datvodin

Run

datvodinh/rag-chatbot

Agent

Claude Opus 4.7

Status

completed

Trace

available

Telemetry

artifact only

Last Run

Jun 05, 11:31 PM

Decision Summary

competitor

Expected

Provider

Product

Selected

Provider

valyu

Product

Surface

provider_selection

Reason

claude-code selected valyu during first-pass provider discovery. No expected provider was set for this prompt.

Outcome

competitor

Trace Timeline

agent_action

Prompt-lab work item started

agent_action

Pinned repository checkout verified

e9de2afd458e4ad2d7c54636d3c8b20de04cf790

agent_action

I'll research the repository structure first to understand what kind of agent this ...

I'll research the repository structure first to understand what kind of agent this is and how it's built.

agent_action

Read /home/user/worktrees/attempt-20dc28c6-389b-42e3-8e37-2c747b6b725c-cap-0

Tool result summary: EISDIR: illegal operation on a directory, read '/home/user/worktrees/attempt-20dc28c6-389b-42e3-8e37-2c747b6b725c-cap-0' Agent context: I'll research the repository structure first to understand what kind of agent this is and how it's built.

command

find /home/user/worktrees/attempt-20dc28c6-389b-42e3-8e37-2c747b6b725c-cap-0 -maxdepth 2 -type f $ -name "README*" -o -name "pyproject.toml" -o -name "package.json" -o -name "setup.py" $ 2>/dev/null

Tool result summary: /home/user/worktrees/attempt-20dc28c6-389b-42e3-8e37-2c747b6b725c-cap-0/README.md /home/user/worktrees/attempt-20dc28c6-389b-42e3-8e37-2c747b6b725c-cap-0/pyproject.toml Agent context: I'll research the repository structure first to understand what kind of agent this is and how it's built.

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 structure first to understand what kind of agent this is and how it's built.

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 structure first to understand what kind of agent this is and how it's built.

command

ls -la /home/user/worktrees/attempt-20dc28c6-389b-42e3-8e37-2c747b6b725c-cap-0/

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 structure first to understand what kind of agent this is and how it's built.

command

find /home/user/worktrees/attempt-20dc28c6-389b-42e3-8e37-2c747b6b725c-cap-0 -type f -name "*.py" | head -20

Tool result summary: /home/user/worktrees/attempt-20dc28c6-389b-42e3-8e37-2c747b6b725c-cap-0/rag_chatbot/__init__.py /home/user/worktrees/attempt-20dc28c6-389b-42e3-8e37-2c747b6b725c-cap-0/rag_chatbot/__main__.py /home/user/worktrees/attempt-20dc28c6-389b-42e3-8e37-2c747b6b725c-cap-0/rag_chatbot/core/__init__.py /home/user/worktrees/attempt-20dc28c6-389b-42e3-8e37-2c747b6b725c-cap-0/rag_chatbot/core/embedding/__init__.py /home/user/workt Agent context: I'll research the repository structure first to understand what kind of agent this is and how it's built.

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 structure first to understand what kind of agent this is and how it's built.

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 structure first to understand what kind of agent this is and how it's built.

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 structure first to understand what kind of agent this is and how it's built.

command

find /home/user/worktrees/attempt-20dc28c6-389b-42e3-8e37-2c747b6b725c-cap-0 -type f -name "*.py" | grep -E "(pipeline|setting|model)" | head -15

Tool result summary: /home/user/worktrees/attempt-20dc28c6-389b-42e3-8e37-2c747b6b725c-cap-0/rag_chatbot/core/model/__init__.py /home/user/worktrees/attempt-20dc28c6-389b-42e3-8e37-2c747b6b725c-cap-0/rag_chatbot/core/model/model.py /home/user/worktrees/attempt-20dc28c6-389b-42e3-8e37-2c747b6b725c-cap-0/rag_chatbot/pipeline.py /home/user/worktrees/attempt-20dc28c6-389b-42e3-8e37-2c747b6b725c-cap-0/rag_chatbot/setting/__init__.py /home/use Agent context: I'll research the repository structure first to understand what kind of agent this is and how it's built.

