Run

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Agent context: I’ll inspect the repository first to understand the agent architecture and where web content ingestion would fit, then I’ll check current provider docs before making a recommendation. I’ll inspect the repository first to understand the agent architecture and where web content ingestion would fit, then I’ll check current provider docs before making a recommendation.

Agent context: I’ll inspect the repository first to understand the agent architecture and where web content ingestion would fit, then I’ll check current provider docs before making a recommendation.

Agent context: I’ll inspect the repository first to understand the agent architecture and where web content ingestion would fit, then I’ll check current provider docs before making a recommendation. exit code 0 Output: /bin/bash: line 1: rg: command not found /home/user/worktrees/attempt-1c2cc84e-9a80-4143-b958-f824f6ffb464-cap-0

Agent context: I’ll inspect the repository first to understand the agent architecture and where web content ingestion would fit, then I’ll check current provider docs before making a recommendation.

Agent context: I’ll inspect the repository first to understand the agent architecture and where web content ingestion would fit, then I’ll check current provider docs before making a recommendation. exit code 0

Agent context: `rg` is not installed in this workspace, so I’m falling back to `find`/standard shell tools. The worktree is clean. `rg` is not installed in this workspace, so I’m falling back to `find`/standard shell tools. The worktree is clean.

Agent context: `rg` is not installed in this workspace, so I’m falling back to `find`/standard shell tools. The worktree is clean.

Agent context: `rg` is not installed in this workspace, so I’m falling back to `find`/standard shell tools. The worktree is clean. exit code 0 Output: .gitignore .vercelignore LICENSE Makefile README.md backend/.buildpacks.env backend/.cnignore backend/.dockerignore backend/.env.development.example backend/.env.production.example backend/.gitignore backend/Dockerfile backend/Procfile backend/README.md backend/poetry.lock backend/pyproject.toml backend/requirements.txt backend/sensei_search/__init__.py backend/sensei_search/agents/__init__.py backend/sensei_search/agents/samurai/__init__.py backend/sensei_search/agents/samurai/agent.py backend/sensei_search/agents/samurai/prompts.py backend/sensei_search/agents/shogun/__init__.py backend/sensei_search/agents/shogun/agent_v1.py backend/sensei_search/agents/shogun/agent_v2.py backend/sensei_search/agents/shogun/prompts.py backend/sensei_search/base_agent.py backend/sensei_search/chat_store.py backend/sensei_search/config.py backend/sensei_search/env.py backend/sensei_search/logger.py back

Agent context: `rg` is not installed in this workspace, so I’m falling back to `find`/standard shell tools. The worktree is clean.

Agent context: `rg` is not installed in this workspace, so I’m falling back to `find`/standard shell tools. The worktree is clean. exit code 0 Output: ./README.md ./backend/README.md ./backend/pyproject.toml ./backend/requirements.txt ./frontend/README.md ./frontend/package.json

Agent context: The repo is a search/chat app with a Python backend and Next frontend. I’m reading the backend agent and search-tool code now to see whether URL extraction belongs as a new tool, inside existing search results, or both. The repo is a search/chat app with a Python backend and Next frontend. I’m reading the backend agent and search-tool code now to see whether URL extraction belongs as a new tool, inside existing search results, or both.

Agent context: The repo is a search/chat app with a Python backend and Next frontend. I’m reading the backend agent and search-tool code now to see whether URL extraction belongs as a new tool, inside existing search results, or both.

Agent context: The repo is a search/chat app with a Python backend and Next frontend. I’m reading the backend agent and search-tool code now to see whether URL extraction belongs as a new tool, inside existing search results, or both. exit code 0 Output: # Sensei Search Sensei Search is an AI-powered answer engine. ## 📸 Screenshots ### Light Mode <div align="center"> <img src="./docs/lennon.png" alt="Screenshot - Lennon" max-width="600"> </div> ### Dark Mode <div align="center"> <img src="./docs/programming.png" alt="Screenshot - Programming" max-width="600"> </div> ## 💡 Insights from Utilizing Open Source LLMs The key takeaways and experiences of working with open source Large Language Models are summarized in a detailed discussion. For more information, you can read the full discussion on Reddit: - [Building an Open Source Perplexity AI with Open Source LLMs - Reddit Post](https://www.reddit.com/r/LocalLLaMA/comments/1dj7mkq/building_an_open_source_perplexity_ai_with_open/) ## 🛠️ Tech Stack Sensei Search is built using the following technologies: - Frontend: Next.js, Tailwind CSS - Backend: FastAPI, OpenAI client - LLMs: Command-R, Provider mentions: openai

Agent context: The repo is a search/chat app with a Python backend and Next frontend. I’m reading the backend agent and search-tool code now to see whether URL extraction belongs as a new tool, inside existing search results, or both.

