From a24920df0a00389eb3174ada19442547403b5deb Mon Sep 17 00:00:00 2001 From: Rishabh Srivastava Date: Sat, 12 Aug 2023 04:19:47 +0800 Subject: [PATCH] added utilities for pruning a database --- data/embeddings.pkl | Bin 0 -> 630772 bytes data/ner_metadata.pkl | Bin 0 -> 9660 bytes eval/{runner.py => openai_runner.py} | 0 utils/pruning.py | 183 +++++++++++++++++++++++++++ 4 files changed, 183 insertions(+) create mode 100644 data/embeddings.pkl create mode 100644 data/ner_metadata.pkl rename eval/{runner.py => openai_runner.py} (100%) create mode 100644 utils/pruning.py diff --git a/data/embeddings.pkl b/data/embeddings.pkl new file mode 100644 index 0000000000000000000000000000000000000000..381c34f9b2cd3fd9ddd575fd5f694fa71c7b7729 GIT binary patch literal 630772 zcmZU4c|28L)V>TEGRxGUBt$7grS92_Oo&s}S;z1Q=3lP=J%Un}#0ogOI`47Y z@9CNnE9~!cz|~{9(;NQTn>49?Q!yV^FHWvz$rjGC3*S|k^eJ0IQ`-5 z?v@fO?&Rg;yvOOk13vqmTmt>w4o+JcPW<8GI>~)n$m>t$`pJcRi>>6BpX9c9&og}v 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z_rIal%PztfPN>v>V37V@3z#JoOqF8nD!9FgeX#-WV*X#vJWo{cA{z%8j5=HaD{a&+ zAye1#1@mQods(&fHg3dBeU9y&Wok1co<{#9BOv%kEl9>J7)4gT8v$7`Q|xQ>9}#eb z0PwF}AOy(lx-LLW-<#22h2?1dVuq&){F@$RTOgZ#iy_;Uxvq&#bS?P+7T;I{{rVS5 zxTzB|7YPQC`r2=*0h>pQ5|0?gS+Xe6%_xpNqd5Ew=O{DWO!1@*YeK4$AZY0k`o&{9 h_LRa|yBMMJNYtQRRt>>L4H{+PNE%sqSzfIC{{xg0esll; literal 0 HcmV?d00001 diff --git a/eval/runner.py b/eval/openai_runner.py similarity index 100% rename from eval/runner.py rename to eval/openai_runner.py diff --git a/utils/pruning.py b/utils/pruning.py new file mode 100644 index 0000000..ad86640 --- /dev/null +++ b/utils/pruning.py @@ -0,0 +1,183 @@ +from typing import Dict, List, Tuple +from sentence_transformers import SentenceTransformer +import spacy +import pickle +import torch +import torch.nn.functional as F + +encoder = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2") +nlp = spacy.load("en_core_web_sm") + +def load_all_emb() -> Tuple[Dict[str, torch.tensor], List[str]]: + """ + Load all embeddings from pickle file. + """ + try: + with open(f"data/embeddings.pkl", "rb") as f: + all_emb, col_descriptions = pickle.load(f) + return all_emb, col_descriptions + except FileNotFoundError: + print("Embeddings not found.") + exit(1) + +def load_ner_md() -> Tuple[Dict[str, Dict], Dict[str, Dict], Dict[str, Dict]]: + """ + Load all NER and join metadata from pickle file. + """ + try: + with open(f"data/ner_metadata.pkl", "rb") as f: + column_ner, column_join = pickle.load(f) + return column_ner, column_join + except FileNotFoundError: + print("NER and join metadata not found.") + exit(1) + +def knn( + query: str, + all_emb: torch.tensor, + k: int, + threshold: float, +) -> Tuple[torch.tensor, torch.tensor]: + """ + Get top most similar columns' embeddings to query using cosine similarity. + """ + query_emb = encoder.encode(query, convert_to_tensor=True) + similarity_scores = F.cosine_similarity(query_emb, all_emb) + top_results = torch.nonzero(similarity_scores > threshold).squeeze() + # if top_results is empty, return empty tensors + if top_results.numel() == 0: + return torch.tensor([]), torch.tensor([]) + # if only 1 result is returned, we need to convert it to a tensor + elif top_results.numel() == 1: + return torch.tensor([similarity_scores[top_results]]), torch.tensor([top_results]) + else: + top_k_scores, top_k_indices = torch.topk( + similarity_scores[top_results], k=min(k, top_results.numel()) + ) + return top_k_scores, top_results[top_k_indices] + +def get_entity_types(sentence, verbose: bool = False): + """ + Get entity types from sentence using spaCy. + """ + doc = nlp(sentence) + named_entities = set() + for ent in doc.ents: + if verbose: + print(f"ent {ent}, {ent.label_}") + named_entities.add(ent.label_) + + return named_entities + +def format_topk_sql(topk_table_columns: Dict[str, List[Tuple[str, str, str]]], exclude_column_descriptions: bool = False) -> str: + md_str = "```\n" + for table_name in topk_table_columns: + columns_str = "" + for column_tuple in topk_table_columns[table_name]: + if exclude_column_descriptions: + columns_str += f"\n {column_tuple[0]} {column_tuple[1]}," + else: + columns_str += f"\n {column_tuple[0]} {column_tuple[1]}, --{column_tuple[2]}" + md_str += f"CREATE TABLE {table_name} ({columns_str}\n)\n-----------\n" + return md_str + +def get_md_emb( + question: