embedding-optimizer 0.0.1

Strategies for Efficient Data Embedding: Creating Embeddings Optimized for Accuracy - Creating Embeddings Optimized for Storage

Strategies for Efficient Data Embedding

Two approaches to generating optimized embeddings.

1. Creating Embeddings Optimized for Accuracy If you’re optimizing for accuracy, a good practice is to first summarize the entire document, then store the summary text and the embedding together. For the rest of the document, you can simply create overlapping chunks and store the embedding and the chunk text together.

2. Creating Embeddings Optimized for Storage If you’re optimizing for space, you can chunk the data, summarize each chunk, concatenate all the summarizations, then create an embedding for the final summary.

Example

import os

from langchain_community.document_loaders import TextLoader
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter
from langchain.docstore.document import Document
from langchain_community.vectorstores import FAISS
from openai import OpenAI

from embedding_optimizer.optimizer import EmbeddingOptimizer

# Set your OpenAI API Key
os.environ['OPENAI_API_KEY'] = ''

# Load your document
raw_document = TextLoader('test_data.txt').load()

# If your document is long, you might want to split it into chunks
text_splitter = CharacterTextSplitter(separator=".", chunk_size=1000, chunk_overlap=0)
documents = text_splitter.split_documents(raw_document)

embedding_optimizer = EmbeddingOptimizer(openai_api_key='')

# documents_optimizer = embedding_optimizer.optimized_documents_for_storage(raw_document[0].page_content, documents)
documents_optimizer = embedding_optimizer.optimized_documents_for_accuracy(raw_document[0].page_content, documents)

# Embed the document chunks and the summary
embedding_model = OpenAIEmbeddings(openai_api_key=os.environ["OPENAI_API_KEY"])

db = FAISS.from_documents(documents_optimizer, embedding_model)

# query it
query = "What motivated Alex to create the Function of Everything (FoE)?"
docs = db.similarity_search(query)

print(docs[0].page_content)

Issues

Feel free to submit issues and enhancement requests.