Run

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f9c910d6025e8060c89fe6d3bb59b181b26461b1

Agent context: I’ll inspect the repo guidance and the agent/LLM flow first, then verify current provider capabilities from official docs so the recommendation is grounded in what the product actually needs. I’ll inspect the repo guidance and the agent/LLM flow first, then verify current provider capabilities from official docs so the recommendation is grounded in what the product actually needs.

Agent context: I’ll inspect the repo guidance and the agent/LLM flow first, then verify current provider capabilities from official docs so the recommendation is grounded in what the product actually needs.

Agent context: I’ll inspect the repo guidance and the agent/LLM flow first, then verify current provider capabilities from official docs so the recommendation is grounded in what the product actually needs. exit code 127 Output: /home/user/worktrees/attempt-8922cda0-b75c-4de1-80a0-0b5121458d83-cap-0 /bin/bash: line 1: rg: command not found

Agent context: I’ll inspect the repo guidance and the agent/LLM flow first, then verify current provider capabilities from official docs so the recommendation is grounded in what the product actually needs.

Agent context: I’ll inspect the repo guidance and the agent/LLM flow first, then verify current provider capabilities from official docs so the recommendation is grounded in what the product actually needs. exit code 127

Agent context: `rg` is not installed in this workspace, so I’m falling back to `find` and `grep`. I’m looking for the existing retrieval and LLM boundaries before judging provider fit. `rg` is not installed in this workspace, so I’m falling back to `find` and `grep`. I’m looking for the existing retrieval and LLM boundaries before judging provider fit.

Agent context: `rg` is not installed in this workspace, so I’m falling back to `find` and `grep`. I’m looking for the existing retrieval and LLM boundaries before judging provider fit.

Agent context: `rg` is not installed in this workspace, so I’m falling back to `find` and `grep`. I’m looking for the existing retrieval and LLM boundaries before judging provider fit. exit code 0 Output: ./AGENTS.md ./CLAUDE.md ./README.md ./README.zh-CN.md ./tests/README.md

Agent context: `rg` is not installed in this workspace, so I’m falling back to `find` and `grep`. I’m looking for the existing retrieval and LLM boundaries before judging provider fit.

Agent context: `rg` is not installed in this workspace, so I’m falling back to `find` and `grep`. I’m looking for the existing retrieval and LLM boundaries before judging provider fit. exit code 0 Output: backend/api/endpoints/auth.py:428: # After downgrade, agent access must be explicitly re-assigned via /agents/{id}/members backend/api/v1/endpoints.py:13: BackgroundTasks, backend/api/v1/endpoints.py:101:from services.llm_service import get_llm_service backend/api/v1/endpoints.py:103:from services.kb_retrieval_service import KbRetrievalService backend/api/v1/endpoints.py:116: "general": """Role: You are an AI chatbot that helps users resolve their inquiries, questions, and requests. Your goal is always to provide high-quality, friendly, and efficient responses. Your responsibility is to carefully listen to users, understand their needs, and do your best to assist them or guide them to appropriate resources. If a question is not sufficiently clear, you should proactively ask clarifying questions. Be sure to maintain a positive and constructive tone at the end of your response. backend/api

Agent context: I’ve found the relevant chat path: the backend already builds `sources`, emits them over SSE, and has placeholder replacement for trusted citations. Next I’m reading the project docs plus the exact service code around LLM calls and KB retrieval. I’ve found the relevant chat path: the backend already builds `sources`, emits them over SSE, and has placeholder replacement for trusted citations. Next I’m reading the project docs plus the exact service code around LLM calls and KB retrieval.

Agent context: I’ve found the relevant chat path: the backend already builds `sources`, emits them over SSE, and has placeholder replacement for trusted citations. Next I’m reading the project docs plus the exact service code around LLM calls and KB retrieval.

Agent context: I’ve found the relevant chat path: the backend already builds `sources`, emits them over SSE, and has placeholder replacement for trusted citations. Next I’m reading the project docs plus the exact service code around LLM calls and KB retrieval. exit code 0 Output: # CLAUDE.md This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository. ## Repo layout - `frontend-nextjs/` is the active admin/dashboard frontend. Treat the older `frontend/` directory as legacy/reference only. - `backend/` is a FastAPI app with SQLite persistence, Redis-backed rate limiting/cache fallbacks, and self-KB retrieval/indexing (Qdrant). - `widget/` builds the embeddable chat widget SDK that talks to the backend streaming chat endpoints. - `nginx/` contains the reverse-proxy config used in Docker deployments. - `scrapling-service/` is a standalone FastAPI microservice that performs HTTP fetching with `curl_cffi` (TLS-impersonated Chrome 120) and `readability-lxml` content extraction, with `httpx` fallback when `curl_cffi` fails. The backend talks to it via HTTP on port 8001 (internal Docker network). - `docker-compose.yml` is the pri Provider mentions: openai

Agent context: I’ve found the relevant chat path: the backend already builds `sources`, emits them over SSE, and has placeholder replacement for trusted citations. Next I’m reading the project docs plus the exact service code around LLM calls and KB retrieval.

