├── Castor
├── Castor-data
└── models

2. Compile Anserini

cd Anserini
mvn package
cd ..

This creates anserini-0.0.1-SNAPSHOT.jar at Anserini/target

We highly recommend the use of virtualenv as the dependencies are subjected to frequent changes.

Install the dependency packages:

cd Castor
pip install -r requirements.txt

3. Download Dependencies

  • Download the TrecQA lucene index
  • Download the Google word2vec file from here

To run RetrieveSentences:

python ./anserini_dependency/RetrieveSentences.py

Possible parameters are:

option input format default description
-index string N/A Path of the Lucene index
-embeddings string "" Path of the word2vec index
-topics string "" topics file
-query string "" a single query
-hits [1, inf) 100 max number of hits to return
-scorer string Idf passage scores (Idf or Wmd)
-k [1, inf) 1 top-k passages to be retrieved

Note: Either a query or a topic must be passed in as an argument; they can't be both empty.

NB: The speech UI cannot be run in Ubuntu. To test the pipeline in Ubuntu, make the following changes:

  • Comment out the JavaScript part and run the Bash script
  • Make a REST API query to the endpoint using Postman, Curl etc.

To setup the demo

1. Installing libraries for demo

cd anserini_dependency/js
npm install
cd ../..

2. Flask

  • Flask is used as the server for the API
  • Copy config.cfg.example to config.cfg and make necessary changes, such as setting the index path and API keys.

3. Run the Demo

./run_ui.sh

Additional Notes

  • This is the documentation for the API call to send a question to the model and get back the predicted answer.
  • The request body fields are: question(required )num_hits(optional) and k(optional).

# REQUEST:
HTTP Method: POST
Endpoint: [host]:[port]/answer
Content-Type: application/json
text of body in raw format:
{
    "question": "What is the birthdate of Einstein?",
    "num_hits": 50,
    "k": 30
}
  • The response body contains answers which is a list of objects with two fields - passage, score.
# RESPONSE:
Content-Type: application/json
text of body in raw format:
{
  "answers": [
                {"passage": "Einstein was born in the 1800s", 'score': 0.976},
                {"passage": "Einstein was a physicist", 'score': 0.524}
            ]
}
You can’t perform that action at this time.