Push

.env

@@ -0,0 +1,19 @@
+# To overwrite these variables, create a .env.local file
+
+# The path to the directory where the data will be downloaded on machine
+LILAC_DATA_PATH=./data
+
+# Set to 1 for duckdb to use views instead of materialized tables (lower memory usage, but slower).
+DUCKDB_USE_VIEWS=0
+
+# Variables that can be set in .env.local
+#
+# Get key from https://www.cohere.ai/api-keys
+# COHERE_API_KEY=
+
+# GCS_REGION=
+# GCS_ACCESS_KEY=
+# GCS_SECRET_KEY=
+
+# Get key from https://platform.openai.com/account/api-keys
+# OPENAI_API_KEY=

Dockerfile

@@ -0,0 +1,24 @@
+# NOTE: When we upgrade to 3.11 we can use a slimmer docker image which comes with gcc.
+FROM python:3.9-bullseye
+
+# Allow statements and log messages to immediately appear in the Knative logs
+ENV PYTHONUNBUFFERED True
+
+# Set the working directory in the container.
+WORKDIR /code
+
+COPY .env .
+COPY LICENSE .
+
+# Copy static files.
+COPY /web/blueprint/build ./web/blueprint/build
+
+# Copy python files.
+COPY /src ./src/
+
+# Install the dependencies. This requires exporting requirements.txt from poetry first, which
+# happens from ./build_docker.sh.
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+CMD ["uvicorn", "src.server:app", "--host", "0.0.0.0", "--port", "5432"]

LICENSE

@@ -0,0 +1,201 @@
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
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requirements.txt

@@ -0,0 +1,130 @@
+aiohttp==3.8.4 ; python_version >= "3.9" and python_version < "3.10"
+aiosignal==1.3.1 ; python_version >= "3.9" and python_version < "3.10"
+anyio==3.7.0 ; python_version >= "3.9" and python_version < "3.10"
+async-timeout==4.0.2 ; python_version >= "3.9" and python_version < "3.10"
+attrs==23.1.0 ; python_version >= "3.9" and python_version < "3.10"
+blis==0.7.9 ; python_version >= "3.9" and python_version < "3.10"
+cachetools==5.3.1 ; python_version >= "3.9" and python_version < "3.10"
+catalogue==2.0.8 ; python_version >= "3.9" and python_version < "3.10"
+certifi==2023.5.7 ; python_version >= "3.9" and python_version < "3.10"
+charset-normalizer==3.1.0 ; python_version >= "3.9" and python_version < "3.10"
+click==8.1.3 ; python_version >= "3.9" and python_version < "3.10"
+cloudpickle==2.2.1 ; python_version >= "3.9" and python_version < "3.10"
+cohere==3.10.0 ; python_version >= "3.9" and python_version < "3.10"
+colorama==0.4.6 ; python_version >= "3.9" and python_version < "3.10" and (platform_system == "Windows" or sys_platform == "win32")
+confection==0.0.4 ; python_version >= "3.9" and python_version < "3.10"
+cymem==2.0.7 ; python_version >= "3.9" and python_version < "3.10"
+cytoolz==0.12.1 ; python_version >= "3.9" and python_version < "3.10"
+dask==2023.5.1 ; python_version >= "3.9" and python_version < "3.10"
+datasets==2.12.0 ; python_version >= "3.9" and python_version < "3.10"
+decorator==5.1.1 ; python_version >= "3.9" and python_version < "3.10"
+dill==0.3.6 ; python_version >= "3.9" and python_version < "3.10"
+distributed==2023.5.1 ; python_version >= "3.9" and python_version < "3.10"
+duckdb==0.8.0 ; python_version >= "3.9" and python_version < "3.10"
+email-reply-parser==0.5.12 ; python_version >= "3.9" and python_version < "3.10"
+exceptiongroup==1.1.1 ; python_version >= "3.9" and python_version < "3.10"
+fastapi==0.95.2 ; python_version >= "3.9" and python_version < "3.10"
+filelock==3.12.0 ; python_version >= "3.9" and python_version < "3.10"
+floret==0.10.3 ; python_version >= "3.9" and python_version < "3.10"
+frozenlist==1.3.3 ; python_version >= "3.9" and python_version < "3.10"
+fsspec==2023.5.0 ; python_version >= "3.9" and python_version < "3.10"
+fsspec[http]==2023.5.0 ; python_version >= "3.9" and python_version < "3.10"
