transient-in-memory-semantic-search-engine 0.1.0a0

A transient, in-memory semantic search engine for small document collections, powered by sentence embeddings from an OpenAI Embeddings-compatible API.

transient-in-memory-semantic-search-engine

A transient, in-memory semantic search engine for small document collections, powered by sentence embeddings from an OpenAI Embeddings-compatible API. That includes:

• OpenAI's embedding models (base_url='https://api.openai.com/v1)
• Google Gemini (base_url='https://generativelanguage.googleapis.com/v1beta/openai')
• ollama (base_url='http://localhost:11434/v1').

with compatible models.

Features

• Simple in-memory construction from mapping or iterable of key-value pairs.
  • Values must be of type Text.
• Uses cosine similarity for nearest neighbor ranking.

Installation

pip install transient-in-memory-semantic-search-engine

Example

# coding=utf-8
from __future__ import print_function, unicode_literals
from transient_in_memory_semantic_search_engine import TransientInMemorySemanticSearchEngine

# For this example, api_key and base_url are ignored by the dummy backend.
engine = TransientInMemorySemanticSearchEngine(
    api_key='OPENAI_API_KEY',
    base_url='https://api.openai.com/v1',
    model='text-embedding-ada-002',
    key_value_mapping_or_key_value_pairs=[
        ('a', 'The quick brown fox jumps over the lazy dog.'),
        ('b', 'A fast, dark-colored fox leaped above a sleeping canine.'),
        ('c', 'Unrelated sentence about software engineering.'),
    ]
)

# Search with a query:
query = 'A fox jumping over a dog'
results = engine(query)

# Results are returned as a list of (similarity, key) pairs, sorted by similarity descending.
print('Results:')
for score, key in results:
    print('Key: %s, Similarity: %.4f' % (key, score))

Output:

Results:
Key: b, Similarity: 0.9236
Key: a, Similarity: 0.9130
Key: c, Similarity: 0.7434

Notes

• Values must be of type Text.
• Engine construction is fast for moderate document sizes, as all embeddings are precomputed.
• Suitable for prototyping and small-scale applications.

Command-line Usage

You can also use semantic search from the command line using:

python -m transient_in_memory_semantic_search_engine \
    --api-key YOUR_API_KEY \
    --base-url https://api.openai.com/v1 \
    --model text-embedding-ada-002 \
    --key-value-json path/to/your_documents.json

Where your_documents.json is a file containing your documents as a mapping from keys to text values, for example:

{
    "doc1": "The quick brown fox jumps over the lazy dog.",
    "doc2": "A fast, dark-colored fox leaped above a sleeping canine.",
    "doc3": "Unrelated sentence about software engineering."
}

Once started, you will be prompted:

Enter a query:

Type your search query. The engine will print a ranked list of matches:

score,key
0.9236,"doc2"
0.9130,"doc1"
0.7434,"doc3"

Contributing

Contributions are welcome! Please submit pull requests or open issues on the GitHub repository.

License

This project is licensed under the MIT License.