Merge pull request 'fix(indexer): restore module-level embedding/batch constants (cronjobs cassés)' (#59) from fix/indexer-missing-constants into main
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Reviewed-on: #59
This commit was merged in pull request #59.
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@@ -22,7 +22,9 @@ jobs:
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with:
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python-version: "3.14"
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- name: Install dependencies
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run: pip install -r requirements.txt pytest
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run: pip install -r requirements.txt pytest ruff==0.17.1
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- name: Lint (undefined names)
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run: ruff check src/ tests/ --select F821
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- name: Run unit tests
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run: pytest tests/ -v
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env:
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@@ -29,6 +29,13 @@ MAILDIR_FOLDERS = os.environ.get("MAILDIR_FOLDERS", "")
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QDRANT_URL = os.environ.get("QDRANT_URL", "")
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COLLECTION_NAME = os.environ.get("COLLECTION_NAME", "")
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# Embedding model and batching. EMBEDDING_MODEL_NAME MUST stay in sync with the
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# one used by server.py: indexer and server have to embed with the same model,
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# otherwise the vectors stored in Qdrant are not comparable with query vectors.
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EMBEDDING_MODEL_NAME = os.environ.get("EMBEDDING_MODEL_NAME", "BAAI/bge-small-en-v1.5")
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BATCH_SIZE = int(os.environ.get("BATCH_SIZE", "100"))
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EMBEDDING_BATCH_SIZE = int(os.environ.get("EMBEDDING_BATCH_SIZE", "64"))
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METADATA_COLLECTION = "mcp_indexer_metadata"
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INCREMENTAL_DAYS = int(os.environ.get("INCREMENTAL_DAYS", "7"))
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FORCE_REINDEX = os.environ.get("FORCE_REINDEX", "").lower() in ("1", "true", "yes")
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@@ -21,6 +21,9 @@ from indexer import (
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parse_email_message,
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get_recent_keys,
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METADATA_COLLECTION,
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EMBEDDING_MODEL_NAME,
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BATCH_SIZE,
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EMBEDDING_BATCH_SIZE,
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)
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@@ -227,3 +230,25 @@ class TestGetRecentKeys:
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assert key.split(":")[0] in keys
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for k in keys:
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assert ":" not in k
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# ---------------------------------------------------------------------------
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# Regression: module-level constants consumed by main()
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# ---------------------------------------------------------------------------
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def test_indexer_module_constants_are_defined():
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"""Regression (e32806a): the "make indexer importable without env" refactor
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dropped these three module-level definitions while main() kept using them,
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so every indexer run died with NameError before touching Qdrant."""
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assert isinstance(EMBEDDING_MODEL_NAME, str) and EMBEDDING_MODEL_NAME
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assert isinstance(BATCH_SIZE, int) and BATCH_SIZE > 0
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assert isinstance(EMBEDDING_BATCH_SIZE, int) and EMBEDDING_BATCH_SIZE > 0
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def test_embedding_model_is_the_same_as_the_server_one():
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"""Indexer and server must embed with the same model, otherwise the vectors
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stored by the indexer are not comparable with the query vectors."""
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from server import EMBEDDING_MODEL_NAME as server_embedding_model
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assert EMBEDDING_MODEL_NAME == server_embedding_model
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