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 structure first to understand what kind of agent this is and how it's built.

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 structure first to understand what kind of agent this is and how it's built.

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 structure first to understand what kind of agent this is and how it's built.

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 structure first to understand what kind of agent this is and how it's built.

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 structure first to understand what kind of agent this is and how it's built.

command

Tool result summary: /home/user/worktrees/attempt-20dc28c6-389b-42e3-8e37-2c747b6b725c-cap-0/rag_chatbot/core/embedding/embedding.py /home/user/worktrees/attempt-20dc28c6-389b-42e3-8e37-2c747b6b725c-cap-0/rag_chatbot/core/model/model.py /home/user/worktrees/attempt-20dc28c6-389b-42e3-8e37-2c747b6b725c-cap-0/rag_chatbot/core/prompt/query_gen_prompt.py /home/user/worktrees/attempt-20dc28c6-389b-42e3-8e37-2c747b6b725c-cap-0/rag_chatbot/logg Agent context: I'll research the repository structure first to understand what kind of agent this is and how it's built.

agent_action

Grep /home/user/worktrees/attempt-20dc28c6-389b-42e3-8e37-2c747b6b725c-cap-0

Tool result summary: Found 2 files rag_chatbot/core/embedding/embedding.py rag_chatbot/core/model/model.py Agent context: I'll research the repository structure first to understand what kind of agent this is and how it's built.

command

find /home/user/worktrees/attempt-20dc28c6-389b-42e3-8e37-2c747b6b725c-cap-0/rag_chatbot -type f -name "*.py" | sort

agent_action

Read rag_chatbot

Tool result summary: EISDIR: illegal operation on a directory, read '/home/user/worktrees/attempt-20dc28c6-389b-42e3-8e37-2c747b6b725c-cap-0/rag_chatbot' Agent context: I'll research the repository structure first to understand what kind of agent this is and how it's built.

agent_action

Read rag_chatbot/ui/ui.py

Tool result summary: 1 import os 2 import shutil 3 import json 4 import sys 5 import time 6 import gradio as gr 7 from dataclasses import dataclass 8 from typing import ClassVar 9 from llama_index.core.chat_engine.types import StreamingAgentChatResponse 10 from .theme import JS_LIGHT_THEME, CSS 11 from ..pipeline import LocalRAGPipeline 12 from ..logger import Logger 13 14 15 @dataclass 16 class DefaultElement: 17 DEFAULT_MESSAGE: ClassV Agent context: I'll research the repository structure first to understand what kind of agent this is and how it's built.

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 structure first to understand what kind of agent this is and how it's built.

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 structure first to understand what kind of agent this is and how it's built.

command

ls -la /home/user/worktrees/attempt-20dc28c6-389b-42e3-8e37-2c747b6b725c-cap-0/rag_chatbot/core/

Tool result summary: total 36 drwxr-xr-x 8 user user 4096 Jun 5 23:28 . drwxr-xr-x 7 user user 4096 Jun 5 23:28 .. -rw-r--r-- 1 user user 386 Jun 5 23:28 __init__.py drwxr-xr-x 2 user user 4096 Jun 5 23:28 embedding drwxr-xr-x 2 user user 4096 Jun 5 23:28 engine drwxr-xr-x 2 user user 4096 Jun 5 23:28 ingestion drwxr-xr-x 2 user user 4096 Jun 5 23:28 model drwxr-xr-x 2 user user 4096 Jun 5 23:28 prompt drwxr-xr-x 2 user user 4096 Jun 5 2 Agent context: I'll research the repository structure first to understand what kind of agent this is and how it's built.

agent_action

Read rag_chatbot/core/prompt/__init__.py

Tool result summary: 1 from .qa_prompt import get_system_prompt, get_context_prompt 2 from .query_gen_prompt import get_query_gen_prompt 3 from .select_prompt import get_single_select_prompt 4 5 __all__ = [ 6 "get_qa_and_refine_prompt", 7 "get_system_prompt", 8 "get_context_prompt", 9 "get_query_gen_prompt", 10 "get_single_select_prompt", 11 ] 12 Agent context: I'll research the repository structure first to understand what kind of agent this is and how it's built.

