54df6310c5
- Fix OpenMP library linking in DiskANN CMake configuration - Add timeout protection for HuggingFace model loading to prevent hangs - Improve embedding server process termination with better timeouts - Make DiskANN backend default enabled alongside HNSW - Update documentation to reflect both backends included by default
745 lines
28 KiB
Python
745 lines
28 KiB
Python
#!/usr/bin/env python3
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"""
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This file contains the chat generation logic for the LEANN project,
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supporting different backends like Ollama, Hugging Face Transformers, and a simulation mode.
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"""
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import difflib
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import logging
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import os
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from abc import ABC, abstractmethod
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from typing import Any
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import torch
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# Configure logging
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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def check_ollama_models() -> list[str]:
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"""Check available Ollama models and return a list"""
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try:
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import requests
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response = requests.get("http://localhost:11434/api/tags", timeout=5)
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if response.status_code == 200:
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data = response.json()
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return [model["name"] for model in data.get("models", [])]
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return []
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except Exception:
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return []
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def check_ollama_model_exists_remotely(model_name: str) -> tuple[bool, list[str]]:
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"""Check if a model exists in Ollama's remote library and return available tags
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Returns:
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(model_exists, available_tags): bool and list of matching tags
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"""
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try:
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import re
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import requests
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# Split model name and tag
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if ":" in model_name:
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base_model, requested_tag = model_name.split(":", 1)
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else:
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base_model, requested_tag = model_name, None
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# First check if base model exists in library
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library_response = requests.get("https://ollama.com/library", timeout=8)
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if library_response.status_code != 200:
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return True, [] # Assume exists if can't check
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# Extract model names from library page
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models_in_library = re.findall(r'href="/library/([^"]+)"', library_response.text)
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if base_model not in models_in_library:
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return False, [] # Base model doesn't exist
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# If base model exists, get available tags
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tags_response = requests.get(f"https://ollama.com/library/{base_model}/tags", timeout=8)
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if tags_response.status_code != 200:
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return True, [] # Base model exists but can't get tags
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# Extract tags for this model - be more specific to avoid HTML artifacts
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tag_pattern = rf"{re.escape(base_model)}:[a-zA-Z0-9\.\-_]+"
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raw_tags = re.findall(tag_pattern, tags_response.text)
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# Clean up tags - remove HTML artifacts and duplicates
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available_tags = []
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seen = set()
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for tag in raw_tags:
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# Skip if it looks like HTML (contains < or >)
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if "<" in tag or ">" in tag:
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continue
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if tag not in seen:
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seen.add(tag)
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available_tags.append(tag)
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# Check if exact model exists
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if requested_tag is None:
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# User just requested base model, suggest tags
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return True, available_tags[:10] # Return up to 10 tags
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else:
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exact_match = model_name in available_tags
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return exact_match, available_tags[:10]
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except Exception:
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pass
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# If scraping fails, assume model might exist (don't block user)
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return True, []
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def search_ollama_models_fuzzy(query: str, available_models: list[str]) -> list[str]:
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"""Use intelligent fuzzy search for Ollama models"""
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if not available_models:
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return []
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query_lower = query.lower()
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suggestions = []
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# 1. Exact matches first
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exact_matches = [m for m in available_models if query_lower == m.lower()]
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suggestions.extend(exact_matches)
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# 2. Starts with query
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starts_with = [
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m for m in available_models if m.lower().startswith(query_lower) and m not in suggestions
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]
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suggestions.extend(starts_with)
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# 3. Contains query
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contains = [m for m in available_models if query_lower in m.lower() and m not in suggestions]
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suggestions.extend(contains)
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# 4. Base model name matching (remove version numbers)
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def get_base_name(model_name: str) -> str:
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"""Extract base name without version (e.g., 'llama3:8b' -> 'llama3')"""
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return model_name.split(":")[0].split("-")[0]
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query_base = get_base_name(query_lower)
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base_matches = [
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m
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for m in available_models
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if get_base_name(m.lower()) == query_base and m not in suggestions
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]
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suggestions.extend(base_matches)
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# 5. Family/variant matching
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model_families = {
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"llama": ["llama2", "llama3", "alpaca", "vicuna", "codellama"],
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"qwen": ["qwen", "qwen2", "qwen3"],
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"gemma": ["gemma", "gemma2"],
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"phi": ["phi", "phi2", "phi3"],
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"mistral": ["mistral", "mixtral", "openhermes"],
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"dolphin": ["dolphin", "openchat"],
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"deepseek": ["deepseek", "deepseek-coder"],
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}
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query_family = None
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for family, variants in model_families.items():
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if any(variant in query_lower for variant in variants):
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query_family = family
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break
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if query_family:
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family_variants = model_families[query_family]
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family_matches = [
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m
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for m in available_models
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if any(variant in m.lower() for variant in family_variants) and m not in suggestions
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]
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suggestions.extend(family_matches)
