Run

P5

e9de2afd458e4ad2d7c54636d3c8b20de04cf790

Agent context: I’ll inspect the repository first to understand the agent architecture and extension points, then I’ll verify current official docs for likely managed search/retrieval providers before making a single recommendation. I’ll inspect the repository first to understand the agent architecture and extension points, then I’ll verify current official docs for likely managed search/retrieval providers before making a single recommendation.

Agent context: I’ll inspect the repository first to understand the agent architecture and extension points, then I’ll verify current official docs for likely managed search/retrieval providers before making a single recommendation.

Agent context: I’ll inspect the repository first to understand the agent architecture and extension points, then I’ll verify current official docs for likely managed search/retrieval providers before making a single recommendation. exit code 127 Output: /home/user/worktrees/attempt-c75727fe-a859-43dc-b8bd-614bd8da878e-cap-2 /bin/bash: line 1: rg: command not found

Agent context: I’ll inspect the repository first to understand the agent architecture and extension points, then I’ll verify current official docs for likely managed search/retrieval providers before making a single recommendation.

Agent context: I’ll inspect the repository first to understand the agent architecture and extension points, then I’ll verify current official docs for likely managed search/retrieval providers before making a single recommendation. exit code 0

Agent context: `rg` is not installed in this workspace, so I’m falling back to `find` and targeted reads. The worktree is clean, which keeps the recommendation scoped to the repository as-is. `rg` is not installed in this workspace, so I’m falling back to `find` and targeted reads. The worktree is clean, which keeps the recommendation scoped to the repository as-is.

Agent context: `rg` is not installed in this workspace, so I’m falling back to `find` and targeted reads. The worktree is clean, which keeps the recommendation scoped to the repository as-is.

Agent context: `rg` is not installed in this workspace, so I’m falling back to `find` and targeted reads. The worktree is clean, which keeps the recommendation scoped to the repository as-is. exit code 0 Output: ./.dockerignore ./.github/actions/build-python/action.yaml ./.github/workflows/ci.yaml ./.gitignore ./Dockerfile ./LICENSE ./Makefile ./README.md ./assets/bot.png ./assets/demo.png ./assets/rag-flow.svg ./assets/retriever.svg ./assets/user.png ./docker-compose.yml ./notebooks/kaggle.ipynb ./pyproject.toml ./rag_chatbot/__init__.py ./rag_chatbot/__main__.py ./rag_chatbot/core/__init__.py ./rag_chatbot/core/embedding/__init__.py ./rag_chatbot/core/embedding/embedding.py ./rag_chatbot/core/engine/__init__.py ./rag_chatbot/core/engine/engine.py ./rag_chatbot/core/engine/retriever.py ./rag_chatbot/core/ingestion/__init__.py ./rag_chatbot/core/ingestion/ingestion.py ./rag_chatbot/core/model/__init__.py ./rag_chatbot/core/model/model.py ./rag_chatbot/core/prompt/__init__.py ./rag_chatbot/core/prompt/qa_prompt.py ./rag_chatbot/core/prompt/query_gen_prompt.py ./rag_chatbot/core/prompt/select_prom

Agent context: `rg` is not installed in this workspace, so I’m falling back to `find` and targeted reads. The worktree is clean, which keeps the recommendation scoped to the repository as-is.

Agent context: `rg` is not installed in this workspace, so I’m falling back to `find` and targeted reads. The worktree is clean, which keeps the recommendation scoped to the repository as-is. exit code 0 Output: ./README.md ./pyproject.toml

Agent context: I’ve found a compact Python RAG app rather than a broad agent framework. Next I’m reading the pipeline, retriever, prompt, and settings code to see whether live web retrieval should look like another retriever source, a pre-answer tool, or a separate ingestion path. I’ve found a compact Python RAG app rather than a broad agent framework. Next I’m reading the pipeline, retriever, prompt, and settings code to see whether live web retrieval should look like another retriever source, a pre-answer tool, or a separate ingestion path.

Agent context: I’ve found a compact Python RAG app rather than a broad agent framework. Next I’m reading the pipeline, retriever, prompt, and settings code to see whether live web retrieval should look like another retriever source, a pre-answer tool, or a separate ingestion path.

Agent context: I’ve found a compact Python RAG app rather than a broad agent framework. Next I’m reading the pipeline, retriever, prompt, and settings code to see whether live web retrieval should look like another retriever source, a pre-answer tool, or a separate ingestion path. exit code 0 Output: # 🤖 Chat with multiple PDFs locally  ## 📖 Table of Contents - [📖 Table of Contents](#-table-of-contents) - [⭐️ Key Features](#️-key-features) - [💡 Idea (Experiment)](#-idea-experiment) - [💻 Setup](#-setup) - [1. Kaggle (Recommended)](#1-kaggle-recommended) - [2. Local](#2-local) - [2.1. Clone project](#21-clone-project) - [2.2 Install](#22-install) - [2.3 Run](#23-run) - [3. Go to: `http://0.0.0.0:7860/` or Ngrok link after setup completed](#3-go-to-http00007860-or-ngrok-link-after-setup-completed) - [🌟 Star History](#-star-history) ## ⭐️ Key Features - Easy to run on `Local` or `Kaggle` (new) - Using any model from `Huggingface` and `Ollama` - Process multiple PDF inputs. - Chat with multiples languages (Coming soon). - Simple UI with `Gradio`. ## 💡 Idea (Experiment)   ## 💻 Setup ##

Agent context: I’ve found a compact Python RAG app rather than a broad agent framework. Next I’m reading the pipeline, retriever, prompt, and settings code to see whether live web retrieval should look like another retriever source, a pre-answer tool, or a separate ingestion path.

