datvodin

Run

datvodinh/rag-chatbot

Agent

Claude Opus 4.7

Status

completed

Trace

available

Telemetry

artifact only

Last Run

Jun 05, 11:30 PM

Decision Summary

target

Expected

Provider

Product

Selected

Provider

linkup

Product

Surface

provider_selection

Reason

claude-code selected Linkup during first-pass provider discovery.

Outcome

target

Trace Timeline

agent_action

Prompt-lab work item started

agent_action

Pinned repository checkout verified

e9de2afd458e4ad2d7c54636d3c8b20de04cf790

agent_action

I'll research the repository first to understand its architecture and how an agent ...

I'll research the repository first to understand its architecture and how an agent uses it, then evaluate web-search providers.

agent_action

Read /home/user/worktrees/attempt-ba850f9f-53b2-489c-9631-d01fc8e179c0-cap-1

Tool result summary: EISDIR: illegal operation on a directory, read '/home/user/worktrees/attempt-ba850f9f-53b2-489c-9631-d01fc8e179c0-cap-1' Agent context: I'll research the repository first to understand its architecture and how an agent uses it, then evaluate web-search providers.

command

find /home/user/worktrees/attempt-ba850f9f-53b2-489c-9631-d01fc8e179c0-cap-1 -maxdepth 2 -type f \( -name "README*" -o -name "pyproject.toml" -o -name "package.json" -o -name "*.md" \) | head -20

Tool result summary: /home/user/worktrees/attempt-ba850f9f-53b2-489c-9631-d01fc8e179c0-cap-1/README.md /home/user/worktrees/attempt-ba850f9f-53b2-489c-9631-d01fc8e179c0-cap-1/pyproject.toml Agent context: I'll research the repository first to understand its architecture and how an agent uses it, then evaluate web-search providers.

agent_action

Read README.md

Tool result summary: 1 # 🤖 Chat with multiple PDFs locally 2 3 ![alt text](assets/demo.png) 4 5 ## 📖 Table of Contents 6 7 - [📖 Table of Contents](#-table-of-contents) 8 - [⭐️ Key Features](#️-key-features) 9 - [💡 Idea (Experiment)](#-idea-experiment) 10 - [💻 Setup](#-setup) 11 - [1. Kaggle (Recommended)](#1-kaggle-recommended) 12 - [2. Local](#2-local) 13 - [2.1. Clone project](#21-clone-project) 14 - [2.2 Install](#22-install) 15 Agent context: I'll research the repository first to understand its architecture and how an agent uses it, then evaluate web-search providers.

agent_action

Read pyproject.toml

Tool result summary: 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-in Agent context: I'll research the repository first to understand its architecture and how an agent uses it, then evaluate web-search providers.

command

ls -la /home/user/worktrees/attempt-ba850f9f-53b2-489c-9631-d01fc8e179c0-cap-1

Tool result summary: total 836 drwxr-xr-x 7 user user 4096 Jun 5 23:28 . drwxr-xr-x 4 user user 4096 Jun 5 23:28 .. -rw-r--r-- 1 user user 45 Jun 5 23:28 .dockerignore -rw-r--r-- 1 user user 170 Jun 5 23:28 .git drwxr-xr-x 4 user user 4096 Jun 5 23:28 .github -rw-r--r-- 1 user user 3184 Jun 5 23:28 .gitignore -rw-r--r-- 1 user user 865 Jun 5 23:28 Dockerfile -rw-r--r-- 1 user user 11357 Jun 5 23:28 LICENSE -rw-r--r-- 1 user user 3845 Jun Agent context: I'll research the repository first to understand its architecture and how an agent uses it, then evaluate web-search providers.

command

find /home/user/worktrees/attempt-ba850f9f-53b2-489c-9631-d01fc8e179c0-cap-1/rag_chatbot -type f -name "*.py" | head -30

Tool result summary: /home/user/worktrees/attempt-ba850f9f-53b2-489c-9631-d01fc8e179c0-cap-1/rag_chatbot/__init__.py /home/user/worktrees/attempt-ba850f9f-53b2-489c-9631-d01fc8e179c0-cap-1/rag_chatbot/__main__.py /home/user/worktrees/attempt-ba850f9f-53b2-489c-9631-d01fc8e179c0-cap-1/rag_chatbot/core/__init__.py /home/user/worktrees/attempt-ba850f9f-53b2-489c-9631-d01fc8e179c0-cap-1/rag_chatbot/core/embedding/__init__.py /home/user/workt Agent context: I'll research the repository first to understand its architecture and how an agent uses it, then evaluate web-search providers.