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 structure first to understand what kind of agent this is and how it's built.

command

Tool result summary: /home/user/worktrees/attempt-20dc28c6-389b-42e3-8e37-2c747b6b725c-cap-0/rag_chatbot/core/embedding/embedding.py:import requests /home/user/worktrees/attempt-20dc28c6-389b-42e3-8e37-2c747b6b725c-cap-0/rag_chatbot/core/embedding/embedding.py:from llama_index.embeddings.openai import OpenAIEmbedding /home/user/worktrees/attempt-20dc28c6-389b-42e3-8e37-2c747b6b725c-cap-0/rag_chatbot/core/model/model.py:from llama_index.l Agent context: I'll research the repository structure first to understand what kind of agent this is and how it's built.

command

cat /home/user/worktrees/attempt-20dc28c6-389b-42e3-8e37-2c747b6b725c-cap-0/pyproject.toml | grep -A 20 "dependencies ="

Tool result summary: 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,< Agent context: I'll research the repository structure first to understand what kind of agent this is and how it's built.

agent_action

Read rag_chatbot/ui/ui.py

agent_action

The repo is a LlamaIndex-based local RAG chatbot (Python, Ollama/OpenAI LLMs, Chrom...

The repo is a LlamaIndex-based local RAG chatbot (Python, Ollama/OpenAI LLMs, Chroma vectors, BM25+vector hybrid retrieval). No existing web access. Now let me research the actual providers, since the emphasis on "licensed, reputable sources over scraped results" is a strong differentiator. Let me load web tools.

agent_action

The licensing/quality emphasis is the deciding factor here. Let me research the mai...

The licensing/quality emphasis is the deciding factor here. Let me research the main candidates' actual documentation in parallel — focusing on which providers license premium content vs. scraping generic search results.

Valyu valyu.network web search API documentation sources licensed academic

Query: Valyu valyu.network web search API documentation sources licensed academic 8 search result links captured Tool result summary: Based on the search results, here's what I found about Valyu's web search API documentation and sources: ## Overview Valyu provides an AI Search API for building AI agents with access to web search and 36+ proprietary data sources. ## Documentation Documentation is available at https://docs.valyu.ai/, with a complete documentation index accessible at https://docs.valyu.ai/llms.txt. ## Proprietary Data Sources Valyu e Agent context: The licensing/quality emphasis is the deciding factor here. Let me research the main candidates' actual documentation in parallel — focusing on which providers license premium content vs. scraping generic search results.

Linkup linkup.so search API documentation sources pricing LangChain

Query: Linkup linkup.so search API documentation sources pricing LangChain 8 search result links captured Tool result summary: Based on the search results, here's comprehensive information about Linkup's search API, documentation, sources, pricing, and LangChain integration: ## Overview Linkup is a French AI startup that has developed a search API specifically designed for Large Language Models (LLMs), allowing them to search the web and access current information to overcome the limitation of outdated training data. ## LangChain Integration Agent context: The licensing/quality emphasis is the deciding factor here. Let me research the main candidates' actual documentation in parallel — focusing on which providers license premium content vs. scraping generic search results.

url

https://docs.valyu.network

Fetch prompt: What sources does Valyu cover? Do they license premium/proprietary content (academic papers, financial data, news)? Is there a Python SDK/API? What does an API call look like (endpoint, request/response shape)? Pricing model? AI/RAG/agent use cases, LlamaIndex/LangChain integrations? List the doc page URLs. Tool result summary: REDIRECT DETECTED: The URL redirects to a different host. Original URL: https://docs.valyu.network Redirect URL: https://docs.valyu.ai/ Status: 301 Moved Permanently To complete your request, I need to fetch content from the redirected URL. Please use WebFetch again with these parameters: - url: "https://docs.valyu.ai/" - prompt: "What sources does Valyu cover? Do they license premium/proprietary content (academic pa Agent context: The licensing/quality emphasis is the deciding factor here. Let me research the main candidates' actual documentation in parallel — focusing on which providers license premium content vs. scraping generic search results.

