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node_lines:
- node_line_name: retrieve_node_line # Arbitrary node line name
nodes:
- node_type: retrieval
strategy:
metrics: [ retrieval_f1, retrieval_recall, retrieval_precision,
retrieval_ndcg, retrieval_map, retrieval_mrr ]
speed_threshold: 10
top_k: 10
modules:
- module_type: bm25
bm25_tokenizer: [ porter_stemmer, space, gpt2 ]
- module_type: vectordb
embedding_model: openai
embedding_batch: 256
- module_type: hybrid_rrf
weight_range: (4,80)
- module_type: hybrid_cc
normalize_method: [ mm, tmm, z, dbsf ]
weight_range: (0.0, 1.0)
test_weight_size: 101
- node_type: passage_augmenter
strategy:
metrics: [ retrieval_f1, retrieval_recall, retrieval_precision ]
speed_threshold: 5
top_k: 5
embedding_model: openai
modules:
- module_type: pass_passage_augmenter
- module_type: prev_next_augmenter
mode: next
- node_type: passage_filter
strategy:
metrics: [ retrieval_f1, retrieval_recall, retrieval_precision ]
speed_threshold: 5
modules:
- module_type: pass_passage_filter
- module_type: similarity_threshold_cutoff
threshold: 0.85
- module_type: similarity_percentile_cutoff
percentile: 0.6
- module_type: threshold_cutoff
threshold: 0.85
- module_type: percentile_cutoff
percentile: 0.6
- node_type: passage_compressor
strategy:
metrics: [retrieval_token_f1, retrieval_token_recall, retrieval_token_precision]
speed_threshold: 10
modules:
- module_type: pass_compressor
- module_type: tree_summarize
llm: openai
model: gpt-4o-mini
- module_type: refine
llm: openai
model: gpt-4o-mini
- module_type: longllmlingua
- node_line_name: post_retrieve_node_line # Arbitrary node line name
nodes:
- node_type: prompt_maker
strategy:
metrics:
- metric_name: bleu
- metric_name: meteor
- metric_name: rouge
- metric_name: sem_score
embedding_model: openai
speed_threshold: 10
generator_modules:
- module_type: llama_index_llm
llm: openai
model: [gpt-4o-mini]
modules:
- module_type: fstring
prompt: ["Tell me something about the question: {query} \n\n {retrieved_contents}",
"Question: {query} \n Something to read: {retrieved_contents} \n What's your answer?"]
- module_type: long_context_reorder
prompt: [ "Tell me something about the question: {query} \n\n {retrieved_contents}",
"Question: {query} \n Something to read: {retrieved_contents} \n What's your answer?" ]
- node_type: generator
strategy:
metrics:
- metric_name: bleu
- metric_name: meteor
- metric_name: rouge
- metric_name: sem_score
embedding_model: openai
speed_threshold: 10
modules:
- module_type: llama_index_llm
llm: [openai]
model: [gpt-4o-mini]
temperature: [0.5, 1.0]
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