chore(main): release 0.6.1 (#539)

Bumps the npm_and_yarn group with 6 updates in the /www directory:

| Package | From | To |
| --- | --- | --- |
| [axios](https://github.com/axios/axios) | `1.6.2` | `1.8.2` |
| [postcss](https://github.com/postcss/postcss) | `8.4.25` | `8.4.31` |
| [braces](https://github.com/micromatch/braces) | `3.0.2` | `3.0.3` |
| [cross-spawn](https://github.com/moxystudio/node-cross-spawn) | `7.0.3` | `7.0.6` |
| [micromatch](https://github.com/micromatch/micromatch) | `4.0.5` | `4.0.8` |
| [nanoid](https://github.com/ai/nanoid) | `3.3.6` | `3.3.11` |



Updates `axios` from 1.6.2 to 1.8.2
- [Release notes](https://github.com/axios/axios/releases)
- [Changelog](https://github.com/axios/axios/blob/v1.x/CHANGELOG.md)
- [Commits](https://github.com/axios/axios/compare/v1.6.2...v1.8.2)

Updates `postcss` from 8.4.25 to 8.4.31
- [Release notes](https://github.com/postcss/postcss/releases)
- [Changelog](https://github.com/postcss/postcss/blob/main/CHANGELOG.md)
- [Commits](https://github.com/postcss/postcss/compare/8.4.25...8.4.31)

Updates `braces` from 3.0.2 to 3.0.3
- [Changelog](https://github.com/micromatch/braces/blob/master/CHANGELOG.md)
- [Commits](https://github.com/micromatch/braces/compare/3.0.2...3.0.3)

Updates `cross-spawn` from 7.0.3 to 7.0.6
- [Changelog](https://github.com/moxystudio/node-cross-spawn/blob/master/CHANGELOG.md)
- [Commits](https://github.com/moxystudio/node-cross-spawn/compare/v7.0.3...v7.0.6)

Updates `follow-redirects` from 1.15.2 to 1.15.6
- [Release notes](https://github.com/follow-redirects/follow-redirects/releases)
- [Commits](https://github.com/follow-redirects/follow-redirects/compare/v1.15.2...v1.15.6)

Updates `micromatch` from 4.0.5 to 4.0.8
- [Release notes](https://github.com/micromatch/micromatch/releases)
- [Changelog](https://github.com/micromatch/micromatch/blob/master/CHANGELOG.md)
- [Commits](https://github.com/micromatch/micromatch/compare/4.0.5...4.0.8)

Updates `nanoid` from 3.3.6 to 3.3.11
- [Release notes](https://github.com/ai/nanoid/releases)
- [Changelog](https://github.com/ai/nanoid/blob/main/CHANGELOG.md)
- [Commits](https://github.com/ai/nanoid/compare/3.3.6...3.3.11)

---
updated-dependencies:
- dependency-name: axios
  dependency-version: 1.8.2
  dependency-type: direct:production
  dependency-group: npm_and_yarn
- dependency-name: postcss
  dependency-version: 8.4.31
  dependency-type: direct:production
  dependency-group: npm_and_yarn
- dependency-name: braces
  dependency-version: 3.0.3
  dependency-type: indirect
  dependency-group: npm_and_yarn
- dependency-name: cross-spawn
  dependency-version: 7.0.6
  dependency-type: indirect
  dependency-group: npm_and_yarn
- dependency-name: follow-redirects
  dependency-version: 1.15.6
  dependency-type: indirect
  dependency-group: npm_and_yarn
- dependency-name: micromatch
  dependency-version: 4.0.8
  dependency-type: indirect
  dependency-group: npm_and_yarn
- dependency-name: nanoid
  dependency-version: 3.3.11
  dependency-type: indirect
  dependency-group: npm_and_yarn
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>

.gitignore

@@ -13,3 +13,4 @@ restart-dev.sh
 data/
 www/REFACTOR.md
 www/reload-frontend
+server/test.sqlite

CHANGELOG.md

@@ -1,5 +1,32 @@
 # Changelog
 
+## [0.6.1](https://github.com/Monadical-SAS/reflector/compare/v0.6.0...v0.6.1) (2025-08-06)
+
+
+### Bug Fixes
+
+* delayed waveform loading ([#538](https://github.com/Monadical-SAS/reflector/issues/538)) ([ef64146](https://github.com/Monadical-SAS/reflector/commit/ef64146325d03f64dd9a1fe40234fb3e7e957ae2))
+
+## [0.6.0](https://github.com/Monadical-SAS/reflector/compare/v0.5.0...v0.6.0) (2025-08-05)
+
+
+### ⚠ BREAKING CHANGES
+
+* Configuration keys have changed. Update your .env file:
+    - TRANSCRIPT_MODAL_API_KEY → TRANSCRIPT_API_KEY
+    - LLM_MODAL_API_KEY → (removed, use TRANSCRIPT_API_KEY)
+    - Add DIARIZATION_API_KEY and TRANSLATE_API_KEY if using those services
+
+### Features
+
+* implement service-specific Modal API keys with auto processor pattern ([#528](https://github.com/Monadical-SAS/reflector/issues/528)) ([650befb](https://github.com/Monadical-SAS/reflector/commit/650befb291c47a1f49e94a01ab37d8fdfcd2b65d))
+* use llamaindex everywhere ([#525](https://github.com/Monadical-SAS/reflector/issues/525)) ([3141d17](https://github.com/Monadical-SAS/reflector/commit/3141d172bc4d3b3d533370c8e6e351ea762169bf))
+
+
+### Miscellaneous Chores
+
+* **main:** release 0.6.0 ([ecdbf00](https://github.com/Monadical-SAS/reflector/commit/ecdbf003ea2476c3e95fd231adaeb852f2943df0))
+
 ## [0.5.0](https://github.com/Monadical-SAS/reflector/compare/v0.4.0...v0.5.0) (2025-07-31)
 
 

CLAUDE.md

@@ -144,7 +144,9 @@ All endpoints prefixed `/v1/`:
 **Backend** (`server/.env`):
 - `DATABASE_URL` - Database connection string
 - `REDIS_URL` - Redis broker for Celery
-- `MODAL_TOKEN_ID`, `MODAL_TOKEN_SECRET` - Modal.com GPU processing
+- `TRANSCRIPT_BACKEND=modal` + `TRANSCRIPT_MODAL_API_KEY` - Modal.com transcription
+- `DIARIZATION_BACKEND=modal` + `DIARIZATION_MODAL_API_KEY` - Modal.com diarization
+- `TRANSLATION_BACKEND=modal` + `TRANSLATION_MODAL_API_KEY` - Modal.com translation
 - `WHEREBY_API_KEY` - Video platform integration
 - `REFLECTOR_AUTH_BACKEND` - Authentication method (none, jwt)
 

server/env.example

@@ -24,7 +24,6 @@ AUTH_JWT_AUDIENCE=
 ## Using serverless modal.com (require reflector-gpu-modal deployed)
 #TRANSCRIPT_BACKEND=modal
 #TRANSCRIPT_URL=https://xxxxx--reflector-transcriber-web.modal.run
-#TRANSLATE_URL=https://xxxxx--reflector-translator-web.modal.run
 #TRANSCRIPT_MODAL_API_KEY=xxxxx
 
 TRANSCRIPT_BACKEND=modal
@@ -32,11 +31,13 @@ TRANSCRIPT_URL=https://monadical-sas--reflector-transcriber-web.modal.run
 TRANSCRIPT_MODAL_API_KEY=
 
 ## =======================================================
-## Transcription backend
+## Translation backend
 ##
 ## Only available in modal atm
 ## =======================================================
+TRANSLATION_BACKEND=modal
 TRANSLATE_URL=https://monadical-sas--reflector-translator-web.modal.run
+#TRANSLATION_MODAL_API_KEY=xxxxx
 
 ## =======================================================
 ## LLM backend
@@ -46,38 +47,11 @@ TRANSLATE_URL=https://monadical-sas--reflector-translator-web.modal.run
 ## llm backend implementation
 ## =======================================================
 
-## Using serverless modal.com (require reflector-gpu-modal deployed)
-LLM_BACKEND=modal
-LLM_URL=https://monadical-sas--reflector-llm-web.modal.run
-LLM_MODAL_API_KEY=
-ZEPHYR_LLM_URL=https://monadical-sas--reflector-llm-zephyr-web.modal.run
-
-
-## Using OpenAI
-#LLM_BACKEND=openai
-#LLM_OPENAI_KEY=xxx
-#LLM_OPENAI_MODEL=gpt-3.5-turbo
-
-## Using GPT4ALL
-#LLM_BACKEND=openai
-#LLM_URL=http://localhost:4891/v1/completions
-#LLM_OPENAI_MODEL="GPT4All Falcon"
-
-## Default LLM MODEL NAME
-#DEFAULT_LLM=lmsys/vicuna-13b-v1.5
-
-## Cache directory to store models
-CACHE_DIR=data
-
-## =======================================================
-## Summary LLM configuration
-## =======================================================
-
 ## Context size for summary generation (tokens)
-SUMMARY_LLM_CONTEXT_SIZE_TOKENS=16000
+# LLM_MODEL=microsoft/phi-4
-SUMMARY_LLM_URL=
+LLM_CONTEXT_WINDOW=16000
-SUMMARY_LLM_API_KEY=sk-
+LLM_URL=
-SUMMARY_MODEL=
+LLM_API_KEY=sk-
 
 ## =======================================================
 ## Diarization
@@ -86,7 +60,9 @@ SUMMARY_MODEL=
 ## To allow diarization, you need to expose expose the files to be dowloded by the pipeline
 ## =======================================================
 DIARIZATION_ENABLED=false
+DIARIZATION_BACKEND=modal
 DIARIZATION_URL=https://monadical-sas--reflector-diarizer-web.modal.run
+#DIARIZATION_MODAL_API_KEY=xxxxx
 
 
 ## =======================================================

server/gpu/modal_deployments/README.md

@@ -3,8 +3,9 @@
 This repository hold an API for the GPU implementation of the Reflector API service,
 and use [Modal.com](https://modal.com)
 
-- `reflector_llm.py` - LLM API
+- `reflector_diarizer.py` - Diarization API
 - `reflector_transcriber.py` - Transcription API
+- `reflector_translator.py` - Translation API
 
 ## Modal.com deployment
 
@@ -23,16 +24,20 @@ $ modal deploy reflector_llm.py
 └── 🔨 Created web => https://xxxx--reflector-llm-web.modal.run
 ```
 
-Then in your reflector api configuration `.env`, you can set theses keys:
+Then in your reflector api configuration `.env`, you can set these keys:
 
 ```
 TRANSCRIPT_BACKEND=modal
 TRANSCRIPT_URL=https://xxxx--reflector-transcriber-web.modal.run
 TRANSCRIPT_MODAL_API_KEY=REFLECTOR_APIKEY
 
