datvodin

Run

datvodinh/rag-chatbot

Agent

GPT-5.3-Codex

Status

completed

Trace

available

Telemetry

artifact only

Last Run

Jun 05, 11:31 PM

Decision Summary

competitor

Expected

Provider

Product

Selected

Provider

tavily

Product

Surface

provider_selection

Reason

codex selected tavily during first-pass provider discovery. No expected provider was set for this prompt.

Outcome

competitor

Trace Timeline

agent_action

Prompt-lab work item started

agent_action

Pinned repository checkout verified

e9de2afd458e4ad2d7c54636d3c8b20de04cf790

agent_action

I’ll inspect the repo first to understand the agent stack and integration points, t...

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.

command

/bin/bash -lc "pwd && rg --files -g '"'!*node_modules*'"' -g '"'!*.png'"' -g '"'!*.jpg'"' -g '"'!*.jpeg'"' -g '"'!*.gif'"' -g '"'!*.lock'"'"

command

/bin/bash -lc "pwd && rg --files -g '"'!*node_modules*'"' -g '"'!*.png'"' -g '"'!*.jpg'"' -g '"'!*.jpeg'"' -g '"'!*.gif'"' -g '"'!*.lock'"'"

exit code 127 Output: /bin/bash: line 1: rg: command not found /home/user/worktrees/attempt-40bea178-8361-4aef-81b7-b90efed0859d-cap-0

command

/bin/bash -lc 'git status --short'

command

/bin/bash -lc 'git status --short'

exit code 0

agent_action

`rg` is not installed in this workspace, so I’m falling back to `find` plus targete...

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

command

/bin/bash -lc "find . -path './.git' -prune -o -path './node_modules' -prune -o -path './.next' -prune -o -path './dist' -prune -o -path './build' -prune -o -type f -print | sed 's#"'^./##'"' | sort | head -300"

command

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

command

/bin/bash -lc "find . -maxdepth 2 -type f \$ -name 'README*' -o -name 'package.json' -o -name 'pyproject.toml' -o -name 'requirements*.txt' -o -name 'Cargo.toml' -o -name 'go.mod' -o -name 'pom.xml' -o -name '*.md' \$ -print | sort"

command

exit code 0 Output: ./README.md ./pyproject.toml

agent_action

The project is a Python RAG chatbot with local ingestion, embeddings, vector store,...

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.

command

/bin/bash -lc "sed -n '1,260p' README.md"

command

/bin/bash -lc "sed -n '1,260p' README.md"

exit code 0 Output: # 🤖 Chat with multiple PDFs locally ![alt text](assets/demo.png) ## 📖 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) ![RAG Flow](./assets/rag-flow.svg) ![Retriever](./assets/retriever.svg) ## 💻 Setup ##

command

/bin/bash -lc "sed -n '1,260p' pyproject.toml"

command

/bin/bash -lc "sed -n '1,260p' pyproject.toml"

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 =

command

/bin/bash -lc "sed -n '1,260p' rag_chatbot/core/engine/engine.py"

command

/bin/bash -lc "sed -n '1,260p' rag_chatbot/core/engine/engine.py"

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

command

/bin/bash -lc "sed -n '1,260p' rag_chatbot/core/engine/retriever.py"

command

/bin/bash -lc "sed -n '1,260p' rag_chatbot/core/engine/retriever.py"

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

command

/bin/bash -lc "sed -n '1,260p' rag_chatbot/setting/setting.py"

command

/bin/bash -lc "sed -n '1,260p' rag_chatbot/setting/setting.py"

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:

command

/bin/bash -lc "sed -n '1,260p' rag_chatbot/pipeline.py"

command

/bin/bash -lc "sed -n '1,260p' rag_chatbot/pipeline.py"

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_

command

/bin/bash -lc "sed -n '1,260p' rag_chatbot/core/ingestion/ingestion.py"

command

/bin/bash -lc "sed -n '1,260p' rag_chatbot/core/ingestion/ingestion.py"

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

command

/bin/bash -lc "sed -n '1,220p' rag_chatbot/core/prompt/qa_prompt.py"

command

/bin/bash -lc "sed -n '1,220p' rag_chatbot/core/prompt/qa_prompt.py"

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_action

The integration point is straightforward: this app already uses LlamaIndex retrieve...