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# 6. Use difflib for remaining fuzzy matches
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remaining_models = [m for m in available_models if m not in suggestions]
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difflib_matches = difflib.get_close_matches(query_lower, remaining_models, n=3, cutoff=0.4)
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suggestions.extend(difflib_matches)
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return suggestions[:8] # Return top 8 suggestions
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# Remove this function entirely - we don't need external API calls for Ollama
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# Remove this too - no need for fallback
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def suggest_similar_models(invalid_model: str, available_models: list[str]) -> list[str]:
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"""Use difflib to find similar model names"""
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if not available_models:
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return []
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# Get close matches using fuzzy matching
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suggestions = difflib.get_close_matches(invalid_model, available_models, n=3, cutoff=0.3)
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return suggestions
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def check_hf_model_exists(model_name: str) -> bool:
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"""Quick check if HuggingFace model exists without downloading"""
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try:
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from huggingface_hub import model_info
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model_info(model_name)
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return True
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except Exception:
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return False
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def get_popular_hf_models() -> list[str]:
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"""Return a list of popular HuggingFace models for suggestions"""
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try:
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from huggingface_hub import list_models
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# Get popular text-generation models, sorted by downloads
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models = list_models(
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filter="text-generation",
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sort="downloads",
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direction=-1,
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limit=20, # Get top 20 most downloaded
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)
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# Extract model names and filter for chat/conversation models
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model_names = []
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chat_keywords = ["chat", "instruct", "dialog", "conversation", "assistant"]
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for model in models:
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model_name = model.id if hasattr(model, "id") else str(model)
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# Prioritize models with chat-related keywords
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if any(keyword in model_name.lower() for keyword in chat_keywords):
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model_names.append(model_name)
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elif len(model_names) < 10: # Fill up with other popular models
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model_names.append(model_name)
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return model_names[:10] if model_names else _get_fallback_hf_models()
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except Exception:
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# Fallback to static list if API call fails
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return _get_fallback_hf_models()
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def _get_fallback_hf_models() -> list[str]:
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"""Fallback list of popular HuggingFace models"""
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return [
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"microsoft/DialoGPT-medium",
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"microsoft/DialoGPT-large",
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"facebook/blenderbot-400M-distill",
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"microsoft/phi-2",
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"deepseek-ai/deepseek-llm-7b-chat",
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"microsoft/DialoGPT-small",
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"facebook/blenderbot_small-90M",
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"microsoft/phi-1_5",
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"facebook/opt-350m",
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"EleutherAI/gpt-neo-1.3B",
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]
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def search_hf_models_fuzzy(query: str, limit: int = 10) -> list[str]:
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"""Use HuggingFace Hub's native fuzzy search for model suggestions"""
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try:
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from huggingface_hub import list_models
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# HF Hub's search is already fuzzy! It handles typos and partial matches
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models = list_models(
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search=query,
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filter="text-generation",
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sort="downloads",
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direction=-1,
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limit=limit,
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)
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model_names = [model.id if hasattr(model, "id") else str(model) for model in models]
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# If direct search doesn't return enough results, try some variations
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if len(model_names) < 3:
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# Try searching for partial matches or common variations
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variations = []
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# Extract base name (e.g., "gpt3" from "gpt-3.5")
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base_query = query.lower().replace("-", "").replace(".", "").replace("_", "")
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if base_query != query.lower():
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variations.append(base_query)
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# Try common model name patterns
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if "gpt" in query.lower():
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variations.extend(["gpt2", "gpt-neo", "gpt-j", "dialoGPT"])
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elif "llama" in query.lower():
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variations.extend(["llama2", "alpaca", "vicuna"])
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elif "bert" in query.lower():
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variations.extend(["roberta", "distilbert", "albert"])
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# Search with variations
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for var in variations[:2]: # Limit to 2 variations to avoid too many API calls
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try:
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var_models = list_models(
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search=var,
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filter="text-generation",
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sort="downloads",
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direction=-1,
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limit=3,
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)
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var_names = [
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model.id if hasattr(model, "id") else str(model) for model in var_models
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]
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model_names.extend(var_names)
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except Exception:
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continue
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# Remove duplicates while preserving order
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seen = set()
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unique_models = []
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for model in model_names:
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if model not in seen:
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seen.add(model)
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unique_models.append(model)
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return unique_models[:limit]
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except Exception:
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# If search fails, return empty list
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return []
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def search_hf_models(query: str, limit: int = 10) -> list[str]:
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"""Simple search for HuggingFace models based on query (kept for backward compatibility)"""
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return search_hf_models_fuzzy(query, limit)
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def validate_model_and_suggest(model_name: str, llm_type: str) -> str | None:
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"""Validate model name and provide suggestions if invalid"""
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if llm_type == "ollama":
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available_models = check_ollama_models()
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if available_models and model_name not in available_models:
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error_msg = f"Model '{model_name}' not found in your local Ollama installation."
