feat(models): 组织、用户、角色、客户、会话与消息模型

按组织边界定义数据关系，补齐仓储层与通用工具，迁移脚本可重复执行。

backend/app/utils/model_context.py

@@ -0,0 +1,114 @@
+"""
+Copyright 2024-2026 ReplyPier
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+
+Curated context-window lookup for the org-configurable chat models, used to
+size FAQ-generation batches to the selected model instead of a fixed constant.
+Deliberately a small hand-maintained table (kept conservative) rather than a
+dependency on litellm's model map; unknown models fall back to a per-provider
+floor, and FAQ_CONTEXT_TOKENS_OVERRIDE forces a value for anything exotic.
+"""
+
+from app.core.config import settings
+
+# Conservative floors when the model name is unrecognised. Keys match
+# AIModelType values, uppercased.
+_PROVIDER_DEFAULT_TOKENS = {
+    "OPENAI": 16_000,
+    "REPLYPIER": 128_000,
+    "ANTHROPIC": 200_000,
+    "DEEPSEEK": 64_000,
+    "GOOGLE": 32_000,
+    "GOOGLEVERTEX": 32_000,
+    "GROQ": 8_000,
+    "MISTRAL": 32_000,
+    "HUGGINGFACE": 8_000,
+    "OLLAMA": 8_000,
+    "XAI": 128_000,
+}
+_FALLBACK_TOKENS = 8_000
+
+# Lowercase model-name prefixes; the longest matching prefix wins so
+# "gpt-4o" beats "gpt-4" and "mistral-large" beats "mistral".
+_MODEL_PREFIX_TOKENS = {
+    "gpt-5": 400_000,
+    "gpt-4.1": 1_000_000,
+    "gpt-4o": 128_000,
+    "gpt-4-turbo": 128_000,
+    "gpt-4": 8_000,
+    "gpt-3.5": 16_000,
+    "o1": 200_000,
+    "o3": 200_000,
+    "o4": 200_000,
+    "claude": 200_000,
+    "gemini": 1_000_000,
+    "deepseek": 64_000,
+    "llama-3.1": 128_000,
+    "llama-3.2": 128_000,
+    "llama-3.3": 128_000,
+    "llama-4": 128_000,
+    "mixtral": 32_000,
+    "mistral-large": 128_000,
+    "mistral": 32_000,
+    "grok": 128_000,
+    "qwen": 32_000,
+    "kimi": 128_000,
+}
+
+# Batch-size math. CHARS_PER_TOKEN is conservative for English prose (~4);
+# SAFETY absorbs tokenizer variance and the packed-batch separators.
+_PROMPT_OVERHEAD_TOKENS = 2_500  # instructions + existing-questions block
+_OUTPUT_TOKENS_PER_FAQ = 120
+_OUTPUT_TOKENS_BASE = 500
+_CHARS_PER_TOKEN = 3.5
+_SAFETY = 0.8
+_MIN_FAQS_PER_BATCH = 15
+_MAX_FAQS_PER_BATCH = 40
+_CHARS_PER_FAQ = 3_000  # scale FAQ yield with batch size so density stays flat
+
+
+def context_tokens_for(model_type: str, model_name: str) -> int:
+    """Best-known context window (tokens) for the configured model."""
+    if settings.FAQ_CONTEXT_TOKENS_OVERRIDE > 0:
+        return settings.FAQ_CONTEXT_TOKENS_OVERRIDE
+    name = (model_name or "").casefold()
+    best = None
+    for prefix, tokens in _MODEL_PREFIX_TOKENS.items():
+        if name.startswith(prefix) and (best is None or len(prefix) > best[0]):
+            best = (len(prefix), tokens)
+    if best:
+        return best[1]
+    return _PROVIDER_DEFAULT_TOKENS.get((model_type or "").upper(), _FALLBACK_TOKENS)
+
+
+def output_tokens_for(max_faqs: int) -> int:
+    """Output reserve for a structured batch of up to max_faqs FAQs."""
+    return max_faqs * _OUTPUT_TOKENS_PER_FAQ + _OUTPUT_TOKENS_BASE
+
+
+def faq_batch_chars_for(model_type: str, model_name: str) -> tuple[int, int]:
+    """(batch_chars, max_faqs) for FAQ extraction with this model.
+
+    batch_chars is floored at FAQ_MAX_BATCH_CHARS (today's behaviour for
+    small-context models) and ceilinged at FAQ_MAX_BATCH_CHARS_CEILING — a
+    quality guard, not a token limit: grounded extraction degrades on very
+    long contexts. max_faqs scales with the batch so bigger batches don't
+    reduce total FAQ yield.
+    """
+    context = context_tokens_for(model_type, model_name)
+    usable = context - _PROMPT_OVERHEAD_TOKENS - output_tokens_for(_MAX_FAQS_PER_BATCH)
+    raw_chars = int(usable * _CHARS_PER_TOKEN * _SAFETY)
+    batch_chars = max(settings.FAQ_MAX_BATCH_CHARS, min(raw_chars, settings.FAQ_MAX_BATCH_CHARS_CEILING))
+    max_faqs = max(_MIN_FAQS_PER_BATCH, min(batch_chars // _CHARS_PER_FAQ, _MAX_FAQS_PER_BATCH))
+    return batch_chars, max_faqs