Find
Search dashboard pages and product views.
Search dashboard pages and product views.
datvodinh/rag-chatbot
Add live web search/retrieval to this repository so its agent can ground its answers in current web information. Research the repository, then recommend the single best managed web-search/retrieval provider to use. The agent needs access to high-quality, reputable, and licensed sources rather than generic scraped search-engine results, so weigh source coverage and quality heavily. Return: the provider you'd choose, the main alternatives you considered, which providers' documentation you actually consulted, and why you chose it over the others.
P3
e9de2afd458e4ad2d7c54636d3c8b20de04cf790
Agent context: I’ll inspect the repo first to understand the agent stack and integration constraints, then I’ll verify provider docs and compare options against the licensed-source requirement. I’ll inspect the repo first to understand the agent stack and integration constraints, then I’ll verify provider docs and compare options against the licensed-source requirement.
Agent context: I’ll inspect the repo first to understand the agent stack and integration constraints, then I’ll verify provider docs and compare options against the licensed-source requirement.
Agent context: I’ll inspect the repo first to understand the agent stack and integration constraints, then I’ll verify provider docs and compare options against the licensed-source requirement. exit code 0 Output: /home/user/worktrees/attempt-8f210ddd-c3f8-4835-a230-f71a5c1ae210-cap-0 /bin/bash: line 1: rg: command not found
Agent context: I’ll inspect the repo first to understand the agent stack and integration constraints, then I’ll verify provider docs and compare options against the licensed-source requirement. Provider mentions: openai
Agent context: I’ll inspect the repo first to understand the agent stack and integration constraints, then I’ll verify provider docs and compare options against the licensed-source requirement. exit code 127 Output: /bin/bash: line 1: rg: command not found Provider mentions: openai
Agent context: I’ll inspect the repo first to understand the agent stack and integration constraints, then I’ll verify provider docs and compare options against the licensed-source requirement.
Agent context: I’ll inspect the repo first to understand the agent stack and integration constraints, then I’ll verify provider docs and compare options against the licensed-source requirement. exit code 0
Agent context: `rg` is not installed in this environment, so I’m falling back to `find`/`grep` and keeping the scan focused. The git tree is currently clean. `rg` is not installed in this environment, so I’m falling back to `find`/`grep` and keeping the scan focused. The git tree is currently clean.
Agent context: `rg` is not installed in this environment, so I’m falling back to `find`/`grep` and keeping the scan focused. The git tree is currently clean.
Agent context: `rg` is not installed in this environment, so I’m falling back to `find`/`grep` and keeping the scan focused. The git tree is currently 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`/`grep` and keeping the scan focused. The git tree is currently clean. Provider mentions: openai
Agent context: `rg` is not installed in this environment, so I’m falling back to `find`/`grep` and keeping the scan focused. The git tree is currently clean. exit code 0 Output: ./README.md:22:- Using any model from `Huggingface` and `Ollama` ./README.md:31: ./README.md:65:#### 2.2.2 Using script (Ollama, Ngrok, python package) ./README.md:73:##### 1. `Ollama` ./README.md:75:- MacOS, Window: [Download](https://ollama.com/) ./README.md:80:curl -fsSL https://ollama.com/install.sh | sh ./README.md:94:curl -s https://ngrok-agent.s3.amazonaws.com/ngrok.asc \ ./README.md:96:&& echo "deb https://ngrok-agent.s3.amazonaws.com buster main" \ ./docker-compose.yml:21: ollama: ./docker-compose.yml:22: container_name: ollama ./docker-compose.yml:23: image: ollama/ollama:latest ./docker-compose.yml:27: - ./data/ollama:/root/.ollama ./pyproject.toml:11: "llama-index-embeddings-huggingface>=0.1.4,<0.2", ./pyproject.toml:12: "llama-index-llms-openai>=0.1.12,<0.2", ./pyproject.toml:13: "llama-index-vector-stores-chroma>=0.1.6,<0.2", ./pyproject. Provider mentions: openai
Agent context: The repo is a LlamaIndex RAG chatbot: local document ingestion, Chroma/vector retrieval, BM25 fusion, Ollama/OpenAI model options. I’m reading the engine and settings paths now so the provider recommendation fits the current architecture rather than assuming a generic agent framework. The repo is a LlamaIndex RAG chatbot: local document ingestion, Chroma/vector retrieval, BM25 fusion, Ollama/OpenAI model options. I’m reading the engine and settings paths now so the provider recommendation fits the current architecture rather than assuming a generic agent framework. Provider mentions: openai
Agent context: The repo is a LlamaIndex RAG chatbot: local document ingestion, Chroma/vector retrieval, BM25 fusion, Ollama/OpenAI model options. I’m reading the engine and settings paths now so the provider recommendation fits the current architecture rather than assuming a generic agent framework.
