remove unsued file

ragatouille_search.py

@@ -1,109 +0,0 @@
-from pathlib import Path
-from typing import Any, Dict, List
-
-import gradio as gr
-from huggingface_hub import snapshot_download
-from ragatouille import RAGPretrainedModel
-from toolz import unique
-
-# Top-level variables
-INDEX_PATH = Path(".ragatouille/colbert/indexes/my_index_with_ids_and_metadata/")
-REPO_ID = "davanstrien/search-index"
-
-INITIAL_QUERY = "hello world"
-DEFAULT_K = 10
-
-
-def initialize_index():
-    INDEX_PATH.mkdir(parents=True, exist_ok=True)
-    snapshot_download(REPO_ID, repo_type="dataset", local_dir=INDEX_PATH)
-    rag = RAGPretrainedModel.from_index(INDEX_PATH)
-    # Warm up index
-    rag.search(INITIAL_QUERY)
-    return rag
-
-
-def format_results_as_markdown(results: List[Dict[str, Any]]) -> str:
-    markdown = ""
-    for result in results:
-        content = result["content"]
-        score = result["score"]
-        rank = result["rank"]
-        document_id = result["document_id"]
-        passage_id = result["passage_id"]
-        link = f"https://huggingface.co/datasets/{document_id}"
-
-        markdown += f"### Result {rank}\n"
-        markdown += f"**Score:** {score}\n\n"
-        markdown += f"**Document ID:** [{document_id}]({link})\n\n"
-        markdown += f"**Passage ID:** {passage_id}\n\n"
-
-        # Limit initial content display to 1000 characters
-        preview = f"{content[:1000]}..." if len(content) > 1000 else content
-        markdown += f"{preview}\n\n"
-
-        # Add expandable section for full content if it's longer than 1000 characters
-        if len(content) > 1000:
-            markdown += "<details>\n"
-            markdown += "<summary>Click to expand full content</summary>\n\n"
-            markdown += f"{content}\n\n"
-            markdown += "</details>\n\n"
-
-        markdown += "---\n\n"
-
-    return markdown
-
-
-def search_with_ragatouille(query, k=DEFAULT_K, make_unique=False):
-    results = RAG.search(query, k=k)
-    if make_unique:
-        results = make_results_unique(results)
-    return format_results_as_markdown(results)
-
-
-def make_results_unique(results: List[Dict[str, Any]]):
-    unique_results = unique(results, lambda x: x["document_id"])
-    return list(unique_results)
-
-
-def create_ragatouille_interface():
-    with gr.Blocks() as ragatouille_demo:
-        gr.Markdown("### RAGatouille Dataset Search")
-        gr.Markdown(
-            """This interface allows you to search inside dataset cards on the Hub using the [answerai-colbert-small-v1](https://huggingface.co/answerdotai/answerai-colbert-small-v1) ColBERT model via [RAGatouille](https://github.com/AnswerDotAI/RAGatouille). Please be aware that this is an early prototype and may not work as expected!
-
-            ## Notes:
-            **Not all datasets are indexed yet!**
-            For a dataset to be indexed:
-            - It must have a dataset card on the Hub. You can find documentation on how to write a good dataset card [here](https://huggingface.co/docs/hub/datasets-cards).
-            - The dataset must have at least 1 like and 1 download
-            - The card must be a minimum length (to weed out low quality cards)
-            **At the moment the index is refreshed when I decide to do it, so it may not be up to date.** If there is sufficient interest I will implement a daily refresh (give this repo a like if you'd like this feature!)
-            Feel free to open a discussion to give feedback or request features &#129303;
-            """
-        )
-        with gr.Column():
-            query = gr.Textbox(label="Search query", placeholder="medieval handwriting")
-        with gr.Row():
-            k = gr.Slider(1, 100, value=DEFAULT_K, step=1, label="Number of Results")
-            make_unique = gr.Checkbox(False, label="Show each dataset only once?")
-        search_button = gr.Button("Search")
-        search_button.click(
-            search_with_ragatouille,
-            inputs=[query, k, make_unique],
-            outputs=gr.Markdown(label="Results"),
-        )
-    return ragatouille_demo
-
-
-# Initialize RAG globally
-RAG = initialize_index()
-
-
-def main():
-    demo = create_ragatouille_interface()
-    demo.launch()
-
-
-if __name__ == "__main__":
-    main()