+gcsfs==2023.5.0 ; python_version >= "3.9" and python_version < "3.10"
+google-api-core==2.11.0 ; python_version >= "3.9" and python_version < "3.10"
+google-api-python-client==2.88.0 ; python_version >= "3.9" and python_version < "3.10"
+google-auth-httplib2==0.1.0 ; python_version >= "3.9" and python_version < "3.10"
+google-auth-oauthlib==1.0.0 ; python_version >= "3.9" and python_version < "3.10"
+google-auth==2.19.1 ; python_version >= "3.9" and python_version < "3.10"
+google-cloud-core==2.3.2 ; python_version >= "3.9" and python_version < "3.10"
+google-cloud-storage==2.9.0 ; python_version >= "3.9" and python_version < "3.10"
+google-crc32c==1.5.0 ; python_version >= "3.9" and python_version < "3.10"
+google-resumable-media==2.5.0 ; python_version >= "3.9" and python_version < "3.10"
+googleapis-common-protos==1.59.0 ; python_version >= "3.9" and python_version < "3.10"
+h11==0.14.0 ; python_version >= "3.9" and python_version < "3.10"
+httplib2==0.22.0 ; python_version >= "3.9" and python_version < "3.10"
+httptools==0.5.0 ; python_version >= "3.9" and python_version < "3.10"
+huggingface-hub==0.15.1 ; python_version >= "3.9" and python_version < "3.10"
+idna==3.4 ; python_version >= "3.9" and python_version < "3.10"
+importlib-metadata==6.6.0 ; python_version >= "3.9" and python_version < "3.10"
+jellyfish==0.11.2 ; python_version >= "3.9" and python_version < "3.10"
+jinja2==3.1.2 ; python_version >= "3.9" and python_version < "3.10"
+joblib==1.2.0 ; python_version >= "3.9" and python_version < "3.10"
+langcodes==3.3.0 ; python_version >= "3.9" and python_version < "3.10"
+locket==1.0.0 ; python_version >= "3.9" and python_version < "3.10"
+markupsafe==2.1.3 ; python_version >= "3.9" and python_version < "3.10"
+mpmath==1.3.0 ; python_version >= "3.9" and python_version < "3.10"
+msgpack==1.0.5 ; python_version >= "3.9" and python_version < "3.10"
+multidict==6.0.4 ; python_version >= "3.9" and python_version < "3.10"
+multiprocess==0.70.14 ; python_version >= "3.9" and python_version < "3.10"
+murmurhash==1.0.9 ; python_version >= "3.9" and python_version < "3.10"
+networkx==3.1 ; python_version >= "3.9" and python_version < "3.10"
+nltk==3.8.1 ; python_version >= "3.9" and python_version < "3.10"
+numpy==1.24.3 ; python_version >= "3.9" and python_version < "3.10"
+oauthlib==3.2.2 ; python_version >= "3.9" and python_version < "3.10"
+openai-function-call==0.0.5 ; python_version >= "3.9" and python_version < "3.10"
+openai==0.27.8 ; python_version >= "3.9" and python_version < "3.10"
+orjson==3.9.0 ; python_version >= "3.9" and python_version < "3.10"
+packaging==23.1 ; python_version >= "3.9" and python_version < "3.10"
+pandas==2.0.2 ; python_version >= "3.9" and python_version < "3.10"
+partd==1.4.0 ; python_version >= "3.9" and python_version < "3.10"
+pathy==0.10.1 ; python_version >= "3.9" and python_version < "3.10"
+pillow==9.5.0 ; python_version >= "3.9" and python_version < "3.10"
+preshed==3.0.8 ; python_version >= "3.9" and python_version < "3.10"
+protobuf==4.23.2 ; python_version >= "3.9" and python_version < "3.10"
+psutil==5.9.5 ; python_version >= "3.9" and python_version < "3.10"
+pyarrow==9.0.0 ; python_version >= "3.9" and python_version < "3.10"
+pyasn1-modules==0.3.0 ; python_version >= "3.9" and python_version < "3.10"
+pyasn1==0.5.0 ; python_version >= "3.9" and python_version < "3.10"
+pydantic==1.10.9 ; python_version >= "3.9" and python_version < "3.10"
+pyparsing==3.0.9 ; python_version >= "3.9" and python_version < "3.10"
+pyphen==0.14.0 ; python_version >= "3.9" and python_version < "3.10"
+python-dateutil==2.8.2 ; python_version >= "3.9" and python_version < "3.10"