Agent context: I’ve found the relevant chat path: the backend already builds `sources`, emits them over SSE, and has placeholder replacement for trusted citations. Next I’m reading the project docs plus the exact service code around LLM calls and KB retrieval. exit code 0 Output: logger.info(f"Resetting daily message quota for workspace {agent.workspace_id}") quota.used_messages_today = 0 quota.last_message_reset = now quota.updated_at = now await db.flush() return quota def build_chat_sources(retrieval_results: List[Dict[str, Any]]) -> List[Dict[str, Any]]: """Build normalized source payloads for chat responses.""" sources: List[Dict[str, Any]] = [] for result in retrieval_results: snippet = result.get("content", "")[:200].strip() if snippet and len(result.get("content", "")) > 200: snippet += "..." if result["type"] == "url": sources.append( { "type": "url", "title": result.get("metadata", {}).get("title", "文档"), "url": result.get("metadata", {}).get("url", ""), "snippet": snippet or None, } ) return sources _SOURCE_PLACEHOLDER_PATTERN = re.compile(r"\[([^\]]+)\]\(#source-(\d+)\)") def replace_source_placeholders(reply: str, sources: List[Dict[str, Any]]) -> st

Agent context: I’ve found the relevant chat path: the backend already builds `sources`, emits them over SSE, and has placeholder replacement for trusted citations. Next I’m reading the project docs plus the exact service code around LLM calls and KB retrieval.

Agent context: I’ve found the relevant chat path: the backend already builds `sources`, emits them over SSE, and has placeholder replacement for trusted citations. Next I’m reading the project docs plus the exact service code around LLM calls and KB retrieval. exit code 0 Output: """ LLM 服务抽象层 - 支持多个 AI 提供商 支持的提供商： - OpenAI Native (官方接口) - OpenAI Compatible (兼容接口，如DeepSeek) - Google (Gemini) - Mock (用于测试) """ import asyncio import random from abc import ABC, abstractmethod from typing import AsyncGenerator, List, Dict, Optional, Awaitable, Callable, TypeVar import logging import html from config import settings logger = logging.getLogger(__name__) T = TypeVar("T") class LLMError(Exception): """Base exception for classified LLM failures.""" code = "PROVIDER_ERROR" class APIKeyInvalidError(LLMError): code = "API_KEY_INVALID" class APIKeyMissingError(LLMError): code = "API_KEY_MISSING" class ProviderRateLimitedError(LLMError): code = "PROVIDER_RATE_LIMITED" class ProviderUnavailableError(LLMError): code = "PROVIDER_UNAVAILABLE" class ModelNotFoundError(LLMError): code = "MODEL_NOT_FOUND" def classify_llm_error(error: Exception) -> LLMError: """Normalize provider-spe Provider mentions: openai

Agent context: I’ve found the relevant chat path: the backend already builds `sources`, emits them over SSE, and has placeholder replacement for trusted citations. Next I’m reading the project docs plus the exact service code around LLM calls and KB retrieval.

Agent context: I’ve found the relevant chat path: the backend already builds `sources`, emits them over SSE, and has placeholder replacement for trusted citations. Next I’m reading the project docs plus the exact service code around LLM calls and KB retrieval. exit code 0 Output: # Basjoo [](https://www.docker.com/) [](https://fastapi.tiangolo.com/) [](https://nextjs.org/) [](https://www.python.org/) [](https://www.typescriptlang.org/) [](https://www.postgresql.org/) [](https://redis.io/) [](https://qdrant.tech/) [![ Provider mentions: openai

Agent context: I’ve found the relevant chat path: the backend already builds `sources`, emits them over SSE, and has placeholder replacement for trusted citations. Next I’m reading the project docs plus the exact service code around LLM calls and KB retrieval.

Agent context: I’ve found the relevant chat path: the backend already builds `sources`, emits them over SSE, and has placeholder replacement for trusted citations. Next I’m reading the project docs plus the exact service code around LLM calls and KB retrieval. exit code 0 Output: """KB retrieval service: validate agent/kb/tenant, embed query, Qdrant search + threshold filter.""" import logging from typing import Any from sqlalchemy import select from database import AsyncSessionLocal from models import Agent, KnowledgeBase from services.document_parser import DocumentParser from services.kb_document_processor import get_embedding_api_key from services.kb_service import KbService from services.qdrant_service import QdrantKbService logger = logging.getLogger(__name__) class KbRetrievalService: def __init__(self): self.parser = DocumentParser() self.qdrant = QdrantKbService() self.kb_svc = KbService() self.default_threshold = 0.6 # Fallback default, but agent threshold is preferred async def retrieve( self, tenant_id: str | None, agent_id: str, query: str, top_k: int = 5, threshold: float | None = None, ) -> list[dict[str, Any]]: """Retrieve top-K chunks from agent'

