diff --git a/.github/dependabot.yml b/.github/dependabot.yml deleted file mode 100644 index 0e71ac3..0000000 --- a/.github/dependabot.yml +++ /dev/null @@ -1,18 +0,0 @@ -# To get started with Dependabot version updates, you'll need to specify which -# package ecosystems to update and where the package manifests are located. -# Please see the documentation for all configuration options: -# https://docs.github.com/github/administering-a-repository/configuration-options-for-dependency-updates - -version: 2 -updates: - # Maintain dependencies for GitHub Actions - - package-ecosystem: "github-actions" - directory: "/" - schedule: - interval: "daily" - - # Maintain dependencies for npm - - package-ecosystem: "pip" - directory: "/" - schedule: - interval: "daily" diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index f529e92..997205c 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,29 +1,28 @@ default_language_version: - python: python3.14 # set for project python version + python: python3.14 +default_stages: + - pre-push repos: - repo: local hooks: - - id: black-apply - name: black-apply - entry: uv run black + - id: ruff-format + name: ruff-format + entry: uv run ruff format --diff language: system pass_filenames: true - types: ["python"] + types: [ "python" ] + - id: mypy name: mypy entry: uv run mypy language: system pass_filenames: true - types: ["python"] - exclude: "tests/" - - id: ruff-apply - name: ruff-apply - entry: uv run ruff check --fix + types: [ "python" ] + exclude: "(tests/|output/|migrations/)" + + - id: ruff-check + name: ruff-check + entry: uv run ruff check language: system pass_filenames: true - types: ["python"] - # - id: pip-audit - # name: pip-audit - # entry: uv run pip-audit - # language: system - # pass_filenames: false + types: [ "python" ] diff --git a/Dockerfile b/Dockerfile index 2b5801b..b14d29e 100644 --- a/Dockerfile +++ b/Dockerfile @@ -16,4 +16,4 @@ FROM public.ecr.aws/lambda/python:3.14 COPY --from=builder ${LAMBDA_TASK_ROOT} ${LAMBDA_TASK_ROOT} -CMD ["lambdas.my_function.lambda_handler"] +CMD ["lambdas.tokenizer_handler.lambda_handler"] diff --git a/LICENSE b/LICENSE index 261eeb9..7cdd900 100644 --- a/LICENSE +++ b/LICENSE @@ -186,7 +186,7 @@ same "printed page" as the copyright notice for easier identification within third-party archives. - Copyright [yyyy] [name of copyright owner] + Copyright 2026 MIT Libraries Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. diff --git a/Makefile b/Makefile index f009fa4..f8f6556 100644 --- a/Makefile +++ b/Makefile @@ -15,22 +15,26 @@ help: # Preview Makefile commands /^[-_[:alpha:]]+:.?*#/ { printf " %-15s%s\n", $$1, $$2 }' $(MAKEFILE_LIST) # ensure OS binaries aren't called if naming conflict with Make recipes -.PHONY: help venv install update test coveralls lint black mypy ruff safety lint-apply black-apply ruff-apply check-arch dist-dev publish-dev update-lambda-dev docker-clean +.PHONY: help venv install update test coveralls lint lint-fix security sam-build sam-invoke console check-arch dist-dev publish-dev update-lambda-dev docker-clean ############################################## # Python Environment and Dependency commands ############################################## -install: .venv .git/hooks/pre-commit # Install Python dependencies, hooks, and create virtual environment if not exists +install: .venv .git/hooks/pre-commit .git/hooks/pre-push # Install Python dependencies and create virtual environment if not exists uv sync --dev .venv: # Creates virtual environment if not found @echo "Creating virtual environment at .venv..." uv venv .venv -.git/hooks/pre-commit: # Sets up pre-commit hook if not setup - @echo "Installing pre-commit hooks..." - uv run pre-commit install +.git/hooks/pre-commit: # Sets up pre-commit commit hooks if not setup + @echo "Installing pre-commit commit hooks..." + uv run pre-commit install --hook-type pre-commit + +.git/hooks/pre-push: # Sets up pre-commit push hooks if not setup + @echo "Installing pre-commit push hooks..." + uv run pre-commit install --hook-type pre-push venv: .venv # Create the Python virtual environment @@ -50,44 +54,36 @@ coveralls: test # Write coverage data to an LCOV report uv run coverage lcov -o ./coverage/lcov.info #################################### -# Code quality and safety commands +# Code linting and formatting #################################### -lint: black mypy ruff # Run linters - -black: # Run 'black' linter and print a preview of suggested changes - uv