005-retrievers.py

import os
from dotenv import load_dotenv, find_dotenv
_ = load_dotenv(find_dotenv())
openai_api_key = os.environ["OPENAI_API_KEY"]

from langchain_openai import ChatOpenAI

chatModel = ChatOpenAI(model="gpt-3.5-turbo-0125")

from langchain_community.document_loaders import TextLoader
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter
from langchain_chroma import Chroma

# Load the document, split it into chunks, embed each chunk and load it into the vector store.
loaded_document = TextLoader('./data/state_of_the_union.txt').load()

text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)

chunks_of_text = text_splitter.split_documents(loaded_document)

vector_db = Chroma.from_documents(chunks_of_text, OpenAIEmbeddings())

question = "What did the president say about the John Lewis Voting Rights Act?"

response = vector_db.similarity_search(question)

print("\n----------\n")

print("Ask the RAG App: What did the president say about the John Lewis Voting Rights Act?")

print("\n----------\n")
#print(response[0].page_content)

print("\n----------\n")

from langchain_community.document_loaders import TextLoader

loader = TextLoader("./data/state_of_the_union.txt")

from langchain_community.vectorstores import FAISS
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter

loaded_document = loader.load()

text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)

chunks_of_text = text_splitter.split_documents(loaded_document)

embeddings = OpenAIEmbeddings()

vector_db = FAISS.from_documents(chunks_of_text, embeddings)

retriever = vector_db.as_retriever()

response = retriever.invoke("what did he say about ketanji brown jackson?")

print("\n----------\n")

print("Ask the RAG App with Retriever: What did he say about ketanji brown jackson?")

print("\n----------\n")
print(response[0].page_content)

print("\n----------\n")