Run

P1

e9de2afd458e4ad2d7c54636d3c8b20de04cf790

Agent context: I’ll inspect the repository first to understand the agent stack and integration points, then I’ll verify current provider docs before making a recommendation. After that I’ll give you a focused provider choice and comparison. I’ll inspect the repository first to understand the agent stack and integration points, then I’ll verify current provider docs before making a recommendation. After that I’ll give you a focused provider choice and comparison.

Agent context: I’ll inspect the repository first to understand the agent stack and integration points, then I’ll verify current provider docs before making a recommendation. After that I’ll give you a focused provider choice and comparison.

Agent context: I’ll inspect the repository first to understand the agent stack and integration points, then I’ll verify current provider docs before making a recommendation. After that I’ll give you a focused provider choice and comparison. exit code 0 Output: /home/user/worktrees/attempt-86c762ee-be10-4bea-85de-6bcf4691083a-cap-1 /bin/bash: line 1: rg: command not found

Agent context: I’ll inspect the repository first to understand the agent stack and integration points, then I’ll verify current provider docs before making a recommendation. After that I’ll give you a focused provider choice and comparison.

Agent context: I’ll inspect the repository first to understand the agent stack and integration points, then I’ll verify current provider docs before making a recommendation. After that I’ll give you a focused provider choice and comparison. exit code 0

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

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

Agent context: `rg` is not installed in this environment, so I’m falling back to `find` and targeted file reads. The worktree is clean. exit code 0 Output: .git .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

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

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

Agent context: This is a compact Python RAG chatbot, so I’m reading the retrieval, prompt, model, and settings layers now. That should identify whether “web retrieval” belongs as another retriever, an ingestion source, or a model/tool call. This is a compact Python RAG chatbot, so I’m reading the retrieval, prompt, model, and settings layers now. That should identify whether “web retrieval” belongs as another retriever, an ingestion source, or a model/tool call.

Agent context: This is a compact Python RAG chatbot, so I’m reading the retrieval, prompt, model, and settings layers now. That should identify whether “web retrieval” belongs as another retriever, an ingestion source, or a model/tool call.

Agent context: This is a compact Python RAG chatbot, so I’m reading the retrieval, prompt, model, and settings layers now. That should identify whether “web retrieval” belongs as another retriever, an ingestion source, or a model/tool call. 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: This is a compact Python RAG chatbot, so I’m reading the retrieval, prompt, model, and settings layers now. That should identify whether “web retrieval” belongs as another retriever, an ingestion source, or a model/tool call.

Agent context: This is a compact Python RAG chatbot, so I’m reading the retrieval, prompt, model, and settings layers now. That should identify whether “web retrieval” belongs as another retriever, an ingestion source, or a model/tool call. 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: This is a compact Python RAG chatbot, so I’m reading the retrieval, prompt, model, and settings layers now. That should identify whether “web retrieval” belongs as another retriever, an ingestion source, or a model/tool call.

Agent context: This is a compact Python RAG chatbot, so I’m reading the retrieval, prompt, model, and settings layers now. That should identify whether “web retrieval” belongs as another retriever, an ingestion source, or a model/tool call. 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: This is a compact Python RAG chatbot, so I’m reading the retrieval, prompt, model, and settings layers now. That should identify whether “web retrieval” belongs as another retriever, an ingestion source, or a model/tool call.

Agent context: This is a compact Python RAG chatbot, so I’m reading the retrieval, prompt, model, and settings layers now. That should identify whether “web retrieval” belongs as another retriever, an ingestion source, or a model/tool call. 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 local path is LlamaIndex-based: PDF nodes go into vector/BM25 retrieval, then `CondensePlusContextChatEngine` uses retrieved context. For current web grounding, the cleanest fit is a web retriever/query source that returns cited snippets or answer-level citations into that same context path. The local path is LlamaIndex-based: PDF nodes go into vector/BM25 retrieval, then `CondensePlusContextChatEngine` uses retrieved context. For current web grounding, the cleanest fit is a web retriever/query source that returns cited snippets or answer-level citations into that same context path.