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.

command

/bin/bash -lc "sed -n '1,320p' rag_chatbot/ui/ui.py"

command

/bin/bash -lc "sed -n '1,320p' rag_chatbot/ui/ui.py"

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

command

/bin/bash -lc "sed -n '1,220p' rag_chatbot/core/model/model.py"

command

/bin/bash -lc "sed -n '1,220p' rag_chatbot/core/model/model.py"

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

command

/bin/bash -lc "sed -n '1,220p' rag_chatbot/core/vector_store/vector_store.py"

command

/bin/bash -lc "sed -n '1,220p' rag_chatbot/core/vector_store/vector_store.py"

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

command

command

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

Tavily API documentation search extract raw_content LlamaIndex integration

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

OpenAI web search tool API documentation Responses API web_search_preview

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

site:docs.llamaindex.ai Tavily LlamaIndex TavilyToolSpec

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

Tavily pricing API credits search extract crawl 2026

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

url

https://docs.tavily.com/documentation/api-reference/endpoint/search

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.

url

https://pypi.org/project/llama-index-tools-tavily-research/0.4.2/

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.

Tavily Extract API documentation raw content markdown text official docs

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

command

/bin/bash -lc "nl -ba rag_chatbot/core/engine/retriever.py | sed -n '83,190p'"

command

/bin/bash -lc "nl -ba rag_chatbot/core/engine/retriever.py | sed -n '83,190p'"

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

command

/bin/bash -lc "nl -ba rag_chatbot/pipeline.py | sed -n '1,130p'"

command

/bin/bash -lc "nl -ba rag_chatbot/pipeline.py | sed -n '1,130p'"

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(

command

/bin/bash -lc "nl -ba rag_chatbot/core/ingestion/ingestion.py | sed -n '1,105p'"

command

/bin/bash -lc "nl -ba rag_chatbot/core/ingestion/ingestion.py | sed -n '1,105p'"

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

command

/bin/bash -lc "nl -ba pyproject.toml | sed -n '1,45p'"

command

/bin/bash -lc "nl -ba pyproject.toml | sed -n '1,45p'"

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

command

/bin/bash -lc "nl -ba rag_chatbot/core/engine/engine.py | sed -n '1,45p'"

command

/bin/bash -lc "nl -ba rag_chatbot/core/engine/engine.py | sed -n '1,45p'"

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_action

**Provider Choice** I’d choose **Tavily**, specifically **Tavily Search + Extract**...

**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

result

Selected unknown

Commands

/bin/bash -lc "pwd && rg --files -g '"'!*node_modules*'"' -g '"'!*.png'"' -g '"'!*.jpg'"' -g '"'!*.jpeg'"' -g '"'!*.gif'"' -g '"'!*.lock'"'"

exit 127

/bin/bash -lc 'git status --short'

exit 0

/bin/bash -lc "sed -n '1,260p' README.md"

exit 0

/bin/bash -lc "sed -n '1,260p' pyproject.toml"

exit 0

/bin/bash -lc "sed -n '1,260p' rag_chatbot/core/engine/engine.py"

exit 0

/bin/bash -lc "sed -n '1,260p' rag_chatbot/core/engine/retriever.py"

exit 0

/bin/bash -lc "sed -n '1,260p' rag_chatbot/setting/setting.py"

exit 0

/bin/bash -lc "sed -n '1,260p' rag_chatbot/pipeline.py"

exit 0

/bin/bash -lc "sed -n '1,260p' rag_chatbot/core/ingestion/ingestion.py"

exit 0

/bin/bash -lc "sed -n '1,220p' rag_chatbot/core/prompt/qa_prompt.py"

exit 0

/bin/bash -lc "sed -n '1,320p' rag_chatbot/ui/ui.py"

exit 0

/bin/bash -lc "sed -n '1,220p' rag_chatbot/core/model/model.py"

exit 0

/bin/bash -lc "sed -n '1,220p' rag_chatbot/core/vector_store/vector_store.py"

exit 0

exit 0

/bin/bash -lc "nl -ba rag_chatbot/core/engine/retriever.py | sed -n '83,190p'"

exit 0

/bin/bash -lc "nl -ba rag_chatbot/pipeline.py | sed -n '1,130p'"

exit 0

/bin/bash -lc "nl -ba rag_chatbot/core/ingestion/ingestion.py | sed -n '1,105p'"

exit 0

/bin/bash -lc "nl -ba pyproject.toml | sed -n '1,45p'"

exit 0

/bin/bash -lc "nl -ba rag_chatbot/core/engine/engine.py | sed -n '1,45p'"

exit 0

URLs Fetched

https://docs.tavily.com/documentation/api-reference/endpoint/search

status not materialized

https://pypi.org/project/llama-index-tools-tavily-research/0.4.2/

status not materialized

Changed Files

Artifact only

Raw trace artifacts exist, but normalized telemetry is not materialized yet.

Runs

Find

Run

Decision Summary

Trace Timeline

Commands

URLs Fetched

Changed Files

Runs

Run

Decision Summary

Trace Timeline

Commands

URLs Fetched

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