mocker-db is a python module that contains mock vector database like solution built around
python dictionary data type. It contains methods necessary to interact with this 'database',
embed, search and persist.
from mocker_db import MockerDB, MockerConnector, SentenceTransformerEmbedder
MockerDB can be used as ephemeral database where everything is saved in memory, but also can be persisted in one file for the database and another for embeddings storage.
Embedder is set to sentence_transformer by default and processed locally, custom embedders that connect to an api or use other open source models could be used as long as they have the same interface.
# Initialization
handler = MockerDB(
# optional
embedder_params = {'model_name_or_path' : 'paraphrase-multilingual-mpnet-base-v2',
'processing_type' : 'batch',
'tbatch_size' : 500},
similarity_search_type = 'linear_torch',
use_embedder = True,
embedder = SentenceTransformerEmbedder,
persist = True
)
# Initialize empty database
handler.establish_connection(
# optional for persist
file_path = "./mock_persist",
embs_file_path = "./mock_embs_persist",
)
sentences = [
"The cat slept.",
"It rained today.",
"She smiled gently.",
"Books hold knowledge.",
"The sun set behind the mountains, casting a golden glow over the valley.",
"He quickly realized that time was slipping away, and he needed to act fast.",
"The concert was an unforgettable experience, filled with laughter and joy.",
"Despite the challenges, they managed to build a beautiful home together.",
"As the wind howled through the ancient trees, scattering leaves and whispering secrets of the forest, she stood there, gazing up at the endless expanse of stars, feeling both infinitely small and profoundly connected to the universe.",
"While the project seemed daunting at first, requiring countless hours of research, planning, and execution, the team worked tirelessly, motivated by their shared goal of creating something truly remarkable and innovative in their field.",
"In the bustling city streets, amidst the constant hum of traffic and chatter, he found himself contemplating life's mysteries, pondering the choices that had brought him to this very moment and wondering where the path ahead would lead.",
"The conference was a gathering of minds from around the globe, each participant bringing their unique perspectives and insights to the table, fostering a vibrant exchange of ideas that would shape the future of their respective fields for years to come."
]
# Insert Data
values_list = [
{'text' : t, 'n_words' : len(t.split())} for t in sentences
]
handler.insert_values(values_list, "text")
print(f"Items in the database {len(handler.data)}")
Items in the database 12
There are multiple options for search which could be used together or separately:
results = handler.search_database(
query = "cat",
filter_criteria = {
"n_words" : 3,
}
)
print([{k: str(v)[:30] + "..." for k, v in result.items()} for result in results])
[{'text': 'The cat slept....', 'n_words': '3...'}, {'text': 'She smiled gently....', 'n_words': '3...'}, {'text': 'It rained today....', 'n_words': '3...'}, {'text': 'Books hold knowledge....', 'n_words': '3...'}]
results = handler.search_database(
# when keyword key is provided filter is used to pass keywords
filter_criteria = {
"text" : ["sun"],
},
keyword_check_keys = ['text'],
# percentage of filter keyword allowed to be different
keyword_check_cutoff = 1,
return_keys_list=['text']
)
print([{k: str(v)[:30] + "..." for k, v in result.items()} for result in results])
[{'text': 'The sun set behind the mountai...'}]
results = handler.search_database(
query = "cat",
filter_criteria = {
"n_words" : 3,
},
return_keys_list=["-n_words"])
print([{k: str(v)[:30] + "..." for k, v in result.items()} for result in results])
[{'text': 'The cat slept....'}, {'text': 'She smiled gently....'}, {'text': 'It rained today....'}, {'text': 'Books hold knowledge....'}]
results = handler.search_database(
query = "cat slept",
filter_criteria = {
"n_words" : 3,
},
return_keys_list=["+&distance"]
)
print([{k: str(v)[:30] + "..." for k, v in result.items()} for result in results])
[{'text': 'The cat slept....', 'n_words': '3...', '&distance': '0.9757655893784214...'}, {'text': 'She smiled gently....', 'n_words': '3...', '&distance': '0.25537100167603033...'}, {'text': 'It rained today....', 'n_words': '3...', '&distance': '0.049663180663929454...'}, {'text': 'Books hold knowledge....', 'n_words': '3...', '&distance': '0.011214834039176086...'}]
results = handler.search_database(
query = "cat slept",
filter_criteria = {
"n_words" : 3,
},
return_keys_list=["&distance"]
)
print([{k: str(v)[:30] + "..." for k, v in result.items()} for result in results])
[{'&distance': '0.9757655893784214...'}, {'&distance': '0.25537100167603033...'}, {'&distance': '0.049663180663929454...'}, {'&distance': '0.011214834039176086...'}]
results = handler.search_database(
query = "cat slept",
filter_criteria = {
"n_words" : 3,
},
return_keys_list=["+embedding"]
)
print([{k: str(v)[:30] + "..." for k, v in result.items()} for result in results])
[{'text': 'The cat slept....', 'n_words': '3...', 'embedding': '[-3.86438444e-02 1.23167984e-...'}, {'text': 'She smiled gently....', 'n_words': '3...', 'embedding': '[-2.46711876e-02 2.37020180e-...'}, {'text': 'It rained today....', 'n_words': '3...', 'embedding': '[-1.35887727e-01 -2.52719879e-...'}, {'text': 'Books hold knowledge....', 'n_words': '3...', 'embedding': '[ 6.20863438e-02 1.13785945e-...'}]
