Answers 2.4

Theory

The primary purpose of converting textual information into embeddings is to transform text into numerical vectors in a way that captures semantic meaning, enabling computers to process and understand textual data more effectively.
Embeddings capture semantic similarity by positioning words or sentences with similar meanings closer to each other in a high-dimensional vector space, facilitating tasks like semantic search.
The process of creating word embeddings involves training models on large text corpora to learn vector representations of words based on their context, ensuring that words used in similar contexts have similar vector representations.
In semantic search, embeddings allow the system to understand the intent and contextual meaning behind queries, enabling it to retrieve documents that are semantically related to the query, even without exact keyword matches.
Document embeddings represent the semantic essence of entire documents, while query embeddings represent the semantic intent of search queries. Comparing these embeddings enables the identification of documents that are semantically relevant to the query.
A vector store is a database optimized for storing and retrieving high-dimensional vector data (embeddings), crucial for performing efficient similarity searches in applications like semantic search.
When choosing a vector store, considerations include the size of the dataset, persistence requirements, and the specific use case, such as whether the application is for research, development, or production use.
Chroma is suitable for rapid prototyping and small datasets due to its in-memory nature, which allows for fast data retrieval. Its limitations include lack of persistence and scalability for larger datasets.
The workflow involves document splitting, embedding generation, vector store indexing, query processing, and response generation, collectively enabling efficient semantic search capabilities.
Document splitting improves search granularity and relevance by breaking down documents into semantically coherent chunks, allowing for more precise matching of document parts to queries.
Embedding generation for document chunks involves mapping text to high-dimensional vectors, capturing the semantic features of the text and enabling efficient computational processing and comparison.
Vector store indexing allows for the efficient storage and retrieval of embeddings, enabling quick similarity searches to find document chunks most relevant to a given query.
Query processing involves generating an embedding for the user's query and searching the vector store for document embeddings that are most similar, using metrics like Euclidean distance or cosine similarity.
Response generation enhances user experience by using retrieved document chunks and the original query to generate coherent and contextually relevant responses, leveraging large language models.
Setting up the environment involves importing necessary libraries, setting up API keys, and configuring the system to ensure it is properly prepared for embedding and vector store operations.
Document loading and splitting are crucial for managing textual data more effectively, breaking it down into smaller, manageable, and semantically meaningful chunks for better processing.
Generating embeddings transforms textual information into numerical vectors that encapsulate semantic meanings, with similarity demonstrated through metrics like the dot product between vectors.
When setting up Chroma, considerations include the directory for persistence, clearing existing data for a fresh start, and initializing the store with document splits and embeddings for retrieval.
Similarity searches facilitate the retrieval of relevant document chunks by comparing the query embedding with document embeddings to find the closest matches based on semantic similarity.
Potential failure modes in semantic searches include duplicate entries and irrelevant document inclusion. Addressing these involves refining the search process to ensure relevance and distinctiveness of results.

Practice

def generate_embeddings(sentences):
    """
    Generates a simple placeholder embedding for each sentence based on its length.

    Parameters:
    - sentences (list of str): A list of sentences to generate embeddings for.

    Returns:
    - list of int: A list of embeddings, where each embedding is the length of the corresponding sentence.
    """
    return [len(sentence) for sentence in sentences]

def cosine_similarity(vector_a, vector_b):
    """
    Calculates the cosine similarity between two vectors.

    Parameters:
    - vector_a (list of float): The first vector.
    - vector_b (list of float): The second vector.

    Returns:
    - float: The cosine similarity between vector_a and vector_b.
    """
    dot_product = sum(a*b for a, b in zip(vector_a, vector_b))
    magnitude_a = sum(a**2 for a in vector_a) ** 0.5
    magnitude_b = sum(b**2 for b in vector_b) ** 0.5
    return dot_product / (magnitude_a * magnitude_b)

# Example usage:
sentences = ["Hello, world!", "This is a longer sentence.", "Short"]
embeddings = generate_embeddings(sentences)
print("Embeddings:", embeddings)

vector_a = [1, 2, 3]
vector_b = [2, 3, 4]
similarity = cosine_similarity(vector_a, vector_b)
print("Cosine Similarity:", similarity)

def cosine_similarity(vector_a, vector_b):
    """Calculates the cosine similarity between two vectors."""
    dot_product = sum(a*b for a, b in zip(vector_a, vector_b))
    magnitude_a = sum(a**2 for a in vector_a) ** 0.5
    magnitude_b = sum(b**2 for b in vector_b) ** 0.5
    if magnitude_a == 0 or magnitude_b == 0:
        return 0  # Avoid division by zero
    return dot_product / (magnitude_a * magnitude_b)

class SimpleVectorStore:
    def __init__(self):
        self.vectors = []

    def add_vector(self, vector):
        """Adds a vector to the store."""
        self.vectors.append(vector)

    def find_most_similar(self, query_vector):
        """Finds and returns the most similar vector to the query_vector."""
        similarities = [cosine_similarity(query_vector, vector) for vector in self.vectors]
        if not similarities:
            return None
        max_index = similarities.index(max(similarities))
        return self.vectors[max_index]

import sys

def split_text_into_chunks(text, chunk_size):
    """Splits the given text into chunks of the specified size."""
    return [text[i:i+chunk_size] for i in range(0, len(text), chunk_size)]

