update
This commit is contained in:
sam
parent
5b4bd51199
commit
6a7ca88e63
@ -1,7 +1,6 @@
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"""Streamlit UI scaffold for the investment assistant."""
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from __future__ import annotations
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import json
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import sys
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from dataclasses import asdict
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from datetime import date, timedelta
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@ -219,10 +218,10 @@ def render_settings() -> None:
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if model_options:
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options_with_custom = model_options + [custom_model_label]
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if primary.model in model_options:
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if primary.provider == selected_provider and primary.model in model_options:
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model_index = options_with_custom.index(primary.model)
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else:
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model_index = len(options_with_custom) - 1
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model_index = 0
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selected_model_option = st.selectbox(
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"LLM 模型",
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options_with_custom,
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@ -232,17 +231,17 @@ def render_settings() -> None:
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if selected_model_option == custom_model_label:
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custom_model_value = st.text_input(
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"自定义模型名称",
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value=primary.model if primary.model not in model_options else "",
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value="" if primary.provider != selected_provider or primary.model in model_options else primary.model,
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)
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chosen_model = custom_model_value.strip() or default_model_hint
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else:
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custom_model_value = selected_model_option
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chosen_model = selected_model_option
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else:
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custom_model_value = st.text_input(
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chosen_model = st.text_input(
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"LLM 模型",
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value=primary.model or default_model_hint,
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help="未预设该 Provider 的模型列表，请手动填写",
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)
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selected_model_option = custom_model_label
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).strip() or default_model_hint
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default_base_hint = DEFAULT_LLM_BASE_URLS.get(selected_provider, "")
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provider_default_temp = float(provider_info.get("temperature", 0.2))
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provider_default_timeout = int(provider_info.get("timeout", 30.0))
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@ -257,11 +256,16 @@ def render_settings() -> None:
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timeout_value = provider_default_timeout
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llm_base = st.text_input(
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"LLM Base URL (可选)",
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"LLM Base URL",
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value=base_value,
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help=f"默认推荐：{default_base_hint or '按供应商要求填写'}",
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)
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llm_api_key = st.text_input("LLM API Key (OpenAI 类需要)", value=primary.api_key or "", type="password")
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llm_api_key = st.text_input(
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"LLM API Key",
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value=primary.api_key or "",
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type="password",
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help="点击右侧小图标可查看当前 Key，该值会写入 config.json（已被 gitignore 排除）",
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)
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llm_temperature = st.slider(
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"LLM 温度",
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min_value=0.0,
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@ -292,61 +296,97 @@ def render_settings() -> None:
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format="%d",
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)
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ensemble_display = []
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for endpoint in llm_cfg.ensemble:
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data = asdict(endpoint)
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if data.get("api_key"):
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data["api_key"] = ""
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ensemble_display.append(data)
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ensemble_text = st.text_area(
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"LLM 集群配置 (JSON 数组)",
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value=json.dumps(ensemble_display or [], ensure_ascii=False, indent=2),
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height=220,
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existing_api_keys = {ep.provider: ep.api_key or None for ep in llm_cfg.ensemble}
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ensemble_rows = [
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{
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"provider": ep.provider,
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"model": ep.model,
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"base_url": ep.base_url or "",
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"api_key": ep.api_key or "",
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"temperature": float(ep.temperature),
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"timeout": float(ep.timeout),
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}
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for ep in llm_cfg.ensemble
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]
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if not ensemble_rows:
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ensemble_rows = [
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{
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"provider": "",
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"model": "",
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"base_url": "",
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"api_key": "",
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"temperature": provider_default_temp,
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"timeout": provider_default_timeout,
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}
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]
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ensemble_rows = st.data_editor(
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ensemble_rows,
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num_rows="dynamic",
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key="llm_ensemble_editor",
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column_config={
