api/alembic/versions/20260327_0003_add_pos_and_transcript_to_jobs.py

@@ -1,29 +0,0 @@
-"""add source_pos_data, target_pos_data, audio_transcript to jobs
-
-Revision ID: 0003
-Revises: 0002
-Create Date: 2026-03-27
-
-"""
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-from alembic import op
-from sqlalchemy.dialects import postgresql
-
-revision: str = "0003"
-down_revision: Union[str, None] = "0002"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
-    op.add_column("jobs", sa.Column("source_pos_data", postgresql.JSONB(), nullable=True))
-    op.add_column("jobs", sa.Column("target_pos_data", postgresql.JSONB(), nullable=True))
-    op.add_column("jobs", sa.Column("audio_transcript", postgresql.JSONB(), nullable=True))
-
-
-def downgrade() -> None:
-    op.drop_column("jobs", "audio_transcript")
-    op.drop_column("jobs", "target_pos_data")
-    op.drop_column("jobs", "source_pos_data")

api/app/config.py

@@ -6,7 +6,6 @@ class Settings(BaseSettings):
     jwt_secret: str
     anthropic_api_key: str
     deepl_api_key: str
-    deepgram_api_key: str
     gemini_api_key: str
     storage_endpoint_url: str
     storage_access_key: str

api/app/domain/models/summarise_job.py

@@ -15,9 +15,6 @@ class SummariseJob:
     translated_text: str
     error_message: str
     audio_url: str
-    source_pos_data: dict | None
-    target_pos_data: dict | None
-    audio_transcript: dict | None
     created_at: datetime
     started_at: datetime | None = None
     completed_at: datetime | None = None

api/app/outbound/anthropic/anthropic_client.py

@@ -19,17 +19,18 @@ class AnthropicClient():
     ) -> str:
         return (
             f"You are a language learning content creator.\n"
-            f"You generate markdown summaries of user-provided content.\n"
-            f"You will provide content in {to_language} at {complexity_level} proficiency level on the CEFR scale.\n"
+            f"The user will provide input, you will generate an engaging realistic summary text in {to_language} "
+            f"at {complexity_level} proficiency level (CEFR scale).\n\n"
             f"The text you generate will:\n"
-            f"- Contain ONLY the generated summary text in {to_language}.\n"
-            f"- Never contain inappropriate (hateful, sexual, violent) content.  It is preferable to return no text than to generate such content.\n"
+            f"- Contain ONLY the generated text in {to_language}.\n"
+            f"- Be appropriate for a {complexity_level} {to_language} speaker.\n"
+            f"- Never generate inappropriate (hateful, sexual, violent) content.  It is preferable to return no text than to generate such content.\n"
             f"- Speak directly to the reader/listener, adopting the tone and style of a semi-formal news reporter or podcaster.\n"
             f"- Where appropriate (fluency level, content), use a small number of idiomatic expressions.\n"
-            f"- Where appropriate use at least one additional tense, beyond the default of the content.\n"
-            f"- Be formatted in markdown.  Contain a single level 1 header (#) at the top, followed by paragraphs and line breaks.\n"
-            f"- Be around {length_preference} long.\n"
-            f"- Be inspired by the content, but not the tone, of the source material."
+            f"- Be formatted in markdown with paragraphs and line breaks.\n"
+            f"- Be {length_preference} long.\n"
+            f"- Be inspired by the following source material "
+            f"(but written originally in {from_language}):\n\n"
         )
 
     def _create_prompt_summarise_text(

api/app/outbound/deepgram/deepgram_client.py

@@ -1,24 +0,0 @@
-import asyncio
-from deepgram import (
-    AsyncDeepgramClient,
-)
-
-class LocalDeepgramClient:
-    def __init__(self, api_key: str):
-        self.deepgram_client = AsyncDeepgramClient(api_key=api_key)
-
-    async def transcribe_bytes(self, audio_bytes: bytes, language_code: str) -> dict:
-        response = await self.deepgram_client.listen.v1.media.transcribe_file(
-            request=audio_bytes,
-            model="nova-3",
-            language=language_code,
-            utterances=True,
-            smart_format=True,
-        )
-        return response.results.json()
-
-    async def transcribe_local_file(self, local_file_path: str, language_code: str) -> dict:
-        with open(local_file_path, "rb") as audio_file:
-            return await self.transcribe_bytes(audio_file, language_code)
-
-

