Run

P0

e9de2afd458e4ad2d7c54636d3c8b20de04cf790

Agent context: I’ll inspect the repo first to understand the agent stack and integration points, then I’ll check current provider docs so the recommendation is grounded in what each managed service actually supports. I’ll inspect the repo first to understand the agent stack and integration points, then I’ll check current provider docs so the recommendation is grounded in what each managed service actually supports.

Agent context: I’ll inspect the repo first to understand the agent stack and integration points, then I’ll check current provider docs so the recommendation is grounded in what each managed service actually supports.

Agent context: I’ll inspect the repo first to understand the agent stack and integration points, then I’ll check current provider docs so the recommendation is grounded in what each managed service actually supports. exit code 127 Output: /bin/bash: line 1: rg: command not found /home/user/worktrees/attempt-40bea178-8361-4aef-81b7-b90efed0859d-cap-0

Agent context: I’ll inspect the repo first to understand the agent stack and integration points, then I’ll check current provider docs so the recommendation is grounded in what each managed service actually supports.

Agent context: I’ll inspect the repo first to understand the agent stack and integration points, then I’ll check current provider docs so the recommendation is grounded in what each managed service actually supports. exit code 0

Agent context: `rg` is not installed in this workspace, so I’m falling back to `find` plus targeted file reads. The worktree is clean. `rg` is not installed in this workspace, so I’m falling back to `find` plus targeted file reads. The worktree is clean.

Agent context: `rg` is not installed in this workspace, so I’m falling back to `find` plus targeted file reads. The worktree is clean.

Agent context: `rg` is not installed in this workspace, so I’m falling back to `find` plus targeted file reads. The worktree is clean. 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_prompt.py rag_chatbot/core/vector_store/__init__.py rag_chatbot/core

Agent context: `rg` is not installed in this workspace, so I’m falling back to `find` plus targeted file reads. The worktree is clean.

Agent context: `rg` is not installed in this workspace, so I’m falling back to `find` plus targeted file reads. The worktree is clean. exit code 0 Output: ./README.md ./pyproject.toml

Agent context: The project is a Python RAG chatbot with local ingestion, embeddings, vector store, and Ollama-backed generation. I’m reading the engine, retriever, prompts, settings, and dependency file to see whether web retrieval should be a retriever source, a tool, or a pre-ingestion path. The project is a Python RAG chatbot with local ingestion, embeddings, vector store, and Ollama-backed generation. I’m reading the engine, retriever, prompts, settings, and dependency file to see whether web retrieval should be a retriever source, a tool, or a pre-ingestion path.

Agent context: The project is a Python RAG chatbot with local ingestion, embeddings, vector store, and Ollama-backed generation. I’m reading the engine, retriever, prompts, settings, and dependency file to see whether web retrieval should be a retriever source, a tool, or a pre-ingestion path.

Agent context: The project is a Python RAG chatbot with local ingestion, embeddings, vector store, and Ollama-backed generation. I’m reading the engine, retriever, prompts, settings, and dependency file to see whether web retrieval should be a retriever source, a tool, or a pre-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: The project is a Python RAG chatbot with local ingestion, embeddings, vector store, and Ollama-backed generation. I’m reading the engine, retriever, prompts, settings, and dependency file to see whether web retrieval should be a retriever source, a tool, or a pre-ingestion path.

Agent context: The project is a Python RAG chatbot with local ingestion, embeddings, vector store, and Ollama-backed generation. I’m reading the engine, retriever, prompts, settings, and dependency file to see whether web retrieval should be a retriever source, a tool, or a pre-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: The project is a Python RAG chatbot with local ingestion, embeddings, vector store, and Ollama-backed generation. I’m reading the engine, retriever, prompts, settings, and dependency file to see whether web retrieval should be a retriever source, a tool, or a pre-ingestion path.

