A Retrieval-Augmented Generation (RAG) framework with MongoDB and Sentence Transformers
Project description
Example usage
if __name__ == "__main__":
mongo_url = 'your_mongo_url_here'
db_name = 'chat-service'
collection_name = 'rag'
model_name = 'sentence-transformers/all-MiniLM-L6-v2'
similarity_threshold = 0.3
rag = RAGFramework(mongo_url, db_name, collection_name, model_name, similarity_threshold)
# Update embeddings for existing documents
rag.update_embeddings()
# Insert new data
raw_text = "This is a sample document text that needs to be inserted into MongoDB."
result = rag.insert_data(raw_text)
print(result)
# Retrieve data based on a query
query = "sample document text"
retrieved_data = rag.retrieve_data(query)
print(retrieved_data)
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