Agent context: The repo is a LlamaIndex RAG chatbot: local document ingestion, Chroma/vector retrieval, BM25 fusion, Ollama/OpenAI model options. I’m reading the engine and settings paths now so the provider recommendation fits the current architecture rather than assuming a generic agent framework. 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 repo is a LlamaIndex RAG chatbot: local document ingestion, Chroma/vector retrieval, BM25 fusion, Ollama/OpenAI model options. I’m reading the engine and settings paths now so the provider recommendation fits the current architecture rather than assuming a generic agent framework.
Agent context: The repo is a LlamaIndex RAG chatbot: local document ingestion, Chroma/vector retrieval, BM25 fusion, Ollama/OpenAI model options. I’m reading the engine and settings paths now so the provider recommendation fits the current architecture rather than assuming a generic agent framework. exit code 0 Output: from llama_index.core.chat_engine import CondensePlusContextChatEngine, SimpleChatEngine from llama_index.core.memory import ChatMemoryBuffer from llama_index.core.llms.llm import LLM from llama_index.core.schema import BaseNode from typing import List from .retriever import LocalRetriever from ...setting import RAGSettings class LocalChatEngine: def __init__( self, setting: RAGSettings | None = None, host: str = "host.docker.internal" ): super().__init__() self._setting = setting or RAGSettings() self._retriever = LocalRetriever(self._setting) self._host = host def set_engine( self, llm: LLM, nodes: List[BaseNode], language: str = "eng", ) -> CondensePlusContextChatEngine | SimpleChatEngine: # Normal chat engine if len(nodes) == 0: return SimpleChatEngine.from_defaults( llm=llm, memory=ChatMemoryBuffer( token_limit=self._setting.ollama.chat_token_limit ), ) # Chat engine with documents
Agent context: The repo is a LlamaIndex RAG chatbot: local document ingestion, Chroma/vector retrieval, BM25 fusion, Ollama/OpenAI model options. I’m reading the engine and settings paths now so the provider recommendation fits the current architecture rather than assuming a generic agent framework.
Agent context: The repo is a LlamaIndex RAG chatbot: local document ingestion, Chroma/vector retrieval, BM25 fusion, Ollama/OpenAI model options. I’m reading the engine and settings paths now so the provider recommendation fits the current architecture rather than assuming a generic agent framework. exit code 0 Output: [project] name = "rag-chatbot" version = "0.1.0" description = "" authors = [{ name = "Võ Đình Đạt", email = "vodinhdat03@gmail.com" }] requires-python = ">=3.11" readme = "README.md" dependencies = [ "gradio<5", "python-dotenv>=1.0.1,<2", "llama-index-embeddings-huggingface>=0.1.4,<0.2", "llama-index-llms-openai>=0.1.12,<0.2", "llama-index-vector-stores-chroma>=0.1.6,<0.2", "llama-index-llms-ollama>=0.1.2,<0.2", "llama-index-readers-file>=0.1.11,<0.2", "llama-index>=0.10.22,<0.11", "transformers>=4.39.1,<5", "torch", "llama-index-callbacks-wandb>=0.1.2,<0.2", "llama-index-retrievers-bm25>=0.1.3,<0.2", "pymupdf>=1.24.3,<2", "tqdm>=4.66.4,<5", "requests>=2.32.3,<3", "pandas>=2.2.3,<3", "sentence-transformers>=3.2.0,<4", "pydantic==2.8.2", ] [dependency-groups] dev = [ "ruff>=0.6.9,<0.7", "deptry>=0.20.0,<0.21", "pytest>=8.3.3,<9", ] [build-system] requires = ["hatchling"] build-backend = Provider mentions: openai
Agent context: The repo is a LlamaIndex RAG chatbot: local document ingestion, Chroma/vector retrieval, BM25 fusion, Ollama/OpenAI model options. I’m reading the engine and settings paths now so the provider recommendation fits the current architecture rather than assuming a generic agent framework.
