Add LiteLLMComponent to the project

src/backend/langflow/components/llms/LiteLLM.py

@@ -0,0 +1,118 @@
+from dataclasses import Field
+from langflow import CustomComponent
+from typing import Optional, Union, Callable, Any, Dict
+from langflow.field_typing import BaseLanguageModel
+from langchain_community.chat_models import ChatLiteLLM
+import os
+
+
+class LiteLLMComponent(CustomComponent):
+    display_name = "LiteLLM"
+    description = "`LiteLLM` collection of large language models."
+    documentation = "https://python.langchain.com/docs/integrations/chat/litellm"
+
+    def build_config(self):
+        return {
+            "model": {
+                "display_name": "Model name",
+                "field_type": "str",
+                "advanced": False,
+                "required": True,
+                "info": "The name of the model to use. For example, `gpt-3.5-turbo`.",
+            },
+            "api_key": {
+                "display_name": "API key",
+                "field_type": "str",
+                "advanced": False,
+                "required": False,
+                "password": True,
+            },
+            "streaming": {
+                "display_name": "Streaming",
+                "field_type": "bool",
+                "advanced": True,
+                "required": False,
+                "default": True,
+            },
+            "temperature": {
+                "display_name": "Temperature",
+                "field_type": "float",
+                "advanced": True,
+                "required": False,
+                "default": 0.7,
+            },
+            "model_kwargs": {
+                "display_name": "Model kwargs",
+                "field_type": "dict",
+                "advanced": True,
+                "required": False,
+                "default": {},
+            },
+            "top_p": {
+                "display_name": "Top p",
+                "field_type": "float",
+                "advanced": True,
+                "required": False,
+            },
+            "top_k": {
+                "display_name": "Top k",
+                "field_type": "int",
+                "advanced": True,
+                "required": False,
+            },
+            "n": {
+                "display_name": "N",
+                "field_type": "int",
+                "advanced": True,
+                "required": False,
+                "info": "Number of chat completions to generate for each prompt. "
+                "Note that the API may not return the full n completions if duplicates are generated.",
+                "default": 1,
+            },
+            "max_tokens": {
+                "display_name": "Max tokens",
+                "field_type": "int",
+                "advanced": True,
+                "required": False,
+                "default": 256,
+                "info": "The maximum number of tokens to generate for each chat completion.",
+            },
+            "max_retries": {
+                "display_name": "Max retries",
+                "field_type": "int",
+                "advanced": True,
+                "required": False,
+                "default": 6,
+            },
+        }
+
+    def build(
+        self,
+        model: str,
+        api_key: str,
+        streaming: bool = True,
+        temperature: Optional[float] = 0.7,
+        model_kwargs: Optional[Dict[str, Any]] = {},
+        top_p: Optional[float] = None,
+        top_k: Optional[int] = None,
+        n: Optional[int] = 1,
+        max_tokens: Optional[int] = 256,
+        max_retries: Optional[int] = 6,
+    ) -> Union[BaseLanguageModel, Callable]:
+        if api_key:
+            if "perplexity" in model:
+                os.environ["PERPLEXITYAI_API_KEY"] = api_key
+            elif "replicate" in model:
+                os.environ["REPLICATE_API_KEY"] = api_key
+        LLM = ChatLiteLLM(
+            model=model,
+            streaming=streaming,
+            temperature=temperature,
+            model_kwargs=model_kwargs,
+            top_p=top_p,
+            top_k=top_k,
+            n=n,
+            max_tokens=max_tokens,
+            max_retries=max_retries,
+        )
+        return LLM