Agent context: I’ve found a compact Python RAG app rather than a broad agent framework. Next I’m reading the pipeline, retriever, prompt, and settings code to see whether live web retrieval should look like another retriever source, a pre-answer tool, or a separate ingestion path. exit code 0 Output: [project] name = "rag-chatbot" version = "0.1.0" description = "" authors = [{ name = "Võ Đình Đạt", email = "vodinhdat03@gmail.com" }] requires-python = ">=3.11" readme = "README.md" dependencies = [ "gradio<5", "python-dotenv>=1.0.1,<2", "llama-index-embeddings-huggingface>=0.1.4,<0.2", "llama-index-llms-openai>=0.1.12,<0.2", "llama-index-vector-stores-chroma>=0.1.6,<0.2", "llama-index-llms-ollama>=0.1.2,<0.2", "llama-index-readers-file>=0.1.11,<0.2", "llama-index>=0.10.22,<0.11", "transformers>=4.39.1,<5", "torch", "llama-index-callbacks-wandb>=0.1.2,<0.2", "llama-index-retrievers-bm25>=0.1.3,<0.2", "pymupdf>=1.24.3,<2", "tqdm>=4.66.4,<5", "requests>=2.32.3,<3", "pandas>=2.2.3,<3", "sentence-transformers>=3.2.0,<4", "pydantic==2.8.2", ] [dependency-groups] dev = [ "ruff>=0.6.9,<0.7", "deptry>=0.20.0,<0.21", "pytest>=8.3.3,<9", ] [build-system] requires = ["hatchling"] build-backend = Provider mentions: openai

Agent context: I’ve found a compact Python RAG app rather than a broad agent framework. Next I’m reading the pipeline, retriever, prompt, and settings code to see whether live web retrieval should look like another retriever source, a pre-answer tool, or a separate ingestion path.

Agent context: I’ve found a compact Python RAG app rather than a broad agent framework. Next I’m reading the pipeline, retriever, prompt, and settings code to see whether live web retrieval should look like another retriever source, a pre-answer tool, or a separate ingestion path. exit code 0 Output: from typing import List from dotenv import load_dotenv from llama_index.core.retrievers import ( BaseRetriever, QueryFusionRetriever, VectorIndexRetriever, RouterRetriever, ) from llama_index.core.callbacks.base import CallbackManager from llama_index.core.retrievers.fusion_retriever import FUSION_MODES from llama_index.core.postprocessor import SentenceTransformerRerank from llama_index.core.tools import RetrieverTool from llama_index.core.selectors import LLMSingleSelector from llama_index.core.schema import BaseNode, NodeWithScore, QueryBundle, IndexNode from llama_index.core.llms.llm import LLM from llama_index.retrievers.bm25 import BM25Retriever from llama_index.core import Settings, VectorStoreIndex from ..prompt import get_query_gen_prompt from ...setting import RAGSettings load_dotenv() class TwoStageRetriever(QueryFusionRetriever): def __init__( self, retrievers: List[BaseRetri

Agent context: I’ve found a compact Python RAG app rather than a broad agent framework. Next I’m reading the pipeline, retriever, prompt, and settings code to see whether live web retrieval should look like another retriever source, a pre-answer tool, or a separate ingestion path.

Agent context: I’ve found a compact Python RAG app rather than a broad agent framework. Next I’m reading the pipeline, retriever, prompt, and settings code to see whether live web retrieval should look like another retriever source, a pre-answer tool, or a separate ingestion path. exit code 0 Output: from .core import ( LocalChatEngine, LocalDataIngestion, LocalRAGModel, LocalEmbedding, LocalVectorStore, get_system_prompt, ) from llama_index.core import Settings from llama_index.core.chat_engine.types import StreamingAgentChatResponse from llama_index.core.prompts import ChatMessage, MessageRole class LocalRAGPipeline: def __init__(self, host: str = "host.docker.internal") -> None: self._host = host self._language = "eng" self._model_name = "" self._system_prompt = get_system_prompt("eng", is_rag_prompt=False) self._engine = LocalChatEngine(host=host) self._default_model = LocalRAGModel.set(self._model_name, host=host) self._query_engine = None self._ingestion = LocalDataIngestion() self._vector_store = LocalVectorStore(host=host) Settings.llm = LocalRAGModel.set(host=host) Settings.embed_model = LocalEmbedding.set(host=host) def get_model_name(self): return self._model_name def set_

Agent context: The app already uses LlamaIndex retrievers over uploaded PDFs, with a `LocalRetriever` deciding between vector-only, hybrid BM25/vector, and router retrievers. A managed web provider will fit best as a new retriever/tool feeding citation-bearing web snippets or page text into the existing chat engine. The app already uses LlamaIndex retrievers over uploaded PDFs, with a `LocalRetriever` deciding between vector-only, hybrid BM25/vector, and router retrievers. A managed web provider will fit best as a new retriever/tool feeding citation-bearing web snippets or page text into the existing chat engine.