url

https://www.linkup.so

Fetch prompt: What sources does Linkup cover? Do they license premium/proprietary content or scrape the open web? Is there a Python SDK/API? What does an API call look like (endpoint, request/response shape)? Pricing model (per-search, subscription, free tier)? Is it built for AI/RAG/agent grounding? LlamaIndex/LangChain integrations? What's distinctive about source quality? Tool result summary: # Linkup Web Search API - Key Details ## Sources & Content The page doesn't specify which sources Linkup covers or whether they use licensed vs. scraped content. No licensing details are provided. ## Python SDK & API **Yes, Python SDK available.** Example code shown: ```python from linkup import LinkupClient client = LinkupClient(api_key="<your-api-key>") search_response = client.search( query="What is Microsoft 2024 Agent context: The licensing/quality emphasis is the deciding factor here. Let me research the main candidates' actual documentation in parallel — focusing on which providers license premium content vs. scraping generic search results.

url

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

Fetch prompt: Extract the search API endpoint URL, HTTP method, request parameters/body shape, and response shape. Include details on what fields are returned. Tool result summary: # Tavily Search API Extraction ## Endpoint - **URL:** `https://api.tavily.com/search` - **HTTP Method:** POST ## Request Body Parameters The API accepts a JSON object with the following properties: **Required:** - `query` (string): The search query to execute **Optional Parameters:** - `search_depth`: "advanced", "basic", "fast", or "ultra-fast" (default: basic) - `chunks_per_source`: Integer 1-3 (default: 3; advance Agent context: The licensing/quality emphasis is the deciding factor here. Let me research the main candidates' actual documentation in parallel — focusing on which providers license premium content vs. scraping generic search results.

url

https://docs.exa.ai/reference/search

Fetch prompt: Extract the search API endpoint URL, HTTP method, request parameters/body shape, and response shape. Include details on what content/fields are returned and how Exa indexes the web. Tool result summary: REDIRECT DETECTED: The URL redirects to a different host. Original URL: https://docs.exa.ai/reference/search Redirect URL: https://exa.ai/docs/reference/search 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/search" - prompt: "Extract the search API endpoint URL, HTTP method Agent context: The licensing/quality emphasis is the deciding factor here. Let me research the main candidates' actual documentation in parallel — focusing on which providers license premium content vs. scraping generic search results.

url

https://brave.com/search/api/

Fetch prompt: Extract: what sources/index they use (independent index vs scraped vs licensed), source quality/reputability handling, API endpoints, pricing model and free tier, AI/RAG/agent grounding use cases, content licensing details. Tool result summary: # Brave Search API: Key Technical & Business Details ## Index Source Brave operates its **own independent web index**, not a scraper. The company states: "it's our own independent index of the Web packaged with our own ranking models." The index includes **over 30 billion pages** with **100+ million daily updates**. Data comes from two sources: 1. **Web crawler**: Brave's bot crawls pages indexable by standard search Agent context: The licensing/quality emphasis is the deciding factor here. Let me research the main candidates' actual documentation in parallel — focusing on which providers license premium content vs. scraping generic search results.

url

https://docs.perplexity.ai/

Fetch prompt: Extract: what sources they cover (licensed vs scraped vs index), source quality handling, API endpoint and request/response shape, pricing model and free tier, AI/RAG/agent grounding focus, content licensing. Tool result summary: # Perplexity API Platform Analysis Based on the provided documentation overview, here's what can be determined: ## Sources Coverage The documentation doesn't specify whether sources are licensed, scraped, or indexed. However, the Search API description mentions "raw, ranked web search results with advanced filtering," suggesting real-time web access without detailing acquisition methods. ## Source Quality Handling No Agent context: The licensing/quality emphasis is the deciding factor here. Let me research the main candidates' actual documentation in parallel — focusing on which providers license premium content vs. scraping generic search results.