Agent context: The project is a Python RAG chatbot with local ingestion, embeddings, vector store, and Ollama-backed generation. I’m reading the engine, retriever, prompts, settings, and dependency file to see whether web retrieval should be a retriever source, a tool, or a pre-ingestion path. 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 project is a Python RAG chatbot with local ingestion, embeddings, vector store, and Ollama-backed generation. I’m reading the engine, retriever, prompts, settings, and dependency file to see whether web retrieval should be a retriever source, a tool, or a pre-ingestion path.

Agent context: The project is a Python RAG chatbot with local ingestion, embeddings, vector store, and Ollama-backed generation. I’m reading the engine, retriever, prompts, settings, and dependency file to see whether web retrieval should be a retriever source, a tool, or a pre-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: The project is a Python RAG chatbot with local ingestion, embeddings, vector store, and Ollama-backed generation. I’m reading the engine, retriever, prompts, settings, and dependency file to see whether web retrieval should be a retriever source, a tool, or a pre-ingestion path.

Agent context: The project is a Python RAG chatbot with local ingestion, embeddings, vector store, and Ollama-backed generation. I’m reading the engine, retriever, prompts, settings, and dependency file to see whether web retrieval should be a retriever source, a tool, or a pre-ingestion path. 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 project is a Python RAG chatbot with local ingestion, embeddings, vector store, and Ollama-backed generation. I’m reading the engine, retriever, prompts, settings, and dependency file to see whether web retrieval should be a retriever source, a tool, or a pre-ingestion path.

Agent context: The project is a Python RAG chatbot with local ingestion, embeddings, vector store, and Ollama-backed generation. I’m reading the engine, retriever, prompts, settings, and dependency file to see whether web retrieval should be a retriever source, a tool, or a pre-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 project is a Python RAG chatbot with local ingestion, embeddings, vector store, and Ollama-backed generation. I’m reading the engine, retriever, prompts, settings, and dependency file to see whether web retrieval should be a retriever source, a tool, or a pre-ingestion path.

Agent context: The project is a Python RAG chatbot with local ingestion, embeddings, vector store, and Ollama-backed generation. I’m reading the engine, retriever, prompts, settings, and dependency file to see whether web retrieval should be a retriever source, a tool, or a pre-ingestion path. exit code 0 Output: import re import fitz from llama_index.core import Document, Settings from llama_index.core.schema import BaseNode from llama_index.core.node_parser import SentenceSplitter from dotenv import load_dotenv from typing import Any, List from tqdm import tqdm from ...setting import RAGSettings load_dotenv() class LocalDataIngestion: def __init__(self, setting: RAGSettings | None = None) -> None: self._setting = setting or RAGSettings() self._node_store = {} self._ingested_file = [] def _filter_text(self, text): # Define the regex pattern. pattern = r'[a-zA-Z0-9 \u00C0-\u01B0\u1EA0-\u1EF9`~!@#$%^&*()_\-+=\[\]{}|\\;:\'",.<>/?]+' matches = re.findall(pattern, text) # Join all matched substrings into a single string filtered_text = " ".join(matches) # Normalize the text by removing extra whitespaces normalized_text = re.sub(r"\s+", " ", filtered_text.strip()) return normalized_text def store_node

Agent context: The project is a Python RAG chatbot with local ingestion, embeddings, vector store, and Ollama-backed generation. I’m reading the engine, retriever, prompts, settings, and dependency file to see whether web retrieval should be a retriever source, a tool, or a pre-ingestion path.

Agent context: The project is a Python RAG chatbot with local ingestion, embeddings, vector store, and Ollama-backed generation. I’m reading the engine, retriever, prompts, settings, and dependency file to see whether web retrieval should be a retriever source, a tool, or a pre-ingestion path. 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 integration point is straightforward: this app already uses LlamaIndex retrievers feeding `CondensePlusContextChatEngine`, with `requests` already in dependencies. A managed web provider that can return search results plus crawled page text/snippets with URLs would fit as another retriever/source without changing the local Ollama model path. The integration point is straightforward: this app already uses LlamaIndex retrievers feeding `CondensePlusContextChatEngine`, with `requests` already in dependencies. A managed web provider that can return search results plus crawled page text/snippets with URLs would fit as another retriever/source without changing the local Ollama model path.