Agent context: The repo is a search/chat app with a Python backend and Next frontend. I’m reading the backend agent and search-tool code now to see whether URL extraction belongs as a new tool, inside existing search results, or both. exit code 0 Output: from __future__ import annotations import asyncio import uuid from abc import ABC, abstractmethod from enum import Enum from typing import Dict, List, Literal, Optional, Protocol, Union import trafilatura # type: ignore[import] from aiohttp import ClientSession, ClientTimeout from pydantic import BaseModel, Field from typing_extensions import TypedDict from sensei_search.chat_store import ChatHistoryItem, ChatStore, ThreadMetadata from sensei_search.logger import logger from sensei_search.models import MediumImage, MediumVideo, MetaData, WebResult from sensei_search.tools import GeneralResult, TopResults FETCH_WEBPAGE_TIMEOUT = 3 class NoAccessError(Exception): """ Raised when a user attempts to access a resource they do not have permission to access. """ def __init__( self, message: str = "You do not have access to this resource." ) -> None: self.message = message super().__init__(self.

Agent context: The repo is a search/chat app with a Python backend and Next frontend. I’m reading the backend agent and search-tool code now to see whether URL extraction belongs as a new tool, inside existing search results, or both.

Agent context: The repo is a search/chat app with a Python backend and Next frontend. I’m reading the backend agent and search-tool code now to see whether URL extraction belongs as a new tool, inside existing search results, or both. exit code 0 Output: import asyncio import re from datetime import datetime from typing import Any, List, Optional from openai import AsyncOpenAI, OpenAI from sensei_search.agents.samurai.prompts import ( answer_prompt, classification_prompt, related_questions_prompt, search_prompt, ) from sensei_search.base_agent import BaseAgent, EnrichedQuery, NoAccessError, QueryTags from sensei_search.chat_store import ThreadMetadata from sensei_search.config import ( MD_MODEL, MD_MODEL_API_KEY, MD_MODEL_URL, SM_MODEL, SM_MODEL_API_KEY, SM_MODEL_URL, ) from sensei_search.logger import logger from sensei_search.models import MetaData from sensei_search.tools.search import Category from sensei_search.tools.search import Input as SearchInput from sensei_search.tools.search import TopResults, get_search_tool from sensei_search.utils import create_slug FETCH_WEBPAGE_TIMEOUT = 3 async def noop() -> None: return None class Sam Provider mentions: openai

Agent context: The repo is a search/chat app with a Python backend and Next frontend. I’m reading the backend agent and search-tool code now to see whether URL extraction belongs as a new tool, inside existing search results, or both.

Agent context: The repo is a search/chat app with a Python backend and Next frontend. I’m reading the backend agent and search-tool code now to see whether URL extraction belongs as a new tool, inside existing search results, or both. exit code 0 Output: from __future__ import annotations import asyncio import os from abc import ABC, abstractmethod from enum import Enum from typing import Any, List, Union from urllib.parse import urljoin from aiohttp import ClientSession from pydantic import BaseModel, Field from typing_extensions import TypedDict from sensei_search.logger import logger class BaseResult(TypedDict): url: str title: str content: str engines: List[str] score: float category: str class ImageResult(BaseResult): img_src: str class VideoResult(BaseResult): ... class GeneralResult(BaseResult): ... class Category(str, Enum): general = "general" images = "images" videos = "videos" class TopResults(TypedDict): general: List[GeneralResult] images: List[ImageResult] videos: List[VideoResult] async def is_url_accessible(url: str) -> bool: async with ClientSession() as session: try: async with session.head(url) as response: return resp

Agent context: There’s already a local `aiohttp` + `trafilatura` fetch path in `BaseAgent.fetch_web_pages`, with a three-second timeout and no browser/proxy/anti-bot handling. I’m checking how it is wired into the answer flow and what deployment configuration already exists before comparing providers. There’s already a local `aiohttp` + `trafilatura` fetch path in `BaseAgent.fetch_web_pages`, with a three-second timeout and no browser/proxy/anti-bot handling. I’m checking how it is wired into the answer flow and what deployment configuration already exists before comparing providers.

Agent context: There’s already a local `aiohttp` + `trafilatura` fetch path in `BaseAgent.fetch_web_pages`, with a three-second timeout and no browser/proxy/anti-bot handling. I’m checking how it is wired into the answer flow and what deployment configuration already exists before comparing providers.

Agent context: There’s already a local `aiohttp` + `trafilatura` fetch path in `BaseAgent.fetch_web_pages`, with a three-second timeout and no browser/proxy/anti-bot handling. I’m checking how it is wired into the answer flow and what deployment configuration already exists before comparing providers. exit code 0 Output: "1. Ensure that the user's query does not violate the Safety Preamble. If it does, reject the request and provide no response. " "2. Retrieve relevant documents related to the user's query. " "3. Determine which of the retrieved documents contain facts pertinent to crafting an informative response. " "4. Construct your answer based on the information extracted from the relevant documents. Avoid directly copying any grounding markup or references, such as [1][2], from the source material. Always attribute the information by citing the corresponding document(s) using the format `[1][2]` while composing the answer. NEVER include a References or Sources section at the end of your answer. " "5. When relevant documents are available, prioritize the information obtained from the search results over the knowledge from your pre-training data." "Now answer user's latest query using the same langua

Agent context: There’s already a local `aiohttp` + `trafilatura` fetch path in `BaseAgent.fetch_web_pages`, with a three-second timeout and no browser/proxy/anti-bot handling. I’m checking how it is wired into the answer flow and what deployment configuration already exists before comparing providers.

