feat(llm): add alternative support of OpenAI API

.env.example

@@ -21,6 +21,10 @@ LLM_PROVIDER='ollama'
 OLLAMA_MODEL='dolphin-llama3:8b-v2.9-q4_K_M'
 # Smaller option
 # OLLAMA_MODEL='tinydolphin:1.1b-v2.8-q4_K_M'
+GROQ_API_KEY=
+GROQ_MODEL='llama3-8b-8192'
+OPENAI_API_KEY=
+OPENAI_MODEL='gpt-4o-2024-05-13'
 LLM_TEMPERATURE=0
 JWT_SECRET=
 SENTRY_DSN=

.github/workflows/push.yml

@@ -72,11 +72,11 @@ jobs:
             docker rmi -f $(docker images -f "dangling=true" -q)
             docker volume rm -f $(docker volume ls -f "dangling=true" -q)
             # Update the service
-            docker compose pull backend gradio
-            docker compose stop backend gradio && docker compose up -d --wait
+            docker compose pull backend chat
+            docker compose stop backend chat && docker compose up -d --wait
             # Check update
             docker inspect -f '{{ .Created }}' $(docker compose images -q backend)
-            docker inspect -f '{{ .Created }}' $(docker compose images -q gradio)
+            docker inspect -f '{{ .Created }}' $(docker compose images -q chat)
             # Clean up
             docker rm -fv $(docker ps -aq)
             docker rmi -f $(docker images -f "dangling=true" -q)

CONTRIBUTING.md

@@ -77,10 +77,12 @@ If you are wondering how to do something with Companion API, or a more general q
 - [Git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git)
 - [Docker](https://docs.docker.com/engine/install/)
 - [Docker compose](https://docs.docker.com/compose/)
-- [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html) and a GPU (>= 6 Gb VRAM for good performance/latency balance)
+- [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html) and a GPU (>= 6 Gb VRAM for good performance/latency balance)*
 - [Poetry](https://python-poetry.org/docs/)
 - [Make](https://www.gnu.org/software/make/) (optional)
 
+_*If you don't have a GPU, you can use alternative LLM providers (currently supported: Groq, OpenAI)_
+
 
 ### Configure your fork
 
@@ -130,8 +132,12 @@ This file contains all the information to run the project.
 - `SUPERADMIN_PWD`: the password of the initial admin user
 
 #### Other optional values
-- `SECRET_KEY`: if set, tokens can be reused between sessions. All instances sharing the same secret key can use the same token.
+- `JWT_SECRET`: if set, tokens can be reused between sessions. All instances sharing the same secret key can use the same token.
 - `OLLAMA_MODEL`: the model tag in [Ollama library](https://ollama.com/library) that will be used for the API.
+- `GROQ_API_KEY`: your [Groq API KEY](https://console.groq.com/keys), required if you select `groq` as `LLM_PROVIDER`.
+- `GROQ_MODEL`: the model tag in [Groq supported models](https://console.groq.com/docs/models) that will be used for the API.
+- `OPENAI_API_KEY`: your [OpenAI API KEY](https://platform.openai.com/api-keys), required if you select `openai` as `LLM_PROVIDER`.
+- `OPENAI_MODEL`: the model tag in [OpenAI supported models](https://platform.openai.com/docs/models) that will be used for the API.
 - `SENTRY_DSN`: the DSN for your [Sentry](https://sentry.io/) project, which monitors back-end errors and report them back.
 - `SERVER_NAME`: the server tag that will be used to report events to Sentry.
 - `POSTHOG_HOST`: the host for PostHog [PostHog](https://eu.posthog.com/settings/project-details).

docker-compose.prod.yml

@@ -72,7 +72,7 @@ services:
       - POSTGRES_URL=postgresql+asyncpg://${POSTGRES_USER}:${POSTGRES_PASSWORD}@${POSTGRES_HOST}:${POSTGRES_PORT}/${POSTGRES_DB}
       - SUPERADMIN_LOGIN=${SUPERADMIN_LOGIN}
       - SUPERADMIN_PWD=${SUPERADMIN_PWD}
-      - SECRET_KEY=${SECRET_KEY}
+      - JWT_SECRET=${JWT_SECRET}
       - OLLAMA_ENDPOINT=http://ollama:11434
       - OLLAMA_MODEL=${OLLAMA_MODEL}
       - OLLAMA_TIMEOUT=${OLLAMA_TIMEOUT:-60}

