🚀 Maaany things

src/engines/LLMEngine/AnthropicLLMEngine.py

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+import anthropic
+import gradio as gr
+import orjson
+
+from .BaseLLMEngine import BaseLLMEngine
+
+# Assuming these are the models supported by Anthropics that you wish to include
+ANTHROPIC_POSSIBLE_MODELS = [
+    "claude-2.1",
+    # Add more models as needed
+]
+
+class AnthropicsLLMEngine(BaseLLMEngine):
+    num_options = 1
+    name = "Anthropics"
+    description = "Anthropics language model engine."
+
+    def __init__(self, options: list) -> None:
+        self.model = options[0]
+        self.client = anthropic.Anthropic(api_key="YourAnthropicAPIKeyHere")  # Ensure API key is securely managed
+        super().__init__()
+
+    def generate(self, system_prompt: str, chat_prompt: str, max_tokens: int = 1024, temperature: float = 1.0, json_mode: bool = False, top_p: float = 1, frequency_penalty: float = 0, presence_penalty: float = 0) -> str | dict:
+        # Note: Adjust the parameters as per Anthropics API capabilities
+        prompt = f"""{anthropic.HUMAN_PROMPT} {system_prompt} {anthropic.HUMAN_PROMPT} {chat_prompt} {anthropic.AI_PROMPT}"""
+        if json_mode:
+            # anthopic does not officially support JSON mode, but we can bias the output towards a JSON-like format
+            prompt += " {"
+        response: anthropic.types.Completion = self.client.completions.create(
+            max_tokens_to_sample=max_tokens,
+            prompt=prompt,
+            model=self.model,
+            top_p=top_p,
+            temperature=temperature,
+            frequency_penalty=frequency_penalty,
+        )
+
+        content = response.completion
+        if json_mode:
+            #we add back the opening curly brace wich is not included in the response since it is in the prompt
+            content = "{" + content
+            #we remove everything after the last closing curly brace
+            content = content[:content.rfind("}") + 1]
+            return orjson.loads(content)
+        else:
+            return content
+
+    @classmethod
+    def get_options(cls) -> list:
+        return [
+            gr.Dropdown(
+                label="Model",
+                choices=ANTHROPIC_POSSIBLE_MODELS,
+                max_choices=1,
+                value=ANTHROPIC_POSSIBLE_MODELS[0]
+            )
+        ]