run black --check --diff . - -mypy: # Run 'mypy' linter +lint: # Run linters + uv run ruff format --diff uv run mypy . - -ruff: # Run 'ruff' linter and print a preview of errors uv run ruff check . -safety: # Check for security vulnerabilities - uv run pip-audit - -lint-apply: black-apply ruff-apply # Apply changes with 'black' and resolve 'fixable errors' with 'ruff' - -black-apply: # Apply changes with 'black' - uv run black . - -ruff-apply: # Resolve 'fixable errors' with 'ruff' +lint-fix: # Run linting, auto fix behaviors where supported + uv run ruff format . uv run ruff check --fix . +security: # Check for security vulnerabilities + uv run pip-audit + #################################### # SAM Lambda #################################### -sam-build: # Build SAM image for running Lambda locally - sam build --template tests/sam/template.yaml +sam-build: # Build Docker image for running Lambda locally + sam build --template tests/sam/template.yaml --debug -sam-http-run: # Run lambda locally as an HTTP server - sam local start-api --template tests/sam/template.yaml --env-vars tests/sam/env.json +sam-invoke: # Invoke lambda locally with tests/sam/event.json + sam local invoke Tokenizer --template tests/sam/template.yaml --env-vars tests/sam/env.json --event tests/sam/event.json -sam-http-ping: # Send curl command to SAM HTTP server - curl --location 'http://localhost:3000/myapp' \ - --header 'Content-Type: application/json' \ - --data '{"msg":"in a bottle"}' +#################################### +# Convenience commands +#################################### +console: # Start a Python REPL with the project environment loaded + @echo "'exit' to quit console" + uv run ipython #################################### # Deployment to ECR @@ -126,7 +122,7 @@ publish-dev: dist-dev ## Build, tag and push (intended for developer-based manua docker image prune -f --filter "dangling=true" ## If this is a Lambda repo, uncomment the two lines below -update-lambda-dev: ## Updates the lambda with whatever is the most recent image in the ecr (intended for developer-based manual update) +update-lambda-dev: check-arch ## Updates the lambda with whatever is the most recent image in the ecr (intended for developer-based manual update) @ARCH_TAG=$$(cat .arch_tag); \ aws lambda update-function-code \ --region us-east-1 \ diff --git a/README.md b/README.md index cce3c39..b9db8ed 100644 --- a/README.md +++ b/README.md @@ -1,28 +1,6 @@ -# python-lambda-template +# timdex-semantic-builder -A template repository for creating Python lambda functions. - -## Repo Setup (delete this section and above after initial function setup) - -1. Rename "my_function" to the desired initial function name across the repo. (May be helpful to do a project-wide find-and-replace). -2. Update Python version if needed (note: AWS lambda cannot currently support versions higher than 3.9). -3. Install all dependencies with `make install` to create initial Pipfile.lock with latest dependency versions. -4. Add initial function description to README and update initial required ENV variable documentation as needed. -5. Update license if needed (check app-specific dependencies for licensing terms). -6. Check Github repository settings: - - Confirm repo branch protection settings are correct (see [dev docs](https://mitlibraries.github.io/guides/basics/github.html) for details) - - Confirm that all of the following are enabled in the repo's code security and analysis settings: - - Dependabot alerts - - Dependabot security updates - - Secret scanning -7. Create a Sentry project for the app if needed (we want this for most apps): - - Send initial exceptions to Sentry project for dev, stage, and prod environments to create them. - - Create an alert for the prod environment only, with notifications sent to the appropriate team(s). - - If *not* using Sentry, delete Sentry configuration from my_function.py and test_my_function_.py, and remove sentry_sdk from project dependencies. - -# my_function - -Description of the function/functions. +Accepts query string (keyword) and returns OpenSearch-ready token weights. ## Development @@ -32,17 +10,25 @@ Description of the function/functions. - To run unit tests: `make test` - To lint the repo: `make lint` +## opensearch-project directory + +This directory stores 3 files extracted from the `opensearch-neural-sparse-encoding-doc-v3-gte` model. + +We store them locally to reduce unnecessary downloads from huggingface, and because we don't need