Agent context: The repo is a LlamaIndex RAG chatbot: local document ingestion, Chroma/vector retrieval, BM25 fusion, Ollama/OpenAI model options. I’m reading the engine and settings paths now so the provider recommendation fits the current architecture rather than assuming a generic agent framework. 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 repo is a LlamaIndex RAG chatbot: local document ingestion, Chroma/vector retrieval, BM25 fusion, Ollama/OpenAI model options. I’m reading the engine and settings paths now so the provider recommendation fits the current architecture rather than assuming a generic agent framework.
Agent context: The repo is a LlamaIndex RAG chatbot: local document ingestion, Chroma/vector retrieval, BM25 fusion, Ollama/OpenAI model options. I’m reading the engine and settings paths now so the provider recommendation fits the current architecture rather than assuming a generic agent framework. 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: This codebase does not currently have tool-calling or web retrieval; it builds a `CondensePlusContextChatEngine` over local ingested nodes. A practical integration would add a LlamaIndex-compatible web retriever/tool beside the existing local retriever, with settings and API key configuration kept in `RAGSettings`. This codebase does not currently have tool-calling or web retrieval; it builds a `CondensePlusContextChatEngine` over local ingested nodes. A practical integration would add a LlamaIndex-compatible web retriever/tool beside the existing local retriever, with settings and API key configuration kept in `RAGSettings`.
Agent context: This codebase does not currently have tool-calling or web retrieval; it builds a `CondensePlusContextChatEngine` over local ingested nodes. A practical integration would add a LlamaIndex-compatible web retriever/tool beside the existing local retriever, with settings and API key configuration kept in `RAGSettings`.
Agent context: This codebase does not currently have tool-calling or web retrieval; it builds a `CondensePlusContextChatEngine` over local ingested nodes. A practical integration would add a LlamaIndex-compatible web retriever/tool beside the existing local retriever, with settings and API key configuration kept in `RAGSettings`. 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 codebase does not currently have tool-calling or web retrieval; it builds a `CondensePlusContextChatEngine` over local ingested nodes. A practical integration would add a LlamaIndex-compatible web retriever/tool beside the existing local retriever, with settings and API key configuration kept in `RAGSettings`.
Agent context: This codebase does not currently have tool-calling or web retrieval; it builds a `CondensePlusContextChatEngine` over local ingested nodes. A practical integration would add a LlamaIndex-compatible web retriever/tool beside the existing local retriever, with settings and API key configuration kept in `RAGSettings`. exit code 0 Output: import os import shutil import json import sys import time import gradio as gr from dataclasses import dataclass from typing import ClassVar from llama_index.core.chat_engine.types import StreamingAgentChatResponse from .theme import JS_LIGHT_THEME, CSS from ..pipeline import LocalRAGPipeline from ..logger import Logger @dataclass class DefaultElement: DEFAULT_MESSAGE: ClassVar[dict] = {"text": ""} DEFAULT_MODEL: str = "" DEFAULT_HISTORY: ClassVar[list] = [] DEFAULT_DOCUMENT: ClassVar[list] = [] HELLO_MESSAGE: str = "Hi 👋, how can I help you today?" SET_MODEL_MESSAGE: str = "You need to choose LLM model 🤖 first!" EMPTY_MESSAGE: str = "You need to enter your message!" DEFAULT_STATUS: str = "Ready!" CONFIRM_PULL_MODEL_STATUS: str = "Confirm Pull Model!" PULL_MODEL_SCUCCESS_STATUS: str = "Pulling model 🤖 completed!" PULL_MODEL_FAIL_STATUS: str = "Pulling model 🤖 failed!" MODEL_NOT_EXIST
Agent context: This codebase does not currently have tool-calling or web retrieval; it builds a `CondensePlusContextChatEngine` over local ingested nodes. A practical integration would add a LlamaIndex-compatible web retriever/tool beside the existing local retriever, with settings and API key configuration kept in `RAGSettings`.
