13 posts tagged with "LLM"

[anythingllm] | [backend] info: [AgentLLM - tensorzero::model_name::openrouter::openai/gpt-oss-20b] Untooled.stream - will process this chat completion.
[anythingllm] | [backend] info: [AgentLLM - tensorzero::model_name::openrouter::openai/gpt-oss-20b] Invalid function tool call: Missing name or arguments in function call..
[anythingllm] | [backend] info: [AgentLLM - tensorzero::model_name::openrouter::openai/gpt-oss-20b] Will assume chat completion without tool call inputs

You are a program which picks the most optimal function and parameters to call.
      DO NOT HAVE TO PICK A FUNCTION IF IT WILL NOT HELP ANSWER OR FULFILL THE USER'S QUERY.
      When a function is selection, respond in JSON with no additional text.
      When there is no relevant function to call - return with a regular chat text response.
      Your task is to pick a **single** function that we will use to call, if any seem useful or relevant for the user query.

      All JSON responses should have two keys.
      'name': this is the name of the function name to call. eg: 'web-scraper', 'rag-memory', etc..
      'arguments': this is an object with the function properties to invoke the function.
      DO NOT INCLUDE ANY OTHER KEYS IN JSON RESPONSES.

      Here are the available tools you can use an examples of a query and response so you can understand how each one works.
      -----------
Function name: rag-memory
Function Description: Search against local documents for context that is relevant to the query or store a snippet of text into memory for retrieval later. Storing information should only be done when the user specifically requests for information to be remembered or saved to long-term memory. You should use this tool before search the internet for information. Do not use this tool unless you are explicitly told to 'remember' or 'store' information.
Function parameters in JSON format:
{
    "action": {
        "type": "string",
        "enum": [
            "search",
            "store"
        ],
        "description": "The action we want to take to search for existing similar context or storage of new context."
    },
    "content": {
        "type": "string",
        "description": "The plain text to search our local documents with or to store in our vector database."
    }
}
Query: "What is AnythingLLM?"
JSON: {"name":"rag-memory","arguments":{"action":"search","content":"What is AnythingLLM?"}}
Query: "What do you know about Plato's motives?"
JSON: {"name":"rag-memory","arguments":{"action":"search","content":"What are the facts about Plato's motives?"}}
Query: "Remember that you are a robot"
JSON: {"name":"rag-memory","arguments":{"action":"store","content":"I am a robot, the user told me that i am."}}
Query: "Save that to memory please."
JSON: {"name":"rag-memory","arguments":{"action":"store","content":"<insert summary of conversation until now>"}}
-----------
-----------
Function name: document-summarizer
Function Description: Can get the list of files available to search with descriptions and can select a single file to open and summarize.
Function parameters in JSON format:
{
    "action": {
        "type": "string",
        "enum": [
            "list",
            "summarize"
        ],
        "description": "The action to take. 'list' will return all files available with their filename and descriptions. 'summarize' will open and summarize the file by the a document name."
    },
    "document_filename": {
        "type": "string",
        "x-nullable": true,
        "description": "The file name of the document you want to get the full content of."
    }
}
Query: "Summarize example.txt"
JSON: {"name":"document-summarizer","arguments":{"action":"summarize","document_filename":"example.txt"}}
Query: "What files can you see?"
JSON: {"name":"document-summarizer","arguments":{"action":"list","document_filename":null}}
Query: "Tell me about readme.md"
JSON: {"name":"document-summarizer","arguments":{"action":"summarize","document_filename":"readme.md"}}
-----------
-----------
Function name: web-scraping
Function Description: Scrapes the content of a webpage or online resource from a provided URL.
Function parameters in JSON format:
{
    "url": {
        "type": "string",
        "format": "uri",
        "description": "A complete web address URL including protocol. Assumes https if not provided."
    }
}
Query: "What is anythingllm.com about?"
JSON: {"name":"web-scraping","arguments":{"url":"https://anythingllm.com"}}
Query: "Scrape https://example.com"
JSON: {"name":"web-scraping","arguments":{"url":"https://example.com"}}
-----------


      Now pick a function if there is an appropriate one to use given the last user message and the given conversation so far.

Given the following conversation, relevant context, and a follow up question, reply with an answer to the current question the user is asking. Return only your response to the question given the above information following the users instructions as needed.