Agent context: The integration point is straightforward: this app already uses LlamaIndex retrievers feeding `CondensePlusContextChatEngine`, with `requests` already in dependencies. A managed web provider that can return search results plus crawled page text/snippets with URLs would fit as another retriever/source without changing the local Ollama model path.

Agent context: The integration point is straightforward: this app already uses LlamaIndex retrievers feeding `CondensePlusContextChatEngine`, with `requests` already in dependencies. A managed web provider that can return search results plus crawled page text/snippets with URLs would fit as another retriever/source without changing the local Ollama model path. 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: The integration point is straightforward: this app already uses LlamaIndex retrievers feeding `CondensePlusContextChatEngine`, with `requests` already in dependencies. A managed web provider that can return search results plus crawled page text/snippets with URLs would fit as another retriever/source without changing the local Ollama model path.

Agent context: The integration point is straightforward: this app already uses LlamaIndex retrievers feeding `CondensePlusContextChatEngine`, with `requests` already in dependencies. A managed web provider that can return search results plus crawled page text/snippets with URLs would fit as another retriever/source without changing the local Ollama model path. exit code 0 Output: from llama_index.llms.ollama import Ollama from llama_index.llms.openai import OpenAI from ...setting import RAGSettings from dotenv import load_dotenv import requests load_dotenv() class LocalRAGModel: def __init__(self) -> None: pass @staticmethod def set( model_name: str = "llama3:8b-instruct-q8_0", system_prompt: str | None = None, host: str = "host.docker.internal", setting: RAGSettings | None = None, ): setting = setting or RAGSettings() if model_name in ["gpt-3.5-turbo", "gpt-4", "gpt-4o", "gpt-4-turbo"]: return OpenAI(model=model_name, temperature=setting.ollama.temperature) else: settings_kwargs = { "tfs_z": setting.ollama.tfs_z, "top_k": setting.ollama.top_k, "top_p": setting.ollama.top_p, "repeat_last_n": setting.ollama.repeat_last_n, "repeat_penalty": setting.ollama.repeat_penalty, } return Ollama( model=model_name, system_prompt=system_prompt, base_url=f"http://{host}:{setti Provider mentions: openai

Agent context: The integration point is straightforward: this app already uses LlamaIndex retrievers feeding `CondensePlusContextChatEngine`, with `requests` already in dependencies. A managed web provider that can return search results plus crawled page text/snippets with URLs would fit as another retriever/source without changing the local Ollama model path.

Agent context: The integration point is straightforward: this app already uses LlamaIndex retrievers feeding `CondensePlusContextChatEngine`, with `requests` already in dependencies. A managed web provider that can return search results plus crawled page text/snippets with URLs would fit as another retriever/source without changing the local Ollama model path. exit code 0 Output: from llama_index.core import VectorStoreIndex from dotenv import load_dotenv from ...setting import RAGSettings load_dotenv() class LocalVectorStore: def __init__( self, host: str = "host.docker.internal", setting: RAGSettings | None = None, ) -> None: # TODO # CHROMA VECTOR STORE self._setting = setting or RAGSettings() def get_index(self, nodes): if len(nodes) == 0: return None index = VectorStoreIndex(nodes=nodes) return index

Agent context: The integration point is straightforward: this app already uses LlamaIndex retrievers feeding `CondensePlusContextChatEngine`, with `requests` already in dependencies. A managed web provider that can return search results plus crawled page text/snippets with URLs would fit as another retriever/source without changing the local Ollama model path. Provider mentions: openai

