Enable Semantic Search for Beancount transactions

src/search_type/symmetric_ledger.py

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+# Standard Packages
+import json
+import time
+import gzip
+import os
+import sys
+import re
+import argparse
+import pathlib
+
+# External Packages
+import torch
+from sentence_transformers import SentenceTransformer, CrossEncoder, util
+
+# Internal Packages
+from utils.helpers import get_absolute_path
+from processor.ledger.beancount_to_jsonl import beancount_to_jsonl
+
+
+def initialize_model():
+    "Initialize model for symetric semantic search. That is, where query of similar size to results"
+    bi_encoder = SentenceTransformer('sentence-transformers/paraphrase-MiniLM-L6-v2') # The encoder encodes all entries to use for semantic search
+    top_k = 100                                                                       # Number of entries we want to retrieve with the bi-encoder
+    cross_encoder = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')              # The cross-encoder re-ranks the results to improve quality
+    return bi_encoder, cross_encoder, top_k
+
+
+def extract_entries(notesfile, verbose=0):
+    "Load entries from compressed jsonl"
+    entries = []
+    with gzip.open(get_absolute_path(notesfile), 'rt', encoding='utf8') as jsonl:
+        for line in jsonl:
+            note = json.loads(line.strip())
+
+            note_string = f'{note["Title"]} \t {note["Tags"] if "Tags" in note else ""} \n {note["Body"] if "Body" in note else ""}'
+            entries.extend([note_string])
+
+    if verbose > 0:
+        print(f"Loaded {len(entries)} entries from {notesfile}")
+
+    return entries
+
+
+def compute_embeddings(entries, bi_encoder, embeddings_file, regenerate=False, verbose=0):
+    "Compute (and Save) Embeddings or Load Pre-Computed Embeddings"
+    # Load pre-computed embeddings from file if exists
+    if embeddings_file.exists() and not regenerate:
+        corpus_embeddings = torch.load(get_absolute_path(embeddings_file))
+        if verbose > 0:
+            print(f"Loaded embeddings from {embeddings_file}")
+
+    else:  # Else compute the corpus_embeddings from scratch, which can take a while
+        corpus_embeddings = bi_encoder.encode(entries, convert_to_tensor=True, show_progress_bar=True)
+        torch.save(corpus_embeddings, get_absolute_path(embeddings_file))
+        if verbose > 0:
+            print(f"Computed embeddings and save them to {embeddings_file}")
+
+    return corpus_embeddings
+
+
+def query_transactions(raw_query, corpus_embeddings, entries, bi_encoder, cross_encoder, top_k=100):
+    "Search all notes for entries that answer the query"
+    # Separate natural query from explicit required, blocked words filters
+    query = " ".join([word for word in raw_query.split() if not word.startswith("+") and not word.startswith("-")])
+    required_words = set([word[1:].lower() for word in raw_query.split() if word.startswith("+")])
+    blocked_words = set([word[1:].lower() for word in raw_query.split() if word.startswith("-")])
+
+    # Encode the query using the bi-encoder
+    question_embedding = bi_encoder.encode(query, convert_to_tensor=True)
+
+    # Find relevant entries for the query
+    hits = util.semantic_search(question_embedding, corpus_embeddings, top_k=top_k)
+    hits = hits[0]  # Get the hits for the first query
+
+    # Filter results using explicit filters
+    hits = explicit_filter(hits, entries, required_words, blocked_words)
+    if hits is None or len(hits) == 0:
+        return hits
+
+    # Score all retrieved entries using the cross-encoder
+    cross_inp = [[query, entries[hit['corpus_id']]] for hit in hits]
+    cross_scores = cross_encoder.predict(cross_inp)
+
+    # Store cross-encoder scores in results dictionary for ranking
+    for idx in range(len(cross_scores)):
+        hits[idx]['cross-score'] = cross_scores[idx]
+
+    # Order results by cross encoder score followed by biencoder score
+    hits.sort(key=lambda x: x['score'], reverse=True) # sort by biencoder score
+    hits.sort(key=lambda x: x['cross-score'], reverse=True) # sort by cross encoder score
+
+    return hits
+
+
+def explicit_filter(hits, entries, required_words, blocked_words):
+    hits_by_word_set = [(set(word.lower()
+                             for word
+                             in re.split(
+                                 ',|\.| |\]|\[\(|\)|\{|\}',
