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README.md

@@ -1,12 +1,52 @@
 ---
-title: Llmops Database Mcp
-emoji: 🐠
-colorFrom: yellow
-colorTo: gray
+title: LLMOps Database MCP Server
+emoji: 🔍
+colorFrom: blue
+colorTo: purple
 sdk: gradio
-sdk_version: 6.1.0
+sdk_version: "5.0.0"
 app_file: app.py
 pinned: false
+license: apache-2.0
+tags:
+  - mcp-server
+  - llmops
+  - datasets
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# LLMOps Database MCP Server
+
+An MCP server for querying the [ZenML LLMOps Database](https://huggingface.co/datasets/zenml/llmops-database) - a collection of 1,100+ real-world LLMOps case studies.
+
+## Tools
+
+- **search** - Search with optional filters (query, industry, company, year, tag)
+- **get_case_study_details** - Get full details of a case study
+- **get_statistics** - Database statistics
+- **list_options** - Available filter values
+
+## Use as MCP Server
+
+Add to your MCP client (Cursor, Claude Desktop, etc.):
+
+```json
+{
+  "mcpServers": {
+    "llmops-database": {
+      "command": "npx",
+      "args": [
+        "mcp-remote",
+        "https://hugobowne-llmops-database-mcp.hf.space/gradio_api/mcp/"
+      ]
+    }
+  }
+}
+```
+
+## Dataset
+
+The [ZenML LLMOps Database](https://huggingface.co/datasets/zenml/llmops-database) contains:
+- 1,100+ case studies of real-world LLM deployments
+- Metadata: company, industry, year, source URL
+- Tags: tools, techniques, applications
+- Short and full summaries

