{
    "query": "You are a super intelligent assistant. Please answer all my questions precisely and comprehensively.\n\nThrough our system KIOS you have a Knowledge Base named ILB-30.10.2024 with all the informations that the user requests. In this knowledge base are following Documents \n\nThis is the initial message to start the chat. Based on the following summary/context you should formulate an initial message greeting the user with the following user name [Gender] [Vorname] [Surname] tell them that you are the AI Chatbot Simon using the Large Language Model [Used Model] to answer all questions.\n\nFormulate the initial message in the Usersettings Language German\n\nPlease use the following context to suggest some questions or topics to chat about this knowledge base. List at least 3-10 possible topics or suggestions up and use emojis. The chat should be professional and in business terms.  At the end ask an open question what the user would like to check on the list. Please keep the wildcards incased in brackets and make it easy to replace the wildcards. \n\n The provided context contains several files related to AI models and copyright law. \n\n**File: TA-9-2024-0138_EN.pdf (Page 98)**\n\nThis file discusses the need for transparency in the data used to train general-purpose AI models. It proposes that providers of such models should create and publicly share a detailed summary of the training data, including the main data collections or sets used. This summary should be comprehensive in scope but not technically detailed, allowing copyright holders to exercise and enforce their rights. The AI Office should provide a template for this summary.\n\n**File: ilb_merkblatt_auflagen-im-zuwendungsbescheid-ergaenzende-hinweise-zu-ausgabebelegen_st2211160827.pdf (Page 1)**\n\nThis file appears to be a table formatted in Markdown, but the content is not visible. It is likely a table related to financial data or a budget.\n\n**File: ilb_anlag_beiblaetter-ausgaben-big-digital_w2404160845_3.0.xlsx (Page 1)**\n\nThis file also appears to be a table formatted in Markdown, but the content is not visible. It is likely a table related to financial data or a budget.\n\n**File: docs-pinecone-io-examples-sample-apps-namespace-notes-further-optimizations-for-the-rag-pipeline-44536.txt (Page 1)**\n\nThis file provides code snippets and explanations related to a Retrieval Augmented Generation (RAG) pipeline. It focuses on document deletion and management within a Pinecone namespace. The code demonstrates how to identify chunks associated with a specific document using id prefixing and perform targeted deletions.\n\n**File: docs-pinecone-io-examples-sample-apps-namespace-notes-43975.txt (Page 1)**\n\nThis file also provides code snippets and explanations related to a RAG pipeline. It focuses on creating a prompt for an AI assistant, incorporating a CONTEXT BLOCK that includes system prompt instructions and the provided context. The code ensures that the AI assistant does not share external URLs, apologizes, or invents information.\n\n**File: docs-pinecone-io-examples-sample-apps-namespace-notes-project-structure-44597.txt (Page 1)**\n\nThis file provides code snippets and explanations related to a RAG pipeline. It focuses on document deletion and management within a Pinecone namespace. The code demonstrates how to identify chunks associated with a specific document using id prefixing and perform targeted deletions.\n\n**File: docs-pinecone-io-examples-sample-apps-namespace-notes-built-with-44594.txt (Page 1)**\n\nThis file provides code snippets and explanations related to a RAG pipeline. It focuses on creating a prompt for an AI assistant, incorporating a CONTEXT BLOCK that includes system prompt instructions and the provided context. The code ensures that the AI assistant does not share external URLs, apologizes, or invents information.\n\n**File: docs-pinecone-io-examples-sample-apps-namespace-notes-start-the-project-44524.txt (Page 1)**\n\nThis file provides code snippets and explanations related to a RAG pipeline. It focuses on creating a prompt for an AI assistant, incorporating a CONTEXT BLOCK that includes system prompt instructions and the provided context. The code ensures that the AI assistant does not share external URLs, apologizes, or invents information.\n\n**File: docs-pinecone-io-examples-sample-apps-namespace-notes-troubleshooting-44601.txt (Page 1)**\n\nThis file provides code snippets and explanations related to a RAG pipeline. It focuses on creating a prompt for an AI assistant, incorporating a CONTEXT BLOCK that includes system prompt instructions and the provided context. The code ensures that the AI assistant does not share external URLs, apologizes, or invents information.\n\n**File: docs-pinecone-io-examples-sample-apps-namespace-notes-run-the-sample-app-44523.txt (Page 1)**\n\nThis file provides code snippets and explanations related to a RAG pipeline. It focuses on creating a prompt for an AI assistant, incorporating a CONTEXT BLOCK that includes system prompt instructions and the provided context. The code ensures that the AI assistant does not share external URLs, apologizes, or invents information.\n\n**File: docs-pinecone-io-examples-sample-apps-namespace-notes-simple-multi-tenant-rag-methodology-44526.txt (Page 1)**\n\nThis file provides code snippets and explanations related to a RAG pipeline. It focuses on creating a prompt for an AI assistant, incorporating a CONTEXT BLOCK that includes system prompt instructions and the provided context. The code ensures that the AI assistant does not share external URLs, apologizes, or invents information.\n\n**File: docs-pinecone-io-examples-sample-apps-namespace-notes-create-a-pinecone-serverless-index-44622.txt (Page 1)**\n\nThis file provides code snippets and explanations related to a RAG pipeline. It focuses on creating a prompt for an AI assistant, incorporating a CONTEXT BLOCK that includes system prompt instructions and the provided context. The code ensures that the AI assistant does not share external URLs, apologizes, or invents information.\n\n**File: docs-pinecone-io-examples-sample-apps-namespace-notes-get-your-api-key-44621.txt (Page 1)**\n\nThis file provides code snippets and explanations related to a RAG pipeline. It focuses on creating a prompt for an AI assistant, incorporating a CONTEXT BLOCK that includes system prompt instructions and the provided context. The code ensures that the AI assistant does not share external URLs, apologizes, or invents information.\n\nThe context primarily focuses on the use of Pinecone for storing and retrieving information for a Retrieval Augmented Generation (RAG) pipeline. The code snippets demonstrate how to chunk text, embed it using OpenAI's text-embedding-3-small model, and upsert the embeddings into a Pinecone namespace. The context also highlights the importance of id prefixing for targeted document management and the need to consider LLM context windows when retrieving and presenting information. \n",
    "namespace": "5bc3d877-9ebf-4c01-a67f-8ea66cc90d64",
    "messages": [],
    "stream": false,
    "language_level": "",
    "chat_channel": "",
    "language": "German",
    "tone": "neutral",
    "writing_style": "standard",
    "model": "gemini-1.5-flash",
    "knowledgebase": "ki-dev-large",
    "seed": 0,
    "client_id": 0,
    "all_context": true,
    "follow_up_for": null,
    "knowledgebase_files_count": 0,
    "override_command": "",
    "disable_clarity_check": true,
    "custom_primer": "",
    "logging": true,
    "query_route": ""
}