the full model, just these files. + +This repository should refresh these files whenever our pipeline repository updates the model version. + ## Testing Locally with AWS SAM ### SAM Installation -Ensure that AWS SAM CLI is installed: https://docs.aws.amazon.com/serverless-application-model/latest/developerguide/install-sam-cli.html. +Ensure that AWS SAM CLI is installed: . All following actions and commands should be performed from the root of the project (i.e. same directory as the `Dockerfile`). ### Building and Configuration -1- Create a JSON file for SAM that has environment variables for the container +1- Create a JSON file for SAM that has environment variables for the container - copy `tests/sam/env.json.template` to `tests/sam/env.json` (which is git ignored) - fill in missing sensitive env vars @@ -51,52 +37,61 @@ All following actions and commands should be performed from the root of the proj 2- Build Docker image: -```shell -make sam-build -``` - -### Invoking Lambda via HTTP requests - -The following outlines how to run the Lambda SAM docker image as an HTTP endpoint, accepting requests and returning respnoses similar to a lambda behind an ALB, Function URL, or API Gateway. - -1- Ensure any required AWS credentials set in terminal, and any other env vars in `tests/sam/env.json` up-to-date. - -2- Run HTTP server: - -```shell -make sam-http-run -``` - -This starts a server at `http://localhost:3000`. Requests must include a path, e.g. `/myapp`, but are arbitrary insofar as the lambda does not utilize them in the request payload. - -3- In another terminal, perform an HTTP request via another `Makefile` command: +> [!TIP] +> This step can take a few minutes and may appear to hang. ```shell -make sam-http-ping +make sam-build ``` -Response should have an HTTP status of `200` and respond with: - -```json -You have successfully called this lambda! -``` +> [!IMPORTANT] +> You need to rebuild when you change code. ### Invoking Lambda directly -While Lambdas can be invoked via HTTP methods (ALB, Function URL, etc.), they are also often invoked directly with an `event` payload. To do so with SAM, you do **not** need to first start an HTTP server with `make sam-run`, you can invoke the function image directly: +While Lambdas can be invoked via HTTP methods (ALB, Function URL, etc.), they are also often invoked directly with an `event` payload. You can invoke the function image directly: ```shell -echo '{"action": "ping"}' | sam local invoke -e - +echo '{"query": "hello world"}' | sam local invoke -e - ``` Response: -```text -You have successfully called this lambda! +```json +{ + "query": { + "bool": { + "should": [ + { + "rank_feature": { + "field": "embedding_full_record.[CLS]", + "boost": 1.0 + } + }, + { + "rank_feature": { + "field": "embedding_full_record.[SEP]", + "boost": 1.0 + } + }, + { + "rank_feature": { + "field": "embedding_full_record.world", + "boost": 3.4208686351776123 + } + }, + { + "rank_feature": { + "field": "embedding_full_record.hello", + "boost": 6.937756538391113 + } + } + ] + } + } +} ``` -As you can see from this response, the returning the same content even though it was invoked directly. - ### Troubleshoot #### Encounter error `botocore.exceptions.TokenRetrievalError` @@ -109,11 +104,11 @@ When running a Lambda via SAM, it attempts to parse and setup AWS credentials ju ### Build the container -`docker build -t my_function:latest .` +`docker build -t tokenizer:latest .` ### Run the container -`docker run -e WORKSPACE=dev -p 9000:8080 my_function:latest` +`docker run -e WORKSPACE=dev -p 9000:8080 tokenizer:latest` ### Call the container via HTTP from another terminal window @@ -123,17 +118,62 @@ If you have `jq` installed, you can pipe the output to get better formatted outp `curl -XPOST "http://localhost:9000/2015-03-31/functions/function/invocations" -d '{ "query": "hello world"}' | jq` +### Open a python console with application context + +- `make console` +- `from lambdas import tokenizer_handler` +- `tokenizer_handler.lambda_handler({"query": "hello world"}, {})` + +Response: + +```json +{ + "query":{ + "bool":{ + "should":[ + { + "rank_feature":{ + "field":"embedding_full_record.[CLS]", + "boost":1.0 + } + }, + { + "rank_feature":{ + "field":"embedding_full_record.