Store Message embedding

ansible/dev.yaml

@@ -55,6 +55,7 @@
           DEBUG_USE_SEED_DATA: "true"
           MAX_WORKERS: "1"
           RATE_LIMIT: "false"
+          DEBUG_SKIP_EMBEDDING_COMPUTATION: "true"
         ports:
           - 8080:8080
 

backend/alembic/versions/2023_01_08_1106-3d96bb92e33a_added_minilm_embedding_column_to_message.py

@@ -0,0 +1,27 @@
+"""added miniLM_embedding column to message
+
+Revision ID: 023548d474f7
+Revises: ba61fe17fb6e
+Create Date: 2023-01-08 11:06:25.613290
+
+"""
+import sqlalchemy as sa
+from alembic import op
+
+# revision identifiers, used by Alembic.
+revision = "023548d474f7"
+down_revision = "ba61fe17fb6e"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    # ### commands auto generated by Alembic - please adjust! ###
+    op.add_column("message", sa.Column("miniLM_embedding", sa.ARRAY(sa.Float()), nullable=True))
+    # ### end Alembic commands ###
+
+
+def downgrade() -> None:
+    # ### commands auto generated by Alembic - please adjust! ###
+    op.drop_column("message", "miniLM_embedding")
+    # ### end Alembic commands ###

backend/alembic/versions/2023_01_08_1603-35bdc1a08bb8_embedding_for_message_now_in_its_own_.py

@@ -0,0 +1,49 @@
+"""embedding for message now in its own table
+
+Revision ID: 35bdc1a08bb8
+Revises: 023548d474f7
+Create Date: 2023-01-08 16:03:48.454207
+
+"""
+import sqlalchemy as sa
+import sqlmodel
+from alembic import op
+from sqlalchemy.dialects import postgresql
+
+# revision identifiers, used by Alembic.
+revision = "35bdc1a08bb8"
+down_revision = "023548d474f7"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    # ### commands auto generated by Alembic - please adjust! ###
+    op.create_table(
+        "message_embedding",
+        sa.Column("message_id", postgresql.UUID(as_uuid=True), nullable=False),
+        sa.Column("embedding", sa.ARRAY(sa.Float()), nullable=True),
+        sa.Column("model", sqlmodel.sql.sqltypes.AutoString(length=256), nullable=False),
+        sa.ForeignKeyConstraint(
+            ["message_id"],
+            ["message.id"],
+        ),
+        sa.PrimaryKeyConstraint("message_id", "model"),
+    )
+    op.drop_column("message", "miniLM_embedding")
+    # ### end Alembic commands ###
+
+
+def downgrade() -> None:
+    # ### commands auto generated by Alembic - please adjust! ###
+    op.add_column(
+        "message",
+        sa.Column(
+            "miniLM_embedding",
+            postgresql.ARRAY(postgresql.DOUBLE_PRECISION(precision=53)),
+            autoincrement=False,
+            nullable=True,
+        ),
+    )
+    op.drop_table("message_embedding")
+    # ### end Alembic commands ###

backend/alembic/versions/2023_01_08_2128-aac6b2f66006_created_date.py

@@ -0,0 +1,30 @@
+"""Created date
+
+Revision ID: aac6b2f66006
+Revises: 35bdc1a08bb8
+Create Date: 2023-01-08 21:28:27.342729
+
+"""
+import sqlalchemy as sa
+from alembic import op
+
+# revision identifiers, used by Alembic.
+revision = "aac6b2f66006"
+down_revision = "35bdc1a08bb8"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    # ### commands auto generated by Alembic - please adjust! ###
+    op.add_column(
+        "message_embedding",
+        sa.Column("created_date", sa.DateTime(), server_default=sa.text("CURRENT_TIMESTAMP"), nullable=False),
+    )
+    # ### end Alembic commands ###
+
+
+def downgrade() -> None:
+    # ### commands auto generated by Alembic - please adjust! ###
+    op.drop_column("message_embedding", "created_date")
+    # ### end Alembic commands ###

backend/oasst_backend/api/v1/hugging_face.py

@@ -1,19 +1,14 @@
-from enum import Enum
 from typing import List
 
 from fastapi import APIRouter, Depends
 from oasst_backend.api import deps
 from oasst_backend.models import ApiClient
 from oasst_backend.schemas.hugging_face import ToxicityClassification
-from oasst_backend.utils.hugging_face import HuggingFaceAPI
+from oasst_backend.utils.hugging_face import HfUrl, HuggingFaceAPI
 
 router = APIRouter()
 
