Run

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448265f1fedfc0f6084499458ad9c445b54d1f68

Agent context: I’ll inspect the repo first to understand what “agent” means here and where web-content fetching would fit, then I’ll verify current provider capabilities from their docs before making the recommendation. I’ll inspect the repo first to understand what “agent” means here and where web-content fetching would fit, then I’ll verify current provider capabilities from their docs before making the recommendation.

Agent context: I’ll inspect the repo first to understand what “agent” means here and where web-content fetching would fit, then I’ll verify current provider capabilities from their docs before making the recommendation.

Agent context: I’ll inspect the repo first to understand what “agent” means here and where web-content fetching would fit, then I’ll verify current provider capabilities from their docs before making the recommendation. exit code 127 Output: /bin/bash: line 1: rg: command not found

Agent context: I’ll inspect the repo first to understand what “agent” means here and where web-content fetching would fit, then I’ll verify current provider capabilities from their docs before making the recommendation.

Agent context: I’ll inspect the repo first to understand what “agent” means here and where web-content fetching would fit, then I’ll verify current provider capabilities from their docs before making the recommendation. exit code 0

Agent context: `rg` isn’t installed in this environment, so I’m falling back to standard filesystem commands. The worktree is currently clean. `rg` isn’t installed in this environment, so I’m falling back to standard filesystem commands. The worktree is currently clean.

Agent context: `rg` isn’t installed in this environment, so I’m falling back to standard filesystem commands. The worktree is currently clean.

Agent context: `rg` isn’t installed in this environment, so I’m falling back to standard filesystem commands. The worktree is currently clean. exit code 0 Output: .git .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/base_agent.py backend/sensei_search/chat_store.py backend/sensei_search/config.py backend/sensei_search/env.py backend/sensei_search/logger.py backend/sensei_search/models.py backend/sensei_search/server.py backend/sensei_search/utils.py cluster.yaml docker-compose.yaml docs/lennon.png docs/programming.png frontend/.buildpacks.env frontend/.cnignore frontend/.dockerignore frontend/.env.development frontend/.env.production frontend/.eslintrc.json frontend/.gitignore frontend/Dockerfile frontend/Procfile frontend/

Agent context: `rg` isn’t installed in this environment, so I’m falling back to standard filesystem commands. The worktree is currently clean.

Agent context: `rg` isn’t installed in this environment, so I’m falling back to standard filesystem commands. The worktree is currently clean. exit code 0 Output: . .agents .codex backend backend/sensei_search docs frontend frontend/app frontend/public ops ops/k8s ops/searxng

Agent context: I found a small full-stack app with a Python backend and a `sensei_search` package. I’m reading the agent, search, and config paths now to see the current retrieval boundary. I found a small full-stack app with a Python backend and a `sensei_search` package. I’m reading the agent, search, and config paths now to see the current retrieval boundary.

Agent context: I found a small full-stack app with a Python backend and a `sensei_search` package. I’m reading the agent, search, and config paths now to see the current retrieval boundary.

Agent context: I found a small full-stack app with a Python backend and a `sensei_search` package. I’m reading the agent, search, and config paths now to see the current retrieval boundary. 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: I found a small full-stack app with a Python backend and a `sensei_search` package. I’m reading the agent, search, and config paths now to see the current retrieval boundary.

Agent context: I found a small full-stack app with a Python backend and a `sensei_search` package. I’m reading the agent, search, and config paths now to see the current retrieval boundary. 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: I found a small full-stack app with a Python backend and a `sensei_search` package. I’m reading the agent, search, and config paths now to see the current retrieval boundary.

Agent context: I found a small full-stack app with a Python backend and a `sensei_search` package. I’m reading the agent, search, and config paths now to see the current retrieval boundary. 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: I found a small full-stack app with a Python backend and a `sensei_search` package. I’m reading the agent, search, and config paths now to see the current retrieval boundary.

Agent context: I found a small full-stack app with a Python backend and a `sensei_search` package. I’m reading the agent, search, and config paths now to see the current retrieval boundary. exit code 0 Output: from __future__ import annotations import asyncio import os from typing import Dict, List, Optional import socketio # type: ignore[import-untyped] from fastapi import FastAPI from fastapi.middleware.cors import CORSMiddleware from sensei_search.agents.shogun.agent_v2 import ShogunAgent from sensei_search.base_agent import NoAccessError from sensei_search.chat_store import ChatStore from sensei_search.logger import logger from sensei_search.models import ChatThread env = os.getenv("ENV", "development") origins: Optional[List[str]] = None if env == "production": origins_env = os.getenv("CORS_ORIGINS", "") if origins_env: origins = origins_env.split(",") class SocketIOEmitter: def __init__(self, sio: socketio.AsyncServer, sid: str): self.sio = sio self.sid = sid async def emit(self, event: str, data: Dict) -> None: await self.sio.emit(event, data, room=self.sid) app = FastAPI() app.add_midd

Agent context: I found a small full-stack app with a Python backend and a `sensei_search` package. I’m reading the agent, search, and config paths now to see the current retrieval boundary.

Agent context: I found a small full-stack app with a Python backend and a `sensei_search` package. I’m reading the agent, search, and config paths now to see the current retrieval boundary. exit code 0 Output: "id": str(uuid.uuid4()), "thread_id": self.thread_id, "mediums": mediums, "web_results": web_results, "query": user_message, "answer": answer, # We use the metadata to give the client extra info if they need to load the Math plugin "metadata": metadata, } await chat_store.save_chat_history(self.thread_id, chat_history) async def load_chat_history( self, thread_id: str, roles: Optional[List[Literal["user", "assistant"]]] = None ) -> None: """ Load the chat history for the current thread from Redis. We don't store system messages in the chat history, so we only load user and assistant messages. """ if self.chat_messages_loaded: return if roles is None: roles = ["user", "assistant"] chat_store = ChatStore() chat_history = await chat_store.get_chat_history(thread_id) for m in chat_history: if "user" in roles: self.chat_messages.append({"role": "user", "content": m["query"]}) if "assistant" i

Agent context: I found a small full-stack app with a Python backend and a `sensei_search` package. I’m reading the agent, search, and config paths now to see the current retrieval boundary.

