Building a Search Engine With Pre-Trained Transformers: A Step By Step Guide

We all use search engines. We search for information about the best item to purchase, a nice place to hangout, or to answer our questions about anything we want to know. 

We also rely heavily on search to check emails, documents and financial transactions. A lot of these search interactions happen through text or speech converted to voice input. This means a lot of language processing happens on search engines, so NLP plays a pretty important role in modern search engines

Let’s take a quick look into what happens when we search. When you search using a query, the search engine collects a ranked list of documents that matches the query. For this to happen, an “index” of documents and vocabulary used in them should be constructed first, and then used to search and rank results. One of the popular forms of indexing textual data and ranking search results for search is TF-IDF.

Recent development in deep learning models for NLP can be used for this. For example, Google recently started ranking search results and showing snippets using the BERT model. They claim that this has improved the quality and relevance of search results.

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There are 2 types of search engines:

• Generic search engines, such as Google and Bing, that crawl the web and aim to cover as much as possible by constantly looking for new webpages.
• Enterprise search engines, where our search space is restricted to a smaller set of already existing documents within an organization.

The second form of search is the most common use case you will encounter at any workplace. It’s clear when you look at the diagram below.

You can use state-of-the-art sentence embeddings with transformers, and use them in downstream tasks for semantic textual similarity.

In this article, we’ll explore how to build a vector-based search engine.

Why would you need a vector-based search engine?

Keyword-based search engine struggle with:

• Complex queries or words that have dual meaning.
• Long search queries.
• Users not familiar with important keywords to retrieve best results.

Vector-based (also known as semantic search) search solves these problems by finding a numerical representation of text queries using SOTA language models. Then it indexes them in high dimensional vector space, and measures how similar a query vector is to the indexed documents.

Lets see what the pre-trained models have to offer:

• They produce high quality embeddings, as they were trained on large amounts of text data.
• They don’t force you to create a custom tokenizer, as transformers come with their own methods.
• They’re really simple and handy to fine tune the model to your downstream task.

These models produce a fixed size vector for each token in the document.

Learn more

Training, Visualizing, and Understanding Word Embeddings: Deep Dive Into Custom Datasets

Now, let’s see how we can use a pre-trained BERT model to build a feature extractor for search engines.

Step 1: Load the pre-trained model

!wget https://storage.googleapis.com/bert_models/2018_10_18/uncased_L-12_H-768_A-12.zip
!unzip uncased_L-12_H-768_A-12.zip
!pip install bert-serving-server --no-deps

For this implementation, I’ll be using BERT uncased. There are other variations of BERT available – bert-as-a-service uses BERT as a sentence encoder and hosts it as a service via ZeroMQ, letting you map sentences into fixed length representations with just 2 lines of code. This is useful if you want to avoid additional latency and potential modes introduced by a client-server architecture.

Step 2: Optimizing the inference graph

To modify the model graph, we need some low level Tensorflow programming. Since we’re using bert-as-a-service, we can configure the inference graph using a simple CLI interface. 

(The version of tensorflow used for this implementation was tensorflow==1.15.2)

import os
import tensorflow as tf
import tensorflow.compat.v1 as tfc


sess = tfc.InteractiveSession()

from bert_serving.server.graph import optimize_graph
from bert_serving.server.helper import get_args_parser


# input dir
MODEL_DIR = '/content/uncased_L-12_H-768_A-12' #@param {type:"string"}
# output dir
GRAPH_DIR = '/content/graph/' #@param {type:"string"}
# output filename
GRAPH_OUT = 'extractor.pbtxt' #@param {type:"string"}

POOL_STRAT = 'REDUCE_MEAN' #@param ['REDUCE_MEAN', 'REDUCE_MAX', "NONE"]
POOL_LAYER = '-2' #@param {type:"string"}
SEQ_LEN = '256' #@param {type:"string"}


tf.io.gfile.mkdir(GRAPH_DIR)


carg = get_args_parser().parse_args(args=['-model_dir', MODEL_DIR,
                              '-graph_tmp_dir', GRAPH_DIR,
                              '-max_seq_len', str(SEQ_LEN),
                              '-pooling_layer', str(POOL_LAYER),
                              '-pooling_strategy', POOL_STRAT])

tmp_name, config = optimize_graph(carg)
graph_fout = os.path.join(GRAPH_DIR, GRAPH_OUT)

tf.gfile.Rename(
   tmp_name,
   graph_fout,
   overwrite=True
)
print("nSerialized graph to {}".format(graph_fout))

Take a look at a few parameters in the above snippet.

