Integrates LiteLLM for Unified Access to Multiple LLM Models

.env.example

@@ -27,9 +27,16 @@ FILE_SIZE_LIMIT=5242880
 
 # GPT settings
 OPENAI_API_BASE="https://api.openai.com/v1" # deprecated
-OPENAI_API_KEY="sk-" # deprecated
 GPT_ENGINE="gpt-3.5-turbo" # deprecated
 
+# AI Assistant Settings
+LLM_PROVIDER=openai  # Can be "openai", "anthropic", or "google"
+LLM_MODEL=gpt-4o-mini  # The specific model you want to use
+
+OPENAI_API_KEY=your-openai-api-key
+ANTHROPIC_API_KEY=your-anthropic-api-key
+GEMINI_API_KEY=your-gemini-api-key
+
 # Settings related to Docker
 DOCKERIZED=1  # deprecated
 

apiserver/plane/app/views/external/base.py

@@ -1,71 +1,165 @@
-# Python imports
-import requests
+# Python import
 import os
+from typing import List, Dict, Tuple
+
+# Third party import
+import litellm
+import requests
 
-# Third party imports
-from openai import OpenAI
-from rest_framework.response import Response
 from rest_framework import status
+from rest_framework.response import Response
 
-# Django imports
+# Module import
+from plane.app.permissions import ROLE, allow_permission
+from plane.app.serializers import (ProjectLiteSerializer,
+                                   WorkspaceLiteSerializer)
+from plane.db.models import Project, Workspace
+from plane.license.utils.instance_value import get_configuration_value
+from plane.utils.exception_logger import log_exception
 
-# Module imports
 from ..base import BaseAPIView
-from plane.app.permissions import allow_permission, ROLE
-from plane.db.models import Workspace, Project
-from plane.app.serializers import ProjectLiteSerializer, WorkspaceLiteSerializer
-from plane.license.utils.instance_value import get_configuration_value
+
+
+class LLMProvider:
+    """Base class for LLM provider configurations"""
+    name: str = ""
+    models: List[str] = []
+    api_key_env: str = ""
+    default_model: str = ""
+
+    @classmethod
+    def get_config(cls) -> Dict[str, str | List[str]]:
+        return {
+            "name": cls.name,
+            "models": cls.models,
+            "default_model": cls.default_model,
+        }
+
+class OpenAIProvider(LLMProvider):
+    name = "OpenAI"
+    models = ["gpt-3.5-turbo", "gpt-4o-mini", "gpt-4o", "o1-mini", "o1-preview"]
+    api_key_env = "OPENAI_API_KEY"
+    default_model = "gpt-4o-mini"
+
+class AnthropicProvider(LLMProvider):
+    name = "Anthropic"
+    models = [
+        "claude-3-5-sonnet-20240620",
+        "claude-3-haiku-20240307",
+        "claude-3-opus-20240229",
+        "claude-3-sonnet-20240229",
+        "claude-2.1",
+        "claude-2",
+        "claude-instant-1.2",
+        "claude-instant-1"
+    ]
+    api_key_env = "ANTHROPIC_API_KEY"
+    default_model = "claude-3-sonnet-20240229"
+
+class GeminiProvider(LLMProvider):
+    name = "Gemini"
+    models = ["gemini-pro", "gemini-1.5-pro-latest", "gemini-pro-vision"]
+    api_key_env = "GEMINI_API_KEY"
+    default_model = "gemini-pro"
+
+SUPPORTED_PROVIDERS = {
+    "openai": OpenAIProvider,
+    "anthropic": AnthropicProvider,
+    "gemini": GeminiProvider,
+}
+
+def get_llm_config() -> Tuple[str | None, str | None, str | None]:
+    """
+    Helper to get LLM configuration values, returns:
+        - api_key, model, provider
+    """
+    provider_key, model = get_configuration_value([
+        {
+            "key": "LLM_PROVIDER",
+            "default": os.environ.get("LLM_PROVIDER", "openai"),
+        },
+        {
+            "key": "LLM_MODEL",
+            "default": None,
+        },
+    ])
+
+    provider = SUPPORTED_PROVIDERS.get(provider_key.lower())
+    if not provider:
+        return None, None, None
+
+    api_key, _ = get_configuration_value([
+        {
+            "key": provider.api_key_env,
+            "default": os.environ.get(provider.api_key_env, None),
+        }
+    ])
+
+    if not api_key:
+        return None, None, None
+
+    # If no model specified, use provider's default
+    if not model:
+        model = provider.default_model
+
+    # Validate model is supported by provider
+    if model not in provider.models:
+        return None, None, None
+
+    return api_key, model, provider_key
+
+
+def get_llm_response(task, prompt, api_key: str, model: str, provider: str) -> Tuple[str | None, str | None]:
+    """Helper to get LLM completion response"""
+    final_text = task + "\n" + prompt
+    try:
+        # For Gemini, prepend provider name to model
+        if provider.lower() == "gemini":
+            model = f"gemini/{model}"
+
+        response = litellm.completion(
+            model=model,
+            messages=[{"role": "user", "content": final_text}],
+            api_key=api_key,
+        )
+        text = response.choices[0].message.content.strip()
+        return text, None
+    except Exception as e:
+        log_exception(e)
+        return None, "Error has occurred while generating reponse from OpenAI GPT"
 