Agent context: There’s already a local `aiohttp` + `trafilatura` fetch path in `BaseAgent.fetch_web_pages`, with a three-second timeout and no browser/proxy/anti-bot handling. I’m checking how it is wired into the answer flow and what deployment configuration already exists before comparing providers. exit code 0 Output: from __future__ import annotations from urllib.parse import urljoin from aiohttp import ClientSession from sensei_search.config import SEARXNG_URL from sensei_search.logger import logger from sensei_search.tools.search.base import ( Input, SearchTool, TopResults, filter_medium_by_accessibility, get_top_results, ) MAX_RESULTS = 5 async def filter_medium_by_scores(results: TopResults) -> TopResults: """ Filter out low-score ('searxng' score) images and videos. """ logger.info("Filtering medium by scores") # We want to use some heuristics to filter out low-quality results # Remove any images that have a score less than 1 images = [image for image in results["images"] if float(image["score"] or 0) >= 1] # Remove any videos that have a score less than 4 videos = [video for video in results["videos"] if float(video["score"] or 0) >= 4] results["images"] = images results["videos"] = videos retu

Agent context: There’s already a local `aiohttp` + `trafilatura` fetch path in `BaseAgent.fetch_web_pages`, with a three-second timeout and no browser/proxy/anti-bot handling. I’m checking how it is wired into the answer flow and what deployment configuration already exists before comparing providers.

Agent context: There’s already a local `aiohttp` + `trafilatura` fetch path in `BaseAgent.fetch_web_pages`, with a three-second timeout and no browser/proxy/anti-bot handling. I’m checking how it is wired into the answer flow and what deployment configuration already exists before comparing providers. exit code 0 Output: from __future__ import annotations import asyncio import os from typing import Any, Callable, Coroutine, Dict, List, TypeVar from aiohttp import ClientSession from sensei_search.config import BING_API_KEY from sensei_search.logger import logger from sensei_search.tools.search.base import ( Category, GeneralResult, ImageResult, Input, SearchTool, TopResults, VideoResult, filter_medium_by_accessibility, ) MAX_RESULTS = 5 T = TypeVar("T", GeneralResult, ImageResult, VideoResult) class Bing(SearchTool): @staticmethod async def search(args: Input) -> TopResults: """ A search engine tool. Useful when you need to answer questions about nouns, current events or the current state of the world. To use the tool, you need to pass in parameters described by the OpenAPI spec. """ logger.info("Searching with bing") logger.debug(f"Calling bing with args: {args}") query, categories = args.query, args.cat

Agent context: There’s already a local `aiohttp` + `trafilatura` fetch path in `BaseAgent.fetch_web_pages`, with a three-second timeout and no browser/proxy/anti-bot handling. I’m checking how it is wired into the answer flow and what deployment configuration already exists before comparing providers.

Agent context: There’s already a local `aiohttp` + `trafilatura` fetch path in `BaseAgent.fetch_web_pages`, with a three-second timeout and no browser/proxy/anti-bot handling. I’m checking how it is wired into the answer flow and what deployment configuration already exists before comparing providers. exit code 0 Output: [tool.poetry] name = "sensei-search" version = "0.1.0" description = "" authors = ["Jijun Leng"] readme = "README.md" [tool.poetry.dependencies] python = "^3.9" python-dotenv = "^1.0.1" openai = "^1.30.5" loguru = "^0.7.2" fastapi = "^0.111.0" python-socketio = "^5.11.2" uvicorn = "^0.30.1" aiohttp = "^3.9.5" trafilatura = "^1.10.0" redis = "^5.0.5" typing-extensions = "^4.12.2" python-slugify = "^8.0.4" [tool.mypy] ignore_missing_imports = true disallow_untyped_defs = true check_untyped_defs = true plugins = "pydantic.mypy" warn_unused_configs = true [tool.isort] profile = "black" [tool.poetry.group.dev.dependencies] isort = "^5.13.2" black = "^24.4.2" mypy = "^1.10.0" jinja2 = "^3.1.4" paka = "^0.1.9" [build-system] requires = ["poetry-core"] build-backend = "poetry.core.masonry.api" Provider mentions: openai

Agent context: There’s already a local `aiohttp` + `trafilatura` fetch path in `BaseAgent.fetch_web_pages`, with a three-second timeout and no browser/proxy/anti-bot handling. I’m checking how it is wired into the answer flow and what deployment configuration already exists before comparing providers.

