first commit

llms/vllm.py

@@ -0,0 +1,107 @@
+import json
+import os
+from typing import Dict, List, Optional, Union
+
+from openai import OpenAI
+
+from mem0.configs.llms.base import BaseLlmConfig
+from mem0.configs.llms.vllm import VllmConfig
+from mem0.llms.base import LLMBase
+from mem0.memory.utils import extract_json
+
+
+class VllmLLM(LLMBase):
+    def __init__(self, config: Optional[Union[BaseLlmConfig, VllmConfig, Dict]] = None):
+        # Convert to VllmConfig if needed
+        if config is None:
+            config = VllmConfig()
+        elif isinstance(config, dict):
+            config = VllmConfig(**config)
+        elif isinstance(config, BaseLlmConfig) and not isinstance(config, VllmConfig):
+            # Convert BaseLlmConfig to VllmConfig
+            config = VllmConfig(
+                model=config.model,
+                temperature=config.temperature,
+                api_key=config.api_key,
+                max_tokens=config.max_tokens,
+                top_p=config.top_p,
+                top_k=config.top_k,
+                enable_vision=config.enable_vision,
+                vision_details=config.vision_details,
+                http_client_proxies=config.http_client,
+            )
+
+        super().__init__(config)
+
+        if not self.config.model:
+            self.config.model = "Qwen/Qwen2.5-32B-Instruct"
+
+        self.config.api_key = self.config.api_key or os.getenv("VLLM_API_KEY") or "vllm-api-key"
+        base_url = self.config.vllm_base_url or os.getenv("VLLM_BASE_URL")
+        self.client = OpenAI(api_key=self.config.api_key, base_url=base_url)
+
+    def _parse_response(self, response, tools):
+        """
+        Process the response based on whether tools are used or not.
+
+        Args:
+            response: The raw response from API.
+            tools: The list of tools provided in the request.
+
+        Returns:
+            str or dict: The processed response.
+        """
+        if tools:
+            processed_response = {
+                "content": response.choices[0].message.content,
+                "tool_calls": [],
+            }
+
+            if response.choices[0].message.tool_calls:
+                for tool_call in response.choices[0].message.tool_calls:
+                    processed_response["tool_calls"].append(
+                        {
+                            "name": tool_call.function.name,
+                            "arguments": json.loads(extract_json(tool_call.function.arguments)),
+                        }
+                    )
+
+            return processed_response
+        else:
+            return response.choices[0].message.content
+
+    def generate_response(
+        self,
+        messages: List[Dict[str, str]],
+        response_format=None,
+        tools: Optional[List[Dict]] = None,
+        tool_choice: str = "auto",
+        **kwargs,
+    ):
+        """
+        Generate a response based on the given messages using vLLM.
+
+        Args:
+            messages (list): List of message dicts containing 'role' and 'content'.
+            response_format (str or object, optional): Format of the response. Defaults to "text".
+            tools (list, optional): List of tools that the model can call. Defaults to None.
+            tool_choice (str, optional): Tool choice method. Defaults to "auto".
+            **kwargs: Additional vLLM-specific parameters.
+
+        Returns:
+            str: The generated response.
+        """
+        params = self._get_supported_params(messages=messages, **kwargs)
+        params.update(
+            {
+                "model": self.config.model,
+                "messages": messages,
+            }
+        )
+
+        if tools:
+            params["tools"] = tools
+            params["tool_choice"] = tool_choice
+
+        response = self.client.chat.completions.create(**params)
+        return self._parse_response(response, tools)