-LLM_BACKEND=modal
+DIARIZATION_BACKEND=modal
-LLM_URL=https://xxxx--reflector-llm-web.modal.run
+DIARIZATION_URL=https://xxxx--reflector-diarizer-web.modal.run
-LLM_MODAL_API_KEY=REFLECTOR_APIKEY
+DIARIZATION_MODAL_API_KEY=REFLECTOR_APIKEY
+
+TRANSLATION_BACKEND=modal
+TRANSLATION_URL=https://xxxx--reflector-translator-web.modal.run
+TRANSLATION_MODAL_API_KEY=REFLECTOR_APIKEY
 ```
 
 ## API

server/gpu/modal_deployments/reflector_llm.py

@@ -1,213 +0,0 @@
-"""
-Reflector GPU backend - LLM
-===========================
-
-"""
-
-import json
-import os
-import threading
-from typing import Optional
-
-from modal import App, Image, Secret, asgi_app, enter, exit, method
-
-# LLM
-LLM_MODEL: str = "lmsys/vicuna-13b-v1.5"
-LLM_LOW_CPU_MEM_USAGE: bool = True
-LLM_TORCH_DTYPE: str = "bfloat16"
-LLM_MAX_NEW_TOKENS: int = 300
-
-IMAGE_MODEL_DIR = "/root/llm_models"
-
-app = App(name="reflector-llm")
-
-
-def download_llm():
-    from huggingface_hub import snapshot_download
-
-    print("Downloading LLM model")
-    snapshot_download(LLM_MODEL, cache_dir=IMAGE_MODEL_DIR)
-    print("LLM model downloaded")
-
-
-def migrate_cache_llm():
-    """
-    XXX The cache for model files in Transformers v4.22.0 has been updated.
-    Migrating your old cache. This is a one-time only operation. You can
-    interrupt this and resume the migration later on by calling
-    `transformers.utils.move_cache()`.
-    """
-    from transformers.utils.hub import move_cache
-
-    print("Moving LLM cache")
-    move_cache(cache_dir=IMAGE_MODEL_DIR, new_cache_dir=IMAGE_MODEL_DIR)
-    print("LLM cache moved")
-
-
-llm_image = (
-    Image.debian_slim(python_version="3.10.8")
-    .apt_install("git")
-    .pip_install(
-        "transformers",
-        "torch",
-        "sentencepiece",
-        "protobuf",
-        "jsonformer==0.12.0",
-        "accelerate==0.21.0",
-        "einops==0.6.1",
-        "hf-transfer~=0.1",
-        "huggingface_hub==0.16.4",
-    )
-    .env({"HF_HUB_ENABLE_HF_TRANSFER": "1"})
-    .run_function(download_llm)
-    .run_function(migrate_cache_llm)
-)
-
-
-@app.cls(
-    gpu="A100",
-    timeout=60 * 5,
-    scaledown_window=60 * 5,
-    allow_concurrent_inputs=15,
-    image=llm_image,
-)
-class LLM:
-    @enter()
-    def enter(self):
-        import torch
-        from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
-
-        print("Instance llm model")
-        model = AutoModelForCausalLM.from_pretrained(
-            LLM_MODEL,
-            torch_dtype=getattr(torch, LLM_TORCH_DTYPE),
-            low_cpu_mem_usage=LLM_LOW_CPU_MEM_USAGE,
-            cache_dir=IMAGE_MODEL_DIR,
-            local_files_only=True,
-        )
-
-        # JSONFormer doesn't yet support generation configs
-        print("Instance llm generation config")
-        model.config.max_new_tokens = LLM_MAX_NEW_TOKENS
-
-        # generation configuration
-        gen_cfg = GenerationConfig.from_model_config(model.config)
-        gen_cfg.max_new_tokens = LLM_MAX_NEW_TOKENS
-
-        # load tokenizer
-        print("Instance llm tokenizer")
-        tokenizer = AutoTokenizer.from_pretrained(
-            LLM_MODEL, cache_dir=IMAGE_MODEL_DIR, local_files_only=True
-        )
-
-        # move model to gpu
-        print("Move llm model to GPU")
-        model = model.cuda()
-
-        print("Warmup llm done")
-        self.model = model
-        self.tokenizer = tokenizer
-        self.gen_cfg = gen_cfg
-        self.GenerationConfig = GenerationConfig
-
-        self.lock = threading.Lock()
-
-    @exit()
-    def exit():
-        print("Exit llm")
-
-    @method()
-    def generate(
-        self, prompt: str, gen_schema: str | None, gen_cfg: str | None
-    ) -> dict:
-        """
-        Perform a generation action using the LLM
-        """
-        print(f"Generate {prompt=}")
-        if gen_cfg:
-            gen_cfg = self.GenerationConfig.from_dict(json.loads(gen_cfg))
-        else:
-            gen_cfg = self.gen_cfg
-
-        # If a gen_schema is given, conform to gen_schema
-        with self.lock:
-            if gen_schema:
-                import jsonformer
-
-                print(f"Schema {gen_schema=}")
-                jsonformer_llm = jsonformer.Jsonformer(
-                    model=self.model,
-                    tokenizer=self.tokenizer,
-                    json_schema=json.loads(gen_schema),
-                    prompt=prompt,
-                    max_string_token_length=gen_cfg.max_new_tokens,
-                )
-                response = jsonformer_llm()
-            else:
-                # If no gen_schema, perform prompt only generation
-
-                # tokenize prompt
-                input_ids = self.tokenizer.encode(prompt, return_tensors="pt").to(
-                    self.model.device
-                )
-                output = self.model.generate(input_ids, generation_config=gen_cfg)
-
-                # decode output
-                response = self.tokenizer.decode(
-                    output[0].cpu(), skip_special_tokens=True
-                )
-                response = response[len(prompt) :]
-        print(f"Generated {response=}")
-        return {"text": response}
-
-
-# -------------------------------------------------------------------
-# Web API
-# -------------------------------------------------------------------
-
-
-@app.function(
-    scaledown_window=60 * 10,
-    timeout=60 * 5,
-    allow_concurrent_inputs=45,
-    secrets=[
-        Secret.from_name("reflector-gpu"),
-    ],
-)
-@asgi_app()
-def web():
-    from fastapi import Depends, FastAPI, HTTPException, status
-    from fastapi.security import OAuth2PasswordBearer
-    from pydantic import BaseModel
-
-    llmstub = LLM()
-
-    app = FastAPI()
-    oauth2_scheme = OAuth2PasswordBearer(tokenUrl="token")
-
-    def apikey_auth(apikey: str = Depends(oauth2_scheme)):
-        if apikey != os.environ["REFLECTOR_GPU_APIKEY"]:
-            raise HTTPException(
-                status_code=status.HTTP_401_UNAUTHORIZED,
-                detail="Invalid API key",
-                headers={"WWW-Authenticate": "Bearer"},
-            )
-
-    class LLMRequest(BaseModel):
-        prompt: str
-        gen_schema: Optional[dict] = None
-        gen_cfg: Optional[dict] = None
-
-    @app.post("/llm", dependencies=[Depends(apikey_auth)])
-    def llm(
-        req: LLMRequest,
-    ):
-        gen_schema = json.dumps(req.gen_schema) if req.gen_schema else None
-        gen_cfg = json.dumps(req.gen_cfg) if req.gen_cfg else None
-        func = llmstub.generate.spawn(
-            prompt=req.prompt, gen_schema=gen_schema, gen_cfg=gen_cfg
-        )
-        result = func.get()
-        return result
-
-    return app

server/gpu/modal_deployments/reflector_llm_zephyr.py

@@ -1,219 +0,0 @@
-"""
-Reflector GPU backend - LLM
-===========================
-
-"""
-
-import json
-import os
-import threading
-from typing import Optional
-
-from modal import App, Image, Secret, asgi_app, enter, exit, method
-
-# LLM
-LLM_MODEL: str = "HuggingFaceH4/zephyr-7b-alpha"
-LLM_LOW_CPU_MEM_USAGE: bool = True
-LLM_TORCH_DTYPE: str = "bfloat16"
-LLM_MAX_NEW_TOKENS: int = 300
-
-IMAGE_MODEL_DIR = "/root/llm_models/zephyr"
-
-app = App(name="reflector-llm-zephyr")
-
-
-def download_llm():
-    from huggingface_hub import snapshot_download
-
-    print("Downloading LLM model")
-    snapshot_download(LLM_MODEL, cache_dir=IMAGE_MODEL_DIR)
-    print("LLM model downloaded")
-
-
-def migrate_cache_llm():
-    """
-    XXX The cache for model files in Transformers v4.22.0 has been updated.
-    Migrating your old cache. This is a one-time only operation. You can
-    interrupt this and resume the migration later on by calling
-    `transformers.utils.move_cache()`.
-    """
-    from transformers.utils.hub import move_cache
-
-    print("Moving LLM cache")
-    move_cache(cache_dir=IMAGE_MODEL_DIR, new_cache_dir=IMAGE_MODEL_DIR)
-    print("LLM cache moved")
-
-
-llm_image = (
-    Image.debian_slim(python_version="3.10.8")
-    .apt_install("git")
-    .pip_install(
-        "transformers==4.34.0",
-        "torch",
-        "sentencepiece",
-        "protobuf",
-        "jsonformer==0.12.0",
-        "accelerate==0.21.0",
-        "einops==0.6.1",
-        "hf-transfer~=0.1",
-        "huggingface_hub==0.16.4",
-    )
-    .env({"HF_HUB_ENABLE_HF_TRANSFER": "1"})
-    .run_function(download_llm)
-    .run_function(migrate_cache_llm)
-)
-
-
-@app.cls(
-    gpu="A10G",
-    timeout=60 * 5,
-    scaledown_window=60 * 5,
-    allow_concurrent_inputs=10,
-    image=llm_image,
-)
-class LLM:
-    @enter()
-    def enter(self):
-        import torch
-        from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
-
-        print("Instance llm model")
-        model = AutoModelForCausalLM.from_pretrained(
-            LLM_MODEL,
-            torch_dtype=getattr(torch, LLM_TORCH_DTYPE),
-            low_cpu_mem_usage=LLM_LOW_CPU_MEM_USAGE,
-            cache_dir=IMAGE_MODEL_DIR,
-            local_files_only=True,
-        )
-
-        # JSONFormer doesn't yet support generation configs
-        print("Instance llm generation config")
-        model.config.max_new_tokens = LLM_MAX_NEW_TOKENS
-
-        # generation configuration
-        gen_cfg = GenerationConfig.from_model_config(model.config)
-        gen_cfg.max_new_tokens = LLM_MAX_NEW_TOKENS
-
-        # load tokenizer
-        print("Instance llm tokenizer")
-        tokenizer = AutoTokenizer.from_pretrained(
-            LLM_MODEL, cache_dir=IMAGE_MODEL_DIR, local_files_only=True
-        )
-        gen_cfg.pad_token_id = tokenizer.eos_token_id
-        gen_cfg.eos_token_id = tokenizer.eos_token_id
-        tokenizer.pad_token = tokenizer.eos_token
-        model.config.pad_token_id = tokenizer.eos_token_id
-
-        # move model to gpu
-        print("Move llm model to GPU")
-        model = model.cuda()
-
-        print("Warmup llm done")
-        self.model = model
-        self.tokenizer = tokenizer
-        self.gen_cfg = gen_cfg
-        self.GenerationConfig = GenerationConfig
-        self.lock = threading.Lock()
-
-    @exit()
-    def exit():
-        print("Exit llm")
-
-    @method()
-    def generate(
-        self, prompt: str, gen_schema: str | None, gen_cfg: str | None
-    ) -> dict:
-        """
-        Perform a generation action using the LLM
-        """
-        print(f"Generate {prompt=}")
-        if gen_cfg:
-            gen_cfg = self.GenerationConfig.from_dict(json.loads(gen_cfg))
-            gen_cfg.pad_token_id = self.tokenizer.eos_token_id
-            gen_cfg.eos_token_id = self.tokenizer.eos_token_id
-        else:
-            gen_cfg = self.gen_cfg
-
-        # If a gen_schema is given, conform to gen_schema
-        with self.lock:
-            if gen_schema:
-                import jsonformer
-
-                print(f"Schema {gen_schema=}")
-                jsonformer_llm = jsonformer.Jsonformer(
-                    model=self.model,
-                    tokenizer=self.tokenizer,
-                    json_schema=json.loads(gen_schema),
-                    prompt=prompt,
-                    max_string_token_length=gen_cfg.max_new_tokens,
-                )
-                response = jsonformer_llm()
-            else:
-                # If no gen_schema, perform prompt only generation
-
-                # tokenize prompt
-                input_ids = self.tokenizer.encode(prompt, return_tensors="pt").to(
-                    self.model.device
-                )
-                output = self.model.generate(input_ids, generation_config=gen_cfg)
-
-                # decode output
-                response = self.tokenizer.decode(
-                    output[0].cpu(), skip_special_tokens=True
-                )
-                response = response[len(prompt) :]
-                response = {"long_summary": response}
-        print(f"Generated {response=}")
-        return {"text": response}
-
-
-# -------------------------------------------------------------------
-# Web API
-# -------------------------------------------------------------------
-
-
-@app.function(
-    scaledown_window=60 * 10,
-    timeout=60 * 5,
-    allow_concurrent_inputs=30,
-    secrets=[
-        Secret.from_name("reflector-gpu"),
-    ],
-)
-@asgi_app()
-def web():
-    from fastapi import Depends, FastAPI, HTTPException, status
-    from fastapi.security import OAuth2PasswordBearer
-    from pydantic import BaseModel
-
-    llmstub = LLM()
-
-    app = FastAPI()
-    oauth2_scheme = OAuth2PasswordBearer(tokenUrl="token")
-
-    def apikey_auth(apikey: str = Depends(oauth2_scheme)):
-        if apikey != os.environ["REFLECTOR_GPU_APIKEY"]:
-            raise HTTPException(
-                status_code=status.HTTP_401_UNAUTHORIZED,
-                detail="Invalid API key",
-                headers={"WWW-Authenticate": "Bearer"},
-            )
-
-    class LLMRequest(BaseModel):
-        prompt: str
-        gen_schema: Optional[dict] = None
-        gen_cfg: Optional[dict] = None
-
-    @app.post("/llm", dependencies=[Depends(apikey_auth)])
-    def llm(
-        req: LLMRequest,
-    ):
-        gen_schema = json.dumps(req.gen_schema) if req.gen_schema else None
-        gen_cfg = json.dumps(req.gen_cfg) if req.gen_cfg else None
-        func = llmstub.generate.spawn(
-            prompt=req.prompt, gen_schema=gen_schema, gen_cfg=gen_cfg
-        )
-        result = func.get()
-        return result
-
-    return app

server/pyproject.toml

@@ -34,12 +34,12 @@ dependencies = [
     "python-multipart>=0.0.6",
     "faster-whisper>=0.10.0",
     "transformers>=4.36.2",
-    "black==24.1.1",
     "jsonschema>=4.23.0",
     "openai>=1.59.7",
     "psycopg2-binary>=2.9.10",
     "llama-index>=0.12.52",
     "llama-index-llms-openai-like>=0.4.0",
+    "pytest-env>=1.1.5",
 ]
 
 [dependency-groups]
@@ -83,6 +83,10 @@ packages = ["reflector"]
 [tool.coverage.run]
 source = ["reflector"]
 
+[tool.pytest_env]
+ENVIRONMENT = "pytest"
+DATABASE_URL = "sqlite:///test.sqlite"
+
 [tool.pytest.ini_options]
 addopts = "-ra -q --disable-pytest-warnings --cov --cov-report html -v"
 testpaths = ["tests"]

server/reflector/llm.py

@@ -0,0 +1,83 @@
+from typing import Type, TypeVar
+
+from llama_index.core import Settings
+from llama_index.core.output_parsers import PydanticOutputParser
+from llama_index.core.program import LLMTextCompletionProgram
+from llama_index.core.response_synthesizers import TreeSummarize
+from llama_index.llms.openai_like import OpenAILike
+from pydantic import BaseModel
+
+T = TypeVar("T", bound=BaseModel)
+
+STRUCTURED_RESPONSE_PROMPT_TEMPLATE = """
+Based on the following analysis, provide the information in the requested JSON format:
+
+Analysis:
+{analysis}
+
+{format_instructions}
+"""
+
+
+class LLM:
+    def __init__(self, settings, temperature: float = 0.4, max_tokens: int = 2048):
+        self.settings_obj = settings
+        self.model_name = settings.LLM_MODEL
+        self.url = settings.LLM_URL
+        self.api_key = settings.LLM_API_KEY
+        self.context_window = settings.LLM_CONTEXT_WINDOW
+        self.temperature = temperature
+        self.max_tokens = max_tokens
+
+        # Configure llamaindex Settings
+        self._configure_llamaindex()
+
+    def _configure_llamaindex(self):
+        """Configure llamaindex Settings with OpenAILike LLM"""
+        Settings.llm = OpenAILike(
+            model=self.model_name,
+            api_base=self.url,
+            api_key=self.api_key,
+            context_window=self.context_window,
+            is_chat_model=True,
+            is_function_calling_model=False,
+            temperature=self.temperature,
+            max_tokens=self.max_tokens,
+        )
+
+    async def get_response(
+        self, prompt: str, texts: list[str], tone_name: str | None = None
+    ) -> str:
+        """Get a text response using TreeSummarize for non-function-calling models"""
+        summarizer = TreeSummarize(verbose=False)
+        response = await summarizer.aget_response(prompt, texts, tone_name=tone_name)
+        return str(response).strip()
+
+    async def get_structured_response(
+        self,
+        prompt: str,
+        texts: list[str],
+        output_cls: Type[T],
+        tone_name: str | None = None,
+    ) -> T:
+        """Get structured output from LLM for non-function-calling models"""
+        summarizer = TreeSummarize(verbose=True)
+        response = await summarizer.aget_response(prompt, texts, tone_name=tone_name)
+
+        output_parser = PydanticOutputParser(output_cls)
+
+        program = LLMTextCompletionProgram.from_defaults(
+            output_parser=output_parser,
+            prompt_template_str=STRUCTURED_RESPONSE_PROMPT_TEMPLATE,
+            verbose=False,
+        )
+
+        format_instructions = output_parser.format(
+            "Please structure the above information in the following JSON format:"
+        )
+
+        output = await program.acall(
+            analysis=str(response), format_instructions=format_instructions
+        )
+
+        return output

server/reflector/llm/__init__.py

@@ -1,2 +0,0 @@
-from .base import LLM  # noqa: F401
-from .llm_params import LLMTaskParams  # noqa: F401

server/reflector/llm/base.py

@@ -1,347 +0,0 @@
-import importlib
-import json
-import re
-from typing import TypeVar
-
-import nltk
-from prometheus_client import Counter, Histogram
-from transformers import GenerationConfig
-
-from reflector.llm.llm_params import TaskParams
-from reflector.logger import logger as reflector_logger