 
-class HF_url(str, Enum):
-    HUGGINGFACE_TOXIC_ROBERTA = "https://api-inference.huggingface.co/models/unitary/multilingual-toxic-xlm-roberta"
-
-
 @router.get("/text_toxicity")
 async def get_text_toxicity(
     msg: str,
@@ -30,7 +25,7 @@ async def get_text_toxicity(
         ToxicityClassification: the score of toxicity of the message.
     """
 
-    api_url: str = HF_url.HUGGINGFACE_TOXIC_ROBERTA.value
+    api_url: str = HfUrl.HUGGINGFACE_TOXIC_ROBERTA.value
     hugging_face_api = HuggingFaceAPI(api_url)
     response = await hugging_face_api.post(msg)
 

backend/oasst_backend/api/v1/tasks.py

@@ -7,7 +7,9 @@
 from loguru import logger
 from oasst_backend.api import deps
 from oasst_backend.api.v1.utils import prepare_conversation
+from oasst_backend.config import settings
 from oasst_backend.prompt_repository import PromptRepository, TaskRepository
+from oasst_backend.utils.hugging_face import HfEmbeddingModel, HfUrl, HuggingFaceAPI
 from oasst_shared.exceptions import OasstError, OasstErrorCode
 from oasst_shared.schemas import protocol as protocol_schema
 from sqlmodel import Session
@@ -253,7 +255,7 @@ def tasks_acknowledge_failure(
 
 
 @router.post("/interaction", response_model=protocol_schema.TaskDone)
-def tasks_interaction(
+async def tasks_interaction(
     *,
     db: Session = Depends(deps.get_db),
     api_key: APIKey = Depends(deps.get_api_key),
@@ -274,12 +276,26 @@ def tasks_interaction(
                 )
 
                 # here we store the text reply in the database
-                pr.store_text_reply(
+                newMessage = pr.store_text_reply(
                     text=interaction.text,
                     frontend_message_id=interaction.message_id,
                     user_frontend_message_id=interaction.user_message_id,
                 )
 
+                if not settings.DEBUG_SKIP_EMBEDDING_COMPUTATION:
+                    try:
+                        hugging_face_api = HuggingFaceAPI(
+                            f"{HfUrl.HUGGINGFACE_FEATURE_EXTRACTION.value}/{HfEmbeddingModel.MINILM.value}"
+                        )
+                        embedding = await hugging_face_api.post(interaction.text)
+                        pr.insert_message_embedding(
+                            message_id=newMessage.id, model=HfEmbeddingModel.MINILM.value, embedding=embedding
+                        )
+                    except OasstError:
+                        logger.error(
+                            f"Could not fetch embbeddings for text reply to {interaction.message_id=} with {interaction.text=} by {interaction.user=}."
+                        )
+
                 return protocol_schema.TaskDone()
             case protocol_schema.MessageRating:
                 logger.info(

backend/oasst_backend/config.py

@@ -25,6 +25,7 @@ class Settings(BaseSettings):
     DEBUG_USE_SEED_DATA_PATH: Optional[FilePath] = (
         Path(__file__).parent.parent / "test_data/generic/test_generic_data.json"
     )
+    DEBUG_SKIP_EMBEDDING_COMPUTATION: bool = False
 
     HUGGING_FACE_API_KEY: str = ""
 

backend/oasst_backend/models/__init__.py

@@ -1,6 +1,7 @@
 from .api_client import ApiClient
 from .journal import Journal, JournalIntegration
 from .message import Message
+from .message_embedding import MessageEmbedding
 from .message_reaction import MessageReaction
 from .message_tree_state import MessageTreeState
 from .task import Task
@@ -13,6 +14,7 @@
     "User",
     "UserStats",
     "Message",
+    "MessageEmbedding",
     "MessageReaction",
     "MessageTreeState",
     "Task",

backend/oasst_backend/models/message_embedding.py

@@ -0,0 +1,21 @@
+from datetime import datetime
+from typing import List, Optional
+from uuid import UUID
+
+import sqlalchemy as sa
+import sqlalchemy.dialects.postgresql as pg
+from sqlmodel import ARRAY, Field, Float, SQLModel
+
+
+class MessageEmbedding(SQLModel, table=True):
+    __tablename__ = "message_embedding"
+    __table_args__ = (sa.PrimaryKeyConstraint("message_id", "model"),)
+
+    message_id: UUID = Field(sa_column=sa.Column(pg.UUID(as_uuid=True), sa.ForeignKey("message.id"), nullable=False))
+    model: str = Field(max_length=256, nullable=False)
+    embedding: List[float] = Field(sa_column=sa.Column(ARRAY(Float)), nullable=True)
+
+    # In the case that the Message Embedding is created afterwards
+    created_date: Optional[datetime] = Field(
+        sa_column=sa.Column(sa.DateTime(), nullable=False, server_default=sa.func.current_timestamp())
+    )

