Build Your Own Search Engine

Code for the "Build Your Own Search Engine" workshop

What we will do:

Use Zoomcamp FAQ documents
Create a search engine for retreiving these documents
Later the results can be used for a Q&A RAG system
Reference implementation for text search

Workshop Outline

Preparing the Environment
Basics of Text Search
- Basics of Information Retrieval
- Introduction to vector spaces, bag of words, and TF-IDF
Implementing Basic Text Search
- TF-IDF scoring with sklearn
- Keyword filtering using pandas
- Creating a class for relevance search
Embeddings and Vector Search
- Vector embeddings
- Word2Vec and other approaches for word embeddings
- LSA (Latent Semantic Analysis) for document embeddings
- Implementing vector search with LSA
- BERT embeddings
Combining Text and Vector Search (5 minutes)
Practical Implementation Aspects and Tools (10 minutes)
- Real-world implementation tools:
  - inverted indexes for text search
  - LSH for vector search (using random projections)
- Technologies:
  - Lucene/Elasticsearch for text search
  - FAISS and and other vector databases

1. Preparing the environment

In the workshop, we'll use Github Codespaces, but you can use any env

We need to install the following libraries:

pip install requests pandas scikit-learn jupyter

Start jupyter:

jupyter notebook

Download the data:

import requests 

docs_url = 'https://github.com/alexeygrigorev/llm-rag-workshop/raw/main/notebooks/documents.json'
docs_response = requests.get(docs_url)
documents_raw = docs_response.json()

documents = []

for course in documents_raw:
    course_name = course['course']

    for doc in course['documents']:
        doc['course'] = course_name
        documents.append(doc)

Creating the dataframe:

import pandas as pd

df = pd.DataFrame(documents, columns=['course', 'section', 'question', 'text'])
df.head()

2. Basics of Text Search

Information Retrieval - The process of obtaining relevant information from large datasets based on user queries.
Vector Spaces - A mathematical representation where text is converted into vectors (points in space) allowing for quantitative comparison.
Bag of Words - A simple text representation model treating each document as a collection of words disregarding grammar and word order but keeping multiplicity.
TF-IDF (Term Frequency-Inverse Document Frequency) - A statistical measure used to evaluate how important a word is to a document in a collection or corpus. It increases with the number of times a word appears in the document but is offset by the frequency of the word in the corpus.

3. Implementing Basic Text Search

Let's implement it ourselves.

Keyword filtering

First, keyword filtering:

df[df.course == 'data-engineering-zoomcamp'].head()

Vectorization

For Count Vectorizer and TF-IDF we will first use a simple example

documents = [
    "Course starts on 15th Jan 2024",
    "Prerequisites listed on GitHub",
    "Submit homeworks after start date",
    "Registration not required for participation",
    "Setup Google Cloud and Python before course"
]

Let's use a count vectorizer first:

from sklearn.feature_extraction.text import CountVectorizer

cv = CountVectorizer(stop_words='english')
X = cv.fit_transform(docs_example)

names = cv.get_feature_names_out()

df_docs = pd.DataFrame(X.toarray(), columns=names).T
df_docs

This representation is called "bag of words" - here we ignore the order of words, just focus on the words themselves. In many cases this is sufficient and gives pretty good results already.

Now let's replace it with TfidfVectorizer:

from sklearn.feature_extraction.text import TfidfVectorizer

cv = TfidfVectorizer(stop_words='english')
X = cv.fit_transform(docs_example)

names = cv.get_feature_names_out()

df_docs = pd.DataFrame(X.toarray(), columns=names).T
df_docs.round(2)

Query-Document Similarity

We represent the query in the same vector space - i.e. using the same vectorizer:

query = "Do I need to know python to sign up for the January course?"

q = cv.transform([query])
q.toarray()

We can see the words of the query and the words of some document:

query_dict = dict(zip(names, q.toarray()[0]))
query_dict

doc_dict = dict(zip(names, X.toarray()[1]))
doc_dict

The more words in common - the better the matching score. Let's calculate it:

df_qd = pd.DataFrame([query_dict, doc_dict], index=['query', 'doc']).T

(df_qd['query'] * df_qd['doc']).sum()

This is a dot-product. So we can use matrix multiplication to compute the score:

X.dot(q.T).toarray()

Watch this linear algebra refresher if you're a bit rusty on matrix multiplication (don't worry - it's developer friendly)

Bottom line: it's a very fast and effective method of computing similarities

In practice, we usually use cosine similarity:

cosine_similarity(X, q)

The TF-IDF vectorizer already outputs a normalized vectors, so the results are identical. We won't go into details of how it works, but you can check "Introduction to Infromation Retrieval" if you want to learn more.

