mocker-db 0.0.4

A mock handler for simulating a vector database.

Mocker DB

This class is a mock handler for simulating a vector database, designed primarily for testing and development scenarios. It offers functionalities such as text embedding, hierarchical navigable small world (HNSW) search, and basic data management within a simulated environment resembling a vector database.

import sys
import numpy as np
sys.path.append('../')
from python_modules.mocker_db import MockerDB, SentenceTransformerEmbedder, MockerSimilaritySearch

Usage examples

The examples contain:

1. Basic data insertion and retrieval
2. Text embedding and searching
3. Advanced filtering and removal
4. Testing the HNSW search algorithm
5. Simulating database connection and persistence

1. Basic Data Insertion and Retrieval

# Initialization
handler = MockerDB(
    # optional
    embedder_params = {'model_name_or_path' : 'paraphrase-multilingual-mpnet-base-v2',
                        'processing_type' : 'batch',
                        'tbatch_size' : 500},
    embedder = SentenceTransformerEmbedder,
    ## optional/ for similarity search
    similarity_search_h = MockerSimilaritySearch,
    return_keys_list = [],
    search_results_n = 3,
    similarity_search_type = 'linear',
    similarity_params = {'space':'cosine'},
    ## optional/ inputs with defaults
    file_path = "./mock_persist",
    persist = True,
    embedder_error_tolerance = 0.0
)
# Initialize empty database
handler.establish_connection()

# Insert Data
values_list = [
    {"text": "Sample text 1"},
    {"text": "Sample text 2"}
]
handler.insert_values(values_list, "text")
print(f"Items in the database {len(handler.data)}")

# Retrieve Data
handler.filter_keys(subkey="text", subvalue="Sample text 1")
handler.search_database_keys(query='text')
results = handler.get_dict_results(return_keys_list=["text"])
print(results)

.gitattributes:   0%|          | 0.00/690 [00:00<?, ?B/s]



1_Pooling/config.json:   0%|          | 0.00/190 [00:00<?, ?B/s]



README.md:   0%|          | 0.00/4.13k [00:00<?, ?B/s]



config.json:   0%|          | 0.00/723 [00:00<?, ?B/s]



config_sentence_transformers.json:   0%|          | 0.00/122 [00:00<?, ?B/s]



pytorch_model.bin:   0%|          | 0.00/1.11G [00:00<?, ?B/s]



sentence_bert_config.json:   0%|          | 0.00/53.0 [00:00<?, ?B/s]



sentencepiece.bpe.model:   0%|          | 0.00/5.07M [00:00<?, ?B/s]



special_tokens_map.json:   0%|          | 0.00/239 [00:00<?, ?B/s]



tokenizer.json:   0%|          | 0.00/9.08M [00:00<?, ?B/s]



tokenizer_config.json:   0%|          | 0.00/402 [00:00<?, ?B/s]



modules.json:   0%|          | 0.00/229 [00:00<?, ?B/s]


Items in the database 2
[{'text': 'Sample text 1'}]

2. Text Embedding and Searching

ste = SentenceTransformerEmbedder(# optional / adaptor parameters
                                  processing_type = '',
                                  tbatch_size = 500,
                                  max_workers = 2,
                                  # sentence transformer parameters
                                  model_name_or_path = 'paraphrase-multilingual-mpnet-base-v2',)

# Single Text Embedding
query = "Sample query"
embedded_query = ste.embed(query,
                           # optional
                           processing_type='')
print(embedded_query[0:50])

[-0.04973586  0.09520268 -0.01219508  0.09253863 -0.02301829 -0.02721018
  0.0568395   0.09710983  0.10683874  0.05812277  0.1322755   0.01142832
 -0.06957253  0.0698075  -0.05259365 -0.05755996  0.00816183 -0.0083684
 -0.00861256  0.01442069  0.01188816 -0.09503672  0.07125735 -0.04827785
  0.01473162  0.01084185 -0.1048248   0.07012521 -0.04720647  0.10030048
  0.04455933  0.02131893  0.00667914 -0.05259187  0.06822995 -0.09520472
 -0.00581363 -0.02451877 -0.00384987  0.02750723  0.06960277  0.2401375
 -0.01220019  0.05890937 -0.08468664  0.11379692 -0.03594767 -0.0565297
 -0.01621809  0.09546725]