Agent context: The integration point is straightforward: this app already uses LlamaIndex retrievers feeding `CondensePlusContextChatEngine`, with `requests` already in dependencies. A managed web provider that can return search results plus crawled page text/snippets with URLs would fit as another retriever/source without changing the local Ollama model path. exit code 0 Output: rag_chatbot/core/embedding/embedding.py:3:import requests rag_chatbot/core/embedding/embedding.py:38: return requests.post(f"http://{host}:11434/api/pull", json=payload, stream=True) rag_chatbot/core/embedding/embedding.py:43: data = requests.get(f"http://{host}:11434/api/tags").json() rag_chatbot/core/engine/__init__.py:2:from .retriever import LocalRetriever rag_chatbot/core/engine/__init__.py:4:__all__ = ["LocalChatEngine", "LocalRetriever"] rag_chatbot/core/engine/engine.py:6:from .retriever import LocalRetriever rag_chatbot/core/engine/engine.py:16: self._retriever = LocalRetriever(self._setting) rag_chatbot/core/engine/retriever.py:4: BaseRetriever, rag_chatbot/core/engine/retriever.py:5: QueryFusionRetriever, rag_chatbot/core/engine/retriever.py:6: VectorIndexRetriever, rag_chatbot/core/engine/retriever.py:7: RouterRetriever, rag_chatbot/core/engine/retriever.py:12:from llama_inde Provider mentions: openai

Query/prompt: Tavily API documentation search extract raw_content LlamaIndex integration Agent context: The integration point is straightforward: this app already uses LlamaIndex retrievers feeding `CondensePlusContextChatEngine`, with `requests` already in dependencies. A managed web provider that can return search results plus crawled page text/snippets with URLs would fit as another retriever/source without changing the local Ollama model path. Query: Tavily API documentation search extract raw_content LlamaIndex integration Agent context: The integration point is straightforward: this app already uses LlamaIndex retrievers feeding `CondensePlusContextChatEngine`, with `requests` already in dependencies. A managed web provider that can return search results plus crawled page text/snippets with URLs would fit as another retriever/source without changing the local Ollama model path. Expanded queries: Tavily API documentation search extract raw_content LlamaIndex integration | Exa API documentation search contents LlamaIndex integration | Brave Search API documentation Web Search API | SerpAPI documentation organic results API

Query/prompt: OpenAI web search tool API documentation Responses API web_search_preview Agent context: The integration point is straightforward: this app already uses LlamaIndex retrievers feeding `CondensePlusContextChatEngine`, with `requests` already in dependencies. A managed web provider that can return search results plus crawled page text/snippets with URLs would fit as another retriever/source without changing the local Ollama model path. Query: OpenAI web search tool API documentation Responses API web_search_preview Agent context: The integration point is straightforward: this app already uses LlamaIndex retrievers feeding `CondensePlusContextChatEngine`, with `requests` already in dependencies. A managed web provider that can return search results plus crawled page text/snippets with URLs would fit as another retriever/source without changing the local Ollama model path. Expanded queries: OpenAI web search tool API documentation Responses API web_search_preview | Tavily docs search API extract crawl docs official | Perplexity Sonar API documentation search grounding citations official | LlamaIndex Tavily integration documentation Provider mentions: openai

Query/prompt: site:docs.llamaindex.ai Tavily LlamaIndex TavilyToolSpec Agent context: The integration point is straightforward: this app already uses LlamaIndex retrievers feeding `CondensePlusContextChatEngine`, with `requests` already in dependencies. A managed web provider that can return search results plus crawled page text/snippets with URLs would fit as another retriever/source without changing the local Ollama model path. Query: site:docs.llamaindex.ai Tavily LlamaIndex TavilyToolSpec Agent context: The integration point is straightforward: this app already uses LlamaIndex retrievers feeding `CondensePlusContextChatEngine`, with `requests` already in dependencies. A managed web provider that can return search results plus crawled page text/snippets with URLs would fit as another retriever/source without changing the local Ollama model path. Expanded queries: site:docs.llamaindex.ai Tavily LlamaIndex TavilyToolSpec | site:developers.llamaindex.ai/python TavilyToolSpec tavily LlamaIndex | llama-index-tools-tavily-research PyPI tavily tool spec