+                                 entries[hit['corpus_id']])
+                             if word != ""),
+                         hit)
+                        for hit in hits]
+
+    if len(required_words) == 0 and len(blocked_words) == 0:
+        return hits
+    if len(required_words) > 0:
+        return [hit for (words_in_entry, hit) in hits_by_word_set
+                if required_words.intersection(words_in_entry) and not blocked_words.intersection(words_in_entry)]
+    if len(blocked_words) > 0:
+        return [hit for (words_in_entry, hit) in hits_by_word_set
+                if not blocked_words.intersection(words_in_entry)]
+    return hits
+
+
+def render_results(hits, entries, count=5, display_biencoder_results=False):
+    "Render the Results returned by Search for the Query"
+    if display_biencoder_results:
+        # Output of top hits from bi-encoder
+        print("\n-------------------------\n")
+        print(f"Top-{count} Bi-Encoder Retrieval hits")
+        hits = sorted(hits, key=lambda x: x['score'], reverse=True)
+        for hit in hits[0:count]:
+            print(f"Score: {hit['score']:.3f}\n------------\n{entries[hit['corpus_id']]}")
+
+    # Output of top hits from re-ranker
+    print("\n-------------------------\n")
+    print(f"Top-{count} Cross-Encoder Re-ranker hits")
+    hits = sorted(hits, key=lambda x: x['cross-score'], reverse=True)
+    for hit in hits[0:count]:
+        print(f"CrossScore: {hit['cross-score']:.3f}\n-----------------\n{entries[hit['corpus_id']]}")
+
+
+def collate_results(hits, entries, count=5):
+    return [
+        {
+            "Entry": entries[hit['corpus_id']],
+            "Score": f"{hit['cross-score']:.3f}"
+        }
+        for hit
+        in hits[0:count]]
+
+
+def setup(input_files, input_filter, compressed_jsonl, embeddings, regenerate=False, verbose=False):
+    # Initialize Model
+    bi_encoder, cross_encoder, top_k = initialize_model()
+
+    # Map notes in Org-Mode files to (compressed) JSONL formatted file
+    if not compressed_jsonl.exists() or regenerate:
+        beancount_to_jsonl(input_files, input_filter, compressed_jsonl, verbose)
+
+    # Extract Entries
+    entries = extract_entries(compressed_jsonl, verbose)
+
+    # Compute or Load Embeddings
+    corpus_embeddings = compute_embeddings(entries, bi_encoder, embeddings, regenerate=regenerate, verbose=verbose)
+
+    return entries, corpus_embeddings, bi_encoder, cross_encoder, top_k
+
+
+if __name__ == '__main__':
+    # Setup Argument Parser
+    parser = argparse.ArgumentParser(description="Map Beancount transactions into (compressed) JSONL format")
+    parser.add_argument('--input-files', '-i', nargs='*', help="List of Beancount files to process")
+    parser.add_argument('--input-filter', type=str, default=None, help="Regex filter for Beancount files to process")
+    parser.add_argument('--compressed-jsonl', '-j', type=pathlib.Path, default=pathlib.Path(".transactions.jsonl.gz"), help="Compressed JSONL formatted transactions file to compute embeddings from")
+    parser.add_argument('--embeddings', '-e', type=pathlib.Path, default=pathlib.Path(".transaction_embeddings.pt"), help="File to save/load model embeddings to/from")
+    parser.add_argument('--regenerate', action='store_true', default=False, help="Regenerate embeddings from Beancount files. Default: false")
+    parser.add_argument('--results-count', '-n', default=5, type=int, help="Number of results to render. Default: 5")
+    parser.add_argument('--interactive', action='store_true', default=False, help="Interactive mode allows user to run queries on the model. Default: true")
+    parser.add_argument('--verbose', action='count', default=0, help="Show verbose conversion logs. Default: 0")
+    args = parser.parse_args()
+
+    entries, corpus_embeddings, bi_encoder, cross_encoder, top_k = setup(args.input_files, args.input_filter, args.compressed_jsonl, args.embeddings, args.regenerate, args.verbose)
+
+    # Run User Queries on Entries in Interactive Mode
+    while args.interactive:
+        # get query from user
+        user_query = input("Enter your query: ")
+        if user_query == "exit":
+            exit(0)
+
+        # query notes
+        hits = query_transactions(user_query, corpus_embeddings, entries, bi_encoder, cross_encoder, top_k)
+
+        # render results
+        render_results(hits, entries, count=args.results_count)