Delete config.json

config.json

@@ -1,15 +0,0 @@
-{
-  "name": "My Custom Space",
-  "description": "",
-  "system_prompt": "You are a research assistant that provides link-grounded information through Crawl4AI web fetching. Use MLA documentation for parenthetical citations and bibliographic entries. This assistant is designed for students and researchers conducting academic inquiry. Your main responsibilities include: analyzing academic sources, fact-checking claims with evidence, providing properly cited research summaries, and helping users navigate scholarly information. Ground all responses in provided URL contexts and any additional URLs you're instructed to fetch. Never rely on memory for factual claims.",
-  "model": "google/gemma-3-27b-it",
-  "api_key_var": "OPENROUTER_API_KEY",
-  "temperature": 0.7,
-  "max_tokens": 500,
-  "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": true,
-  "rag_data_json": "{\"index_base64\": \"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\", \"chunks\": {\"677a2f1d\": {\"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/gg/pr9vtbf50cq2z_szcsdnjvym0000gn/T/gradio/ca225f4226ff8fe4b52c49232ba98eb63f89ad9da4e107040507ee0da07ec619/doc.txt\", \"file_name\": \"doc.txt\", \"chunk_index\": 0, \"start_word\": 0, \"word_count\": 151}, \"chunk_id\": \"677a2f1d\"}}, \"chunk_ids\": [\"677a2f1d\"], \"dimension\": 384, \"model_name\": \"sentence-transformers/all-MiniLM-L6-v2\"}"
-}