Query/prompt: Tavily pricing API credits search extract crawl 2026 Agent context: The integration point is straightforward: this app already uses LlamaIndex retrievers feeding `CondensePlusContextChatEngine`, with `requests` already in dependencies. A managed web provider that can return search results plus crawled page text/snippets with URLs would fit as another retriever/source without changing the local Ollama model path. Query: Tavily pricing API credits search extract crawl 2026 Agent context: The integration point is straightforward: this app already uses LlamaIndex retrievers feeding `CondensePlusContextChatEngine`, with `requests` already in dependencies. A managed web provider that can return search results plus crawled page text/snippets with URLs would fit as another retriever/source without changing the local Ollama model path. Expanded queries: Tavily pricing API credits search extract crawl 2026 | Exa pricing API search contents 2026 | Brave Search API pricing 2026 | Perplexity Sonar API pricing 2026

Query/prompt: https://docs.tavily.com/documentation/api-reference/endpoint/search Agent context: The integration point is straightforward: this app already uses LlamaIndex retrievers feeding `CondensePlusContextChatEngine`, with `requests` already in dependencies. A managed web provider that can return search results plus crawled page text/snippets with URLs would fit as another retriever/source without changing the local Ollama model path. Query: https://docs.tavily.com/documentation/api-reference/endpoint/search Agent context: The integration point is straightforward: this app already uses LlamaIndex retrievers feeding `CondensePlusContextChatEngine`, with `requests` already in dependencies. A managed web provider that can return search results plus crawled page text/snippets with URLs would fit as another retriever/source without changing the local Ollama model path.

Query/prompt: https://pypi.org/project/llama-index-tools-tavily-research/0.4.2/ Agent context: The integration point is straightforward: this app already uses LlamaIndex retrievers feeding `CondensePlusContextChatEngine`, with `requests` already in dependencies. A managed web provider that can return search results plus crawled page text/snippets with URLs would fit as another retriever/source without changing the local Ollama model path. Query: https://pypi.org/project/llama-index-tools-tavily-research/0.4.2/ Agent context: The integration point is straightforward: this app already uses LlamaIndex retrievers feeding `CondensePlusContextChatEngine`, with `requests` already in dependencies. A managed web provider that can return search results plus crawled page text/snippets with URLs would fit as another retriever/source without changing the local Ollama model path.

Query/prompt: Tavily Extract API documentation raw content markdown text official docs Agent context: The integration point is straightforward: this app already uses LlamaIndex retrievers feeding `CondensePlusContextChatEngine`, with `requests` already in dependencies. A managed web provider that can return search results plus crawled page text/snippets with URLs would fit as another retriever/source without changing the local Ollama model path. Query: Tavily Extract API documentation raw content markdown text official docs Agent context: The integration point is straightforward: this app already uses LlamaIndex retrievers feeding `CondensePlusContextChatEngine`, with `requests` already in dependencies. A managed web provider that can return search results plus crawled page text/snippets with URLs would fit as another retriever/source without changing the local Ollama model path. Expanded queries: Tavily Extract API documentation raw content markdown text official docs | Tavily API response result raw_content score title url docs

Agent context: The integration point is straightforward: this app already uses LlamaIndex retrievers feeding `CondensePlusContextChatEngine`, with `requests` already in dependencies. A managed web provider that can return search results plus crawled page text/snippets with URLs would fit as another retriever/source without changing the local Ollama model path.