command

ls -la /home/user/worktrees/attempt-ba850f9f-53b2-489c-9631-d01fc8e179c0-cap-1/rag_chatbot

Tool result summary: total 52 drwxr-xr-x 7 user user 4096 Jun 5 23:28 . drwxr-xr-x 7 user user 4096 Jun 5 23:28 .. -rw-r--r-- 1 user user 141 Jun 5 23:28 __init__.py -rw-r--r-- 1 user user 1169 Jun 5 23:28 __main__.py drwxr-xr-x 8 user user 4096 Jun 5 23:28 core drwxr-xr-x 2 user user 4096 Jun 5 23:28 eval -rw-r--r-- 1 user user 1788 Jun 5 23:28 logger.py -rw-r--r-- 1 user user 1547 Jun 5 23:28 ollama.py -rw-r--r-- 1 user user 4100 Jun 5 Agent context: I'll research the repository first to understand its architecture and how an agent uses it, then evaluate web-search providers.

agent_action

Read rag_chatbot/__main__.py

Tool result summary: 1 import argparse 2 import llama_index 3 from dotenv import load_dotenv 4 from .ui import LocalChatbotUI 5 from .pipeline import LocalRAGPipeline 6 from .logger import Logger 7 from .ollama import run_ollama_server, is_port_open 8 9 load_dotenv() 10 11 # CONSTANTS 12 LOG_FILE = "logging.log" 13 DATA_DIR = "data/data" 14 AVATAR_IMAGES = ["./assets/user.png", "./assets/bot.png"] 15 16 # PARSER 17 parser = argparse.Argu Agent context: I'll research the repository first to understand its architecture and how an agent uses it, then evaluate web-search providers.

agent_action

Read rag_chatbot/pipeline.py

Tool result summary: 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") -> N Agent context: I'll research the repository first to understand its architecture and how an agent uses it, then evaluate web-search providers.

agent_action

Read rag_chatbot/setting/setting.py

Tool result summary: 1 from pydantic import BaseModel, Field 2 from typing import List 3 4 5 class OllamaSettings(BaseModel): 6 llm: str = Field(default="llama3:8b-instruct-q8_0", description="LLM model") 7 keep_alive: str = Field(default="1h", description="Keep alive time for the server") 8 tfs_z: float = Field(default=1.0, description="TFS normalization factor") 9 top_k: int = Field(default=40, description="Top k sampling") 10 top_p: f Agent context: I'll research the repository first to understand its architecture and how an agent uses it, then evaluate web-search providers.

agent_action

Read rag_chatbot/core/engine/engine.py

Tool result summary: 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 | N Agent context: I'll research the repository first to understand its architecture and how an agent uses it, then evaluate web-search providers.

agent_action

Read rag_chatbot/core/model/model.py

Tool result summary: 1 from llama_index.llms.ollama import Ollama 2 from llama_index.llms.openai import OpenAI 3 from ...setting import RAGSettings 4 from dotenv import load_dotenv 5 import requests 6 7 load_dotenv() 8 9 10 class LocalRAGModel: 11 def __init__(self) -> None: 12 pass 13 14 @staticmethod 15 def set( 16 model_name: str = "llama3:8b-instruct-q8_0", 17 system_prompt: str | None = None, 18 host: str = "host.docker.internal", 1 Agent context: I'll research the repository first to understand its architecture and how an agent uses it, then evaluate web-search providers.

agent_action

Read rag_chatbot/core/embedding/embedding.py

Tool result summary: 1 import os 2 import torch 3 import requests 4 from llama_index.embeddings.huggingface import HuggingFaceEmbedding 5 from llama_index.embeddings.openai import OpenAIEmbedding 6 from transformers import AutoModel, AutoTokenizer 7 from ...setting import RAGSettings 8 from dotenv import load_dotenv 9 10 11 load_dotenv() 12 13 14 class LocalEmbedding: 15 @staticmethod 16 def set(setting: RAGSettings | None = None, **kwar Agent context: I'll research the repository first to understand its architecture and how an agent uses it, then evaluate web-search providers.

agent_action

Read rag_chatbot/core/engine/retriever.py

Tool result summary: 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.co Agent context: I'll research the repository first to understand its architecture and how an agent uses it, then evaluate web-search providers.