Agent context: There’s already a local `aiohttp` + `trafilatura` fetch path in `BaseAgent.fetch_web_pages`, with a three-second timeout and no browser/proxy/anti-bot handling. I’m checking how it is wired into the answer flow and what deployment configuration already exists before comparing providers. exit code 0 Output: import os from sensei_search.env import load_envs load_envs() SEARXNG_URL = os.environ["SEARXNG_URL"] REDIS_HOST = os.environ["REDIS_HOST"] BING_API_KEY = os.environ["BING_API_KEY"] # Small model SM_MODEL_URL = os.environ["SM_MODEL_URL"] SM_MODEL = os.environ["SM_MODEL"] SM_MODEL_API_KEY = os.environ["SM_MODEL_API_KEY"] # Medium model MD_MODEL_URL = os.environ["MD_MODEL_URL"] MD_MODEL = os.environ["MD_MODEL"] MD_MODEL_API_KEY = os.environ["MD_MODEL_API_KEY"]

Agent context: There’s already a local `aiohttp` + `trafilatura` fetch path in `BaseAgent.fetch_web_pages`, with a three-second timeout and no browser/proxy/anti-bot handling. I’m checking how it is wired into the answer flow and what deployment configuration already exists before comparing providers.

Agent context: There’s already a local `aiohttp` + `trafilatura` fetch path in `BaseAgent.fetch_web_pages`, with a three-second timeout and no browser/proxy/anti-bot handling. I’m checking how it is wired into the answer flow and what deployment configuration already exists before comparing providers. exit code 0 Output: import asyncio import json import re from datetime import datetime from typing import Any, List, Optional from openai import AsyncOpenAI from openai.types.chat import ChatCompletionMessageParam, ChatCompletionMessageToolCall from openai.types.chat.chat_completion_chunk import ChoiceDeltaToolCall from openai.types.chat.chat_completion_message_tool_call import Function from sensei_search.agents.shogun.prompts import ( answer_prompt, general_prompt, related_questions_prompt, ) from sensei_search.base_agent import BaseAgent, NoAccessError from sensei_search.chat_store import ThreadMetadata from sensei_search.config import ( MD_MODEL, MD_MODEL_API_KEY, MD_MODEL_URL, SM_MODEL, SM_MODEL_API_KEY, SM_MODEL_URL, ) from sensei_search.logger import logger from sensei_search.models import MetaData from sensei_search.tools.search import Input as SearchInput from sensei_search.tools.search import TopRe Provider mentions: openai

Agent context: There’s already a local `aiohttp` + `trafilatura` fetch path in `BaseAgent.fetch_web_pages`, with a three-second timeout and no browser/proxy/anti-bot handling. I’m checking how it is wired into the answer flow and what deployment configuration already exists before comparing providers.

Agent context: There’s already a local `aiohttp` + `trafilatura` fetch path in `BaseAgent.fetch_web_pages`, with a three-second timeout and no browser/proxy/anti-bot handling. I’m checking how it is wired into the answer flow and what deployment configuration already exists before comparing providers. exit code 0 Output: import asyncio import json import re from datetime import datetime from typing import Any, List, Optional, Tuple from openai import AsyncOpenAI from openai.types.chat import ChatCompletionMessageParam, ChatCompletionMessageToolCall from openai.types.chat.chat_completion_message_tool_call import Function from sensei_search.agents.shogun.prompts import ( answer_prompt, classification_prompt, general_prompt, related_questions_prompt, search_prompt, ) from sensei_search.base_agent import BaseAgent, NoAccessError from sensei_search.chat_store import ThreadMetadata from sensei_search.config import ( MD_MODEL, MD_MODEL_API_KEY, MD_MODEL_URL, SM_MODEL, SM_MODEL_API_KEY, SM_MODEL_URL, ) from sensei_search.logger import logger from sensei_search.models import MetaData from sensei_search.tools.search import Category from sensei_search.tools.search import Input as SearchInput from sensei_search.tool Provider mentions: openai

Agent context: There’s already a local `aiohttp` + `trafilatura` fetch path in `BaseAgent.fetch_web_pages`, with a three-second timeout and no browser/proxy/anti-bot handling. I’m checking how it is wired into the answer flow and what deployment configuration already exists before comparing providers.

Agent context: There’s already a local `aiohttp` + `trafilatura` fetch path in `BaseAgent.fetch_web_pages`, with a three-second timeout and no browser/proxy/anti-bot handling. I’m checking how it is wired into the answer flow and what deployment configuration already exists before comparing providers. exit code 0 Output: backend/sensei_search/agents/samurai/agent.py:28:from sensei_search.tools.search import TopResults, get_search_tool backend/sensei_search/agents/samurai/agent.py:148: async def gen_related_questions(self, web_pages: List[str]) -> List[str]: backend/sensei_search/agents/samurai/agent.py:150: [f"Document: {i + 1}\n{page}" for i, page in enumerate(web_pages)] backend/sensei_search/agents/samurai/agent.py:184: async def gen_answer(self, web_pages: List[str]) -> str: backend/sensei_search/agents/samurai/agent.py:194: [f"Document: {i + 1}\n{page}" for i, page in enumerate(web_pages)] backend/sensei_search/agents/samurai/agent.py:254: medium_results = await get_search_tool().search(search_input) backend/sensei_search/agents/samurai/agent.py:298: get_search_tool().search(search_input), backend/sensei_search/agents/samurai/agent.py:306: _, web_pages = await asyncio.gather( backend/sensei_search/a