Agent context: The integration point is straightforward: this app already uses LlamaIndex retrievers feeding `CondensePlusContextChatEngine`, with `requests` already in dependencies. A managed web provider that can return search results plus crawled page text/snippets with URLs would fit as another retriever/source without changing the local Ollama model path. exit code 0 Output: 83 class LocalRetriever: 84 def __init__( 85 self, setting: RAGSettings | None = None, host: str = "host.docker.internal" 86 ): 87 super().__init__() 88 self._setting = setting or RAGSettings() 89 self._host = host 90 91 def _get_normal_retriever( 92 self, 93 vector_index: VectorStoreIndex, 94 llm: LLM | None = None, 95 language: str = "eng", 96 ): 97 llm = llm or Settings.llm 98 return VectorIndexRetriever( 99 index=vector_index, 100 similarity_top_k=self._setting.retriever.similarity_top_k, 101 embed_model=Settings.embed_model, 102 verbose=True, 103 ) 104 105 def _get_hybrid_retriever( 106 self, 107 vector_index: VectorStoreIndex, 108 llm: LLM | None = None, 109 language: str = "eng", 110 gen_query: bool = True, 111 ): 112 # VECTOR INDEX RETRIEVER 113 vector_retriever = VectorIndexRetriever( 114 index=vector_index, 115 similarity_top_k=self._setting.retriever.similarity_top_k, 116 embe

Agent context: The integration point is straightforward: this app already uses LlamaIndex retrievers feeding `CondensePlusContextChatEngine`, with `requests` already in dependencies. A managed web provider that can return search results plus crawled page text/snippets with URLs would fit as another retriever/source without changing the local Ollama model path.

Agent context: The integration point is straightforward: this app already uses LlamaIndex retrievers feeding `CondensePlusContextChatEngine`, with `requests` already in dependencies. A managed web provider that can return search results plus crawled page text/snippets with URLs would fit as another retriever/source without changing the local Ollama model path. 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 integration point is straightforward: this app already uses LlamaIndex retrievers feeding `CondensePlusContextChatEngine`, with `requests` already in dependencies. A managed web provider that can return search results plus crawled page text/snippets with URLs would fit as another retriever/source without changing the local Ollama model path.

Agent context: The integration point is straightforward: this app already uses LlamaIndex retrievers feeding `CondensePlusContextChatEngine`, with `requests` already in dependencies. A managed web provider that can return search results plus crawled page text/snippets with URLs would fit as another retriever/source without changing the local Ollama model path. exit code 0 Output: 1 import re 2 import fitz 3 from llama_index.core import Document, Settings 4 from llama_index.core.schema import BaseNode 5 from llama_index.core.node_parser import SentenceSplitter 6 from dotenv import load_dotenv 7 from typing import Any, List 8 from tqdm import tqdm 9 from ...setting import RAGSettings 10 11 load_dotenv() 12 13 14 class LocalDataIngestion: 15 def __init__(self, setting: RAGSettings | None = None) -> None: 16 self._setting = setting or RAGSettings() 17 self._node_store = {} 18 self._ingested_file = [] 19 20 def _filter_text(self, text): 21 # Define the regex pattern. 22 pattern = r'[a-zA-Z0-9 \u00C0-\u01B0\u1EA0-\u1EF9`~!@#$%^&*()_\-+=\[\]{}|\\;:\'",.<>/?]+' 23 matches = re.findall(pattern, text) 24 # Join all matched substrings into a single string 25 filtered_text = " ".join(matches) 26 # Normalize the text by removing extra whitespaces 27 normalized_text = re.sub(r

Agent context: The integration point is straightforward: this app already uses LlamaIndex retrievers feeding `CondensePlusContextChatEngine`, with `requests` already in dependencies. A managed web provider that can return search results plus crawled page text/snippets with URLs would fit as another retriever/source without changing the local Ollama model path.