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# Check if the model exists remotely and get available tags
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model_exists_remotely, available_tags = check_ollama_model_exists_remotely(model_name)
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if model_exists_remotely and model_name in available_tags:
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# Exact model exists remotely - suggest pulling it
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error_msg += "\n\nTo install the requested model:\n"
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error_msg += f" ollama pull {model_name}\n"
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# Show local alternatives
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suggestions = search_ollama_models_fuzzy(model_name, available_models)
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if suggestions:
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error_msg += "\nOr use one of these similar installed models:\n"
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for i, suggestion in enumerate(suggestions, 1):
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error_msg += f" {i}. {suggestion}\n"
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elif model_exists_remotely and available_tags:
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# Base model exists but requested tag doesn't - suggest correct tags
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base_model = model_name.split(":")[0]
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requested_tag = model_name.split(":", 1)[1] if ":" in model_name else None
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error_msg += (
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f"\n\nModel '{base_model}' exists, but tag '{requested_tag}' is not available."
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)
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error_msg += f"\n\nAvailable {base_model} models you can install:\n"
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for i, tag in enumerate(available_tags[:8], 1):
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error_msg += f" {i}. ollama pull {tag}\n"
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if len(available_tags) > 8:
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error_msg += f" ... and {len(available_tags) - 8} more variants\n"
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# Also show local alternatives
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suggestions = search_ollama_models_fuzzy(model_name, available_models)
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if suggestions:
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error_msg += "\nOr use one of these similar installed models:\n"
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for i, suggestion in enumerate(suggestions, 1):
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error_msg += f" {i}. {suggestion}\n"
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else:
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# Model doesn't exist remotely - show fuzzy suggestions
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suggestions = search_ollama_models_fuzzy(model_name, available_models)
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error_msg += f"\n\nModel '{model_name}' was not found in Ollama's library."
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if suggestions:
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error_msg += "\n\nDid you mean one of these installed models?\n"
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for i, suggestion in enumerate(suggestions, 1):
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error_msg += f" {i}. {suggestion}\n"
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else:
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error_msg += "\n\nYour installed models:\n"
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for i, model in enumerate(available_models[:8], 1):
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error_msg += f" {i}. {model}\n"
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if len(available_models) > 8:
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error_msg += f" ... and {len(available_models) - 8} more\n"
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error_msg += "\n\nCommands:"
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error_msg += "\n ollama list # List installed models"
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if model_exists_remotely and available_tags:
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if model_name in available_tags:
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error_msg += f"\n ollama pull {model_name} # Install requested model"
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else:
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error_msg += (
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f"\n ollama pull {available_tags[0]} # Install recommended variant"
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)
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error_msg += "\n https://ollama.com/library # Browse available models"
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return error_msg
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elif llm_type == "hf":
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# For HF models, we can do a quick existence check
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if not check_hf_model_exists(model_name):
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# Use HF Hub's native fuzzy search directly
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search_suggestions = search_hf_models_fuzzy(model_name, limit=8)
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error_msg = f"Model '{model_name}' not found on HuggingFace Hub."
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if search_suggestions:
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error_msg += "\n\nDid you mean one of these?\n"
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for i, suggestion in enumerate(search_suggestions, 1):
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error_msg += f" {i}. {suggestion}\n"
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else:
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# Fallback to popular models if search returns nothing
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popular_models = get_popular_hf_models()
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error_msg += "\n\nPopular chat models:\n"
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for i, model in enumerate(popular_models[:5], 1):
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error_msg += f" {i}. {model}\n"
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error_msg += f"\nSearch more: https://huggingface.co/models?search={model_name}&pipeline_tag=text-generation"
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return error_msg
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return None # Model is valid or we can't check
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class LLMInterface(ABC):
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"""Abstract base class for a generic Language Model (LLM) interface."""