+python-dotenv==1.0.0 ; python_version >= "3.9" and python_version < "3.10"
+pytz==2023.3 ; python_version >= "3.9" and python_version < "3.10"
+pyyaml==6.0 ; python_version >= "3.9" and python_version < "3.10"
+regex==2023.6.3 ; python_version >= "3.9" and python_version < "3.10"
+requests-oauthlib==1.3.1 ; python_version >= "3.9" and python_version < "3.10"
+requests==2.31.0 ; python_version >= "3.9" and python_version < "3.10"
+responses==0.18.0 ; python_version >= "3.9" and python_version < "3.10"
+rsa==4.9 ; python_version >= "3.9" and python_version < "3.10"
+scikit-learn==1.2.2 ; python_version >= "3.9" and python_version < "3.10"
+scipy==1.10.1 ; python_version >= "3.9" and python_version < "3.10"
+sentence-transformers==2.2.2 ; python_version >= "3.9" and python_version < "3.10"
+sentencepiece==0.1.99 ; python_version >= "3.9" and python_version < "3.10"
+setuptools==65.7.0 ; python_version >= "3.9" and python_version < "3.10"
+six==1.16.0 ; python_version >= "3.9" and python_version < "3.10"
+smart-open==6.3.0 ; python_version >= "3.9" and python_version < "3.10"
+sniffio==1.3.0 ; python_version >= "3.9" and python_version < "3.10"
+sortedcontainers==2.4.0 ; python_version >= "3.9" and python_version < "3.10"
+spacy-legacy==3.0.12 ; python_version >= "3.9" and python_version < "3.10"
+spacy-loggers==1.0.4 ; python_version >= "3.9" and python_version < "3.10"
+spacy==3.5.3 ; python_version >= "3.9" and python_version < "3.10"
+srsly==2.4.6 ; python_version >= "3.9" and python_version < "3.10"
+starlette==0.27.0 ; python_version >= "3.9" and python_version < "3.10"
+sympy==1.12 ; python_version >= "3.9" and python_version < "3.10"
+tblib==1.7.0 ; python_version >= "3.9" and python_version < "3.10"
+textacy==0.13.0 ; python_version >= "3.9" and python_version < "3.10"
+thinc==8.1.10 ; python_version >= "3.9" and python_version < "3.10"
+threadpoolctl==3.1.0 ; python_version >= "3.9" and python_version < "3.10"
+tokenizers==0.13.3 ; python_version >= "3.9" and python_version < "3.10"
+toolz==0.12.0 ; python_version >= "3.9" and python_version < "3.10"
+torch==2.0.1 ; python_version >= "3.9" and python_version < "3.10"
+torchvision==0.15.2 ; python_version >= "3.9" and python_version < "3.10"
+tornado==6.3.2 ; python_version >= "3.9" and python_version < "3.10"
+tqdm==4.65.0 ; python_version >= "3.9" and python_version < "3.10"
+transformers==4.29.2 ; python_version >= "3.9" and python_version < "3.10"
+typer==0.7.0 ; python_version >= "3.9" and python_version < "3.10"
+types-psutil==5.9.5.13 ; python_version >= "3.9" and python_version < "3.10"
+typing-extensions==4.6.3 ; python_version >= "3.9" and python_version < "3.10"
+tzdata==2023.3 ; python_version >= "3.9" and python_version < "3.10"
+uritemplate==4.1.1 ; python_version >= "3.9" and python_version < "3.10"
+urllib3==1.26.16 ; python_version >= "3.9" and python_version < "3.10"
+uvicorn[standard]==0.20.0 ; python_version >= "3.9" and python_version < "3.10"
+uvloop==0.17.0 ; (sys_platform != "win32" and sys_platform != "cygwin") and platform_python_implementation != "PyPy" and python_version >= "3.9" and python_version < "3.10"
+wasabi==1.1.1 ; python_version >= "3.9" and python_version < "3.10"
+watchfiles==0.19.0 ; python_version >= "3.9" and python_version < "3.10"
+websockets==11.0.3 ; python_version >= "3.9" and python_version < "3.10"
+xxhash==3.2.0 ; python_version >= "3.9" and python_version < "3.10"
+yarl==1.9.2 ; python_version >= "3.9" and python_version < "3.10"
+zict==3.0.0 ; python_version >= "3.9" and python_version < "3.10"
+zipp==3.15.0 ; python_version >= "3.9" and python_version < "3.10"

src/concepts/concept.py

@@ -0,0 +1,271 @@
+"""Defines the concept and the concept models."""