Agent context: The app already uses LlamaIndex retrievers over uploaded PDFs, with a `LocalRetriever` deciding between vector-only, hybrid BM25/vector, and router retrievers. A managed web provider will fit best as a new retriever/tool feeding citation-bearing web snippets or page text into the existing chat engine.

Agent context: The app already uses LlamaIndex retrievers over uploaded PDFs, with a `LocalRetriever` deciding between vector-only, hybrid BM25/vector, and router retrievers. A managed web provider will fit best as a new retriever/tool feeding citation-bearing web snippets or page text into the existing chat engine. exit code 0 Output: def get_context_prompt(language: str) -> str: if language == "vi": return CONTEXT_PROMPT_VI return CONTEXT_PROMPT_EN def get_system_prompt(language: str, is_rag_prompt: bool = True) -> str: if language == "vi": return SYSTEM_PROMPT_RAG_VI if is_rag_prompt else SYSTEM_PROMPT_VI return SYSTEM_PROMPT_RAG_EN if is_rag_prompt else SYSTEM_PROMPT_EN SYSTEM_PROMPT_EN = """\ This is a chat between a user and an artificial intelligence assistant. \ The assistant gives helpful, detailed, and polite answers to the user's questions based on the context. \ The assistant should also indicate when the answer cannot be found in the context.""" SYSTEM_PROMPT_RAG_EN = """\ This is a chat between a user and an artificial intelligence assistant. \ The assistant gives helpful, detailed, and polite answers to the user's questions based on the context. \ The assistant should also indicate when the answer cannot

Agent context: The app already uses LlamaIndex retrievers over uploaded PDFs, with a `LocalRetriever` deciding between vector-only, hybrid BM25/vector, and router retrievers. A managed web provider will fit best as a new retriever/tool feeding citation-bearing web snippets or page text into the existing chat engine.

Agent context: The app already uses LlamaIndex retrievers over uploaded PDFs, with a `LocalRetriever` deciding between vector-only, hybrid BM25/vector, and router retrievers. A managed web provider will fit best as a new retriever/tool feeding citation-bearing web snippets or page text into the existing chat engine. exit code 0 Output: from llama_index.core.chat_engine import CondensePlusContextChatEngine, SimpleChatEngine from llama_index.core.memory import ChatMemoryBuffer from llama_index.core.llms.llm import LLM from llama_index.core.schema import BaseNode from typing import List from .retriever import LocalRetriever from ...setting import RAGSettings class LocalChatEngine: def __init__( self, setting: RAGSettings | None = None, host: str = "host.docker.internal" ): super().__init__() self._setting = setting or RAGSettings() self._retriever = LocalRetriever(self._setting) self._host = host def set_engine( self, llm: LLM, nodes: List[BaseNode], language: str = "eng", ) -> CondensePlusContextChatEngine | SimpleChatEngine: # Normal chat engine if len(nodes) == 0: return SimpleChatEngine.from_defaults( llm=llm, memory=ChatMemoryBuffer( token_limit=self._setting.ollama.chat_token_limit ), ) # Chat engine with documents

Agent context: The app already uses LlamaIndex retrievers over uploaded PDFs, with a `LocalRetriever` deciding between vector-only, hybrid BM25/vector, and router retrievers. A managed web provider will fit best as a new retriever/tool feeding citation-bearing web snippets or page text into the existing chat engine.

Agent context: The app already uses LlamaIndex retrievers over uploaded PDFs, with a `LocalRetriever` deciding between vector-only, hybrid BM25/vector, and router retrievers. A managed web provider will fit best as a new retriever/tool feeding citation-bearing web snippets or page text into the existing chat engine.