Agent context: I found a small full-stack app with a Python backend and a `sensei_search` package. I’m reading the agent, search, and config paths now to see the current retrieval boundary. exit code 0 Output: 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 backend/sensei_search/models.py backend/sensei_search/server.py backend/sensei_search/tools/__init__.py backend/sensei_search/tools/search/__init__.py backend/sensei_search/tools/search/base.py backend/sensei_search/tools/search/bing.py backend/sensei_search/tools/search/searxng.py backend/sensei_search/utils.py

Agent context: I found a small full-stack app with a Python backend and a `sensei_search` package. I’m reading the agent, search, and config paths now to see the current retrieval boundary.

Agent context: I found a small full-stack app with a Python backend and a `sensei_search` package. I’m reading the agent, search, and config paths now to see the current retrieval boundary. 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: I found a small full-stack app with a Python backend and a `sensei_search` package. I’m reading the agent, search, and config paths now to see the current retrieval boundary.

Agent context: I found a small full-stack app with a Python backend and a `sensei_search` package. I’m reading the agent, search, and config paths now to see the current retrieval boundary. exit code 0 Output: aiohttp==3.9.5 ; python_version >= "3.9" and python_version < "4.0" \ --hash=sha256:0605cc2c0088fcaae79f01c913a38611ad09ba68ff482402d3410bf59039bfb8 \ --hash=sha256:0a158704edf0abcac8ac371fbb54044f3270bdbc93e254a82b6c82be1ef08f3c \ --hash=sha256:0cbf56238f4bbf49dab8c2dc2e6b1b68502b1e88d335bea59b3f5b9f4c001475 \ --hash=sha256:1732102949ff6087589408d76cd6dea656b93c896b011ecafff418c9661dc4ed \ --hash=sha256:18f634d540dd099c262e9f887c8bbacc959847cfe5da7a0e2e1cf3f14dbf2daf \ --hash=sha256:239f975589a944eeb1bad26b8b140a59a3a320067fb3cd10b75c3092405a1372 \ --hash=sha256:2faa61a904b83142747fc6a6d7ad8fccff898c849123030f8e75d5d967fd4a81 \ --hash=sha256:320e8618eda64e19d11bdb3bd04ccc0a816c17eaecb7e4945d01deee2a22f95f \ --hash=sha256:38d80498e2e169bc61418ff36170e0aad0cd268da8b38a17c4cf29d254a8b3f1 \ --hash=sha256:3916c8692dbd9d55c523374a3b8213e628424d19116ac4308e434dbf6d95bbdd \ --hash=sha256:393c7a

Agent context: I found a small full-stack app with a Python backend and a `sensei_search` package. I’m reading the agent, search, and config paths now to see the current retrieval boundary.

Agent context: I found a small full-stack app with a Python backend and a `sensei_search` package. I’m reading the agent, search, and config paths now to see the current retrieval boundary. exit code 0 Output: answer_prompt = """\ You are Sensei, a helpful search assistant. Your task is to deliver a concise and accurate response to a Query, drawing from the given search results. Answer only the last Query using its provided search results and the context of previous queries. Do not repeat information from previous answers.Your answer must be precise, of high-quality, and written by an expert using an unbiased and journalistic tone. You MUST cite the most relevant search results that answer the Query. Do not mention any irrelevant results. You MUST ADHERE to the following instructions for citing search results: - To cite a search result, enclose its index located above the summary with brackets at the end of the corresponding sentence, for example "Ice is less dense than water[1][3]." or "Paris is the capital of France[1][4][5]." - NO SPACE between the last word and the citation, and ALWAYS use Provider mentions: together-ai

Agent context: I found a small full-stack app with a Python backend and a `sensei_search` package. I’m reading the agent, search, and config paths now to see the current retrieval boundary.

Agent context: I found a small full-stack app with a Python backend and a `sensei_search` package. I’m reading the agent, search, and config paths now to see the current retrieval boundary. 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: I found a small full-stack app with a Python backend and a `sensei_search` package. I’m reading the agent, search, and config paths now to see the current retrieval boundary.

Agent context: I found a small full-stack app with a Python backend and a `sensei_search` package. I’m reading the agent, search, and config paths now to see the current retrieval boundary. 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: I found a small full-stack app with a Python backend and a `sensei_search` package. I’m reading the agent, search, and config paths now to see the current retrieval boundary.

Agent context: I found a small full-stack app with a Python backend and a `sensei_search` package. I’m reading the agent, search, and config paths now to see the current retrieval boundary. 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: I found a small full-stack app with a Python backend and a `sensei_search` package. I’m reading the agent, search, and config paths now to see the current retrieval boundary.

Agent context: I found a small full-stack app with a Python backend and a `sensei_search` package. I’m reading the agent, search, and config paths now to see the current retrieval boundary. 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: The backend already has a local fetch path in `BaseAgent.fetch_web_pages`: `aiohttp` with a 3-second timeout, followed by `trafilatura.extract`. That is useful for static HTML, but it will miss client-rendered content and is not currently wired into the Shogun answer path, which only passes search snippets to the model. The backend already has a local fetch path in `BaseAgent.fetch_web_pages`: `aiohttp` with a 3-second timeout, followed by `trafilatura.extract`. That is useful for static HTML, but it will miss client-rendered content and is not currently wired into the Shogun answer path, which only passes search snippets to the model.

Agent context: The backend already has a local fetch path in `BaseAgent.fetch_web_pages`: `aiohttp` with a 3-second timeout, followed by `trafilatura.extract`. That is useful for static HTML, but it will miss client-rendered content and is not currently wired into the Shogun answer path, which only passes search snippets to the model.