url

https://exa.ai/docs/reference/search

Fetch prompt: Extract the search API endpoint URL, HTTP method, request parameters/body shape, and response shape. Include details on what content/fields are returned and how Exa indexes the web. Tool result summary: # Exa Search API Specification ## Endpoint - **URL:** `https://api.exa.ai/search` - **Method:** POST - **Authentication:** API key via `x-api-key` header or `Authorization: Bearer` token ## Request Body Schema The API accepts a JSON payload with these key parameters: **Required:** - `query` (string, min 1 char): The search query string **Optional parameters include:** - `type`: Search mode—`instant`, `fast`, `auto` ( Agent context: The licensing/quality emphasis is the deciding factor here. Let me research the main candidates' actual documentation in parallel — focusing on which providers license premium content vs. scraping generic search results.

url

https://docs.tavily.com/documentation/about

Fetch prompt: What sources does Tavily cover? Does it license premium/proprietary content or scrape/index the open web? How does it ensure source quality? Is it built for AI/RAG/agent use cases? Mention LlamaIndex or LangChain integrations and Python SDK. Tool result summary: # Tavily Search Engine Overview ## Source Coverage & Content The documentation doesn't specify which particular sources Tavily covers. However, it indicates that the service "aggregates up to 20 sites per a single API call" and handles "searching, scraping, filtering and extracting the most relevant information from online sources." This suggests web-wide indexing rather than licensed proprietary content, though the Agent context: The licensing/quality emphasis is the deciding factor here. Let me research the main candidates' actual documentation in parallel — focusing on which providers license premium content vs. scraping generic search results.

url

https://docs.valyu.ai/overview

Fetch prompt: What sources/datasets does Valyu cover? Do they license premium/proprietary content (academic papers like arXiv/PubMed, SEC filings, financial data, clinical trials, patents)? Is there a Python SDK/API? What does an API call look like (endpoint URL, request parameters, response shape)? Pricing model (per-search, subscription, free tier)? Built for AI/RAG/agent grounding? LlamaIndex or LangChain integration? What's distinctive about source quality and licensing? List the specific doc page URLs. Tool result summary: # Valyu AI Search API - Overview ## Data Sources & Coverage Valyu provides access to **36+ proprietary data sources** including: - PubMed (medical research) - SEC filings (financial documents) - Clinical trials - arXiv (academic papers) - Patents - Real-time financial data - Web search results The platform licenses premium/proprietary academic and financial content through partnerships. ## SDK & API Access **Yes**, V Agent context: The licensing/quality emphasis is the deciding factor here. Let me research the main candidates' actual documentation in parallel — focusing on which providers license premium content vs. scraping generic search results.

Exa.ai sources index neural search quality licensed content open web 2026

Query: Exa.ai sources index neural search quality licensed content open web 2026 10 search result links captured Tool result summary: Based on the search results, here's what I found about Exa.ai's sources, index, neural search quality, and relation to licensed content and the open web in 2026: ## Index and Sources Exa uses neural embeddings over a proprietary index of tens of billions of pages, with Exa indexing tens of billions of web pages with minute-level refresh rates. They track, index, crawl, and monitor billions of webpages and serve them Agent context: The licensing/quality emphasis is the deciding factor here. Let me research the main candidates' actual documentation in parallel — focusing on which providers license premium content vs. scraping generic search results.