Agent context: The app already uses LlamaIndex retrievers over uploaded PDFs, with a `LocalRetriever` deciding between vector-only, hybrid BM25/vector, and router retrievers. A managed web provider will fit best as a new retriever/tool feeding citation-bearing web snippets or page text into the existing chat engine. exit code 0 Output: from pydantic import BaseModel, Field from typing import List class OllamaSettings(BaseModel): llm: str = Field(default="llama3:8b-instruct-q8_0", description="LLM model") keep_alive: str = Field(default="1h", description="Keep alive time for the server") tfs_z: float = Field(default=1.0, description="TFS normalization factor") top_k: int = Field(default=40, description="Top k sampling") top_p: float = Field(default=0.9, description="Top p sampling") repeat_last_n: int = Field(default=64, description="Repeat last n tokens") repeat_penalty: float = Field(default=1.1, description="Repeat penalty") request_timeout: float = Field(default=300, description="Request timeout") port: int = Field(default=11434, description="Port number") context_window: int = Field(default=8000, description="Context window size") temperature: float = Field(default=0.1, description="Temperature") chat_token_limit:

Agent context: The app already uses LlamaIndex retrievers over uploaded PDFs, with a `LocalRetriever` deciding between vector-only, hybrid BM25/vector, and router retrievers. A managed web provider will fit best as a new retriever/tool feeding citation-bearing web snippets or page text into the existing chat engine. exit code 0 Output: import os import shutil import json import sys import time import gradio as gr from dataclasses import dataclass from typing import ClassVar from llama_index.core.chat_engine.types import StreamingAgentChatResponse from .theme import JS_LIGHT_THEME, CSS from ..pipeline import LocalRAGPipeline from ..logger import Logger @dataclass class DefaultElement: DEFAULT_MESSAGE: ClassVar[dict] = {"text": ""} DEFAULT_MODEL: str = "" DEFAULT_HISTORY: ClassVar[list] = [] DEFAULT_DOCUMENT: ClassVar[list] = [] HELLO_MESSAGE: str = "Hi 👋, how can I help you today?" SET_MODEL_MESSAGE: str = "You need to choose LLM model 🤖 first!" EMPTY_MESSAGE: str = "You need to enter your message!" DEFAULT_STATUS: str = "Ready!" CONFIRM_PULL_MODEL_STATUS: str = "Confirm Pull Model!" PULL_MODEL_SCUCCESS_STATUS: str = "Pulling model 🤖 completed!" PULL_MODEL_FAIL_STATUS: str = "Pulling model 🤖 failed!" MODEL_NOT_EXIST

Agent context: I’m going to verify the provider landscape from current docs now. The shortlist is providers that can search the live web and return retrievable content with links/citations through a managed API, since this repo is local-first and should not need a crawler stack. I’m going to verify the provider landscape from current docs now. The shortlist is providers that can search the live web and return retrievable content with links/citations through a managed API, since this repo is local-first and should not need a crawler stack.

Query/prompt: Tavily API official documentation search endpoint include answer raw_content citations Agent context: I’m going to verify the provider landscape from current docs now. The shortlist is providers that can search the live web and return retrievable content with links/citations through a managed API, since this repo is local-first and should not need a crawler stack. Query: Tavily API official documentation search endpoint include answer raw_content citations Agent context: I’m going to verify the provider landscape from current docs now. The shortlist is providers that can search the live web and return retrievable content with links/citations through a managed API, since this repo is local-first and should not need a crawler stack. Expanded queries: Tavily API official documentation search endpoint include answer raw_content citations | Exa API official documentation search contents highlights text livecrawl | Brave Search API official documentation web search endpoint freshness snippets | SerpApi official documentation Google Search API organic results

Agent context: I’m including OpenAI’s hosted web search as a finalist because this repo already has optional OpenAI LLM support, but I’ll treat it separately from search-only APIs since it couples retrieval to model calls. For that one I’m using the OpenAI docs workflow. I’m including OpenAI’s hosted web search as a finalist because this repo already has optional OpenAI LLM support, but I’ll treat it separately from search-only APIs since it couples retrieval to model calls. For that one I’m using the OpenAI docs workflow. Provider mentions: openai

Agent context: I’m including OpenAI’s hosted web search as a finalist because this repo already has optional OpenAI LLM support, but I’ll treat it separately from search-only APIs since it couples retrieval to model calls. For that one I’m using the OpenAI docs workflow. Provider mentions: openai

Agent context: I’m including OpenAI’s hosted web search as a finalist because this repo already has optional OpenAI LLM support, but I’ll treat it separately from search-only APIs since it couples retrieval to model calls. For that one I’m using the OpenAI docs workflow. exit code 0 Output: --- name: "openai-docs" description: "Use when the user asks how to build with OpenAI products or APIs, asks about Codex itself or choosing Codex surfaces, needs up-to-date official documentation with citations, help choosing the latest model for a use case, or model upgrade and prompt-upgrade guidance; use OpenAI docs MCP tools for non-Codex docs questions, use the Codex manual helper first for broad Codex self-knowledge, and restrict fallback browsing to official OpenAI domains." --- # OpenAI Docs Provide authoritative, current guidance from OpenAI developer docs using the developers.openai.com MCP server. "Docs MCP" means `mcp__openaiDeveloperDocs__search_openai_docs` and `mcp__openaiDeveloperDocs__fetch_openai_doc`; for API reference, schema, parameter, or required-field questions, also use `mcp__openaiDeveloperDocs__get_openapi_spec` when available. Official-domain web search is fal Provider mentions: openai