Agent context: The local path is LlamaIndex-based: PDF nodes go into vector/BM25 retrieval, then `CondensePlusContextChatEngine` uses retrieved context. For current web grounding, the cleanest fit is a web retriever/query source that returns cited snippets or answer-level citations into that same context path.

Agent context: The local path is LlamaIndex-based: PDF nodes go into vector/BM25 retrieval, then `CondensePlusContextChatEngine` uses retrieved context. For current web grounding, the cleanest fit is a web retriever/query source that returns cited snippets or answer-level citations into that same context 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 local path is LlamaIndex-based: PDF nodes go into vector/BM25 retrieval, then `CondensePlusContextChatEngine` uses retrieved context. For current web grounding, the cleanest fit is a web retriever/query source that returns cited snippets or answer-level citations into that same context path.

Agent context: The local path is LlamaIndex-based: PDF nodes go into vector/BM25 retrieval, then `CondensePlusContextChatEngine` uses retrieved context. For current web grounding, the cleanest fit is a web retriever/query source that returns cited snippets or answer-level citations into that same context 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 local path is LlamaIndex-based: PDF nodes go into vector/BM25 retrieval, then `CondensePlusContextChatEngine` uses retrieved context. For current web grounding, the cleanest fit is a web retriever/query source that returns cited snippets or answer-level citations into that same context path.

Agent context: The local path is LlamaIndex-based: PDF nodes go into vector/BM25 retrieval, then `CondensePlusContextChatEngine` uses retrieved context. For current web grounding, the cleanest fit is a web retriever/query source that returns cited snippets or answer-level citations into that same context 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 local path is LlamaIndex-based: PDF nodes go into vector/BM25 retrieval, then `CondensePlusContextChatEngine` uses retrieved context. For current web grounding, the cleanest fit is a web retriever/query source that returns cited snippets or answer-level citations into that same context path.

Agent context: The local path is LlamaIndex-based: PDF nodes go into vector/BM25 retrieval, then `CondensePlusContextChatEngine` uses retrieved context. For current web grounding, the cleanest fit is a web retriever/query source that returns cited snippets or answer-level citations into that same context 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

Query/prompt: Perplexity Sonar API documentation citations search results official Agent context: The local path is LlamaIndex-based: PDF nodes go into vector/BM25 retrieval, then `CondensePlusContextChatEngine` uses retrieved context. For current web grounding, the cleanest fit is a web retriever/query source that returns cited snippets or answer-level citations into that same context path. Query: Perplexity Sonar API documentation citations search results official Agent context: The local path is LlamaIndex-based: PDF nodes go into vector/BM25 retrieval, then `CondensePlusContextChatEngine` uses retrieved context. For current web grounding, the cleanest fit is a web retriever/query source that returns cited snippets or answer-level citations into that same context path. Expanded queries: Perplexity Sonar API documentation citations search results official | Tavily API documentation search extract citations official | Exa API documentation search contents official | Brave Search API documentation web search official

Agent context: I’m also considering OpenAI’s managed web search because this repo already has an OpenAI LLM adapter, so I’m using the OpenAI docs skill for that part and official docs for the other providers. I’m also considering OpenAI’s managed web search because this repo already has an OpenAI LLM adapter, so I’m using the OpenAI docs skill for that part and official docs for the other providers. Provider mentions: openai

Agent context: I’m also considering OpenAI’s managed web search because this repo already has an OpenAI LLM adapter, so I’m using the OpenAI docs skill for that part and official docs for the other providers. Provider mentions: openai