+import random
+from typing import Iterable, Literal, Optional, Union
+
+import numpy as np
+from pydantic import BaseModel, validator
+from sklearn.exceptions import NotFittedError
+from sklearn.linear_model import LogisticRegression
+
+from ..db_manager import get_dataset
+from ..embeddings.embedding import get_embed_fn
+from ..schema import Path, RichData, SignalInputType, normalize_path
+from ..signals.signal import TextEmbeddingSignal, get_signal_cls
+from ..utils import DebugTimer
+
+LOCAL_CONCEPT_NAMESPACE = 'local'
+
+# Number of randomly sampled negative examples to use for training. This is used to obtain a more
+# balanced model that works with a specific dataset.
+DEFAULT_NUM_NEG_EXAMPLES = 300
+
+
+class ConceptColumnInfo(BaseModel):
+  """Information about a dataset associated with a concept."""
+  # Namespace of the dataset.
+  namespace: str
+  # Name of the dataset.
+  name: str
+  # Path holding the text to use for negative examples.
+  path: Path
+
+  num_negative_examples = DEFAULT_NUM_NEG_EXAMPLES
+
+
+class ExampleOrigin(BaseModel):
+  """The origin of an example."""
+  # The namespace that holds the dataset.
+  dataset_namespace: str
+
+  # The name of the dataset.
+  dataset_name: str
+
+  # The id of row in the dataset that the example was added from.
+  dataset_row_id: str
+
+
+DraftId = Union[Literal['main'], str]
+DRAFT_MAIN = 'main'
+
+
+class ExampleIn(BaseModel):
+  """An example in a concept without the id (used for adding new examples)."""
+  label: bool
+  text: Optional[str]
+  img: Optional[bytes]
+  origin: Optional[ExampleOrigin]
+  # The name of the draft to put the example in. If None, puts it in the main draft.
+  draft: Optional[DraftId] = DRAFT_MAIN
+
+  @validator('text')
+  def parse_text(cls, text: str) -> str:
+    """Fixes surrogate errors in text: https://github.com/ijl/orjson/blob/master/README.md#str ."""
+    return text.encode('utf-8', 'replace').decode('utf-8')
+
+
+class Example(ExampleIn):
+  """A single example in a concept used for training a concept model."""
+  id: str
+
+
+class Concept(BaseModel):
+  """A concept is a collection of examples."""
+  # The namespace of the concept.
+  namespace: str = LOCAL_CONCEPT_NAMESPACE
+  # The name of the concept.
+  concept_name: str
+  # The type of the data format that this concept represents.
+  type: SignalInputType
+  data: dict[str, Example]
+  version: int = 0
+
+  def drafts(self) -> list[DraftId]:
+    """Gets all the drafts for the concept."""
+    drafts: set[DraftId] = set([DRAFT_MAIN])  # Always return the main draft.