Agent context: The active Samurai flow already fetches the top five result URLs and sends extracted text into the answer prompt; Shogun currently uses snippets only. A managed extractor would mainly replace or backstop `BaseAgent.fetch_web_pages`, while optionally giving Shogun full-page context later. The active Samurai flow already fetches the top five result URLs and sends extracted text into the answer prompt; Shogun currently uses snippets only. A managed extractor would mainly replace or backstop `BaseAgent.fetch_web_pages`, while optionally giving Shogun full-page context later.

Query/prompt: Firecrawl extract markdown web page API docs scrape Agent context: The active Samurai flow already fetches the top five result URLs and sends extracted text into the answer prompt; Shogun currently uses snippets only. A managed extractor would mainly replace or backstop `BaseAgent.fetch_web_pages`, while optionally giving Shogun full-page context later. Query: Firecrawl extract markdown web page API docs scrape Agent context: The active Samurai flow already fetches the top five result URLs and sends extracted text into the answer prompt; Shogun currently uses snippets only. A managed extractor would mainly replace or backstop `BaseAgent.fetch_web_pages`, while optionally giving Shogun full-page context later. Expanded queries: Firecrawl extract markdown web page API docs scrape | Jina AI Reader API docs URL markdown | Tavily Extract API docs web page content extraction | Exa contents API docs web page contents

Query/prompt: https://docs.firecrawl.dev/api-reference/v2-introduction Agent context: The active Samurai flow already fetches the top five result URLs and sends extracted text into the answer prompt; Shogun currently uses snippets only. A managed extractor would mainly replace or backstop `BaseAgent.fetch_web_pages`, while optionally giving Shogun full-page context later. Query: https://docs.firecrawl.dev/api-reference/v2-introduction Agent context: The active Samurai flow already fetches the top five result URLs and sends extracted text into the answer prompt; Shogun currently uses snippets only. A managed extractor would mainly replace or backstop `BaseAgent.fetch_web_pages`, while optionally giving Shogun full-page context later.

Query/prompt: https://r.jina.ai/docs Agent context: The active Samurai flow already fetches the top five result URLs and sends extracted text into the answer prompt; Shogun currently uses snippets only. A managed extractor would mainly replace or backstop `BaseAgent.fetch_web_pages`, while optionally giving Shogun full-page context later. Query: https://r.jina.ai/docs Agent context: The active Samurai flow already fetches the top five result URLs and sends extracted text into the answer prompt; Shogun currently uses snippets only. A managed extractor would mainly replace or backstop `BaseAgent.fetch_web_pages`, while optionally giving Shogun full-page context later.

Query/prompt: Diffbot Article API docs extract webpage article markdown Agent context: The active Samurai flow already fetches the top five result URLs and sends extracted text into the answer prompt; Shogun currently uses snippets only. A managed extractor would mainly replace or backstop `BaseAgent.fetch_web_pages`, while optionally giving Shogun full-page context later. Query: Diffbot Article API docs extract webpage article markdown Agent context: The active Samurai flow already fetches the top five result URLs and sends extracted text into the answer prompt; Shogun currently uses snippets only. A managed extractor would mainly replace or backstop `BaseAgent.fetch_web_pages`, while optionally giving Shogun full-page context later. Expanded queries: Diffbot Article API docs extract webpage article markdown | ScrapingBee scraping API extract rules docs javascript rendering | Browserless content API scrape markdown docs | Apify Website Content Crawler markdown API docs

Query/prompt: Firecrawl pricing credits scrape API pricing Agent context: The active Samurai flow already fetches the top five result URLs and sends extracted text into the answer prompt; Shogun currently uses snippets only. A managed extractor would mainly replace or backstop `BaseAgent.fetch_web_pages`, while optionally giving Shogun full-page context later. Query: Firecrawl pricing credits scrape API pricing Agent context: The active Samurai flow already fetches the top five result URLs and sends extracted text into the answer prompt; Shogun currently uses snippets only. A managed extractor would mainly replace or backstop `BaseAgent.fetch_web_pages`, while optionally giving Shogun full-page context later. Expanded queries: Firecrawl pricing credits scrape API pricing | Tavily pricing API extract credits | Exa API pricing contents pages cost | Jina AI Reader pricing API

Query/prompt: https://www.firecrawl.dev/pricing Agent context: The active Samurai flow already fetches the top five result URLs and sends extracted text into the answer prompt; Shogun currently uses snippets only. A managed extractor would mainly replace or backstop `BaseAgent.fetch_web_pages`, while optionally giving Shogun full-page context later. Query: https://www.firecrawl.dev/pricing Agent context: The active Samurai flow already fetches the top five result URLs and sends extracted text into the answer prompt; Shogun currently uses snippets only. A managed extractor would mainly replace or backstop `BaseAgent.fetch_web_pages`, while optionally giving Shogun full-page context later.

