fix(llms/xai): forward tools, add XAIConfig, parse tool_calls (#5190)
This commit is contained in:
Aarkin Karnik
@@ -0,0 +1,55 @@
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from typing import Optional
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from mem0.configs.llms.base import BaseLlmConfig
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class XAIConfig(BaseLlmConfig):
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"""
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Configuration class for X.AI (Grok) provider parameters.
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Inherits from BaseLlmConfig and adds X.AI-specific settings.
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"""
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def __init__(
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self,
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# Base parameters
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model: Optional[str] = None,
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temperature: float = 0.1,
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api_key: Optional[str] = None,
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max_tokens: int = 2000,
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top_p: float = 0.1,
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top_k: int = 1,
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enable_vision: bool = False,
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vision_details: Optional[str] = "auto",
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http_client_proxies: Optional[dict] = None,
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# X.AI-specific parameters
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xai_base_url: Optional[str] = None,
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):
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"""
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Initialize X.AI configuration.
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Args:
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model: X.AI / Grok model to use, defaults to None
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temperature: Controls randomness, defaults to 0.1
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api_key: X.AI API key, defaults to None
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max_tokens: Maximum tokens to generate, defaults to 2000
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top_p: Nucleus sampling parameter, defaults to 0.1
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top_k: Top-k sampling parameter, defaults to 1
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enable_vision: Enable vision capabilities, defaults to False
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vision_details: Vision detail level, defaults to "auto"
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http_client_proxies: HTTP client proxy settings, defaults to None
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xai_base_url: X.AI API base URL, defaults to None
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"""
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super().__init__(
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model=model,
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temperature=temperature,
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api_key=api_key,
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max_tokens=max_tokens,
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top_p=top_p,
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top_k=top_k,
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enable_vision=enable_vision,
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vision_details=vision_details,
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http_client_proxies=http_client_proxies,
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)
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# X.AI-specific parameters
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self.xai_base_url = xai_base_url
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+71
-13
@@ -1,14 +1,36 @@
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import json
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import os
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from typing import Dict, List, Optional
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from typing import Dict, List, Optional, Union
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from openai import OpenAI
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from mem0.configs.llms.base import BaseLlmConfig
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from mem0.configs.llms.xai import XAIConfig
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from mem0.llms.base import LLMBase
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from mem0.memory.utils import extract_json
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class XAILLM(LLMBase):
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def __init__(self, config: Optional[BaseLlmConfig] = None):
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def __init__(self, config: Optional[Union[BaseLlmConfig, XAIConfig, Dict]] = None):
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# Convert to XAIConfig if needed
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if config is None:
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config = XAIConfig()
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elif isinstance(config, dict):
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config = XAIConfig(**config)
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elif isinstance(config, BaseLlmConfig) and not isinstance(config, XAIConfig):
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# Convert BaseLlmConfig to XAIConfig so xai_base_url is available
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config = XAIConfig(
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model=config.model,
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temperature=config.temperature,
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api_key=config.api_key,
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max_tokens=config.max_tokens,
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top_p=config.top_p,
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top_k=config.top_k,
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enable_vision=config.enable_vision,
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vision_details=config.vision_details,
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http_client_proxies=config.http_client,
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)
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super().__init__(config)
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if not self.config.model:
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@@ -18,35 +40,71 @@ class XAILLM(LLMBase):
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base_url = self.config.xai_base_url or os.getenv("XAI_API_BASE") or "https://api.x.ai/v1"
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self.client = OpenAI(api_key=api_key, base_url=base_url)
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def _parse_response(self, response, tools):
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"""
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Process the response based on whether tools are used or not.
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Args:
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response: The raw response from API.
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tools: The list of tools provided in the request.
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Returns:
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str or dict: The processed response.
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"""
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if tools:
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processed_response = {
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"content": response.choices[0].message.content,
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"tool_calls": [],
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}
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if response.choices[0].message.tool_calls:
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for tool_call in response.choices[0].message.tool_calls:
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processed_response["tool_calls"].append(
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{
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"name": tool_call.function.name,
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"arguments": json.loads(extract_json(tool_call.function.arguments)),
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}
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)
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return processed_response
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else:
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return response.choices[0].message.content
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def generate_response(
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self,
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messages: List[Dict[str, str]],
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response_format=None,
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tools: Optional[List[Dict]] = None,
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tool_choice: str = "auto",
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**kwargs,
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):
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"""
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Generate a response based on the given messages using XAI.