-from reflector.settings import settings
-from reflector.utils.retry import retry
-
-T = TypeVar("T", bound="LLM")
-
-
-class LLM:
-    _nltk_downloaded = False
-    _registry = {}
-    model_name: str
-    m_generate = Histogram(
-        "llm_generate",
-        "Time spent in LLM.generate",
-        ["backend"],
-    )
-    m_generate_call = Counter(
-        "llm_generate_call",
-        "Number of calls to LLM.generate",
-        ["backend"],
-    )
-    m_generate_success = Counter(
-        "llm_generate_success",
-        "Number of successful calls to LLM.generate",
-        ["backend"],
-    )
-    m_generate_failure = Counter(
-        "llm_generate_failure",
-        "Number of failed calls to LLM.generate",
-        ["backend"],
-    )
-
-    @classmethod
-    def ensure_nltk(cls):
-        """
-        Make sure NLTK package is installed. Searches in the cache and
-        downloads only if needed.
-        """
-        if not cls._nltk_downloaded:
-            nltk.download("punkt_tab")
-            # For POS tagging
-            nltk.download("averaged_perceptron_tagger_eng")
-            cls._nltk_downloaded = True
-
-    @classmethod
-    def register(cls, name, klass):
-        cls._registry[name] = klass
-
-    @classmethod
-    def get_instance(cls, model_name: str | None = None, name: str = None) -> T:
-        """
-        Return an instance depending on the settings.
-        Settings used:
-
-        - `LLM_BACKEND`: key of the backend
-        - `LLM_URL`: url of the backend
-        """
-        if name is None:
-            name = settings.LLM_BACKEND
-        if name not in cls._registry:
-            module_name = f"reflector.llm.llm_{name}"
-            importlib.import_module(module_name)
-        cls.ensure_nltk()
-
-        return cls._registry[name](model_name)
-
-    def get_model_name(self) -> str:
-        """
-        Get the currently set model name
-        """
-        return self._get_model_name()
-
-    def _get_model_name(self) -> str:
-        pass
-
-    def set_model_name(self, model_name: str) -> bool:
-        """
-        Update the model name with the provided model name
-        """
-        return self._set_model_name(model_name)
-
-    def _set_model_name(self, model_name: str) -> bool:
-        raise NotImplementedError
-
-    @property
-    def template(self) -> str:
-        """
-        Return the LLM Prompt template
-        """
-        return """
-        ### Human:
-        {instruct}
-
-        {text}
-
-        ### Assistant:
-        """
-
-    def __init__(self):
-        name = self.__class__.__name__
-        self.m_generate = self.m_generate.labels(name)
-        self.m_generate_call = self.m_generate_call.labels(name)
-        self.m_generate_success = self.m_generate_success.labels(name)
-        self.m_generate_failure = self.m_generate_failure.labels(name)
-        self.detokenizer = nltk.tokenize.treebank.TreebankWordDetokenizer()
-
-    @property
-    def tokenizer(self):
-        """
-        Return the tokenizer instance used by LLM
-        """
-        return self._get_tokenizer()
-
-    def _get_tokenizer(self):
-        pass
-
-    def has_structured_output(self):
-        # whether implementation supports structured output
-        # on the model side (otherwise it's prompt engineering)
-        return False
-
-    async def generate(
-        self,
-        prompt: str,
-        logger: reflector_logger,
-        gen_schema: dict | None = None,
-        gen_cfg: GenerationConfig | None = None,
-        **kwargs,
-    ) -> dict:
-        logger.info("LLM generate", prompt=repr(prompt))
-
-        if gen_cfg:
-            gen_cfg = gen_cfg.to_dict()
-        self.m_generate_call.inc()
-        try:
-            with self.m_generate.time():
-                result = await retry(self._generate)(
-                    prompt=prompt,
-                    gen_schema=gen_schema,
-                    gen_cfg=gen_cfg,
-                    logger=logger,
-                    **kwargs,
-                )
-            self.m_generate_success.inc()
-
-        except Exception:
-            logger.exception("Failed to call llm after retrying")
-            self.m_generate_failure.inc()
-            raise
-
-        logger.debug("LLM result [raw]", result=repr(result))
-        if isinstance(result, str):
-            result = self._parse_json(result)
-        logger.debug("LLM result [parsed]", result=repr(result))
-
-        return result
-
-    async def completion(
-        self, messages: list, logger: reflector_logger, **kwargs
-    ) -> dict:
-        """
-        Use /v1/chat/completion Open-AI compatible endpoint from the URL
-        It's up to the user to validate anything or transform the result
-        """
-        logger.info("LLM completions", messages=messages)
-
-        try:
-            with self.m_generate.time():
-                result = await retry(self._completion)(
-                    messages=messages, **{**kwargs, "logger": logger}
-                )
-            self.m_generate_success.inc()
-        except Exception:
-            logger.exception("Failed to call llm after retrying")
-            self.m_generate_failure.inc()
-            raise
-
-        logger.debug("LLM completion result", result=repr(result))
-        return result
-
-    def ensure_casing(self, title: str) -> str:
-        """
-        LLM takes care of word casing, but in rare cases this
-        can falter. This is a fallback to ensure the casing of
-        topics is in a proper format.
-
-        We select nouns, verbs and adjectives and check if camel
-         casing is present and fix it, if not. Will not perform
-         any other changes.
-        """
-        tokens = nltk.word_tokenize(title)
-        pos_tags = nltk.pos_tag(tokens)
-        camel_cased = []
-
-        whitelisted_pos_tags = [
-            "NN",
-            "NNS",
-            "NNP",
-            "NNPS",  # Noun POS
-            "VB",
-            "VBD",
-            "VBG",
-            "VBN",
-            "VBP",
-            "VBZ",  # Verb POS
-            "JJ",
-            "JJR",
-            "JJS",  # Adjective POS
-        ]
-
-        # If at all there is an exception, do not block other reflector
-        # processes. Return the LLM generated title, at the least.
-        try:
-            for word, pos in pos_tags:
-                if pos in whitelisted_pos_tags and word[0].islower():
-                    camel_cased.append(word[0].upper() + word[1:])
-                else:
-                    camel_cased.append(word)
-            modified_title = self.detokenizer.detokenize(camel_cased)
-
-            # Irrespective of casing changes, the starting letter
-            # of title is always upper-cased
-            title = modified_title[0].upper() + modified_title[1:]
-        except Exception as e:
-            reflector_logger.info(
-                f"Failed to ensure casing on {title=} with exception : {str(e)}"
-            )
-
-        return title
-
-    def trim_title(self, title: str) -> str:
-        """
-        List of manual trimming to the title.
-
-        Longer titles are prone to run into A prefix of phrases that don't
-        really add any descriptive information and in some cases, this
-        behaviour can be repeated for several consecutive topics. Trim the
-        titles to maintain quality of titles.
-        """
-        phrases_to_remove = ["Discussing", "Discussion on", "Discussion about"]
-        try:
-            pattern = (
-                r"\b(?:"
-                + "|".join(re.escape(phrase) for phrase in phrases_to_remove)
-                + r")\b"
-            )
-            title = re.sub(pattern, "", title, flags=re.IGNORECASE)
-        except Exception as e:
-            reflector_logger.info(f"Failed to trim {title=} with exception : {str(e)}")
-        return title
-
-    async def _generate(
-        self, prompt: str, gen_schema: dict | None, gen_cfg: dict | None, **kwargs
-    ) -> str:
-        raise NotImplementedError
-
-    async def _completion(self, messages: list, **kwargs) -> dict:
-        raise NotImplementedError
-
-    def _parse_json(self, result: str) -> dict:
-        result = result.strip()
-        # try detecting code block if exist
-        # starts with ```json\n, ends with ```
-        # or starts with ```\n, ends with ```
-        # or starts with \n```javascript\n, ends with ```
-
-        regex = r"```(json|javascript|)?(.*)```"
-        matches = re.findall(regex, result.strip(), re.MULTILINE | re.DOTALL)
-        if matches:
-            result = matches[0][1]
-
-        else:
-            # maybe the prompt has been started with ```json
-            # so if text ends with ```, just remove it and use it as json
-            if result.endswith("```"):
-                result = result[:-3]
-
-        return json.loads(result.strip())
-
-    def text_token_threshold(self, task_params: TaskParams | None) -> int:
-        """
-        Choose the token size to set as the threshold to pack the LLM calls
-        """
-        buffer_token_size = 100
-        default_output_tokens = 1000
-        context_window = self.tokenizer.model_max_length
-        tokens = self.tokenizer.tokenize(
-            self.create_prompt(instruct=task_params.instruct, text="")
-        )
-        threshold = context_window - len(tokens) - buffer_token_size
-        if task_params.gen_cfg:
-            threshold -= task_params.gen_cfg.max_new_tokens
-        else:
-            threshold -= default_output_tokens
-        return threshold
-
-    def split_corpus(
-        self,
-        corpus: str,
-        task_params: TaskParams,
-        token_threshold: int | None = None,
-    ) -> list[str]:
-        """
-        Split the input to the LLM due to CUDA memory limitations and LLM context window
-        restrictions.
-
-        Accumulate tokens from full sentences till threshold and yield accumulated
-        tokens. Reset accumulation when threshold is reached and repeat process.
-        """
-        if not token_threshold:
-            token_threshold = self.text_token_threshold(task_params=task_params)
-
-        accumulated_tokens = []
-        accumulated_sentences = []
-        accumulated_token_count = 0
-        corpus_sentences = nltk.sent_tokenize(corpus)
-
-        for sentence in corpus_sentences:
-            tokens = self.tokenizer.tokenize(sentence)
-            if accumulated_token_count + len(tokens) <= token_threshold:
-                accumulated_token_count += len(tokens)
-                accumulated_tokens.extend(tokens)
-                accumulated_sentences.append(sentence)
-            else:
-                yield "".join(accumulated_sentences)
-                accumulated_token_count = len(tokens)
-                accumulated_tokens = tokens
-                accumulated_sentences = [sentence]
-
-        if accumulated_tokens:
-            yield " ".join(accumulated_sentences)
-
-    def create_prompt(self, instruct: str, text: str) -> str:
-        """
-        Create a consumable prompt based on the prompt template
-        """
-        return self.template.format(instruct=instruct, text=text)

server/reflector/llm/llm_modal.py

@@ -1,155 +0,0 @@
-import httpx
-from transformers import AutoTokenizer, GenerationConfig
-
-from reflector.llm.base import LLM
-from reflector.logger import logger as reflector_logger
-from reflector.settings import settings
-from reflector.utils.retry import retry
-
-
-class ModalLLM(LLM):
-    def __init__(self, model_name: str | None = None):
-        super().__init__()
-        self.timeout = settings.LLM_TIMEOUT
-        self.llm_url = settings.LLM_URL + "/llm"
-        self.headers = {
-            "Authorization": f"Bearer {settings.LLM_MODAL_API_KEY}",
-        }
-        self._set_model_name(model_name if model_name else settings.DEFAULT_LLM)
-
-    @property
-    def supported_models(self):
-        """
-        List of currently supported models on this GPU platform
-        """
-        # TODO: Query the specific GPU platform
-        # Replace this with a HTTP call
-        return [
-            "lmsys/vicuna-13b-v1.5",
-            "HuggingFaceH4/zephyr-7b-alpha",
-            "NousResearch/Hermes-3-Llama-3.1-8B",
-        ]
-
-    async def _generate(
-        self, prompt: str, gen_schema: dict | None, gen_cfg: dict | None, **kwargs
-    ) -> str:
-        json_payload = {"prompt": prompt}
-        if gen_schema:
-            json_payload["gen_schema"] = gen_schema
-        if gen_cfg:
-            json_payload["gen_cfg"] = gen_cfg
-
-        # Handing over generation of the final summary to Zephyr model
-        # but replacing the Vicuna model will happen after more testing
-        # TODO: Create a mapping of model names and cloud deployments
-        if self.model_name == "HuggingFaceH4/zephyr-7b-alpha":
-            self.llm_url = settings.ZEPHYR_LLM_URL + "/llm"
-
-        async with httpx.AsyncClient() as client:
-            response = await retry(client.post)(
-                self.llm_url,
-                headers=self.headers,
-                json=json_payload,
-                timeout=self.timeout,
-                retry_timeout=60 * 5,
-                follow_redirects=True,
-                logger=kwargs.get("logger", reflector_logger),
-            )
-            response.raise_for_status()
-            text = response.json()["text"]
-            return text
-
-    async def _completion(self, messages: list, **kwargs) -> dict:
-        # returns full api response
-        kwargs.setdefault("temperature", 0.3)
-        kwargs.setdefault("max_tokens", 2048)
-        kwargs.setdefault("stream", False)
-        kwargs.setdefault("repetition_penalty", 1)
-        kwargs.setdefault("top_p", 1)
-        kwargs.setdefault("top_k", -1)
-        kwargs.setdefault("min_p", 0.05)
-        data = {"messages": messages, "model": self.model_name, **kwargs}
-
-        if self.model_name == "NousResearch/Hermes-3-Llama-3.1-8B":
-            self.llm_url = settings.HERMES_3_8B_LLM_URL + "/v1/chat/completions"
-
-        async with httpx.AsyncClient() as client:
-            response = await retry(client.post)(
-                self.llm_url,
-                headers=self.headers,
-                json=data,
-                timeout=self.timeout,
-                retry_timeout=60 * 5,
-                follow_redirects=True,
-                logger=kwargs.get("logger", reflector_logger),
-            )
-            response.raise_for_status()
-            return response.json()
-
-    def _set_model_name(self, model_name: str) -> bool:
-        """
-        Set the model name
-        """
-        # Abort, if the model is not supported
-        if model_name not in self.supported_models:
-            reflector_logger.info(
-                f"Attempted to change {model_name=}, but is not supported."
-                f"Setting model and tokenizer failed !"
-            )
-            return False
-        # Abort, if the model is already set
-        elif hasattr(self, "model_name") and model_name == self._get_model_name():
-            reflector_logger.info("No change in model. Setting model skipped.")
-            return False
-        # Update model name and tokenizer
-        self.model_name = model_name
-        self.llm_tokenizer = AutoTokenizer.from_pretrained(
-            self.model_name, cache_dir=settings.CACHE_DIR
-        )
-        reflector_logger.info(f"Model set to {model_name=}. Tokenizer updated.")
-        return True
-
-    def _get_tokenizer(self) -> AutoTokenizer:
-        """
-        Return the currently used LLM tokenizer
-        """
-        return self.llm_tokenizer
-
-    def _get_model_name(self) -> str:
-        """
-        Return the current model name from the instance details
-        """
-        return self.model_name
-
-
-LLM.register("modal", ModalLLM)
-
-if __name__ == "__main__":
-    from reflector.logger import logger
-
-    async def main():
-        llm = ModalLLM()
-        prompt = llm.create_prompt(
-            instruct="Complete the following task",
-            text="Tell me a joke about programming.",
-        )
-        result = await llm.generate(prompt=prompt, logger=logger)
-        print(result)
-
-        gen_schema = {
-            "type": "object",
-            "properties": {"response": {"type": "string"}},
-        }
-
-        result = await llm.generate(prompt=prompt, gen_schema=gen_schema, logger=logger)
-        print(result)
-
-        gen_cfg = GenerationConfig(max_new_tokens=150)
-        result = await llm.generate(
-            prompt=prompt, gen_cfg=gen_cfg, gen_schema=gen_schema, logger=logger
-        )
-        print(result)
-
-    import asyncio
-
-    asyncio.run(main())

server/reflector/llm/llm_openai.py

@@ -1,48 +0,0 @@
-import httpx
-from transformers import GenerationConfig
-
-from reflector.llm.base import LLM
-from reflector.logger import logger
-from reflector.settings import settings
-
-
-class OpenAILLM(LLM):
-    def __init__(self, model_name: str | None = None, **kwargs):
-        super().__init__(**kwargs)
-        self.openai_key = settings.LLM_OPENAI_KEY
-        self.openai_url = settings.LLM_URL
-        self.openai_model = settings.LLM_OPENAI_MODEL
-        self.openai_temperature = settings.LLM_OPENAI_TEMPERATURE
-        self.timeout = settings.LLM_TIMEOUT
-        self.max_tokens = settings.LLM_MAX_TOKENS
-        logger.info(f"LLM use openai backend at {self.openai_url}")
-
-    async def _generate(
-        self,
-        prompt: str,
