embeddings.py

# Model	              Dimensions	Cost per 1M tokens	Best For
# text-embedding-3-small	1536	    $0.02	            General use
# text-embedding-3-large	3072	    $0.13	                High accuracy
# text-embedding-ada-002	1536	    $0.10	                Legacy

from langchain_openai.embeddings import OpenAIEmbeddings
from langchain_community.embeddings import HuggingFaceEmbeddings
from dotenv import load_dotenv

load_dotenv()

embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2") # 384 dimensions


# Ollama 
from langchain_ollama import OllamaEmbeddings
embeddings = OllamaEmbeddings(model="llama2-7b-embedding-q4_0")















# embeddings = OpenAIEmbeddings(model="text-embedding-3-small")

# # single text
# text = "This is a sample text to be embedded."
# embedding = embeddings.embed_query(text)
# # print(f"Embedding for single text: {embedding}")

# print(len(embedding))  # Should print 1536 for text-embedding-3-small


# # multiple texts
# embeds = embeddings.embed_documents(
#     ["This is the first document.", "This is the second document."]
# )
# print(f"Embeddings for multiple texts: {embeds}")
# print(f"Number of embeddings returned: {len(embeds)}")  # Should print 2
# print(
#     f"Length of each embedding: {len(embeds[0])}"
# )  # Should print 1536 for text-embedding-3-small