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https://github.com/makeplane/plane
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Integrates LiteLLM for Unified Access to Multiple LLM Models (#5925)
* adds litellm gateway * Fixes repeating code * Fixes error exposing * Fixes error for None text * handles logging exception * Adds multiple providers support * handling edge cases * adds new envs to instance store * strategy pattern for llm config --------- Co-authored-by: akash5100 <akashzsh08@gmail.com>
This commit is contained in:
Akash Verma
GitHub
222
apiserver/plane/app/views/external/base.py
vendored
222
apiserver/plane/app/views/external/base.py
vendored
@@ -1,71 +1,169 @@
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# Python imports
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import requests
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# Python import
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import os
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from typing import List, Dict, Tuple
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# Third party import
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import litellm
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import requests
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# Third party imports
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from openai import OpenAI
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from rest_framework.response import Response
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from rest_framework import status
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from rest_framework.response import Response
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# Django imports
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# Module imports
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from ..base import BaseAPIView
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from plane.app.permissions import allow_permission, ROLE
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from plane.db.models import Workspace, Project
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from plane.app.serializers import ProjectLiteSerializer, WorkspaceLiteSerializer
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# Module import
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from plane.app.permissions import ROLE, allow_permission
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from plane.app.serializers import (ProjectLiteSerializer,
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WorkspaceLiteSerializer)
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from plane.db.models import Project, Workspace
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from plane.license.utils.instance_value import get_configuration_value
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from plane.utils.exception_logger import log_exception
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from ..base import BaseAPIView
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class LLMProvider:
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"""Base class for LLM provider configurations"""
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name: str = ""
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models: List[str] = []
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default_model: str = ""
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@classmethod
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def get_config(cls) -> Dict[str, str | List[str]]:
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return {
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"name": cls.name,
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"models": cls.models,
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"default_model": cls.default_model,
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}
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class OpenAIProvider(LLMProvider):
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name = "OpenAI"
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models = ["gpt-3.5-turbo", "gpt-4o-mini", "gpt-4o", "o1-mini", "o1-preview"]
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default_model = "gpt-4o-mini"
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class AnthropicProvider(LLMProvider):
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name = "Anthropic"
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models = [
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"claude-3-5-sonnet-20240620",
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"claude-3-haiku-20240307",
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"claude-3-opus-20240229",
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"claude-3-sonnet-20240229",
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"claude-2.1",
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"claude-2",
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"claude-instant-1.2",
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"claude-instant-1"
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]
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default_model = "claude-3-sonnet-20240229"
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class GeminiProvider(LLMProvider):
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name = "Gemini"
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models = ["gemini-pro", "gemini-1.5-pro-latest", "gemini-pro-vision"]
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default_model = "gemini-pro"
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SUPPORTED_PROVIDERS = {
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"openai": OpenAIProvider,
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"anthropic": AnthropicProvider,
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"gemini": GeminiProvider,
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}
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def get_llm_config() -> Tuple[str | None, str | None, str | None]:
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"""
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Helper to get LLM configuration values, returns:
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- api_key, model, provider
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"""
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api_key, provider_key, model = get_configuration_value([
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{
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"key": "LLM_API_KEY",
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"default": os.environ.get("LLM_API_KEY", None),
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},
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{
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"key": "LLM_PROVIDER",
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"default": os.environ.get("LLM_PROVIDER", "openai"),
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},
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{
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"key": "LLM_MODEL",
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"default": os.environ.get("LLM_MODEL", None),
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},
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])
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provider = SUPPORTED_PROVIDERS.get(provider_key.lower())
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if not provider:
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log_exception(ValueError(f"Unsupported provider: {provider_key}"))
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return None, None, None
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if not api_key:
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log_exception(ValueError(f"Missing API key for provider: {provider.name}"))
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return None, None, None
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# If no model specified, use provider's default
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if not model:
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model = provider.default_model
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# Validate model is supported by provider
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if model not in provider.models:
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log_exception(ValueError(
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f"Model {model} not supported by {provider.name}. "
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f"Supported models: {', '.join(provider.models)}"
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))
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return None, None, None
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return api_key, model, provider_key
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def get_llm_response(task, prompt, api_key: str, model: str, provider: str) -> Tuple[str | None, str | None]:
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"""Helper to get LLM completion response"""
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final_text = task + "\n" + prompt
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try:
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# For Gemini, prepend provider name to model
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if provider.lower() == "gemini":
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model = f"gemini/{model}"
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response = litellm.completion(
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model=model,
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messages=[{"role": "user", "content": final_text}],
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api_key=api_key,
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)
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text = response.choices[0].message.content.strip()
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return text, None
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except Exception as e:
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log_exception(e)
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error_type = e.__class__.__name__
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if error_type == "AuthenticationError":
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return None, f"Invalid API key for {provider}"
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elif error_type == "RateLimitError":
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return None, f"Rate limit exceeded for {provider}"
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else:
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return None, f"Error occurred while generating response from {provider}"
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class GPTIntegrationEndpoint(BaseAPIView):
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@allow_permission([ROLE.ADMIN, ROLE.MEMBER])
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def post(self, request, slug, project_id):
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OPENAI_API_KEY, GPT_ENGINE = get_configuration_value(
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[
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{
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"key": "OPENAI_API_KEY",
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"default": os.environ.get("OPENAI_API_KEY", None),
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},