Agent context: The codebase has a good insertion point: `prepare_chat_request` retrieves tenant KB chunks, then passes a single prompt to the configured chat model. The source pipeline currently only exposes URL KB sources, so live web retrieval should be a backend service that returns normalized source objects and grounded context before the LLM call. The codebase has a good insertion point: `prepare_chat_request` retrieves tenant KB chunks, then passes a single prompt to the configured chat model. The source pipeline currently only exposes URL KB sources, so live web retrieval should be a backend service that returns normalized source objects and grounded context before the LLM call.

Agent context: The codebase has a good insertion point: `prepare_chat_request` retrieves tenant KB chunks, then passes a single prompt to the configured chat model. The source pipeline currently only exposes URL KB sources, so live web retrieval should be a backend service that returns normalized source objects and grounded context before the LLM call. Provider mentions: openai

Agent context: The codebase has a good insertion point: `prepare_chat_request` retrieves tenant KB chunks, then passes a single prompt to the configured chat model. The source pipeline currently only exposes URL KB sources, so live web retrieval should be a backend service that returns normalized source objects and grounded context before the LLM call. exit code 0 Output: backend/models.py:101: provider_type = Column( backend/models.py:121: # Azure OpenAI特定配置 backend/models.py:126: # Anthropic特定配置 backend/models.py:129: # Google特定配置 backend/models.py:199: url_sources = relationship( backend/models.py:217: __tablename__ = "url_sources" backend/models.py:249: agent = relationship("Agent", back_populates="url_sources") backend/models.py:253: Index("ix_url_sources_agent_status", "agent_id", "status"), backend/models.py:372: sources = Column( backend/config.py:113: # DeepSeek API (optional - can be set per-agent in dashboard) backend/services/llm_service.py:5:- OpenAI Native (官方接口) backend/services/llm_service.py:6:- OpenAI Compatible (兼容接口，如DeepSeek) backend/services/llm_service.py:7:- Google (Gemini) backend/services/llm_service.py:25:class LLMError(Exception): backend/services/llm_service.py:31:class APIKeyInvalidError(LLMError): backend/services/llm_servic Provider mentions: openai

Agent context: The codebase has a good insertion point: `prepare_chat_request` retrieves tenant KB chunks, then passes a single prompt to the configured chat model. The source pipeline currently only exposes URL KB sources, so live web retrieval should be a backend service that returns normalized source objects and grounded context before the LLM call.

Agent context: The codebase has a good insertion point: `prepare_chat_request` retrieves tenant KB chunks, then passes a single prompt to the configured chat model. The source pipeline currently only exposes URL KB sources, so live web retrieval should be a backend service that returns normalized source objects and grounded context before the LLM call. exit code 0 Output: def __init__(self, model: str = "mock-model"): """初始化 Mock LLM""" super().__init__(model=model) logger.warning("使用Mock LLM服务 - 仅用于测试和演示环境") async def chat_completion( self, messages: List[Dict[str, str]], system_prompt: Optional[str] = None, stream: bool = True, temperature: Optional[float] = None, max_tokens: Optional[int] = None, ) -> AsyncGenerator[str, None]: """ Mock 聊天完成 Args: messages: 消息列表 system_prompt: 系统提示词 stream: 是否流式返回 Yields: str: 模拟的回复内容 """ # 获取最后一条用户消息 user_message = "" for msg in reversed(messages): if msg.get("role") == "user": user_message = msg.get("content", "") break # 生成模拟回复 mock_response = self._generate_mock_response(user_message, system_prompt) if stream: # 流式返回（模拟逐字输出） words = mock_response.split() for i, word in enumerate(words): if i > 0: word = " " + word yield word else: # 非流式返回 yield mock_response def _generate_mock_response( self, user_message: str, sys Provider mentions: openai

Agent context: The codebase has a good insertion point: `prepare_chat_request` retrieves tenant KB chunks, then passes a single prompt to the configured chat model. The source pipeline currently only exposes URL KB sources, so live web retrieval should be a backend service that returns normalized source objects and grounded context before the LLM call.