app.py

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+"""
+Gradio MCP Server for querying the ZenML LLMOps Database.
+
+Exposes 4 tools:
+1. search - flexible search with optional filters
+2. get_case_study_details - get full details of a case study
+3. get_statistics - database statistics
+4. list_options - available industries, companies, years
+"""
+
+import gradio as gr
+from datasets import load_dataset
+import pandas as pd
+
+# Load the dataset once at startup
+print("Loading ZenML LLMOps Database...")
+ds = load_dataset("zenml/llmops-database", split="train")
+df = ds.to_pandas()
+print(f"Loaded {len(df)} case studies")
+
+
+def search(
+    query: str = None,
+    industry: str = None,
+    company: str = None,
+    year: int = None,
+    tag: str = None,
+    limit: int = 20
+) -> str:
+    """
+    Search the LLMOps database with optional filters. All parameters can be combined.
+    
+    Args:
+        query: Text to search for in titles and summaries (e.g., 'RAG', 'fine-tuning', 'agents')
+        industry: Filter by industry (e.g., 'Tech', 'Finance', 'Healthcare')
+        company: Filter by company (e.g., 'meta', 'google', 'openai')
+        year: Filter by year (e.g., 2023, 2024)
+        tag: Filter by tag in any tag field (e.g., 'pytorch', 'monitoring', 'rag')
+        limit: Maximum results to return (default 20)
+    
+    Returns:
+        Matching case studies with title, company, industry, year, and summary
+    """
+    # Start with all rows
+    mask = pd.Series([True] * len(df))
+    
+    # Apply text search if provided
+    if query and query.strip():
+        query_lower = query.lower()
+        text_mask = (
+            df["title"].str.lower().str.contains(query_lower, na=False) |
+            df["short_summary"].str.lower().str.contains(query_lower, na=False) |
+            df["full_summary"].str.lower().str.contains(query_lower, na=False)
+        )
+        mask = mask & text_mask
+    
+    # Apply industry filter if provided
+    if industry and industry.strip():
+        mask = mask & df["industry"].str.lower().str.contains(industry.lower(), na=False)
+    
+    # Apply company filter if provided
+    if company and company.strip():
+        mask = mask & df["company"].str.lower().str.contains(company.lower(), na=False)
+    
+    # Apply year filter if provided
+    if year:
+        mask = mask & (df["year"] == year)
+    
+    # Apply tag filter if provided
+    if tag and tag.strip():
+        tag_lower = tag.lower()
+        tag_mask = (
+            df["tools_tags"].str.lower().str.contains(tag_lower, na=False) |
+            df["techniques_tags"].str.lower().str.contains(tag_lower, na=False) |
+            df["application_tags"].str.lower().str.contains(tag_lower, na=False) |
+            df["extra_tags"].str.lower().str.contains(tag_lower, na=False)
+        )
+        mask = mask & tag_mask
+    
+    results = df[mask].head(limit)
+    
+    if len(results) == 0:
+        filters = []
+        if query: filters.append(f"query='{query}'")
+        if industry: filters.append(f"industry='{industry}'")
+        if company: filters.append(f"company='{company}'")
+        if year: filters.append(f"year={year}")
+        if tag: filters.append(f"tag='{tag}'")
+        return f"No case studies found with filters: {', '.join(filters) if filters else 'none'}"
+    
+    output = f"Found {len(results)} case studies:\n\n"
+    for _, row in results.iterrows():
+        output += f"## {row['title']}\n"
+        output += f"**Company:** {row['company']} | **Industry:** {row['industry']} | **Year:** {row['year']}\n"
+        output += f"**Tags:** {row['application_tags']}\n"
+        output += f"**Summary:** {row['short_summary']}\n"
+        output += f"**Source:** {row['source_url']}\n\n"
+        output += "---\n\n"
+    
+    return output
+
+
+def get_case_study_details(title: str) -> str:
+    """
+    Get the full details of a specific case study by title.
+    
+    Args:
+        title: The title (or part of the title) of the case study to retrieve
+    
+    Returns:
+        Complete case study including full summary, all tags, and source URL
+    """
+    if not title or not title.strip():
+        return "Please provide a title to search for"
+    
+    mask = df["title"].str.lower().str.contains(title.lower(), na=False)
+    results = df[mask]
+    
+    if len(results) == 0:
+        return f"No case study found with title containing '{title}'"
+    
+    row = results.iloc[0]
+    
+    output = f"# {row['title']}\n\n"
+    output += f"**Company:** {row['company']}\n"
+    output += f"**Industry:** {row['industry']}\n"
+    output += f"**Year:** {row['year']}\n"
+    output += f"**Source:** {row['source_url']}\n\n"
+    output += f"## Tags\n"
+    output += f"- **Application:** {row['application_tags']}\n"
+    output += f"- **Tools:** {row['tools_tags']}\n"
+    output += f"- **Techniques:** {row['techniques_tags']}\n"
+    output += f"- **Extra:** {row['extra_tags']}\n\n"
+    output += f"## Full Summary\n\n{row['full_summary']}\n"
+    
+    return output
+
+
+def get_statistics() -> str:
+    """
+    Get statistics about the LLMOps Database.
+    
+    Returns:
+        Summary statistics including total count, breakdown by industry, year, and top companies
+    """
+    output = "# LLMOps Database Statistics\n\n"
+    output += f"**Total case studies:** {len(df)}\n\n"
+    
+    output += "## By Industry\n"
+    industry_counts = df["industry"].value_counts()
+    for industry, count in industry_counts.items():
+        output += f"- {industry}: {count}\n"
+    
+    output += "\n## By Year\n"
+    year_counts = df["year"].value_counts().sort_index()
+    for year, count in year_counts.items():
+        output += f"- {int(year)}: {count}\n"
+    
+    output += "\n## Top 15 Companies\n"
+    company_counts = df["company"].value_counts().head(15)
+    for company, count in company_counts.items():
+        output += f"- {company}: {count}\n"
+    
+    return output
+
+
+def list_options() -> str:
+    """
+    List available filter options (industries, top companies, years).
+    Use this to know what values you can filter by in the search function.
+    
+    Returns:
+        Lists of available industries, companies, and years
+    """
+    output = "# Available Filter Options\n\n"
+    
+    output += "## Industries\n"
+    for industry in df["industry"].dropna().unique():
+        output += f"- {industry}\n"
+    
+    output += "\n## Years\n"
+    for year in sorted(df["year"].dropna().unique()):
+        output += f"- {int(year)}\n"
+    
+    output += "\n## Top 30 Companies\n"
+    for company in df["company"].value_counts().head(30).index:
+        output += f"- {company}\n"
+    
+    return output
+
+
+# Create the Gradio interface
+with gr.Blocks(title="LLMOps Database MCP Server") as demo:
+    gr.Markdown("""
+    # 🔍 ZenML LLMOps Database MCP Server
+    
+    Query the [ZenML LLMOps Database](https://huggingface.co/datasets/zenml/llmops-database) - 
+    a collection of 1,100+ real-world LLMOps case studies.
+    
+    **This app is an MCP server** - add it to your AI assistant (like Cursor) to query the database!
+    """)
+    
+    with gr.Tab("Search"):
+        gr.Markdown("### Search with optional filters (all can be combined)")
+        with gr.Row():
+            query_input = gr.Textbox(label="Text Search", placeholder="e.g., RAG, fine-tuning, agents")
+            industry_input = gr.Textbox(label="Industry", placeholder="e.g., Tech, Finance, Healthcare")
+        with gr.Row():
+            company_input = gr.Textbox(label="Company", placeholder="e.g., meta, google, openai")
+            year_input = gr.Number(label="Year", value=None)
+        with gr.Row():
+            tag_input = gr.Textbox(label="Tag", placeholder="e.g., pytorch, monitoring, rag")
+            limit_input = gr.Slider(minimum=1, maximum=50, value=20, step=1, label="Max Results")
+        search_btn = gr.Button("Search")
+        search_output = gr.Markdown()
+        search_btn.click(
+            search, 
+            inputs=[query_input, industry_input, company_input, year_input, tag_input, limit_input], 
+            outputs=search_output
+        )
+    
+    with gr.Tab("Details"):
+        title_input = gr.Textbox(label="Case Study Title", placeholder="Enter part of the title")
+        details_btn = gr.Button("Get Details")
+        details_output = gr.Markdown()
+        details_btn.click(get_case_study_details, inputs=[title_input], outputs=details_output)
+    
+    with gr.Tab("Statistics"):
+        stats_btn = gr.Button("Get Statistics")
+        stats_output = gr.Markdown()
+        stats_btn.click(get_statistics, outputs=stats_output)
+        
+        gr.Markdown("---")
+        options_btn = gr.Button("List Filter Options")
+        options_output = gr.Markdown()
+        options_btn.click(list_options, outputs=options_output)
+
+# Launch with MCP server enabled
+if __name__ == "__main__":
+    demo.launch(mcp_server=True)

requirements.txt

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+gradio[mcp]>=5.0.0
+datasets>=4.4.1
+pandas