Agent context: I’m also considering OpenAI’s managed web search because this repo already has an OpenAI LLM adapter, so I’m using the OpenAI docs skill for that part and official docs for the other providers. exit code 0 Output: --- name: "openai-docs" description: "Use when the user asks how to build with OpenAI products or APIs, asks about Codex itself or choosing Codex surfaces, needs up-to-date official documentation with citations, help choosing the latest model for a use case, or model upgrade and prompt-upgrade guidance; use OpenAI docs MCP tools for non-Codex docs questions, use the Codex manual helper first for broad Codex self-knowledge, and restrict fallback browsing to official OpenAI domains." --- # OpenAI Docs Provide authoritative, current guidance from OpenAI developer docs using the developers.openai.com MCP server. "Docs MCP" means `mcp__openaiDeveloperDocs__search_openai_docs` and `mcp__openaiDeveloperDocs__fetch_openai_doc`; for API reference, schema, parameter, or required-field questions, also use `mcp__openaiDeveloperDocs__get_openapi_spec` when available. Official-domain web search is fal Provider mentions: openai

Query/prompt: site:platform.openai.com/docs web search tool citations Responses API OpenAI Agent context: I’m also considering OpenAI’s managed web search because this repo already has an OpenAI LLM adapter, so I’m using the OpenAI docs skill for that part and official docs for the other providers. Query: site:platform.openai.com/docs web search tool citations Responses API OpenAI Agent context: I’m also considering OpenAI’s managed web search because this repo already has an OpenAI LLM adapter, so I’m using the OpenAI docs skill for that part and official docs for the other providers. Expanded queries: site:platform.openai.com/docs web search tool citations Responses API OpenAI | site:developers.openai.com web search tool citations Responses API OpenAI Provider mentions: openai

Query/prompt: https://platform.openai.com/docs/guides/retrieval Agent context: I’m also considering OpenAI’s managed web search because this repo already has an OpenAI LLM adapter, so I’m using the OpenAI docs skill for that part and official docs for the other providers. Query: https://platform.openai.com/docs/guides/retrieval Agent context: I’m also considering OpenAI’s managed web search because this repo already has an OpenAI LLM adapter, so I’m using the OpenAI docs skill for that part and official docs for the other providers. Provider mentions: openai

Query/prompt: https://docs.perplexity.ai/docs/sonar/quickstart Agent context: I’m also considering OpenAI’s managed web search because this repo already has an OpenAI LLM adapter, so I’m using the OpenAI docs skill for that part and official docs for the other providers. Query: https://docs.perplexity.ai/docs/sonar/quickstart Agent context: I’m also considering OpenAI’s managed web search because this repo already has an OpenAI LLM adapter, so I’m using the OpenAI docs skill for that part and official docs for the other providers. Provider mentions: openai

Query/prompt: 'include_raw_content' in https://docs.tavily.com/documentation/api-reference/endpoint/search Agent context: I’m also considering OpenAI’s managed web search because this repo already has an OpenAI LLM adapter, so I’m using the OpenAI docs skill for that part and official docs for the other providers. Query: 'include_raw_content' in https://docs.tavily.com/documentation/api-reference/endpoint/search Agent context: I’m also considering OpenAI’s managed web search because this repo already has an OpenAI LLM adapter, so I’m using the OpenAI docs skill for that part and official docs for the other providers. Provider mentions: openai

Query/prompt: https://docs.tavily.com/documentation/api-reference/endpoint/search Agent context: I’m also considering OpenAI’s managed web search because this repo already has an OpenAI LLM adapter, so I’m using the OpenAI docs skill for that part and official docs for the other providers. Query: https://docs.tavily.com/documentation/api-reference/endpoint/search Agent context: I’m also considering OpenAI’s managed web search because this repo already has an OpenAI LLM adapter, so I’m using the OpenAI docs skill for that part and official docs for the other providers. Provider mentions: openai

Query/prompt: https://docs.perplexity.ai/docs/sonar/models Agent context: I’m also considering OpenAI’s managed web search because this repo already has an OpenAI LLM adapter, so I’m using the OpenAI docs skill for that part and official docs for the other providers. Query: https://docs.perplexity.ai/docs/sonar/models Agent context: I’m also considering OpenAI’s managed web search because this repo already has an OpenAI LLM adapter, so I’m using the OpenAI docs skill for that part and official docs for the other providers. Provider mentions: openai