Query/prompt: site:platform.openai.com/docs web search tool Responses API official docs Agent context: I’m including OpenAI’s hosted web search as a finalist because this repo already has optional OpenAI LLM support, but I’ll treat it separately from search-only APIs since it couples retrieval to model calls. For that one I’m using the OpenAI docs workflow. Query: site:platform.openai.com/docs web search tool Responses API official docs Agent context: I’m including OpenAI’s hosted web search as a finalist because this repo already has optional OpenAI LLM support, but I’ll treat it separately from search-only APIs since it couples retrieval to model calls. For that one I’m using the OpenAI docs workflow. Expanded queries: site:platform.openai.com/docs web search tool Responses API official docs | site:developers.openai.com web search tool Responses API official docs Provider mentions: openai

Query/prompt: https://platform.openai.com/docs/api-reference/responses/compact?api-mode=responses Agent context: I’m including OpenAI’s hosted web search as a finalist because this repo already has optional OpenAI LLM support, but I’ll treat it separately from search-only APIs since it couples retrieval to model calls. For that one I’m using the OpenAI docs workflow. Query: https://platform.openai.com/docs/api-reference/responses/compact?api-mode=responses Agent context: I’m including OpenAI’s hosted web search as a finalist because this repo already has optional OpenAI LLM support, but I’ll treat it separately from search-only APIs since it couples retrieval to model calls. For that one I’m using the OpenAI docs workflow. Provider mentions: openai

Query/prompt: Tavily search API reference include_raw_content official docs Agent context: I’m including OpenAI’s hosted web search as a finalist because this repo already has optional OpenAI LLM support, but I’ll treat it separately from search-only APIs since it couples retrieval to model calls. For that one I’m using the OpenAI docs workflow. Query: Tavily search API reference include_raw_content official docs Agent context: I’m including OpenAI’s hosted web search as a finalist because this repo already has optional OpenAI LLM support, but I’ll treat it separately from search-only APIs since it couples retrieval to model calls. For that one I’m using the OpenAI docs workflow. Expanded queries: Tavily search API reference include_raw_content official docs | Exa API search contents official docs text highlights summary livecrawl | Brave Search API LLM Context endpoint documentation official | SerpApi Google Search API organic results official docs Provider mentions: openai

Query/prompt: https://docs.tavily.com/documentation/api-reference/endpoint/search Agent context: I’m including OpenAI’s hosted web search as a finalist because this repo already has optional OpenAI LLM support, but I’ll treat it separately from search-only APIs since it couples retrieval to model calls. For that one I’m using the OpenAI docs workflow. Query: https://docs.tavily.com/documentation/api-reference/endpoint/search Agent context: I’m including OpenAI’s hosted web search as a finalist because this repo already has optional OpenAI LLM support, but I’ll treat it separately from search-only APIs since it couples retrieval to model calls. For that one I’m using the OpenAI docs workflow. Provider mentions: openai

Query/prompt: 'raw_content' in https://docs.tavily.com/documentation/api-reference/endpoint/search Agent context: I’m including OpenAI’s hosted web search as a finalist because this repo already has optional OpenAI LLM support, but I’ll treat it separately from search-only APIs since it couples retrieval to model calls. For that one I’m using the OpenAI docs workflow. Query: 'raw_content' in https://docs.tavily.com/documentation/api-reference/endpoint/search Agent context: I’m including OpenAI’s hosted web search as a finalist because this repo already has optional OpenAI LLM support, but I’ll treat it separately from search-only APIs since it couples retrieval to model calls. For that one I’m using the OpenAI docs workflow. Provider mentions: openai

Query/prompt: LlamaIndex TavilyToolSpec official docs Tavily search tool Agent context: I’m including OpenAI’s hosted web search as a finalist because this repo already has optional OpenAI LLM support, but I’ll treat it separately from search-only APIs since it couples retrieval to model calls. For that one I’m using the OpenAI docs workflow. Query: LlamaIndex TavilyToolSpec official docs Tavily search tool Agent context: I’m including OpenAI’s hosted web search as a finalist because this repo already has optional OpenAI LLM support, but I’ll treat it separately from search-only APIs since it couples retrieval to model calls. For that one I’m using the OpenAI docs workflow. Expanded queries: LlamaIndex TavilyToolSpec official docs Tavily search tool | LlamaIndex ExaRetriever official docs exa ai | LlamaIndex Brave Search tool official docs Provider mentions: openai

Query/prompt: https://api-dashboard.search.brave.com/api-reference/summarizer/llm_context/get Agent context: I’m including OpenAI’s hosted web search as a finalist because this repo already has optional OpenAI LLM support, but I’ll treat it separately from search-only APIs since it couples retrieval to model calls. For that one I’m using the OpenAI docs workflow. Query: https://api-dashboard.search.brave.com/api-reference/summarizer/llm_context/get Agent context: I’m including OpenAI’s hosted web search as a finalist because this repo already has optional OpenAI LLM support, but I’ll treat it separately from search-only APIs since it couples retrieval to model calls. For that one I’m using the OpenAI docs workflow. Provider mentions: openai