str, + column_emb: torch.tensor, + column_info_csv: List[str], + column_ner: Dict[str, List[str]], + column_join: Dict[str, dict], + k: int = 20, + threshold: float = 0.2, + exclude_column_descriptions: bool = False, +) -> str: + """ + Given question, generated metadata csv string with top k columns and tables + that are most similar to the question. `column_emb`, `column_info_csv`, `column_ner`, + `column_join` are all specific to the db_name. `column_info_csv` is a list of csv strings + with 1 row per column info, where each row is in the format: + `table_name.column_name,column_type,column_description`. + Steps are: + 1. Get top k columns from question to `column_emb` using `knn` and add the corresponding column info to topk_table_columns. + 2. Get entity types from question. If entity type is in `column_ner`, add the corresponding list of column info to topk_table_columns. + 3. Generate the metadata string using the column info so far. + 4. Get joinable columns between tables in topk_table_columns and add to final metadata string. + """ + # 1) get top k columns + top_k_scores, top_k_indices = knn(question, column_emb, k, threshold) + topk_table_columns = {} + table_column_names = set() + for score, index in zip(top_k_scores, top_k_indices): + table_name, column_info = column_info_csv[index].split(".", 1) + column_tuple = tuple(column_info.split(",", 2)) + if table_name not in topk_table_columns: + topk_table_columns[table_name] = [] + topk_table_columns[table_name].append(column_tuple) + table_column_names.add(f"{table_name}.{column_tuple[0]}") + + # 2) get entity types from question + add corresponding columns + entity_types = get_entity_types(question) + for entity_type in entity_types: + if entity_type in column_ner: + for column_info in column_ner[entity_type]: + table_column_name, column_type, column_description = column_info.split(",", 2) + table_name, column_name = table_column_name.split(".", 1) + if table_name not in topk_table_columns: + topk_table_columns[table_name] = [] + column_tuple = (column_name, column_type, column_description) + if column_tuple not in topk_table_columns[table_name]: + topk_table_columns[table_name].append(column_tuple) + topk_tables = sorted(list(topk_table_columns.keys())) + + # 3) get table pairs that can be joined + # create dict of table_column_name -> column_tuple for lookups + column_name_to_tuple = {} + ncols = len(column_info_csv) + for i in range(ncols): + table_column_name, column_type, column_description = column_info_csv[i].split(",", 2) + table_name, column_name = table_column_name.split(".", 1) + column_tuple = (column_name, column_type, column_description) + column_name_to_tuple[table_column_name] = column_tuple + # go through list of top k tables and see if pairs can be joined + join_list = [] + for i in range(len(topk_tables)): + for j in range(i + 1, len(topk_tables)): + table1, table2 = topk_tables[i], topk_tables[j] + assert table1 <= table2 + if (table1, table2) in column_join: + for table_col_1, table_col_2 in column_join[(table1, table2)]: + # add to topk_table_columns + if table_col_1 not in table_column_names: + column_tuple = column_name_to_tuple[table_col_1] + topk_table_columns[table1].append(column_tuple) + table_column_names.add(table_col_1) + if table_col_2 not in table_column_names: + column_tuple = column_name_to_tuple[table_col_2] + topk_table_columns[table2].append(column_tuple) + table_column_names.add(table_col_2) + # add to join_list + join_str = f"{table_col_1} can be joined with {table_col_2}" + if join_str not in join_list: + join_list.append(join_str) + + # 4) format metadata string + md_str = format_topk_sql(topk_table_columns, exclude_column_descriptions) + + if join_list: + md_str += "```\n\nAdditionally, is a list of joinable columns in this database schema:\n```\n" + md_str += "\n".join(join_list) + md_str += "\n```" + return md_str + +def prune_metadata_str(question, db_name, exclude_column_descriptions=False): + emb, csv_descriptions = load_all_emb() + columns_ner, columns_join = load_ner_md() + table_metadata_csv = get_md_emb( + question, + emb[db_name], + csv_descriptions[db_name], + columns_ner[db_name], + columns_join[db_name], + exclude_column_descriptions=exclude_column_descriptions, + ) + return table_metadata_csv \ No newline at end of file