Vectorizing all the documents

Let's now do it for all the documents:

fields = ['section', 'question', 'text']
transformers = {}
matrices = {}

for field in fields:
    cv = TfidfVectorizer(stop_words='english', min_df=3)
    X = cv.fit_transform(df[field])

    transformers[field] = cv
    matrices[field] = X

transformers['text'].get_feature_names_out()
matrices['text']

Search

Let's now do search with the text field:

query = "I just singned up. Is it too late to join the course?"

q = transformers['text'].transform([query])
score = cosine_similarity(matrices['text'], q).flatten()

Let's do it only for the data engineering course:

mask = (df.course == 'data-engineering-zoomcamp').values
score = score * mask

And get the top results:

import numpy as np

idx = np.argsort(-score)[:10]

Note: np.argpartition is a more efficient way of doing the same thing

Get the docs:

df.iloc[idx].text

Search with all the fields & boosting + filtering

We can do it for all the fields. Let's also boost one of the fields - question - to give it more importance than to others

boost = {'question': 3.0}

score = np.zeros(len(df))

for f in fields:
    b = boost.get(f, 1.0)
    q = transformers[f].transform([query])
    s = cosine_similarity(matrices[f], q).flatten()
    score = score + b * s

And add filters (in this case, only one):

filters = {
    'course': 'data-engineering-zoomcamp'
}

for field, value in filters.items():
    mask = (df[field] == value).values
    score = score * mask

Getting the results:

idx = np.argsort(-score)[:10]
results = df.iloc[idx]
results.to_dict(orient='records')

Putting it all together

Let's create a class for us to use:

class TextSearch:

    def __init__(self, text_fields):
        self.text_fields = text_fields
        self.matrices = {}
        self.vectorizers = {}

    def fit(self, records, vectorizer_params={}):
        self.df = pd.DataFrame(records)

        for f in self.text_fields:
            cv = TfidfVectorizer(**vectorizer_params)
            X = cv.fit_transform(self.df[f])
            self.matrices[f] = X
            self.vectorizers[f] = cv

    def search(self, query, n_results=10, boost={}, filters={}):
        score = np.zeros(len(self.df))

        for f in self.text_fields:
            b = boost.get(f, 1.0)
            q = self.vectorizers[f].transform([query])
            s = cosine_similarity(self.matrices[f], q).flatten()
            score = score + b * s

        for field, value in filters.items():
            mask = (self.df[field] == value).values
            score = score * mask

        idx = np.argsort(-score)[:n_results]
        results = self.df.iloc[idx]
        return results.to_dict(orient='records')

Using it:

index = TextSearch(
    text_fields=['section', 'question', 'text']
)
index.fit(documents)

index.search(
    query='I just singned up. Is it too late to join the course?',
    n_results=5,
    boost={'question': 3.0},
    filters={'course': 'data-engineering-zoomcamp'}
)

You can fild the implementation here too if you want to use it: https://github.com/alexeygrigorev/minsearch

Note: this is a toy example for illustrating how relevance search works. It's not meant to be used in production.

4. Embeddings and Vector Search

Problem with text - only exact matches. How about synonyms?

What are Embeddings?

Conversion to Numbers: Embeddings transform different words, sentences and documents into dense vectors (arrays with numbers).
Capturing Similarity: They ensure similar items have similar numerical vectors, illustrating their closeness in terms of characteristics.
Dimensionality Reduction: Embeddings reduce complex characteristics into vectors.
Use in Machine Learning: These numerical vectors are used in machine learning models for tasks such as recommendations, text analysis, and pattern recognition.

SVD

Singular Value Decomposition is the simplest way to turn Bag-of-Words representation into embeddings

This way we still don't preserve the word order (because it wasn't in the Bag-of-Words representation) but we reduce dimensionality and capture synonyms.

We won't go into mathematics, it's sufficient to know that SVD "compresses" our input vectors in such a way that as much as possible of the original information is retained.

This compression is lossy compression - meaning that we won't be able to restore the 100% of the original vector, but the result is close enough.