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Generate a response based on the given messages using X.AI (Grok).
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Args:
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messages (list): List of message dicts containing 'role' and 'content'.
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response_format (str or object, optional): Format of the response. Defaults to "text".
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response_format (str or object, optional): Format of the response. Defaults to None.
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tools (list, optional): List of tools that the model can call. Defaults to None.
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tool_choice (str, optional): Tool choice method. Defaults to "auto".
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**kwargs: Additional X.AI-specific parameters.
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Returns:
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str: The generated response.
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str or dict: The generated response. A string when tools are not requested;
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a dict ``{"content": ..., "tool_calls": [...]}`` when tools are requested.
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"""
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params = {
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"model": self.config.model,
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"messages": messages,
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"temperature": self.config.temperature,
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"max_tokens": self.config.max_tokens,
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"top_p": self.config.top_p,
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}
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params = self._get_supported_params(messages=messages, **kwargs)
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params.update(
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{
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"model": self.config.model,
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"messages": messages,
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}
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)
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if response_format:
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params["response_format"] = response_format
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if tools:
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params["tools"] = tools
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params["tool_choice"] = tool_choice
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response = self.client.chat.completions.create(**params)
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return response.choices[0].message.content
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return self._parse_response(response, tools)
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@@ -7,17 +7,18 @@ from mem0.configs.llms.aws_bedrock import AWSBedrockConfig
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from mem0.configs.llms.azure import AzureOpenAIConfig
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from mem0.configs.llms.base import BaseLlmConfig
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from mem0.configs.llms.deepseek import DeepSeekConfig
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from mem0.configs.llms.minimax import MinimaxConfig
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from mem0.configs.llms.lmstudio import LMStudioConfig
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from mem0.configs.llms.minimax import MinimaxConfig
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from mem0.configs.llms.ollama import OllamaConfig
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from mem0.configs.llms.openai import OpenAIConfig
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from mem0.configs.llms.vllm import VllmConfig
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from mem0.configs.llms.xai import XAIConfig
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from mem0.configs.rerankers.base import BaseRerankerConfig
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from mem0.configs.rerankers.cohere import CohereRerankerConfig
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from mem0.configs.rerankers.huggingface import HuggingFaceRerankerConfig
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from mem0.configs.rerankers.llm import LLMRerankerConfig
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from mem0.configs.rerankers.sentence_transformer import SentenceTransformerRerankerConfig
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from mem0.configs.rerankers.zero_entropy import ZeroEntropyRerankerConfig
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from mem0.configs.rerankers.llm import LLMRerankerConfig
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from mem0.configs.rerankers.huggingface import HuggingFaceRerankerConfig
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from mem0.embeddings.mock import MockEmbeddings
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@@ -48,7 +49,7 @@ class LlmFactory:
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"gemini": ("mem0.llms.gemini.GeminiLLM", BaseLlmConfig),
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"deepseek": ("mem0.llms.deepseek.DeepSeekLLM", DeepSeekConfig),
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"minimax": ("mem0.llms.minimax.MiniMaxLLM", MinimaxConfig),
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"xai": ("mem0.llms.xai.XAILLM", BaseLlmConfig),
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"xai": ("mem0.llms.xai.XAILLM", XAIConfig),
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"sarvam": ("mem0.llms.sarvam.SarvamLLM", BaseLlmConfig),
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"lmstudio": ("mem0.llms.lmstudio.LMStudioLLM", LMStudioConfig),
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"vllm": ("mem0.llms.vllm.VllmLLM", VllmConfig),
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@@ -209,7 +210,6 @@ class VectorStoreFactory:
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return instance
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class RerankerFactory:
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"""
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Factory for creating reranker instances with appropriate configurations.