results = handler.search_database(
query = "cat slept",
filter_criteria = {
"n_words" : 3,
},
return_keys_list=["embedding"]
)
print([{k: str(v)[:30] + "..." for k, v in result.items()} for result in results])
[{'embedding': '[-3.86438444e-02 1.23167984e-...'}, {'embedding': '[-2.46711876e-02 2.37020180e-...'}, {'embedding': '[-1.35887727e-01 -2.52719879e-...'}, {'embedding': '[ 6.20863438e-02 1.13785945e-...'}]
results = handler.search_database(
query = "cat slept",
filter_criteria = {
"n_words" : 3,
},
return_keys_list=["embedding", "+&embedded_field"]
)
print([{k: str(v)[:30] + "..." for k, v in result.items()} for result in results])
[{'embedding': '[-3.86438444e-02 1.23167984e-...', '&embedded_field': 'text...'}, {'embedding': '[-2.46711876e-02 2.37020180e-...', '&embedded_field': 'text...'}, {'embedding': '[-1.35887727e-01 -2.52719879e-...', '&embedded_field': 'text...'}, {'embedding': '[ 6.20863438e-02 1.13785945e-...', '&embedded_field': 'text...'}]
Ollama
import logging
logging.disable(logging.INFO)
# Initialization
handler = MockerDB(
# optional
persist = True,
llm_conn_params = {
'llm_h_type' : 'OllamaConn',
'llm_h_params' : {
'connection_string' : 'http://127.0.0.1:11434',
'model_name' : 'llama3.1:latest'
}
}
)
# Initialize empty database
handler.establish_connection(
# optional for persist
file_path = "./mock_persist",
embs_file_path = "./mock_embs_persist",
)
results = await handler.search_database_async(
llm_search_keys=['text'],
filter_criteria = {
"text" : ["cat", "nature"],
},
return_keys_list=["+&cats"],
ignore_cats_cache=False
)
print([{k: str(v)[:30] + "..." for k, v in result.items()} for result in results])
[{'text': 'The cat slept....', 'n_words': '3...', '&cats': "{'text': ['cat']}..."}, {'text': 'The sun set behind the mountai...', 'n_words': '13...', '&cats': "{'text': ['nature']}..."}, {'text': 'As the wind howled through the...', 'n_words': '37...', '&cats': "{'text': ['nature']}..."}]
handler.cats
{'The cat slept.': {1: ['cat'], 0: ['nature']},
'It rained today.': {1: [], 0: ['cat', 'nature']},
'She smiled gently.': {1: [], 0: ['cat', 'nature']},
'Books hold knowledge.': {1: [], 0: ['cat', 'nature']},
'The sun set behind the mountains, casting a golden glow over the valley.': {1: ['nature'],
0: ['cat']},
'He quickly realized that time was slipping away, and he needed to act fast.': {1: [],
0: ['cat', 'nature']},
'The concert was an unforgettable experience, filled with laughter and joy.': {1: [],
0: ['cat', 'nature']},
'Despite the challenges, they managed to build a beautiful home together.': {1: [],
0: ['cat', 'nature']},
'As the wind howled through the ancient trees, scattering leaves and whispering secrets of the forest, she stood there, gazing up at the endless expanse of stars, feeling both infinitely small and profoundly connected to the universe.': {1: ['nature'],
0: ['cat']},
'While the project seemed daunting at first, requiring countless hours of research, planning, and execution, the team worked tirelessly, motivated by their shared goal of creating something truly remarkable and innovative in their field.': {1: [],
0: ['cat', 'nature']},
"In the bustling city streets, amidst the constant hum of traffic and chatter, he found himself contemplating life's mysteries, pondering the choices that had brought him to this very moment and wondering where the path ahead would lead.": {1: [],
0: ['cat', 'nature']},
'The conference was a gathering of minds from around the globe, each participant bringing their unique perspectives and insights to the table, fostering a vibrant exchange of ideas that would shape the future of their respective fields for years to come.': {1: [],
0: ['cat', 'nature']}}
OpenAI
import logging
logging.disable(logging.INFO)
from dotenv import load_dotenv
import os
load_dotenv("../../credentials")
# Initialization
handler = MockerDB(
# optional
persist = True,
llm_conn_params = {
'llm_h_type' : 'OpenAIConn',
'llm_h_params' : {
'model_name' : 'gpt-4o-mini',
'env_mapping' : {
'api_key' : "OPENAI_KEY"
}
}
}
)
# Initialize empty database
handler.establish_connection(
# optional for persist
file_path = "./mock_persist",
embs_file_path = "./mock_embs_persist",
)
results = await handler.search_database_async(
llm_search_keys=['text'],
filter_criteria = {
"text" : ["cat", "nature"],
}
)
print([{k: str(v)[:30] + "..." for k, v in result.items()} for result in results])
[{'text': 'The cat slept....', 'n_words': '3...'}, {'text': 'The sun set behind the mountai...', 'n_words': '13...'}, {'text': 'As the wind howled through the...', 'n_words': '37...'}]
print(f"Items in the database {len(handler.data)}")
handler.remove_from_database(filter_criteria = {"n_words" : 11})
print(f"Items left in the database {len(handler.data)}")
Items in the database 14
Items left in the database 12
results = handler.embed_texts(
texts = [
"Short. Variation 1: Short.",
"Another medium-length example, aiming to test the variability in processing different lengths of text inputs. Variation 2: processing lengths medium-length example, in inputs. to variability aiming test of text different the Another"
]
)
print(str(results)[0:300] + "...")