+    for example in self.data.values():
+      if example.draft:
+        drafts.add(example.draft)
+    return list(sorted(drafts))
+
+
+class LogisticEmbeddingModel(BaseModel):
+  """A model that uses logistic regression with embeddings."""
+
+  class Config:
+    arbitrary_types_allowed = True
+    underscore_attrs_are_private = True
+
+  version: int = -1
+
+  # The following fields are excluded from JSON serialization, but still pickleable.
+  # See `notebooks/Toxicity.ipynb` for an example of training a concept model.
+  _model: LogisticRegression = LogisticRegression(
+    class_weight=None, C=30, tol=1e-5, warm_start=True, max_iter=1_000, n_jobs=-1)
+
+  def score_embeddings(self, embeddings: np.ndarray) -> np.ndarray:
+    """Get the scores for the provided embeddings."""
+    try:
+      return self._model.predict_proba(embeddings)[:, 1]
+    except NotFittedError:
+      return np.random.rand(len(embeddings))
+
+  def fit(self, embeddings: np.ndarray, labels: list[bool], sample_weights: list[float]) -> None:
+    """Fit the model to the provided embeddings and labels."""
+    if len(set(labels)) < 2:
+      return
+    if len(labels) != len(embeddings):
+      raise ValueError(
+        f'Length of embeddings ({len(embeddings)}) must match length of labels ({len(labels)})')
+    if len(sample_weights) != len(labels):
+      raise ValueError(
+        f'Length of sample_weights ({len(sample_weights)}) must match length of labels '
+        f'({len(labels)})')
+    self._model.fit(embeddings, labels, sample_weights)
+
+
+def draft_examples(concept: Concept, draft: DraftId) -> dict[str, Example]:
+  """Get the examples in the provided draft by overriding the main draft."""
+  draft_examples: dict[str, dict[str, Example]] = {}
+  for id, example in concept.data.items():
+    draft_examples.setdefault(example.draft or DRAFT_MAIN, {})[example.id] = example
+
+  if draft == DRAFT_MAIN:
+    return draft_examples.get(DRAFT_MAIN, {})
+
+  if draft not in draft_examples:
+    raise ValueError(
+      f'Draft {draft} not found in concept. Found drafts: {list(draft_examples.keys())}')
+
+  # Map the text of the draft to its id so we can dedup with main.
+  draft_text_ids = {example.text: id for id, example in draft_examples[draft].items()}
+
+  # Write each of examples from main to the draft examples only if the text does not appear in the
+  # draft.
+  for id, example in draft_examples[DRAFT_MAIN].items():
+    if example.text not in draft_text_ids:
+      draft_examples[draft][id] = example
+
+  return draft_examples[draft]
+
+
+class ConceptModel(BaseModel):
+  """A concept model. Stores all concept model drafts and manages syncing."""
+  # The concept that this model is for.
+  namespace: str
+  concept_name: str
+
+  # The name of the embedding for this model.
+  embedding_name: str
+  version: int = -1
+
+  # The following fields are excluded from JSON serialization, but still pickleable.
+  # Maps a concept id to the embeddings.
+  _embeddings: dict[str, np.ndarray] = {}
+  _logistic_models: dict[DraftId, LogisticEmbeddingModel] = {}
+  _negative_vectors: Optional[np.ndarray] = None
+
+  class Config:
+    arbitrary_types_allowed = True
+    underscore_attrs_are_private = True
+
+  def calibrate_on_dataset(self, column_info: ConceptColumnInfo) -> None:
+    """Calibrate the model on the embeddings in the provided vector store."""
+    db = get_dataset(column_info.namespace, column_info.name)
+    vector_store = db.get_vector_store(normalize_path(column_info.path))
+    keys = vector_store.keys()
+    num_samples = min(column_info.num_negative_examples, len(keys))
+    sample_keys = random.sample(keys, num_samples)
+    self._negative_vectors = vector_store.get(sample_keys)
+
+  def score_embeddings(self, draft: DraftId, embeddings: np.ndarray) -> np.ndarray:
+    """Get the scores for the provided embeddings."""