Agent context: The backend already has a local fetch path in `BaseAgent.fetch_web_pages`: `aiohttp` with a 3-second timeout, followed by `trafilatura.extract`. That is useful for static HTML, but it will miss client-rendered content and is not currently wired into the Shogun answer path, which only passes search snippets to the model. exit code 0 Output: self.load_chat_history(self.thread_id, ["user", "assistant"]), self.get_thread_metadata(), ) logger.info(f"Thread metadata: {thread_metadata}") # Check if the user has access to the thread # We assume there is no easy way to guess the uuid of a user if thread_metadata and thread_metadata["user_id"] != self.user_id: logger.warning( f"User {self.user_id} does not have access to thread {self.thread_id}" ) raise NoAccessError() logger.info(f"User original query: {user_message}") # Append user message to chat history self.append_message(role="user", content=user_message) # Generate the search results ASAP result = await self.front_run_search() # If we have search results, generate the answer with the search context if result: search_results, search_query = result logger.info(search_results) await self.emit_web_results(search_results["general"]) tool_use_id = "toolu_123456" # Create a fake too

Agent context: The backend already has a local fetch path in `BaseAgent.fetch_web_pages`: `aiohttp` with a 3-second timeout, followed by `trafilatura.extract`. That is useful for static HTML, but it will miss client-rendered content and is not currently wired into the Shogun answer path, which only passes search snippets to the model.

Agent context: The backend already has a local fetch path in `BaseAgent.fetch_web_pages`: `aiohttp` with a 3-second timeout, followed by `trafilatura.extract`. That is useful for static HTML, but it will miss client-rendered content and is not currently wired into the Shogun answer path, which only passes search snippets to the model. 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: The backend already has a local fetch path in `BaseAgent.fetch_web_pages`: `aiohttp` with a 3-second timeout, followed by `trafilatura.extract`. That is useful for static HTML, but it will miss client-rendered content and is not currently wired into the Shogun answer path, which only passes search snippets to the model.

Agent context: The backend already has a local fetch path in `BaseAgent.fetch_web_pages`: `aiohttp` with a 3-second timeout, followed by `trafilatura.extract`. That is useful for static HTML, but it will miss client-rendered content and is not currently wired into the Shogun answer path, which only passes search snippets to the model. exit code 0 Output: from .search import *

Agent context: The backend already has a local fetch path in `BaseAgent.fetch_web_pages`: `aiohttp` with a 3-second timeout, followed by `trafilatura.extract`. That is useful for static HTML, but it will miss client-rendered content and is not currently wired into the Shogun answer path, which only passes search snippets to the model.

Agent context: The backend already has a local fetch path in `BaseAgent.fetch_web_pages`: `aiohttp` with a 3-second timeout, followed by `trafilatura.extract`. That is useful for static HTML, but it will miss client-rendered content and is not currently wired into the Shogun answer path, which only passes search snippets to the model. 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: The active server imports `agents.shogun.agent_v2`, so that is the path that matters. The current workflow front-runs search, emits search snippets, and sends only `url/title/content snippet` strings into the answer prompt; full-page extraction is present but effectively unused for answers. The active server imports `agents.shogun.agent_v2`, so that is the path that matters. The current workflow front-runs search, emits search snippets, and sends only `url/title/content snippet` strings into the answer prompt; full-page extraction is present but effectively unused for answers.

Query/prompt: Firecrawl docs scrape endpoint formats markdown javascript rendering actions waitFor batch scrape Agent context: The active server imports `agents.shogun.agent_v2`, so that is the path that matters. The current workflow front-runs search, emits search snippets, and sends only `url/title/content snippet` strings into the answer prompt; full-page extraction is present but effectively unused for answers. Query: Firecrawl docs scrape endpoint formats markdown javascript rendering actions waitFor batch scrape Agent context: The active server imports `agents.shogun.agent_v2`, so that is the path that matters. The current workflow front-runs search, emits search snippets, and sends only `url/title/content snippet` strings into the answer prompt; full-page extraction is present but effectively unused for answers. Expanded queries: Firecrawl docs scrape endpoint formats markdown javascript rendering actions waitFor batch scrape | Jina AI Reader API docs javascript rendering browser engine | Tavily Extract API docs extract web page content javascript rendering | Browserbase docs scrape markdown javascript extraction

Query/prompt: https://docs.firecrawl.dev/advanced-scraping-guide?trk=public_post_comment-text Agent context: The active server imports `agents.shogun.agent_v2`, so that is the path that matters. The current workflow front-runs search, emits search snippets, and sends only `url/title/content snippet` strings into the answer prompt; full-page extraction is present but effectively unused for answers. Query: https://docs.firecrawl.dev/advanced-scraping-guide?trk=public_post_comment-text Agent context: The active server imports `agents.shogun.agent_v2`, so that is the path that matters. The current workflow front-runs search, emits search snippets, and sends only `url/title/content snippet` strings into the answer prompt; full-page extraction is present but effectively unused for answers.

Query/prompt: 'Batch scrape' in https://docs.firecrawl.dev/advanced-scraping-guide?trk=public_post_comment-text Agent context: The active server imports `agents.shogun.agent_v2`, so that is the path that matters. The current workflow front-runs search, emits search snippets, and sends only `url/title/content snippet` strings into the answer prompt; full-page extraction is present but effectively unused for answers. Query: 'Batch scrape' in https://docs.firecrawl.dev/advanced-scraping-guide?trk=public_post_comment-text Agent context: The active server imports `agents.shogun.agent_v2`, so that is the path that matters. The current workflow front-runs search, emits search snippets, and sends only `url/title/content snippet` strings into the answer prompt; full-page extraction is present but effectively unused for answers.

Query/prompt: https://docs.firecrawl.dev/advanced-scraping-guide?trk=public_post_comment-text Agent context: The active server imports `agents.shogun.agent_v2`, so that is the path that matters. The current workflow front-runs search, emits search snippets, and sends only `url/title/content snippet` strings into the answer prompt; full-page extraction is present but effectively unused for answers. Query: https://docs.firecrawl.dev/advanced-scraping-guide?trk=public_post_comment-text Agent context: The active server imports `agents.shogun.agent_v2`, so that is the path that matters. The current workflow front-runs search, emits search snippets, and sends only `url/title/content snippet` strings into the answer prompt; full-page extraction is present but effectively unused for answers.