agent_action

Read rag_chatbot/core/vector_store/vector_store.py

Tool result summary: 1 from llama_index.core import VectorStoreIndex 2 from dotenv import load_dotenv 3 from ...setting import RAGSettings 4 5 load_dotenv() 6 7 8 class LocalVectorStore: 9 def __init__( 10 self, 11 host: str = "host.docker.internal", 12 setting: RAGSettings | None = None, 13 ) -> None: 14 # TODO 15 # CHROMA VECTOR STORE 16 self._setting = setting or RAGSettings() 17 18 def get_index(self, nodes): 19 if len(nodes) == 0: 2 Agent context: I'll research the repository first to understand its architecture and how an agent uses it, then evaluate web-search providers.

agent_action

Read rag_chatbot/core/prompt/qa_prompt.py

Tool result summary: 1 def get_context_prompt(language: str) -> str: 2 if language == "vi": 3 return CONTEXT_PROMPT_VI 4 return CONTEXT_PROMPT_EN 5 6 7 def get_system_prompt(language: str, is_rag_prompt: bool = True) -> str: 8 if language == "vi": 9 return SYSTEM_PROMPT_RAG_VI if is_rag_prompt else SYSTEM_PROMPT_VI 10 return SYSTEM_PROMPT_RAG_EN if is_rag_prompt else SYSTEM_PROMPT_EN 11 12 13 SYSTEM_PROMPT_EN = """\ 14 This is a chat bet Agent context: I'll research the repository first to understand its architecture and how an agent uses it, then evaluate web-search providers.

agent_action

Read rag_chatbot/core/ingestion/ingestion.py

Tool result summary: 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) Agent context: I'll research the repository first to understand its architecture and how an agent uses it, then evaluate web-search providers.

agent_action

Read rag_chatbot/core/__init__.py

Tool result summary: 1 from .embedding import LocalEmbedding 2 from .model import LocalRAGModel 3 from .ingestion import LocalDataIngestion 4 from .vector_store import LocalVectorStore 5 from .engine import LocalChatEngine 6 from .prompt import get_system_prompt 7 8 __all__ = [ 9 "LocalEmbedding", 10 "LocalRAGModel", 11 "LocalDataIngestion", 12 "LocalVectorStore", 13 "LocalChatEngine", 14 "get_system_prompt", 15 ] 16 Agent context: I'll research the repository first to understand its architecture and how an agent uses it, then evaluate web-search providers.

command

find /home/user/worktrees/attempt-ba850f9f-53b2-489c-9631-d01fc8e179c0-cap-1 -name "ui*" -type d

Tool result summary: /home/user/worktrees/attempt-ba850f9f-53b2-489c-9631-d01fc8e179c0-cap-1/rag_chatbot/ui Agent context: I'll research the repository first to understand its architecture and how an agent uses it, then evaluate web-search providers.

command

ls -la /home/user/worktrees/attempt-ba850f9f-53b2-489c-9631-d01fc8e179c0-cap-1/rag_chatbot/ui/

Tool result summary: total 36 drwxr-xr-x 2 user user 4096 Jun 5 23:28 . drwxr-xr-x 7 user user 4096 Jun 5 23:28 .. -rw-r--r-- 1 user user 68 Jun 5 23:28 __init__.py -rw-r--r-- 1 user user 392 Jun 5 23:28 theme.py -rw-r--r-- 1 user user 17484 Jun 5 23:28 ui.py Agent context: I'll research the repository first to understand its architecture and how an agent uses it, then evaluate web-search providers.

command

grep -E "(openai|anthropic|llm|api)" /home/user/worktrees/attempt-ba850f9f-53b2-489c-9631-d01fc8e179c0-cap-1/pyproject.toml

Tool result summary: "llama-index-llms-openai>=0.1.12,<0.2", "llama-index-llms-ollama>=0.1.2,<0.2", Agent context: I'll research the repository first to understand its architecture and how an agent uses it, then evaluate web-search providers.

command

find /home/user/worktrees/attempt-ba850f9f-53b2-489c-9631-d01fc8e179c0-cap-1 -name ".env*" -o -name "*.example" | head -5

Tool result summary: (Bash completed with no output) Agent context: I'll research the repository first to understand its architecture and how an agent uses it, then evaluate web-search providers.

agent_action

This is a LlamaIndex-based local RAG chatbot (Gradio UI, Ollama + OpenAI). No web s...

