Question Answering Using Milvus and Cohere
This page illustrates how to create a question-answering system based on the SQuAD dataset using Milvus as the vector database and Cohere as the embedding system.
Before you begin
Code snippets on this page require pymilvus, cohere, pandas, numpy, and tqdm installed. Among these packages, pymilvus is the client for Milvus. If not present on your system, run the following commands to install them:
pip install pymilvus cohere pandas numpy tqdm
Then you need to load the modules to be used in this guide.
import cohere
import pandas
import numpy as np
from tqdm import tqdm
from pymilvus import connections, FieldSchema, CollectionSchema, DataType, Collection, utility
Parameters
Here we can find the parameters used in the following snippets. Some of them need to be changed to fit your environment. Beside each is a description of what it is.
FILE = 'https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v2.0.json' # The SQuAD dataset url
COLLECTION_NAME = 'question_answering_db' # Collection name
DIMENSION = 1024 # Embeddings size, cohere embeddings default to 4096 with the large model
COUNT = 5000 # How many questions to embed and insert into Milvus
BATCH_SIZE = 96 # How large of batches to use for embedding and insertion
MILVUS_HOST = 'localhost' # Milvus server URI
MILVUS_PORT = '19530'
COHERE_API_KEY = 'replace-this-with-the-cohere-api-key' # API key obtained from Cohere
To know more about the model and dataset used on this page, refer to co:here and SQuAD.
Prepare the dataset
In this example, we are going to use the Stanford Question Answering Dataset (SQuAD) as our truth source for answering questions. This dataset comes in the form of a JSON file and we are going to use pandas to load it in.
# Download the dataset
dataset = pandas.read_json(FILE)
# Clean up the dataset by grabbing all the question answer pairs
simplified_records = []
for x in dataset['data']:
for y in x['paragraphs']:
for z in y['qas']:
if len(z['answers']) != 0:
simplified_records.append({'question': z['question'], 'answer': z['answers'][0]['text']})
# Grab the amount of records based on COUNT
simplified_records = pandas.DataFrame.from_records(simplified_records)
simplified_records = simplified_records.sample(n=min(COUNT, len(simplified_records)), random_state = 42)
# Check the length of the cleaned dataset matches count
print(len(simplified_records))
The output should be the number of records in the dataset
5000
Create a collection
This section deals with Milvus and setting up the database for this use case. Within Milvus, we need to set up a collection and index it.
# Connect to Milvus Database
connections.connect(host=MILVUS_HOST, port=MILVUS_PORT)
# Remove collection if it already exists
if utility.has_collection(COLLECTION_NAME):
utility.drop_collection(COLLECTION_NAME)
# Create collection which includes the id, title, and embedding.
fields = [
FieldSchema(name='id', dtype=DataType.INT64, is_primary=True, auto_id=True),
FieldSchema(name='original_question', dtype=DataType.VARCHAR, max_length=1000),
FieldSchema(name='answer', dtype=DataType.VARCHAR, max_length=1000),
FieldSchema(name='original_question_embedding', dtype=DataType.FLOAT_VECTOR, dim=DIMENSION)
]
schema = CollectionSchema(fields=fields)
collection = Collection(name=COLLECTION_NAME, schema=schema)
# Create an IVF_FLAT index for collection.
index_params = {
'metric_type':'IP',
'index_type':"IVF_FLAT",
'params':{"nlist": 1024}
}
collection.create_index(field_name="original_question_embedding", index_params=index_params)
collection.load()
Insert data
Once we have the collection set up we need to start inserting our data. This is done in three steps
- reading the data,
- embedding the original questions, and
- inserting the data into the collection we've just created on Milvus.
In this example, the data includes the original question, the original question's embedding, and the answer to the original question.
# Set up a co:here client.
cohere_client = cohere.Client(COHERE_API_KEY)
# Extract embeddings from questions using Cohere
def embed(texts, input_type):
res = cohere_client.embed(texts, model='embed-multilingual-v3.0', input_type=input_type)
return res.embeddings
# Insert each question, answer, and qustion embedding
total = pandas.DataFrame()
for batch in tqdm(np.array_split(simplified_records, (COUNT/BATCH_SIZE) + 1)):
questions = batch['question'].tolist()
embeddings = embed(questions, "search_document")
data = [
{
'original_question': x,
'answer': batch['answer'].tolist()[i],
'original_question_embedding': embeddings[i]
} for i, x in enumerate(questions)
]
collection.insert(data=data)
time.sleep(10)
Ask questions
Once all the data is inserted into the Milvus collection, we can ask the system questions by taking our question phrase, embedding it with Cohere, and searching with the collection.
Searches performed on data right after insertion might be a little slower as searching unindexed data is done in a brute-force manner. Once the new data is automatically indexed, the searches will speed up.
# Search the cluster for an answer to a question text
def search(text, top_k = 5):
# AUTOINDEX does not require any search params
search_params = {}
results = collection.search(
data = embed([text], "search_query"), # Embeded the question
anns_field='original_question_embedding',
param=search_params,
limit = top_k, # Limit to top_k results per search
output_fields=['original_question', 'answer'] # Include the original question and answer in the result
)
distances = results[0].distances
entities = [ x.entity.to_dict()['entity'] for x in results[0] ]
ret = [ {
"answer": x[1]["answer"],
"distance": x[0],
"original_question": x[1]['original_question']
} for x in zip(distances, entities)]
return ret
# Ask these questions
search_questions = ['What kills bacteria?', 'What\'s the biggest dog?']
# Print out the results in order of [answer, similarity score, original question]
ret = [ { "question": x, "candidates": search(x) } for x in search_questions ]
The output should be similar to the following:
# Output
#
# [
# {
# "question": "What kills bacteria?",
# "candidates": [
# {
# "answer": "farming",
# "distance": 0.6261022090911865,
# "original_question": "What makes bacteria resistant to antibiotic treatment?"
# },
# {
# "answer": "Phage therapy",
# "distance": 0.6093736886978149,
# "original_question": "What has been talked about to treat resistant bacteria?"
# },
# {
# "answer": "oral contraceptives",
# "distance": 0.5902313590049744,
# "original_question": "In therapy, what does the antibacterial interact with?"
# },
# {
# "answer": "slowing down the multiplication of bacteria or killing the bacteria",
# "distance": 0.5874154567718506,
# "original_question": "How do antibiotics work?"
# },
# {
# "answer": "in intensive farming to promote animal growth",
# "distance": 0.5667208433151245,
# "original_question": "Besides in treating human disease where else are antibiotics used?"
# }
# ]
# },
# {
# "question": "What's the biggest dog?",
# "candidates": [
# {
# "answer": "English Mastiff",
# "distance": 0.7875324487686157,
# "original_question": "What breed was the largest dog known to have lived?"
# },
# {
# "answer": "forest elephants",
# "distance": 0.5886962413787842,
# "original_question": "What large animals reside in the national park?"
# },
# {
# "answer": "Rico",
# "distance": 0.5634892582893372,
# "original_question": "What is the name of the dog that could ID over 200 things?"
# },
# {
# "answer": "Iditarod Trail Sled Dog Race",
# "distance": 0.546872615814209,
# "original_question": "Which dog-sled race in Alaska is the most famous?"
# },
# {
# "answer": "part of the family",
# "distance": 0.5387814044952393,
# "original_question": "Most people today describe their dogs as what?"
# }
# ]
# }
# ]