Tavily pricing API credits free tier subscription 2026

Query: Tavily pricing API credits free tier subscription 2026 10 search result links captured Tool result summary: Based on the search results, here's the current information on Tavily pricing, API credits, and free tier offerings as of 2026: ## Free Tier Tavily provides 1,000 free API credits every month with no credit card required. This free tier is available indefinitely as of May 2026. ## Free Tier Includes Tavily's free plan includes 1,000 API credits/month, community support, and search + extract endpoints. ## Paid Plans P Agent context: The licensing/quality emphasis is the deciding factor here. Let me research the main candidates' actual documentation in parallel — focusing on which providers license premium content vs. scraping generic search results.

url

https://www.valyu.ai/search-api

Fetch prompt: What sources does Valyu license (academic papers, financial data, news, proprietary datasets)? What's distinctive about source quality and licensing vs scraped web? Pricing details? Endpoints offered (DeepSearch, Contents, Answer, DeepResearch)? Tool result summary: # Valyu Search API: Information Available on Landing Page Based on the provided content, here's what can be determined: **Sources Licensed:** The page mentions "proprietary content" including "paywalled content, academic research, reports" but doesn't specify which sources are actually licensed or provide a detailed list. **Quality & Licensing Distinction:** Valyu emphasizes it offers "clean, relevant results from tr Agent context: The licensing/quality emphasis is the deciding factor here. Let me research the main candidates' actual documentation in parallel — focusing on which providers license premium content vs. scraping generic search results.

url

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

Fetch prompt: What sources does Linkup cover? Do they license premium content or scrape open web? API endpoint URL, request shape, response shape? Pricing model (per-search, free tier)? Python SDK? Built for AI/RAG? List doc page URLs. Tool result summary: # Linkup Web Search API: Key Information ## Sources & Content The provided documentation doesn't specify which sources Linkup covers, licensing details, or whether they use premium content or open web scraping. This information isn't available in the introduction page. ## API Endpoints Linkup offers four endpoints: - **Search**: "Query → ranked URLs, sourced answer, or structured JSON" (<1s–~30s, sync) - **Fetch**: " Agent context: The licensing/quality emphasis is the deciding factor here. Let me research the main candidates' actual documentation in parallel — focusing on which providers license premium content vs. scraping generic search results.

url

https://www.linkup.so/pricing

Fetch prompt: What is the exact pricing model? Per-search cost, subscription tiers, free tier / free credits? Standard vs deep search pricing? Tool result summary: # Linkup Pricing Model Based on the page content, here's the pricing structure: ## Pay-Per-Use Model Linkup operates on a "pay as you go" basis with no subscription tiers mentioned. Users get "4,000 queries for free" before charges begin. ## Per-Request Pricing by Service: **Fetch** (URL content extraction): "$0.001 - $0.005" per request **Search** (Web search for AI agents): "$0.005 - $0.006" per request with three Agent context: The licensing/quality emphasis is the deciding factor here. Let me research the main candidates' actual documentation in parallel — focusing on which providers license premium content vs. scraping generic search results.

url

https://docs.perplexity.ai/getting-started/pricing

Fetch prompt: Extract pricing details: per-request costs, subscription tiers, free tier/credits, token costs for Sonar models, and any per-search citation pricing. Tool result summary: # Perplexity API Pricing Summary ## Per-Request Costs **Agent API Tools:** - Web search: $0.005 per invocation - Fetch URL: $0.0005 per invocation - People search: $0.005 per invocation - Finance search: $0.005 per invocation - Sandbox: $0.03 per session (20-minute billing window) **Search API:** "$5.00" per 1,000 requests with no token-based charges ## Sonar Model Token Costs | Model | Input | Output | Citations | R Agent context: The licensing/quality emphasis is the deciding factor here. Let me research the main candidates' actual documentation in parallel — focusing on which providers license premium content vs. scraping generic search results.

url

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

Fetch prompt: Extract the API endpoint URL, request shape (model, messages, parameters), and response shape including citations/search_results. Note any Python SDK or OpenAI-compatible client usage. Tool result summary: # Perplexity AI API Summary ## Endpoint **URL:** `https://api.perplexity.ai/v1/sonar` (POST) ## Request Shape **Required fields:** - `model`: string (enum: sonar, sonar-pro, sonar-deep-research, sonar-reasoning-pro) - `messages`: array of ChatMessage objects with role and content **Key optional parameters:** - `max_tokens`: integer (max 128000) - `temperature`: number (0-2 range) - `top_p`: number (0-1 range) - `stre Agent context: The licensing/quality emphasis is the deciding factor here. Let me research the main candidates' actual documentation in parallel — focusing on which providers license premium content vs. scraping generic search results.