For each text sample, the BERT-base model encoding layer outputs a tensor of shape [sequence_len, encoder_dim], with one vector per input token. To get a fixed representation, we need to apply some sort of pooling.

POOL_STRAT parameter defines the pooling strategy applied to the encoder layer number POOL_LAYER. The default value ‘REDUCE_MEAN’ averages the vector for all tokens in the sequence. This particular strategy works best for most sentence-level tasks, when the model is not fine-tuned. Another option is NONE, in which case no pooling is applied.

SEQ_LEN has an impact on the maximum length of sequences processed by the model. If you want to increase the model inference speed almost linearly, you can give smaller values.

Running the above code snippet will put the model graph and weights into a GraphDef object, which will be serialized to a pbtxt file at GRAPH_OUT. The file will often be smaller than the pre-trained model, because the nodes and the variables required for training will be removed.

Step 3: Creating feature extractor

Let’s use the serialized graph to build a feature extractor using tf.Estimator API. We need to define 2 things: input_fn and model_fn.

input_fn gets data into the model. This includes executing the whole text preprocessing pipeline and preparing a feed_dict for BERT.
Each text sample is converted into a tf.Example instance, with the necessary features listed in the INPUT_NAMES. The bert_tokenizer object contains the WordPiece vocabulary and performs text processing. After that, the examples are regrouped by feature names in feed_dict.

import logging
import numpy as np

from tensorflow.python.estimator.estimator import Estimator
from tensorflow.python.estimator.run_config import RunConfig
from tensorflow.python.estimator.model_fn import EstimatorSpec
from tensorflow.keras.utils import Progbar

from bert_serving.server.bert.tokenization import FullTokenizer
from bert_serving.server.bert.extract_features import convert_lst_to_features

log = logging.getLogger('tensorflow')
log.setLevel(logging.INFO)
log.handlers = []

GRAPH_PATH = "/content/graph/extractor.pbtxt" #@param {type:"string"}
VOCAB_PATH = "/content/uncased_L-12_H-768_A-12/vocab.txt" #@param {type:"string"}

SEQ_LEN = 256 #@param {type:"integer"}

INPUT_NAMES = ['input_ids', 'input_mask', 'input_type_ids']
bert_tokenizer = FullTokenizer(VOCAB_PATH)

def build_feed_dict(texts):

   text_features = list(convert_lst_to_features(
       texts, SEQ_LEN, SEQ_LEN,
       bert_tokenizer, log, False, False))

   target_shape = (len(texts), -1)

   feed_dict = {}
   for iname in INPUT_NAMES:
       features_i = np.array([getattr(f, iname) for f in text_features])
       features_i = features_i.reshape(target_shape).astype("int32")
       feed_dict[iname] = features_i

   return feed_dict

tf.Estimators have a feature which makes them rebuild and reinitialize the whole computational graph at each call to the predict function. 

So, in order to avoid the overhead, we’ll pass the generator to the predict function, and the generator will yield the features to the model in a never ending loop.

def build_input_fn(container):

   def gen():
       while True:
         try:
           yield build_feed_dict(container.get())
         except:
           yield build_feed_dict(container.get())

   def input_fn():
       return tf.data.Dataset.from_generator(
           gen,
           output_types={iname: tf.int32 for iname in INPUT_NAMES},
           output_shapes={iname: (None, None) for iname in INPUT_NAMES})
   return input_fn

class DataContainer:
 def __init__(self):
   self._texts = None
  def set(self, texts):
   if type(texts) is str:
     texts = [texts]
   self._texts = texts

 def get(self):
   return self._texts

The model_fn contains the specification of the model. In our case, it’s loaded from the pbtxt file we saved in the previous step. The features are mapped explicitly to the corresponding input nodes via input_map.

def model_fn(features, mode):
   with tf.gfile.GFile(GRAPH_PATH, 'rb') as f:
       graph_def = tf.GraphDef()
       graph_def.ParseFromString(f.read())

   output = tf.import_graph_def(graph_def,
                                input_map={k + ':0': features[k] for k in INPUT_NAMES},
                                return_elements=['final_encodes:0'])

   return EstimatorSpec(mode=mode, predictions={'output': output[0]})

estimator = Estimator(model_fn=model_fn)

Now that we have things in place, we need to perform inference.

def batch(iterable, n=1):
   l = len(iterable)
   for ndx in range(0, l, n):
       yield iterable[ndx:min(ndx + n, l)]

def build_vectorizer(_estimator, _input_fn_builder, batch_size=128):
 container = DataContainer()
 predict_fn = _estimator.predict(_input_fn_builder(container), yield_single_examples=False)
  def vectorize(text, verbose=False):
   x = []
   bar = Progbar(len(text))
   for text_batch in batch(text, batch_size):
     container.set(text_batch)
     x.append(next(predict_fn)['output'])
     if verbose:
       bar.add(len(text_batch))

   r = np.vstack(x)
   return r
  return vectorize
bert_vectorizer = build_vectorizer(estimator, build_input_fn)

bert_vectorizer(64*['sample text']).shape
o/p: (64, 768)

Step 4: Exploring vector space with projector

Using the vectorizer, we will generate embeddings for articles from the Reuters-221578 benchmark corpus.