Agent context: The integration point is straightforward: this app already uses LlamaIndex retrievers feeding `CondensePlusContextChatEngine`, with `requests` already in dependencies. A managed web provider that can return search results plus crawled page text/snippets with URLs would fit as another retriever/source without changing the local Ollama model path. 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 integration point is straightforward: this app already uses LlamaIndex retrievers feeding `CondensePlusContextChatEngine`, with `requests` already in dependencies. A managed web provider that can return search results plus crawled page text/snippets with URLs would fit as another retriever/source without changing the local Ollama model path.

Agent context: The integration point is straightforward: this app already uses LlamaIndex retrievers feeding `CondensePlusContextChatEngine`, with `requests` already in dependencies. A managed web provider that can return search results plus crawled page text/snippets with URLs would fit as another retriever/source without changing the local Ollama model path. 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: **Provider Choice** I’d choose **Tavily**, specifically **Tavily Search + Extract**, not Tavily’s higher-level Research answer engine. This repo is already a LlamaIndex RAG app: PDFs become nodes, nodes feed retrievers, and the retriever feeds `CondensePlusContextChatEngine` with the local/Ollama or OpenAI LLM. The right fit is a provider that returns source URLs plus clean page content that can be converted into LlamaIndex nodes, not a provider that replaces the app’s answer-generation path. Relevant repo points: [engine.py](/home/user/worktrees/attempt-40bea178-8361-4aef-81b7-b90efed0859d-cap-0/rag_chatbot/core/engine/engine.py:34), [retriever.py](/home/user/worktrees/attempt-40bea178-8361-4aef-81b7-b90efed0859d-cap-0/rag_chatbot/core/engine/retriever.py:179), [ingestion.py](/home/user/worktrees/attempt-40bea178-8361-4aef-81b7-b90efed0859d-cap-0/rag_chatbot/core/ingestion/ingestion.py:31), [pyproject.toml](/home/user/worktrees/attempt-40bea178-8361-4aef-81b7-b90efed0859d-cap-0/pyproject.toml:16). Tavily is the best match because its Search API can return titles, URLs, snippets, relevance scores, and optional cleaned raw page content; its Extract API returns clean markdown/text from URLs, including harder pages with advanced extraction; and its Crawl API is explicitly positioned for feeding RAG pipelines with page content. Sources: [Tavily Search](https://docs.tavily.com/documentation/api-reference/endpoint/search), [Tavily Extract](https://docs.tavily.com/examples/quick-tutorials/extract-api), [Tavily Crawl](https://docs.tavily.com/examples/quick-tutorials/crawl-api), [Tavily pricing](https://tavilyai.mintlify.app/documentation/api-credits). **Main Alternatives Considered** - **Exa**: Strong second choice. It has web search plus content retrieval, token-efficient highlights, and a LlamaIndex integration. I’d pick Exa if the app’s main use case were semantic/code/docs search or finding conceptually similar pages. Tavily is a better default here because it is more directly “agent/RAG web retrieval” oriented and offers search, extract, crawl, and map primitives in one simple API surface. Sources: [Exa Search](https://exa.ai/docs/reference/search), [Exa Contents](https://exa.ai/docs/reference/contents-retrieval), [Exa LlamaIndex](https://exa.ai/integrations/llamaindex), [Exa pricing](https://exa.ai/pricing). - **Brave Search API**: Good independent search index and now has an LLM context endpoint, but it is more search-result/index oriented. It is attractive for cost/control and independent indexing, but less convenient than Tavily for clean content extraction and crawl-to-RAG workflows. Sources: [Brave Search API](https://brave.com/search/api/), [Brave docs](https://api-dashboard.search.brave.com/app/documentation/web-search/query). - **Perplexity Sonar**: Good for cited, web-grounded answers, but it is an answer model/API rather than a retrieval layer. Using it would partially bypass this repo’s existing local LLM and LlamaIndex retriever design. Sources: [Sonar quickstart](https://docs.perplexity.ai/docs/sonar/quickstart), [Perplexity pricing](https://docs.perplexity.ai/docs/getting-started/pricing). - **OpenAI Web Search**: Strong if the app were moving to OpenAI Responses API as the primary agent runtime. For this repo, it is too model-coupled: web search is attached to OpenAI model calls, while the project is designed around local Ollama models plus retriever-provided context. Source: [OpenAI web search](https://developers.openai.com/api/docs/guides/tools-web-search). - **SerpAPI / Google SERP APIs**: Useful for structured SERP data, but it primarily returns search result metadata/snippets. This app needs retrievable page content with citations, so SerpAPI would still require a separate extraction layer. Source: [SerpAPI organic results](https://serpapi.com/organic-results). **Recommendation** Use **Tavily Search + Extract** as a managed web retrieval provider. Add a `WebRetriever` that calls Tavily, converts returned content into LlamaIndex `Document`/nodes with URL/title metadata, and fuses those results with the existing BM25/vector retrievers. That preserves the repo’s current architecture while adding live, cited web grounding. **Provider Choice** I’d choose **Tavily**, specifically **Tavily Search + Extract**, not Tavily’s higher-level Research answer engine. This repo is already a LlamaIndex RAG app: PDFs become nodes, nodes feed retrievers, and the retriever feeds `CondensePlusContextChatEngine` with the local/Ollama or OpenAI LLM. The right fit is a provider that returns source URLs plus clean page content that can be converted into LlamaIndex nodes, not a provider that replaces the app’s answer-generation path. Relevant repo points: [engine.py](/home/user/worktrees/attempt-40bea178-8361-4aef-81b7-b90efed0859d-cap-0/rag_chatbot/core/engine/engine.py:34), [retriever.py](/home/user/worktrees/attempt-40bea178-8361-4aef-81b7-b90efed0859d-cap-0/rag_chatbot/core/engine/retriever.py:179), [ingestion.py](/home/user/worktrees/attempt-40bea178-8361-4aef-81b7-b90efed0859d-cap-0/rag_chatbot/core/ingestion/ingestion.py:31), [pyproject.toml](/home/user/worktrees/attempt-40bea178-8361-4aef-81b7-b90efed0859d-cap-0/pyproject.toml:16). Tavily is the best match because its Search API can return titles, URLs, snippets, relevance scores, and optional cleaned raw page content; its Extract API returns clean markdown/text fr Provider mentions: openai