Perplexity Sonar API LlamaIndex LangChain integration licensed sources publishers

Query: Perplexity Sonar API LlamaIndex LangChain integration licensed sources publishers 10 search result links captured Tool result summary: Based on the search results, here's what I found about Perplexity Sonar API integration with LlamaIndex and LangChain: ## LlamaIndex Integration Perplexity models can easily be wrapped into a llamaindex tool so that it can be called as part of your data processing or conversational workflows. This tool uses real-time generative search powered by Perplexity, and it's configured with the updated default model ("sonar-p Agent context: The licensing/quality emphasis is the deciding factor here. Let me research the main candidates' actual documentation in parallel — focusing on which providers license premium content vs. scraping generic search results.

Brave Search API LangChain LlamaIndex integration Python SDK 2026

Query: Brave Search API LangChain LlamaIndex integration Python SDK 2026 10 search result links captured Tool result summary: Based on the search results, here's what I found about Brave Search API integration with LangChain and LlamaIndex: ## Integration Overview LangChain supports integration with Brave search using Python, and the Brave Search API has official integrations available for major frameworks including LangChain and LlamaIndex. ## Framework Support Native support for LangChain and LlamaIndex reduces integration time from days Agent context: The licensing/quality emphasis is the deciding factor here. Let me research the main candidates' actual documentation in parallel — focusing on which providers license premium content vs. scraping generic search results.

url

https://exa.ai/docs/reference/pricing

Fetch prompt: Extract Exa's pricing model: per-search costs, subscription tiers, free tier. Include any per-1000-search rates and content extraction costs. Tool result summary: Request failed with status code 404 Agent context: The licensing/quality emphasis is the deciding factor here. Let me research the main candidates' actual documentation in parallel — focusing on which providers license premium content vs. scraping generic search results.

url

https://docs.valyu.ai/api-reference/endpoint/deepsearch

Fetch prompt: Give the exact endpoint URL, HTTP method, request parameters/body (query, search_type, etc.), and response shape (fields like results, title, url, content, relevance score). What sources can search_type access (web, proprietary, all)? Tool result summary: Request failed with status code 404 Agent context: The licensing/quality emphasis is the deciding factor here. Let me research the main candidates' actual documentation in parallel — focusing on which providers license premium content vs. scraping generic search results.

Tavily web search API sources licensed content vs scraped open web quality grounding

Query: Tavily web search API sources licensed content vs scraped open web quality grounding 10 search result links captured Tool result summary: Based on the search results, here's what I found about Tavily's web search API regarding sources, content, and grounding: ## Content Sources and Approach Tavily is the web access layer built for AI agents, providing search, extraction, and crawling of real-time web data in formats optimized for RAG and agent workflows. Rather than using exclusively licensed content, Tavily provides fast, reliable, up-to-date access t Agent context: The licensing/quality emphasis is the deciding factor here. Let me research the main candidates' actual documentation in parallel — focusing on which providers license premium content vs. scraping generic search results.

url

https://docs.linkup.so/pages/documentation/endpoints/search/reference

Fetch prompt: Give the exact endpoint URL, HTTP method, request body parameters (q, depth, outputType, etc.), and response shape (fields). What output types are supported? Tool result summary: # Linkup /search Endpoint **Endpoint URL & Method:** `POST https://api.linkup.so/v1/search` **Required Request Parameters:** - `q` (string): "The natural language question for which you want to retrieve context" - `depth` (string): One of `fast`, `standard`, or `deep` - `outputType` (string): One of `searchResults`, `sourcedAnswer`, or `structured` **Optional Request Parameters:** - `structuredOutputSchema` (JSON str Agent context: The licensing/quality emphasis is the deciding factor here. Let me research the main candidates' actual documentation in parallel — focusing on which providers license premium content vs. scraping generic search results.