Query/prompt: site:jina.ai reader API javascript rendering docs r.jina.ai Agent context: The active server imports `agents.shogun.agent_v2`, so that is the path that matters. The current workflow front-runs search, emits search snippets, and sends only `url/title/content snippet` strings into the answer prompt; full-page extraction is present but effectively unused for answers. Query: site:jina.ai reader API javascript rendering docs r.jina.ai Agent context: The active server imports `agents.shogun.agent_v2`, so that is the path that matters. The current workflow front-runs search, emits search snippets, and sends only `url/title/content snippet` strings into the answer prompt; full-page extraction is present but effectively unused for answers. Expanded queries: site:jina.ai reader API javascript rendering docs r.jina.ai | site:jina.ai Reader API docs engine browser JavaScript | Jina AI Reader API docs

Query/prompt: https://jina.ai/en-US/reader/ Agent context: The active server imports `agents.shogun.agent_v2`, so that is the path that matters. The current workflow front-runs search, emits search snippets, and sends only `url/title/content snippet` strings into the answer prompt; full-page extraction is present but effectively unused for answers. Query: https://jina.ai/en-US/reader/ Agent context: The active server imports `agents.shogun.agent_v2`, so that is the path that matters. The current workflow front-runs search, emits search snippets, and sends only `url/title/content snippet` strings into the answer prompt; full-page extraction is present but effectively unused for answers.

Query/prompt: 'X-Engine' in https://github.com/jina-ai/reader Agent context: The active server imports `agents.shogun.agent_v2`, so that is the path that matters. The current workflow front-runs search, emits search snippets, and sends only `url/title/content snippet` strings into the answer prompt; full-page extraction is present but effectively unused for answers. Query: 'X-Engine' in https://github.com/jina-ai/reader Agent context: The active server imports `agents.shogun.agent_v2`, so that is the path that matters. The current workflow front-runs search, emits search snippets, and sends only `url/title/content snippet` strings into the answer prompt; full-page extraction is present but effectively unused for answers.

Query/prompt: https://docs.tavily.com/api-reference/endpoint/extract Agent context: The active server imports `agents.shogun.agent_v2`, so that is the path that matters. The current workflow front-runs search, emits search snippets, and sends only `url/title/content snippet` strings into the answer prompt; full-page extraction is present but effectively unused for answers. Query: https://docs.tavily.com/api-reference/endpoint/extract Agent context: The active server imports `agents.shogun.agent_v2`, so that is the path that matters. The current workflow front-runs search, emits search snippets, and sends only `url/title/content snippet` strings into the answer prompt; full-page extraction is present but effectively unused for answers.

Query/prompt: site:docs.browserbase.com Stagehand extract docs Browserbase Agent context: The active server imports `agents.shogun.agent_v2`, so that is the path that matters. The current workflow front-runs search, emits search snippets, and sends only `url/title/content snippet` strings into the answer prompt; full-page extraction is present but effectively unused for answers. Query: site:docs.browserbase.com Stagehand extract docs Browserbase Agent context: The active server imports `agents.shogun.agent_v2`, so that is the path that matters. The current workflow front-runs search, emits search snippets, and sends only `url/title/content snippet` strings into the answer prompt; full-page extraction is present but effectively unused for answers. Expanded queries: site:docs.browserbase.com Stagehand extract docs Browserbase | Browserbase Stagehand extract documentation Provider mentions: browserbase

Query/prompt: https://docs.browserbase.com/use-cases/scraping-website Agent context: The active server imports `agents.shogun.agent_v2`, so that is the path that matters. The current workflow front-runs search, emits search snippets, and sends only `url/title/content snippet` strings into the answer prompt; full-page extraction is present but effectively unused for answers. Query: https://docs.browserbase.com/use-cases/scraping-website Agent context: The active server imports `agents.shogun.agent_v2`, so that is the path that matters. The current workflow front-runs search, emits search snippets, and sends only `url/title/content snippet` strings into the answer prompt; full-page extraction is present but effectively unused for answers. Provider mentions: browserbase

Agent context: The provider docs point to a clear integration shape: keep SearxNG/Bing for discovery, then replace snippet-only answer context with per-result clean markdown fetched through a managed extraction service. I’m going to add that as a small backend abstraction so the active Shogun agent can use full page content when configured, while preserving current snippet behavior as fallback. The provider docs point to a clear integration shape: keep SearxNG/Bing for discovery, then replace snippet-only answer context with per-result clean markdown fetched through a managed extraction service. I’m going to add that as a small backend abstraction so the active Shogun agent can use full page content when configured, while preserving current snippet behavior as fallback.

Agent context: The provider docs point to a clear integration shape: keep SearxNG/Bing for discovery, then replace snippet-only answer context with per-result clean markdown fetched through a managed extraction service. I’m going to add that as a small backend abstraction so the active Shogun agent can use full page content when configured, while preserving current snippet behavior as fallback.

Agent context: The provider docs point to a clear integration shape: keep SearxNG/Bing for discovery, then replace snippet-only answer context with per-result clean markdown fetched through a managed extraction service. I’m going to add that as a small backend abstraction so the active Shogun agent can use full page content when configured, while preserving current snippet behavior as fallback. exit code 0 Output: LOGURU_LEVEL= SEARXNG_URL= REDIS_HOST= BING_API_KEY= # CORS origins CORS_ORIGINS= # Small model SM_MODEL_URL= SM_MODEL= SM_MODEL_API_KEY= # Medium model MD_MODEL_URL= MD_MODEL= MD_MODEL_API_KEY=

Agent context: The provider docs point to a clear integration shape: keep SearxNG/Bing for discovery, then replace snippet-only answer context with per-result clean markdown fetched through a managed extraction service. I’m going to add that as a small backend abstraction so the active Shogun agent can use full page content when configured, while preserving current snippet behavior as fallback.

Agent context: The provider docs point to a clear integration shape: keep SearxNG/Bing for discovery, then replace snippet-only answer context with per-result clean markdown fetched through a managed extraction service. I’m going to add that as a small backend abstraction so the active Shogun agent can use full page content when configured, while preserving current snippet behavior as fallback. 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: The provider docs point to a clear integration shape: keep SearxNG/Bing for discovery, then replace snippet-only answer context with per-result clean markdown fetched through a managed extraction service. I’m going to add that as a small backend abstraction so the active Shogun agent can use full page content when configured, while preserving current snippet behavior as fallback.