Agent context: This codebase does not currently have tool-calling or web retrieval; it builds a `CondensePlusContextChatEngine` over local ingested nodes. A practical integration would add a LlamaIndex-compatible web retriever/tool beside the existing local retriever, with settings and API key configuration kept in `RAGSettings`. exit code 0 Output: rag_chatbot/core/engine/engine.py:10:class LocalChatEngine: rag_chatbot/core/engine/retriever.py:12:from llama_index.core.tools import RetrieverTool rag_chatbot/core/engine/retriever.py:158: fusion_tool = RetrieverTool.from_defaults( rag_chatbot/core/engine/retriever.py:165: two_stage_tool = RetrieverTool.from_defaults( rag_chatbot/pipeline.py:10:from llama_index.core.chat_engine.types import StreamingAgentChatResponse rag_chatbot/pipeline.py:114: ) -> StreamingAgentChatResponse: rag_chatbot/ui/ui.py:9:from llama_index.core.chat_engine.types import StreamingAgentChatResponse rag_chatbot/ui/ui.py:62: response: StreamingAgentChatResponse,
Query/prompt: managed web search retrieval API AI agents licensed content reputable sources Tavily Exa Linkup You.com Perplexity documentation Agent context: This codebase does not currently have tool-calling or web retrieval; it builds a `CondensePlusContextChatEngine` over local ingested nodes. A practical integration would add a LlamaIndex-compatible web retriever/tool beside the existing local retriever, with settings and API key configuration kept in `RAGSettings`. Query: managed web search retrieval API AI agents licensed content reputable sources Tavily Exa Linkup You.com Perplexity documentation Agent context: This codebase does not currently have tool-calling or web retrieval; it builds a `CondensePlusContextChatEngine` over local ingested nodes. A practical integration would add a LlamaIndex-compatible web retriever/tool beside the existing local retriever, with settings and API key configuration kept in `RAGSettings`. Expanded queries: managed web search retrieval API AI agents licensed content reputable sources Tavily Exa Linkup You.com Perplexity documentation | Linkup search API licensed sources documentation | Tavily search API documentation sources crawl chunks LlamaIndex | LlamaIndex web search retriever Tavily Exa Brave Search API documentation
Query/prompt: Linkup Search API documentation high quality sources licensed content Agent context: This codebase does not currently have tool-calling or web retrieval; it builds a `CondensePlusContextChatEngine` over local ingested nodes. A practical integration would add a LlamaIndex-compatible web retriever/tool beside the existing local retriever, with settings and API key configuration kept in `RAGSettings`. Query: Linkup Search API documentation high quality sources licensed content Agent context: This codebase does not currently have tool-calling or web retrieval; it builds a `CondensePlusContextChatEngine` over local ingested nodes. A practical integration would add a LlamaIndex-compatible web retriever/tool beside the existing local retriever, with settings and API key configuration kept in `RAGSettings`. Expanded queries: Linkup Search API documentation high quality sources licensed content | Linkup API docs search depth output type sources | Valyu AI API documentation web search academic proprietary sources | You.com API documentation search API sources citations
Query/prompt: https://docs.linkup.so/pages/documentation/endpoints/search/best-practices Agent context: This codebase does not currently have tool-calling or web retrieval; it builds a `CondensePlusContextChatEngine` over local ingested nodes. A practical integration would add a LlamaIndex-compatible web retriever/tool beside the existing local retriever, with settings and API key configuration kept in `RAGSettings`. Query: https://docs.linkup.so/pages/documentation/endpoints/search/best-practices Agent context: This codebase does not currently have tool-calling or web retrieval; it builds a `CondensePlusContextChatEngine` over local ingested nodes. A practical integration would add a LlamaIndex-compatible web retriever/tool beside the existing local retriever, with settings and API key configuration kept in `RAGSettings`.
Query/prompt: https://you.com/docs/search/overview Agent context: This codebase does not currently have tool-calling or web retrieval; it builds a `CondensePlusContextChatEngine` over local ingested nodes. A practical integration would add a LlamaIndex-compatible web retriever/tool beside the existing local retriever, with settings and API key configuration kept in `RAGSettings`. Query: https://you.com/docs/search/overview Agent context: This codebase does not currently have tool-calling or web retrieval; it builds a `CondensePlusContextChatEngine` over local ingested nodes. A practical integration would add a LlamaIndex-compatible web retriever/tool beside the existing local retriever, with settings and API key configuration kept in `RAGSettings`.