# Batch Text Embedding
queries = ["Sample query", "Sample query 2"]
embedded_query = ste.embed(queries,
                           # optional
                           processing_type='batch')
print(embedded_query[0][0:50])
print("---")
print(embedded_query[1][0:50])

[-0.04973584  0.09520271 -0.01219508  0.09253865 -0.0230183  -0.02721017
  0.05683954  0.09710982  0.10683876  0.05812274  0.13227552  0.01142829
 -0.06957256  0.06980743 -0.05259361 -0.05755996  0.00816183 -0.00836839
 -0.00861252  0.01442068  0.01188819 -0.09503672  0.07125732 -0.04827787
  0.01473164  0.01084186 -0.1048249   0.07012525 -0.04720649  0.10030047
  0.04455935  0.02131895  0.00667912 -0.05259192  0.06822995 -0.09520471
 -0.00581363 -0.02451887 -0.00384988  0.02750726  0.06960279  0.2401375
 -0.01220022  0.05890937 -0.08468666  0.11379688 -0.03594765 -0.05652964
 -0.0162181   0.09546735]
---
[-0.05087024  0.1231768  -0.0139253   0.10524713 -0.07614321 -0.02349629
  0.05829773  0.15128359  0.18119803  0.03745934  0.12174664  0.00639838
 -0.04045055  0.12758303 -0.06155453 -0.06736137  0.04713943 -0.04134275
 -0.12165949  0.0440988   0.01834145 -0.04796624  0.04922185 -0.00641203
  0.01420631 -0.03602944 -0.01026761  0.09232258 -0.04927172  0.03985452
  0.03566906  0.0833893   0.04922603 -0.09951889  0.0513812  -0.13344644
  0.01626778 -0.01189724  0.0059921   0.05663403  0.04282105  0.26432782
 -0.01122811  0.07177631 -0.11822144  0.08731946 -0.04965353  0.03697515
  0.08965266  0.03107021]

# Search Database
search_results = handler.search_database(query, return_keys_list=["text"])

# Display Results
print(search_results)

[{'text': 'Sample text 1'}]

3. Advanced Filtering and Removal

# Advanced Filtering
filter_criteria = {"text": "Sample text 1"}
handler.filter_database(filter_criteria)
filtered_data = handler.filtered_data
print(f"Filtered data {len(filtered_data)}")

# Data Removal
handler.remove_from_database(filter_criteria)
print(f"Items left in the database {len(handler.data)}")

Filtered data 1
Items left in the database 1

4. Testing the HNSW Search Algorithm

mss = MockerSimilaritySearch(
    # optional
    search_results_n = 3,
    similarity_params = {'space':'cosine'},
    similarity_search_type ='linear'
)

# Create embeddings
embeddings = [ste.embed("example1"), ste.embed("example2")]


# Assuming embeddings are pre-calculated and stored in 'embeddings'
data_with_embeddings = {"record1": {"embedding": embeddings[0]}, "record2": {"embedding": embeddings[1]}}
handler.data = data_with_embeddings

# HNSW Search
query_embedding = embeddings[0]  # Example query embedding
labels, distances = mss.hnsw_search(query_embedding, np.array(embeddings), k=1)
print(labels, distances)

[0] [4.172325e-07]

5. Simulating Database Connection and Persistence

# Establish Connection
handler.establish_connection()

# Change and Persist Data
handler.insert_values([{"text": "New sample text"}], "text")
handler.save_data()

# Reload Data
handler.establish_connection()
print(f"Items in the database {len(handler.data)}")

Items in the database 2