# In this yaml, we do not use tree_summarize for accuracy

# And did not use monoT5, because it can take too long.

node_lines:

- node_line_name: retrieve_node_line # Arbitrary node line name

nodes:

- node_type: retrieval

strategy:

metrics: [retrieval_f1, retrieval_recall, retrieval_precision]

top_k: 20

modules:

- module_type: bm25

- module_type: vectordb

embedding_model: huggingface_baai_bge_small

- module_type: hybrid_rrf

target_modules: ('bm25', 'vectordb')

rrf_k: [3, 5, 10]

- module_type: hybrid_cc

target_modules: ('bm25', 'vectordb')

weights:

- (0.5, 0.5)

- (0.3, 0.7)

- (0.7, 0.3)

- node_type: passage_reranker

strategy:

metrics: [retrieval_f1, retrieval_recall, retrieval_precision]

top_k: 3

modules:

- module_type: pass_reranker

- module_type: tart

- module_type: upr

- node_line_name: post_retrieve_node_line # Arbitrary node line name

nodes:

- node_type: prompt_maker

strategy:

metrics: [bleu, meteor, rouge, sem_score]

generator_modules:

- module_type: vllm

llm: mistralai/Mistral-7B-Instruct-v0.2

modules:

- module_type: fstring

prompt:

- "Answer to given questions with the following passage: {retrieved_contents} \n\n Question: {query} \n\n Answer:"

- "There is a passages related to user question. Please response carefully to the following question. \n\n Passage: {retrieved_contents} \n\n Question: {query} \n\n Answer the question. Think step by step." # Zero-shot CoT prompt

- "{retrieved_contents} \n\n Read the passage carefully, and answer this question. \n\n Question: {query} \n\n Answer the question. Be concise." # concise prompt

- node_type: generator

strategy:

metrics: [bleu, meteor, rouge, sem_score]

modules:

- module_type: vllm

llm: mistralai/Mistral-7B-Instruct-v0.2

temperature: [0.1, 0.5, 1.1]