Query/prompt: site:docs.perplexity.ai/docs/sonar search domain filters search recency filter Sonar API Agent context: I’m also considering OpenAI’s managed web search because this repo already has an OpenAI LLM adapter, so I’m using the OpenAI docs skill for that part and official docs for the other providers. Query: site:docs.perplexity.ai/docs/sonar search domain filters search recency filter Sonar API Agent context: I’m also considering OpenAI’s managed web search because this repo already has an OpenAI LLM adapter, so I’m using the OpenAI docs skill for that part and official docs for the other providers. Expanded queries: site:docs.perplexity.ai/docs/sonar search domain filters search recency filter Sonar API | site:docs.perplexity.ai/docs/sonar "search_domain_filter" "search_recency_filter" Provider mentions: openai

Agent context: I’ve got enough repo context now: this is not currently a tool-calling agent; it is a local LlamaIndex RAG pipeline over uploaded PDFs, with optional OpenAI/Ollama LLM backends. I’m now comparing providers on whether they can return grounded content plus auditable source metadata, not just “an answer from the web.” I’ve got enough repo context now: this is not currently a tool-calling agent; it is a local LlamaIndex RAG pipeline over uploaded PDFs, with optional OpenAI/Ollama LLM backends. I’m now comparing providers on whether they can return grounded content plus auditable source metadata, not just “an answer from the web.” Provider mentions: openai

Agent context: I’ve got enough repo context now: this is not currently a tool-calling agent; it is a local LlamaIndex RAG pipeline over uploaded PDFs, with optional OpenAI/Ollama LLM backends. I’m now comparing providers on whether they can return grounded content plus auditable source metadata, not just “an answer from the web.”

Agent context: I’ve got enough repo context now: this is not currently a tool-calling agent; it is a local LlamaIndex RAG pipeline over uploaded PDFs, with optional OpenAI/Ollama LLM backends. I’m now comparing providers on whether they can return grounded content plus auditable source metadata, not just “an answer from the web.” 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: I’ve got enough repo context now: this is not currently a tool-calling agent; it is a local LlamaIndex RAG pipeline over uploaded PDFs, with optional OpenAI/Ollama LLM backends. I’m now comparing providers on whether they can return grounded content plus auditable source metadata, not just “an answer from the web.”

Agent context: I’ve got enough repo context now: this is not currently a tool-calling agent; it is a local LlamaIndex RAG pipeline over uploaded PDFs, with optional OpenAI/Ollama LLM backends. I’m now comparing providers on whether they can return grounded content plus auditable source metadata, not just “an answer from the web.” exit code 0 Output: 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", 19 setting: RAGSettings | None = None, 20 ): 21 setting = setting or RAGSettings() 22 if model_name in ["gpt-3.5-turbo", "gpt-4", "gpt-4o", "gpt-4-turbo"]: 23 return OpenAI(model=model_name, temperature=setting.ollama.temperature) 24 else: 25 settings_kwargs = { 26 "tfs_z": setting.ollama.tfs_z, 27 "top_k": setting.ollama.top_k, 28 "top_p": setting.ollama.top_p, 29 "repeat_last_n": setting.ollama.repeat_last_n, 30 "repeat_penalty": setting.ollama.repeat_penalty, 31 } 32 return Provider mentions: openai

Agent context: I’ve got enough repo context now: this is not currently a tool-calling agent; it is a local LlamaIndex RAG pipeline over uploaded PDFs, with optional OpenAI/Ollama LLM backends. I’m now comparing providers on whether they can return grounded content plus auditable source metadata, not just “an answer from the web.”