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{
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"key": "GPT_ENGINE",
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"default": os.environ.get("GPT_ENGINE", "gpt-3.5-turbo"),
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},
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]
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)
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api_key, model, provider = get_llm_config()
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# Get the configuration value
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# Check the keys
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if not OPENAI_API_KEY or not GPT_ENGINE:
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if not api_key or not model or not provider:
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return Response(
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{"error": "OpenAI API key and engine is required"},
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{"error": "LLM provider API key and model are required"},
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status=status.HTTP_400_BAD_REQUEST,
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)
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prompt = request.data.get("prompt", False)
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task = request.data.get("task", False)
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if not task:
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return Response(
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{"error": "Task is required"}, status=status.HTTP_400_BAD_REQUEST
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)
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final_text = task + "\n" + prompt
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client = OpenAI(api_key=OPENAI_API_KEY)
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response = client.chat.completions.create(
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model=GPT_ENGINE, messages=[{"role": "user", "content": final_text}]
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)
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text, error = get_llm_response(task, request.data.get("prompt", False), api_key, model, provider)
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if not text and error:
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return Response(
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{"error": "An internal error has occurred."},
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status=status.HTTP_500_INTERNAL_SERVER_ERROR,
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)
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workspace = Workspace.objects.get(slug=slug)
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project = Project.objects.get(pk=project_id)
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text = response.choices[0].message.content.strip()
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text_html = text.replace("\n", "<br/>")
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return Response(
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{
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"response": text,
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"response_html": text_html,
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"response_html": text.replace("\n", "<br/>"),
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"project_detail": ProjectLiteSerializer(project).data,
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"workspace_detail": WorkspaceLiteSerializer(workspace).data,
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},
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@@ -76,47 +174,33 @@ class GPTIntegrationEndpoint(BaseAPIView):
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class WorkspaceGPTIntegrationEndpoint(BaseAPIView):
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@allow_permission(allowed_roles=[ROLE.ADMIN, ROLE.MEMBER], level="WORKSPACE")
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def post(self, request, slug):
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OPENAI_API_KEY, GPT_ENGINE = get_configuration_value(
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[
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{
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"key": "OPENAI_API_KEY",
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"default": os.environ.get("OPENAI_API_KEY", None),
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},
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{
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"key": "GPT_ENGINE",
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"default": os.environ.get("GPT_ENGINE", "gpt-3.5-turbo"),
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},
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]
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)
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# Get the configuration value
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# Check the keys
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if not OPENAI_API_KEY or not GPT_ENGINE:
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api_key, model, provider = get_llm_config()
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if not api_key or not model or not provider:
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return Response(
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{"error": "OpenAI API key and engine is required"},
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{"error": "LLM provider API key and model are required"},
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status=status.HTTP_400_BAD_REQUEST,
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)
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prompt = request.data.get("prompt", False)
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task = request.data.get("task", False)
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if not task:
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return Response(
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{"error": "Task is required"}, status=status.HTTP_400_BAD_REQUEST
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)
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final_text = task + "\n" + prompt
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text, error = get_llm_response(task, request.data.get("prompt", False), api_key, model, provider)
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if not text and error:
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return Response(
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{"error": "An internal error has occurred."},
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status=status.HTTP_500_INTERNAL_SERVER_ERROR,
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)
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client = OpenAI(api_key=OPENAI_API_KEY)
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response = client.chat.completions.create(
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model=GPT_ENGINE, messages=[{"role": "user", "content": final_text}]
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)
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text = response.choices[0].message.content.strip()
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text_html = text.replace("\n", "<br/>")
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return Response(
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{"response": text, "response_html": text_html}, status=status.HTTP_200_OK
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{
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"response": text,
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"response_html": text.replace("\n", "<br/>"),
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},
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status=status.HTTP_200_OK,
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)
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@@ -132,20 +132,33 @@ class Command(BaseCommand):
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"is_encrypted": False,
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},
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{
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"key": "OPENAI_API_KEY",
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"value": os.environ.get("OPENAI_API_KEY"),
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"category": "OPENAI",
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"key": "LLM_API_KEY",
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"value": os.environ.get("LLM_API_KEY"),
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"category": "AI",
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"is_encrypted": True,
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},
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{
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"key": "GPT_ENGINE",
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"key": "LLM_PROVIDER",
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"value": os.environ.get("LLM_PROVIDER", "openai"),
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"category": "AI",
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"is_encrypted": False,
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},
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{
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"key": "LLM_MODEL",
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"value": os.environ.get("LLM_MODEL", "gpt-4o-mini"),
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"category": "AI",
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"is_encrypted": False,
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},
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# Deprecated, use LLM_MODEL
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{
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"key": "GPT_ENGINE",
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"value": os.environ.get("GPT_ENGINE", "gpt-3.5-turbo"),
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"category": "SMTP",
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"is_encrypted": False,
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},
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{
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"key": "UNSPLASH_ACCESS_KEY",
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"value": os.environ.get("UNSPLASH_ACESS_KEY", ""),
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"value": os.environ.get("UNSPLASH_ACCESS_KEY", ""),
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"category": "UNSPLASH",
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"is_encrypted": True,
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},
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@@ -37,7 +37,7 @@ uvicorn==0.29.0
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# sockets
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channels==4.1.0
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# ai
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openai==1.25.0
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litellm==1.51.0
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# slack
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slack-sdk==3.27.1
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# apm
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