WangShen007
20fef2b9f3
One word everywhere: ReplyPier in UI copy, package and image names, the replypier.site domains (app/api/docs/help), REPLYPIER_* settings and the --rp-* design tokens. - public widget API is window.ReplyPier with replypierId / replypierBaseUrl / replypierConfig / replypierToken, loader at webclient/replypier.min.js; both bundles rebuilt from source - NOTICE, llms.txt, CONTRIBUTING, HELP_CENTER_INFRA and the demo runtime config rewritten; the demo config ships no third-party keys - migration adds the ReplyPier managed model value to the AI model enum - local compose project, database, role, volume and Docker network carry the same name
115 lines
4.1 KiB
Python
115 lines
4.1 KiB
Python
"""
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Copyright 2024-2026 ReplyPier
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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Curated context-window lookup for the org-configurable chat models, used to
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size FAQ-generation batches to the selected model instead of a fixed constant.
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Deliberately a small hand-maintained table (kept conservative) rather than a
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dependency on litellm's model map; unknown models fall back to a per-provider
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floor, and FAQ_CONTEXT_TOKENS_OVERRIDE forces a value for anything exotic.
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"""
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from app.core.config import settings
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# Conservative floors when the model name is unrecognised. Keys match
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# AIModelType values, uppercased.
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_PROVIDER_DEFAULT_TOKENS = {
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"OPENAI": 16_000,
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"REPLYPIER": 128_000,
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"ANTHROPIC": 200_000,
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"DEEPSEEK": 64_000,
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"GOOGLE": 32_000,
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"GOOGLEVERTEX": 32_000,
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"GROQ": 8_000,
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"MISTRAL": 32_000,
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"HUGGINGFACE": 8_000,
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"OLLAMA": 8_000,
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"XAI": 128_000,
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}
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_FALLBACK_TOKENS = 8_000
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# Lowercase model-name prefixes; the longest matching prefix wins so
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# "gpt-4o" beats "gpt-4" and "mistral-large" beats "mistral".
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_MODEL_PREFIX_TOKENS = {
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"gpt-5": 400_000,
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"gpt-4.1": 1_000_000,
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"gpt-4o": 128_000,
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"gpt-4-turbo": 128_000,
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"gpt-4": 8_000,
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"gpt-3.5": 16_000,
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"o1": 200_000,
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"o3": 200_000,
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"o4": 200_000,
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"claude": 200_000,
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"gemini": 1_000_000,
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"deepseek": 64_000,
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"llama-3.1": 128_000,
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"llama-3.2": 128_000,
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"llama-3.3": 128_000,
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"llama-4": 128_000,
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"mixtral": 32_000,
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"mistral-large": 128_000,
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"mistral": 32_000,
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"grok": 128_000,
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"qwen": 32_000,
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"kimi": 128_000,
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}
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# Batch-size math. CHARS_PER_TOKEN is conservative for English prose (~4);
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# SAFETY absorbs tokenizer variance and the packed-batch separators.
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_PROMPT_OVERHEAD_TOKENS = 2_500 # instructions + existing-questions block
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_OUTPUT_TOKENS_PER_FAQ = 120
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_OUTPUT_TOKENS_BASE = 500
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_CHARS_PER_TOKEN = 3.5
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_SAFETY = 0.8
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_MIN_FAQS_PER_BATCH = 15
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_MAX_FAQS_PER_BATCH = 40
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_CHARS_PER_FAQ = 3_000 # scale FAQ yield with batch size so density stays flat
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def context_tokens_for(model_type: str, model_name: str) -> int:
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"""Best-known context window (tokens) for the configured model."""
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if settings.FAQ_CONTEXT_TOKENS_OVERRIDE > 0:
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return settings.FAQ_CONTEXT_TOKENS_OVERRIDE
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name = (model_name or "").casefold()
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best = None
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for prefix, tokens in _MODEL_PREFIX_TOKENS.items():
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if name.startswith(prefix) and (best is None or len(prefix) > best[0]):
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best = (len(prefix), tokens)
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if best:
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return best[1]
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return _PROVIDER_DEFAULT_TOKENS.get((model_type or "").upper(), _FALLBACK_TOKENS)
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def output_tokens_for(max_faqs: int) -> int:
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"""Output reserve for a structured batch of up to max_faqs FAQs."""
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return max_faqs * _OUTPUT_TOKENS_PER_FAQ + _OUTPUT_TOKENS_BASE
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def faq_batch_chars_for(model_type: str, model_name: str) -> tuple[int, int]:
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"""(batch_chars, max_faqs) for FAQ extraction with this model.
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batch_chars is floored at FAQ_MAX_BATCH_CHARS (today's behaviour for
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small-context models) and ceilinged at FAQ_MAX_BATCH_CHARS_CEILING — a
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quality guard, not a token limit: grounded extraction degrades on very
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long contexts. max_faqs scales with the batch so bigger batches don't
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reduce total FAQ yield.
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"""
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context = context_tokens_for(model_type, model_name)
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usable = context - _PROMPT_OVERHEAD_TOKENS - output_tokens_for(_MAX_FAQS_PER_BATCH)
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raw_chars = int(usable * _CHARS_PER_TOKEN * _SAFETY)
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batch_chars = max(settings.FAQ_MAX_BATCH_CHARS, min(raw_chars, settings.FAQ_MAX_BATCH_CHARS_CEILING))
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max_faqs = max(_MIN_FAQS_PER_BATCH, min(batch_chars // _CHARS_PER_FAQ, _MAX_FAQS_PER_BATCH))
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return batch_chars, max_faqs
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