Query/prompt: 'Free' in https://www.firecrawl.dev/pricing Agent context: The active Samurai flow already fetches the top five result URLs and sends extracted text into the answer prompt; Shogun currently uses snippets only. A managed extractor would mainly replace or backstop `BaseAgent.fetch_web_pages`, while optionally giving Shogun full-page context later. Query: 'Free' in https://www.firecrawl.dev/pricing Agent context: The active Samurai flow already fetches the top five result URLs and sends extracted text into the answer prompt; Shogun currently uses snippets only. A managed extractor would mainly replace or backstop `BaseAgent.fetch_web_pages`, while optionally giving Shogun full-page context later.

Agent context: At this point the choice is between a purpose-built scraper/extractor and a search API that also extracts. For this repo, the important fit is known-URL extraction for the top search results, batch support, readable Markdown/text, predictable latency, and low operational burden. At this point the choice is between a purpose-built scraper/extractor and a search API that also extracts. For this repo, the important fit is known-URL extraction for the top search results, batch support, readable Markdown/text, predictable latency, and low operational burden.

Query/prompt: https://docs.diffbot.com/docs/getting-started-with-diffbot-extract Agent context: At this point the choice is between a purpose-built scraper/extractor and a search API that also extracts. For this repo, the important fit is known-URL extraction for the top search results, batch support, readable Markdown/text, predictable latency, and low operational burden. Query: https://docs.diffbot.com/docs/getting-started-with-diffbot-extract Agent context: At this point the choice is between a purpose-built scraper/extractor and a search API that also extracts. For this repo, the important fit is known-URL extraction for the top search results, batch support, readable Markdown/text, predictable latency, and low operational burden.

Agent context: At this point the choice is between a purpose-built scraper/extractor and a search API that also extracts. For this repo, the important fit is known-URL extraction for the top search results, batch support, readable Markdown/text, predictable latency, and low operational burden.

Agent context: At this point the choice is between a purpose-built scraper/extractor and a search API that also extracts. For this repo, the important fit is known-URL extraction for the top search results, batch support, readable Markdown/text, predictable latency, and low operational burden. exit code 0 Output: import os from .base import * from .bing import * from .searxng import * def get_search_tool() -> SearchTool: env = os.getenv("ENV", "development") if env == "development": return SearxNG() else: return Bing()

Agent context: At this point the choice is between a purpose-built scraper/extractor and a search API that also extracts. For this repo, the important fit is known-URL extraction for the top search results, batch support, readable Markdown/text, predictable latency, and low operational burden.

Agent context: At this point the choice is between a purpose-built scraper/extractor and a search API that also extracts. For this repo, the important fit is known-URL extraction for the top search results, batch support, readable Markdown/text, predictable latency, and low operational burden. exit code 0 Output: LOGURU_LEVEL=DEBUG SEARXNG_URL="http://searxng:8080" REDIS_HOST="redis" BING_API_KEY="" # Small model SM_MODEL_URL="http://host.docker.internal:11434/v1/" SM_MODEL="llama3:instruct" SM_MODEL_API_KEY="whatever" # Medium model MD_MODEL_URL="http://host.docker.internal:11434/v1/" MD_MODEL="command-r" MD_MODEL_API_KEY="whatever"

Agent context: At this point the choice is between a purpose-built scraper/extractor and a search API that also extracts. For this repo, the important fit is known-URL extraction for the top search results, batch support, readable Markdown/text, predictable latency, and low operational burden.

Agent context: At this point the choice is between a purpose-built scraper/extractor and a search API that also extracts. For this repo, the important fit is known-URL extraction for the top search results, batch support, readable Markdown/text, predictable latency, and low operational burden. exit code 0 Output: import os from pathlib import Path from loguru import logger def load_envs() -> None: from dotenv import load_dotenv if getattr(load_envs, "_loaded", False): return base_dir = Path(__file__).resolve().parent.parent env = os.getenv("ENV", "development") logger.info(f"Loading environment: {env}") if env == "development": dotenv_path = base_dir / ".env.development" elif env == "production": dotenv_path = base_dir / ".env.production" else: raise ValueError(f"Unknown environment: {env}") load_dotenv(dotenv_path) load_envs._loaded = True # type: ignore

Agent context: I’ve got enough repo context and current provider documentation. I’m pulling exact local line references for the current fetch path so the recommendation is tied to where this would land in the codebase. I’ve got enough repo context and current provider documentation. I’m pulling exact local line references for the current fetch path so the recommendation is tied to where this would land in the codebase.

Agent context: I’ve got enough repo context and current provider documentation. I’m pulling exact local line references for the current fetch path so the recommendation is tied to where this would land in the codebase.