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"provider": st.column_config.SelectboxColumn(
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"Provider",
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options=sorted(DEFAULT_LLM_MODEL_OPTIONS.keys()),
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help="选择 LLM 供应商"
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),
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"model": st.column_config.TextColumn("模型", help="留空时使用该 Provider 的默认模型"),
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"base_url": st.column_config.TextColumn("Base URL", help="留空时使用默认地址"),
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"api_key": st.column_config.TextColumn("API Key", help="留空表示使用环境变量或不配置"),
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"temperature": st.column_config.NumberColumn("温度", min_value=0.0, max_value=2.0, step=0.05),
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"timeout": st.column_config.NumberColumn("超时(秒)", min_value=5.0, max_value=120.0, step=5.0),
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},
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hide_index=True,
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use_container_width=True,
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)
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if hasattr(ensemble_rows, "to_dict"):
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ensemble_rows = ensemble_rows.to_dict("records")
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if st.button("保存 LLM 设置"):
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original_provider = primary.provider
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original_model = primary.model
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primary.provider = selected_provider
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if model_options:
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if selected_model_option == custom_model_label:
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model_input = custom_model_value.strip()
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primary.model = model_input or DEFAULT_LLM_MODELS.get(
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selected_provider, DEFAULT_LLM_MODELS["ollama"]
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)
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else:
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primary.model = selected_model_option
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else:
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primary.model = custom_model_value.strip() or DEFAULT_LLM_MODELS.get(
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selected_provider, DEFAULT_LLM_MODELS["ollama"]
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)
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primary.base_url = llm_base.strip() or None
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primary.model = chosen_model
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primary.base_url = llm_base.strip() or DEFAULT_LLM_BASE_URLS.get(selected_provider)
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primary.temperature = llm_temperature
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primary.timeout = llm_timeout
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api_key_value = llm_api_key.strip()
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primary.api_key = api_key_value or None
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if api_key_value:
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primary.api_key = api_key_value
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try:
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parsed = json.loads(ensemble_text or "[]")
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if not isinstance(parsed, list):
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raise ValueError("ensemble 配置必须是数组")
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except Exception as exc: # noqa: BLE001
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LOGGER.exception("解析 LLM 集群配置失败", extra=LOG_EXTRA)
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st.error(f"LLM 集群配置解析失败：{exc}")
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else:
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new_ensemble: List[LLMEndpoint] = []
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invalid = False
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for item in parsed:
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if not isinstance(item, dict):
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st.error("LLM 集群配置中的每个元素都必须是对象")
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invalid = True
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break
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fields = {key: item.get(key) for key in ("provider", "model", "base_url", "api_key", "temperature", "timeout")}
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endpoint = LLMEndpoint(**{k: v for k, v in fields.items() if v not in (None, "")})
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if not endpoint.provider:
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endpoint.provider = "ollama"
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for row in ensemble_rows:
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provider = (row.get("provider") or "").strip().lower()
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if not provider:
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continue
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provider_defaults = DEFAULT_LLM_MODEL_OPTIONS.get(provider, {})
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default_model = DEFAULT_LLM_MODELS.get(provider, DEFAULT_LLM_MODELS["ollama"])
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default_base = DEFAULT_LLM_BASE_URLS.get(provider)
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temp_default = float(provider_defaults.get("temperature", 0.2))
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timeout_default = float(provider_defaults.get("timeout", 30.0))
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model_val = (row.get("model") or "").strip() or default_model
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base_val = (row.get("base_url") or "").strip() or default_base
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api_raw = (row.get("api_key") or "").strip()
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api_value = None
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if api_raw and api_raw != "***":
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api_value = api_raw
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else:
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existing = existing_api_keys.get(provider)
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if existing:
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api_value = existing
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temp_val = row.get("temperature")
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timeout_val = row.get("timeout")
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endpoint = LLMEndpoint(
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provider=provider,
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model=model_val,
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base_url=base_val,
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api_key=api_value,
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temperature=float(temp_val) if temp_val is not None else temp_default,
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timeout=float(timeout_val) if timeout_val is not None else timeout_default,
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)
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new_ensemble.append(endpoint)
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if not invalid:
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llm_cfg.ensemble = new_ensemble
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llm_cfg.strategy = selected_strategy
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llm_cfg.majority_threshold = int(majority_threshold)
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