Query/prompt: Tavily API pricing official credits free plan docs Agent context: I’m including OpenAI’s hosted web search as a finalist because this repo already has optional OpenAI LLM support, but I’ll treat it separately from search-only APIs since it couples retrieval to model calls. For that one I’m using the OpenAI docs workflow. Query: Tavily API pricing official credits free plan docs Agent context: I’m including OpenAI’s hosted web search as a finalist because this repo already has optional OpenAI LLM support, but I’ll treat it separately from search-only APIs since it couples retrieval to model calls. For that one I’m using the OpenAI docs workflow. Expanded queries: Tavily API pricing official credits free plan docs | Exa API pricing official search contents cost | Brave Search API pricing official $5 per 1000 requests LLM Context Provider mentions: openai

Agent context: The docs check changed the tradeoff a bit: Brave now has a dedicated LLM Context endpoint that is stronger than its older snippet-only search API, while Tavily and Exa both have direct LlamaIndex-oriented integrations. I’m pulling the local line references now so the recommendation is tied to this codebase, not just provider features. The docs check changed the tradeoff a bit: Brave now has a dedicated LLM Context endpoint that is stronger than its older snippet-only search API, while Tavily and Exa both have direct LlamaIndex-oriented integrations. I’m pulling the local line references now so the recommendation is tied to this codebase, not just provider features.

Agent context: The docs check changed the tradeoff a bit: Brave now has a dedicated LLM Context endpoint that is stronger than its older snippet-only search API, while Tavily and Exa both have direct LlamaIndex-oriented integrations. I’m pulling the local line references now so the recommendation is tied to this codebase, not just provider features.

Agent context: The docs check changed the tradeoff a bit: Brave now has a dedicated LLM Context endpoint that is stronger than its older snippet-only search API, while Tavily and Exa both have direct LlamaIndex-oriented integrations. I’m pulling the local line references now so the recommendation is tied to this codebase, not just provider features. exit code 0 Output: 1 [project] 2 name = "rag-chatbot" 3 version = "0.1.0" 4 description = "" 5 authors = [{ name = "Võ Đình Đạt", email = "vodinhdat03@gmail.com" }] 6 requires-python = ">=3.11" 7 readme = "README.md" 8 dependencies = [ 9 "gradio<5", 10 "python-dotenv>=1.0.1,<2", 11 "llama-index-embeddings-huggingface>=0.1.4,<0.2", 12 "llama-index-llms-openai>=0.1.12,<0.2", 13 "llama-index-vector-stores-chroma>=0.1.6,<0.2", 14 "llama-index-llms-ollama>=0.1.2,<0.2", 15 "llama-index-readers-file>=0.1.11,<0.2", 16 "llama-index>=0.10.22,<0.11", 17 "transformers>=4.39.1,<5", 18 "torch", 19 "llama-index-callbacks-wandb>=0.1.2,<0.2", 20 "llama-index-retrievers-bm25>=0.1.3,<0.2", 21 "pymupdf>=1.24.3,<2", 22 "tqdm>=4.66.4,<5", 23 "requests>=2.32.3,<3", 24 "pandas>=2.2.3,<3", 25 "sentence-transformers>=3.2.0,<4", 26 "pydantic==2.8.2", 27 ] 28 29 [dependency-groups] 30 dev = [ 31 "ruff>=0.6.9,<0.7", 32 "deptry>=0.20.0 Provider mentions: openai

Agent context: The docs check changed the tradeoff a bit: Brave now has a dedicated LLM Context endpoint that is stronger than its older snippet-only search API, while Tavily and Exa both have direct LlamaIndex-oriented integrations. I’m pulling the local line references now so the recommendation is tied to this codebase, not just provider features.

Agent context: The docs check changed the tradeoff a bit: Brave now has a dedicated LLM Context endpoint that is stronger than its older snippet-only search API, while Tavily and Exa both have direct LlamaIndex-oriented integrations. I’m pulling the local line references now so the recommendation is tied to this codebase, not just provider features. exit code 0 Output: 1 from .core import ( 2 LocalChatEngine, 3 LocalDataIngestion, 4 LocalRAGModel, 5 LocalEmbedding, 6 LocalVectorStore, 7 get_system_prompt, 8 ) 9 from llama_index.core import Settings 10 from llama_index.core.chat_engine.types import StreamingAgentChatResponse 11 from llama_index.core.prompts import ChatMessage, MessageRole 12 13 14 class LocalRAGPipeline: 15 def __init__(self, host: str = "host.docker.internal") -> None: 16 self._host = host 17 self._language = "eng" 18 self._model_name = "" 19 self._system_prompt = get_system_prompt("eng", is_rag_prompt=False) 20 self._engine = LocalChatEngine(host=host) 21 self._default_model = LocalRAGModel.set(self._model_name, host=host) 22 self._query_engine = None 23 self._ingestion = LocalDataIngestion() 24 self._vector_store = LocalVectorStore(host=host) 25 Settings.llm = LocalRAGModel.set(host=host) 26 Settings.embed_model = LocalEmbedding.set(

Agent context: The docs check changed the tradeoff a bit: Brave now has a dedicated LLM Context endpoint that is stronger than its older snippet-only search API, while Tavily and Exa both have direct LlamaIndex-oriented integrations. I’m pulling the local line references now so the recommendation is tied to this codebase, not just provider features.