Example with images:

Let's use the vectorizer for the "text" field and turn it into embeddings

from sklearn.decomposition import TruncatedSVD

X = matrices['text']
cv = transformers['text']

svd = TruncatedSVD(n_components=16)
X_emb = svd.fit_transform(X)

X_emb[0]

For query:

query = 'I just singned up. Is it too late to join the course?'

Q = cv.transform([query])
Q_emb = svd.transform(Q)
Q_emb[0]

Similarity between query and the document:

np.dot(X_emb[0], Q_emb[0])

Let's do it for all the documents. It's the same as previously, except we do it on embeddings, not on sparce matrices:

score = cosine_similarity(X_emb, Q_emb).flatten()
idx = np.argsort(-score)[:10]
list(df.loc[idx].text)

Non-Negative Matrix Factorization

SVD creates values with negative numbers. It's difficult to interpet them.

NMF (Non-Negative Matrix Factorization) is a similar concept, except for non-negative input matrices it produces non-negative results.

We can interpret each of the columns (features) of the embeddings as different topic/concents and to what extent this document is about this concept.

Let's use it for the documents:

nmf = NMF(n_components=16)
X_emb = nmf.fit_transform(X)
X_emb[0]

And the query:

Q = cv.transform([query])
Q_emb = nmf.transform(Q)
Q_emb[0]

We compute the similarity in the same way as previously:

score = cosine_similarity(X_emb, Q_emb).flatten()
idx = np.argsort(-score)[:10]
list(df.loc[idx].text)

BERT

The problem with the previous two approaches is that they don't take into account the word order. They just treat all the words separately (that's why it's called "Bag-of-Words")

BERT and other transformer models don't have this problem.

Let's create embeddings with BERT. We will use the Hugging Face library for that

pip install transformers tqdm

Use it:

import torch
from transformers import BertModel, BertTokenizer

tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
model = BertModel.from_pretrained("bert-base-uncased")
model.eval()  # Set the model to evaluation mode if not training

We need:

tokenizer - for turning text into vectors
model - for compressing the text into embeddings

First, we tokenize the text

texts = [
    "Yes, we will keep all the materials after the course finishes.",
    "You can follow the course at your own pace after it finishes"
]
encoded_input = tokenizer(texts, padding=True, truncation=True, return_tensors='pt')

Then we compute the embeddings:

with torch.no_grad():  # Disable gradient calculation for inference
    outputs = model(**encoded_input)
    hidden_states = outputs.last_hidden_state

Now we need to compress the embeddings:

sentence_embeddings = hidden_states.mean(dim=1)
sentence_embeddings.shape

And convert them to a numpy array

X_emb = sentence_embeddings.numpy()

Note that if use a GPU, first you need to move your tensors to CPU

sentence_embeddings_cpu = sentence_embeddings.cpu()

Let's now compute it for our texts. We'll do it in batches. First, we define a function for batching:

def make_batches(seq, n):
    result = []
    for i in range(0, len(seq), n):
        batch = seq[i:i+n]
        result.append(batch)
    return result

And use it:

texts = df['text'].tolist()
text_batches = make_batches(texts, 8)

all_embeddings = []

for batch in tqdm(text_batches):
    encoded_input = tokenizer(batch, padding=True, truncation=True, return_tensors='pt')

    with torch.no_grad():
        outputs = model(**encoded_input)
        hidden_states = outputs.last_hidden_state
        
        batch_embeddings = hidden_states.mean(dim=1)
        batch_embeddings_np = batch_embeddings.cpu().numpy()
        all_embeddings.append(batch_embeddings_np)

final_embeddings = np.vstack(all_embeddings)

Let's put it into a function:

def compute_embeddings(texts, batch_size=8):
    text_batches = make_batches(texts, 8)
    
    all_embeddings = []
    
    for batch in tqdm(text_batches):
        encoded_input = tokenizer(batch, padding=True, truncation=True, return_tensors='pt')
    
        with torch.no_grad():
            outputs = model(**encoded_input)
            hidden_states = outputs.last_hidden_state
            
            batch_embeddings = hidden_states.mean(dim=1)
            batch_embeddings_np = batch_embeddings.cpu().numpy()
            all_embeddings.append(batch_embeddings_np)
    
    final_embeddings = np.vstack(all_embeddings)
    return final_embeddings

And use it:

X_text = compute_embeddings(df['text'].tolist())

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