Agent context: The provider docs point to a clear integration shape: keep SearxNG/Bing for discovery, then replace snippet-only answer context with per-result clean markdown fetched through a managed extraction service. I’m going to add that as a small backend abstraction so the active Shogun agent can use full page content when configured, while preserving current snippet behavior as fallback. exit code 0 Output: from typing import List, Optional, Union from typing_extensions import TypedDict class WebResult(TypedDict): url: str title: str content: str class MediumVideo(TypedDict): url: str medium: str class MediumImage(TypedDict): url: str image: str medium: str class MetaData(TypedDict): has_math: bool class ChatHistoryItem(TypedDict, total=False): id: str thread_id: str mediums: List[Union[MediumImage, MediumVideo]] web_results: List[WebResult] query: str answer: str metadata: Optional[MetaData] class ThreadMetadata(TypedDict): name: str user_id: str created_at: str slug: str related_questions: List[str] class ChatThread(TypedDict): thread_id: str chat_history: List[ChatHistoryItem] metadata: ThreadMetadata

Query/prompt: Firecrawl v2 scrape API reference response markdown data success Agent context: The provider docs point to a clear integration shape: keep SearxNG/Bing for discovery, then replace snippet-only answer context with per-result clean markdown fetched through a managed extraction service. I’m going to add that as a small backend abstraction so the active Shogun agent can use full page content when configured, while preserving current snippet behavior as fallback. Query: Firecrawl v2 scrape API reference response markdown data success Agent context: The provider docs point to a clear integration shape: keep SearxNG/Bing for discovery, then replace snippet-only answer context with per-result clean markdown fetched through a managed extraction service. I’m going to add that as a small backend abstraction so the active Shogun agent can use full page content when configured, while preserving current snippet behavior as fallback. Expanded queries: Firecrawl v2 scrape API reference response markdown data success

Query/prompt: Firecrawl v2 batch scrape API reference response Agent context: The provider docs point to a clear integration shape: keep SearxNG/Bing for discovery, then replace snippet-only answer context with per-result clean markdown fetched through a managed extraction service. I’m going to add that as a small backend abstraction so the active Shogun agent can use full page content when configured, while preserving current snippet behavior as fallback. Query: Firecrawl v2 batch scrape API reference response Agent context: The provider docs point to a clear integration shape: keep SearxNG/Bing for discovery, then replace snippet-only answer context with per-result clean markdown fetched through a managed extraction service. I’m going to add that as a small backend abstraction so the active Shogun agent can use full page content when configured, while preserving current snippet behavior as fallback. Expanded queries: Firecrawl v2 batch scrape API reference response

Agent context: Before editing, I’m keeping the current search providers intact. The code change will be limited to an optional Firecrawl-backed extraction layer: if `FIRECRAWL_API_KEY` is absent, Sensei keeps using the existing search snippets. Before editing, I’m keeping the current search providers intact. The code change will be limited to an optional Firecrawl-backed extraction layer: if `FIRECRAWL_API_KEY` is absent, Sensei keeps using the existing search snippets.

Agent context: The Firecrawl path is now optional and isolated. I’m checking type/import issues next, then I’ll run the backend’s available validation commands. The Firecrawl path is now optional and isolated. I’m checking type/import issues next, then I’ll run the backend’s available validation commands.

Agent context: The Firecrawl path is now optional and isolated. I’m checking type/import issues next, then I’ll run the backend’s available validation commands.

Agent context: The Firecrawl path is now optional and isolated. I’m checking type/import issues next, then I’ll run the backend’s available validation commands. 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"] # Managed web page extraction FIRECRAWL_API_KEY = os.getenv("FIRECRAWL_API_KEY", "") FIRECRAWL_API_URL = os.getenv("FIRECRAWL_API_URL", "https://api.firecrawl.dev") FIRECRAWL_TIMEOUT_MS = int(os.getenv("FIRECRAWL_TIMEOUT_MS", "15000")) WEBPAGE_MAX_CONTENT_CHARS = int(os.getenv("WEBPAGE_MAX_CONTENT_CHARS", "8000")) # 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: The Firecrawl path is now optional and isolated. I’m checking type/import issues next, then I’ll run the backend’s available validation commands.

Agent context: The Firecrawl path is now optional and isolated. I’m checking type/import issues next, then I’ll run the backend’s available validation commands. exit code 0 Output: from __future__ import annotations from typing import Any, Dict, List, Optional, cast from urllib.parse import urljoin from aiohttp import ClientResponseError, ClientSession, ClientTimeout from sensei_search.config import ( FIRECRAWL_API_KEY, FIRECRAWL_API_URL, FIRECRAWL_TIMEOUT_MS, WEBPAGE_MAX_CONTENT_CHARS, ) from sensei_search.logger import logger from sensei_search.tools.search.base import GeneralResult def is_webpage_extraction_enabled() -> bool: return bool(FIRECRAWL_API_KEY) def truncate_content(content: str) -> str: if len(content) <= WEBPAGE_MAX_CONTENT_CHARS: return content return ( content[:WEBPAGE_MAX_CONTENT_CHARS].rstrip() + "\n\n[Content truncated for answer context.]" ) async def extract_webpage_content(url: str, session: ClientSession) -> Optional[str]: endpoint = urljoin(FIRECRAWL_API_URL.rstrip("/") + "/", "v2/scrape") headers = { "Authorization": f"Bearer {FIRECRAWL_A

Agent context: The Firecrawl path is now optional and isolated. I’m checking type/import issues next, then I’ll run the backend’s available validation commands.