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@abstractmethod
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def ask(self, prompt: str, **kwargs) -> str:
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"""
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Additional keyword arguments (kwargs) for advanced search customization. Example usage:
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chat.ask(
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"What is ANN?",
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top_k=10,
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complexity=64,
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beam_width=8,
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USE_DEFERRED_FETCH=True,
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skip_search_reorder=True,
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recompute_beighbor_embeddings=True,
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dedup_node_dis=True,
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prune_ratio=0.1,
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batch_recompute=True,
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global_pruning=True
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)
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Supported kwargs:
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- complexity (int): Search complexity parameter (default: 32)
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- beam_width (int): Beam width for search (default: 4)
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- USE_DEFERRED_FETCH (bool): Enable deferred fetch mode (default: False)
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- skip_search_reorder (bool): Skip search reorder step (default: False)
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- recompute_beighbor_embeddings (bool): Enable ZMQ embedding server for neighbor recomputation (default: False)
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- dedup_node_dis (bool): Deduplicate nodes by distance (default: False)
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- prune_ratio (float): Pruning ratio for search (default: 0.0)
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- batch_recompute (bool): Enable batch recomputation (default: False)
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- global_pruning (bool): Enable global pruning (default: False)
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"""
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# """
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# Sends a prompt to the LLM and returns the generated text.
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# Args:
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# prompt: The input prompt for the LLM.
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# **kwargs: Additional keyword arguments for the LLM backend.
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# Returns:
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# The response string from the LLM.
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# """
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pass
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class OllamaChat(LLMInterface):
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"""LLM interface for Ollama models."""
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def __init__(self, model: str = "llama3:8b", host: str = "http://localhost:11434"):
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self.model = model
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self.host = host
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logger.info(f"Initializing OllamaChat with model='{model}' and host='{host}'")
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try:
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import requests
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# Check if the Ollama server is responsive
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if host:
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requests.get(host)
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# Pre-check model availability with helpful suggestions
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model_error = validate_model_and_suggest(model, "ollama")
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if model_error:
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raise ValueError(model_error)
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except ImportError:
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raise ImportError(
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"The 'requests' library is required for Ollama. Please install it with 'pip install requests'."
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)
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except requests.exceptions.ConnectionError:
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logger.error(f"Could not connect to Ollama at {host}. Please ensure Ollama is running.")
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raise ConnectionError(
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f"Could not connect to Ollama at {host}. Please ensure Ollama is running."
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)
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def ask(self, prompt: str, **kwargs) -> str:
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import json
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import requests
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full_url = f"{self.host}/api/generate"
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payload = {
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"model": self.model,
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"prompt": prompt,
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"stream": False, # Keep it simple for now
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"options": kwargs,
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}
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logger.debug(f"Sending request to Ollama: {payload}")
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try:
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logger.info("Sending request to Ollama and waiting for response...")
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response = requests.post(full_url, data=json.dumps(payload))
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response.raise_for_status()
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# The response from Ollama can be a stream of JSON objects, handle this
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response_parts = response.text.strip().split("\n")
|
|
full_response = ""
|
|
for part in response_parts:
|
|
if part:
|
|
json_part = json.loads(part)
|
|
full_response += json_part.get("response", "")
|
|
if json_part.get("done"):
|
|
break
|
|
return full_response
|
|
except requests.exceptions.RequestException as e:
|
|
logger.error(f"Error communicating with Ollama: {e}")
|
|
return f"Error: Could not get a response from Ollama. Details: {e}"
|
|
|
|
|
|
class HFChat(LLMInterface):
|
|
"""LLM interface for local Hugging Face Transformers models with proper chat templates."""
|
|
|
|
def __init__(self, model_name: str = "deepseek-ai/deepseek-llm-7b-chat"):
|
|
logger.info(f"Initializing HFChat with model='{model_name}'")
|
|
|
|
# Pre-check model availability with helpful suggestions
|
|
model_error = validate_model_and_suggest(model_name, "hf")
|
|
if model_error:
|
|
raise ValueError(model_error)
|
|
|
|
try:
|
|
import torch
|
|
from transformers import AutoModelForCausalLM, AutoTokenizer
|
|
except ImportError:
|
|
raise ImportError(
|
|
"The 'transformers' and 'torch' libraries are required for Hugging Face models. Please install them with 'pip install transformers torch'."