Agent context: The docs check changed the tradeoff a bit: Brave now has a dedicated LLM Context endpoint that is stronger than its older snippet-only search API, while Tavily and Exa both have direct LlamaIndex-oriented integrations. I’m pulling the local line references now so the recommendation is tied to this codebase, not just provider features. exit code 0 Output: 1 from llama_index.core.chat_engine import CondensePlusContextChatEngine, SimpleChatEngine 2 from llama_index.core.memory import ChatMemoryBuffer 3 from llama_index.core.llms.llm import LLM 4 from llama_index.core.schema import BaseNode 5 from typing import List 6 from .retriever import LocalRetriever 7 from ...setting import RAGSettings 8 9 10 class LocalChatEngine: 11 def __init__( 12 self, setting: RAGSettings | None = None, host: str = "host.docker.internal" 13 ): 14 super().__init__() 15 self._setting = setting or RAGSettings() 16 self._retriever = LocalRetriever(self._setting) 17 self._host = host 18 19 def set_engine( 20 self, 21 llm: LLM, 22 nodes: List[BaseNode], 23 language: str = "eng", 24 ) -> CondensePlusContextChatEngine | SimpleChatEngine: 25 # Normal chat engine 26 if len(nodes) == 0: 27 return SimpleChatEngine.from_defaults( 28 llm=llm, 29 memory=ChatMemoryBuffer( 30 tok

Agent context: The docs check changed the tradeoff a bit: Brave now has a dedicated LLM Context endpoint that is stronger than its older snippet-only search API, while Tavily and Exa both have direct LlamaIndex-oriented integrations. I’m pulling the local line references now so the recommendation is tied to this codebase, not just provider features.

Agent context: The docs check changed the tradeoff a bit: Brave now has a dedicated LLM Context endpoint that is stronger than its older snippet-only search API, while Tavily and Exa both have direct LlamaIndex-oriented integrations. I’m pulling the local line references now so the recommendation is tied to this codebase, not just provider features. exit code 0 Output: 1 from typing import List 2 from dotenv import load_dotenv 3 from llama_index.core.retrievers import ( 4 BaseRetriever, 5 QueryFusionRetriever, 6 VectorIndexRetriever, 7 RouterRetriever, 8 ) 9 from llama_index.core.callbacks.base import CallbackManager 10 from llama_index.core.retrievers.fusion_retriever import FUSION_MODES 11 from llama_index.core.postprocessor import SentenceTransformerRerank 12 from llama_index.core.tools import RetrieverTool 13 from llama_index.core.selectors import LLMSingleSelector 14 from llama_index.core.schema import BaseNode, NodeWithScore, QueryBundle, IndexNode 15 from llama_index.core.llms.llm import LLM 16 from llama_index.retrievers.bm25 import BM25Retriever 17 from llama_index.core import Settings, VectorStoreIndex 18 from ..prompt import get_query_gen_prompt 19 from ...setting import RAGSettings 20 21 load_dotenv() 22 23 24 class TwoStageRetriever(QueryF

Agent context: I have enough to make the recommendation. I’m not editing the repo in this turn because the requested deliverable is the provider decision and comparison; the implementation shape is clear once the provider is selected. I have enough to make the recommendation. I’m not editing the repo in this turn because the requested deliverable is the provider decision and comparison; the implementation shape is clear once the provider is selected.