docker-compose.yml

@@ -56,6 +56,8 @@ services:
       - OLLAMA_MODEL=${OLLAMA_MODEL}
       - GROQ_API_KEY=${GROQ_API_KEY}
       - GROQ_MODEL=${GROQ_MODEL}
+      - OPENAI_API_KEY=${OPENAI_API_KEY}
+      - OPENAI_MODEL=${OPENAI_MODEL}
       - OLLAMA_TIMEOUT=${OLLAMA_TIMEOUT:-60}
       - SUPPORT_EMAIL=${SUPPORT_EMAIL}
       - DEBUG=true

docs/developers/self-hosting.mdx

@@ -10,7 +10,7 @@ Whatever your installation method, you'll need at least the following to be inst
 1. [Docker](https://docs.docker.com/engine/install/) (and [Docker compose](https://docs.docker.com/compose/) if you're using an old version)
 2. [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html) and a GPU
 
-_We recommend min 5Gb of VRAM on your GPU for good performance/latency balance._
+_We recommend min 5Gb of VRAM on your GPU for good performance/latency balance. Please note that by default, this will run your LLM locally (available offline) but if you don't have a GPU, you can use online LLM providers (currently supported: Groq, OpenAI)_
 
 ### 60 seconds setup ⏱️
 

pyproject.toml

@@ -30,6 +30,7 @@ posthog = "^3.0.0"
 prometheus-fastapi-instrumentator = "^6.1.0"
 groq = "^0.5.0"
 ollama = "^0.1.9"
+openai = "^1.29.0"
 uvloop = "^0.19.0"
 httptools = "^0.6.1"
 
@@ -40,6 +41,7 @@ optional = true
 ruff = "==0.4.4"
 mypy = "==1.10.0"
 types-requests = ">=2.0.0"
+types-urllib3 = ">=1.26.25"
 types-passlib = ">=1.7.0"
 pre-commit = "^3.6.0"
 

src/app/api/api_v1/endpoints/code.py

@@ -29,7 +29,7 @@ async def chat(
     guidelines: GuidelineCRUD = Depends(get_guideline_crud),
     token_payload: TokenPayload = Security(get_quack_jwt, scopes=[UserScope.ADMIN, UserScope.USER]),
 ) -> StreamingResponse:
-    telemetry_client.capture(token_payload.sub, event="compute-chat")
+    telemetry_client.capture(token_payload.sub, event="code-chat")
     # Validate payload
     if len(payload.messages) == 0:
         raise HTTPException(

src/app/core/config.py

@@ -53,7 +53,7 @@ def sqlachmey_uri(cls, v: str) -> str:
     GROQ_API_KEY: Union[str, None] = os.environ.get("GROQ_API_KEY")
     GROQ_MODEL: str = os.environ.get("GROQ_MODEL", "llama3-8b-8192")
     OPENAI_API_KEY: Union[str, None] = os.environ.get("OPENAI_API_KEY")
-    OPENAI_MODEL: str = os.environ.get("OPENAI_MODEL", "gpt-4-turbo-2024-04-09")
+    OPENAI_MODEL: str = os.environ.get("OPENAI_MODEL", "gpt-4o-2024-05-13")
 
     # Error monitoring
     SENTRY_DSN: Union[str, None] = os.environ.get("SENTRY_DSN")

src/app/schemas/services.py

@@ -4,65 +4,21 @@
 # See LICENSE or go to <https://www.apache.org/licenses/LICENSE-2.0> for full license details.
 
 from enum import Enum
-from typing import Any, Dict, List, Union
+from typing import List, Union
 
 from pydantic import BaseModel, HttpUrl
 
 __all__ = ["ChatCompletion"]
 
 
-class OpenAIModel(str, Enum):
-    # https://platform.openai.com/docs/models/overview
-    GPT3_5_TURBO: str = "gpt-3.5-turbo-0125"
-    GPT3_5_TURBO_LEGACY: str = "gpt-3.5-turbo-1106"
-    GPT4_TURBO: str = "gpt-4-0125-preview"
-    GPT4_TURBO_LEGACY: str = "gpt-4-1106-preview"
-
-
-class OpenAIChatRole(str, Enum):
+class ChatRole(str, Enum):
     SYSTEM: str = "system"
     USER: str = "user"
     ASSISTANT: str = "assistant"
 
