rqa_prompt_template: The template for the RAG pipeline search prompt. This is used by the model to query the database.
llm_prompt_template: The template for the summary generator LLM prompt.
num_return_documents: Number of documents to return for a query. Too high a number can lead to Out of Memory errors. (Defaults to 50)
embedding_model: THIS IS FROM HUGGINGFACE. The model to use for generating embeddings. This is used to generate embeddings for the documents as a means of comparison using the LLM's embeddings. (Defaults to BAAI/bge-large-en-v1.5)
Other possible tested models
BAAI/bge-base-en-v1.5
BAAI/bge-large-en-v1.5
llm_model: THIS IS FROM OLLAMA. The model used for generating the result summary. (Defaults to qwen2:1.5b)
data_dir: The directory to store the intermediate data like tables/databases etc. (Defaults to ./data/)
persist_dir: The directory to store the cached data. Defaults to ./data/chroma_db/ and stores the embeddings for the documents with a unique hash. (Defaults to ./data/chroma_db/)
testing_flag: Enables testing mode by using subsets of the data for quick debugging. This is used to test the pipeline and is not recommended for normal use. (Defaults to False)
test_subset: Uses a tiny subset of the data for testing.
data_download_n_jobs: Number of jobs to run in parallel for downloading data. (Defaults to 20)
training: Whether to train the model or not. (Defaults to False) this is automatically set to True when when running the training.py script. Do NOT set this to True manually.
search_type : The type of vector comparison to use. (Defaults to "similarity")
reraanking: Whether to rerank the results using the FlashRank algorithm. (Defaults to False)
long_context_reordering: Whether to reorder the results using the Long Context Reordering algorithm. (Defaults to False)
chunk_size: Size of the chunks for the RAG document chunking