This is a LlamaIndex-based local RAG chatbot (Gradio UI, Ollama + OpenAI). No web search today. Now let me consult the actual provider documentation to make a grounded recommendation.

agent_action

Let me consult documentation for the top contenders in parallel.

url

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

Fetch prompt: Summarize Tavily's search API: what it returns (content, raw content, citations/sources, answer field), how it scores/ranks source relevance, any features for factual accuracy and citation grounding, and whether it's designed for LLM/RAG/agent use. Note any LlamaIndex integration mentions. Tool result summary: # Tavily Search API Summary ## What It Returns Tavily's `/search` endpoint returns structured results including: - **Content**: Short descriptions of each result - **Raw Content**: Cleaned, parsed HTML available in markdown or plain text (optional) - **Sources**: Each result includes title, URL, and favicon - **Answer Field**: LLM-generated responses to queries when `include_answer` is enabled ("basic" or "advanced" Agent context: Let me consult documentation for the top contenders in parallel.

url

https://docs.exa.ai/reference/getting-started

Fetch prompt: Summarize Exa's search/retrieval API: neural vs keyword search, content retrieval (text, highlights), how it surfaces high-quality sources, citation/highlight support, and whether it's built for LLM/RAG/agent grounding. Note answer endpoints and any LlamaIndex integration. Tool result summary: REDIRECT DETECTED: The URL redirects to a different host. Original URL: https://docs.exa.ai/reference/getting-started Redirect URL: https://exa.ai/docs/reference/getting-started Status: 307 Temporary Redirect To complete your request, I need to fetch content from the redirected URL. Please use WebFetch again with these parameters: - url: "https://exa.ai/docs/reference/getting-started" - prompt: "Summarize Exa's searc Agent context: Let me consult documentation for the top contenders in parallel.

url

https://docs.perplexity.ai/api-reference/chat-completions-post

Fetch prompt: Summarize Perplexity's Sonar API: does it return grounded answers with citations/sources, what models (sonar, sonar-pro), how it handles factual accuracy and source attribution. Note whether it returns just an answer vs raw search results, and any LlamaIndex integration. Tool result summary: # Perplexity Sonar API Summary ## Grounded Answers & Citations Yes, the API returns grounded responses with source attribution. The response schema includes a `citations` field containing "URLs of sources used to generate the response" and a `search_results` array with detailed source information. ## Available Models Four models are offered: - `sonar` (base) - `sonar-pro` - `sonar-deep-research` - `sonar-reasoning-pr Agent context: Let me consult documentation for the top contenders in parallel.

agent_action

Let me fetch Exa's redirected docs and check LlamaIndex-native integrations plus on...

Let me fetch Exa's redirected docs and check LlamaIndex-native integrations plus one more alternative (Brave/Linkup).

url

https://exa.ai/docs/reference/getting-started

Fetch prompt: Summarize Exa's search/retrieval API: neural vs keyword search, content retrieval (text, highlights), how it surfaces high-quality sources, citation/highlight support, and whether it's built for LLM/RAG/agent grounding. Note answer endpoints and any LlamaIndex integration. Tool result summary: # Exa Search/Retrieval API Summary Based on the documentation page provided, here's what Exa offers: **Core Search Capabilities:** Exa describes itself as "a search engine made for AIs" that employs "embeddings-based search and other intelligent methods" to locate webpages. The page doesn't specify distinct neural versus keyword search modes in the visible content. **Content Retrieval:** The platform offers a dedicat Agent context: Let me fetch Exa's redirected docs and check LlamaIndex-native integrations plus one more alternative (Brave/Linkup).

LlamaIndex web search tool integration Tavily Exa Brave 2026 official

Query: LlamaIndex web search tool integration Tavily Exa Brave 2026 official 10 search result links captured Tool result summary: Based on the search results, here's comprehensive information about LlamaIndex web search tool integrations with Tavily, Exa, and Brave in 2026: ## Official Integrations Tavily is recommended as "best for citation-ready results" with "LangChain/LlamaIndex integration", and Tavily provides official integrations with LangChain, LlamaIndex, and other popular frameworks, making it straightforward to add Tavily search whe Agent context: Let me fetch Exa's redirected docs and check LlamaIndex-native integrations plus one more alternative (Brave/Linkup).