+    return self._get_logistic_model(draft).score_embeddings(embeddings)
+
+  def score(self, draft: DraftId, examples: Iterable[RichData]) -> list[float]:
+    """Get the scores for the provided examples."""
+    embedding_signal = get_signal_cls(self.embedding_name)()
+    if not isinstance(embedding_signal, TextEmbeddingSignal):
+      raise ValueError(f'Only text embedding signals are currently supported for concepts. '
+                       f'"{self.embedding_name}" is a {type(embedding_signal)}.')
+
+    embed_fn = get_embed_fn(self.embedding_name)
+    embeddings = np.array(embed_fn(examples))
+    return self._get_logistic_model(draft).score_embeddings(embeddings).tolist()
+
+  def coef(self, draft: DraftId) -> np.ndarray:
+    """Get the coefficients of the underlying ML model."""
+    return self._get_logistic_model(draft)._model.coef_.reshape(-1)
+
+  def _get_logistic_model(self, draft: DraftId) -> LogisticEmbeddingModel:
+    """Get the logistic model for the provided draft."""
+    if draft not in self._logistic_models:
+      self._logistic_models[draft] = LogisticEmbeddingModel(
+        namespace=self.namespace,
+        concept_name=self.concept_name,
+        embedding_name=self.embedding_name,
+        version=-1)
+    return self._logistic_models[draft]
+
+  def sync(self, concept: Concept) -> bool:
+    """Update the model with the latest labeled concept data."""
+    if concept.version == self.version:
+      # The model is up to date.
+      return False
+
+    concept_path = (f'{self.namespace}/{self.concept_name}/'
+                    f'{self.embedding_name}')
+    with DebugTimer(f'Computing embeddings for "{concept_path}"'):
+      self._compute_embeddings(concept)
+
+    # Fit each of the drafts, sort by draft name for deterministic behavior.
+    for draft in concept.drafts():
+      examples = draft_examples(concept, draft)
+      embeddings = np.array([self._embeddings[id] for id in examples.keys()])
+      labels = [example.label for example in examples.values()]
+      num_pos_labels = len([x for x in labels if x])
+      num_neg_labels = len([x for x in labels if not x])
+      sample_weights = [(1.0 / num_pos_labels if x else 1.0 / num_neg_labels) for x in labels]
+      if self._negative_vectors is not None:
+        num_implicit_labels = len(self._negative_vectors)
+        embeddings = np.concatenate([self._negative_vectors, embeddings])
+        labels = [False] * num_implicit_labels + labels
+        sample_weights = [1.0 / num_implicit_labels] * num_implicit_labels + sample_weights
+
+      model = self._get_logistic_model(draft)
+      with DebugTimer(f'Fitting model for "{concept_path}"'):
+        model.fit(embeddings, labels, sample_weights)
+
+      # Synchronize the model version with the concept version.
+      model.version = concept.version
+
+    # Synchronize the model version with the concept version.
+    self.version = concept.version
+
+    return True
+
+  def _compute_embeddings(self, concept: Concept) -> None:
+    embedding_signal = get_signal_cls(self.embedding_name)()
+    if not isinstance(embedding_signal, TextEmbeddingSignal):
+      raise ValueError(f'Only text embedding signals are currently supported for concepts. '
+                       f'"{self.embedding_name}" is a {type(embedding_signal)}.')
+
+    embed_fn = get_embed_fn(self.embedding_name)
+    concept_embeddings: dict[str, np.ndarray] = {}
+
+    # Compute the embeddings for the examples with cache miss.
+    texts_of_missing_embeddings: dict[str, str] = {}
+    for id, example in concept.data.items():
+      if id in self._embeddings:
+        # Cache hit.
+        concept_embeddings[id] = self._embeddings[id]
+      else:
+        # Cache miss.
+        # TODO(smilkov): Support images.
+        texts_of_missing_embeddings[id] = example.text or ''
+
+    missing_ids = texts_of_missing_embeddings.keys()
+    missing_embeddings = embed_fn(list(texts_of_missing_embeddings.values()))
+
+    for id, embedding in zip(missing_ids, missing_embeddings):
+      concept_embeddings[id] = embedding
+    self._embeddings = concept_embeddings