def load_and_print_chunks(file_path, chunk_size):
    """Loads text from a file, splits it into chunks, and prints each chunk."""
    try:
        with open(file_path, 'r') as file:
            text = file.read()
            chunks = split_text_into_chunks(text, chunk_size)
            for i, chunk in enumerate(chunks, 1):
                print(f"Chunk {i}:\n{chunk}\n{'-'*50}")
    except FileNotFoundError:
        print(f"Error: File '{file_path}' not found.")
    except Exception as e:
        print(f"An unexpected error occurred: {e}")

if __name__ == "__main__":
    if len(sys.argv) != 3:
        print("Usage: python script.py <file_path> <chunk_size>")
        sys.exit(1)
    file_path = sys.argv[1]
    chunk_size = int(sys.argv[2])
    load_and_print_chunks(file_path, chunk_size)

# Assuming the SimpleVectorStore and cosine_similarity function are defined as shown previously

def generate_query_embedding(query):
    """Generates a simple placeholder embedding for the query based on its length."""
    # This is a placeholder. In a real scenario, you would use a model to generate embeddings.
    return [len(query)]

def query_processing(store, query):
    """Processes a query by generating an embedding, searching the vector store, and printing the most similar document chunk."""
    query_embedding = generate_query_embedding(query)
    most_similar = store.find_most_similar(query_embedding)
    if most_similar is not None:
        print("The most similar document chunk is:", most_similar)
    else:
        print("No document chunks found.")

def remove_duplicates(document_chunks):
    """Removes duplicate document chunks based on an exact match."""
    unique_chunks = []
    for chunk in document_chunks:
        if chunk not in unique_chunks:
            unique_chunks.append(chunk)
    return unique_chunks

# Initialization of the SimpleVectorStore
store = SimpleVectorStore()

# Placeholder for document chunks and their embeddings
document_chunks = ["Document chunk 1", "Document chunk 2", "Document chunk 3"]
# Simulating embeddings for these chunks as placeholders (for example, based on their length)
document_embeddings = [[len(chunk)] for chunk in document_chunks]

# Adding document embeddings to the store
for embedding in document_embeddings:
    store.add_vector(embedding)

# Conducting a similarity search with a sample query
query = "Document"
query_embedding = generate_query_embedding(query)

# Finding the most similar document chunks based on cosine similarity
similarities = [(cosine_similarity(query_embedding, doc_embedding), idx) for idx, doc_embedding in enumerate(document_embeddings)]
similarities.sort(reverse=True)  # Sort by similarity in descending order
top_n_indices = [idx for _, idx in similarities[:3]]  # Get the indices of the top 3 most similar chunks

# Printing the IDs or contents of the top 3 most similar document chunks
print("Top 3 most similar document chunks:")
for idx in top_n_indices:
    print(f"{idx + 1}: {document_chunks[idx]}")

def embed_and_store_documents(document_chunks):
    """
    Generates embeddings for each document chunk and stores them in a SimpleVectorStore.

    Parameters:
    - document_chunks (list of str): A list of document chunks as strings.

    Returns:
    - SimpleVectorStore: The vector store initialized with document embeddings.
    """
    store = SimpleVectorStore()
    for chunk in document_chunks:
        # Placeholder for generating an embedding based on the chunk's length
        embedding = [len(chunk)]
        store.add_vector(embedding)
    return store

import json

def save_vector_store(store, filepath):
    """
    Saves the state of a SimpleVectorStore to a file.

    Parameters:
    - store (SimpleVectorStore): The vector store to save.
    - filepath (str): The path to the file where the store should be saved.
    """
    with open(filepath, 'w') as file:
        json.dump(store.vectors, file)

def load_vector_store(filepath):
    """
    Loads the state of a SimpleVectorStore from a file.

    Parameters:
    - filepath (str): The path to the file from which to load the store.

    Returns:
    - SimpleVectorStore: The loaded vector store.
    """
    store = SimpleVectorStore()
    with open(filepath, 'r') as file:
        store.vectors = json.load(file)
    return store

def vector_store_persistence():
    """Demonstrates saving and loading the state of a SimpleVectorStore."""
    store = SimpleVectorStore()  # Assume this is already populated
    filepath = 'vector_store.json'

    # Example of saving and loading
    save_vector_store(store, filepath)
    loaded_store = load_vector_store(filepath)
    print("Vector store loaded with vectors:", loaded_store.vectors)

def evaluate_search_accuracy(queries, expected_chunks):
    """
    Evaluates the accuracy of similarity searches for a list of queries against expected results.

    Parameters:
    - queries (list of str): A list of query strings.
    - expected_chunks (list of str): A list of expected most similar document chunks corresponding to each query.

    Returns:
    - float: The accuracy of the search results.
    """
    correct = 0
    store = embed_and_store_documents(expected_chunks + list(set(expected_chunks) - set(queries)))  # Embedding and storing documents plus some additional to ensure uniqueness

    for query, expected in zip(queries, expected_chunks):
        query_embedding = generate_query_embedding(query)
        most_similar = store.find_most_similar(query_embedding)
        # Assuming the expected_chunks are the document embeddings stored in the same order
        if most_similar and most_similar == [len(expected)]:
            correct += 1

    accuracy = correct / len(queries)
    return accuracy

# Assuming the embed_and_store_documents, generate_query_embedding, and SimpleVectorStore are implemented as described