Agent context: I’ve got enough repo context and current provider documentation. I’m pulling exact local line references for the current fetch path so the recommendation is tied to where this would land in the codebase. exit code 0 Output: 185 await self.emitter.emit( 186 EventEnum.related_questions.value, {"data": related_questions} 187 ) 188 189 async def fetch_web_pages(self, results: List[GeneralResult]) -> List[str]: 190 """ 191 Fetch the web page contents for the search results. 192 """ 193 194 async def fetch_page(url: str, session: ClientSession) -> str: 195 try: 196 async with session.get(url) as response: 197 return await response.text() 198 except asyncio.TimeoutError: 199 logger.warning(f"Timeout occurred when fetching {url}") 200 except Exception as e: 201 logger.exception(f"Error fetching {url}: {e}") 202 return "" 203 204 timeout = ClientTimeout(total=FETCH_WEBPAGE_TIMEOUT) 205 async with ClientSession(timeout=timeout) as session: 206 tasks = [fetch_page(result["url"], session) for result in results] 207 html_web_pages = await asyncio.gather(*tasks) 208 return [trafilatura.extract(page) for page in html_web_

Agent context: I’ve got enough repo context and current provider documentation. I’m pulling exact local line references for the current fetch path so the recommendation is tied to where this would land in the codebase.

Agent context: I’ve got enough repo context and current provider documentation. I’m pulling exact local line references for the current fetch path so the recommendation is tied to where this would land in the codebase. exit code 0 Output: 290 291 # We should check if the tags contain 'needs_search'. But for now, we always perform a search 292 tags = enriched_query["tags"] 293 if tags is None or tags["needs_search"]: 294 search_input = SearchInput(query=query, categories=[Category.general]) 295 metadata = MetaData(has_math=True if tags and tags["has_math"] else False) 296 297 search_results, _ = await asyncio.gather( 298 get_search_tool().search(search_input), 299 self.emit_metadata(metadata=metadata), 300 ) 301 else: 302 search_results = TopResults(general=[], images=[], videos=[]) 303 304 general_results = search_results["general"] 305 306 _, web_pages = await asyncio.gather( 307 # Sending search results to the client ASAP 308 self.emit_web_results(general_results), 309 # Fetch web page contents for llm to use as context 310 self.fetch_web_pages(general_results[:5]), 311 ) 312 313 answer, medium_results, related_question

Agent context: I’ve got enough repo context and current provider documentation. I’m pulling exact local line references for the current fetch path so the recommendation is tied to where this would land in the codebase.

Agent context: I’ve got enough repo context and current provider documentation. I’m pulling exact local line references for the current fetch path so the recommendation is tied to where this would land in the codebase. exit code 0 Output: 1 import os 2 3 from .base import * 4 from .bing import * 5 from .searxng import * 6 7 8 def get_search_tool() -> SearchTool: 9 env = os.getenv("ENV", "development") 10 if env == "development": 11 return SearxNG() 12 else: 13 return Bing()