backend/oasst_backend/prompt_repository.py

@@ -2,13 +2,13 @@
 import random
 from collections import defaultdict
 from http import HTTPStatus
-from typing import Optional
+from typing import List, Optional
 from uuid import UUID, uuid4
 
 import oasst_backend.models.db_payload as db_payload
 from loguru import logger
 from oasst_backend.journal_writer import JournalWriter
-from oasst_backend.models import ApiClient, Message, MessageReaction, TextLabels, User
+from oasst_backend.models import ApiClient, Message, MessageEmbedding, MessageReaction, TextLabels, User
 from oasst_backend.models.payload_column_type import PayloadContainer
 from oasst_backend.task_repository import TaskRepository, validate_frontend_message_id
 from oasst_backend.user_repository import UserRepository
@@ -91,7 +91,12 @@ def insert_message(
         self.db.refresh(message)
         return message
 
-    def store_text_reply(self, text: str, frontend_message_id: str, user_frontend_message_id: str) -> Message:
+    def store_text_reply(
+        self,
+        text: str,
+        frontend_message_id: str,
+        user_frontend_message_id: str,
+    ) -> Message:
         validate_frontend_message_id(frontend_message_id)
         validate_frontend_message_id(user_frontend_message_id)
 
@@ -224,6 +229,30 @@ def store_ranking(self, ranking: protocol_schema.MessageRanking) -> MessageReact
                     OasstErrorCode.TASK_PAYLOAD_TYPE_MISMATCH,
                 )
 
+    def insert_message_embedding(self, message_id: UUID, model: str, embedding: List[float]) -> MessageEmbedding:
+        """Insert the embedding of a new message in the database.
+
+        Args:
+            message_id (UUID): the identifier of the message we want to save its embedding
+            model (str): the model used for creating the embedding
+            embedding (List[float]): the values obtained from the message & model
+
+        Raises:
+            OasstError: if misses some of the before params
+
+        Returns:
+            MessageEmbedding: the instance in the database of the embedding saved for that message
+        """
+
+        if None in (message_id, model, embedding):
+            raise OasstError("Paramters missing to add embedding", OasstErrorCode.GENERIC_ERROR)
+
+        message_embedding = MessageEmbedding(message_id=message_id, model=model, embedding=embedding)
+        self.db.add(message_embedding)
+        self.db.commit()
+        self.db.refresh(message_embedding)
+        return message_embedding
+
     def insert_reaction(self, task_id: UUID, payload: db_payload.ReactionPayload) -> MessageReaction:
         if self.user_id is None:
             raise OasstError("User required", OasstErrorCode.USER_NOT_SPECIFIED)

backend/oasst_backend/utils/hugging_face.py

@@ -1,10 +1,21 @@
+from enum import Enum
 from typing import Any, Dict
 
 import aiohttp
+from loguru import logger
 from oasst_backend.config import settings
 from oasst_shared.exceptions import OasstError, OasstErrorCode
 
 
+class HfUrl(str, Enum):
+    HUGGINGFACE_TOXIC_ROBERTA = ("https://api-inference.huggingface.co/models/unitary/multilingual-toxic-xlm-roberta",)
+    HUGGINGFACE_FEATURE_EXTRACTION = "https://api-inference.huggingface.co/pipeline/feature-extraction"
+
+
+class HfEmbeddingModel(str, Enum):
+    MINILM = "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2"
+
+
 class HuggingFaceAPI:
     """Class Object to make post calls to endpoints for inference in models hosted in HuggingFace"""
 
@@ -41,6 +52,9 @@ async def post(self, input: str) -> Any:
             async with session.post(self.api_url, headers=self.headers, json=payload) as response:
                 # If we get a bad response
                 if response.status != 200:
+
+                    logger.error(response)
+                    logger.info(self.headers)
                     raise OasstError(
                         "Response Error Detoxify HuggingFace", error_code=OasstErrorCode.HUGGINGFACE_API_ERROR
                     )

docker-compose.yaml

@@ -95,6 +95,7 @@ services:
       - DEBUG_SKIP_API_KEY_CHECK=True
       - DEBUG_USE_SEED_DATA=True
       - MAX_WORKERS=1
+      - DEBUG_SKIP_EMBEDDING_COMPUTATION=True
     depends_on:
       db:
         condition: service_healthy

scripts/backend-development/run-local.sh

@@ -6,6 +6,7 @@ pushd "$parent_path/../../backend"
 
 export DEBUG_SKIP_API_KEY_CHECK=True
 export DEBUG_USE_SEED_DATA=True
+export DEBUG_SKIP_EMBEDDING_COMPUTATION=True
 
 uvicorn main:app --reload --port 8080 --host 0.0.0.0