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@@ -219,7 +219,10 @@ class RerankerFactory:
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# Provider mappings with their config classes
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provider_to_class = {
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"cohere": ("mem0.reranker.cohere_reranker.CohereReranker", CohereRerankerConfig),
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"sentence_transformer": ("mem0.reranker.sentence_transformer_reranker.SentenceTransformerReranker", SentenceTransformerRerankerConfig),
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"sentence_transformer": (
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"mem0.reranker.sentence_transformer_reranker.SentenceTransformerReranker",
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SentenceTransformerRerankerConfig,
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),
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"zero_entropy": ("mem0.reranker.zero_entropy_reranker.ZeroEntropyReranker", ZeroEntropyRerankerConfig),
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"llm_reranker": ("mem0.reranker.llm_reranker.LLMReranker", LLMRerankerConfig),
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"huggingface": ("mem0.reranker.huggingface_reranker.HuggingFaceReranker", HuggingFaceRerankerConfig),
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@@ -0,0 +1,209 @@
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import os
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from unittest.mock import Mock, patch
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import pytest
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from mem0.configs.llms.base import BaseLlmConfig
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from mem0.configs.llms.xai import XAIConfig
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from mem0.llms.xai import XAILLM
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from mem0.utils.factory import LlmFactory
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@pytest.fixture
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def mock_xai_client():
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with patch("mem0.llms.xai.OpenAI") as mock_openai:
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mock_client = Mock()
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mock_openai.return_value = mock_client
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yield mock_client
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def test_xai_llm_base_url():
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# case1: default
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config = XAIConfig(model="grok-2-latest", api_key="api_key")
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llm = XAILLM(config)
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assert str(llm.client.base_url) == "https://api.x.ai/v1/"
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# case2: XAI_API_BASE env var
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os.environ["XAI_API_BASE"] = "https://api.provider.com/v1"
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config = XAIConfig(model="grok-2-latest", api_key="api_key")
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llm = XAILLM(config)
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assert str(llm.client.base_url) == "https://api.provider.com/v1/"
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# case3: config.xai_base_url wins over env
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config = XAIConfig(
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model="grok-2-latest",
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api_key="api_key",
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xai_base_url="https://api.config.com/v1",
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)
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llm = XAILLM(config)
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assert str(llm.client.base_url) == "https://api.config.com/v1/"
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def test_xai_accepts_base_llm_config():
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# Used to AttributeError on self.config.xai_base_url because the factory
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# wired XAI with plain BaseLlmConfig.
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llm = XAILLM(BaseLlmConfig(model="grok-2-latest", api_key="k"))
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assert isinstance(llm.config, XAIConfig)
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assert llm.config.xai_base_url is None
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def test_generate_response_without_tools(mock_xai_client):
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config = XAIConfig(model="grok-2-latest", temperature=0.7, max_tokens=100, top_p=1.0, api_key="api_key")
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llm = XAILLM(config)
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messages = [
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{"role": "system", "content": "You are a helpful assistant."},
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{"role": "user", "content": "Hello, how are you?"},
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]
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mock_response = Mock()
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mock_response.choices = [Mock(message=Mock(content="I'm doing well, thank you for asking!"))]
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mock_xai_client.chat.completions.create.return_value = mock_response
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response = llm.generate_response(messages)
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mock_xai_client.chat.completions.create.assert_called_once_with(
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model="grok-2-latest",
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messages=messages,
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temperature=0.7,
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max_tokens=100,
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top_p=1.0,
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)
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assert response == "I'm doing well, thank you for asking!"
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def test_generate_response_with_tools(mock_xai_client):
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config = XAIConfig(model="grok-2-latest", temperature=0.7, max_tokens=100, top_p=1.0, api_key="api_key")
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llm = XAILLM(config)
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messages = [
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{"role": "system", "content": "You are a helpful assistant."},
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{"role": "user", "content": "Add a new memory: Today is a sunny day."},
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]
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tools = [
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{
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"type": "function",
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"function": {
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"name": "add_memory",
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"description": "Add a memory",
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"parameters": {
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"type": "object",
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"properties": {"data": {"type": "string", "description": "Data to add to memory"}},
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"required": ["data"],
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},
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},
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}
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]
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mock_response = Mock()
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mock_message = Mock()
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mock_message.content = "I've added the memory for you."
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mock_tool_call = Mock()
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mock_tool_call.function.name = "add_memory"
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mock_tool_call.function.arguments = '{"data": "Today is a sunny day."}'
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mock_message.tool_calls = [mock_tool_call]
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mock_response.choices = [Mock(message=mock_message)]
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mock_xai_client.chat.completions.create.return_value = mock_response
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response = llm.generate_response(messages, tools=tools)
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mock_xai_client.chat.completions.create.assert_called_once_with(
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model="grok-2-latest",
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messages=messages,
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temperature=0.7,
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max_tokens=100,
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top_p=1.0,
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tools=tools,
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tool_choice="auto",
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)
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assert response["content"] == "I've added the memory for you."