 
 class GPTIntegrationEndpoint(BaseAPIView):
     @allow_permission([ROLE.ADMIN, ROLE.MEMBER])
     def post(self, request, slug, project_id):
-        OPENAI_API_KEY, GPT_ENGINE = get_configuration_value(
-            [
-                {
-                    "key": "OPENAI_API_KEY",
-                    "default": os.environ.get("OPENAI_API_KEY", None),
-                },
-                {
-                    "key": "GPT_ENGINE",
-                    "default": os.environ.get("GPT_ENGINE", "gpt-3.5-turbo"),
-                },
-            ]
-        )
+        api_key, model, provider = get_llm_config()
 
-        # Get the configuration value
-        # Check the keys
-        if not OPENAI_API_KEY or not GPT_ENGINE:
+        if not api_key or not model or not provider:
             return Response(
-                {"error": "OpenAI API key and engine is required"},
+                {"error": "LLM provider API key and model are required"},
                 status=status.HTTP_400_BAD_REQUEST,
             )
 
-        prompt = request.data.get("prompt", False)
         task = request.data.get("task", False)
-
         if not task:
             return Response(
                 {"error": "Task is required"}, status=status.HTTP_400_BAD_REQUEST
             )
 
-        final_text = task + "\n" + prompt
-
-        client = OpenAI(api_key=OPENAI_API_KEY)
-
-        response = client.chat.completions.create(
-            model=GPT_ENGINE, messages=[{"role": "user", "content": final_text}]
-        )
+        text, error = get_llm_response(task, request.data.get("prompt", False), api_key, model, provider)
+        if not text and error:
+            return Response(
+                {"error": "An internal error has occurred."},
+                status=status.HTTP_500_INTERNAL_SERVER_ERROR,
+            )
 
         workspace = Workspace.objects.get(slug=slug)
         project = Project.objects.get(pk=project_id)
 
-        text = response.choices[0].message.content.strip()
-        text_html = text.replace("\n", "<br/>")
         return Response(
             {
                 "response": text,
-                "response_html": text_html,
+                "response_html": text.replace("\n", "<br/>"),
                 "project_detail": ProjectLiteSerializer(project).data,
                 "workspace_detail": WorkspaceLiteSerializer(workspace).data,
             },
@@ -76,47 +170,33 @@ def post(self, request, slug, project_id):
 class WorkspaceGPTIntegrationEndpoint(BaseAPIView):
     @allow_permission(allowed_roles=[ROLE.ADMIN, ROLE.MEMBER], level="WORKSPACE")
     def post(self, request, slug):
-        OPENAI_API_KEY, GPT_ENGINE = get_configuration_value(
-            [
-                {
-                    "key": "OPENAI_API_KEY",
-                    "default": os.environ.get("OPENAI_API_KEY", None),
-                },
-                {
-                    "key": "GPT_ENGINE",
-                    "default": os.environ.get("GPT_ENGINE", "gpt-3.5-turbo"),
-                },
-            ]
-        )
-
-        # Get the configuration value
-        # Check the keys
-        if not OPENAI_API_KEY or not GPT_ENGINE:
+        api_key, model, provider = get_llm_config()
+
+        if not api_key or not model or not provider:
             return Response(
-                {"error": "OpenAI API key and engine is required"},
+                {"error": "LLM provider API key and model are required"},
                 status=status.HTTP_400_BAD_REQUEST,
             )
 
-        prompt = request.data.get("prompt", False)
         task = request.data.get("task", False)
-
         if not task:
             return Response(
                 {"error": "Task is required"}, status=status.HTTP_400_BAD_REQUEST
             )
 
-        final_text = task + "\n" + prompt
-
-        client = OpenAI(api_key=OPENAI_API_KEY)
-
-        response = client.chat.completions.create(
-            model=GPT_ENGINE, messages=[{"role": "user", "content": final_text}]
-        )
+        text, error = get_llm_response(task, request.data.get("prompt", False), api_key, model, provider)
+        if not text and error:
+            return Response(
+                {"error": "An internal error has occurred."},
+                status=status.HTTP_500_INTERNAL_SERVER_ERROR,
+            )
 
-        text = response.choices[0].message.content.strip()
-        text_html = text.replace("\n", "<br/>")
         return Response(
-            {"response": text, "response_html": text_html}, status=status.HTTP_200_OK
+            {
+                "response": text,
+                "response_html": text.replace("\n", "<br/>"),
+            },
+            status=status.HTTP_200_OK,
         )
 
 

apiserver/requirements/base.txt

@@ -37,7 +37,7 @@ uvicorn==0.29.0
 # sockets
 channels==4.1.0
 # ai
-openai==1.25.0
+litellm==1.51.0
 # slack
 slack-sdk==3.27.1
 # apm