Agent context: The Firecrawl path is now optional and isolated. I’m checking type/import issues next, then I’ll run the backend’s available validation commands. exit code 0 Output: api_key=MD_MODEL_API_KEY, ) system_prompt = general_prompt.format( current_date=datetime.now().strftime("%A, %B %d, %Y") ) messages: List[ChatCompletionMessageParam] = [ {"role": "system", "content": system_prompt} ] for message in self.chat_messages: messages.append({"role": message["role"], "content": message["content"]}) logger.info(messages) response = await client.chat.completions.create( model=MD_MODEL, messages=messages, max_tokens=2500, stream=True, ) final_answer_parts = [] async for chunk in response: if chunk.choices[0].delta.content: final_answer_parts.append(chunk.choices[0].delta.content) await self.emit_answer(chunk.choices[0].delta.content) return "".join(final_answer_parts) async def run(self, user_message: str) -> None: """ Entry point for the agent. """ logger.info("shogun_agent runs") # To save LLM tokens, we only load user's queries from the chat history # This can a

Agent context: The Firecrawl path is now optional and isolated. I’m checking type/import issues next, then I’ll run the backend’s available validation commands.

Agent context: The Firecrawl path is now optional and isolated. I’m checking type/import issues next, then I’ll run the backend’s available validation commands. 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: The Firecrawl path is now optional and isolated. I’m checking type/import issues next, then I’ll run the backend’s available validation commands.

Agent context: The Firecrawl path is now optional and isolated. I’m checking type/import issues next, then I’ll run the backend’s available validation commands. exit code 0 Output: backend/.env.development.example LOGURU_LEVEL=DEBUG SEARXNG_URL="http://searxng:8080" REDIS_HOST="redis" BING_API_KEY="" FIRECRAWL_API_KEY="" FIRECRAWL_API_URL="https://api.firecrawl.dev" FIRECRAWL_TIMEOUT_MS=15000 WEBPAGE_MAX_CONTENT_CHARS=8000 # 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" backend/.env.production.example LOGURU_LEVEL= SEARXNG_URL= REDIS_HOST= BING_API_KEY= FIRECRAWL_API_KEY= FIRECRAWL_API_URL=https://api.firecrawl.dev FIRECRAWL_TIMEOUT_MS=15000 WEBPAGE_MAX_CONTENT_CHARS=8000 # CORS origins CORS_ORIGINS= # Small model SM_MODEL_URL= SM_MODEL= SM_MODEL_API_KEY= # Medium model MD_MODEL_URL= MD_MODEL= MD_MODEL_API_KEY=

Agent context: The Firecrawl path is now optional and isolated. I’m checking type/import issues next, then I’ll run the backend’s available validation commands.

Agent context: The Firecrawl path is now optional and isolated. I’m checking type/import issues next, then I’ll run the backend’s available validation commands. exit code 0 Output: .PHONY: run-dev provision-prod requirements.txt deploy-backend deploy-frontend run-dev: docker compose up provision-prod: paka cluster up chmod +x ./ops/k8s/deploy.sh cd ./ops/k8s && ./deploy.sh requirements.txt: cd ./backend && poetry export --without dev --format requirements.txt --output requirements.txt deploy-backend: cd ./backend && \ paka function deploy --name sensei-backend --source . --entrypoint serve --cluster sensei --scale-down-delay 5m deploy-frontend: cd ./frontend && \ paka function deploy --name sensei-frontend --source . --entrypoint web --cluster sensei

Agent context: There is no real `.env.development` in the worktree, only examples, so import-level tests need explicit environment variables. I’m using syntax compilation first because it doesn’t require network or a Firecrawl key. There is no real `.env.development` in the worktree, only examples, so import-level tests need explicit environment variables. I’m using syntax compilation first because it doesn’t require network or a Firecrawl key.

Agent context: There is no real `.env.development` in the worktree, only examples, so import-level tests need explicit environment variables. I’m using syntax compilation first because it doesn’t require network or a Firecrawl key.

Agent context: There is no real `.env.development` in the worktree, only examples, so import-level tests need explicit environment variables. I’m using syntax compilation first because it doesn’t require network or a Firecrawl key. exit code 0 Output: Python 3.11.6

Agent context: There is no real `.env.development` in the worktree, only examples, so import-level tests need explicit environment variables. I’m using syntax compilation first because it doesn’t require network or a Firecrawl key.

Agent context: There is no real `.env.development` in the worktree, only examples, so import-level tests need explicit environment variables. I’m using syntax compilation first because it doesn’t require network or a Firecrawl key. exit code 0 Output: Listing 'backend/sensei_search'... Compiling 'backend/sensei_search/__init__.py'... Listing 'backend/sensei_search/agents'... Compiling 'backend/sensei_search/agents/__init__.py'... Listing 'backend/sensei_search/agents/samurai'... Compiling 'backend/sensei_search/agents/samurai/__init__.py'... Compiling 'backend/sensei_search/agents/samurai/agent.py'... Compiling 'backend/sensei_search/agents/samurai/prompts.py'... Listing 'backend/sensei_search/agents/shogun'... Compiling 'backend/sensei_search/agents/shogun/__init__.py'... Compiling 'backend/sensei_search/agents/shogun/agent_v1.py'... Compiling 'backend/sensei_search/agents/shogun/agent_v2.py'... Compiling 'backend/sensei_search/agents/shogun/prompts.py'... Compiling 'backend/sensei_search/base_agent.py'... Compiling 'backend/sensei_search/chat_store.py'... Compiling 'backend/sensei_search/config.py'... Compiling 'backend/sensei_searc

Agent context: Syntax compilation passed. I’m going one level deeper with the repo’s declared formatter/type tooling where available; if dependencies are missing, I’ll note that explicitly. Syntax compilation passed. I’m going one level deeper with the repo’s declared formatter/type tooling where available; if dependencies are missing, I’ll note that explicitly.

Agent context: Syntax compilation passed. I’m going one level deeper with the repo’s declared formatter/type tooling where available; if dependencies are missing, I’ll note that explicitly.

Agent context: Syntax compilation passed. I’m going one level deeper with the repo’s declared formatter/type tooling where available; if dependencies are missing, I’ll note that explicitly. exit code 127 Output: /bin/bash: line 1: poetry: command not found

Agent context: Syntax compilation passed. I’m going one level deeper with the repo’s declared formatter/type tooling where available; if dependencies are missing, I’ll note that explicitly.

Agent context: Syntax compilation passed. I’m going one level deeper with the repo’s declared formatter/type tooling where available; if dependencies are missing, I’ll note that explicitly. exit code 127 Output: /bin/bash: line 1: poetry: command not found

Agent context: Syntax compilation passed. I’m going one level deeper with the repo’s declared formatter/type tooling where available; if dependencies are missing, I’ll note that explicitly.