INITIALIZATION
Knowledgebase: ki-dev-large
Base Query: You are a super intelligent assistant. Please answer all my questions precisely and comprehensively.

Through our system KIOS you have a Knowledge Base named ILB-30.10.2024 with all the informations that the user requests. In this knowledge base are following Documents 

This is the initial message to start the chat. Based on the following summary/context you should formulate an initial message greeting the user with the following user name [Gender] [Vorname] [Surname] tell them that you are the AI Chatbot Simon using the Large Language Model [Used Model] to answer all questions.

Formulate the initial message in the Usersettings Language German

Please use the following context to suggest some questions or topics to chat about this knowledge base. List at least 3-10 possible topics or suggestions up and use emojis. The chat should be professional and in business terms.  At the end ask an open question what the user would like to check on the list. Please keep the wildcards incased in brackets and make it easy to replace the wildcards. 

 The provided context contains several files related to AI models and copyright law. 

**File: TA-9-2024-0138_EN.pdf (Page 98)**

This file discusses the need for transparency in the data used to train general-purpose AI models. It proposes that providers of such models should create and publicly share a detailed summary of the training data, including the main data collections or sets used. This summary should be comprehensive in scope but not technically detailed, allowing copyright holders to exercise and enforce their rights. The AI Office should provide a template for this summary.

**File: ilb_merkblatt_auflagen-im-zuwendungsbescheid-ergaenzende-hinweise-zu-ausgabebelegen_st2211160827.pdf (Page 1)**

This file appears to be a table formatted in Markdown, but the content is not visible. It is likely a table related to financial data or a budget.

**File: ilb_anlag_beiblaetter-ausgaben-big-digital_w2404160845_3.0.xlsx (Page 1)**

This file also appears to be a table formatted in Markdown, but the content is not visible. It is likely a table related to financial data or a budget.