Agent context: **Provider I’d Choose** Use **Exa Contents API** for managed URL-to-readable-content extraction. Repo fit: this code already searches via SearxNG/Bing, then fetches the top five URLs in [BaseAgent.fetch_web_pages](/home/user/worktrees/attempt-1c2cc84e-9a80-4143-b958-f824f6ffb464-cap-0/backend/sensei_search/base_agent.py:189) and feeds those pages into Samurai’s answer flow at [agent.py](/home/user/worktrees/attempt-1c2cc84e-9a80-4143-b958-f824f6ffb464-cap-0/backend/sensei_search/agents/samurai/agent.py:306). Exa’s `/contents` endpoint is explicitly for known URLs, supports batch URL content retrieval, returns clean LLM-ready text/Markdown, handles JS-rendered pages and PDFs, supports highlights/summaries for token control, and is priced simply at about **$1 / 1k content pages** for text. I would replace or backstop the current `aiohttp + trafilatura` path with an Exa client using `EXA_API_KEY`, passing the top result URLs to `/contents` with `text` and optionally query-targeted `highlights`. **Why Exa Over The Others** - **Best match to this repo’s architecture:** the app already has search providers; it needs extraction for URLs it already has. Exa docs say `/contents` is for when you already know the URLs. - **Batch-friendly:** supports 1-100 URLs per contents request, which fits the current top-five search result workflow. - **Token-efficient:** can return full text, highlights, summaries, or combinations, which is useful because this repo feeds extracted pages directly into prompts. - **Good cost/latency profile:** Exa lists Contents text at `$1 / 1k pages` with low latency, which is attractive for an answer engine where TTFB matters. - **Less operational surface:** no browser pool, proxy strategy, or crawler jobs to manage for the common case. **Main Alternatives Considered** - **Firecrawl:** strongest scraper/crawler feature set. It has Markdown scraping, main-content filtering, ad/cookie blocking, proxies, JS actions, PDF parsing, crawl/map/search, and browser interaction. I’d pick Firecrawl if the product priority were crawling whole sites or fighting anti-bot pages. For this repo’s top-five answer flow, it is heavier than needed. - **Tavily Extract:** very good AI-search-oriented extractor, simple API, Markdown/text output, basic/advanced extraction depth, and low per-page cost. I’d rank it second. Exa wins for this repo because `/contents` is more directly shaped around known URL batches plus text/highlights/summaries. - **Jina Reader:** excellent simplicity and good free/basic usage. It is attractive as a fallback or development option, but I would not make it the primary production provider because rate limits and paid usage are token-based, and it has less explicit batch/content-control structure than Exa. - **ScrapingBee:** strong general scraping infrastructure with JS rendering, proxy options, and Markdown/text output. Better for raw scraping/control than LLM-ready answer grounding. - **Diffbot / Apify:** powerful for structured extraction or large crawls, but too broad/heavy for this repo’s immediate “read these result URLs” need. **Docs Actually Consulted** - Exa: [Contents API guide](https://exa.ai/docs/reference/contents-api-guide), [Contents endpoint](https://exa.ai/docs/reference/get-contents), [pricing](https://exa.sh/pricing?tab=api) - Firecrawl: [API intro](https://docs.firecrawl.dev/api-reference/v2-introduction), [Scrape endpoint](https://docs.firecrawl.dev/api-reference/endpoint/scrape), [Extract endpoint](https://docs.firecrawl.dev/api-reference/endpoint/extract), [pricing](https://www.firecrawl.dev/pricing) - Tavily: [Extract endpoint](https://docs.tavily.com/documentation/api-reference/endpoint/extract), [Search endpoint](https://docs.tavily.com/documentation/api-reference/endpoint/search), [credits/pricing](https://docs.tavily.com/documentation/api-credits) - Jina: [Reader API page](https://jina.ai/reader/), [Reader GitHub docs](https://github.com/jina-ai/reader) - Diffbot: [Extract guide](https://docs.diffbot.com/docs/getting-started-with-diffbot-extract), [Article API](https://docs.diffbot.com/reference/article) - Apify: [Website Content Crawler docs](https://docs.apify.com/platform/integrations/n8n/website-content-crawler) - ScrapingBee: [HTML API docs](https://www.scrapingbee.com/documentation/) **Provider I’d Choose** Use **Exa Contents API** for managed URL-to-readable-content extraction. Repo fit: this code already searches via SearxNG/Bing, then fetches the top five URLs in [BaseAgent.fetch_web_pages](/home/user/worktrees/attempt-1c2cc84e-9a80-4143-b958-f824f6ffb464-cap-0/backend/sensei_search/base_agent.py:189) and feeds those pages into Samurai’s answer flow at [agent.py](/home/user/worktrees/attempt-1c2cc84e-9a80-4143-b958-f824f6ffb464-cap-0/backend/sensei_search/agents/samurai/agent.py:306). Exa’s `/contents` endpoint is explicitly for known URLs, supports batch URL content retrieval, returns clean LLM-ready text/Markdown, handles JS-rendered pages and PDFs, supports highlights/summaries for token control, and is priced simply at about **$1 / 1k content pages** for text. I would replace or backstop the current `aiohttp + trafilatura` path with an Exa client using `EXA_API_KEY`, passing the top result URLs to `/contents` with `text` and optionally query-targeted `highlights`. **Why Exa Over The Others** - **Best match to this repo’s architecture:** the app already has search providers; it needs extraction for URLs it already has. Exa docs say `/contents` is for when

**Provider I’d Choose** Use **Exa Contents API** for managed URL-to-readable-content extraction. Repo fit: this code already searches via SearxNG/Bing, then fetches the top five URLs in [BaseAgent.fetch_web_pages](/home/user/worktrees/attempt-1c2cc84e-9a80-4143-b958-f824f6ffb464-cap-0/backend/sensei_search/base_agent.py:189) and feeds those pages into Samurai’s answer flow at [agent.py](/home/user/worktrees/attempt-1c2cc84e-9a80-4143-b958-f824f6ffb464-cap-0/backend/sensei_search/agents/samurai/agent.py:306). Exa’s `/contents` endpoint is explicitly for known URLs, supports batch URL content retrieval, returns clean LLM-ready text/Markdown, handles JS-rendered pages and PDFs, supports highlights/summaries for token control, and is priced simply at about **$1 / 1k content pages** for text. I would replace or backstop the current `aiohttp + trafilatura` path with an Exa client using `EXA_API_KEY`, passing the top result URLs to `/contents` with `text` and optionally query-targeted `highlights`. **Why Exa Over The Others** - **Best match to this repo’s architecture:** the app already has search providers; it needs extraction for URLs it already has. Exa docs say `/contents` is for when you already know the URLs. - **Batch-friendly:** supports 1-100 URLs per contents request, which fits the current top-five search result workflow. - **Token-efficient:** can return full text, highlig