api/app/outbound/postgres/entities/summarise_job_entity.py

@@ -3,7 +3,7 @@ from datetime import datetime, timezone
 
 from sqlalchemy import String, Text, DateTime, ForeignKey
 from sqlalchemy.orm import Mapped, mapped_column
-from sqlalchemy.dialects.postgresql import UUID, JSONB
+from sqlalchemy.dialects.postgresql import UUID
 
 from ..database import Base
 
@@ -25,9 +25,6 @@ class SummariseJobEntity(Base):
     translated_text: Mapped[str | None] = mapped_column(Text, nullable=True)
     error_message: Mapped[str | None] = mapped_column(Text, nullable=True)
     audio_url: Mapped[str | None] = mapped_column(Text, nullable=True)
-    source_pos_data: Mapped[dict | None] = mapped_column(JSONB, nullable=True)
-    target_pos_data: Mapped[dict | None] = mapped_column(JSONB, nullable=True)
-    audio_transcript: Mapped[dict | None] = mapped_column(JSONB, nullable=True)
     created_at: Mapped[datetime] = mapped_column(
         DateTime(timezone=True),
         default=lambda: datetime.now(timezone.utc),

api/app/outbound/postgres/repositories/summarise_job_repository.py

@@ -46,9 +46,6 @@ class PostgresSummariseJobRepository:
             translated_text=entity.translated_text,
             error_message=entity.error_message,
             audio_url=entity.audio_url,
-            source_pos_data=entity.source_pos_data,
-            target_pos_data=entity.target_pos_data,
-            audio_transcript=entity.audio_transcript,
             created_at=entity.created_at,
             started_at=entity.started_at,
             completed_at=entity.completed_at,
@@ -115,26 +112,6 @@ async def save_translated_text(
     await update(db, job)
 
 
-async def save_pos_data(
-    db: AsyncSession,
-    job: SummariseJobEntity,
-    source_pos_data: dict,
-    target_pos_data: dict,
-) -> None:
-    job.source_pos_data = source_pos_data
-    job.target_pos_data = target_pos_data
-    await update(db, job)
-
-
-async def save_audio_transcript(
-    db: AsyncSession,
-    job: SummariseJobEntity,
-    audio_transcript: dict,
-) -> None:
-    job.audio_transcript = audio_transcript
-    await update(db, job)
-
-
 async def mark_succeeded(db: AsyncSession, job: SummariseJobEntity, audio_url: str) -> None:
     job.status = "succeeded"
     job.audio_url = audio_url

api/app/outbound/spacy/spacy_client.py

@@ -1,46 +0,0 @@
-import spacy
-
-LANGUAGE_MODELS: dict[str, str] = {
-    "en": "en_core_web_sm",
-    "fr": "fr_core_news_sm",
-    "es": "es_core_news_sm",
-    "it": "it_core_news_sm",
-    "de": "de_core_news_sm",
-}
-
-
-class UnsupportedLanguageError(ValueError):
-    def __init__(self, language: str):
-        self.language = language
-        super().__init__(
-            f"Unsupported language '{language}'. Supported: {list(LANGUAGE_MODELS)}"
-        )
-
-
-class SpacyClient:
-    def __init__(self):
-        self._cache: dict[str, spacy.Language] = {}
-
-    def _get_nlp(self, language: str) -> spacy.Language:
-        if language not in LANGUAGE_MODELS:
-            raise UnsupportedLanguageError(language)
-        if language not in self._cache:
-            self._cache[language] = spacy.load(LANGUAGE_MODELS[language])
-        return self._cache[language]
-
-    def get_parts_of_speech(self, text: str, language: str) -> dict:
-        nlp = self._get_nlp(language)
-        doc = nlp(text)
-        tokens = [
-            {
-                "text": token.text,
-                "lemma": token.lemma_,
-                "pos": token.pos_,
-                "tag": token.tag_,
-                "dep": token.dep_,
-                "is_stop": token.is_stop,
-            }
-            for token in doc
-            if not token.is_space
-        ]
-        return {"language": language, "tokens": tokens}

api/app/routers/api/generation.py

@@ -1,10 +1,6 @@
-import asyncio
-import random
 import uuid
 from functools import partial
-from typing import Any, Callable, Coroutine
 