Agent context: The codebase has a good insertion point: `prepare_chat_request` retrieves tenant KB chunks, then passes a single prompt to the configured chat model. The source pipeline currently only exposes URL KB sources, so live web retrieval should be a backend service that returns normalized source objects and grounded context before the LLM call. exit code 0 Output: frontend-nextjs/src/components/ChatPanel.tsx:6:import { formatAssistantMessageContent } from "../utils/citations"; frontend-nextjs/src/components/ChatPanel.tsx:13: sources?: Source[]; frontend-nextjs/src/components/ChatPanel.tsx:123: {t("citations.references")} frontend-nextjs/src/components/ChatPanel.tsx:442: ? formatAssistantMessageContent(msg.content, msg.sources) frontend-nextjs/src/components/SourcesSummary.tsx:42: console.error('Failed to load sources summary:', error); frontend-nextjs/src/components/SourcesSummary.tsx:72: {t('sources.title')} frontend-nextjs/src/components/SourcesSummary.tsx:99: {t('sources.links', { count: data.urls.total })} frontend-nextjs/src/components/SourcesSummary.tsx:131: {t('sources.fileItems', { count: data.files.total })} frontend-nextjs/src/components/SourcesSummary.tsx:135: {data.files.processing} {t('sources.processing')} frontend-nextjs/src/compone

Query/prompt: OpenAI Responses API web search tool citations documentation official Agent context: The codebase has a good insertion point: `prepare_chat_request` retrieves tenant KB chunks, then passes a single prompt to the configured chat model. The source pipeline currently only exposes URL KB sources, so live web retrieval should be a backend service that returns normalized source objects and grounded context before the LLM call. Query: OpenAI Responses API web search tool citations documentation official Agent context: The codebase has a good insertion point: `prepare_chat_request` retrieves tenant KB chunks, then passes a single prompt to the configured chat model. The source pipeline currently only exposes URL KB sources, so live web retrieval should be a backend service that returns normalized source objects and grounded context before the LLM call. Expanded queries: OpenAI Responses API web search tool citations documentation official | Google Gemini Grounding with Google Search citations documentation official | Tavily Search API documentation citations chunks raw content official | Exa API search contents highlights documentation official Provider mentions: openai

Query/prompt: Perplexity Sonar API citations search domain filter documentation official Agent context: The codebase has a good insertion point: `prepare_chat_request` retrieves tenant KB chunks, then passes a single prompt to the configured chat model. The source pipeline currently only exposes URL KB sources, so live web retrieval should be a backend service that returns normalized source objects and grounded context before the LLM call. Query: Perplexity Sonar API citations search domain filter documentation official Agent context: The codebase has a good insertion point: `prepare_chat_request` retrieves tenant KB chunks, then passes a single prompt to the configured chat model. The source pipeline currently only exposes URL KB sources, so live web retrieval should be a backend service that returns normalized source objects and grounded context before the LLM call. Expanded queries: Perplexity Sonar API citations search domain filter documentation official | Brave Search API snippets web search documentation official | Microsoft Bing Web Search API documentation snippets official | SerpAPI Google Search API documentation organic results snippets official

Query/prompt: https://platform.openai.com/docs/guides/tools-web-search?api-mode=responses Agent context: The codebase has a good insertion point: `prepare_chat_request` retrieves tenant KB chunks, then passes a single prompt to the configured chat model. The source pipeline currently only exposes URL KB sources, so live web retrieval should be a backend service that returns normalized source objects and grounded context before the LLM call. Query: https://platform.openai.com/docs/guides/tools-web-search?api-mode=responses Agent context: The codebase has a good insertion point: `prepare_chat_request` retrieves tenant KB chunks, then passes a single prompt to the configured chat model. The source pipeline currently only exposes URL KB sources, so live web retrieval should be a backend service that returns normalized source objects and grounded context before the LLM call. Provider mentions: openai

Query/prompt: 'allowed_domains' in https://platform.openai.com/docs/guides/tools-web-search?api-mode=responses Agent context: The codebase has a good insertion point: `prepare_chat_request` retrieves tenant KB chunks, then passes a single prompt to the configured chat model. The source pipeline currently only exposes URL KB sources, so live web retrieval should be a backend service that returns normalized source objects and grounded context before the LLM call. Query: 'allowed_domains' in https://platform.openai.com/docs/guides/tools-web-search?api-mode=responses Agent context: The codebase has a good insertion point: `prepare_chat_request` retrieves tenant KB chunks, then passes a single prompt to the configured chat model. The source pipeline currently only exposes URL KB sources, so live web retrieval should be a backend service that returns normalized source objects and grounded context before the LLM call. Provider mentions: openai

Query/prompt: https://docs.tavily.com/documentation/api-reference/endpoint/search Agent context: The codebase has a good insertion point: `prepare_chat_request` retrieves tenant KB chunks, then passes a single prompt to the configured chat model. The source pipeline currently only exposes URL KB sources, so live web retrieval should be a backend service that returns normalized source objects and grounded context before the LLM call. Query: https://docs.tavily.com/documentation/api-reference/endpoint/search Agent context: The codebase has a good insertion point: `prepare_chat_request` retrieves tenant KB chunks, then passes a single prompt to the configured chat model. The source pipeline currently only exposes URL KB sources, so live web retrieval should be a backend service that returns normalized source objects and grounded context before the LLM call.