-        gen_schema: dict | None,
-        gen_cfg: GenerationConfig | None,
-        **kwargs,
-    ) -> str:
-        headers = {
-            "Content-Type": "application/json",
-            "Authorization": f"Bearer {self.openai_key}",
-        }
-
-        async with httpx.AsyncClient(timeout=self.timeout) as client:
-            response = await client.post(
-                self.openai_url,
-                headers=headers,
-                json={
-                    "model": self.openai_model,
-                    "prompt": prompt,
-                    "max_tokens": self.max_tokens,
-                    "temperature": self.openai_temperature,
-                },
-            )
-            response.raise_for_status()
-            result = response.json()
-            return result["choices"][0]["text"]
-
-
-LLM.register("openai", OpenAILLM)

server/reflector/llm/llm_params.py

@@ -1,219 +0,0 @@
-from typing import Optional, TypeVar
-
-from pydantic import BaseModel
-from transformers import GenerationConfig
-
-
-class TaskParams(BaseModel, arbitrary_types_allowed=True):
-    instruct: str
-    gen_cfg: Optional[GenerationConfig] = None
-    gen_schema: Optional[dict] = None
-
-
-T = TypeVar("T", bound="LLMTaskParams")
-
-
-class LLMTaskParams:
-    _registry = {}
-
-    @classmethod
-    def register(cls, task, klass) -> None:
-        cls._registry[task] = klass
-
-    @classmethod
-    def get_instance(cls, task: str) -> T:
-        return cls._registry[task]()
-
-    @property
-    def task_params(self) -> TaskParams | None:
-        """
-        Fetch the task related parameters
-        """
-        return self._get_task_params()
-
-    def _get_task_params(self) -> None:
-        pass
-
-
-class FinalLongSummaryParams(LLMTaskParams):
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
-        self._gen_cfg = GenerationConfig(
-            max_new_tokens=1000, num_beams=3, do_sample=True, temperature=0.3
-        )
-        self._instruct = """
-        Take the key ideas and takeaways from the text and create a short
-         summary. Be sure to keep the length of the response to a minimum.
-         Do not include trivial information in the summary.
-          """
-        self._schema = {
-            "type": "object",
-            "properties": {"long_summary": {"type": "string"}},
-        }
-        self._task_params = TaskParams(
-            instruct=self._instruct, gen_schema=self._schema, gen_cfg=self._gen_cfg
-        )
-
-    def _get_task_params(self) -> TaskParams:
-        """gen_schema
-        Return the parameters associated with a specific LLM task
-        """
-        return self._task_params
-
-
-class FinalShortSummaryParams(LLMTaskParams):
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
-        self._gen_cfg = GenerationConfig(
-            max_new_tokens=800, num_beams=3, do_sample=True, temperature=0.3
-        )
-        self._instruct = """
-        Take the key ideas and takeaways from the text and create a short
-         summary. Be sure to keep the length of the response to a minimum.
-         Do not include trivial information in the summary.
-          """
-        self._schema = {
-            "type": "object",
-            "properties": {"short_summary": {"type": "string"}},
-        }
-        self._task_params = TaskParams(
-            instruct=self._instruct, gen_schema=self._schema, gen_cfg=self._gen_cfg
-        )
-
-    def _get_task_params(self) -> TaskParams:
-        """
-        Return the parameters associated with a specific LLM task
-        """
-        return self._task_params
-
-
-class FinalTitleParams(LLMTaskParams):
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
-        self._gen_cfg = GenerationConfig(
-            max_new_tokens=200, num_beams=5, do_sample=True, temperature=0.5
-        )
-        self._instruct = """
-            Combine the following individual titles into one single short title that
-            condenses the essence of all titles.
-        """
-        self._schema = {
-            "type": "object",
-            "properties": {"title": {"type": "string"}},
-        }
-        self._task_params = TaskParams(
-            instruct=self._instruct, gen_schema=self._schema, gen_cfg=self._gen_cfg
-        )
-
-    def _get_task_params(self) -> TaskParams:
-        """
-        Return the parameters associated with a specific LLM task
-        """
-        return self._task_params
-
-
-class TopicParams(LLMTaskParams):
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
-        self._gen_cfg = GenerationConfig(
-            max_new_tokens=500, num_beams=6, do_sample=True, temperature=0.9
-        )
-        self._instruct = """
-                Create a JSON object as response.The JSON object must have 2 fields:
-                i) title and ii) summary.
-                For the title field, generate a very detailed and self-explanatory
-                 title for the given text. Let the title be as descriptive as possible.
-                For the summary field, summarize the given text in a maximum of
-                two sentences.
-            """
-        self._schema = {
-            "type": "object",
-            "properties": {
-                "title": {"type": "string"},
-                "summary": {"type": "string"},
-            },
-        }
-        self._task_params = TaskParams(
-            instruct=self._instruct, gen_schema=self._schema, gen_cfg=self._gen_cfg
-        )
-
-    def _get_task_params(self) -> TaskParams:
-        """
-        Return the parameters associated with a specific LLM task
-        """
-        return self._task_params
-
-
-class BulletedSummaryParams(LLMTaskParams):
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
-        self._gen_cfg = GenerationConfig(
-            max_new_tokens=800,
-            num_beams=1,
-            do_sample=True,
-            temperature=0.2,
-            early_stopping=True,
-        )
-        self._instruct = """
-        Given a meeting transcript, extract the key things discussed in the
-         form of a list.
-
-        While generating the response, follow the constraints mentioned below.
-
-        Summary constraints:
-        i) Do not add new content, except to fix spelling or punctuation.
-        ii) Do not add any prefixes or numbering in the response.
-        iii) The summarization should be as information dense as possible.
-        iv) Do not add any additional sections like Note, Conclusion, etc. in
-        the response.
-
-        Response format:
-        i) The response should be in the form of a bulleted list.
-        ii) Iteratively merge all the relevant paragraphs together to keep the
-         number of paragraphs to a minimum.
-        iii) Remove any unfinished sentences from the final response.
-        iv) Do not include narrative or reporting clauses.
-        v) Use "*" as the bullet icon.
-    """
-        self._task_params = TaskParams(
-            instruct=self._instruct, gen_schema=None, gen_cfg=self._gen_cfg
-        )
-
-    def _get_task_params(self) -> TaskParams:
-        """gen_schema
-        Return the parameters associated with a specific LLM task
-        """
-        return self._task_params
-
-
-class MergedSummaryParams(LLMTaskParams):
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
-        self._gen_cfg = GenerationConfig(
-            max_new_tokens=600,
-            num_beams=1,
-            do_sample=True,
-            temperature=0.2,
-            early_stopping=True,
-        )
-        self._instruct = """
-        Given the key points of a meeting, summarize the points to describe the
-         meeting in the form of paragraphs.
-        """
-        self._task_params = TaskParams(
-            instruct=self._instruct, gen_schema=None, gen_cfg=self._gen_cfg
-        )
-
-    def _get_task_params(self) -> TaskParams:
-        """gen_schema
-        Return the parameters associated with a specific LLM task
-        """
-        return self._task_params
-
-
-LLMTaskParams.register("topic", TopicParams)
-LLMTaskParams.register("final_title", FinalTitleParams)
-LLMTaskParams.register("final_short_summary", FinalShortSummaryParams)
-LLMTaskParams.register("final_long_summary", FinalLongSummaryParams)
-LLMTaskParams.register("bullet_summary", BulletedSummaryParams)
-LLMTaskParams.register("merged_summary", MergedSummaryParams)

server/reflector/llm/openai_llm.py

@@ -1,118 +0,0 @@
-import httpx
-from transformers import AutoTokenizer
-
-from reflector.logger import logger
-
-
-def apply_gen_config(payload: dict, gen_cfg) -> None:
-    """Apply generation config overrides to the payload."""
-    config_mapping = {
-        "temperature": "temperature",
-        "max_new_tokens": "max_tokens",
-        "max_tokens": "max_tokens",
-        "top_p": "top_p",
-        "frequency_penalty": "frequency_penalty",
-        "presence_penalty": "presence_penalty",
-    }
-
-    for cfg_attr, payload_key in config_mapping.items():
-        value = getattr(gen_cfg, cfg_attr, None)
-        if value is not None:
-            payload[payload_key] = value
-            if cfg_attr == "max_new_tokens":  # Handle max_new_tokens taking precedence
-                break
-
-
-class OpenAILLM:
-    def __init__(self, config_prefix: str, settings):
-        self.config_prefix = config_prefix
-        self.settings_obj = settings
-        self.model_name = getattr(settings, f"{config_prefix}_MODEL")
-        self.url = getattr(settings, f"{config_prefix}_LLM_URL")
-        self.api_key = getattr(settings, f"{config_prefix}_LLM_API_KEY")
-
-        timeout = getattr(settings, f"{config_prefix}_LLM_TIMEOUT", 300)
-        self.temperature = getattr(settings, f"{config_prefix}_LLM_TEMPERATURE", 0.7)
-        self.max_tokens = getattr(settings, f"{config_prefix}_LLM_MAX_TOKENS", 1024)
-        self.client = httpx.AsyncClient(timeout=timeout)
-
-        # Use a tokenizer that approximates OpenAI token counting
-        tokenizer_name = getattr(settings, f"{config_prefix}_TOKENIZER", "gpt2")
-        try:
-            self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
-        except Exception:
-            logger.debug(
-                f"Failed to load tokenizer '{tokenizer_name}', falling back to default 'gpt2' tokenizer"
-            )
-            self.tokenizer = AutoTokenizer.from_pretrained("gpt2")
-
-    async def generate(
-        self, prompt: str, gen_schema=None, gen_cfg=None, logger=None
-    ) -> str:
-        if logger:
-            logger.debug(
-                "OpenAI LLM generate",
-                prompt=repr(prompt[:100] + "..." if len(prompt) > 100 else prompt),
-            )
-
-        messages = [{"role": "user", "content": prompt}]
-        result = await self.completion(
-            messages, gen_schema=gen_schema, gen_cfg=gen_cfg, logger=logger
-        )
-        return result["choices"][0]["message"]["content"]
-
-    async def completion(
-        self, messages: list, gen_schema=None, gen_cfg=None, logger=None, **kwargs
-    ) -> dict:
-        if logger:
-            logger.info("OpenAI LLM completion", messages_count=len(messages))
-
-        payload = {
-            "model": self.model_name,
-            "messages": messages,
-            "temperature": self.temperature,
-            "max_tokens": self.max_tokens,
-        }
-
-        # Apply generation config overrides
-        if gen_cfg:
-            apply_gen_config(payload, gen_cfg)
-
-        # Apply structured output schema
-        if gen_schema:
-            payload["response_format"] = {
-                "type": "json_schema",
-                "json_schema": {"name": "response", "schema": gen_schema},
-            }
-
-        headers = {
-            "Content-Type": "application/json",
-            "Authorization": f"Bearer {self.api_key}",
-        }
-
-        url = f"{self.url.rstrip('/')}/chat/completions"
-
-        if logger:
-            logger.debug(
-                "OpenAI API request", url=url, payload_keys=list(payload.keys())
-            )
-
-        response = await self.client.post(url, json=payload, headers=headers)
-        response.raise_for_status()
-
-        result = response.json()
-
-        if logger:
-            logger.debug(
-                "OpenAI API response",
-                status_code=response.status_code,
-                choices_count=len(result.get("choices", [])),
-            )
-
-        return result
-
-    async def __aenter__(self):
-        return self
-
-    async def __aexit__(self, exc_type, exc_val, exc_tb):
-        await self.client.aclose()

server/reflector/pipelines/main_live_pipeline.py

@@ -47,7 +47,7 @@ from reflector.processors import (
     TranscriptFinalTitleProcessor,
     TranscriptLinerProcessor,
     TranscriptTopicDetectorProcessor,
-    TranscriptTranslatorProcessor,
+    TranscriptTranslatorAutoProcessor,
 )
 from reflector.processors.audio_waveform_processor import AudioWaveformProcessor
 from reflector.processors.types import AudioDiarizationInput
@@ -361,7 +361,7 @@ class PipelineMainLive(PipelineMainBase):
             AudioMergeProcessor(),
             AudioTranscriptAutoProcessor.as_threaded(),
             TranscriptLinerProcessor(),
-            TranscriptTranslatorProcessor.as_threaded(callback=self.on_transcript),
+            TranscriptTranslatorAutoProcessor.as_threaded(callback=self.on_transcript),
             TranscriptTopicDetectorProcessor.as_threaded(callback=self.on_topic),
         ]
         pipeline = Pipeline(*processors)

server/reflector/processors/__init__.py

@@ -16,6 +16,7 @@ from .transcript_final_title import TranscriptFinalTitleProcessor  # noqa: F401
 from .transcript_liner import TranscriptLinerProcessor  # noqa: F401
 from .transcript_topic_detector import TranscriptTopicDetectorProcessor  # noqa: F401
 from .transcript_translator import TranscriptTranslatorProcessor  # noqa: F401
+from .transcript_translator_auto import TranscriptTranslatorAutoProcessor  # noqa: F401
 from .types import (  # noqa: F401
     AudioFile,
     FinalLongSummary,

server/reflector/processors/audio_diarization_modal.py

@@ -10,12 +10,17 @@ class AudioDiarizationModalProcessor(AudioDiarizationProcessor):
     INPUT_TYPE = AudioDiarizationInput
     OUTPUT_TYPE = TitleSummary
 
-    def __init__(self, **kwargs):
+    def __init__(self, modal_api_key: str | None = None, **kwargs):
         super().__init__(**kwargs)
+        if not settings.DIARIZATION_URL:
+            raise Exception(
+                "DIARIZATION_URL required to use AudioDiarizationModalProcessor"
+            )
         self.diarization_url = settings.DIARIZATION_URL + "/diarize"
-        self.headers = {
+        self.modal_api_key = modal_api_key
-            "Authorization": f"Bearer {settings.LLM_MODAL_API_KEY}",
+        self.headers = {}
-        }
+        if self.modal_api_key:
+            self.headers["Authorization"] = f"Bearer {self.modal_api_key}"
 
     async def _diarize(self, data: AudioDiarizationInput):
         # Gather diarization data

server/reflector/processors/audio_transcript_modal.py

@@ -21,16 +21,20 @@ from reflector.settings import settings
 
 
 class AudioTranscriptModalProcessor(AudioTranscriptProcessor):
-    def __init__(self, modal_api_key: str):
+    def __init__(self, modal_api_key: str | None = None, **kwargs):
         super().__init__()
+        if not settings.TRANSCRIPT_URL:
+            raise Exception(
+                "TRANSCRIPT_URL required to use AudioTranscriptModalProcessor"
+            )
         self.transcript_url = settings.TRANSCRIPT_URL + "/v1"
         self.timeout = settings.TRANSCRIPT_TIMEOUT
-        self.api_key = settings.TRANSCRIPT_MODAL_API_KEY
+        self.modal_api_key = modal_api_key
 
     async def _transcript(self, data: AudioFile):
         async with AsyncOpenAI(
             base_url=self.transcript_url,
-            api_key=self.api_key,
+            api_key=self.modal_api_key,
             timeout=self.timeout,
         ) as client:
             self.logger.debug(f"Try to transcribe audio {data.name}")

server/reflector/processors/summary/summary_builder.py

@@ -12,15 +12,9 @@ from textwrap import dedent
 from typing import Type, TypeVar
 
 import structlog
-from llama_index.core import Settings
-from llama_index.core.output_parsers import PydanticOutputParser
-from llama_index.core.program import LLMTextCompletionProgram
-from llama_index.core.response_synthesizers import TreeSummarize
-from llama_index.llms.openai_like import OpenAILike
 from pydantic import BaseModel, Field
 
-from reflector.llm.base import LLM
+from reflector.llm import LLM
-from reflector.llm.openai_llm import OpenAILLM
 from reflector.settings import settings
 
 T = TypeVar("T", bound=BaseModel)
@@ -168,23 +162,12 @@ class SummaryBuilder:
         self.summaries: list[dict[str, str]] = []
         self.subjects: list[str] = []
         self.transcription_type: TranscriptionType | None = None
-        self.llm_instance: LLM = llm
+        self.llm: LLM = llm
         self.model_name: str = llm.model_name
         self.logger = logger or structlog.get_logger()
         if filename:
             self.read_transcript_from_file(filename)
 
-        Settings.llm = OpenAILike(
-            model=llm.model_name,
-            api_base=llm.url,
-            api_key=llm.api_key,
-            context_window=settings.SUMMARY_LLM_CONTEXT_SIZE_TOKENS,
-            is_chat_model=True,
-            is_function_calling_model=llm.has_structured_output,
-            temperature=llm.temperature,