{'embeddings': [[0.04973424971103668, -0.43570247292518616, -0.014545125886797905, -0.03648979589343071, -0.04165348783135414, -0.04544278606772423, -0.07025150209665298, 0.10043243318796158, -0.20846229791641235, 0.15596869587898254, 0.11489829421043396, -0.13442179560661316, -0.02425091527402401, ...
Remote Mocker can be used via very similar methods to the local one.
# Initialization
handler = MockerDB(
skip_post_init=True
)
# Initialize empty database
handler.establish_connection(
# optional for connecting to api
connection_details = {
'base_url' : "http://localhost:8000/mocker-db"
}
)
sentences = [
"The cat slept.",
"It rained today.",
"She smiled gently.",
"Books hold knowledge.",
"The sun set behind the mountains, casting a golden glow over the valley.",
"He quickly realized that time was slipping away, and he needed to act fast.",
"The concert was an unforgettable experience, filled with laughter and joy.",
"Despite the challenges, they managed to build a beautiful home together.",
"As the wind howled through the ancient trees, scattering leaves and whispering secrets of the forest, she stood there, gazing up at the endless expanse of stars, feeling both infinitely small and profoundly connected to the universe.",
"While the project seemed daunting at first, requiring countless hours of research, planning, and execution, the team worked tirelessly, motivated by their shared goal of creating something truly remarkable and innovative in their field.",
"In the bustling city streets, amidst the constant hum of traffic and chatter, he found himself contemplating life's mysteries, pondering the choices that had brought him to this very moment and wondering where the path ahead would lead.",
"The conference was a gathering of minds from around the globe, each participant bringing their unique perspectives and insights to the table, fostering a vibrant exchange of ideas that would shape the future of their respective fields for years to come."
]
# Insert Data
values_list = [
{'text' : t, 'n_words' : len(t.split())} for t in sentences
]
handler.insert_values(values_list, "text")
{'status': 'success', 'message': ''}
MockerAPI has multiple handlers stored in memory at a time, they can be displayed with number of items and memory estimate.
handler.show_handlers()
{'results': [{'handler': 'default',
'items': 12,
'memory_usage': 1.4748001098632812}],
'status': 'success',
'message': '',
'handlers': ['default'],
'items': [12],
'memory_usage': [1.4748001098632812]}
results = handler.search_database(
query = "cat",
filter_criteria = {
"n_words" : 3,
}
)
results
{'status': 'success',
'message': '',
'handler': 'default',
'results': [{'text': 'The cat slept.', 'n_words': 3},
{'text': 'Books hold knowledge.', 'n_words': 3},
{'text': 'It rained today.', 'n_words': 3},
{'text': 'She smiled gently.', 'n_words': 3}]}
results = handler.embed_texts(
texts = [
"Short. Variation 1: Short.",
"Another medium-length example, aiming to test the variability in processing different lengths of text inputs. Variation 2: processing lengths medium-length example, in inputs. to variability aiming test of text different the Another"
],
# optional
embedding_model = "intfloat/multilingual-e5-base"
)
print(str(results)[0:500] + "...")
{'status': 'success', 'message': '', 'handler': 'cache_mocker_intfloat_multilingual-e5-base', 'embedding_model': 'intfloat/multilingual-e5-base', 'embeddings': [[-0.021023569628596306, 0.03461984172463417, -0.013103404082357883, 0.030711326748132706, 0.023395603522658348, -0.040545400232076645, -0.01580517739057541, -0.026828577741980553, 0.015833470970392227, 0.017637528479099274, 0.0008703444618731737, -0.011133708991110325, 0.11296682059764862, 0.015158110298216343, -0.04669041559100151, -0.0...
handler.show_handlers()
{'results': [{'handler': 'default',
'items': 12,
'memory_usage': 1.4762191772460938},
{'handler': 'cache_mocker_intfloat_multilingual-e5-base',
'items': 2,
'memory_usage': 1.4075469970703125}],
'status': 'success',
'message': '',
'handlers': ['default', 'cache_mocker_intfloat_multilingual-e5-base'],
'items': [12, 2],
'memory_usage': [1.4762191772460938, 1.4075469970703125]}
FAQs
A mock handler for simulating a vector database.
We found that mocker-db demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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