**File: docs-pinecone-io-examples-sample-apps-namespace-notes-further-optimizations-for-the-rag-pipeline-44536.txt (Page 1)**

This file provides code snippets and explanations related to a Retrieval Augmented Generation (RAG) pipeline. It focuses on document deletion and management within a Pinecone namespace. The code demonstrates how to identify chunks associated with a specific document using id prefixing and perform targeted deletions.

**File: docs-pinecone-io-examples-sample-apps-namespace-notes-43975.txt (Page 1)**

This file also provides code snippets and explanations related to a RAG pipeline. It focuses on creating a prompt for an AI assistant, incorporating a CONTEXT BLOCK that includes system prompt instructions and the provided context. The code ensures that the AI assistant does not share external URLs, apologizes, or invents information.

**File: docs-pinecone-io-examples-sample-apps-namespace-notes-project-structure-44597.txt (Page 1)**

This file provides code snippets and explanations related to a RAG pipeline. It focuses on document deletion and management within a Pinecone namespace. The code demonstrates how to identify chunks associated with a specific document using id prefixing and perform targeted deletions.

**File: docs-pinecone-io-examples-sample-apps-namespace-notes-built-with-44594.txt (Page 1)**

This file provides code snippets and explanations related to a RAG pipeline. It focuses on creating a prompt for an AI assistant, incorporating a CONTEXT BLOCK that includes system prompt instructions and the provided context. The code ensures that the AI assistant does not share external URLs, apologizes, or invents information.

**File: docs-pinecone-io-examples-sample-apps-namespace-notes-start-the-project-44524.txt (Page 1)**

This file provides code snippets and explanations related to a RAG pipeline. It focuses on creating a prompt for an AI assistant, incorporating a CONTEXT BLOCK that includes system prompt instructions and the provided context. The code ensures that the AI assistant does not share external URLs, apologizes, or invents information.

**File: docs-pinecone-io-examples-sample-apps-namespace-notes-troubleshooting-44601.txt (Page 1)**

This file provides code snippets and explanations related to a RAG pipeline. It focuses on creating a prompt for an AI assistant, incorporating a CONTEXT BLOCK that includes system prompt instructions and the provided context. The code ensures that the AI assistant does not share external URLs, apologizes, or invents information.

**File: docs-pinecone-io-examples-sample-apps-namespace-notes-run-the-sample-app-44523.txt (Page 1)**

This file provides code snippets and explanations related to a RAG pipeline. It focuses on creating a prompt for an AI assistant, incorporating a CONTEXT BLOCK that includes system prompt instructions and the provided context. The code ensures that the AI assistant does not share external URLs, apologizes, or invents information.

**File: docs-pinecone-io-examples-sample-apps-namespace-notes-simple-multi-tenant-rag-methodology-44526.txt (Page 1)**

This file provides code snippets and explanations related to a RAG pipeline. It focuses on creating a prompt for an AI assistant, incorporating a CONTEXT BLOCK that includes system prompt instructions and the provided context. The code ensures that the AI assistant does not share external URLs, apologizes, or invents information.

**File: docs-pinecone-io-examples-sample-apps-namespace-notes-create-a-pinecone-serverless-index-44622.txt (Page 1)**

This file provides code snippets and explanations related to a RAG pipeline. It focuses on creating a prompt for an AI assistant, incorporating a CONTEXT BLOCK that includes system prompt instructions and the provided context. The code ensures that the AI assistant does not share external URLs, apologizes, or invents information.

**File: docs-pinecone-io-examples-sample-apps-namespace-notes-get-your-api-key-44621.txt (Page 1)**

This file provides code snippets and explanations related to a RAG pipeline. It focuses on creating a prompt for an AI assistant, incorporating a CONTEXT BLOCK that includes system prompt instructions and the provided context. The code ensures that the AI assistant does not share external URLs, apologizes, or invents information.

The context primarily focuses on the use of Pinecone for storing and retrieving information for a Retrieval Augmented Generation (RAG) pipeline. The code snippets demonstrate how to chunk text, embed it using OpenAI's text-embedding-3-small model, and upsert the embeddings into a Pinecone namespace. The context also highlights the importance of id prefixing for targeted document management and the need to consider LLM context windows when retrieving and presenting information. 

Model: gemini-1.5-flash
**Elapsed Time: 0.00 seconds**