agent_action: Prompt-lab work item started
agent_action: Pinned repository checkout verified
agent_action: I’ll inspect the repo first to understand the agent stack and integration points, t...
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'"'"
command: /bin/bash -lc 'git status --short'
command: /bin/bash -lc 'git status --short'
agent_action: `rg` is not installed in this workspace, so I’m falling back to `find` plus targete...
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: /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: /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: /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"
agent_action: The project is a Python RAG chatbot with local ingestion, embeddings, vector store,...
command: /bin/bash -lc "sed -n '1,260p' README.md"
command: /bin/bash -lc "sed -n '1,260p' README.md"
command: /bin/bash -lc "sed -n '1,260p' pyproject.toml"
command: /bin/bash -lc "sed -n '1,260p' pyproject.toml"
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"
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"
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"
command: /bin/bash -lc "sed -n '1,260p' rag_chatbot/pipeline.py"
command: /bin/bash -lc "sed -n '1,260p' rag_chatbot/pipeline.py"
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"
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"
agent_action: The integration point is straightforward: this app already uses LlamaIndex retrieve...
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"
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"
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"
command: /bin/bash -lc "find rag_chatbot -type f -name '*.py' -print | xargs grep -n \"stream_chat\\|Retriever\\|Tool\\|search\\|requests\\|OPENAI\\|SERP\\|TAVILY\\|EXA\""
command: /bin/bash -lc "find rag_chatbot -type f -name '*.py' -print | xargs grep -n \"stream_chat\\|Retriever\\|Tool\\|search\\|requests\\|OPENAI\\|SERP\\|TAVILY\\|EXA\""
search: Tavily API documentation search extract raw_content LlamaIndex integration
search: OpenAI web search tool API documentation Responses API web_search_preview
search: site:docs.llamaindex.ai Tavily LlamaIndex TavilyToolSpec
search: Tavily pricing API credits search extract crawl 2026
url: https://docs.tavily.com/documentation/api-reference/endpoint/search
url: https://pypi.org/project/llama-index-tools-tavily-research/0.4.2/
search: Tavily Extract API documentation raw content markdown text official 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'"
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'"
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'"
command: /bin/bash -lc "nl -ba pyproject.toml | sed -n '1,45p'"
command: /bin/bash -lc "nl -ba pyproject.toml | sed -n '1,45p'"
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'"
agent_action: **Provider Choice** I’d choose **Tavily**, specifically **Tavily Search + Extract**...
result: Selected unknown