To explore and visualize the embedding vector space in 3D, we will use a dimensionality reduction technique called T-SNE.

First let’s get the article embeddings.

from nltk.corpus import reuters

import nltk
nltk.download("reuters")
nltk.download("punkt")

max_samples = 256
categories = ['wheat', 'tea', 'strategic-metal',
             'housing', 'money-supply', 'fuel']

S, X, Y = [], [], []

for category in categories:
 print(category)
  sents = reuters.sents(categories=category)
 sents = [' '.join(sent) for sent in sents][:max_samples]
 X.append(bert_vectorizer(sents, verbose=True))
 Y += [category] * len(sents)
 S += sents
 X = np.vstack(X)
X.shape

After running the above code, if you face any issues in collab that say: “Resource reuters not found. Please use the NLTK downloader to obtain the resource.”

…then run the following command, where the relative path after -d will give the location where the file will be unzipped:

!unzip /root/nltk_data/corpora/reuters.zip -d /root/nltk_data/corpora

The interactive visualizations of the generated embeddings are available on the Embedding Projector.

From the link, you can run t-SNE yourself, or load a checkpoint using the bookmark in the lower right corner (loading works on Chrome).

To reproduce the input files used for this visualization, run the code snippet below. Then download the files to your machine and upload to Projector.

with open("embeddings.tsv", "w") as fo:
 for x in X.astype('float'):
   line = "t".join([str(v) for v in x])
   fo.write(line+'n')

with open('metadata.tsv', 'w') as fo:
 fo.write("LabeltSentencen")
 for y, s in zip(Y, S):
   fo.write("{}t{}n".format(y, s))

Here’s what I captured using the Projector.

from IPython.display import HTML

HTML("""
<video width="900" height="632" controls>
 <source src="https://storage.googleapis.com/bert_resourses/reuters_tsne_hd.mp4" type="video/mp4">
</video>
""")

Building a supervised model with the generated features is straightforward:

from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
Xtr, Xts, Ytr, Yts = train_test_split(X, Y, random_state=34)

mlp = LogisticRegression()
mlp.fit(Xtr, Ytr)

print(classification_report(Yts, mlp.predict(Xts)))

Step 5: Building a search engine 

Let’s say we have a knowledge base of 50,000 text samples, and we need to quickly answer queries based on this data. How can we retrieve the result most similar to a query from a text database? One of the answers can be nearest neighbour search.

The search problem we’re solving here can be defined as follows:

Given a set of points S in vector space M and a query point Q ∈ M, find the closest point S to Q. There are multiple ways to define ‘closest’ in the vector space – we’ll use Euclidean distance.

To build a search engine for text, we’ll follow these steps:

1. Vectorize all samples from the knowledge base – that gives S.
2. Vectorize the query – that gives Q.
3. Compute euclidean distance D between Q and S.
4. Sort D in ascending order- providing indices of most similar samples.
5. Retrieve labels for said samples from the knowledge base.

We can create the placeholder for Q and S:

graph = tf.Graph()

sess = tf.InteractiveSession(graph=graph)

dim = X.shape[1]

Q = tf.placeholder("float", [dim])
S = tf.placeholder("float", [None, dim])

Define euclidean distance computation:

squared_distance = tf.reduce_sum(tf.pow(Q - S, 2), reduction_indices=1)
distance = tf.sqrt(squared_distance)

Get the most similar indices:

top_k = 10

top_neg_dists, top_indices = tf.math.top_k(tf.negative(distance), k=top_k)
top_dists = tf.negative(top_neg_dists)

from sklearn.metrics.pairwise import euclidean_distances

top_indices.eval({Q:X[0], S:X})

np.argsort(euclidean_distances(X[:1], X)[0])[:10]

Step 6: Accelerating search with math

In tensorflow this can be done as follows:

Q = tf.placeholder("float", [dim])
S = tf.placeholder("float", [None, dim])

Qr = tf.reshape(Q, (1, -1))

PP = tf.keras.backend.batch_dot(S, S, axes=1)
QQ = tf.matmul(Qr, tf.transpose(Qr))
PQ = tf.matmul(S, tf.transpose(Qr))

distance = PP - 2 * PQ + QQ
distance = tf.sqrt(tf.reshape(distance, (-1,)))

top_neg_dists, top_indices = tf.math.top_k(tf.negative(distance), k=top_k)

In the above formula PP and QQ are actually squared L2 norms of the respective vectors. If both vectors are L2 normalized, then:

PP = QQ = 1

Doing L2 normalization discards the information about the vector magnitude, which in many cases you don’t want to do.