[SEP]", + "boost":1.0 + } + }, + { + "rank_feature":{ + "field":"embedding_full_record.world", + "boost":3.4208686351776123 + } + }, + { + "rank_feature":{ + "field":"embedding_full_record.hello", + "boost":6.937756538391113 + } + } + ] + } + } +} +``` + ## Environment Variables +In local development, you can add a `.env` file to manage these. The file is excluded from git and docker builds via +ignore files. + ### Required ```shell -SENTRY_DSN=### If set to a valid Sentry DSN, enables Sentry exception monitoring. This is not needed for local development. WORKSPACE=### Set to `dev` for local development, this will be set to `stage` and `prod` in those environments by Terraform. ``` ### Optional ```shell -=### Description for optional environment variable +SENTRY_DSN=### If set to a valid Sentry DSN, enables Sentry exception monitoring. This is not needed for local development. ``` diff --git a/lambdas/config.py b/lambdas/config.py index 67e310d..34222b9 100644 --- a/lambdas/config.py +++ b/lambdas/config.py @@ -2,18 +2,20 @@ import os import sentry_sdk + +# Load .env file. Generally only used in local dev as we exclude from git and docker +from dotenv import load_dotenv from sentry_sdk.integrations.aws_lambda import AwsLambdaIntegration +load_dotenv() + logger = logging.getLogger(__name__) logger.setLevel(logging.DEBUG) class Config: - REQUIRED_ENV_VARS = ( - "WORKSPACE", - "SENTRY_DSN", - ) - OPTIONAL_ENV_VARS = ("WARNING_ONLY_LOGGERS",) + REQUIRED_ENV_VARS = ("WORKSPACE",) + OPTIONAL_ENV_VARS = ("LOG_LEVEL", "SENTRY_DSN", "WARNING_ONLY_LOGGERS") def check_required_env_vars(self) -> None: """Method to raise exception if required env vars not set.""" @@ -33,28 +35,20 @@ def sentry_dsn(self) -> str | None: return dsn return None + @property + def log_level(self) -> int: + level_str = os.getenv("LOG_LEVEL", "INFO").upper() + return getattr(logging, level_str, logging.INFO) + def configure_logger( root_logger: logging.Logger, *, - verbose: bool = False, warning_only_loggers: str | None = None, ) -> str: - """Configure application via passed application root logger. - - If verbose=True, 3rd party libraries can be quite chatty. For convenience, they can - be set to WARNING level by either passing a comma seperated list of logger names to - 'warning_only_loggers' or by setting the env var WARNING_ONLY_LOGGERS. - """ - if verbose: - root_logger.setLevel(logging.DEBUG) - logging_format = ( - "%(asctime)s %(levelname)s %(name)s.%(funcName)s() " - "line %(lineno)d: %(message)s" - ) - else: - root_logger.setLevel(logging.INFO) - logging_format = "%(asctime)s %(levelname)s %(name)s.%(funcName)s(): %(message)s" + """Configure application via passed application root logger.""" + root_logger.setLevel(Config().log_level) + logging_format = "%(asctime)s %(levelname)s %(name)s.%(funcName)s(): %(message)s" warning_only_loggers = os.getenv("WARNING_ONLY_LOGGERS", warning_only_loggers) if warning_only_loggers: @@ -76,17 +70,6 @@ def configure_logger( ) -def configure_dev_logger( - warning_only_loggers: str = ",".join( # noqa: FLY002 - ["asyncio", "botocore", "urllib3", "boto3", "smart_open"] - ), -) -> None: - """Invoke to setup DEBUG level console logging for development work.""" - os.environ["WARNING_ONLY_LOGGERS"] = warning_only_loggers - root_logger = logging.getLogger() - configure_logger(root_logger, verbose=True) - - def configure_sentry() -> None: CONFIG = Config() # noqa: N806 env = CONFIG.workspace diff --git a/lambdas/query_tokenizer.py b/lambdas/query_tokenizer.py new file mode 100644 index 0000000..fa82612 --- /dev/null +++ b/lambdas/query_tokenizer.py @@ -0,0 +1,98 @@ +import json +import logging +from typing import TYPE_CHECKING, cast + +if TYPE_CHECKING: + from os import PathLike + +from pathlib import Path + +import torch +from transformers import AutoTokenizer + +logger = logging.getLogger(__name__) + + +class QueryTokenizer: + """Tokenizer for generating sparse query vectors for OpenSearch neural sparse search. + + Uses the OpenSearch neural sparse encoding model (doc-v3-gte) to convert query text + into a sparse representation where each token is weighted by its IDF (Inverse Document + Frequency) score. The resulting dict maps vocabulary tokens to float weights, suitable + for use in OpenSearch `rank_feature` queries. + """ + + def __init__(self) -> None: + # Load tokenizer from local path + # Model: opensearch-neural-sparse-encoding-doc-v3-gte, stored in the repo + # to avoid network calls at Lambda cold start + tokenizer_path = "opensearch-project/opensearch-neural-sparse-encoding-doc-v3-gte" + + # Ensure tokenizer_path exists. This avoids a situation in which the AutoTokenizer + # will download the model from Hugging Face if it doesn't