Query/prompt: https://help.tavily.com/articles/3960993389-does-tavily-search-api-provide-citations-for-its-results Agent context: The codebase has a good insertion point: `prepare_chat_request` retrieves tenant KB chunks, then passes a single prompt to the configured chat model. The source pipeline currently only exposes URL KB sources, so live web retrieval should be a backend service that returns normalized source objects and grounded context before the LLM call. Query: https://help.tavily.com/articles/3960993389-does-tavily-search-api-provide-citations-for-its-results Agent context: The codebase has a good insertion point: `prepare_chat_request` retrieves tenant KB chunks, then passes a single prompt to the configured chat model. The source pipeline currently only exposes URL KB sources, so live web retrieval should be a backend service that returns normalized source objects and grounded context before the LLM call.

Query/prompt: site:docs.perplexity.ai API reference chat completions citations Perplexity Sonar search_results Agent context: The codebase has a good insertion point: `prepare_chat_request` retrieves tenant KB chunks, then passes a single prompt to the configured chat model. The source pipeline currently only exposes URL KB sources, so live web retrieval should be a backend service that returns normalized source objects and grounded context before the LLM call. Query: site:docs.perplexity.ai API reference chat completions citations Perplexity Sonar search_results Agent context: The codebase has a good insertion point: `prepare_chat_request` retrieves tenant KB chunks, then passes a single prompt to the configured chat model. The source pipeline currently only exposes URL KB sources, so live web retrieval should be a backend service that returns normalized source objects and grounded context before the LLM call. Expanded queries: site:docs.perplexity.ai API reference chat completions citations Perplexity Sonar search_results | docs.perplexity.ai sonar API citations search results

Agent context: Provider-wise, the main architectural tradeoff is now clear: model-coupled web tools like OpenAI/Gemini give span annotations, while retrieval APIs like Tavily/Exa return source material before generation. Because this repo already streams sources separately and supports multiple LLM providers, I’m weighing auditability and provider independence heavily. Provider-wise, the main architectural tradeoff is now clear: model-coupled web tools like OpenAI/Gemini give span annotations, while retrieval APIs like Tavily/Exa return source material before generation. Because this repo already streams sources separately and supports multiple LLM providers, I’m weighing auditability and provider independence heavily. Provider mentions: openai

Agent context: Provider-wise, the main architectural tradeoff is now clear: model-coupled web tools like OpenAI/Gemini give span annotations, while retrieval APIs like Tavily/Exa return source material before generation. Because this repo already streams sources separately and supports multiple LLM providers, I’m weighing auditability and provider independence heavily.

Agent context: Provider-wise, the main architectural tradeoff is now clear: model-coupled web tools like OpenAI/Gemini give span annotations, while retrieval APIs like Tavily/Exa return source material before generation. Because this repo already streams sources separately and supports multiple LLM providers, I’m weighing auditability and provider independence heavily. exit code 0 Output: 489 def build_chat_sources(retrieval_results: List[Dict[str, Any]]) -> List[Dict[str, Any]]: 490 """Build normalized source payloads for chat responses.""" 491 sources: List[Dict[str, Any]] = [] 492 493 for result in retrieval_results: 494 snippet = result.get("content", "")[:200].strip() 495 if snippet and len(result.get("content", "")) > 200: 496 snippet += "..." 497 498 if result["type"] == "url": 499 sources.append( 500 { 501 "type": "url", 502 "title": result.get("metadata", {}).get("title", "文档"), 503 "url": result.get("metadata", {}).get("url", ""), 504 "snippet": snippet or None, 505 } 506 ) 507 508 return sources 509 510 511 _SOURCE_PLACEHOLDER_PATTERN = re.compile(r"\[([^\]]+)\]\(#source-(\d+)\)") 512 513 514 def replace_source_placeholders(reply: str, sources: List[Dict[str, Any]]) -> str: 515 """Replace trusted source placeholders with real URLs and strip invalid ones.""" 516

Agent context: Provider-wise, the main architectural tradeoff is now clear: model-coupled web tools like OpenAI/Gemini give span annotations, while retrieval APIs like Tavily/Exa return source material before generation. Because this repo already streams sources separately and supports multiple LLM providers, I’m weighing auditability and provider independence heavily.