-            max_tokens=llm.max_tokens,
-        )
-
     def read_transcript_from_file(self, filename: str) -> None:
         """
         Load a transcript from a text file.
@@ -202,40 +185,16 @@ class SummaryBuilder:
         self.transcript = transcript
 
     def set_llm_instance(self, llm: LLM) -> None:
-        self.llm_instance = llm
+        self.llm = llm
 
     async def _get_structured_response(
         self, prompt: str, output_cls: Type[T], tone_name: str | None = None
-    ) -> Type[T]:
+    ) -> T:
         """Generic function to get structured output from LLM for non-function-calling models."""
-        # First, use TreeSummarize to get the response
+        return await self.llm.get_structured_response(
-        summarizer = TreeSummarize(verbose=True)
+            prompt, [self.transcript], output_cls, tone_name=tone_name
-
-        response = await summarizer.aget_response(
-            prompt, [self.transcript], tone_name=tone_name
         )
 
-        # Then, use PydanticOutputParser to structure the response
-        output_parser = PydanticOutputParser(output_cls)
-
-        prompt_template_str = STRUCTURED_RESPONSE_PROMPT_TEMPLATE
-
-        program = LLMTextCompletionProgram.from_defaults(
-            output_parser=output_parser,
-            prompt_template_str=prompt_template_str,
-            verbose=False,
-        )
-
-        format_instructions = output_parser.format(
-            "Please structure the above information in the following JSON format:"
-        )
-
-        output = await program.acall(
-            analysis=str(response), format_instructions=format_instructions
-        )
-
-        return output
-
     # ----------------------------------------------------------------------------
     # Participants
     # ----------------------------------------------------------------------------
@@ -354,19 +313,18 @@ class SummaryBuilder:
     async def generate_subject_summaries(self) -> None:
         """Generate detailed summaries for each extracted subject."""
         assert self.transcript is not None
-        summarizer = TreeSummarize(verbose=False)
         summaries = []
 
         for subject in self.subjects:
             detailed_prompt = DETAILED_SUBJECT_PROMPT_TEMPLATE.format(subject=subject)
 
-            detailed_response = await summarizer.aget_response(
+            detailed_response = await self.llm.get_response(
                 detailed_prompt, [self.transcript], tone_name="Topic assistant"
             )
 
             paragraph_prompt = PARAGRAPH_SUMMARY_PROMPT
 
-            paragraph_response = await summarizer.aget_response(
+            paragraph_response = await self.llm.get_response(
                 paragraph_prompt, [str(detailed_response)], tone_name="Topic summarizer"
             )
 
@@ -377,7 +335,6 @@ class SummaryBuilder:
 
     async def generate_recap(self) -> None:
         """Generate a quick recap from the subject summaries."""
-        summarizer = TreeSummarize(verbose=True)
 
         summaries_text = "\n\n".join(
             [
@@ -388,7 +345,7 @@ class SummaryBuilder:
 
         recap_prompt = RECAP_PROMPT
 
-        recap_response = await summarizer.aget_response(
+        recap_response = await self.llm.get_response(
             recap_prompt, [summaries_text], tone_name="Recap summarizer"
         )
 
@@ -483,7 +440,7 @@ if __name__ == "__main__":
     async def main():
         # build the summary
 
-        llm = OpenAILLM(config_prefix="SUMMARY", settings=settings)
+        llm = LLM(settings=settings)
         sm = SummaryBuilder(llm=llm, filename=args.transcript)
 
         if args.subjects:

server/reflector/processors/transcript_final_summary.py

@@ -1,4 +1,4 @@
-from reflector.llm.openai_llm import OpenAILLM
+from reflector.llm import LLM
 from reflector.processors.base import Processor
 from reflector.processors.summary.summary_builder import SummaryBuilder
 from reflector.processors.types import FinalLongSummary, FinalShortSummary, TitleSummary
@@ -17,7 +17,7 @@ class TranscriptFinalSummaryProcessor(Processor):
         super().__init__(**kwargs)
         self.transcript = transcript
         self.chunks: list[TitleSummary] = []
-        self.llm = OpenAILLM(config_prefix="SUMMARY", settings=settings)
+        self.llm = LLM(settings=settings)
         self.builder = None
 
     async def _push(self, data: TitleSummary):

server/reflector/processors/transcript_final_title.py

@@ -1,67 +1,72 @@
-from reflector.llm import LLM, LLMTaskParams
+from textwrap import dedent
+
+from reflector.llm import LLM
 from reflector.processors.base import Processor
 from reflector.processors.types import FinalTitle, TitleSummary
+from reflector.settings import settings
+from reflector.utils.text import clean_title
+
+TITLE_PROMPT = dedent(
+    """
+    Generate a concise title for this meeting based on the following topic titles.
+    Ignore casual conversation, greetings, or administrative matters.
+
+    The title must:
+    - Be maximum 10 words
+    - Use noun phrases when possible (e.g., "Q1 Budget Review" not "Reviewing the Q1 Budget")
+    - Avoid generic terms like "Team Meeting" or "Discussion"
+
+    If multiple unrelated topics were discussed, prioritize the most significant one.
+    or create a compound title (e.g., "Product Launch and Budget Planning").
+
+    <topics_discussed>
+    {titles}
+    </topics_discussed>
+
+    Do not explain, just output the meeting title as a single line.
+    """
+).strip()
 
 
 class TranscriptFinalTitleProcessor(Processor):
     """
-    Assemble all summary into a line-based json
+    Generate a final title from topic titles using LlamaIndex
     """
 
     INPUT_TYPE = TitleSummary
     OUTPUT_TYPE = FinalTitle
-    TASK = "final_title"
 
     def __init__(self, **kwargs):
         super().__init__(**kwargs)
         self.chunks: list[TitleSummary] = []
-        self.llm = LLM.get_instance()
+        self.llm = LLM(settings=settings, temperature=0.5, max_tokens=200)
-        self.params = LLMTaskParams.get_instance(self.TASK).task_params
 
     async def _push(self, data: TitleSummary):
         self.chunks.append(data)
 
-    async def get_title(self, text: str) -> dict:
+    async def get_title(self, accumulated_titles: str) -> str:
         """
-        Generate a title for the whole recording
+        Generate a title for the whole recording using LLM
         """
-        chunks = list(self.llm.split_corpus(corpus=text, task_params=self.params))
+        prompt = TITLE_PROMPT.format(titles=accumulated_titles)
+        response = await self.llm.get_response(
+            prompt,
+            [accumulated_titles],
+            tone_name="Title generator",
+        )
 
-        if len(chunks) == 1:
+        self.logger.info(f"Generated title response: {response}")
-            chunk = chunks[0]
-            prompt = self.llm.create_prompt(instruct=self.params.instruct, text=chunk)
-            title_result = await self.llm.generate(
-                prompt=prompt,
-                gen_schema=self.params.gen_schema,
-                gen_cfg=self.params.gen_cfg,
-                logger=self.logger,
-            )
-            return title_result
-        else:
-            accumulated_titles = ""
-            for chunk in chunks:
-                prompt = self.llm.create_prompt(
-                    instruct=self.params.instruct, text=chunk
-                )
-                title_result = await self.llm.generate(
-                    prompt=prompt,
-                    gen_schema=self.params.gen_schema,
-                    gen_cfg=self.params.gen_cfg,
-                    logger=self.logger,
-                )
-                accumulated_titles += title_result["title"]
 
-            return await self.get_title(accumulated_titles)
+        return response
 
     async def _flush(self):
         if not self.chunks:
             self.logger.warning("No summary to output")
             return
 
-        accumulated_titles = ".".join([chunk.title for chunk in self.chunks])
+        accumulated_titles = "\n".join([f"- {chunk.title}" for chunk in self.chunks])
-        title_result = await self.get_title(accumulated_titles)
+        title = await self.get_title(accumulated_titles)
-        final_title = self.llm.trim_title(title_result["title"])
+        title = clean_title(title)
-        final_title = self.llm.ensure_casing(final_title)
 
-        final_title = FinalTitle(title=final_title)
+        final_title = FinalTitle(title=title)
         await self.emit(final_title)

server/reflector/processors/transcript_topic_detector.py

@@ -1,7 +1,41 @@
-from reflector.llm import LLM, LLMTaskParams
+from textwrap import dedent
+
+from pydantic import BaseModel, Field
+
+from reflector.llm import LLM
 from reflector.processors.base import Processor
 from reflector.processors.types import TitleSummary, Transcript
 from reflector.settings import settings
+from reflector.utils.text import clean_title
+
+TOPIC_PROMPT = dedent(
+    """
+    Analyze the following transcript segment and extract the main topic being discussed.
+    Focus on the substantive content and ignore small talk or administrative chatter.
+
+    Create a title that:
+    - Captures the specific subject matter being discussed
+    - Is descriptive and self-explanatory
+    - Uses professional language
+    - Is specific rather than generic
+
+    For the summary:
+    - Summarize the key points in maximum two sentences
+    - Focus on what was discussed, decided, or accomplished
+    - Be concise but informative
+
+    <transcript>
+    {text}
+    </transcript>
+    """
+).strip()
+
+
+class TopicResponse(BaseModel):
+    """Structured response for topic detection"""
+
+    title: str = Field(description="A descriptive title for the topic being discussed")
+    summary: str = Field(description="A concise 1-2 sentence summary of the discussion")
 
 
 class TranscriptTopicDetectorProcessor(Processor):
@@ -11,7 +45,6 @@ class TranscriptTopicDetectorProcessor(Processor):
 
     INPUT_TYPE = Transcript
     OUTPUT_TYPE = TitleSummary
-    TASK = "topic"
 
     def __init__(
         self, min_transcript_length: int = int(settings.MIN_TRANSCRIPT_LENGTH), **kwargs
@@ -19,8 +52,7 @@ class TranscriptTopicDetectorProcessor(Processor):
         super().__init__(**kwargs)
         self.transcript = None
         self.min_transcript_length = min_transcript_length
-        self.llm = LLM.get_instance()
+        self.llm = LLM(settings=settings, temperature=0.9, max_tokens=500)
-        self.params = LLMTaskParams.get_instance(self.TASK).task_params
 
     async def _push(self, data: Transcript):
         if self.transcript is None:
@@ -34,18 +66,15 @@ class TranscriptTopicDetectorProcessor(Processor):
             return
         await self.flush()
 
-    async def get_topic(self, text: str) -> dict:
+    async def get_topic(self, text: str) -> TopicResponse:
         """
-        Generate a topic and description for a transcription excerpt
+        Generate a topic and description for a transcription excerpt using LLM
         """
-        prompt = self.llm.create_prompt(instruct=self.params.instruct, text=text)
+        prompt = TOPIC_PROMPT.format(text=text)
-        topic_result = await self.llm.generate(
+        response = await self.llm.get_structured_response(
-            prompt=prompt,
+            prompt, [text], TopicResponse, tone_name="Topic analyzer"
-            gen_schema=self.params.gen_schema,
-            gen_cfg=self.params.gen_cfg,
-            logger=self.logger,
         )
-        return topic_result
+        return response
 
     async def _flush(self):
         if not self.transcript:
@@ -53,13 +82,13 @@ class TranscriptTopicDetectorProcessor(Processor):
 
         text = self.transcript.text
         self.logger.info(f"Topic detector got {len(text)} length transcript")
+
         topic_result = await self.get_topic(text=text)
-        title = self.llm.trim_title(topic_result["title"])
+        title = clean_title(topic_result.title)
-        title = self.llm.ensure_casing(title)
 
         summary = TitleSummary(
             title=title,
-            summary=topic_result["summary"],
+            summary=topic_result.summary,
             timestamp=self.transcript.timestamp,
             duration=self.transcript.duration,
             transcript=self.transcript,

server/reflector/processors/transcript_translator.py

@@ -1,9 +1,5 @@
-import httpx
-
 from reflector.processors.base import Processor
-from reflector.processors.types import Transcript, TranslationLanguages
+from reflector.processors.types import Transcript
-from reflector.settings import settings
-from reflector.utils.retry import retry
 
 
 class TranscriptTranslatorProcessor(Processor):
@@ -13,61 +9,27 @@ class TranscriptTranslatorProcessor(Processor):
 
     INPUT_TYPE = Transcript
     OUTPUT_TYPE = Transcript
-    TASK = "translate"
 
     def __init__(self, **kwargs):
         super().__init__(**kwargs)
         self.transcript = None
-        self.translate_url = settings.TRANSLATE_URL
-        self.timeout = settings.TRANSLATE_TIMEOUT
-        self.headers = {"Authorization": f"Bearer {settings.LLM_MODAL_API_KEY}"}
 
     async def _push(self, data: Transcript):
         self.transcript = data
         await self.flush()
 
-    async def get_translation(self, text: str) -> str | None:
+    async def _translate(self, text: str) -> str | None:
-        # FIXME this should be a processor after, as each user may want
+        raise NotImplementedError
-        # different languages
-
-        source_language = self.get_pref("audio:source_language", "en")
-        target_language = self.get_pref("audio:target_language", "en")
-        if source_language == target_language:
-            return
-
-        languages = TranslationLanguages()
-        # Only way to set the target should be the UI element like dropdown.
-        # Hence, this assert should never fail.
-        assert languages.is_supported(target_language)
-        self.logger.debug(f"Try to translate {text=}")
-        json_payload = {
-            "text": text,
-            "source_language": source_language,
-            "target_language": target_language,
-        }
-
-        async with httpx.AsyncClient() as client:
-            response = await retry(client.post)(
-                self.translate_url + "/translate",
-                headers=self.headers,
-                params=json_payload,
-                timeout=self.timeout,
-                follow_redirects=True,
-                logger=self.logger,
-            )
-            response.raise_for_status()
-            result = response.json()["text"]
-
-            # Sanity check for translation status in the result
-            if target_language in result:
-                translation = result[target_language]
-            self.logger.debug(f"Translation response: {text=}, {translation=}")
-        return translation
 
     async def _flush(self):
         if not self.transcript:
             return
-        self.transcript.translation = await self.get_translation(
+
-            text=self.transcript.text
+        source_language = self.get_pref("audio:source_language", "en")
-        )
+        target_language = self.get_pref("audio:target_language", "en")
+        if source_language == target_language:
+            self.transcript.translation = None
+        else:
+            self.transcript.translation = await self._translate(self.transcript.text)
+
         await self.emit(self.transcript)

server/reflector/processors/transcript_translator_auto.py

@@ -0,0 +1,32 @@
+import importlib
+
+from reflector.processors.transcript_translator import TranscriptTranslatorProcessor
+from reflector.settings import settings
+
+
+class TranscriptTranslatorAutoProcessor(TranscriptTranslatorProcessor):
+    _registry = {}
+
+    @classmethod
+    def register(cls, name, kclass):
+        cls._registry[name] = kclass
+
+    def __new__(cls, name: str | None = None, **kwargs):
+        if name is None:
+            name = settings.TRANSLATION_BACKEND
+        if name not in cls._registry:
+            module_name = f"reflector.processors.transcript_translator_{name}"
+            importlib.import_module(module_name)
+
+        # gather specific configuration for the processor
+        # search `TRANSLATION_BACKEND_XXX_YYY`, push to constructor as `backend_xxx_yyy`
+        config = {}
+        name_upper = name.upper()
+        settings_prefix = "TRANSLATION_"
+        config_prefix = f"{settings_prefix}{name_upper}_"
+        for key, value in settings:
+            if key.startswith(config_prefix):
+                config_name = key[len(settings_prefix) :].lower()
+                config[config_name] = value
+
+        return cls._registry[name](**config | kwargs)

server/reflector/processors/transcript_translator_modal.py

@@ -0,0 +1,66 @@
+import httpx
+
+from reflector.processors.transcript_translator import TranscriptTranslatorProcessor
+from reflector.processors.transcript_translator_auto import (
+    TranscriptTranslatorAutoProcessor,
+)
+from reflector.processors.types import TranslationLanguages
+from reflector.settings import settings
+from reflector.utils.retry import retry
+
+
+class TranscriptTranslatorModalProcessor(TranscriptTranslatorProcessor):
+    """
+    Translate the transcript into the target language using Modal.com
+    """
+
+    def __init__(self, modal_api_key: str | None = None, **kwargs):
+        super().__init__(**kwargs)
+        if not settings.TRANSLATE_URL:
