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from langchain.globals import set_verbose
from loguru import logger
from llm_engineering.application.rag.retriever import ContextRetriever
from llm_engineering.infrastructure.opik_utils import configure_opik
if __name__ == "__main__":
configure_opik()
set_verbose(True)
query = """
My name is Paul Iusztin.
Could you draft a LinkedIn post discussing RAG systems?
I'm particularly interested in:
- how RAG works
- how it is integrated with vector DBs and large language models (LLMs).
"""
retriever = ContextRetriever(mock=False)
documents = retriever.search(query, k=9)
logger.info("Retrieved documents:")
for rank, document in enumerate(documents):
logger.info(f"{rank + 1}: {document}")
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