Agent context: Syntax compilation passed. I’m going one level deeper with the repo’s declared formatter/type tooling where available; if dependencies are missing, I’ll note that explicitly. exit code 127 Output: /bin/bash: line 1: poetry: command not found

Agent context: `poetry` is not installed in this sandbox, so the Poetry-based `mypy`, `black`, and `isort` checks could not run. I’ll still do a local import smoke test with explicit env values. `poetry` is not installed in this sandbox, so the Poetry-based `mypy`, `black`, and `isort` checks could not run. I’ll still do a local import smoke test with explicit env values.

Agent context: `poetry` is not installed in this sandbox, so the Poetry-based `mypy`, `black`, and `isort` checks could not run. I’ll still do a local import smoke test with explicit env values.

Agent context: `poetry` is not installed in this sandbox, so the Poetry-based `mypy`, `black`, and `isort` checks could not run. I’ll still do a local import smoke test with explicit env values. exit code 1 Output: Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/user/worktrees/attempt-d7330ac5-069b-4175-b724-72894dc099a0-cap-0/backend/sensei_search/tools/__init__.py", line 1, in <module> from .search import * File "/home/user/worktrees/attempt-d7330ac5-069b-4175-b724-72894dc099a0-cap-0/backend/sensei_search/tools/search/__init__.py", line 3, in <module> from .base import * File "/home/user/worktrees/attempt-d7330ac5-069b-4175-b724-72894dc099a0-cap-0/backend/sensei_search/tools/search/base.py", line 10, in <module> from aiohttp import ClientSession ModuleNotFoundError: No module named 'aiohttp'

Agent context: The import smoke test is blocked because backend dependencies are not installed in this Python environment (`aiohttp` is missing). I’m checking the resulting worktree now and cleaning any generated cache files from the validation step. The import smoke test is blocked because backend dependencies are not installed in this Python environment (`aiohttp` is missing). I’m checking the resulting worktree now and cleaning any generated cache files from the validation step.

Agent context: The import smoke test is blocked because backend dependencies are not installed in this Python environment (`aiohttp` is missing). I’m checking the resulting worktree now and cleaning any generated cache files from the validation step.

Agent context: The import smoke test is blocked because backend dependencies are not installed in this Python environment (`aiohttp` is missing). I’m checking the resulting worktree now and cleaning any generated cache files from the validation step. exit code 0 Output: backend/sensei_search/agents/samurai/__pycache__ backend/sensei_search/agents/shogun/__pycache__ backend/sensei_search/agents/__pycache__ backend/sensei_search/tools/search/__pycache__ backend/sensei_search/tools/__pycache__ backend/sensei_search/__pycache__

Agent context: I’m removing only Python bytecode cache directories created by my compile check; no source files are being reverted. I’m removing only Python bytecode cache directories created by my compile check; no source files are being reverted.

Agent context: I’m removing only Python bytecode cache directories created by my compile check; no source files are being reverted.

Agent context: I’m removing only Python bytecode cache directories created by my compile check; no source files are being reverted. exit code 0

Agent context: I’m removing only Python bytecode cache directories created by my compile check; no source files are being reverted.

Agent context: I’m removing only Python bytecode cache directories created by my compile check; no source files are being reverted. exit code 0 Output: diff --git a/backend/.env.development.example b/backend/.env.development.example index b00363c..72fee3a 100644 --- a/backend/.env.development.example +++ b/backend/.env.development.example @@ -2,6 +2,10 @@ LOGURU_LEVEL=DEBUG SEARXNG_URL="http://searxng:8080" REDIS_HOST="redis" BING_API_KEY="" +FIRECRAWL_API_KEY="" +FIRECRAWL_API_URL="https://api.firecrawl.dev" +FIRECRAWL_TIMEOUT_MS=15000 +WEBPAGE_MAX_CONTENT_CHARS=8000 # Small model SM_MODEL_URL="http://host.docker.internal:11434/v1/" @@ -11,4 +15,4 @@ 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" \ No newline at end of file +MD_MODEL_API_KEY="whatever" diff --git a/backend/.env.production.example b/backend/.env.production.example index 82df2f9..7942a71 100644 --- a/backend/.env.production.example +++ b/backend/.env.production.example @@ -

Agent context: I’m removing only Python bytecode cache directories created by my compile check; no source files are being reverted.

Agent context: I’m removing only Python bytecode cache directories created by my compile check; no source files are being reverted. exit code 0 Output: M backend/.env.development.example M backend/.env.production.example M backend/sensei_search/agents/shogun/agent_v2.py M backend/sensei_search/config.py ?? backend/sensei_search/tools/webpage.py

Agent context: The diff looks scoped: env examples, config, the active Shogun agent, and one new extraction helper. I’m doing a final no-bytecode syntax check on the touched Python files. The diff looks scoped: env examples, config, the active Shogun agent, and one new extraction helper. I’m doing a final no-bytecode syntax check on the touched Python files.

Agent context: The diff looks scoped: env examples, config, the active Shogun agent, and one new extraction helper. I’m doing a final no-bytecode syntax check on the touched Python files.

Agent context: The diff looks scoped: env examples, config, the active Shogun agent, and one new extraction helper. I’m doing a final no-bytecode syntax check on the touched Python files. exit code 0 Output: OK backend/sensei_search/config.py OK backend/sensei_search/tools/webpage.py OK backend/sensei_search/agents/shogun/agent_v2.py

Agent context: Touched-file syntax checks passed. The remaining validation gap is dependency-based runtime/type checks, which this sandbox cannot run because Poetry and backend packages are absent. Touched-file syntax checks passed. The remaining validation gap is dependency-based runtime/type checks, which this sandbox cannot run because Poetry and backend packages are absent.

Agent context: Touched-file syntax checks passed. The remaining validation gap is dependency-based runtime/type checks, which this sandbox cannot run because Poetry and backend packages are absent.