Agent context: I’ve got enough repo context now: this is not currently a tool-calling agent; it is a local LlamaIndex RAG pipeline over uploaded PDFs, with optional OpenAI/Ollama LLM backends. I’m now comparing providers on whether they can return grounded content plus auditable source metadata, not just “an answer from the web.” 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: I’ve got enough repo context now: this is not currently a tool-calling agent; it is a local LlamaIndex RAG pipeline over uploaded PDFs, with optional OpenAI/Ollama LLM backends. I’m now comparing providers on whether they can return grounded content plus auditable source metadata, not just “an answer from the web.”

Agent context: I’ve got enough repo context now: this is not currently a tool-calling agent; it is a local LlamaIndex RAG pipeline over uploaded PDFs, with optional OpenAI/Ollama LLM backends. I’m now comparing providers on whether they can return grounded content plus auditable source metadata, not just “an answer from the web.” exit code 0 Output: 1 from typing import List 2 from dotenv import load_dotenv 3 from llama_index.core.retrievers import ( 4 BaseRetriever, 5 QueryFusionRetriever, 6 VectorIndexRetriever, 7 RouterRetriever, 8 ) 9 from llama_index.core.callbacks.base import CallbackManager 10 from llama_index.core.retrievers.fusion_retriever import FUSION_MODES 11 from llama_index.core.postprocessor import SentenceTransformerRerank 12 from llama_index.core.tools import RetrieverTool 13 from llama_index.core.selectors import LLMSingleSelector 14 from llama_index.core.schema import BaseNode, NodeWithScore, QueryBundle, IndexNode 15 from llama_index.core.llms.llm import LLM 16 from llama_index.retrievers.bm25 import BM25Retriever 17 from llama_index.core import Settings, VectorStoreIndex 18 from ..prompt import get_query_gen_prompt 19 from ...setting import RAGSettings 20 21 load_dotenv() 22 23 24 class TwoStageRetriever(QueryF