 
-class FieldSchema(BaseModel):
-    type: str
-    description: str
-
-
-class ObjectSchema(BaseModel):
-    type: str = "object"
-    properties: Dict[str, Any]
-    required: List[str]
-
-
-class ArraySchema(BaseModel):
-    type: str = "array"
-    items: ObjectSchema
-
-
-class OpenAIFunction(BaseModel):
-    name: str
-    description: str
-    parameters: ObjectSchema
-
-
-class OpenAITool(BaseModel):
-    type: str = "function"
-    function: OpenAIFunction
-
-
-class _FunctionName(BaseModel):
-    name: str
-
-
-class _OpenAIToolChoice(BaseModel):
-    type: str = "function"
-    function: _FunctionName
-
-
-class OpenAIMessage(BaseModel):
-    role: OpenAIChatRole
+class ChatMessage(BaseModel):
+    role: ChatRole
     content: str
 
 
@@ -72,9 +28,7 @@ class _ResponseFormat(BaseModel):
 
 class ChatCompletion(BaseModel):
     model: str
-    messages: List[OpenAIMessage]
-    functions: List[OpenAIFunction]
-    function_call: Dict[str, str]
+    messages: List[ChatMessage]
     temperature: float = 0.0
     frequency_penalty: float = 1.0
     response_format: _ResponseFormat = _ResponseFormat(type="json_object")

src/app/services/llm/groq.py

@@ -11,6 +11,8 @@
 from groq import Groq, Stream
 from groq.lib.chat_completion_chunk import ChatCompletionChunk
 
+from .utils import CHAT_PROMPT
+
 logger = logging.getLogger("uvicorn.error")
 
 
@@ -20,12 +22,6 @@ class GroqModel(str, Enum):
     MIXTRAL_8X7b: str = "mixtral-8x7b-32768"
 
 
-CHAT_PROMPT = (
-    "You are an AI programming assistant, developed by the company Quack AI, and you only answer questions related to computer science "
-    "(refuse to answer for the rest)."
-)
-
-
 class GroqClient:
     def __init__(
         self,

src/app/services/llm/llm.py

@@ -3,24 +3,48 @@
 # This program is licensed under the Apache License 2.0.
 # See LICENSE or go to <https://www.apache.org/licenses/LICENSE-2.0> for full license details.
 
+import re
 from enum import Enum
-from typing import Union
+from typing import Dict, Union
+
+from fastapi import HTTPException, status
 
 from app.core.config import settings
 
 from .groq import GroqClient
 from .ollama import OllamaClient
+from .openai import OpenAIClient
 
 __all__ = ["llm_client"]
 
+EXAMPLE_PROMPT = (
+    "You are responsible for producing concise illustrations of the company coding guidelines. "
+    "This will be used to teach new developers our way of engineering software. "
+    "Make sure your code is in the specified programming language and functional, don't add extra comments or explanations.\n"
+    # Format
+    "You should output two code blocks: "
+    "a minimal code snippet where the instruction was correctly followed, "
+    "and the same snippet with minimal modifications that invalidates the instruction."
+)
+# Strangely, this doesn't work when compiled
+EXAMPLE_PATTERN = r"```[a-zA-Z]*\n(?P<positive>.*?)```\n.*```[a-zA-Z]*\n(?P<negative>.*?)```"
+
+
+def validate_example_response(response: str) -> Dict[str, str]:
+    matches = re.search(EXAMPLE_PATTERN, response.strip(), re.DOTALL)
+    if matches is None:
+        raise HTTPException(status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, detail="Failed output schema validation")
+
+    return matches.groupdict()
+
 
 class LLMProvider(str, Enum):
     OLLAMA: str = "ollama"
     OPENAI: str = "openai"
     GROQ: str = "groq"
 
 
-llm_client: Union[OllamaClient, GroqClient]
+llm_client: Union[OllamaClient, GroqClient, OpenAIClient]
 if settings.LLM_PROVIDER == LLMProvider.OLLAMA:
     if not settings.OLLAMA_ENDPOINT:
         raise ValueError("Please provide a value for `OLLAMA_ENDPOINT`")
@@ -29,5 +53,9 @@ class LLMProvider(str, Enum):
     if not settings.GROQ_API_KEY:
         raise ValueError("Please provide a value for `GROQ_API_KEY`")
     llm_client = GroqClient(settings.GROQ_API_KEY, settings.GROQ_MODEL, settings.LLM_TEMPERATURE)  # type: ignore[arg-type]
+elif settings.LLM_PROVIDER == LLMProvider.OPENAI:
+    if not settings.OPENAI_API_KEY:
+        raise ValueError("Please provide a value for `OPENAI_API_KEY`")
+    llm_client = OpenAIClient(settings.OPENAI_API_KEY, settings.OPENAI_MODEL, settings.LLM_TEMPERATURE)  # type: ignore[arg-type]
 else:
     raise NotImplementedError("LLM provider is not implemented")