+            raise Exception(
+                "TRANSLATE_URL is required for TranscriptTranslatorModalProcessor"
+            )
+        self.translate_url = settings.TRANSLATE_URL
+        self.timeout = settings.TRANSLATE_TIMEOUT
+        self.modal_api_key = modal_api_key
+        self.headers = {}
+        if self.modal_api_key:
+            self.headers["Authorization"] = f"Bearer {self.modal_api_key}"
+
+    async def _translate(self, text: str) -> str | None:
+        source_language = self.get_pref("audio:source_language", "en")
+        target_language = self.get_pref("audio:target_language", "en")
+
+        languages = TranslationLanguages()
+        # Only way to set the target should be the UI element like dropdown.
+        # Hence, this assert should never fail.
+        assert languages.is_supported(target_language)
+        self.logger.debug(f"Try to translate {text=}")
+        json_payload = {
+            "text": text,
+            "source_language": source_language,
+            "target_language": target_language,
+        }
+
+        async with httpx.AsyncClient() as client:
+            response = await retry(client.post)(
+                self.translate_url + "/translate",
+                headers=self.headers,
+                params=json_payload,
+                timeout=self.timeout,
+                follow_redirects=True,
+                logger=self.logger,
+            )
+            response.raise_for_status()
+            result = response.json()["text"]
+
+            # Sanity check for translation status in the result
+            if target_language in result:
+                translation = result[target_language]
+            else:
+                translation = None
+            self.logger.debug(f"Translation response: {text=}, {translation=}")
+        return translation
+
+
+TranscriptTranslatorAutoProcessor.register("modal", TranscriptTranslatorModalProcessor)

server/reflector/processors/transcript_translator_passthrough.py

@@ -0,0 +1,14 @@
+from reflector.processors.transcript_translator import TranscriptTranslatorProcessor
+from reflector.processors.transcript_translator_auto import (
+    TranscriptTranslatorAutoProcessor,
+)
+
+
+class TranscriptTranslatorPassthroughProcessor(TranscriptTranslatorProcessor):
+    async def _translate(self, text: str) -> None:
+        return None
+
+
+TranscriptTranslatorAutoProcessor.register(
+    "passthrough", TranscriptTranslatorPassthroughProcessor
+)

server/reflector/settings.py

@@ -9,13 +9,14 @@ class Settings(BaseSettings):
     )
 
     # CORS
+    UI_BASE_URL: str = "http://localhost:3000"
     CORS_ORIGIN: str = "*"
     CORS_ALLOW_CREDENTIALS: bool = False
 
     # Database
     DATABASE_URL: str = "sqlite:///./reflector.sqlite3"
 
-    # local data directory (audio for no)
+    # local data directory
     DATA_DIR: str = "./data"
 
     # Audio Transcription
@@ -24,11 +25,7 @@ class Settings(BaseSettings):
     TRANSCRIPT_URL: str | None = None
     TRANSCRIPT_TIMEOUT: int = 90
 
-    # Translate into the target language
+    # Audio Transcription: modal backend
-    TRANSLATE_URL: str | None = None
-    TRANSLATE_TIMEOUT: int = 90
-
-    # Audio transcription modal.com configuration
     TRANSCRIPT_MODAL_API_KEY: str | None = None
 
     # Audio transcription storage
@@ -40,37 +37,28 @@ class Settings(BaseSettings):
     TRANSCRIPT_STORAGE_AWS_ACCESS_KEY_ID: str | None = None
     TRANSCRIPT_STORAGE_AWS_SECRET_ACCESS_KEY: str | None = None
 
+    # Translate into the target language
+    TRANSLATION_BACKEND: str = "passthrough"
+    TRANSLATE_URL: str | None = None
+    TRANSLATE_TIMEOUT: int = 90
+
+    # Translation: modal backend
+    TRANSLATE_MODAL_API_KEY: str | None = None
+
     # LLM
-    # available backend: openai, modal
+    LLM_MODEL: str = "microsoft/phi-4"
-    LLM_BACKEND: str = "modal"
-
-    # LLM common configuration
     LLM_URL: str | None = None
-    LLM_HOST: str = "localhost"
+    LLM_API_KEY: str | None = None
-    LLM_PORT: int = 7860
+    LLM_CONTEXT_WINDOW: int = 16000
-    LLM_OPENAI_KEY: str | None = None
-    LLM_OPENAI_MODEL: str = "gpt-3.5-turbo"
-    LLM_OPENAI_TEMPERATURE: float = 0.7
-    LLM_TIMEOUT: int = 60 * 5  # take cold start into account
-    LLM_MAX_TOKENS: int = 1024
-    LLM_TEMPERATURE: float = 0.7
-    ZEPHYR_LLM_URL: str | None = None
-    HERMES_3_8B_LLM_URL: str | None = None
-
-    # LLM Modal configuration
-    LLM_MODAL_API_KEY: str | None = None
-
-    # per-task cases
-    SUMMARY_MODEL: str = "monadical/private/smart"
-    SUMMARY_LLM_URL: str | None = None
-    SUMMARY_LLM_API_KEY: str | None = None
-    SUMMARY_LLM_CONTEXT_SIZE_TOKENS: int = 16000
 
     # Diarization
     DIARIZATION_ENABLED: bool = True
     DIARIZATION_BACKEND: str = "modal"
     DIARIZATION_URL: str | None = None
 
+    # Diarization: modal backend
+    DIARIZATION_MODAL_API_KEY: str | None = None
+
     # Sentry
     SENTRY_DSN: str | None = None
 
@@ -86,12 +74,6 @@ class Settings(BaseSettings):
     # if set, all anonymous record will be public
     PUBLIC_MODE: bool = False
 
-    # Default LLM model name
-    DEFAULT_LLM: str = "lmsys/vicuna-13b-v1.5"
-
-    # Cache directory for all model storage
-    CACHE_DIR: str = "./data"
-
     # Min transcript length to generate topic + summary
     MIN_TRANSCRIPT_LENGTH: int = 750
 
@@ -116,24 +98,20 @@ class Settings(BaseSettings):
     # Healthcheck
     HEALTHCHECK_URL: str | None = None
 
-    AWS_PROCESS_RECORDING_QUEUE_URL: str | None = None
+    # Whereby integration
-    SQS_POLLING_TIMEOUT_SECONDS: int = 60
-
     WHEREBY_API_URL: str = "https://api.whereby.dev/v1"
-
     WHEREBY_API_KEY: str | None = None
-
+    WHEREBY_WEBHOOK_SECRET: str | None = None
     AWS_WHEREBY_S3_BUCKET: str | None = None
     AWS_WHEREBY_ACCESS_KEY_ID: str | None = None
     AWS_WHEREBY_ACCESS_KEY_SECRET: str | None = None
+    AWS_PROCESS_RECORDING_QUEUE_URL: str | None = None
+    SQS_POLLING_TIMEOUT_SECONDS: int = 60
 
+    # Zulip integration
     ZULIP_REALM: str | None = None
     ZULIP_API_KEY: str | None = None
     ZULIP_BOT_EMAIL: str | None = None
 
-    UI_BASE_URL: str = "http://localhost:3000"
-
-    WHEREBY_WEBHOOK_SECRET: str | None = None
-
 
 settings = Settings()

server/reflector/tools/process.py

@@ -13,7 +13,7 @@ from reflector.processors import (
     TranscriptFinalTitleProcessor,
     TranscriptLinerProcessor,
     TranscriptTopicDetectorProcessor,
-    TranscriptTranslatorProcessor,
+    TranscriptTranslatorAutoProcessor,
 )
 from reflector.processors.base import BroadcastProcessor
 
@@ -31,7 +31,7 @@ async def process_audio_file(
         AudioMergeProcessor(),
         AudioTranscriptAutoProcessor.as_threaded(),
         TranscriptLinerProcessor(),
-        TranscriptTranslatorProcessor.as_threaded(),
+        TranscriptTranslatorAutoProcessor.as_threaded(),
     ]
     if not only_transcript:
         processors += [

server/reflector/tools/process_with_diarization.py

@@ -27,7 +27,7 @@ from reflector.processors import (
     TranscriptFinalTitleProcessor,
     TranscriptLinerProcessor,
     TranscriptTopicDetectorProcessor,
-    TranscriptTranslatorProcessor,
+    TranscriptTranslatorAutoProcessor,
 )
 from reflector.processors.base import BroadcastProcessor, Processor
 from reflector.processors.types import (
@@ -103,7 +103,7 @@ async def process_audio_file_with_diarization(
 
     processors += [
         TranscriptLinerProcessor(),
-        TranscriptTranslatorProcessor.as_threaded(),
+        TranscriptTranslatorAutoProcessor.as_threaded(),
     ]
 
     if not only_transcript:

server/reflector/utils/text.py

@@ -0,0 +1,33 @@
+def clean_title(title: str) -> str:
+    """
+    Clean and format a title string for consistent capitalization.
+
+    Rules:
+    - Strip surrounding quotes (single or double)
+    - Capitalize the first word
+    - Capitalize words longer than 3 characters
+    - Keep words with 3 or fewer characters lowercase (except first word)
+
+    Args:
+        title: The title string to clean
+
+    Returns:
+        The cleaned title with consistent capitalization
+
+    Examples:
+        >>> clean_title("hello world")
+        "Hello World"
+        >>> clean_title("meeting with the team")
+        "Meeting With the Team"
+        >>> clean_title("'Title with quotes'")
+        "Title With Quotes"
+    """
+    title = title.strip("\"'")
+    words = title.split()
+    if words:
+        words = [
+            word.capitalize() if i == 0 or len(word) > 3 else word.lower()
+            for i, word in enumerate(words)
+        ]
+        title = " ".join(words)
+    return title

server/reflector/views/transcripts_audio.py

@@ -51,24 +51,6 @@ async def transcript_get_audio_mp3(
         transcript_id, user_id=user_id
     )
 
-    if transcript.audio_location == "storage":
-        # proxy S3 file, to prevent issue with CORS
-        url = await transcript.get_audio_url()
-        headers = {}
-
-        copy_headers = ["range", "accept-encoding"]
-        for header in copy_headers:
-            if header in request.headers:
-                headers[header] = request.headers[header]
-
-        async with httpx.AsyncClient() as client:
-            resp = await client.request(request.method, url, headers=headers)
-            return Response(
-                content=resp.content,
-                status_code=resp.status_code,
-                headers=resp.headers,
-            )
-
     if transcript.audio_location == "storage":
         # proxy S3 file, to prevent issue with CORS
         url = await transcript.get_audio_url()

server/tests/conftest.py

@@ -7,14 +7,10 @@ import pytest
 @pytest.fixture(scope="function", autouse=True)
 @pytest.mark.asyncio
 async def setup_database():
-    from reflector.settings import settings
+    from reflector.db import engine, metadata  # noqa
-
-    with NamedTemporaryFile() as f:
-        settings.DATABASE_URL = f"sqlite:///{f.name}"
-        from reflector.db import engine, metadata
 
+    metadata.drop_all(bind=engine)
     metadata.create_all(bind=engine)
-
     yield
 
 
@@ -33,17 +29,16 @@ def dummy_processors():
         patch(
             "reflector.processors.transcript_final_summary.TranscriptFinalSummaryProcessor.get_short_summary"
         ) as mock_short_summary,
-        patch(
-            "reflector.processors.transcript_translator.TranscriptTranslatorProcessor.get_translation"
-        ) as mock_translate,
     ):
-        mock_topic.return_value = {"title": "LLM TITLE", "summary": "LLM SUMMARY"}
+        from reflector.processors.transcript_topic_detector import TopicResponse
-        mock_title.return_value = {"title": "LLM TITLE"}
+
+        mock_topic.return_value = TopicResponse(
+            title="LLM TITLE", summary="LLM SUMMARY"
+        )
+        mock_title.return_value = "LLM Title"
         mock_long_summary.return_value = "LLM LONG SUMMARY"
         mock_short_summary.return_value = "LLM SHORT SUMMARY"
-        mock_translate.return_value = "Bonjour le monde"
         yield (
-            mock_translate,
             mock_topic,
             mock_title,
             mock_long_summary,
@@ -101,16 +96,38 @@ async def dummy_diarization():
         yield
 
 
+@pytest.fixture
+async def dummy_transcript_translator():
+    from reflector.processors.transcript_translator import TranscriptTranslatorProcessor
+
+    class TestTranscriptTranslatorProcessor(TranscriptTranslatorProcessor):
+        async def _translate(self, text: str) -> str:
+            source_language = self.get_pref("audio:source_language", "en")
+            target_language = self.get_pref("audio:target_language", "en")
+            return f"{source_language}:{target_language}:{text}"
+
+    def mock_new(cls, *args, **kwargs):
+        return TestTranscriptTranslatorProcessor(*args, **kwargs)
+
+    with patch(
+        "reflector.processors.transcript_translator_auto"
+        ".TranscriptTranslatorAutoProcessor.__new__",
+        mock_new,
+    ):
+        yield
+
+
 @pytest.fixture
 async def dummy_llm():
-    from reflector.llm.base import LLM
+    from reflector.llm import LLM
 
     class TestLLM(LLM):
         def __init__(self):
             self.model_name = "DUMMY MODEL"
             self.llm_tokenizer = "DUMMY TOKENIZER"
 
-    with patch("reflector.llm.base.LLM.get_instance") as mock_llm:
+    # LLM doesn't have get_instance anymore, mocking constructor instead
+    with patch("reflector.llm.LLM") as mock_llm:
         mock_llm.return_value = TestLLM()
         yield
 
@@ -129,22 +146,19 @@ async def dummy_storage():
         async def _get_file_url(self, *args, **kwargs):
             return "http://fake_server/audio.mp3"
 
-    with patch("reflector.storage.base.Storage.get_instance") as mock_storage:
+        async def _get_file(self, *args, **kwargs):
-        mock_storage.return_value = DummyStorage()
+            from pathlib import Path
-        yield
 
+            test_mp3 = Path(__file__).parent / "records" / "test_mathieu_hello.mp3"
+            return test_mp3.read_bytes()
 
-@pytest.fixture
+    dummy = DummyStorage()
-def nltk():
+    with (
-    with patch("reflector.llm.base.LLM.ensure_nltk") as mock_nltk:
+        patch("reflector.storage.base.Storage.get_instance") as mock_storage,
-        mock_nltk.return_value = "NLTK PACKAGE"
+        patch("reflector.storage.get_transcripts_storage") as mock_get_transcripts,
-        yield
+    ):
-
+        mock_storage.return_value = dummy
-
+        mock_get_transcripts.return_value = dummy
-@pytest.fixture
-def ensure_casing():
-    with patch("reflector.llm.base.LLM.ensure_casing") as mock_casing:
-        mock_casing.return_value = "LLM TITLE"
         yield
 
 

server/tests/test_processors_broadcast.py

@@ -2,7 +2,7 @@ import pytest
 
 
 @pytest.mark.asyncio
-async def test_processor_broadcast(nltk):
+async def test_processor_broadcast():
     from reflector.processors.base import BroadcastProcessor, Pipeline, Processor
 
     class TestProcessor(Processor):

server/tests/test_processors_pipeline.py

@@ -3,11 +3,9 @@ import pytest
 
 @pytest.mark.asyncio
 async def test_basic_process(
-    nltk,
     dummy_transcript,
     dummy_llm,
     dummy_processors,
-    ensure_casing,
 ):
     # goal is to start the server, and send rtc audio to it
     # validate the events received
@@ -16,8 +14,8 @@ async def test_basic_process(
     from reflector.settings import settings
     from reflector.tools.process import process_audio_file
 
-    # use an LLM test backend
+    # LLM_BACKEND no longer exists in settings
-    settings.LLM_BACKEND = "test"
+    # settings.LLM_BACKEND = "test"
     settings.TRANSCRIPT_BACKEND = "whisper"
 
     # event callback
@@ -35,7 +33,7 @@ async def test_basic_process(
 
     # validate the events
     assert marks["TranscriptLinerProcessor"] == 1
-    assert marks["TranscriptTranslatorProcessor"] == 1
+    assert marks["TranscriptTranslatorPassthroughProcessor"] == 1
     assert marks["TranscriptTopicDetectorProcessor"] == 1
     assert marks["TranscriptFinalSummaryProcessor"] == 1
     assert marks["TranscriptFinalTitleProcessor"] == 1

server/tests/test_transcripts_process.py

@@ -10,7 +10,6 @@ from httpx import AsyncClient
 @pytest.mark.asyncio
 async def test_transcript_process(
     tmpdir,
-    ensure_casing,
     dummy_llm,
     dummy_processors,
     dummy_diarization,
@@ -69,7 +68,7 @@ async def test_transcript_process(
     transcript = resp.json()
     assert transcript["status"] == "ended"
     assert transcript["short_summary"] == "LLM SHORT SUMMARY"
-    assert transcript["title"] == "LLM TITLE"
+    assert transcript["title"] == "Llm Title"
 
     # check topics and transcript
     response = await ac.get(f"/transcripts/{tid}/topics")

server/tests/test_transcripts_rtc_ws.py

@@ -67,10 +67,9 @@ async def test_transcript_rtc_and_websocket(
     dummy_transcript,
     dummy_processors,
     dummy_diarization,
+    dummy_transcript_translator,
     dummy_storage,
     fake_mp3_upload,
-    ensure_casing,
-    nltk,
     appserver,
 ):
     # goal: start the server, exchange RTC, receive websocket events
@@ -166,7 +165,7 @@ async def test_transcript_rtc_and_websocket(
     assert "TRANSCRIPT" in eventnames
     ev = events[eventnames.index("TRANSCRIPT")]
     assert ev["data"]["text"].startswith("Hello world.")
-    assert ev["data"]["translation"] == "Bonjour le monde"
+    assert ev["data"]["translation"] is None
 
     assert "TOPIC" in eventnames
     ev = events[eventnames.index("TOPIC")]
@@ -185,7 +184,7 @@ async def test_transcript_rtc_and_websocket(
 
     assert "FINAL_TITLE" in eventnames
     ev = events[eventnames.index("FINAL_TITLE")]
-    assert ev["data"]["title"] == "LLM TITLE"
+    assert ev["data"]["title"] == "Llm Title"
 
     assert "WAVEFORM" in eventnames
     ev = events[eventnames.index("WAVEFORM")]
@@ -226,10 +225,9 @@ async def test_transcript_rtc_and_websocket_and_fr(
     dummy_transcript,
     dummy_processors,
     dummy_diarization,
+    dummy_transcript_translator,
     dummy_storage,
     fake_mp3_upload,
-    ensure_casing,
-    nltk,
     appserver,
 ):
     # goal: start the server, exchange RTC, receive websocket events
@@ -334,7 +332,7 @@ async def test_transcript_rtc_and_websocket_and_fr(
     assert "TRANSCRIPT" in eventnames
     ev = events[eventnames.index("TRANSCRIPT")]
     assert ev["data"]["text"].startswith("Hello world.")
-    assert ev["data"]["translation"] == "Bonjour le monde"
+    assert ev["data"]["translation"] == "en:fr:Hello world."
 
     assert "TOPIC" in eventnames