url

https://docs.linkup.so/pages/documentation/get-started/introduction

Fetch prompt: Summarize Linkup's search API: what it returns, source quality, citations, factual accuracy benchmarks (e.g. SimpleQA), and whether it targets LLM/RAG grounding. Note any LlamaIndex integration. Tool result summary: # Linkup Search API Summary **What It Returns:** Linkup's Search endpoint delivers "Query → ranked URLs, sourced answer, or structured JSON" with sub-second to ~30-second latency, designed for synchronous operations. **Source Quality & Citations:** The platform emphasizes customization, allowing teams to "Define which sources to trust, which domains to prefer, which proprietary data to blend in." Research results inc Agent context: Let me fetch Exa's redirected docs and check LlamaIndex-native integrations plus one more alternative (Brave/Linkup).

agent_action

## Recommendation: Linkup For a repo where the existing LlamaIndex pipeline does it...

## Recommendation: Linkup For a repo where the existing LlamaIndex pipeline does its own synthesis + reranking, the right web layer is one that returns **raw, cited, high-quality sources** (not a pre-baked answer) so the local LLM can answer strictly from retrieved snippets with URLs. Given your explicit priority — factual accuracy, source quality, and verifiable citations **above** latency/price/ease — **Linkup** is the best fit. **Why Linkup:** - **Strongest verifiable accuracy claim:** #1 among sub-second web APIs on Verified SimpleQA (92% F-score), with an open-sourced, reproducible eval methodology — exactly the axis you weight highest. - **Source-quality controls:** lets you define which domains to trust/prefer, directly addressing "source quality." - **Verifiable citations:** returns ranked URLs + sourced answers with inline citations; you can feed the cited text into the existing `CondensePlusContextChatEngine` and instruct it to answer only from retrieved sources. - The lack of a native LlamaIndex tool wrapper means more glue code — but you explicitly de-prioritized ease of setup, so this isn't disqualifying. ## Alternatives considered - **Tavily** (strong runner-up / prag

result

Selected unknown

Commands

find /home/user/worktrees/attempt-ba850f9f-53b2-489c-9631-d01fc8e179c0-cap-1 -maxdepth 2 -type f \( -name "README*" -o -name "pyproject.toml" -o -name "package.json" -o -name "*.md" \) | head -20

exit code not materialized

ls -la /home/user/worktrees/attempt-ba850f9f-53b2-489c-9631-d01fc8e179c0-cap-1

exit code not materialized

find /home/user/worktrees/attempt-ba850f9f-53b2-489c-9631-d01fc8e179c0-cap-1/rag_chatbot -type f -name "*.py" | head -30

exit code not materialized

ls -la /home/user/worktrees/attempt-ba850f9f-53b2-489c-9631-d01fc8e179c0-cap-1/rag_chatbot

exit code not materialized

find /home/user/worktrees/attempt-ba850f9f-53b2-489c-9631-d01fc8e179c0-cap-1 -name "ui*" -type d

exit code not materialized

ls -la /home/user/worktrees/attempt-ba850f9f-53b2-489c-9631-d01fc8e179c0-cap-1/rag_chatbot/ui/

exit code not materialized

grep -E "(openai|anthropic|llm|api)" /home/user/worktrees/attempt-ba850f9f-53b2-489c-9631-d01fc8e179c0-cap-1/pyproject.toml

exit code not materialized

find /home/user/worktrees/attempt-ba850f9f-53b2-489c-9631-d01fc8e179c0-cap-1 -name ".env*" -o -name "*.example" | head -5

exit code not materialized

URLs Fetched

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

status not materialized

https://docs.exa.ai/reference/getting-started

status not materialized

https://docs.perplexity.ai/api-reference/chat-completions-post

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https://exa.ai/docs/reference/getting-started

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https://www.firecrawl.dev/blog/best-web-search-apis

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https://www.sona.com/blog/llm-search-api-best-options-for-developers-in-2026

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https://www.firecrawl.dev/blog/exa-alternatives

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https://roundproxies.com/blog/best-exa-ai-alternatives/

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https://www.firecrawl.dev/blog/best-deep-research-apis

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https://docs.tavily.com/documentation/integrations/llamaindex

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https://www.firecrawl.dev/blog/tavily-alternatives

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https://docs.llamaindex.ai/en/stable/api_reference/tools/tavily_research/

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https://brightdata.com/blog/ai/exa-alternatives

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https://medium.com/@unicodeveloper/tavily-alternatives-in-2026-after-the-nebius-acquisition-9de526780686

status not materialized

https://docs.linkup.so/pages/documentation/get-started/introduction

status not materialized

Changed Files

Artifact only

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

Runs

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