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📚 Python Book Search

Source: examples/python/book‑search

This walkthrough guides building a textual book similarity search tool using Python:

  • Creating a DB or AI Store for embedding book descriptions
  • Ingesting book titles and summaries as raw text
  • Querying similar books based on semantic relevance
  • Filtering results by metadata like genre or author
  • Displaying matched books with scores and metadata

🔧 What you’ll learn​

  1. Async setup using grpclib and ahnlich-client-py.
  2. Creating a store and ingesting raw book data.
  3. Running get_sim_n() on text queries like "space opera classics".
  4. Filtering responses by metadata predicates (e.g. author == "Asimov").
  5. Retrieving stored metadata and original summaries for output.

💡 Highlighted snippet​

from grpclib.client import Channel
from ahnlich_client_py.grpc.services import AiServiceStub
from ahnlich_client_py.grpc.ai import query as ai_query
from ahnlich_client_py.grpc.ai.models import AiModel

async with Channel(host="127.0.0.1", port=1370) as channel:
    client = AiServiceStub(channel)
    await client.create_store(ai_query.CreateStore(
        store="books",
        index_model=AiModel.ALL_MINI_LM_L6_V2,
        query_model=AiModel.ALL_MINI_LM_L6_V2,
        predicates=["author", "genre"],
        store_original=True,
        error_if_exists=True
    ))

    # ingest books
    await client.set(ai_query.Set(
        store="books",
        inputs=[
            keyval.AiStoreEntry(
                key=keyval.StoreInput(raw_string="Dune summary..."),
                value=keyval.StoreValue(value={"genre": metadata.MetadataValue(raw_string="SciFi")})
            )
        ],
        preprocess_action=preprocess.PreprocessAction.NoPreprocessing
    ))

➕ Try it yourself​

  • Clone the example project
  • Run both Ahnlich services locally
  • Adjust the book dataset (titles / summaries / metadata)
  • Use the Python script to test querying with new text prompts

🧠 Why these guides matter​

  • Show real usage of the Go/Python/Rust SDKs in fully working apps
  • Let you adapt the patterns for your domain (images, documents, etc.)
  • Emphasize semantic similarity + metadata filtering capabilities
  • Provide production-style, real-world code to build upon

Check out the source on GitHub and follow along as each guide walks you step-by-step through store creation, ingestion, search, and result processing.

📌 Next Steps​

  • Browse the Rust Image Search repo
  • Browse the Python Book Search repo
  • Start customizing: swap embeddings, apply new metadata, or change query logic
  • Use these patterns as templates for your own semantic search pipelines