|
|
)
|
|
|
|
# Auto-detect device
|
|
if torch.cuda.is_available():
|
|
self.device = "cuda"
|
|
logger.info("CUDA is available. Using GPU.")
|
|
elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
|
|
self.device = "mps"
|
|
logger.info("MPS is available. Using Apple Silicon GPU.")
|
|
else:
|
|
self.device = "cpu"
|
|
logger.info("No GPU detected. Using CPU.")
|
|
|
|
# Load tokenizer and model with timeout protection
|
|
try:
|
|
import signal
|
|
|
|
def timeout_handler(signum, frame):
|
|
raise TimeoutError("Model download/loading timed out")
|
|
|
|
# Set timeout for model loading (60 seconds)
|
|
old_handler = signal.signal(signal.SIGALRM, timeout_handler)
|
|
signal.alarm(60)
|
|
|
|
try:
|
|
logger.info(f"Loading tokenizer for {model_name}...")
|
|
self.tokenizer = AutoTokenizer.from_pretrained(model_name)
|
|
|
|
logger.info(f"Loading model {model_name}...")
|
|
self.model = AutoModelForCausalLM.from_pretrained(
|
|
model_name,
|
|
torch_dtype=torch.float16 if self.device != "cpu" else torch.float32,
|
|
device_map="auto" if self.device != "cpu" else None,
|
|
trust_remote_code=True,
|
|
)
|
|
logger.info(f"Successfully loaded {model_name}")
|
|
finally:
|
|
signal.alarm(0) # Cancel the alarm
|
|
signal.signal(signal.SIGALRM, old_handler) # Restore old handler
|
|
|
|
except TimeoutError:
|
|
logger.error(f"Model loading timed out for {model_name}")
|
|
raise RuntimeError(
|
|
f"Model loading timed out for {model_name}. Please check your internet connection or try a smaller model."
|
|
)
|
|
except Exception as e:
|
|
logger.error(f"Failed to load model {model_name}: {e}")
|
|
raise
|
|
|
|
# Move model to device if not using device_map
|
|
if self.device != "cpu" and "device_map" not in str(self.model):
|
|
self.model = self.model.to(self.device)
|
|
|
|
# Set pad token if not present
|
|
if self.tokenizer.pad_token is None:
|
|
self.tokenizer.pad_token = self.tokenizer.eos_token
|
|
|
|
def ask(self, prompt: str, **kwargs) -> str:
|
|
print("kwargs in HF: ", kwargs)
|
|
# Check if this is a Qwen model and add /no_think by default
|
|
is_qwen_model = "qwen" in self.model.config._name_or_path.lower()
|
|
|
|
# For Qwen models, automatically add /no_think to the prompt
|
|
if is_qwen_model and "/no_think" not in prompt and "/think" not in prompt:
|
|
prompt = prompt + " /no_think"
|
|
|
|
# Prepare chat template
|
|
messages = [{"role": "user", "content": prompt}]
|
|
|
|
# Apply chat template if available
|
|
if hasattr(self.tokenizer, "apply_chat_template"):
|
|
try:
|
|
formatted_prompt = self.tokenizer.apply_chat_template(
|
|
messages, tokenize=False, add_generation_prompt=True
|
|
)
|
|
except Exception as e:
|
|
logger.warning(f"Chat template failed, using raw prompt: {e}")
|
|
formatted_prompt = prompt
|
|
else:
|
|
# Fallback for models without chat template
|
|
formatted_prompt = prompt
|
|
|
|
# Tokenize input
|
|
inputs = self.tokenizer(
|
|
formatted_prompt,
|
|
return_tensors="pt",
|
|
padding=True,
|
|
truncation=True,
|
|
max_length=2048,
|
|
)
|
|
|
|
# Move inputs to device
|
|
if self.device != "cpu":
|
|
inputs = {k: v.to(self.device) for k, v in inputs.items()}
|
|
|
|
# Set generation parameters
|
|
generation_config = {
|
|
"max_new_tokens": kwargs.get("max_tokens", kwargs.get("max_new_tokens", 512)),
|
|
"temperature": kwargs.get("temperature", 0.7),
|
|
"top_p": kwargs.get("top_p", 0.9),
|
|
"do_sample": kwargs.get("temperature", 0.7) > 0,
|
|
"pad_token_id": self.tokenizer.eos_token_id,
|
|
"eos_token_id": self.tokenizer.eos_token_id,
|
|
}
|
|
|
|
# Handle temperature=0 for greedy decoding
|
|
if generation_config["temperature"] == 0.0:
|
|
generation_config["do_sample"] = False
|
|
generation_config.pop("temperature")
|
|
|
|
logger.info(f"Generating with HuggingFace model, config: {generation_config}")
|
|
|
|
# Generate
|
|
with torch.no_grad():
|
|
outputs = self.model.generate(**inputs, **generation_config)
|
|
|
|
# Decode response
|
|
generated_tokens = outputs[0][inputs["input_ids"].shape[1] :]
|
|
response = self.tokenizer.decode(generated_tokens, skip_special_tokens=True)
|
|
|
|
return response.strip()
|
|
|
|
|
|
class OpenAIChat(LLMInterface):
|
|
"""LLM interface for OpenAI models."""