-import anthropic
 from fastapi import APIRouter, Depends, HTTPException
 from pydantic import BaseModel
 from sqlalchemy.ext.asyncio import AsyncSession
@@ -15,48 +11,13 @@ from ...storage import upload_audio
 from ...outbound.postgres.database import get_db, AsyncSessionLocal
 from ...outbound.postgres.repositories import summarise_job_repository
 from ...outbound.anthropic.anthropic_client import AnthropicClient
-from ...outbound.deepgram.deepgram_client import LocalDeepgramClient
 from ...outbound.deepl.deepl_client import DeepLClient
 from ...outbound.gemini.gemini_client import GeminiClient 
-from ...outbound.spacy.spacy_client import SpacyClient
 from ...config import settings
 from ... import worker
 
 router = APIRouter(prefix="/generate", tags=["api"])
 
-_ANTHROPIC_RETRYABLE = (
-    anthropic.RateLimitError,
-    anthropic.InternalServerError,
-    anthropic.APITimeoutError,
-    anthropic.APIConnectionError,
-)
-_MAX_RETRIES = 4
-_BASE_DELAY = 1.0   # seconds
-_MAX_DELAY = 60.0   # seconds
-
-
-async def _anthropic_with_backoff(
-    coro_fn: Callable[..., Coroutine[Any, Any, Any]],
-    *args: Any,
-    **kwargs: Any,
-) -> Any:
-    for attempt in range(_MAX_RETRIES + 1):
-        try:
-            return await coro_fn(*args, **kwargs)
-        except _ANTHROPIC_RETRYABLE as exc:
-            if attempt == _MAX_RETRIES:
-                raise
-            retry_after: float | None = None
-            if isinstance(exc, anthropic.RateLimitError):
-                raw = exc.response.headers.get("retry-after")
-                if raw is not None:
-                    retry_after = float(raw)
-            if retry_after is None:
-                retry_after = min(_BASE_DELAY * (2 ** attempt), _MAX_DELAY)
-            # ±20 % jitter to spread out concurrent retries
-            jittered = retry_after * (0.8 + random.random() * 0.4)
-            await asyncio.sleep(jittered)
-
 
 class GenerationRequest(BaseModel):
     target_language: str
@@ -71,10 +32,8 @@ class GenerationResponse(BaseModel):
 
 async def _run_generation(job_id: uuid.UUID, request: GenerationRequest) -> None:
     anthropic_client = AnthropicClient.new(settings.anthropic_api_key)
-    deepgram_client = LocalDeepgramClient(settings.deepgram_api_key)
     deepl_client = DeepLClient(settings.deepl_api_key)
     gemini_client = GeminiClient(settings.gemini_api_key)
-    spacy_client = SpacyClient()
 
     async with AsyncSessionLocal() as db:
         job = await summarise_job_repository.get_by_id(db, job_id)
@@ -85,8 +44,7 @@ async def _run_generation(job_id: uuid.UUID, request: GenerationRequest) -> None
 
             source_material = "\n\n".join(request.input_texts[:3])
 
-            generated_text = await _anthropic_with_backoff(
-                anthropic_client.generate_summary_text,
+            generated_text = await anthropic_client.generate_summary_text(
                 content_to_summarise=source_material,
                 complexity_level=request.complexity_level,
                 from_language=language_name,
@@ -102,19 +60,11 @@ async def _run_generation(job_id: uuid.UUID, request: GenerationRequest) -> None
 
             await summarise_job_repository.save_translated_text(db, job, translated_text)
 
-            target_pos_data = spacy_client.get_parts_of_speech(generated_text, request.target_language)
-            source_pos_data = spacy_client.get_parts_of_speech(translated_text, request.source_language)
-
-            await summarise_job_repository.save_pos_data(db, job, source_pos_data, target_pos_data)
-
             voice = gemini_client.get_voice_by_language(request.target_language)
             wav_bytes = await gemini_client.generate_audio(generated_text, voice)
             audio_key = f"audio/{job_id}.wav"
             upload_audio(audio_key, wav_bytes)
 