     ev = events[eventnames.index("TOPIC")]
@@ -353,7 +351,7 @@ async def test_transcript_rtc_and_websocket_and_fr(
 
     assert "FINAL_TITLE" in eventnames
     ev = events[eventnames.index("FINAL_TITLE")]
-    assert ev["data"]["title"] == "LLM TITLE"
+    assert ev["data"]["title"] == "Llm Title"
 
     # check status order
     statuses = [e["data"]["value"] for e in events if e["event"] == "STATUS"]

server/tests/test_transcripts_upload.py

@@ -10,7 +10,6 @@ from httpx import AsyncClient
 @pytest.mark.asyncio
 async def test_transcript_upload_file(
     tmpdir,
-    ensure_casing,
     dummy_llm,
     dummy_processors,
     dummy_diarization,
@@ -53,7 +52,7 @@ async def test_transcript_upload_file(
     transcript = resp.json()
     assert transcript["status"] == "ended"
     assert transcript["short_summary"] == "LLM SHORT SUMMARY"
-    assert transcript["title"] == "LLM TITLE"
+    assert transcript["title"] == "Llm Title"
 
     # check topics and transcript
     response = await ac.get(f"/transcripts/{tid}/topics")

server/tests/test_utils_text.py

@@ -0,0 +1,21 @@
+import pytest
+
+from reflector.utils.text import clean_title
+
+
+@pytest.mark.parametrize(
+    "input_title,expected",
+    [
+        ("hello world", "Hello World"),
+        ("HELLO WORLD", "Hello World"),
+        ("hello WORLD", "Hello World"),
+        ("the quick brown fox", "The Quick Brown fox"),
+        ("discussion about API design", "Discussion About api Design"),
+        ("Q1 2024 budget review", "Q1 2024 Budget Review"),
+        ("'Title with quotes'", "Title With Quotes"),
+        ("'title with quotes'", "Title With Quotes"),
+        ("MiXeD CaSe WoRdS", "Mixed Case Words"),
+    ],
+)
+def test_clean_title(input_title, expected):
+    assert clean_title(input_title) == expected

server/uv.lock

@@ -2428,6 +2428,18 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/57/79/9dae84c244dabebca6a952e098d6ac9d13719b701fc5323ba6d00abc675a/pytest_docker_tools-3.1.9-py2.py3-none-any.whl", hash = "sha256:36f8e88d56d84ea177df68a175673681243dd991d2807fbf551d90f60341bfdb", size = 29268, upload-time = "2025-03-16T13:48:22.184Z" },
 ]
 
+[[package]]
+name = "pytest-env"
+version = "1.1.5"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "pytest" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/1f/31/27f28431a16b83cab7a636dce59cf397517807d247caa38ee67d65e71ef8/pytest_env-1.1.5.tar.gz", hash = "sha256:91209840aa0e43385073ac464a554ad2947cc2fd663a9debf88d03b01e0cc1cf", size = 8911, upload-time = "2024-09-17T22:39:18.566Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/de/b8/87cfb16045c9d4092cfcf526135d73b88101aac83bc1adcf82dfb5fd3833/pytest_env-1.1.5-py3-none-any.whl", hash = "sha256:ce90cf8772878515c24b31cd97c7fa1f4481cd68d588419fd45f10ecaee6bc30", size = 6141, upload-time = "2024-09-17T22:39:16.942Z" },
+]
+
 [[package]]
 name = "pytest-httpx"
 version = "0.34.0"
@@ -2618,7 +2630,6 @@ dependencies = [
     { name = "aiortc" },
     { name = "alembic" },
     { name = "av" },
-    { name = "black" },
     { name = "celery" },
     { name = "databases", extra = ["aiosqlite", "asyncpg"] },
     { name = "fastapi", extra = ["standard"] },
@@ -2636,6 +2647,7 @@ dependencies = [
     { name = "protobuf" },
     { name = "psycopg2-binary" },
     { name = "pydantic-settings" },
+    { name = "pytest-env" },
     { name = "python-jose", extra = ["cryptography"] },
     { name = "python-multipart" },
     { name = "redis" },
@@ -2681,7 +2693,6 @@ requires-dist = [
     { name = "aiortc", specifier = ">=1.5.0" },
     { name = "alembic", specifier = ">=1.11.3" },
     { name = "av", specifier = ">=10.0.0" },
-    { name = "black", specifier = "==24.1.1" },
     { name = "celery", specifier = ">=5.3.4" },
     { name = "databases", extras = ["aiosqlite", "asyncpg"], specifier = ">=0.7.0" },
     { name = "fastapi", extras = ["standard"], specifier = ">=0.100.1" },
@@ -2699,6 +2710,7 @@ requires-dist = [
     { name = "protobuf", specifier = ">=4.24.3" },
     { name = "psycopg2-binary", specifier = ">=2.9.10" },
     { name = "pydantic-settings", specifier = ">=2.0.2" },
+    { name = "pytest-env", specifier = ">=1.1.5" },
     { name = "python-jose", extras = ["cryptography"], specifier = ">=3.3.0" },
     { name = "python-multipart", specifier = ">=0.0.6" },
     { name = "redis", specifier = ">=5.0.1" },

www/REFACTOR2.md

@@ -1,86 +0,0 @@
-# Chakra UI v3 Migration - Remaining Tasks
-
-## Completed
-
-- ✅ Migrated from Chakra UI v2 to v3 in package.json
-- ✅ Updated theme.ts with whiteAlpha color palette and semantic tokens
-- ✅ Added button recipe with fontWeight 600 and hover states
-- ✅ Moved Poppins font from theme to HTML tag className
-- ✅ Fixed deprecated props across all files:
-  - ✅ `isDisabled` → `disabled` (all occurrences fixed)
-  - ✅ `isChecked` → `checked` (all occurrences fixed)
-  - ✅ `isLoading` → `loading` (all occurrences fixed)
-  - ✅ `isOpen` → `open` (all occurrences fixed)
-  - ✅ `noOfLines` → `lineClamp` (all occurrences fixed)
-  - ✅ `align` → `alignItems` on Flex/Stack components (all occurrences fixed)
-  - ✅ `justify` → `justifyContent` on Flex/Stack components (all occurrences fixed)
-
-## Migration Summary
-
-### Files Modified
-
-1. **app/(app)/rooms/page.tsx**
-
-   - Fixed: isDisabled, isChecked, align, justify on multiple components
-   - Updated temporary Select component props
-
-2. **app/(app)/transcripts/fileUploadButton.tsx**
-
-   - Fixed: isDisabled → disabled
-
-3. **app/(app)/transcripts/shareZulip.tsx**
-
-   - Fixed: isDisabled → disabled
-
-4. **app/(app)/transcripts/shareAndPrivacy.tsx**
-
-   - Fixed: isLoading → loading, isOpen → open
-   - Updated temporary Select component props
-
-5. **app/(app)/browse/page.tsx**
-
-   - Fixed: isOpen → open, align → alignItems, justify → justifyContent
-
-6. **app/(app)/transcripts/transcriptTitle.tsx**
-
-   - Fixed: noOfLines → lineClamp
-
-7. **app/(app)/transcripts/[transcriptId]/correct/topicHeader.tsx**
-
-   - Fixed: noOfLines → lineClamp
-
-8. **app/lib/expandableText.tsx**
-
-   - Fixed: noOfLines → lineClamp
-
-9. **app/[roomName]/page.tsx**
-
-   - Fixed: align → alignItems, justify → justifyContent
-
-10. **app/lib/WherebyWebinarEmbed.tsx**
-    - Fixed: align → alignItems, justify → justifyContent
-
-## Other Potential Issues
-
-1. Check for Modal/Dialog component imports and usage (currently using temporary replacements)
-2. Review Select component usage (using temporary replacements)
-3. Test button hover states for whiteAlpha color palette
-4. Verify all color palettes work correctly with the new semantic tokens
-
-## Testing
-
-After completing migrations:
-
-1. Run `yarn dev` and check all pages
-2. Test buttons with different color palettes
-3. Verify disabled states work correctly
-4. Check that text alignment and flex layouts are correct
-5. Test modal/dialog functionality
-
-## Next Steps
-
-The Chakra UI v3 migration is now largely complete for deprecated props. The main remaining items are:
-
-- Replace temporary Modal and Select components with proper Chakra v3 implementations
-- Thorough testing of all UI components
-- Performance optimization if needed

www/app/(app)/transcripts/[transcriptId]/page.tsx

@@ -32,7 +32,7 @@ export default function TranscriptDetails(details: TranscriptDetails) {
   const topics = useTopics(transcriptId);
   const waveform = useWaveform(
     transcriptId,
-    waiting || mp3.loading || mp3.audioDeleted === true,
+    waiting || mp3.audioDeleted === true,
   );
   const useActiveTopic = useState<Topic | null>(null);
 

www/package.json

@@ -21,7 +21,7 @@
     "@vercel/kv": "^2.0.0",
     "@whereby.com/browser-sdk": "^3.3.4",
     "autoprefixer": "10.4.20",
-    "axios": "^1.6.2",
+    "axios": "^1.8.2",
     "chakra-react-select": "^4.9.1",
     "eslint": "^9.9.1",
     "eslint-config-next": "^14.2.7",
@@ -29,10 +29,10 @@
     "ioredis": "^5.4.1",
     "jest-worker": "^29.6.2",
     "lucide-react": "^0.525.0",
-    "next": "^14.2.7",
+    "next": "^14.2.30",
     "next-auth": "^4.24.7",
     "next-themes": "^0.4.6",
-    "postcss": "8.4.25",
+    "postcss": "8.4.31",
     "prop-types": "^15.8.1",
     "react": "^18.2.0",
     "react-dom": "^18.2.0",

www/yarn.lock

@@ -119,19 +119,10 @@
     chalk "^2.4.2"
     js-tokens "^4.0.0"
 
-"@babel/runtime@^7.12.0", "@babel/runtime@^7.12.5", "@babel/runtime@^7.18.3", "@babel/runtime@^7.23.2", "@babel/runtime@^7.5.5", "@babel/runtime@^7.8.7":
+"@babel/runtime@^7.12.0", "@babel/runtime@^7.12.5", "@babel/runtime@^7.18.3", "@babel/runtime@^7.20.13", "@babel/runtime@^7.23.2", "@babel/runtime@^7.5.5", "@babel/runtime@^7.8.7":
-  version "7.23.6"
+  version "7.28.2"
-  resolved "https://registry.yarnpkg.com/@babel/runtime/-/runtime-7.23.6.tgz#c05e610dc228855dc92ef1b53d07389ed8ab521d"
+  resolved "https://registry.yarnpkg.com/@babel/runtime/-/runtime-7.28.2.tgz#2ae5a9d51cc583bd1f5673b3bb70d6d819682473"
-  integrity sha512-zHd0eUrf5GZoOWVCXp6koAKQTfZV07eit6bGPmJgnZdnSAvvZee6zniW2XMF7Cmc4ISOOnPy3QaSiIJGJkVEDQ==
+  integrity sha512-KHp2IflsnGywDjBWDkR9iEqiWSpc8GIi0lgTT3mOElT0PP1tG26P4tmFI2YvAdzgq9RGyoHZQEIEdZy6Ec5xCA==
-  dependencies:
-    regenerator-runtime "^0.14.0"
-
-"@babel/runtime@^7.20.13":
-  version "7.25.6"
-  resolved "https://registry.yarnpkg.com/@babel/runtime/-/runtime-7.25.6.tgz#9afc3289f7184d8d7f98b099884c26317b9264d2"
-  integrity sha512-VBj9MYyDb9tuLq7yzqjgzt6Q+IBQLrGZfdjOekyEirZPHxXWoTSGUTMrpsfi58Up73d13NfYLv8HT9vmznjzhQ==
-  dependencies:
-    regenerator-runtime "^0.14.0"
 
 "@babel/types@^7.22.15":
   version "7.23.6"
@@ -658,10 +649,10 @@
     semver "^7.3.5"
     tar "^6.1.11"
 
-"@next/env@14.2.7":
+"@next/env@14.2.30":
-  version "14.2.7"
+  version "14.2.30"
-  resolved "https://registry.yarnpkg.com/@next/env/-/env-14.2.7.tgz#40fcd6ccdd53fd7e6788a0604f39032c84bea112"
+  resolved "https://registry.yarnpkg.com/@next/env/-/env-14.2.30.tgz#f955b57975751584722b6b0a2a8cf2bdcc4ffae3"
-  integrity sha512-OTx9y6I3xE/eih+qtthppwLytmpJVPM5PPoJxChFsbjIEFXIayG0h/xLzefHGJviAa3Q5+Fd+9uYojKkHDKxoQ==
+  integrity sha512-KBiBKrDY6kxTQWGzKjQB7QirL3PiiOkV7KW98leHFjtVRKtft76Ra5qSA/SL75xT44dp6hOcqiiJ6iievLOYug==
 
 "@next/eslint-plugin-next@14.2.7":
   version "14.2.7"
@@ -670,50 +661,50 @@
   dependencies:
     glob "10.3.10"
 
-"@next/swc-darwin-arm64@14.2.7":
+"@next/swc-darwin-arm64@14.2.30":
-  version "14.2.7"
+  version "14.2.30"
-  resolved "https://registry.yarnpkg.com/@next/swc-darwin-arm64/-/swc-darwin-arm64-14.2.7.tgz#6cd39ba5d5f43705de44e389d4b4f5d2df391927"
+  resolved "https://registry.yarnpkg.com/@next/swc-darwin-arm64/-/swc-darwin-arm64-14.2.30.tgz#8179a35a068bc6f43a9ab6439875f6e330d02e52"
-  integrity sha512-UhZGcOyI9LE/tZL3h9rs/2wMZaaJKwnpAyegUVDGZqwsla6hMfeSj9ssBWQS9yA4UXun3pPhrFLVnw5KXZs3vw==
+  integrity sha512-EAqfOTb3bTGh9+ewpO/jC59uACadRHM6TSA9DdxJB/6gxOpyV+zrbqeXiFTDy9uV6bmipFDkfpAskeaDcO+7/g==
 
-"@next/swc-darwin-x64@14.2.7":
+"@next/swc-darwin-x64@14.2.30":
-  version "14.2.7"
+  version "14.2.30"
-  resolved "https://registry.yarnpkg.com/@next/swc-darwin-x64/-/swc-darwin-x64-14.2.7.tgz#a1d191a293443cf8df9451b8f13a348caa718cb7"
+  resolved "https://registry.yarnpkg.com/@next/swc-darwin-x64/-/swc-darwin-x64-14.2.30.tgz#87c08d805c0546a73c25a0538a81f8b5f43bd0e9"
-  integrity sha512-ys2cUgZYRc+CbyDeLAaAdZgS7N1Kpyy+wo0b/gAj+SeOeaj0Lw/q+G1hp+DuDiDAVyxLBCJXEY/AkhDmtihUTA==
+  integrity sha512-TyO7Wz1IKE2kGv8dwQ0bmPL3s44EKVencOqwIY69myoS3rdpO1NPg5xPM5ymKu7nfX4oYJrpMxv8G9iqLsnL4A==
 
-"@next/swc-linux-arm64-gnu@14.2.7":
+"@next/swc-linux-arm64-gnu@14.2.30":
-  version "14.2.7"
+  version "14.2.30"
-  resolved "https://registry.yarnpkg.com/@next/swc-linux-arm64-gnu/-/swc-linux-arm64-gnu-14.2.7.tgz#9da3f993b3754b900fe7b469de51898fc51112f2"
+  resolved "https://registry.yarnpkg.com/@next/swc-linux-arm64-gnu/-/swc-linux-arm64-gnu-14.2.30.tgz#eed26d87d96d9ef6fffbde98ceed2c75108a9911"
-  integrity sha512-2xoWtE13sUJ3qrC1lwE/HjbDPm+kBQYFkkiVECJWctRASAHQ+NwjMzgrfqqMYHfMxFb5Wws3w9PqzZJqKFdWcQ==
+  integrity sha512-I5lg1fgPJ7I5dk6mr3qCH1hJYKJu1FsfKSiTKoYwcuUf53HWTrEkwmMI0t5ojFKeA6Vu+SfT2zVy5NS0QLXV4Q==
 
-"@next/swc-linux-arm64-musl@14.2.7":
+"@next/swc-linux-arm64-musl@14.2.30":
-  version "14.2.7"
+  version "14.2.30"
-  resolved "https://registry.yarnpkg.com/@next/swc-linux-arm64-musl/-/swc-linux-arm64-musl-14.2.7.tgz#f75662bdedd2d91ad7e05778274fa17659f1f02f"
+  resolved "https://registry.yarnpkg.com/@next/swc-linux-arm64-musl/-/swc-linux-arm64-musl-14.2.30.tgz#54b38b43c8acf3d3e0b71ae208a0bfca5a9b8563"
-  integrity sha512-+zJ1gJdl35BSAGpkCbfyiY6iRTaPrt3KTl4SF/B1NyELkqqnrNX6cp4IjjjxKpd64/7enI0kf6b9O1Uf3cL0pw==
+  integrity sha512-8GkNA+sLclQyxgzCDs2/2GSwBc92QLMrmYAmoP2xehe5MUKBLB2cgo34Yu242L1siSkwQkiV4YLdCnjwc/Micw==
 
-"@next/swc-linux-x64-gnu@14.2.7":
+"@next/swc-linux-x64-gnu@14.2.30":
-  version "14.2.7"
+  version "14.2.30"
-  resolved "https://registry.yarnpkg.com/@next/swc-linux-x64-gnu/-/swc-linux-x64-gnu-14.2.7.tgz#3c6c5b551a5af4fc8178bd5733c8063266034e79"
+  resolved "https://registry.yarnpkg.com/@next/swc-linux-x64-gnu/-/swc-linux-x64-gnu-14.2.30.tgz#0ee0419da4dc1211a4c925b0841419cd07aa6c59"
-  integrity sha512-m6EBqrskeMUzykBrv0fDX/28lWIBGhMzOYaStp0ihkjzIYJiKUOzVYD1gULHc8XDf5EMSqoH/0/TRAgXqpQwmw==
+  integrity sha512-8Ly7okjssLuBoe8qaRCcjGtcMsv79hwzn/63wNeIkzJVFVX06h5S737XNr7DZwlsbTBDOyI6qbL2BJB5n6TV/w==
 
-"@next/swc-linux-x64-musl@14.2.7":
+"@next/swc-linux-x64-musl@14.2.30":
-  version "14.2.7"
+  version "14.2.30"
-  resolved "https://registry.yarnpkg.com/@next/swc-linux-x64-musl/-/swc-linux-x64-musl-14.2.7.tgz#16f92f00263d1fce91ae80e5f230eb1feea484e4"
+  resolved "https://registry.yarnpkg.com/@next/swc-linux-x64-musl/-/swc-linux-x64-musl-14.2.30.tgz#e88463d8c10dd600087b062f2dea59a515cd66f6"
-  integrity sha512-gUu0viOMvMlzFRz1r1eQ7Ql4OE+hPOmA7smfZAhn8vC4+0swMZaZxa9CSIozTYavi+bJNDZ3tgiSdMjmMzRJlQ==
+  integrity sha512-dBmV1lLNeX4mR7uI7KNVHsGQU+OgTG5RGFPi3tBJpsKPvOPtg9poyav/BYWrB3GPQL4dW5YGGgalwZ79WukbKQ==
 
-"@next/swc-win32-arm64-msvc@14.2.7":
+"@next/swc-win32-arm64-msvc@14.2.30":
-  version "14.2.7"
+  version "14.2.30"
-  resolved "https://registry.yarnpkg.com/@next/swc-win32-arm64-msvc/-/swc-win32-arm64-msvc-14.2.7.tgz#1224cb8a04cd9caad785a2187df9e85b49414a42"
+  resolved "https://registry.yarnpkg.com/@next/swc-win32-arm64-msvc/-/swc-win32-arm64-msvc-14.2.30.tgz#6975cbbab74d519b06d93210ed86cd4f3dbc1c4d"
-  integrity sha512-PGbONHIVIuzWlYmLvuFKcj+8jXnLbx4WrlESYlVnEzDsa3+Q2hI1YHoXaSmbq0k4ZwZ7J6sWNV4UZfx1OeOlbQ==
+  integrity sha512-6MMHi2Qc1Gkq+4YLXAgbYslE1f9zMGBikKMdmQRHXjkGPot1JY3n5/Qrbg40Uvbi8//wYnydPnyvNhI1DMUW1g==
 
-"@next/swc-win32-ia32-msvc@14.2.7":
+"@next/swc-win32-ia32-msvc@14.2.30":
-  version "14.2.7"
+  version "14.2.30"
-  resolved "https://registry.yarnpkg.com/@next/swc-win32-ia32-msvc/-/swc-win32-ia32-msvc-14.2.7.tgz#9494aaf9cc50ddef600f8c1b2ed0f216b19f9294"
+  resolved "https://registry.yarnpkg.com/@next/swc-win32-ia32-msvc/-/swc-win32-ia32-msvc-14.2.30.tgz#08ad4de2e082bc6b07d41099b4310daec7885748"
-  integrity sha512-BiSY5umlx9ed5RQDoHcdbuKTUkuFORDqzYKPHlLeS+STUWQKWziVOn3Ic41LuTBvqE0TRJPKpio9GSIblNR+0w==
+  integrity sha512-pVZMnFok5qEX4RT59mK2hEVtJX+XFfak+/rjHpyFh7juiT52r177bfFKhnlafm0UOSldhXjj32b+LZIOdswGTg==
 
-"@next/swc-win32-x64-msvc@14.2.7":
+"@next/swc-win32-x64-msvc@14.2.30":
-  version "14.2.7"
+  version "14.2.30"
-  resolved "https://registry.yarnpkg.com/@next/swc-win32-x64-msvc/-/swc-win32-x64-msvc-14.2.7.tgz#75e1d90758cb10a547e1cdfb878871da28123682"
+  resolved "https://registry.yarnpkg.com/@next/swc-win32-x64-msvc/-/swc-win32-x64-msvc-14.2.30.tgz#94d3ddcc1e97572a0514a6180c8e3bb415e1dc98"
-  integrity sha512-pxsI23gKWRt/SPHFkDEsP+w+Nd7gK37Hpv0ngc5HpWy2e7cKx9zR/+Q2ptAUqICNTecAaGWvmhway7pj/JLEWA==
+  integrity sha512-4KCo8hMZXMjpTzs3HOqOGYYwAXymXIy7PEPAXNEcEOyKqkjiDlECumrWziy+JEF0Oi4ILHGxzgQ3YiMGG2t/Lg==
 
 "@nodelib/fs.scandir@2.1.5":
   version "2.1.5"
@@ -2624,24 +2615,15 @@ axe-core@=4.7.0:
   resolved "https://registry.yarnpkg.com/axe-core/-/axe-core-4.7.0.tgz#34ba5a48a8b564f67e103f0aa5768d76e15bbbbf"
   integrity sha512-M0JtH+hlOL5pLQwHOLNYZaXuhqmvS8oExsqB1SBYgA4Dk7u/xx+YdGHXaK5pyUfed5mYXdlYiphWq3G8cRi5JQ==
 
-axios@^1.2.3:
+axios@^1.2.3, axios@^1.8.2:
-  version "1.7.2"
+  version "1.8.2"
-  resolved "https://registry.yarnpkg.com/axios/-/axios-1.7.2.tgz#b625db8a7051fbea61c35a3cbb3a1daa7b9c7621"
+  resolved "https://registry.yarnpkg.com/axios/-/axios-1.8.2.tgz#fabe06e241dfe83071d4edfbcaa7b1c3a40f7979"
-  integrity sha512-2A8QhOMrbomlDuiLeK9XibIBzuHeRcqqNOHp0Cyp5EoJ1IFDh+XZH3A6BkXtv0K4gFGCI0Y4BM7B1wOEi0Rmgw==
+  integrity sha512-ls4GYBm5aig9vWx8AWDSGLpnpDQRtWAfrjU+EuytuODrFBkqesN2RkOQCBzrA1RQNHw1SmRMSDDDSwzNAYQ6Rg==
   dependencies:
     follow-redirects "^1.15.6"
     form-data "^4.0.0"
     proxy-from-env "^1.1.0"
 
-axios@^1.6.2:
-  version "1.6.2"
-  resolved "https://registry.yarnpkg.com/axios/-/axios-1.6.2.tgz#de67d42c755b571d3e698df1b6504cde9b0ee9f2"
-  integrity sha512-7i24Ri4pmDRfJTR7LDBhsOTtcm+9kjX5WiY1X3wIisx6G9So3pfMkEiU7emUBe46oceVImccTEM3k6C5dbVW8A==
-  dependencies:
-    follow-redirects "^1.15.0"
-    form-data "^4.0.0"
-    proxy-from-env "^1.1.0"
-
 axobject-query@^3.2.1:
   version "3.2.1"
   resolved "https://registry.yarnpkg.com/axobject-query/-/axobject-query-3.2.1.tgz#39c378a6e3b06ca679f29138151e45b2b32da62a"
@@ -2700,14 +2682,7 @@ brace-expansion@^2.0.1:
   dependencies:
     balanced-match "^1.0.0"
 
-braces@^3.0.2, braces@~3.0.2:
+braces@^3.0.3, braces@~3.0.2:
-  version "3.0.2"
-  resolved "https://registry.npmjs.org/braces/-/braces-3.0.2.tgz"
-  integrity sha512-b8um+L1RzM3WDSzvhm6gIz1yfTbBt6YTlcEKAvsmqCZZFw46z626lVj9j1yEPW33H5H+lBQpZMP1k8l+78Ha0A==