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assert response["tool_calls"] == [{"name": "add_memory", "arguments": {"data": "Today is a sunny day."}}]
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def test_empty_tools_list_not_forwarded(mock_xai_client):
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# tools=[] would otherwise get rejected by some OpenAI-compatible backends
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config = XAIConfig(model="grok-2-latest", api_key="api_key")
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llm = XAILLM(config)
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mock_response = Mock()
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mock_response.choices = [Mock(message=Mock(content="hello"))]
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mock_xai_client.chat.completions.create.return_value = mock_response
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response = llm.generate_response([{"role": "user", "content": "hi"}], tools=[])
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call_kwargs = mock_xai_client.chat.completions.create.call_args.kwargs
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assert "tools" not in call_kwargs
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assert "tool_choice" not in call_kwargs
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assert response == "hello"
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def test_tools_requested_but_model_returns_no_calls(mock_xai_client):
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# Model can decline to call any tool even when offered
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config = XAIConfig(model="grok-2-latest", api_key="api_key")
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llm = XAILLM(config)
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tools = [{"type": "function", "function": {"name": "f", "parameters": {"type": "object", "properties": {}}}}]
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mock_message = Mock(content="just a chat reply")
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mock_message.tool_calls = None
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mock_xai_client.chat.completions.create.return_value = Mock(choices=[Mock(message=mock_message)])
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response = llm.generate_response([{"role": "user", "content": "x"}], tools=tools)
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assert response == {"content": "just a chat reply", "tool_calls": []}
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def test_generate_response_with_response_format(mock_xai_client):
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config = XAIConfig(model="grok-2-latest", temperature=0.7, max_tokens=100, top_p=1.0, api_key="api_key")
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llm = XAILLM(config)
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messages = [
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{"role": "system", "content": "You are a memory extraction assistant."},
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{"role": "user", "content": "I like hiking on weekends."},
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]
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mock_response = Mock()
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mock_response.choices = [Mock(message=Mock(content='{"facts": ["User likes hiking on weekends"]}'))]
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mock_xai_client.chat.completions.create.return_value = mock_response
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response = llm.generate_response(messages, response_format={"type": "json_object"})
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mock_xai_client.chat.completions.create.assert_called_once_with(
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model="grok-2-latest",
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messages=messages,
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temperature=0.7,
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max_tokens=100,
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top_p=1.0,
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response_format={"type": "json_object"},
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)
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assert response == '{"facts": ["User likes hiking on weekends"]}'
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def test_generate_response_without_response_format(mock_xai_client):
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config = XAIConfig(model="grok-2-latest", api_key="api_key")
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llm = XAILLM(config)
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mock_response = Mock()
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mock_response.choices = [Mock(message=Mock(content="hi"))]
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mock_xai_client.chat.completions.create.return_value = mock_response
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llm.generate_response([{"role": "user", "content": "hi"}])
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call_kwargs = mock_xai_client.chat.completions.create.call_args.kwargs
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assert "response_format" not in call_kwargs
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def test_factory_creates_xai_from_dict():
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with patch("mem0.llms.xai.OpenAI") as mock_openai:
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mock_openai.return_value = Mock()
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llm = LlmFactory.create(
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"xai",
|
||||
{"model": "grok-2-latest", "api_key": "k", "xai_base_url": "https://example.com/v1"},
|
||||
)
|
||||
assert isinstance(llm, XAILLM)
|
||||
assert isinstance(llm.config, XAIConfig)
|
||||
assert llm.config.xai_base_url == "https://example.com/v1"
|
||||
|
||||
|
||||
def test_factory_creates_xai_from_base_config():
|
||||
# Legacy callers still hand the factory a plain BaseLlmConfig
|
||||
with patch("mem0.llms.xai.OpenAI") as mock_openai:
|
||||
mock_openai.return_value = Mock()
|
||||
llm = LlmFactory.create("xai", BaseLlmConfig(model="grok-2-latest", api_key="k"))
|
||||
assert isinstance(llm.config, XAIConfig)
|
||||
Reference in New Issue
Block a user