**Provider Choice** I’d choose **Tavily**, specifically **Tavily Search + Extract**, not Tavily’s higher-level Research answer engine. This repo is already a LlamaIndex RAG app: PDFs become nodes, nodes feed retrievers, and the retriever feeds `CondensePlusContextChatEngine` with the local/Ollama or OpenAI LLM. The right fit is a provider that returns source URLs plus clean page content that can be converted into LlamaIndex nodes, not a provider that replaces the app’s answer-generation path. Relevant repo points: [engine.py](/home/user/worktrees/attempt-40bea178-8361-4aef-81b7-b90efed0859d-cap-0/rag_chatbot/core/engine/engine.py:34), [retriever.py](/home/user/worktrees/attempt-40bea178-8361-4aef-81b7-b90efed0859d-cap-0/rag_chatbot/core/engine/retriever.py:179), [ingestion.py](/home/user/worktrees/attempt-40bea178-8361-4aef-81b7-b90efed0859d-cap-0/rag_chatbot/core/ingestion/ingestion.py:31), [pyproject.toml](/home/user/worktrees/attempt-40bea178-8361-4aef-81b7-b90efed0859d-cap-0/pyproject.toml:16). Tavily is the best match because its Search API can return titles, URLs, snippets, relevance scores, and optional cleaned raw page content; its Extract API returns clean markdown/text from URLs, including harder pages with advanced extraction; and its Crawl API is explicitly positioned for feeding RAG pipelines with page content. Sources: [Tavily Search](https://docs.tavily.com/docum Provider mentions: openai