Query/prompt: site:docs.valyu.ai LlamaIndex Valyu integration Agent context: This codebase does not currently have tool-calling or web retrieval; it builds a `CondensePlusContextChatEngine` over local ingested nodes. A practical integration would add a LlamaIndex-compatible web retriever/tool beside the existing local retriever, with settings and API key configuration kept in `RAGSettings`. Query: site:docs.valyu.ai LlamaIndex Valyu integration Agent context: This codebase does not currently have tool-calling or web retrieval; it builds a `CondensePlusContextChatEngine` over local ingested nodes. A practical integration would add a LlamaIndex-compatible web retriever/tool beside the existing local retriever, with settings and API key configuration kept in `RAGSettings`. Expanded queries: site:docs.valyu.ai LlamaIndex Valyu integration | Valyu LlamaIndex integration docs | Valyu Python SDK docs API reference search
Query/prompt: Exa search API docs neural search contents API LlamaIndex integration Agent context: This codebase does not currently have tool-calling or web retrieval; it builds a `CondensePlusContextChatEngine` over local ingested nodes. A practical integration would add a LlamaIndex-compatible web retriever/tool beside the existing local retriever, with settings and API key configuration kept in `RAGSettings`. Query: Exa search API docs neural search contents API LlamaIndex integration Agent context: This codebase does not currently have tool-calling or web retrieval; it builds a `CondensePlusContextChatEngine` over local ingested nodes. A practical integration would add a LlamaIndex-compatible web retriever/tool beside the existing local retriever, with settings and API key configuration kept in `RAGSettings`. Expanded queries: Exa search API docs neural search contents API LlamaIndex integration | Exa documentation search API contents livecrawl LlamaIndex | Perplexity Search API docs source customization citations | Brave Search API LLM context docs independent index
Query/prompt: https://docs.exa.ai/reference/livecrawling-contents Agent context: This codebase does not currently have tool-calling or web retrieval; it builds a `CondensePlusContextChatEngine` over local ingested nodes. A practical integration would add a LlamaIndex-compatible web retriever/tool beside the existing local retriever, with settings and API key configuration kept in `RAGSettings`. Query: https://docs.exa.ai/reference/livecrawling-contents Agent context: This codebase does not currently have tool-calling or web retrieval; it builds a `CondensePlusContextChatEngine` over local ingested nodes. A practical integration would add a LlamaIndex-compatible web retriever/tool beside the existing local retriever, with settings and API key configuration kept in `RAGSettings`.
Query/prompt: https://docs.linkup.so/pages/documentation/endpoints/search/best-practices Agent context: This codebase does not currently have tool-calling or web retrieval; it builds a `CondensePlusContextChatEngine` over local ingested nodes. A practical integration would add a LlamaIndex-compatible web retriever/tool beside the existing local retriever, with settings and API key configuration kept in `RAGSettings`. Query: https://docs.linkup.so/pages/documentation/endpoints/search/best-practices Agent context: This codebase does not currently have tool-calling or web retrieval; it builds a `CondensePlusContextChatEngine` over local ingested nodes. A practical integration would add a LlamaIndex-compatible web retriever/tool beside the existing local retriever, with settings and API key configuration kept in `RAGSettings`.
Query/prompt: site:docs.valyu.ai data coverage licensed sources Valyu datasets proprietary source list Agent context: This codebase does not currently have tool-calling or web retrieval; it builds a `CondensePlusContextChatEngine` over local ingested nodes. A practical integration would add a LlamaIndex-compatible web retriever/tool beside the existing local retriever, with settings and API key configuration kept in `RAGSettings`. Query: site:docs.valyu.ai data coverage licensed sources Valyu datasets proprietary source list Agent context: This codebase does not currently have tool-calling or web retrieval; it builds a `CondensePlusContextChatEngine` over local ingested nodes. A practical integration would add a LlamaIndex-compatible web retriever/tool beside the existing local retriever, with settings and API key configuration kept in `RAGSettings`. Expanded queries: site:docs.valyu.ai data coverage licensed sources Valyu datasets proprietary source list | site:docs.valyu.ai guides datasources Valyu proprietary licensed data sources | site:docs.valyu.ai academic proprietary datasets licensed data sources Valyu
Agent context: I’ve got enough repo context and provider evidence. I’m checking exact line references for the local integration points so the recommendation can include where it would land if you implement it. I’ve got enough repo context and provider evidence. I’m checking exact line references for the local integration points so the recommendation can include where it would land if you implement it.
Agent context: I’ve got enough repo context and provider evidence. I’m checking exact line references for the local integration points so the recommendation can include where it would land if you implement it.
Agent context: I’ve got enough repo context and provider evidence. I’m checking exact line references for the local integration points so the recommendation can include where it would land if you implement it. 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: I’ve got enough repo context and provider evidence. I’m checking exact line references for the local integration points so the recommendation can include where it would land if you implement it.