Agent context: **Recommendation** Choose **OpenAI Responses API with the `web_search` tool**, using a strong reasoning model for web-grounded turns. This repo is a local LlamaIndex PDF RAG app: uploaded PDF nodes are retrieved via vector/BM25 fusion in [retriever.py](/home/user/worktrees/attempt-86c762ee-be10-4bea-85de-6bcf4691083a-cap-1/rag_chatbot/core/engine/retriever.py:83), then passed into [LocalChatEngine](/home/user/worktrees/attempt-86c762ee-be10-4bea-85de-6bcf4691083a-cap-1/rag_chatbot/core/engine/engine.py:19). It has no current web path today; empty-document chat falls back to plain `SimpleChatEngine` without retrieval. Since this product prioritizes factuality and citations over cost/latency, I would add a separate web-grounded answer path rather than trying to stuff transient web pages into the local Chroma/PDF pipeline. Why OpenAI: its web search tool is explicitly for latest web information with sourced citations, returns citation annotations, supports domain allow/block filters, exposes the searched/opened sources, and reasoning models can perform search/open-page/find-in-page actions before answering. Those controls matter more than raw speed here. OpenAI’s docs also say the Responses API is the recommended new API surface for tool-using agent workflows. **Alternatives Considered** - **Perplexity Sonar / Search API**: strong runner-up. Sonar is purpose-built for web-grounded responses and returns citations plus `search_results`; Search API gives raw ranked web results. I would use it if the product wanted a dedicated answer-engine vendor separate from the model provider. I ranked it below OpenAI because OpenAI gives tighter citation annotations, source inclusion, and reasoning-tool orchestration in one agent API. - **Tavily**: good agent-oriented search/retrieval API with ranked results and optional LLM answer generation. I would use it for raw retrieval into an existing RAG stack, but its answer path is less compelling for high-stakes citation discipline than OpenAI’s annotated web-search responses. - **Exa**: strong semantic/neural web search and content extraction, useful for research discovery. I would not make it the default here because semantic relevance is not the same as source authority, and the repo needs current factual grounding more than exploratory web discovery. - **Brave Search API**: best “pure search index” alternative. It has an independent web index, freshness claims, spam-quality positioning, and LLM-oriented context. I would choose it if avoiding model-provider coupling mattered. For this request, I still prefer OpenAI because it provides end-to-end grounded answer generation with verifiable citation metadata. **Docs Actually Consulted** - OpenAI: [Web search guide](https://developers.openai.com/api/docs/guides/tools-web-search), [Responses migration guide](https://platform.openai.com/docs/guides/migrate-to-responses), [Retrieval guide](https://developers.openai.com/api/docs/guides/retrieval) - Perplexity: [Sonar quickstart](https://docs.perplexity.ai/docs/sonar/quickstart), [Search API quickstart](https://docs.perplexity.ai/docs/search/quickstart), [filters](https://docs.perplexity.ai/docs/search/filters/domain-filter) - Tavily: [Search endpoint docs](https://docs.tavily.com/documentation/api-reference/endpoint/search) - Exa: [Search endpoint docs](https://docs.exa.ai/reference/search) - Brave: [Search API overview](https://brave.com/search/api/) I did not change code in this pass because your requested deliverable was the provider recommendation. The implementation I’d make next is a `WebGroundedProvider` called from `LocalRAGPipeline.query()` for current-info questions, preserving OpenAI citation annotations and showing clickable citations in the Gradio UI. **Recommendation** Choose **OpenAI Responses API with the `web_search` tool**, using a strong reasoning model for web-grounded turns. This repo is a local LlamaIndex PDF RAG app: uploaded PDF nodes are retrieved via vector/BM25 fusion in [retriever.py](/home/user/worktrees/attempt-86c762ee-be10-4bea-85de-6bcf4691083a-cap-1/rag_chatbot/core/engine/retriever.py:83), then passed into [LocalChatEngine](/home/user/worktrees/attempt-86c762ee-be10-4bea-85de-6bcf4691083a-cap-1/rag_chatbot/core/engine/engine.py:19). It has no current web path today; empty-document chat falls back to plain `SimpleChatEngine` without retrieval. Since this product prioritizes factuality and citations over cost/latency, I would add a separate web-grounded answer path rather than trying to stuff transient web pages into the local Chroma/PDF pipeline. Why OpenAI: its web search tool is explicitly for latest web information with sourced citations, returns citation annotations, supports domain allow/block filters, exposes the searched/opened sources, and reasoning models can perform search/open-page/find-in-page actions before answering. Those controls matter more than raw speed here. OpenAI’s docs also say the Res Provider mentions: openai

**Recommendation** Choose **OpenAI Responses API with the `web_search` tool**, using a strong reasoning model for web-grounded turns. This repo is a local LlamaIndex PDF RAG app: uploaded PDF nodes are retrieved via vector/BM25 fusion in [retriever.py](/home/user/worktrees/attempt-86c762ee-be10-4bea-85de-6bcf4691083a-cap-1/rag_chatbot/core/engine/retriever.py:83), then passed into [LocalChatEngine](/home/user/worktrees/attempt-86c762ee-be10-4bea-85de-6bcf4691083a-cap-1/rag_chatbot/core/engine/engine.py:19). It has no current web path today; empty-document chat falls back to plain `SimpleChatEngine` without retrieval. Since this product prioritizes factuality and citations over cost/latency, I would add a separate web-grounded answer path rather than trying to stuff transient web pages into the local Chroma/PDF pipeline. Why OpenAI: its web search tool is explicitly for latest web information with sourced citations, returns citation annotations, supports domain allow/block filters, exposes the searched/opened sources, and reasoning models can perform search/open-page/find-in-page actions before answering. Those controls matter more than raw speed here. OpenAI’s docs also say the Responses API is the recommended new API surface for tool-using agent workflows. **Alternatives Considered** - **Perplexity Sonar / Search API**: strong runner-up. Sonar is purpose-built for web-grounded Provider mentions: openai