find the local files. + # We want to always use our local files or error if they don't exist. + if not Path(tokenizer_path).exists(): + msg = f"Tokenizer path not found: {tokenizer_path}" + raise FileNotFoundError(msg) + + self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_path) + + # Load IDF weights from local file + idf_path = tokenizer_path + "/idf.json" + self.idf = self._load_idf(idf_path) + + def _load_idf(self, idf_path: str | PathLike[str]) -> torch.Tensor: + """Load IDF weights from a local JSON file into a vocabulary-sized tensor. + + IDF (Inverse Document Frequency) weights down-score tokens that appear + frequently across documents (e.g. "the", "and") and up-score rare, more + meaningful tokens. The resulting tensor is indexed by token ID, aligned + with the tokenizer's vocabulary. + """ + with open(idf_path) as f: + idf_dict = json.load(f) + + # Convert to tensor + idf_vector = [0] * self.tokenizer.vocab_size + for token, weight in idf_dict.items(): + token_id = cast("int", self.tokenizer.convert_tokens_to_ids(token)) + idf_vector[token_id] = weight + return torch.tensor(idf_vector) + + def tokenize_query(self, query_text: str) -> dict[str, float]: + """Convert query text into sparse token weights for OpenSearch rank_feature query. + + Tokenizes the input, marks which vocabulary tokens are present (known as a one-hot + vector in machine learning), then scales each by its IDF weight. Only tokens + present in the query with a non-zero IDF weight appear in the result. Token + strings are vocabulary tokens (not necessarily whole words) and float values are + their IDF-weighted scores. + """ + feature_query = self.tokenizer( + [query_text], + padding=True, + truncation=True, + return_tensors="pt", + return_token_type_ids=False, + ) + input_ids = feature_query["input_ids"] + + # Create one-hot vector and apply IDF weights + batch_size = input_ids.shape[0] + query_vector = torch.zeros(batch_size, self.tokenizer.vocab_size) + query_vector[torch.arange(batch_size).unsqueeze(-1), input_ids] = 1 + query_sparse_vector = query_vector * self.idf + + # Convert to dict format + return self._sparse_vector_to_dict(query_sparse_vector[0]) + + def _sparse_vector_to_dict(self, sparse_vector: torch.Tensor) -> dict[str, float]: + """Convert a 1D sparse tensor into a dict of token strings to float weights. + + Only non-zero entries are included in the output, keeping the result compact + and directly usable as an OpenSearch rank_feature query payload. + """ + token_indices = torch.nonzero(sparse_vector, as_tuple=True)[0].tolist() + non_zero_values = sparse_vector[token_indices].tolist() + tokens = self.tokenizer.convert_ids_to_tokens(token_indices) + return dict(zip(tokens, non_zero_values, strict=True)) diff --git a/lambdas/tokenizer_handler.py b/lambdas/tokenizer_handler.py new file mode 100644 index 0000000..051bc1f --- /dev/null +++ b/lambdas/tokenizer_handler.py @@ -0,0 +1,73 @@ +import logging +import time +from functools import lru_cache +from typing import TYPE_CHECKING + +if TYPE_CHECKING: + from aws_lambda_typing.context import Context + +from lambdas.config import Config, configure_logger, configure_sentry +from lambdas.query_tokenizer import QueryTokenizer + +# --------------------------------------- +# One-time, Lambda cold start setup +# --------------------------------------- +CONFIG = Config() +CONFIG.check_required_env_vars() + +root_logger = logging.getLogger() +log_config_message = configure_logger(root_logger) +logger = logging.getLogger(__name__) +logger.info(log_config_message) + +configure_sentry() + + +@lru_cache(maxsize=1) +def _get_tokenizer() -> QueryTokenizer: + """Return the module-level QueryTokenizer, created once and cached.""" + logger.info("Initializing QueryTokenizer (cold start)") + return QueryTokenizer() + + +# --------------------------------------- +# Lambda handler entrypoint +# --------------------------------------- +def lambda_handler(event: dict, lambda_context: Context) -> dict: + """Main Lambda handler for tokenizing queries for OpenSearch. + + Returns a JSON-serializable dict; the AWS Lambda runtime handles serialization. + """ + logger.debug("Received event: %s", event) + logger.debug("Lambda context: %s", lambda_context) + + query_tokenizer = _get_tokenizer() + + # Generate query tokens + query = event.get("query", "") + + if not query.strip(): + logger.warning("Received empty query in event: %s", event) + return {"error": "Query is required in the event payload."