|
|
|
|
def __init__(self, model: str = "gpt-4o", api_key: str | None = None):
|
|
self.model = model
|
|
self.api_key = api_key or os.getenv("OPENAI_API_KEY")
|
|
|
|
if not self.api_key:
|
|
raise ValueError(
|
|
"OpenAI API key is required. Set OPENAI_API_KEY environment variable or pass api_key parameter."
|
|
)
|
|
|
|
logger.info(f"Initializing OpenAI Chat with model='{model}'")
|
|
|
|
try:
|
|
import openai
|
|
|
|
self.client = openai.OpenAI(api_key=self.api_key)
|
|
except ImportError:
|
|
raise ImportError(
|
|
"The 'openai' library is required for OpenAI models. Please install it with 'pip install openai'."
|
|
)
|
|
|
|
def ask(self, prompt: str, **kwargs) -> str:
|
|
# Default parameters for OpenAI
|
|
params = {
|
|
"model": self.model,
|
|
"messages": [{"role": "user", "content": prompt}],
|
|
"max_tokens": kwargs.get("max_tokens", 1000),
|
|
"temperature": kwargs.get("temperature", 0.7),
|
|
**{k: v for k, v in kwargs.items() if k not in ["max_tokens", "temperature"]},
|
|
}
|
|
|
|
logger.info(f"Sending request to OpenAI with model {self.model}")
|
|
|
|
try:
|
|
response = self.client.chat.completions.create(**params)
|
|
return response.choices[0].message.content.strip()
|
|
except Exception as e:
|
|
logger.error(f"Error communicating with OpenAI: {e}")
|
|
return f"Error: Could not get a response from OpenAI. Details: {e}"
|
|
|
|
|
|
class SimulatedChat(LLMInterface):
|
|
"""A simple simulated chat for testing and development."""
|
|
|
|
def ask(self, prompt: str, **kwargs) -> str:
|
|
logger.info("Simulating LLM call...")
|
|
print("Prompt sent to LLM (simulation):", prompt[:500] + "...")
|
|
return "This is a simulated answer from the LLM based on the retrieved context."
|
|
|
|
|
|
def get_llm(llm_config: dict[str, Any] | None = None) -> LLMInterface:
|
|
"""
|
|
Factory function to get an LLM interface based on configuration.
|
|
|
|
Args:
|
|
llm_config: A dictionary specifying the LLM type and its parameters.
|
|
Example: {"type": "ollama", "model": "llama3"}
|
|
{"type": "hf", "model": "distilgpt2"}
|
|
None (for simulation mode)
|
|
|
|
Returns:
|
|
An instance of an LLMInterface subclass.
|
|
"""
|
|
if llm_config is None:
|
|
llm_config = {
|
|
"type": "openai",
|
|
"model": "gpt-4o",
|
|
"api_key": os.getenv("OPENAI_API_KEY"),
|
|
}
|
|
|
|
llm_type = llm_config.get("type", "openai")
|
|
model = llm_config.get("model")
|
|
|
|
logger.info(f"Attempting to create LLM of type='{llm_type}' with model='{model}'")
|
|
|
|
if llm_type == "ollama":
|
|
return OllamaChat(
|
|
model=model or "llama3:8b",
|
|
host=llm_config.get("host", "http://localhost:11434"),
|
|
)
|
|
elif llm_type == "hf":
|
|
return HFChat(model_name=model or "deepseek-ai/deepseek-llm-7b-chat")
|
|
elif llm_type == "openai":
|
|
return OpenAIChat(model=model or "gpt-4o", api_key=llm_config.get("api_key"))
|
|
elif llm_type == "simulated":
|
|
return SimulatedChat()
|
|
else:
|
|
raise ValueError(f"Unknown LLM type: '{llm_type}'")
|