Agent context: I’ve got enough repo context and provider evidence. I’m checking exact line references for the local integration points so the recommendation can include where it would land if you implement it. 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 and provider evidence. I’m checking exact line references for the local integration points so the recommendation can include where it would land if you implement it.
Agent context: I’ve got enough repo context and provider evidence. I’m checking exact line references for the local integration points so the recommendation can include where it would land if you implement it. 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 and provider evidence. I’m checking exact line references for the local integration points so the recommendation can include where it would land if you implement it.
Agent context: I’ve got enough repo context and provider evidence. I’m checking exact line references for the local integration points so the recommendation can include where it would land if you implement it. exit code 0 Output: 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: float = Field(default=0.9, description="Top p sampling") 11 repeat_last_n: int = Field(default=64, description="Repeat last n tokens") 12 repeat_penalty: float = Field(default=1.1, description="Repeat penalty") 13 request_timeout: float = Field(default=300, description="Request timeout") 14 port: int = Field(default=11434, description="Port number") 15 context_window: int = Field(default=8000, description="Context window size") 16 temperature: float = Field(default=0.1, descri
Agent context: **Provider** I’d choose **Valyu Search API**. This repo is a LlamaIndex RAG chatbot, not a tool-calling agent yet. Its current grounding path is local-only: `LocalChatEngine` builds a `CondensePlusContextChatEngine` over ingested nodes in [engine.py](/home/user/worktrees/attempt-8f210ddd-c3f8-4835-a230-f71a5c1ae210-cap-0/rag_chatbot/core/engine/engine.py:34), and retrieval is local vector/BM25 fusion in [retriever.py](/home/user/worktrees/attempt-8f210ddd-c3f8-4835-a230-f71a5c1ae210-cap-0/rag_chatbot/core/engine/retriever.py:105). Valyu fits because it has a LlamaIndex integration, but also a plain Python SDK if the integration package conflicts with this repo’s older `llama-index>=0.10.22,<0.11` pin. Why Valyu: its docs explicitly cover **real-time web plus proprietary/licensed data sources**, including papers/books from licensed data sources, academic content, finance, medical research, and source filtering. That maps best to your requirement for reputable/licensed sources instead of generic scraped SERP output. Its Search API returns RAG-ready content, source metadata, dates, relevance scores, and supports `search_type="all" | "web" | "proprietary" | "news"`, date filters, source filters, and long/full content responses. **Docs Consulted** I actually consulted these provider docs: - Valyu: [overview](https://docs.valyu.ai/overview), [Search quickstart](https://docs.valyu.ai/search/quickstart), [data coverage](https://docs.valyu.ai/data-coverage), [source filtering](https://docs.valyu.ai/search-filtering/sources), [LlamaIndex integration](https://docs.valyu.ai/integrations/llamaindex) - You.com: [Search API overview](https://you.com/docs/search/overview), [Research API overview](https://you.com/docs/research/overview) - Tavily: [Search API](https://docs.tavily.com/documentation/api-reference/endpoint/search), [LlamaIndex integration](https://docs.tavily.com/documentation/integrations/llamaindex) - Exa: [content freshness/live crawling](https://exa.ai/docs/reference/livecrawling-contents), [LlamaIndex integration](https://exa.ai/docs/reference/llamaindex) - Brave: [Search API](https://brave.com/search/api/), [LLM Context API](https://api-dashboard.search.brave.com/documentation/services/llm-context) - Perplexity: [Search API quickstart](https://docs.perplexity.ai/docs/search/quickstart), [API platform overview](https://www.perplexity.ai/help-center/en/articles/10354842-what-is-the-perplexity-api-platform) - Linkup: [Search overview](https://docs.linkup.so/pages/documentation/endpoints/search/overview), [Search best practices](https://docs.linkup.so/pages/documentation/endpoints/search/best-practices), [for agents](https://docs.linkup.so/pages/documentation/get-started/for-agents) **Main Alternatives** You.com was the closest alternative: strong AI/RAG-oriented web and news retrieval, full-page live crawling, citations, and good source controls. I’d pick it if the priority were broad current web/news quality over specialized