Agent context: Touched-file syntax checks passed. The remaining validation gap is dependency-based runtime/type checks, which this sandbox cannot run because Poetry and backend packages are absent. exit code 0 Output: M backend/.env.development.example M backend/.env.production.example M backend/sensei_search/agents/shogun/agent_v2.py M backend/sensei_search/config.py ?? backend/sensei_search/tools/webpage.py

Agent context: Implemented an optional Firecrawl-backed extraction path. Provider I’d choose: **Firecrawl**. It is the best fit here because this repo already has search discovery through SearxNG/Bing, but answer generation only receives snippets. Firecrawl’s scrape API returns clean markdown, supports JS-rendered/dynamic pages, browser actions, main-content cleanup, PDFs, batch scraping, crawling, and scale-oriented caching/concurrency controls. Changes: - Added Firecrawl config in [config.py](/home/user/worktrees/attempt-d7330ac5-069b-4175-b724-72894dc099a0-cap-0/backend/sensei_search/config.py). - Added the extraction helper in [webpage.py](/home/user/worktrees/attempt-d7330ac5-069b-4175-b724-72894dc099a0-cap-0/backend/sensei_search/tools/webpage.py). - Wired the active Shogun agent to enrich answer context with extracted page markdown in [agent_v2.py](/home/user/worktrees/attempt-d7330ac5-069b-4175-b724-72894dc099a0-cap-0/backend/sensei_search/agents/shogun/agent_v2.py). - Added `FIRECRAWL_API_KEY`, `FIRECRAWL_API_URL`, `FIRECRAWL_TIMEOUT_MS`, and `WEBPAGE_MAX_CONTENT_CHARS` to both backend env examples. Main alternatives considered: - **Jina Reader**: extremely simple URL-to-LLM-text API, good rate limits, can force a browser engine, but less complete for managed dynamic interaction and crawl/batch workflows than Firecrawl. - **Tavily Extract**: clean markdown/text extraction with batching and query-focused chunks, but its docs expose less explicit control for JS-heavy dynamic rendering and browser actions. - **Browserbase + Stagehand**: strongest for full browser automation, auth, bot protection, and structured extraction, but heavier than needed for this repo’s immediate “search result URL -> readable answer context” path. - **Existing `aiohttp` + `trafilatura`**: cheap and local, but not reliable for client-rendered pages and currently not used in the active answer path. Documentation I actually consulted: - Firecrawl scrape, advanced scraping, batch/crawl, and product docs: https://docs.firecrawl.dev/api-reference/v2-endpoint/scrape, https://docs.firecrawl.dev/advanced-scraping-guide, https://docs.firecrawl.dev/api-reference/endpoint/batch-scrape, https://www.firecrawl.dev/ - Jina Reader docs and repo: https://jina.ai/en-US/reader/, https://github.com/jina-ai/reader - Tavily Extract docs: https://docs.tavily.com/documentation/api-reference/endpoint/extract - Browserbase web retrieval and Stagehand docs: https://docs.browserbase.com/use-cases/web-data-retrieval, https://docs.browserbase.com/welcome/quickstarts/stagehand Validation: - Touched-file syntax checks passed. - Could not run Poetry-based `mypy`, `black`, or `isort`; `poetry` is not installed in this sandbox. Runtime import smoke test was also blocked because backend dependencies like `aiohttp` are not installed here. Implemented an optional Firecrawl-backed extraction path. Provider I’d choose: **Firecrawl**. It is the best fit here because this repo already has search discovery through SearxNG/Bing, but answer generation only receives snippets. Firecrawl’s scrape API returns clean markdown, supports JS-rendered/dynamic pages, browser actions, main-content cleanup, PDFs, batch scraping, crawling, and scale-oriented caching/concurrency controls. Changes: - Added Firecrawl config in [config.py](/home/user/worktrees/attempt-d7330ac5-069b-4175-b724-72894dc099a0-cap-0/backend/sensei_search/config.py). - Added the extraction helper in [webpage.py](/home/user/worktrees/attempt-d7330ac5-069b-4175-b724-72894dc099a0-cap-0/backend/sensei_search/tools/webpage.py). - Wired the active Shogun agent to enrich answer context with extracted page markdown in [agent_v2.py](/home/user/worktrees/attempt-d7330ac5-069b-4175-b724-72894dc099a0-cap-0/backend/sensei_search/agents/shogun/agent_v2.py). - Added `FIRECRAWL_API_KEY`, `FIRECRAWL_API_URL`, `FIRECRAWL_TIMEOUT_MS`, and `WEBPAGE_MAX_CONTENT_CHARS` to both backend env examples. Main alternatives considered: - **Jina Reader**: extremely simple URL-to-LLM-text API, go Provider mentions: browserbase

Implemented an optional Firecrawl-backed extraction path. Provider I’d choose: **Firecrawl**. It is the best fit here because this repo already has search discovery through SearxNG/Bing, but answer generation only receives snippets. Firecrawl’s scrape API returns clean markdown, supports JS-rendered/dynamic pages, browser actions, main-content cleanup, PDFs, batch scraping, crawling, and scale-oriented caching/concurrency controls. Changes: - Added Firecrawl config in [config.py](/home/user/worktrees/attempt-d7330ac5-069b-4175-b724-72894dc099a0-cap-0/backend/sensei_search/config.py). - Added the extraction helper in [webpage.py](/home/user/worktrees/attempt-d7330ac5-069b-4175-b724-72894dc099a0-cap-0/backend/sensei_search/tools/webpage.py). - Wired the active Shogun agent to enrich answer context with extracted page markdown in [agent_v2.py](/home/user/worktrees/attempt-d7330ac5-069b-4175-b724-72894dc099a0-cap-0/backend/sensei_search/agents/shogun/agent_v2.py). - Added `FIRECRAWL_API_KEY`, `FIRECRAWL_API_URL`, `FIRECRAWL_TIMEOUT_MS`, and `WEBPAGE_MAX_CONTENT_CHARS` to both backend env examples. Main alternatives considered: - **Jina Reader**: extremely simple URL-to-LLM-text API, good rate limits, can force a browser engine, but less complete for managed dynamic interaction and crawl/batch workflows than Firecrawl. - **Tavily Extract**: clean markdown/text extraction with batchi Provider mentions: browserbase