Agent context: Provider-wise, the main architectural tradeoff is now clear: model-coupled web tools like OpenAI/Gemini give span annotations, while retrieval APIs like Tavily/Exa return source material before generation. Because this repo already streams sources separately and supports multiple LLM providers, I’m weighing auditability and provider independence heavily. exit code 0 Output: 28 sources?: Source[]; 29 timestamp: Date; 30 } 31 32 interface Source { 33 type: 'url' | 'qa'; 34 title?: string; 35 url?: string; 36 snippet?: string; 37 question?: string; 38 id?: string; 39 } 40 1646 1647 const payload = JSON.parse(dataLines.join('\n')) as StreamEventPayload | StreamDoneMeta | StreamErrorPayload; 1648 1649 switch (eventName) { 1650 case 'sources': 1651 this.currentStreamSources = Array.isArray((payload as StreamEventPayload).sources) 1652 ? (payload as StreamEventPayload).sources! 1653 : []; 1654 break; 1655 case 'thinking':

Agent context: Provider-wise, the main architectural tradeoff is now clear: model-coupled web tools like OpenAI/Gemini give span annotations, while retrieval APIs like Tavily/Exa return source material before generation. Because this repo already streams sources separately and supports multiple LLM providers, I’m weighing auditability and provider independence heavily.

Agent context: Provider-wise, the main architectural tradeoff is now clear: model-coupled web tools like OpenAI/Gemini give span annotations, while retrieval APIs like Tavily/Exa return source material before generation. Because this repo already streams sources separately and supports multiple LLM providers, I’m weighing auditability and provider independence heavily. exit code 0 Output: 751 def get_llm_service( 752 agent=None, 753 use_mock: bool = False, 754 *, 755 api_key: Optional[str] = None, 756 api_base: Optional[str] = None, 757 model: Optional[str] = None, 758 provider_type: Optional[str] = None, 759 ) -> BaseLLMService: 760 """ 761 获取 LLM 服务实例（工厂函数） 762 763 根据Agent的服务商类型返回相应的服务实例 764 765 Args: 766 agent: Agent 模型实例（可选） 767 use_mock: 是否使用Mock服务（用于测试） 768 api_key: 显式传入的 API Key 769 api_base: 显式传入的 API Base 770 model: 显式传入的模型名 771 provider_type: 显式传入的服务商类型 772 773 Returns: 774 BaseLLMService: LLM 服务实例 775 """ 776 resolved_api_key = api_key if api_key is not None else getattr(agent, "api_key", None) 777 resolved_api_base = api_base if api_base is not None else getattr(agent, "api_base", None) 778 resolved_model = model if model is not None else getattr(agent, "model", None) 779 resolved_provider_type = provider_type if provider_type is not None else getattr(agent, " Provider mentions: openai

Agent context: Provider-wise, the main architectural tradeoff is now clear: model-coupled web tools like OpenAI/Gemini give span annotations, while retrieval APIs like Tavily/Exa return source material before generation. Because this repo already streams sources separately and supports multiple LLM providers, I’m weighing auditability and provider independence heavily.

Agent context: Provider-wise, the main architectural tradeoff is now clear: model-coupled web tools like OpenAI/Gemini give span annotations, while retrieval APIs like Tavily/Exa return source material before generation. Because this repo already streams sources separately and supports multiple LLM providers, I’m weighing auditability and provider independence heavily. exit code 0 Output: 20 total_tokens: number; 21 } 22 23 export interface ChatResponse { 24 reply: string; 25 sources: Source[]; 26 usage?: UsageInfo; 27 session_id?: string; 28 message_id?: number; 29 taken_over?: boolean; 30 } 31 32 export interface StreamDoneMeta { 33 message_id: number | null; 34 session_id?: string; 35 usage?: UsageInfo | null; 36 taken_over?: boolean; 37 } 38 39 export interface Source { 40 type: "url" | "file"; 41 title?: string; 42 url?: string; 43 snippet?: string; 44 question?: string; 45 id?: string; 46 } 47 48 export type ProviderType = 420 } 421 422 if (dataLines.length === 0) { 423 return; 424 } 425 426 const payload = JSON.parse(dataLines.join("\n")); 427 428 switch (eventName) { 429 case "sources": 430 callbacks.onSources( 431 Array.isArray(payload.sources) ? payload.sources : [], 432 ); 433 break; 434 case "thinking": 435 callbacks.onThinking?.( 436 typeof payload.elapsed ==

Query/prompt: https://docs.perplexity.ai/docs/sonar/quickstart Agent context: Provider-wise, the main architectural tradeoff is now clear: model-coupled web tools like OpenAI/Gemini give span annotations, while retrieval APIs like Tavily/Exa return source material before generation. Because this repo already streams sources separately and supports multiple LLM providers, I’m weighing auditability and provider independence heavily. Query: https://docs.perplexity.ai/docs/sonar/quickstart Agent context: Provider-wise, the main architectural tradeoff is now clear: model-coupled web tools like OpenAI/Gemini give span annotations, while retrieval APIs like Tavily/Exa return source material before generation. Because this repo already streams sources separately and supports multiple LLM providers, I’m weighing auditability and provider independence heavily. Provider mentions: openai