Instead, we may notice that as long as the knowledge base stays the same – PP – its squared vector norm also stays the same. So, instead of recomputing it every time, we can just do it once and then use the precomputed result, further accelerating the distance computation.

Let’s bring this all together.

class L2Retriever:
 def __init__(self, dim, top_k=3, use_norm=False, use_gpu=True):
   self.dim = dim
   self.top_k = top_k
   self.use_norm = use_norm
   config = tf.ConfigProto(
       device_count = {'GPU': (1 if use_gpu else 0)}
   )
   self.session = tf.Session(config=config)

   self.norm = None
   self.query = tf.placeholder("float", [self.dim])
   self.kbase = tf.placeholder("float", [None, self.dim])

   self.build_graph()

 def build_graph():
   if self.use_norm:
     self.norm = tf.placeholder("float", [None, 1])

   distance = dot_l2_distances(self.kbase, self.query, self.norm)
   top_neg_dists, top_indices = tf.math.top_k(tf.negative(distance), k=self.top_k)
   top_dists = tf.negative(top_neg_dists)

   self.top_distances = top_dists
   self.top_indices = top_indices

 def predict(self, kbase, query, norm=None):
   query = np.squeeze(query)
   feed_dict = {self.query: query, self.kbase: kbase}
   if self.use_norm:
     feed_dict[self.norm] = norm

   I, D = self.session.run([self.top_indices, self.top_distances],
                           feed_dict=feed_dict)
   return I, D

def dot_l2_distances(kbase, query, norm=None):
 query = tf.reshape(query, (1, 1))

 if norm is None:
   XX = tf.keras.backend.batch_dot(kbase, kbase, axes=1)
 else:
   XX = norm
 YY = tf.matmul(query, tf.transpose(query))
 XY = tf.matmul(kbase, tf.transpose(query))

 distance = XX - 2 * XY + YY
 distance = tf.sqrt(tf.reshape(distance, (-1, 1)))

 return distance

We can use this implementation with any vectorizer model, not just BERT. It’s quite effective at the nearest neighbour retrieval, able to process dozens of requests per second on a 2-core colab CPU.

There are some extra aspects you need to consider when building machine learning applications:

• How do you ensure the scalability of  your solution?

• Pick the right framework/languages. 
• Use the right processors. 
• Collect and warehouse data. 
• Input pipeline.
• Model training.
• Distributed systems.
• Other optimizations.
• Resource Utilization and monitoring.
• Deploy.

• How do you train, test and deploy your model to production?

• Create a notebook instance that you can use to download and process your data.
• Prepare the data/preprocess it that you need to train your ML model and then upload the data (ex: Amazon S3).
• Use your training dataset to train your machine learning model. 
• Deploy the model to an endpoint, reformat and load the csv data, then run the model to create predictions.
• Evaluate the performance and accuracy of the ML model.

Might interest you

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Side note – make ML easier with experiment tracking 

One tool can take care of all your experiment tracking and collaboration needs –  neptune.ai 

Neptune records your entire experimentation process – exploratory notebooks, model training runs, code, hyperparameters, metrics, data versions, results, exploration visualizations, and more. 

It’s the metadata store for MLOps, built for research and production teams that run a lot of experiments. Focus on ML, and leave metadata management to Neptune. To get started with Neptune, visit their extensive guide.

An ML metadata store like Neptune is an essential part of the MLOps stack. It takes care of metadata management when you’re building your models.

It logs, stores, displays, organizes, compares and queries all metadata generated during the ML model lifecycle. 

You can use an ML metastore to track, organize, and compare everything you care about in your ML experiments. 

Neptune integrates with all of your favorite frameworks and tools – one of the most popular integrations is Tensorflow/Keras, done directly via TensorBoard.

Conclusion

The main area of exploration for search with BERT is similarity. Similarity for documents, for recommendations, and similarity between queries and documents for returning and ranking search results. 

If you can use similarity to solve this problem with highly accurate results, then you have a pretty great search for your product or application. 

I hope you learned something new here. Thanks for reading. Keep learning.