-  dependencies:
-    fill-range "^7.0.1"
-
-braces@^3.0.3:
   version "3.0.3"
   resolved "https://registry.yarnpkg.com/braces/-/braces-3.0.3.tgz#490332f40919452272d55a8480adc0c441358789"
   integrity sha512-yQbXgO/OSZVD2IsiLlro+7Hf6Q18EJrKSEsdoMzKePKXct3gvD8oLcOQdIzGupr5Fj+EDe8gO/lxc1BzfMpxvA==
@@ -2772,6 +2747,14 @@ c12@1.11.1:
     pkg-types "^1.1.1"
     rc9 "^2.1.2"
 
+call-bind-apply-helpers@^1.0.1, call-bind-apply-helpers@^1.0.2:
+  version "1.0.2"
+  resolved "https://registry.yarnpkg.com/call-bind-apply-helpers/-/call-bind-apply-helpers-1.0.2.tgz#4b5428c222be985d79c3d82657479dbe0b59b2d6"
+  integrity sha512-Sp1ablJ0ivDkSzjcaJdxEunN5/XvksFJ2sMBFfq6x0ryhQV/2b/KwFe21cMpmHtPOSij8K99/wSfoEuTObmuMQ==
+  dependencies:
+    es-errors "^1.3.0"
+    function-bind "^1.1.2"
+
 call-bind@^1.0.0:
   version "1.0.2"
   resolved "https://registry.yarnpkg.com/call-bind/-/call-bind-1.0.2.tgz#b1d4e89e688119c3c9a903ad30abb2f6a919be3c"
@@ -3073,9 +3056,9 @@ create-require@^1.1.0:
   integrity sha512-dcKFX3jn0MpIaXjisoRvexIJVEKzaq7z2rZKxf+MSr9TkdmHmsU4m2lcLojrj/FHl8mk5VxMmYA+ftRkP/3oKQ==
 
 cross-spawn@^7.0.0, cross-spawn@^7.0.2, cross-spawn@^7.0.3:
-  version "7.0.3"
+  version "7.0.6"
-  resolved "https://registry.yarnpkg.com/cross-spawn/-/cross-spawn-7.0.3.tgz#f73a85b9d5d41d045551c177e2882d4ac85728a6"
+  resolved "https://registry.yarnpkg.com/cross-spawn/-/cross-spawn-7.0.6.tgz#8a58fe78f00dcd70c370451759dfbfaf03e8ee9f"
-  integrity sha512-iRDPJKUPVEND7dHPO8rkbOnPpyDygcDFtWjpeWNCgy8WP2rXcxXL8TskReQl6OrB2G7+UJrags1q15Fudc7G6w==
+  integrity sha512-uV2QOWP2nWzsy2aMp8aRibhi9dlzF5Hgh5SHaB9OiTGEyDTiJJyx0uy51QXdyWbtAHNua4XJzUKca3OzKUd3vA==
   dependencies:
     path-key "^3.1.0"
     shebang-command "^2.0.0"
@@ -3298,6 +3281,15 @@ dotenv@^16.4.5:
   resolved "https://registry.yarnpkg.com/dotenv/-/dotenv-16.4.5.tgz#cdd3b3b604cb327e286b4762e13502f717cb099f"
   integrity sha512-ZmdL2rui+eB2YwhsWzjInR8LldtZHGDoQ1ugH85ppHKwpUHL7j7rN0Ti9NCnGiQbhaZ11FpR+7ao1dNsmduNUg==
 
+dunder-proto@^1.0.1:
+  version "1.0.1"
+  resolved "https://registry.yarnpkg.com/dunder-proto/-/dunder-proto-1.0.1.tgz#d7ae667e1dc83482f8b70fd0f6eefc50da30f58a"
+  integrity sha512-KIN/nDJBQRcXw0MLVhZE9iQHmG68qAVIBg9CqmUYjmQIhgij9U5MFvrqkUL5FbtyyzZuOeOt0zdeRe4UY7ct+A==
+  dependencies:
+    call-bind-apply-helpers "^1.0.1"
+    es-errors "^1.3.0"
+    gopd "^1.2.0"
+
 eastasianwidth@^0.2.0:
   version "0.2.0"
   resolved "https://registry.yarnpkg.com/eastasianwidth/-/eastasianwidth-0.2.0.tgz#696ce2ec0aa0e6ea93a397ffcf24aa7840c827cb"
@@ -3428,6 +3420,16 @@ es-abstract@^1.22.1:
     unbox-primitive "^1.0.2"
     which-typed-array "^1.1.13"
 
+es-define-property@^1.0.1:
+  version "1.0.1"
+  resolved "https://registry.yarnpkg.com/es-define-property/-/es-define-property-1.0.1.tgz#983eb2f9a6724e9303f61addf011c72e09e0b0fa"
+  integrity sha512-e3nRfgfUZ4rNGL232gUgX06QNyyez04KdjFrF+LTRoOXmrOgFKDg4BCdsjW8EnT69eqdYGmRpJwiPVYNrCaW3g==
+
+es-errors@^1.3.0:
+  version "1.3.0"
+  resolved "https://registry.yarnpkg.com/es-errors/-/es-errors-1.3.0.tgz#05f75a25dab98e4fb1dcd5e1472c0546d5057c8f"
+  integrity sha512-Zf5H2Kxt2xjTvbJvP2ZWLEICxA6j+hAmMzIlypy4xcBg1vKVnx89Wy0GbS+kf5cwCVFFzdCFh2XSCFNULS6csw==
+
 es-iterator-helpers@^1.0.12, es-iterator-helpers@^1.0.15:
   version "1.0.15"
   resolved "https://registry.yarnpkg.com/es-iterator-helpers/-/es-iterator-helpers-1.0.15.tgz#bd81d275ac766431d19305923707c3efd9f1ae40"
@@ -3453,6 +3455,13 @@ es-module-lexer@1.4.1:
   resolved "https://registry.yarnpkg.com/es-module-lexer/-/es-module-lexer-1.4.1.tgz#41ea21b43908fe6a287ffcbe4300f790555331f5"
   integrity sha512-cXLGjP0c4T3flZJKQSuziYoq7MlT+rnvfZjfp7h+I7K9BNX54kP9nyWvdbwjQ4u1iWbOL4u96fgeZLToQlZC7w==
 
+es-object-atoms@^1.0.0, es-object-atoms@^1.1.1:
+  version "1.1.1"
+  resolved "https://registry.yarnpkg.com/es-object-atoms/-/es-object-atoms-1.1.1.tgz#1c4f2c4837327597ce69d2ca190a7fdd172338c1"
+  integrity sha512-FGgH2h8zKNim9ljj7dankFPcICIK9Cp5bm+c2gQSYePhpaG5+esrLODihIorn+Pe6FGJzWhXQotPv73jTaldXA==
+  dependencies:
+    es-errors "^1.3.0"
+
 es-set-tostringtag@^2.0.1:
   version "2.0.2"
   resolved "https://registry.yarnpkg.com/es-set-tostringtag/-/es-set-tostringtag-2.0.2.tgz#11f7cc9f63376930a5f20be4915834f4bc74f9c9"
@@ -3462,6 +3471,16 @@ es-set-tostringtag@^2.0.1:
     has-tostringtag "^1.0.0"
     hasown "^2.0.0"
 
+es-set-tostringtag@^2.1.0:
+  version "2.1.0"
+  resolved "https://registry.yarnpkg.com/es-set-tostringtag/-/es-set-tostringtag-2.1.0.tgz#f31dbbe0c183b00a6d26eb6325c810c0fd18bd4d"
+  integrity sha512-j6vWzfrGVfyXxge+O0x5sh6cvxAog0a/4Rdd2K36zCMV5eJ+/+tOAngRO8cODMNWbVRdVlmGZQL2YS3yR8bIUA==
+  dependencies:
+    es-errors "^1.3.0"
+    get-intrinsic "^1.2.6"
+    has-tostringtag "^1.0.2"
+    hasown "^2.0.2"
+
 es-shim-unscopables@^1.0.0:
   version "1.0.2"
   resolved "https://registry.yarnpkg.com/es-shim-unscopables/-/es-shim-unscopables-1.0.2.tgz#1f6942e71ecc7835ed1c8a83006d8771a63a3763"
@@ -3963,13 +3982,6 @@ file-uri-to-path@1.0.0:
   resolved "https://registry.yarnpkg.com/file-uri-to-path/-/file-uri-to-path-1.0.0.tgz#553a7b8446ff6f684359c445f1e37a05dacc33dd"
   integrity sha512-0Zt+s3L7Vf1biwWZ29aARiVYLx7iMGnEUl9x33fbB/j3jR81u/O2LbqK+Bm1CDSNDKVtJ/YjwY7TUd5SkeLQLw==
 
-fill-range@^7.0.1:
-  version "7.0.1"
-  resolved "https://registry.npmjs.org/fill-range/-/fill-range-7.0.1.tgz"
-  integrity sha512-qOo9F+dMUmC2Lcb4BbVvnKJxTPjCm+RRpe4gDuGrzkL7mEVl/djYSu2OdQ2Pa302N4oqkSg9ir6jaLWJ2USVpQ==
-  dependencies:
-    to-regex-range "^5.0.1"
-
 fill-range@^7.1.1:
   version "7.1.1"
   resolved "https://registry.yarnpkg.com/fill-range/-/fill-range-7.1.1.tgz#44265d3cac07e3ea7dc247516380643754a05292"
@@ -4003,11 +4015,6 @@ flatted@^3.2.9:
   resolved "https://registry.yarnpkg.com/flatted/-/flatted-3.2.9.tgz#7eb4c67ca1ba34232ca9d2d93e9886e611ad7daf"
   integrity sha512-36yxDn5H7OFZQla0/jFJmbIKTdZAQHngCedGxiMmpNfEZM0sdEeT+WczLQrjK6D7o2aiyLYDnkw0R3JK0Qv1RQ==
 
-follow-redirects@^1.15.0:
-  version "1.15.2"
-  resolved "https://registry.yarnpkg.com/follow-redirects/-/follow-redirects-1.15.2.tgz#b460864144ba63f2681096f274c4e57026da2c13"
-  integrity sha512-VQLG33o04KaQ8uYi2tVNbdrWp1QWxNNea+nmIB4EVM28v0hmP17z7aG1+wAkNzVq4KeXTq3221ye5qTJP91JwA==
-
 follow-redirects@^1.15.6:
   version "1.15.6"
   resolved "https://registry.yarnpkg.com/follow-redirects/-/follow-redirects-1.15.6.tgz#7f815c0cda4249c74ff09e95ef97c23b5fd0399b"
@@ -4034,12 +4041,14 @@ foreground-child@^3.1.0:
     signal-exit "^4.0.1"
 
 form-data@^4.0.0:
-  version "4.0.0"
+  version "4.0.4"
-  resolved "https://registry.yarnpkg.com/form-data/-/form-data-4.0.0.tgz#93919daeaf361ee529584b9b31664dc12c9fa452"
+  resolved "https://registry.yarnpkg.com/form-data/-/form-data-4.0.4.tgz#784cdcce0669a9d68e94d11ac4eea98088edd2c4"
-  integrity sha512-ETEklSGi5t0QMZuiXoA/Q6vcnxcLQP5vdugSpuAyi6SVGi2clPPp+xgEhuMaHC+zGgn31Kd235W35f7Hykkaww==
+  integrity sha512-KrGhL9Q4zjj0kiUt5OO4Mr/A/jlI2jDYs5eHBpYHPcBEVSiipAvn2Ko2HnPe20rmcuuvMHNdZFp+4IlGTMF0Ow==
   dependencies:
     asynckit "^0.4.0"
     combined-stream "^1.0.8"
+    es-set-tostringtag "^2.1.0"
+    hasown "^2.0.2"
     mime-types "^2.1.12"
 
 formidable@^2.1.2:
@@ -4174,11 +4183,35 @@ get-intrinsic@^1.1.1, get-intrinsic@^1.1.3, get-intrinsic@^1.2.0, get-intrinsic@
     has-symbols "^1.0.3"
     hasown "^2.0.0"
 
+get-intrinsic@^1.2.6:
+  version "1.3.0"
+  resolved "https://registry.yarnpkg.com/get-intrinsic/-/get-intrinsic-1.3.0.tgz#743f0e3b6964a93a5491ed1bffaae054d7f98d01"
+  integrity sha512-9fSjSaos/fRIVIp+xSJlE6lfwhES7LNtKaCBIamHsjr2na1BiABJPo0mOjjz8GJDURarmCPGqaiVg5mfjb98CQ==
+  dependencies:
+    call-bind-apply-helpers "^1.0.2"
+    es-define-property "^1.0.1"
+    es-errors "^1.3.0"
+    es-object-atoms "^1.1.1"
+    function-bind "^1.1.2"
+    get-proto "^1.0.1"
+    gopd "^1.2.0"
+    has-symbols "^1.1.0"
+    hasown "^2.0.2"
+    math-intrinsics "^1.1.0"
+
 get-nonce@^1.0.0:
   version "1.0.1"
   resolved "https://registry.yarnpkg.com/get-nonce/-/get-nonce-1.0.1.tgz#fdf3f0278073820d2ce9426c18f07481b1e0cdf3"
   integrity sha512-FJhYRoDaiatfEkUK8HKlicmu/3SGFD51q3itKDGoSTysQJBnfOcxU5GxnhE1E6soB76MbT0MBtnKJuXyAx+96Q==
 
+get-proto@^1.0.1:
+  version "1.0.1"
+  resolved "https://registry.yarnpkg.com/get-proto/-/get-proto-1.0.1.tgz#150b3f2743869ef3e851ec0c49d15b1d14d00ee1"
+  integrity sha512-sTSfBjoXBp89JvIKIefqw7U2CCebsc74kiY6awiGogKtoSGbgjYE/G/+l9sF3MWFPNc9IcoOC4ODfKHfxFmp0g==
+  dependencies:
+    dunder-proto "^1.0.1"
+    es-object-atoms "^1.0.0"
+
 get-stream@^5.0.0:
   version "5.2.0"
   resolved "https://registry.yarnpkg.com/get-stream/-/get-stream-5.2.0.tgz#4966a1795ee5ace65e706c4b7beb71257d6e22d3"
@@ -4311,6 +4344,11 @@ gopd@^1.0.1:
   dependencies:
     get-intrinsic "^1.1.3"
 
+gopd@^1.2.0:
+  version "1.2.0"
+  resolved "https://registry.yarnpkg.com/gopd/-/gopd-1.2.0.tgz#89f56b8217bdbc8802bd299df6d7f1081d7e51a1"
+  integrity sha512-ZUKRh6/kUFoAiTAtTYPZJ3hw9wNxx+BIBOijnlG9PnrJsCcSjs1wyyD6vJpaYtgnzDrKYRSqf3OO6Rfa93xsRg==
+
 graceful-fs@^4.1.6, graceful-fs@^4.2.0, graceful-fs@^4.2.11, graceful-fs@^4.2.4, graceful-fs@^4.2.9:
   version "4.2.11"
   resolved "https://registry.yarnpkg.com/graceful-fs/-/graceful-fs-4.2.11.tgz#4183e4e8bf08bb6e05bbb2f7d2e0c8f712ca40e3"
@@ -4368,6 +4406,11 @@ has-symbols@^1.0.2, has-symbols@^1.0.3:
   resolved "https://registry.yarnpkg.com/has-symbols/-/has-symbols-1.0.3.tgz#bb7b2c4349251dce87b125f7bdf874aa7c8b39f8"
   integrity sha512-l3LCuF6MgDNwTDKkdYGEihYjt5pRPbEg46rtlmnSPlUbgmB8LOIrKJbYYFBSbnPaJexMKtiPO8hmeRjRz2Td+A==
 
+has-symbols@^1.1.0:
+  version "1.1.0"
+  resolved "https://registry.yarnpkg.com/has-symbols/-/has-symbols-1.1.0.tgz#fc9c6a783a084951d0b971fe1018de813707a338"
+  integrity sha512-1cDNdwJ2Jaohmb3sg4OmKaMBwuC48sYni5HUw2DvsC8LjGTLK9h+eb1X6RyuOHe4hT0ULCW68iomhjUoKUqlPQ==
+
 has-tostringtag@^1.0.0:
   version "1.0.0"
   resolved "https://registry.yarnpkg.com/has-tostringtag/-/has-tostringtag-1.0.0.tgz#7e133818a7d394734f941e73c3d3f9291e658b25"
@@ -4375,6 +4418,13 @@ has-tostringtag@^1.0.0:
   dependencies:
     has-symbols "^1.0.2"
 
+has-tostringtag@^1.0.2:
+  version "1.0.2"
+  resolved "https://registry.yarnpkg.com/has-tostringtag/-/has-tostringtag-1.0.2.tgz#2cdc42d40bef2e5b4eeab7c01a73c54ce7ab5abc"
+  integrity sha512-NqADB8VjPFLM2V0VvHUewwwsw0ZWBaIdgo+ieHtK3hasLz4qeCRjYcqfB6AQrBggRKppKF8L52/VqdVsO47Dlw==
+  dependencies:
+    has-symbols "^1.0.3"
+
 has-unicode@^2.0.1:
   version "2.0.1"
   resolved "https://registry.yarnpkg.com/has-unicode/-/has-unicode-2.0.1.tgz#e0e6fe6a28cf51138855e086d1691e771de2a8b9"
@@ -4394,6 +4444,13 @@ hasown@^2.0.0:
   dependencies:
     function-bind "^1.1.2"
 
+hasown@^2.0.2:
+  version "2.0.2"
+  resolved "https://registry.yarnpkg.com/hasown/-/hasown-2.0.2.tgz#003eaf91be7adc372e84ec59dc37252cedb80003"
+  integrity sha512-0hJU9SCPvmMzIBdZFqNPXWa6dqh7WdH0cII9y+CyS8rG3nL48Bclra9HmKhVVUHyPWNH5Y7xDwAB7bfgSjkUMQ==
+  dependencies:
+    function-bind "^1.1.2"
+
 hast-util-to-jsx-runtime@^2.0.0:
   version "2.2.0"
   resolved "https://registry.yarnpkg.com/hast-util-to-jsx-runtime/-/hast-util-to-jsx-runtime-2.2.0.tgz#ffd59bfcf0eb8321c6ed511bfc4b399ac3404bc2"
@@ -5095,6 +5152,11 @@ make-error@^1.1.1:
   resolved "https://registry.yarnpkg.com/make-error/-/make-error-1.3.6.tgz#2eb2e37ea9b67c4891f684a1394799af484cf7a2"
   integrity sha512-s8UhlNe7vPKomQhC1qFelMokr/Sc3AgNbso3n74mVPA5LTZwkB9NlXf4XPamLxJE8h0gh73rM94xvwRT2CVInw==
 
+math-intrinsics@^1.1.0:
+  version "1.1.0"
+  resolved "https://registry.yarnpkg.com/math-intrinsics/-/math-intrinsics-1.1.0.tgz#a0dd74be81e2aa5c2f27e65ce283605ee4e2b7f9"
+  integrity sha512-/IXtbwEk5HTPyEwyKX6hGkYXxM9nbj64B+ilVJnC/R6B0pH5G4V3b0pVbL7DBj4tkhBAppbQUlf6F6Xl9LHu1g==
+
 mdast-util-from-markdown@^2.0.0:
   version "2.0.0"
   resolved "https://registry.yarnpkg.com/mdast-util-from-markdown/-/mdast-util-from-markdown-2.0.0.tgz#52f14815ec291ed061f2922fd14d6689c810cb88"
@@ -5374,7 +5436,7 @@ micromark@^4.0.0:
     micromark-util-symbol "^2.0.0"
     micromark-util-types "^2.0.0"
 
-micromatch@^4.0.2:
+micromatch@^4.0.2, micromatch@^4.0.4, micromatch@^4.0.5:
   version "4.0.8"
   resolved "https://registry.yarnpkg.com/micromatch/-/micromatch-4.0.8.tgz#d66fa18f3a47076789320b9b1af32bd86d9fa202"
   integrity sha512-PXwfBhYu0hBCPw8Dn0E+WDYb7af3dSLVWKi3HGv84IdF4TyFoC0ysxFd0Goxw7nSv4T/PzEJQxsYsEiFCKo2BA==
@@ -5382,14 +5444,6 @@ micromatch@^4.0.2:
     braces "^3.0.3"
     picomatch "^2.3.1"
 
-micromatch@^4.0.4, micromatch@^4.0.5:
-  version "4.0.5"
-  resolved "https://registry.npmjs.org/micromatch/-/micromatch-4.0.5.tgz"
-  integrity sha512-DMy+ERcEW2q8Z2Po+WNXuw3c5YaUSFjAO5GsJqfEl7UjvtIuFKO6ZrKvcItdy98dwFI2N1tg3zNIdKaQT+aNdA==
-  dependencies:
-    braces "^3.0.2"
-    picomatch "^2.3.1"
-
 mime-db@1.52.0:
   version "1.52.0"
   resolved "https://registry.yarnpkg.com/mime-db/-/mime-db-1.52.0.tgz#bbabcdc02859f4987301c856e3387ce5ec43bf70"
@@ -5542,9 +5596,9 @@ mz@^2.7.0:
     thenify-all "^1.0.0"
 
 nanoid@^3.3.6:
-  version "3.3.6"
+  version "3.3.11"
-  resolved "https://registry.npmjs.org/nanoid/-/nanoid-3.3.6.tgz"
+  resolved "https://registry.yarnpkg.com/nanoid/-/nanoid-3.3.11.tgz#4f4f112cefbe303202f2199838128936266d185b"
-  integrity sha512-BGcqMMJuToF7i1rt+2PWSNVnWIkGCU78jBG3RxO/bZlnZPK2Cmi2QaffxGO/2RvWi9sL+FAiRiXMgsyxQ1DIDA==
+  integrity sha512-N8SpfPUnUp1bK+PMYW8qSWdl9U+wwNWI4QKxOYDy9JAro3WMX7p2OeVRF9v+347pnakNevPmiHhNmZ2HbFA76w==
 
 natural-compare@^1.4.0:
   version "1.4.0"
@@ -5576,12 +5630,12 @@ next-themes@^0.4.6:
   resolved "https://registry.yarnpkg.com/next-themes/-/next-themes-0.4.6.tgz#8d7e92d03b8fea6582892a50a928c9b23502e8b6"
   integrity sha512-pZvgD5L0IEvX5/9GWyHMf3m8BKiVQwsCMHfoFosXtXBMnaS0ZnIJ9ST4b4NqLVKDEm8QBxoNNGNaBv2JNF6XNA==
 
-next@^14.2.7:
+next@^14.2.30:
-  version "14.2.7"
+  version "14.2.30"
-  resolved "https://registry.yarnpkg.com/next/-/next-14.2.7.tgz#e02d5d9622ff4b998e5c89adfd660c9bf6435970"
+  resolved "https://registry.yarnpkg.com/next/-/next-14.2.30.tgz#7b7288859794574067f65d6e2ea98822f2173006"
-  integrity sha512-4Qy2aK0LwH4eQiSvQWyKuC7JXE13bIopEQesWE0c/P3uuNRnZCQanI0vsrMLmUQJLAto+A+/8+sve2hd+BQuOQ==
+  integrity sha512-+COdu6HQrHHFQ1S/8BBsCag61jZacmvbuL2avHvQFbWa2Ox7bE+d8FyNgxRLjXQ5wtPyQwEmk85js/AuaG2Sbg==
   dependencies:
-    "@next/env" "14.2.7"
+    "@next/env" "14.2.30"
     "@swc/helpers" "0.5.5"
     busboy "1.6.0"
     caniuse-lite "^1.0.30001579"
@@ -5589,15 +5643,15 @@ next@^14.2.7:
     postcss "8.4.31"
     styled-jsx "5.1.1"
   optionalDependencies:
-    "@next/swc-darwin-arm64" "14.2.7"
+    "@next/swc-darwin-arm64" "14.2.30"
-    "@next/swc-darwin-x64" "14.2.7"
+    "@next/swc-darwin-x64" "14.2.30"
-    "@next/swc-linux-arm64-gnu" "14.2.7"
+    "@next/swc-linux-arm64-gnu" "14.2.30"
-    "@next/swc-linux-arm64-musl" "14.2.7"
+    "@next/swc-linux-arm64-musl" "14.2.30"
-    "@next/swc-linux-x64-gnu" "14.2.7"
+    "@next/swc-linux-x64-gnu" "14.2.30"
-    "@next/swc-linux-x64-musl" "14.2.7"
+    "@next/swc-linux-x64-musl" "14.2.30"
-    "@next/swc-win32-arm64-msvc" "14.2.7"
+    "@next/swc-win32-arm64-msvc" "14.2.30"
-    "@next/swc-win32-ia32-msvc" "14.2.7"
+    "@next/swc-win32-ia32-msvc" "14.2.30"
-    "@next/swc-win32-x64-msvc" "14.2.7"
+    "@next/swc-win32-x64-msvc" "14.2.30"
 
 node-abort-controller@^3.0.1:
   version "3.1.1"
@@ -5982,12 +6036,12 @@ perfect-freehand@^1.2.2:
   resolved "https://registry.yarnpkg.com/perfect-freehand/-/perfect-freehand-1.2.2.tgz#292f65b72df0c7f57a89c4b346b50d7139014172"
   integrity sha512-eh31l019WICQ03pkF3FSzHxB8n07ItqIQ++G5UV8JX0zVOXzgTGCqnRR0jJ2h9U8/2uW4W4mtGJELt9kEV0CFQ==
 
-picocolors@1.0.0, picocolors@^1.0.0:
+picocolors@1.0.0:
   version "1.0.0"
   resolved "https://registry.npmjs.org/picocolors/-/picocolors-1.0.0.tgz"
   integrity sha512-1fygroTLlHu66zi26VoTDv8yRgm0Fccecssto+MhsZ0D/DGW2sm8E8AjW7NU5VVTRt5GxbeZ5qBuJr+HyLYkjQ==
 
-picocolors@^1.0.1:
+picocolors@^1.0.0, picocolors@^1.0.1:
   version "1.0.1"
   resolved "https://registry.yarnpkg.com/picocolors/-/picocolors-1.0.1.tgz#a8ad579b571952f0e5d25892de5445bcfe25aaa1"
   integrity sha512-anP1Z8qwhkbmu7MFP5iTt+wQKXgwzf7zTyGlcdzabySa9vd0Xt392U0rVmz9poOaBj0uHJKyyo9/upk0HrEQew==
@@ -6060,16 +6114,7 @@ postcss-value-parser@^4.0.0, postcss-value-parser@^4.2.0:
   resolved "https://registry.npmjs.org/postcss-value-parser/-/postcss-value-parser-4.2.0.tgz"
   integrity sha512-1NNCs6uurfkVbeXG4S8JFT9t19m45ICnif8zWLd5oPSZ50QnwMfK+H3jv408d4jw/7Bttv5axS5IiHoLaVNHeQ==
 
-postcss@8.4.25, postcss@^8.4.23:
+postcss@8.4.31, postcss@^8.4.23:
-  version "8.4.25"
-  resolved "https://registry.npmjs.org/postcss/-/postcss-8.4.25.tgz"
-  integrity sha512-7taJ/8t2av0Z+sQEvNzCkpDynl0tX3uJMCODi6nT3PfASC7dYCWV9aQ+uiCf+KBD4SEFcu+GvJdGdwzQ6OSjCw==
-  dependencies:
-    nanoid "^3.3.6"
-    picocolors "^1.0.0"
-    source-map-js "^1.0.2"
-
-postcss@8.4.31:
   version "8.4.31"
   resolved "https://registry.yarnpkg.com/postcss/-/postcss-8.4.31.tgz#92b451050a9f914da6755af352bdc0192508656d"
   integrity sha512-PS08Iboia9mts/2ygV3eLpY5ghnUcfLV/EXTOW1E2qYxJKGGBUtNjN76FYHnMs36RmARn41bC0AZmn+rR0OVpQ==
@@ -6402,11 +6447,6 @@ reflect.getprototypeof@^1.0.4:
     globalthis "^1.0.3"
     which-builtin-type "^1.1.3"
 
-regenerator-runtime@^0.14.0:
-  version "0.14.0"
-  resolved "https://registry.yarnpkg.com/regenerator-runtime/-/regenerator-runtime-0.14.0.tgz#5e19d68eb12d486f797e15a3c6a918f7cec5eb45"
-  integrity sha512-srw17NI0TUWHuGa5CFGGmhfNIeja30WMBfbslPNhf6JrqQlLN5gcrvig1oqPxiVaXb0oW0XRKtH6Nngs5lKCIA==
-
 regexp.prototype.flags@^1.5.0, regexp.prototype.flags@^1.5.1:
   version "1.5.1"
   resolved "https://registry.yarnpkg.com/regexp.prototype.flags/-/regexp.prototype.flags-1.5.1.tgz#90ce989138db209f81492edd734183ce99f9677e"