-            transcript = await deepgram_client.transcribe_bytes(wav_bytes, request.target_language)
-            await summarise_job_repository.save_audio_transcript(db, job, transcript)
-
             await summarise_job_repository.mark_succeeded(db, job, audio_key)
 
         except Exception as exc:

api/app/routers/api/jobs.py

@@ -28,12 +28,9 @@ class JobResponse(BaseModel):
     completed_at: datetime | None = None
     # only present on success
     generated_text: str | None = None
-    generated_text_pos: dict | None = None
     translated_text: str | None = None
-    translated_text_pos: dict | None = None
     input_summary: str | None = None
     audio_url: str | None = None
-    audio_transcript: dict | None = None
     # only present on failure
     error_message: str | None = None
     model_config = {"from_attributes": True}
@@ -43,8 +40,6 @@ class JobSummary(BaseModel):
     id: uuid.UUID
     status: str
     created_at: datetime
-    completed_at: datetime | None = None
-    error_message: str | None = None
 
 
 class JobListResponse(BaseModel):
@@ -90,12 +85,9 @@ async def get_job(
 
     if job.status == "succeeded":
         response.generated_text = job.generated_text
-        response.generated_text_pos = job.target_pos_data
         response.translated_text = job.translated_text
-        response.translated_text_pos = job.source_pos_data
         response.input_summary = job.input_summary
         response.audio_url = job.audio_url
-        response.audio_transcript = job.audio_transcript
     elif job.status == "failed":
         response.error_message = job.error_message
 

api/app/routers/api/pos.py

@@ -1,12 +1,31 @@
 from fastapi import APIRouter, Depends, HTTPException
 from pydantic import BaseModel
+import spacy
 
 from ...auth import verify_token
-from ...outbound.spacy.spacy_client import SpacyClient, UnsupportedLanguageError
 
 router = APIRouter(prefix="/pos", tags=["api", "pos"])
 
-_spacy_client = SpacyClient()
+LANGUAGE_MODELS: dict[str, str] = {
+    "en": "en_core_web_sm",
+    "fr": "fr_core_news_sm",
+    "es": "es_core_news_sm",
+    "it": "it_core_news_sm",
+    "de": "de_core_news_sm",
+}
+
+_nlp_cache: dict[str, spacy.Language] = {}
+
+
+def _get_nlp(language: str) -> spacy.Language:
+    if language not in LANGUAGE_MODELS:
+        raise HTTPException(
+            status_code=400,
+            detail=f"Unsupported language '{language}'. Supported: {list(LANGUAGE_MODELS)}",
+        )
+    if language not in _nlp_cache:
+        _nlp_cache[language] = spacy.load(LANGUAGE_MODELS[language])
+    return _nlp_cache[language]
 
 
 class POSRequest(BaseModel):
@@ -30,8 +49,18 @@ class POSResponse(BaseModel):
 
 @router.post("/", response_model=POSResponse)
 def analyze_pos(request: POSRequest, _: dict = Depends(verify_token)) -> POSResponse:
-    try:
-        result = _spacy_client.get_parts_of_speech(request.text, request.language)
-    except UnsupportedLanguageError as e:
-        raise HTTPException(status_code=400, detail=str(e))
-    return POSResponse(**result)
+    nlp = _get_nlp(request.language)
+    doc = nlp(request.text)
+    tokens = [
+        TokenInfo(
+            text=token.text,
+            lemma=token.lemma_,
+            pos=token.pos_,
+            tag=token.tag_,
+            dep=token.dep_,
+            is_stop=token.is_stop,
+        )
+        for token in doc
+        if not token.is_space
+    ]
+    return POSResponse(language=request.language, tokens=tokens)

api/pyproject.toml

@@ -17,7 +17,6 @@ dependencies = [
     "google-genai>=1.0.0",
     "boto3>=1.35.0",
     "httpx>=0.28.1",
-    "deepgram-sdk>=6.1.0"
 ]
 
 [build-system]