Query/prompt: 'Search results and metadata are delivered' in https://docs.perplexity.ai/docs/sonar/features Agent context: Provider-wise, the main architectural tradeoff is now clear: model-coupled web tools like OpenAI/Gemini give span annotations, while retrieval APIs like Tavily/Exa return source material before generation. Because this repo already streams sources separately and supports multiple LLM providers, I’m weighing auditability and provider independence heavily. Query: 'Search results and metadata are delivered' in https://docs.perplexity.ai/docs/sonar/features Agent context: Provider-wise, the main architectural tradeoff is now clear: model-coupled web tools like OpenAI/Gemini give span annotations, while retrieval APIs like Tavily/Exa return source material before generation. Because this repo already streams sources separately and supports multiple LLM providers, I’m weighing auditability and provider independence heavily. Provider mentions: openai

Query/prompt: https://docs.perplexity.ai/docs/grounded-llm/chat-completions/filters/context-size Agent context: Provider-wise, the main architectural tradeoff is now clear: model-coupled web tools like OpenAI/Gemini give span annotations, while retrieval APIs like Tavily/Exa return source material before generation. Because this repo already streams sources separately and supports multiple LLM providers, I’m weighing auditability and provider independence heavily. Query: https://docs.perplexity.ai/docs/grounded-llm/chat-completions/filters/context-size Agent context: Provider-wise, the main architectural tradeoff is now clear: model-coupled web tools like OpenAI/Gemini give span annotations, while retrieval APIs like Tavily/Exa return source material before generation. Because this repo already streams sources separately and supports multiple LLM providers, I’m weighing auditability and provider independence heavily. Provider mentions: openai

Query/prompt: https://docs.tavily.com/documentation/api-reference/endpoint/search Agent context: Provider-wise, the main architectural tradeoff is now clear: model-coupled web tools like OpenAI/Gemini give span annotations, while retrieval APIs like Tavily/Exa return source material before generation. Because this repo already streams sources separately and supports multiple LLM providers, I’m weighing auditability and provider independence heavily. Query: https://docs.tavily.com/documentation/api-reference/endpoint/search Agent context: Provider-wise, the main architectural tradeoff is now clear: model-coupled web tools like OpenAI/Gemini give span annotations, while retrieval APIs like Tavily/Exa return source material before generation. Because this repo already streams sources separately and supports multiple LLM providers, I’m weighing auditability and provider independence heavily. Provider mentions: openai

