Delete config.json

config.json

@@ -1,16 +0,0 @@
-{
-  "name": "My Custom Space",
-  "description": "",
-  "system_prompt": "You are an advanced research assistant specializing in academic literature search and analysis. Your expertise includes finding peer-reviewed sources, critically evaluating research methodology, synthesizing insights across multiple papers, and providing properly formatted citations. When responding, ground all claims in specific sources from provided URL contexts, distinguish between direct evidence and analytical interpretation, and highlight any limitations or conflicting findings. Use clear, accessible language that makes complex research understandable, and suggest related areas of inquiry when relevant. Your goal is to be a knowledgeable research partner who helps users navigate academic information with precision and clarity.",
-  "model": "google/gemini-2.0-flash-001",
-  "api_key_var": "OPENROUTER_API_KEY",
-  "temperature": 0.7,
-  "max_tokens": 1500,
-  "examples": "[\"Hello! How can you help me?\", \"Tell me something interesting\", \"What can you do?\"]",
-  "grounding_urls": "[]",
-  "access_code": "",
-  "enable_dynamic_urls": true,
-  "enable_vector_rag": false,
-  "enable_web_search": true,
-  "rag_data_json": "{\"index_base64\": \"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\", \"chunks\": {\"8570c8c5\": {\"text\": \"Vector Database Test Document This is a test document for evaluating the vector database functionality. Section 1: Introduction to Vector Databases Vector databases store and query high-dimensional vector representations of data. They enable semantic search by finding vectors similar to a query vector in an embedding space. Section 2: Use Cases Common applications include: - Document retrieval and question answering - Similarity search for products or content - Recommendation systems - Semantic search in chatbots Section 3: Technical Implementation Vector databases typically use embedding models to convert text into dense vectors, then use algorithms like cosine similarity or approximate nearest neighbor search to find relevant results. Section 4: Benefits - Semantic understanding beyond keyword matching - Scalable retrieval for large document collections - Integration with modern AI systems and large language models - Support for multi-modal data (text, images, audio) This document should generate multiple chunks when processed by the system.\", \"metadata\": {\"file_path\": \"/private/var/folders/0m/_clrz0_d1tzf_fns8rxyy1jr0000gn/T/gradio/c4c745f9c7f069f694a492715df7f50d07f18cee76e93e198029acd8a6c38532/doc.txt\", \"file_name\": \"doc.txt\", \"chunk_index\": 0, \"start_word\": 0, \"word_count\": 151}, \"chunk_id\": \"8570c8c5\"}}, \"chunk_ids\": [\"8570c8c5\"], \"dimension\": 384, \"model_name\": \"all-MiniLM-L6-v2\"}"
-}