licensed/proprietary datasets. Tavily is easy to integrate and agent-friendly, with search depth, raw content, domain filters, and LlamaIndex support. I would not choose it first here because it is primarily web search/extraction, not clearly stronger on licensed/proprietary source coverage. Exa is strong for semantic web search and has freshness controls and LlamaIndex support. I would choose it for neural discovery over the open web, but not for this requirement because Valyu has clearer documented proprietary/academic/financial/healthcare source coverage. Brave is the best “real search index” option: independent index, LLM Context API, custom source reranking via Goggles, and clear AI app usage rights. It is still general web search, so it does not satisfy the licensed/premium-source requirement as well as Valyu. Perplexity has strong hosted search/answer products with citations and a refreshed index. I would avoid making it the repository’s retrieval substrate because the Search API is more of a ranked web result layer, while Sonar/Agent API couples retrieval with Perplexity’s generation path. Linkup is promising for agentic search/scrape workflows and sourced answers, but its docs emphasize web search modes more than licensed/proprietary data coverage. It is a good fallback for company intelligence and structured web extraction, not my top choice for this source-quality requirement. **Implementation Direction** Add `valyu` and, if compatible, `llama-index-tools-valyu`; otherwise wrap the Valyu Python SDK directly. I’d add web settings beside `RetrieverSettings` in [setting.py](/home/user/worktrees/attempt-8f210ddd-c3f8-4835-a230-f71a5c1ae210-cap-0/rag_chatbot/setting/setting.py:20), then add a Valyu-backed retriever/tool alongside `LocalRetriever` in [retriever.py](/home/user/worktrees/attempt-8f210ddd-c3f8-4835-a230-f71a5c1ae210-cap-0/rag_chatbot/core/engine/retriever.py:83), returning LlamaIndex `Document`/node-like context with URL, title, publication date, source type, and relevance metadata. **Provider** I’d choose **Valyu Search API**. This repo is a LlamaIndex RAG chatbot, not a tool-calling agent yet. Its current grounding path is local-only: `LocalChatEngine` builds a `CondensePlusContextChatEngine` over ingested nodes in [engine.py](/home/user/worktrees/attempt-8f210ddd-c3f8-4835-a230-f71a5c1ae210-cap-0/rag_chatbot/core/engine/engine.py:34), and retrieval is local vector/BM25 fusion in [retriever.py](/home/user/worktrees/attempt-8f210ddd-c3f8-4835-a230-f71a5c1ae210-cap-0/rag_chatbot/core/engine/retriever.py:105). Valyu fits because it has a LlamaIndex integration, but also a plain Python SDK if the integration package conflicts with this repo’s older `llama-index>=0.10.22,<0.11` pin. Why Valyu: its docs explicitly cover **real-time web plus proprietary/licensed data sources**, including papers/books from licensed data sources, academic content, finance, medical research, and source filtering. That maps best to your requirement for reputable/licensed sources instead of generic scraped SERP output. Its Search API returns RAG-ready content, source metadata, dates, relevance scores, and supports `search_type="all" | "web" | "proprietary" | "news"`, date filters, sourc
**Provider** I’d choose **Valyu Search API**. This repo is a LlamaIndex RAG chatbot, not a tool-calling agent yet. Its current grounding path is local-only: `LocalChatEngine` builds a `CondensePlusContextChatEngine` over ingested nodes in [engine.py](/home/user/worktrees/attempt-8f210ddd-c3f8-4835-a230-f71a5c1ae210-cap-0/rag_chatbot/core/engine/engine.py:34), and retrieval is local vector/BM25 fusion in [retriever.py](/home/user/worktrees/attempt-8f210ddd-c3f8-4835-a230-f71a5c1ae210-cap-0/rag_chatbot/core/engine/retriever.py:105). Valyu fits because it has a LlamaIndex integration, but also a plain Python SDK if the integration package conflicts with this repo’s older `llama-index>=0.10.22,<0.11` pin. Why Valyu: its docs explicitly cover **real-time web plus proprietary/licensed data sources**, including papers/books from licensed data sources, academic content, finance, medical research, and source filtering. That maps best to your requirement for reputable/licensed sources instead of generic scraped SERP output. Its Search API returns RAG-ready content, source metadata, dates, relevance scores, and supports `search_type="all" | "web" | "proprietary" | "news"`, date filters, source filters, and long/full content responses. **Docs Consulted** I actually consulted these provider docs: - Valyu: [overview](https://docs.valyu.ai/overview), [Search quickstart](https://docs.valyu.ai/