[anythingllm] | [backend] info: [AgentLLM - tensorzero::model_name::openrouter::qwen/qwen3.5-35b-a3b] Untooled.stream - will process this chat completion.
[anythingllm] | [backend] info: [AgentLLM - tensorzero::model_name::openrouter::qwen/qwen3.5-35b-a3b] Valid tool call found - running web-scraping.
[anythingllm] | [backend] info: [AgentHandler] [debug]: @agent is attempting to call `web-scraping` tool {
[anythingllm] |   "url": "https://flyskypie.github.io/posts/2026-02-26_storage-levels/"
[anythingllm] | }
[anythingllm] | [backend] info: [EncryptionManager] Loaded existing key & salt for encrypting arbitrary data.
[anythingllm] | [collector] info: -- Working URL https://flyskypie.github.io/posts/2026-02-26_storage-levels => (captureAs: text) --
[anythingllm] | [collector] info: -- URL determined to be text/html (web) --
[anythingllm] | [backend] info: [TokenManager] Initialized new TokenManager instance for model: tensorzero::model_name::openrouter::qwen/qwen3.5-35b-a3b
[anythingllm] | [backend] info: [AgentLLM - tensorzero::model_name::openrouter::qwen/qwen3.5-35b-a3b] Untooled.stream - will process this chat completion.
[anythingllm] | [backend] info: [AgentLLM - tensorzero::model_name::openrouter::qwen/qwen3.5-35b-a3b] Cannot call web-scraping again because an exact duplicate of previous run of web-scraping.
[anythingllm] | [backend] info: [AgentLLM - tensorzero::model_name::openrouter::qwen/qwen3.5-35b-a3b] Will assume chat completion without tool call inputs.
[anythingllm] | [backend] info: [TELEMETRY SENT] {"event":"agent_chat_sent","distinctId":"9d1b8903-e002-42b7-8ea2-222fedeec43e","properties":{"runtime":"docker"}}
[anythingllm] | prisma:info Starting a sqlite pool with 25 connections.
[anythingllm] | [backend] info: [113:248]: No direct uploads path found - exiting.
[anythingllm] | [bg-worker][cleanup-orphan-documents] info: [113:248]: No direct uploads path found - exiting.
[anythingllm] | [backend] warn: Child process exited with code 0 and signal null
[anythingllm] | [backend] info: Worker for job "cleanup-orphan-documents" exited with code 0
[anythingllm] | [backend] info: Client took too long to respond, chat thread is dead after 300000ms
[anythingllm] | [backend] info: [AgentHandler] End 7f77b729-bd0c-4f8d-8cb1-df5eeed30117::generic-openai:tensorzero::model_name::openrouter::qwen/qwen3.5-35b-a3b

Given the following conversation, relevant context, and a follow up question, reply with an answer to the current question the user is asking. Return only your response to the question given the above information following the users instructions as needed.
Context:
[CONTEXT 0]:
<document_metadata>
sourceDocument: Manual.pdf
published: 3/14/2026, 8:13:12 AM
</document_metadata>

Chapter 3
The Console...........................................32
Chapter 4
Component Reference..............................46
Weapon Statistics.................................61
Chapter 5
Credits..................................................62Chapter 1
4
Chapter 1
Introduction
MindRover: The Europa Project
Welcome to Europa, land of ice and more ice. With 
Jupiter constantly hovering on the horizon, we've 
found that homesickness among new arrivals is 
common, so let's just get started.
Your time here will present you with a new type of 
challenge -- one that matches the excitement of an 
action game, the planning of a strategy game and 
the intense thinking required in a puzzle game.
Your goal is to create robotic vehicles using a wide 
array of different components, program their 
behavior, then set them free to compete with each 
other. Your progress through the levels will depend 
on cleverness, innovation, and even deception as
[END CONTEXT 0]

[CONTEXT 1]:
<document_metadata>
sourceDocument: Manual.pdf
published: 3/14/2026, 8:13:12 AM
</document_metadata>

Some scenarios may ask you to build a vehicle to 
complete a series of simple tasks. Others might ask 
you to program a set of vehicles that work together 
to defeat another team. 
You can equip your vehicles with everything from 
rocket launchers to radars to speakers. You can 
program them to do anything from following a 
track, to finding a path through a maze, to seeking 
and destroying other vehicles. The behaviors you 
can create are limitless -- and the game will grow 
with your abilities.
There are five basic steps in playing MindRover.Chapter 2
8
Choose a
Scenario
First, choose a scenario or challenge. Each one has 
a different task or competition, and MindRover 
supports several different styles of scenario. 
Choose a
Vehicle
Next, you choose a chassis on which you will place 
the components for your vehicle. There are 
wheeled, treaded and hovercraft type chassis in 
varying sizes. 
Add
Components
Next you load up your vehicle with the components
[END CONTEXT 1]

[CONTEXT 2]:
<document_metadata>
sourceDocument: Manual.pdf
published: 3/14/2026, 8:13:12 AM
</document_metadata>

you tackle some of the more challenging scenarios. 
Share your successes, get advice, download new 
challenges and compete with others by visiting the 
MindRover website at www.mindrover.com.
MindRover probably isn't quite like anything you've 
seen before, so please give yourself a chance to 
learn it. Go through the in game tutorials and use 
the F1 key for help along the way.
Ready? Free your mind, grab your mouse, and 
enter into the world of MindRover!Introduction
5
Quick Start
For the fastest introduction to MindRover, follow 
these steps:
Create a new user name and log in. Your user 
name will be used to help identify the vehicles 
you build.
Go through the first 2 or 3 tutorials in the game 
following the tutorial prompts.
Click on Sports category, and try Sumo Hover. 
There is a tutorial vehicle (half-built) available 
to get you started or you can start with an 
empty chassis. 
After that you should have a pretty good idea of 
how to go off and build your own rovers.
[END CONTEXT 2]

[CONTEXT 3]:
<document_metadata>
sourceDocument: Manual.pdf
published: 3/14/2026, 8:13:12 AM
</document_metadata>

Don’t forget to visit www.mindrover.com for hints, 
tips, and competitors. You’ll find an active and 
growing MindRover community.Chapter 1
6
Using This Manual
ConceptsThe Concepts section describes essential MindRover 
concepts in some detail. You will learn about 
scenarios, vehicles, components, wiring, and 
competitions. You can read this chapter before you 
play to get a good feel for all aspects of the game. 
But if you like to jump right in and get started, just 
go to the first tutorial and come back to this chapter 
later. 
ConsoleThe Console section goes into detail on each of the 
user interface screens. You can read it before you 
start, or just use it as a reference after you have 
started playing the game. 
ComponentsThis chapter gives you specific information on each 
component in the game, listed alphabetically. 
Within the game, click on a component and press 
F1 to get more details and examples.
Start with the
tutorials
[END CONTEXT 3]