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Update llm/llm_spark.py
Browse files- llm/llm_spark.py +115 -115
llm/llm_spark.py
CHANGED
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@@ -1,116 +1,116 @@
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"""
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Spark LLM Implementation
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"""
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import os
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import httpx
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import json
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from typing import Dict, List, Any, AsyncIterator
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from llm_interface import LLMInterface
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from utils.logger import log_info, log_error, log_warning, log_debug
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# Get timeout from environment
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DEFAULT_LLM_TIMEOUT = int(os.getenv("LLM_TIMEOUT_SECONDS", "60"))
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MAX_RESPONSE_LENGTH = int(os.getenv("LLM_MAX_RESPONSE_LENGTH", "4096"))
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class SparkLLM(LLMInterface):
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"""Spark LLM integration with improved error handling"""
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def __init__(self, spark_endpoint: str, spark_token: str, provider_variant: str = "cloud", settings: Dict[str, Any] = None):
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super().__init__(settings)
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self.spark_endpoint = spark_endpoint.rstrip("/")
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self.spark_token = spark_token
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self.provider_variant = provider_variant
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self.timeout = self.settings.get("timeout", DEFAULT_LLM_TIMEOUT)
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log_info(f"π SparkLLM initialized", endpoint=self.spark_endpoint, timeout=self.timeout)
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async def generate(self, system_prompt: str, user_input: str, context: List[Dict]) -> str:
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"""Generate response with improved error handling and streaming support"""
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headers = {
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"Authorization": f"Bearer {self.spark_token}",
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"Content-Type": "application/json"
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}
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# Build context messages
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messages = []
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if system_prompt:
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messages.append({
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"role": "system",
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"content": system_prompt
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})
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for msg in context[-10:]: # Last 10 messages for context
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messages.append({
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"role": msg.get("role", "user"),
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"content": msg.get("content", "")
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})
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messages.append({
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"role": "user",
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"content": user_input
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})
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payload = {
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"messages": messages,
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"mode": self.provider_variant,
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"max_tokens": self.settings.get("max_tokens", 2048),
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"temperature": self.settings.get("temperature", 0.7),
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"stream": False # For now, no streaming
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}
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try:
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async with httpx.AsyncClient(timeout=self.timeout) as client:
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with LogTimer(f"Spark LLM request"):
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response = await client.post(
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f"{self.spark_endpoint}/generate",
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json=payload,
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headers=headers
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)
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# Check for rate limiting
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if response.status_code == 429:
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retry_after = response.headers.get("Retry-After", "60")
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log_warning(f"Rate limited by Spark", retry_after=retry_after)
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raise httpx.HTTPStatusError(
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f"Rate limited. Retry after {retry_after}s",
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request=response.request,
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response=response
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)
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response.raise_for_status()
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result = response.json()
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# Extract response
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content = result.get("model_answer", "")
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# Check response length
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if len(content) > MAX_RESPONSE_LENGTH:
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log_warning(f"Response exceeded max length, truncating",
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original_length=len(content),
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max_length=MAX_RESPONSE_LENGTH)
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content = content[:MAX_RESPONSE_LENGTH] + "..."
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return content
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except httpx.TimeoutException:
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log_error(f"Spark request timed out", timeout=self.timeout)
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raise
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except httpx.HTTPStatusError as e:
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log_error(f"Spark HTTP error",
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status_code=e.response.status_code,
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response=e.response.text[:500])
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raise
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except Exception as e:
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log_error("Spark unexpected error", error=str(e))
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raise
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def get_provider_name(self) -> str:
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return f"spark-{self.provider_variant}"
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def get_model_info(self) -> Dict[str, Any]:
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return {
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"provider": "spark",
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"variant": self.provider_variant,
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"endpoint": self.spark_endpoint,
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"max_tokens": self.settings.get("max_tokens", 2048),
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"temperature": self.settings.get("temperature", 0.7)
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}
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"""
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Spark LLM Implementation
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"""
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import os
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import httpx
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import json
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from typing import Dict, List, Any, AsyncIterator
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from .llm_interface import LLMInterface
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from utils.logger import log_info, log_error, log_warning, log_debug
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+
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# Get timeout from environment
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DEFAULT_LLM_TIMEOUT = int(os.getenv("LLM_TIMEOUT_SECONDS", "60"))
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MAX_RESPONSE_LENGTH = int(os.getenv("LLM_MAX_RESPONSE_LENGTH", "4096"))
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class SparkLLM(LLMInterface):
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"""Spark LLM integration with improved error handling"""
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def __init__(self, spark_endpoint: str, spark_token: str, provider_variant: str = "cloud", settings: Dict[str, Any] = None):
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super().__init__(settings)
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self.spark_endpoint = spark_endpoint.rstrip("/")
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self.spark_token = spark_token
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self.provider_variant = provider_variant
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self.timeout = self.settings.get("timeout", DEFAULT_LLM_TIMEOUT)
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log_info(f"π SparkLLM initialized", endpoint=self.spark_endpoint, timeout=self.timeout)
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async def generate(self, system_prompt: str, user_input: str, context: List[Dict]) -> str:
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"""Generate response with improved error handling and streaming support"""
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headers = {
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"Authorization": f"Bearer {self.spark_token}",
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"Content-Type": "application/json"
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}
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# Build context messages
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messages = []
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if system_prompt:
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messages.append({
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"role": "system",
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"content": system_prompt
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})
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for msg in context[-10:]: # Last 10 messages for context
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messages.append({
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"role": msg.get("role", "user"),
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"content": msg.get("content", "")
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})
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messages.append({
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"role": "user",
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"content": user_input
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})
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payload = {
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"messages": messages,
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"mode": self.provider_variant,
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"max_tokens": self.settings.get("max_tokens", 2048),
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"temperature": self.settings.get("temperature", 0.7),
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"stream": False # For now, no streaming
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}
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try:
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async with httpx.AsyncClient(timeout=self.timeout) as client:
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with LogTimer(f"Spark LLM request"):
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response = await client.post(
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f"{self.spark_endpoint}/generate",
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json=payload,
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headers=headers
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)
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# Check for rate limiting
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if response.status_code == 429:
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retry_after = response.headers.get("Retry-After", "60")
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log_warning(f"Rate limited by Spark", retry_after=retry_after)
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raise httpx.HTTPStatusError(
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f"Rate limited. Retry after {retry_after}s",
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request=response.request,
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response=response
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)
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response.raise_for_status()
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result = response.json()
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# Extract response
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content = result.get("model_answer", "")
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# Check response length
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if len(content) > MAX_RESPONSE_LENGTH:
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log_warning(f"Response exceeded max length, truncating",
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original_length=len(content),
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max_length=MAX_RESPONSE_LENGTH)
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content = content[:MAX_RESPONSE_LENGTH] + "..."
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return content
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except httpx.TimeoutException:
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log_error(f"Spark request timed out", timeout=self.timeout)
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raise
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except httpx.HTTPStatusError as e:
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log_error(f"Spark HTTP error",
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status_code=e.response.status_code,
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response=e.response.text[:500])
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raise
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except Exception as e:
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log_error("Spark unexpected error", error=str(e))
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raise
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def get_provider_name(self) -> str:
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return f"spark-{self.provider_variant}"
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def get_model_info(self) -> Dict[str, Any]:
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return {
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"provider": "spark",
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"variant": self.provider_variant,
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"endpoint": self.spark_endpoint,
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"max_tokens": self.settings.get("max_tokens", 2048),
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"temperature": self.settings.get("temperature", 0.7)
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}
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