Perplexity Publishers Program revenue sharing licensed content sources Sonar API gr...

Query: Perplexity Publishers Program revenue sharing licensed content sources Sonar API grounding 10 search result links captured Tool result summary: Based on the search results, here's what I found regarding Perplexity's Publishers Program, revenue sharing, licensed content sources, and Sonar API grounding: ## Publishers Program & Revenue Sharing The 80/20 revenue split gives publishers 80% of subscription revenue attributed to their cited content, with over 2,400 publishers participating as of Q1 2026. Perplexity has allocated $42.5 million for a revenue sharing Agent context: The licensing/quality emphasis is the deciding factor here. Let me research the main candidates' actual documentation in parallel — focusing on which providers license premium content vs. scraping generic search results.

url

https://docs.valyu.ai/quickstart

Fetch prompt: Show the exact Python SDK usage and REST API call for DeepSearch: endpoint URL, method, request parameters (query, search_type values like 'web'/'proprietary'/'all', max_num_results, relevance_threshold), and response shape fields (results, title, url, content, source, relevance_score). What proprietary sources/datasets are listed? Tool result summary: # Valyu AI Search API Summary ## Python SDK Usage ```python from valyu import Valyu valyu = Valyu("your-api-key-here") response = valyu.search( "Implementation details of agentic search-enhanced large reasoning models", search_type="proprietary", max_num_results=10, relevance_threshold=0.5, category="agentic retrieval-augmented generation", included_sources=["valyu/valyu-arxiv"], is_tool_call=True ) ``` ## REST API C Agent context: The licensing/quality emphasis is the deciding factor here. Let me research the main candidates' actual documentation in parallel — focusing on which providers license premium content vs. scraping generic search results.

url

https://exa.ai/pricing

Fetch prompt: Extract Exa's pricing model: per-search or per-1000-search costs, subscription tiers, free tier credits, and content extraction costs. Tool result summary: # Exa Pricing Model Summary ## Pay-Per-Request Structure Exa operates on a **per-1,000-requests pricing model** rather than subscriptions: - **Search**: "$7/1k requests" - **Deep Search**: "$12–15/1k requests" - **Contents**: "$1/1k pages per content type" - **Monitors**: "$15/1k requests" ## Free Tier "Run up to 1,000 requests per month for free" with access to core search features. ## Additional Costs - **Extra res Agent context: The licensing/quality emphasis is the deciding factor here. Let me research the main candidates' actual documentation in parallel — focusing on which providers license premium content vs. scraping generic search results.

agent_action

I've researched the repo and six providers' live documentation. Here's my recommend...

I've researched the repo and six providers' live documentation. Here's my recommendation. ## Recommendation: **Valyu** For this repository — a LlamaIndex RAG chatbot that retrieves passages, reranks them, and feeds them to a local LLM — **Valyu** (valyu.ai) is the best fit, specifically because your priority is *licensed, reputable sources over scraped search results*. **Why it wins for this codebase:** - **Licensed, not scraped.** Valyu's pitch is literally "One API. No scraping. No noise." It licenses arXiv, PubMed, SEC filings, clinical trials, patents, and real-time financial data through partnerships — exactly the "high-quality, reputable, licensed" requirement. A `search_type=proprietary` mode isolates licensed content from open web. - **Returns passages, not answers.** It gives `{title, url, content, relevance_score}` plus scholarly metadata (DOI, authors, citation_count). That drops cleanly into your existing retrieve → `SentenceTransformerRerank` → `CondensePlusContextChatEngine` pipeline. No architectural fight. - **Stack match.** Native **LlamaIndex** integration (the only one of the six besides Tavily/Perplexity to advertise it), Python SDK (`pip install valyu`), `$10`

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