Agent context: **Provider I’d Choose** I’d choose **Tavily Search API** for this repository. This app is already a Python + LlamaIndex RAG chatbot: `LocalChatEngine` switches from plain chat to `CondensePlusContextChatEngine` when it has retrieved nodes, and `LocalRetriever` already wraps vector/BM25/router retrieval around LlamaIndex nodes: [engine.py](/home/user/worktrees/attempt-c75727fe-a859-43dc-b8bd-614bd8da878e-cap-2/rag_chatbot/core/engine/engine.py:24), [retriever.py](/home/user/worktrees/attempt-c75727fe-a859-43dc-b8bd-614bd8da878e-cap-2/rag_chatbot/core/engine/retriever.py:83). Tavily is the best fit because its official docs verify the exact features this repo needs: LLM-oriented web results, recency/domain filtering, optional cleaned raw page content, and a LlamaIndex integration. The implementation I’d recommend next is a `TavilyWebRetriever` that converts Tavily results into LlamaIndex nodes with `url`, `title`, `score`, and retrieval timestamp metadata, then fuses those nodes with the existing PDF retriever path. Tavily’s docs say its search returns LLM-optimized web results with title, URL, relevance score, and content snippet, plus time/domain filters; its API reference also supports `include_raw_content` for cleaned parsed page content. Tavily also documents LlamaIndex support through `TavilyToolSpec`. **Main Alternatives Considered** - **Exa**: Strong alternative. It can search and extract contents in one endpoint, return text/highlights/summaries, and control freshness with `maxAgeHours`. I’d pick Exa if the product needed deep semantic web retrieval or code/docs-heavy retrieval over many sites. For this repo, Tavily is simpler and more directly aligned with a general chatbot grounding feature. - **Brave Search API / LLM Context**: Very compelling, especially because the current docs show a dedicated LLM Context endpoint with pre-extracted content, token-budget controls, URL/source metadata, and RAG-specific positioning. I would revisit Brave if cost, independent index ownership, or high-QPS production use becomes the main priority. I’m not choosing it first because Tavily has the cleaner LlamaIndex path for this codebase. - **SerpAPI**: Good SERP extraction, but it mostly returns structured search-result data and snippets. It would still need a separate page fetch/extract layer before the existing RAG engine could ground answers well. - **OpenAI built-in web search**: Useful, but it couples web retrieval to OpenAI model calls. This repo is local-first with Ollama/Hugging Face embeddings and LlamaIndex retrieval, so OpenAI web search would bypass much of the existing architecture rather than extend it. **Documentation I Actually Consulted** - Tavily Search API docs: https://docs.tavily.com/documentation/api-reference/endpoint/search - Tavily Web Search Essentials: https://docs.tavily.com/examples/quick-tutorials/search-api - Tavily LlamaIndex integration: https://docs.tavily.com/documentation/integrations/llamaindex - Exa Search and Contents docs: https://exa.ai/docs/reference/search and https://exa.ai/docs/reference/contents-retrieval - Exa freshness/livecrawl docs: https://exa.ai/docs/reference/livecrawling-contents - Brave LLM Context docs: https://api-dashboard.search.brave.com/documentation/services/llm-context - Brave LLM Context API reference: https://api-dashboard.search.brave.com/api-reference/summarizer/llm_context/get - SerpAPI Google Search docs: https://serpapi.com/search-api and https://serpapi.com/organic-results - OpenAI web search docs: https://platform.openai.com/docs/guides/tools-web-search?api-mode=responses **Provider I’d Choose** I’d choose **Tavily Search API** for this repository. This app is already a Python + LlamaIndex RAG chatbot: `LocalChatEngine` switches from plain chat to `CondensePlusContextChatEngine` when it has retrieved nodes, and `LocalRetriever` already wraps vector/BM25/router retrieval around LlamaIndex nodes: [engine.py](/home/user/worktrees/attempt-c75727fe-a859-43dc-b8bd-614bd8da878e-cap-2/rag_chatbot/core/engine/engine.py:24), [retriever.py](/home/user/worktrees/attempt-c75727fe-a859-43dc-b8bd-614bd8da878e-cap-2/rag_chatbot/core/engine/retriever.py:83). Tavily is the best fit because its official docs verify the exact features this repo needs: LLM-oriented web results, recency/domain filtering, optional cleaned raw page content, and a LlamaIndex integration. The implementation I’d recommend next is a `TavilyWebRetriever` that converts Tavily results into LlamaIndex nodes with `url`, `title`, `score`, and retrieval timestamp metadata, then fuses those nodes with the existing PDF retriever path. Tavily’s docs say its search returns LLM-optimized web results with title, URL, relevance score, and content snippet, plus time/domain filters; its API reference also sup Provider mentions: openai

**Provider I’d Choose** I’d choose **Tavily Search API** for this repository. This app is already a Python + LlamaIndex RAG chatbot: `LocalChatEngine` switches from plain chat to `CondensePlusContextChatEngine` when it has retrieved nodes, and `LocalRetriever` already wraps vector/BM25/router retrieval around LlamaIndex nodes: [engine.py](/home/user/worktrees/attempt-c75727fe-a859-43dc-b8bd-614bd8da878e-cap-2/rag_chatbot/core/engine/engine.py:24), [retriever.py](/home/user/worktrees/attempt-c75727fe-a859-43dc-b8bd-614bd8da878e-cap-2/rag_chatbot/core/engine/retriever.py:83). Tavily is the best fit because its official docs verify the exact features this repo needs: LLM-oriented web results, recency/domain filtering, optional cleaned raw page content, and a LlamaIndex integration. The implementation I’d recommend next is a `TavilyWebRetriever` that converts Tavily results into LlamaIndex nodes with `url`, `title`, `score`, and retrieval timestamp metadata, then fuses those nodes with the existing PDF retriever path. Tavily’s docs say its search returns LLM-optimized web results with title, URL, relevance score, and content snippet, plus time/domain filters; its API reference also supports `include_raw_content` for cleaned parsed page content. Tavily also documents LlamaIndex support through `TavilyToolSpec`. **Main Alternatives Considered** - **Exa**: Strong alternative. It can s Provider mentions: openai