} + + start = time.perf_counter() + query_tokens = query_tokenizer.tokenize_query(query) + end = time.perf_counter() + logger.debug("Tokenization and IDF weighting took: %.4f seconds", end - start) + + # Build OpenSearch query + return { + "query": { + "bool": { + "should": [ + { + "rank_feature": { + "field": f"embedding_full_record.{token}", + "boost": weight, + } + } + for token, weight in query_tokens.items() + ] + } + } + } diff --git a/opensearch-project/opensearch-neural-sparse-encoding-doc-v3-gte/README.md b/opensearch-project/opensearch-neural-sparse-encoding-doc-v3-gte/README.md new file mode 100644 index 0000000..0e8c6de --- /dev/null +++ b/opensearch-project/opensearch-neural-sparse-encoding-doc-v3-gte/README.md @@ -0,0 +1,13 @@ +# OpenSearch Tokenizer files + +The files in this directory are sourced from a model hosted on [Huggingface](https://huggingface.co/opensearch-project/opensearch-neural-sparse-encoding-doc-v3-gte/tree/main). + +This is the license and copyright statement supplied with the files. + +## License + +This project is licensed under the [Apache v2.0 License](https://github.com/opensearch-project/neural-search/blob/main/LICENSE). + +## Copyright + +Copyright OpenSearch Contributors. See [NOTICE](https://github.com/opensearch-project/neural-search/blob/main/NOTICE) for details. diff --git a/opensearch-project/opensearch-neural-sparse-encoding-doc-v3-gte/idf.json b/opensearch-project/opensearch-neural-sparse-encoding-doc-v3-gte/idf.json new file mode 100644 index 0000000..43a6696 --- /dev/null +++ b/opensearch-project/opensearch-neural-sparse-encoding-doc-v3-gte/idf.json @@ -0,0 +1 @@ +{"[PAD]": 1.0, "[unused0]": 1.0, "[unused1]": 1.0, "[unused2]": 1.0, "[unused3]": 1.0, "[unused4]": 1.0, "[unused5]": 1.0, "[unused6]": 1.0, "[unused7]": 1.0, "[unused8]": 1.0, "[unused9]": 1.0, "[unused10]": 1.0, "[unused11]": 1.0, "[unused12]": 1.0, "[unused13]": 1.0, "[unused14]": 1.0, "[unused15]": 1.0, "[unused16]": 1.0, "[unused17]": 1.0, "[unused18]": 1.0, "[unused19]": 1.0, "[unused20]": 1.0, "[unused21]": 1.0, "[unused22]": 1.0, "[unused23]": 1.0, "[unused24]": 1.0, "[unused25]": 1.0, "[unused26]": 1.0, "[unused27]": 1.0, "[unused28]": 1.0, "[unused29]": 1.0, "[unused30]": 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"special": true + }, + "100": { + "content": "[UNK]", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "101": { + "content": "[CLS]", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "102": { + "content": "[SEP]", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "103": { + "content": "[MASK]", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + } + }, + "clean_up_tokenization_spaces": true, + "cls_token": "[CLS]", + "do_lower_case": true, + "mask_token": "[MASK]", + "model_max_length": 8192, + "pad_token": "[PAD]", + "sep_token": "[SEP]", + "strip_accents": null, + "tokenize_chinese_chars": true, + "tokenizer_class": "DistilBertTokenizer", + "unk_token": "[UNK]" +} diff --git a/pyproject.toml b/pyproject.toml index 8890af5..8a0c82f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,12 +4,14 @@ version = "2.0.0" requires-python = ">=3.14" dependencies = [ + "python-dotenv>=1.2.2", "sentry-sdk>=2.29.1", + "transformers[torch]>=5.2.0", ] [dependency-groups] dev = [ - "black>=26.1.0", + "aws-lambda-typing>=2.2.0", "coveralls>=4.0.1", "ipython>=9.2.0", "mypy>=1.15.0", @@ -19,9 +21,6 @@ dev = [ "ruff>=0.11.11", ] -[tool.black] -line-length = 90 - [tool.mypy] disallow_untyped_calls = true disallow_untyped_defs = true @@ -58,9 +57,6 @@ ignore = [ "S321", ] -# allow autofix behavior for specified rules -fixable = ["E", "F", "I", "Q"] - [tool.ruff.lint.flake8-annotations] mypy-init-return = true @@ -72,6 +68,8 @@ fixture-parentheses = false "ANN", "ARG001", "S101", + "SLF001", + "PLR2004", ] [tool.ruff.lint.pycodestyle] diff --git a/tests/conftest.py b/tests/conftest.py index 6fdf7de..f14613f 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -1,6 +1,7 @@ -import pytest +import os - -@pytest.fixture(autouse=True) -def _test_env(monkeypatch): - monkeypatch.setenv("WORKSPACE", "test") +# Set required environment variables for testing here +# Failure to do so will result in errors during initialization +# You can override these with test-specific values in individual test files as needed +# with monkeypatch.setenv("VARIABLE", "custom_value") +os.environ["WORKSPACE"] = "test" diff --git a/tests/sam/env.json.template b/tests/sam/env.json.template index b740b91..c384eea 100644 --- a/tests/sam/env.json.template +++ b/tests/sam/env.json.template @@ -1,3 +1,5 @@ { - "MyApp": {} + "Tokenizer": { + "SENTRY_DSN": "None" + } } diff --git a/tests/sam/event.json b/tests/sam/event.json new file mode 100644 index 0000000..80d7564 --- /dev/null +++ b/tests/sam/event.json @@ -0,0 +1,3 @@ +{ + "query": "machine learning" +} diff --git a/tests/sam/template.yaml b/tests/sam/template.yaml index 94d2a1e..0f0cfc1 100644 --- a/tests/sam/template.yaml +++ b/tests/sam/template.yaml @@ -1,28 +1,25 @@ AWSTemplateFormatVersion: "2010-09-09" Transform: AWS::Serverless-2016-10-31 Resources: - MyApp: + Tokenizer: Type: AWS::Serverless::Function Properties: PackageType: Image Timeout: 900 MemorySize: 1024 - Events: - BagitApi: - Type: HttpApi - Properties: - Path: /{proxy+} - Method: ANY - ImageUri: myapp:latest + Architectures: + - arm64 + ImageUri: tokenizer:latest Environment: # While tests/sam/env.json is required for sensitive env vars, ALL env vars # used in the lambda must exist here as well, even just as "..." placeholders. Variables: + LOG_LEVEL: "INFO" WORKSPACE: "dev" - WARNING_ONLY_LOGGERS: "asyncio,botocore,urllib3,s3transfer,boto3" - #SENTRY_DSN: "None" + WARNING_ONLY_LOGGERS: "asyncio" + SENTRY_DSN: "None" Metadata: DockerContext: ../../. # Build Lambda Dockerfile DockerTag: latest Dockerfile: Dockerfile - SamResourceId: MyApp + SamResourceId: Tokenizer diff --git a/tests/test_query_tokenizer.py b/tests/test_query_tokenizer.py new file mode 100644 index 0000000..e1d450d --- /dev/null +++ b/tests/test_query_tokenizer.py @@ -0,0 +1,131 @@ +import json +from unittest.mock import MagicMock, mock_open, patch + +import pytest +import torch + +from lambdas.query_tokenizer import QueryTokenizer + +VOCAB_SIZE = 100 +MOCK_IDF = {"hello": 1.5, "world": 2.0} +# token ids for "hello" and "world" in mock vocab +HELLO_ID = 5 +WORLD_ID = 10 + + +def make_mock_tokenizer(): + mock_tokenizer = MagicMock() + mock_tokenizer.vocab_size = VOCAB_SIZE + mock_tokenizer.convert_tokens_to_ids.side_effect = lambda t: ( + HELLO_ID if t == "hello" else WORLD_ID + ) + mock_tokenizer.convert_ids_to_tokens.side_effect = lambda ids: [ + "hello" if i == HELLO_ID else "world" for i in ids + ] + return mock_tokenizer + + +@pytest.fixture +def query_tokenizer(): + mock_tokenizer = make_mock_tokenizer() + with ( + patch( + "lambdas.query_tokenizer.AutoTokenizer.from_pretrained", + return_value=mock_tokenizer, + ), + patch("builtins.open", mock_open(read_data=json.dumps(MOCK_IDF))), + ): + return QueryTokenizer() + + +def test_idf_loaded_as_tensor(query_tokenizer): + assert isinstance(query_tokenizer.idf, torch.Tensor) + assert query_tokenizer.idf.shape[0] == VOCAB_SIZE + assert query_tokenizer.idf[HELLO_ID].item() == pytest.approx(1.5) + assert query_tokenizer.idf[WORLD_ID].item() == pytest.approx(2.0) + + +def test_tokenize_query_returns_dict(query_tokenizer): + # Simulate tokenizer returning input_ids containing HELLO_ID and WORLD_ID + query_tokenizer.tokenizer.return_value = { + "input_ids": torch.tensor([[HELLO_ID, WORLD_ID]]) + } + result = query_tokenizer.tokenize_query("hello world") + assert isinstance(result, dict) + assert len(result) > 0 + + +def test_tokenize_query_weights_are_floats(query_tokenizer): + query_tokenizer.tokenizer.return_value = { + "input_ids": torch.tensor([[HELLO_ID, WORLD_ID]]) + } + result = query_tokenizer.tokenize_query("hello world") + for weight in result.values(): + assert isinstance(weight, float) + + +def test_sparse_vector_to_dict_maps_tokens_to_weights(query_tokenizer): + sparse = torch.zeros(VOCAB_SIZE) + sparse[HELLO_ID] = 1.5 + sparse[WORLD_ID] = 2.0 + result = query_tokenizer._sparse_vector_to_dict(sparse) + assert result == {"hello": pytest.approx(1.5), "world": pytest.approx(2.0)} + + +def test_sparse_vector_to_dict_excludes_zero_weights(query_tokenizer): + sparse = torch.zeros(VOCAB_SIZE) + sparse[HELLO_ID] = 1.5 + result = query_tokenizer._sparse_vector_to_dict(sparse) + assert "world" not in result + + +def test_tokenize_query_returns_correct_weights(query_tokenizer): + query_tokenizer.tokenizer.return_value = { + "input_ids": torch.tensor([[HELLO_ID, WORLD_ID]]) + } + result = query_tokenizer.tokenize_query("hello world") + assert result == {"hello": pytest.approx(1.5), "world": pytest.approx(2.0)} + + +def test_tokenize_query_excludes_tokens_not_in_idf(query_tokenizer): + # ID 20 is not in MOCK_IDF so its IDF weight is 0; it should be absent from result + query_tokenizer.tokenizer.convert_ids_to_tokens.side_effect = lambda ids: [ + "hello" if i == HELLO_ID else "unknown" for i in ids + ] + query_tokenizer.tokenizer.return_value = {"input_ids": torch.tensor([[HELLO_ID, 20]])} + result = query_tokenizer.tokenize_query("hello unknown") + assert "hello" in result + assert "unknown" not in result + + +def test_init_raises_filenotfounderror_when_tokenizer_path_missing(): + """Test that FileNotFoundError is raised when tokenizer_path doesn't exist.""" + with ( + patch("pathlib.Path.exists", return_value=False), + pytest.raises(FileNotFoundError, match="Tokenizer path not found"), + ): + QueryTokenizer() + + +def test_init_raises_filenotfounderror_with_correct_path_message(): + """Test that FileNotFoundError message includes the missing path.""" + missing_path = "opensearch-project/opensearch-neural-sparse-encoding-doc-v3-gte" + with patch("pathlib.Path.exists", return_value=False): + with pytest.raises(FileNotFoundError) as exc_info: + QueryTokenizer() + assert missing_path in str(exc_info.value) + + +def test_load_idf_raises_filenotfounderror_when_idf_file_missing(): + """Test that FileNotFoundError is raised when IDF file doesn't exist.""" + mock_tokenizer = make_mock_tokenizer() + with ( + patch( + "lambdas.query_tokenizer.AutoTokenizer.from_pretrained", + return_value=mock_tokenizer, + ), + patch("pathlib.Path.exists", return_value=True), + patch("builtins.open", side_effect=FileNotFoundError()), + pytest.raises(FileNotFoundError), + ): + QueryTokenizer() diff --git a/tests/test_tokenizer_handler.py b/tests/test_tokenizer_handler.py new file mode 100644 index 0000000..9c21cba --- /dev/null +++ b/tests/test_tokenizer_handler.py @@ -0,0 +1,61 @@ +from unittest.mock import MagicMock, patch + +import pytest + +from lambdas import tokenizer_handler + + +@pytest.fixture +def mock_query_tokenizer(): + mock_instance = MagicMock() + with patch.object(tokenizer_handler, "_get_tokenizer", return_value=mock_instance): + yield mock_instance + + +def test_returns_opensearch_query_structure(mock_query_tokenizer): + mock_query_tokenizer.tokenize_query.return_value = {"hello": 1.5, "world": 2.0} + result = tokenizer_handler.lambda_handler({"query": "hello world"}, {}) + assert "query" in result + assert "bool" in result["query"] + assert "should" in result["query"]["bool"] + + +def test_opensearch_query_contains_rank_features(mock_query_tokenizer): + mock_query_tokenizer.tokenize_query.return_value = {"hello": 1.5} + result = tokenizer_handler.lambda_handler({"query": "hello"}, {}) + should_clauses = result["query"]["bool"]["should"] + assert len(should_clauses) == 1 + assert should_clauses[0]["rank_feature"]["field"] == "embedding_full_record.hello" + assert should_clauses[0]["rank_feature"]["boost"] == pytest.approx(1.5) + + +def test_returns_no_query_provided_for_empty_query(mock_query_tokenizer): + result = tokenizer_handler.lambda_handler({"query": ""}, {}) + assert result == {"error": "Query is required in the event payload."} + mock_query_tokenizer.tokenize_query.assert_not_called() + + +def test_returns_no_query_provided_when_query_key_missing(mock_query_tokenizer): + result = tokenizer_handler.lambda_handler({}, {}) + assert result == {"error": "Query is required in the event payload."} + mock_query_tokenizer.tokenize_query.assert_not_called() + + +def test_each_token_weight_pair_becomes_rank_feature(mock_query_tokenizer): + mock_query_tokenizer.tokenize_query.return_value = {"foo": 1.0, "bar": 3.5} + result = tokenizer_handler.lambda_handler({"query": "foo bar"}, {}) + should_clauses = result["query"]["bool"]["should"] + fields = {c["rank_feature"]["field"] for c in should_clauses} + assert fields == {"embedding_full_record.foo", "embedding_full_record.bar"} + + +def test_tokenize_query_called_with_correct_query(mock_query_tokenizer): + mock_query_tokenizer.tokenize_query.return_value = {"fakedata": 1.5} + tokenizer_handler.lambda_handler({"query": "hello world"}, {}) + mock_query_tokenizer.tokenize_query.assert_called_once_with("hello world") + + +def test_returns_error_for_whitespace_only_query(mock_query_tokenizer): + result = tokenizer_handler.lambda_handler({"query": " "}, {}) + assert result == {"error": "Query is required in the event payload."} + mock_query_tokenizer.tokenize_query.assert_not_called() diff --git a/uv.lock b/uv.lock index 7231743..30bbc8c 100644 --- a/uv.lock +++ b/uv.lock @@ -2,6 +2,24 @@ version = 1 revision = 3 requires-python = ">=3.14" 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