Agent context: **Recommendation** Use **Tavily Search API**, as a retrieval-only layer, not its generated `answer` field. For this repo, Tavily is the best fit because Basjoo already has the right architecture for pre-generation evidence: backend chat builds `sources`, replaces trusted `#source-N` placeholders, and streams sources to the frontend/widget before the LLM response starts in [backend/api/v1/endpoints.py](/home/user/worktrees/attempt-8922cda0-b75c-4de1-80a0-0b5121458d83-cap-0/backend/api/v1/endpoints.py:489) and [backend/api/v1/endpoints.py](/home/user/worktrees/attempt-8922cda0-b75c-4de1-80a0-0b5121458d83-cap-0/backend/api/v1/endpoints.py:1289). The agent currently retrieves tenant KB context, then calls whichever LLM provider is configured in [backend/api/v1/endpoints.py](/home/user/worktrees/attempt-8922cda0-b75c-4de1-80a0-0b5121458d83-cap-0/backend/api/v1/endpoints.py:865), while LLMs are provider-pluggable in [backend/services/llm_service.py](/home/user/worktrees/attempt-8922cda0-b75c-4de1-80a0-0b5121458d83-cap-0/backend/services/llm_service.py:751). A standalone retrieval provider fits that better than coupling web grounding to one model vendor. I would configure Tavily with `search_depth=advanced`, `include_answer=false`, `include_raw_content="text"` or `"markdown"` where needed, strict `include_domains`/`exclude_domains`, date filters for current-event queries, and a hard answer policy: no cited source evidence, no answer. Tavily’s docs say advanced search is for high-precision queries, chunks are pulled directly from sources, `news` is for real-time updates, and raw content returns cleaned parsed page content; it also supports include/exclude domain controls up to 300/150 domains respectively. Tavily also states its API includes citations for retrieved information. Sources: Tavily Search API docs and Tavily citation FAQ. ([docs.tavily.com](https://docs.tavily.com/documentation/api-reference/endpoint/search)) ([docs.tavily.com](https://docs.tavily.com/documentation/api-reference/endpoint/search)) ([help.tavily.com](https://help.tavily.com/articles/3960993389-does-tavily-search-api-provide-citations-for-its-results)) **Main Alternatives Considered** - **Google Gemini / Vertex Grounding with Google Search**: strongest search corpus signal, and Google returns `groundingMetadata`, source chunks, and citation supports. I did not choose it because grounding is coupled to Gemini generation, the model decides when search is useful, and this repo would lose provider independence. It is a good answer if the product standardizes on Gemini for all grounded answers. ([docs.perplexity.ai](https://docs.perplexity.ai/docs/sonar/prompt-guide)) ([docs.perplexity.ai](https://docs.perplexity.ai/docs/grounded-llm/chat-completions/filters/context-size)) - **OpenAI Responses API Web Search**: strong model-integrated option with inline URL citations and citation annotations, plus live web access. I did not choose it because Basjoo currently uses Chat Completions-style provider abstraction and many non-OpenAI providers; OpenAI web search would require a separate Responses path and citations arrive as model annotations rather than a clean pre-generation retrieval layer. ([docs.perplexity.ai](https://docs.perplexity.ai/docs/sonar/features)) - **Perplexity Sonar / Search API**: compelling for web-grounded answers and top-level `citations`/`search_results`; Search API returns ranked structured results. I did not choose it because Sonar is another answer model, search metadata arrives at the end of streaming, and Perplexity’s own docs note the system prompt does not influence search, so source-control must be done through request parameters. ([docs.perplexity.ai](https://docs.perplexity.ai/docs/sonar/quickstart)) ([docs.perplexity.ai](https://docs.perplexity.ai/docs/sonar/features)) ([docs.perplexity.ai](https://docs.perplexity.ai/docs/sonar/prompt-guide)) ([docs.perplexity.ai](https://docs.perplexity.ai/guides/search-quickstart)) - **Exa**: good semantic search and content retrieval; it can return extractive text from pages. I did not choose it as the first option because its strongest differentiator is semantic discovery, while this use case needs conservative, auditable current-fact grounding. Its summaries are generated, so I would only use its extractive content path if chosen. - **Brave Search API / SerpApi Google results**: useful raw search/SERP providers. I did not choose them because they primarily provide ranked links and snippets or excerpts, so Basjoo would still need a separate extraction/reranking layer to reach the same retrieval quality. **Docs Actually Consulted** Tavily Search API and citation FAQ; OpenAI Web Search docs; Google Gemini Grounding and Vertex/Gemini grounding docs; Perplexity Sonar/Search/filter docs; Exa search/content docs; Brave Search API docs; SerpApi organic results docs. **Recommendation** Use **Tavily Search API**, as a retrieval-only layer, not its generated `answer` field. For this repo, Tavily is the best fit because Basjoo already has the right architecture for pre-generation evidence: backend chat builds `sources`, replaces trusted `#source-N` placeholders, and streams sources to the frontend/widget before the LLM response starts in [backend/api/v1/endpoints.py](/home/user/worktrees/attempt-8922cda0-b75c-4de1-80a0-0b5121458d83-cap-0/backend/api/v1/endpoints.py:489) and [backend/api/v1/endpoints.py](/home/user/worktrees/attempt-8922cda0-b75c-4de1-80a0-0b5121458d83-cap-0/backend/api/v1/endpoints.py:1289). The agent currently retrieves tenant KB context, then calls whichever LLM provider is configured in [backend/api/v1/endpoints.py](/home/user/worktrees/attempt-8922cda0-b75c-4de1-80a0-0b5121458d83-cap-0/backend/api/v1/endpoints.py:865), while LLMs are provider-pluggable in [backend/services/llm_service.py](/home/user/worktrees/attempt-8922cda0-b75c-4de1-80a0-0b5121458d83-cap-0/backend/services/llm_service.py:751). A standalone retrieval provider fits that better than coupling web grounding to one model vendor. I would configure Tavily with `sea Provider mentions: openai

**Recommendation** Use **Tavily Search API**, as a retrieval-only layer, not its generated `answer` field. For this repo, Tavily is the best fit because Basjoo already has the right architecture for pre-generation evidence: backend chat builds `sources`, replaces trusted `#source-N` placeholders, and streams sources to the frontend/widget before the LLM response starts in [backend/api/v1/endpoints.py](/home/user/worktrees/attempt-8922cda0-b75c-4de1-80a0-0b5121458d83-cap-0/backend/api/v1/endpoints.py:489) and [backend/api/v1/endpoints.py](/home/user/worktrees/attempt-8922cda0-b75c-4de1-80a0-0b5121458d83-cap-0/backend/api/v1/endpoints.py:1289). The agent currently retrieves tenant KB context, then calls whichever LLM provider is configured in [backend/api/v1/endpoints.py](/home/user/worktrees/attempt-8922cda0-b75c-4de1-80a0-0b5121458d83-cap-0/backend/api/v1/endpoints.py:865), while LLMs are provider-pluggable in [backend/services/llm_service.py](/home/user/worktrees/attempt-8922cda0-b75c-4de1-80a0-0b5121458d83-cap-0/backend/services/llm_service.py:751). A standalone retrieval provider fits that better than coupling web grounding to one model vendor. I would configure Tavily with `search_depth=advanced`, `include_answer=false`, `include_raw_content="text"` or `"markdown"` where needed, strict `include_domains`/`exclude_domains`, date filters for current-event queries, and a hard a Provider mentions: openai