Upload folder using huggingface_hub

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+chroma_db/chroma.sqlite3 filter=lfs diff=lfs merge=lfs -text

.github/workflows/update_space.yml

@@ -0,0 +1,28 @@
+name: Run Python script
+
+on:
+  push:
+    branches:
+      - main
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+
+    steps:
+    - name: Checkout
+      uses: actions/checkout@v2
+
+    - name: Set up Python
+      uses: actions/setup-python@v2
+      with:
+        python-version: '3.9'
+
+    - name: Install Gradio
+      run: python -m pip install gradio
+
+    - name: Log in to Hugging Face
+      run: python -c 'import huggingface_hub; huggingface_hub.login(token="${{ secrets.hf_token }}")'
+
+    - name: Deploy to Spaces
+      run: gradio deploy

.gitignore

@@ -0,0 +1,104 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.pyc
+*.pyo
+*.pyd
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# IDEs and editors
+.idea/
+.vscode/
+*.swp
+*~
+
+# OS generated files
+.DS_Store
+Thumbs.db
+
+# Ansible
+*.retry
+.ansible/
+.ansible-lint
+vault.yml
+vault.yaml
+vault_pass.txt
+*.log
+
+# Ansible AVD generated files
+intended/
+documentation/
+reports/
+fabric-documentation/
+
+# Ansible Collections and Roles
+# These should be managed via requirements.yml
+ansible_collections/
+collections/
+roles/
+
+# Credentials
+credentials.yml
+credentials.yaml
+*.pem
+*.key
+
+# ChromaDB - Keep SQLite but ignore vector index files
+chroma_db/*/
+!chroma_db/
+!chroma_db/chroma.sqlite3

.python-version

@@ -0,0 +1 @@
+3.12

README.md

@@ -1,12 +1,6 @@
 ---
-title: Mcp Network Doc Dem
-emoji: 🐨
-colorFrom: red
-colorTo: gray
+title: mcp-network-doc-dem
+app_file: app.py
 sdk: gradio
 sdk_version: 5.38.2
-app_file: app.py
-pinned: false
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,39 @@
+# /// script
+# dependencies = [
+#     "gradio[mcp]",
+#     "langchain_community",
+#     "chromadb",
+#     "huggingface_hub",
+#     "langchain_community",
+#     "sentence_transformers",
+# ]
+# ///
+
+import gradio as gr
+from query_interface import AristaDocumentQuery
+
+def search_docs(query: str, k: int = 5) -> str:
+   """
+    Search the Arista AVD documentation vector database.
+    Args:
+         query (str): The search query.
+         k (int): Number of results to return.
+    Returns:
+         str: Formatted string of search results.
+   """
+   query_interface = AristaDocumentQuery()
+   results = query_interface.similarity_search(query, k=k)
+   return query_interface.format_results(results)
+
+# Create a standard Gradio interface
+demo = gr.Interface(
+    fn=search_docs,
+    inputs=["textbox", "number"],
+    outputs="text",
+    title="Document Search",
+    description="Enter a search query and the number of results to return."
+)
+
+# Launch both the Gradio web interface and the MCP server
+if __name__ == "__main__":
+    demo.launch(mcp_server=True)

chroma_db/chroma.sqlite3

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:579d6b92c38712c65f3d6982a9ff9869602348c201875a9ec529f5b99826abfd
+size 3698688

pyproject.toml

@@ -0,0 +1,9 @@
+[project]
+name = "mcp-arista-avd"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.12"
+dependencies = [
+    "gradio>=5.38.2",
+]

query_interface.py

@@ -0,0 +1,210 @@
+#!/usr/bin/env python3
+# /// script
+# dependencies = [
+#     "langchain_community",
+#     "chromadb",
+#     "huggingface_hub",
+#     "langchain_community",
+#     "sentence_transformers",
+#     "pydantic"
+# ]
+# ///
+#!/usr/bin/env python3
+"""
+Query interface for Arista AVD documentation vector database.
+Provides search and retrieval capabilities.
+"""
+
+import argparse
+import json
+from typing import List, Dict, Any, Optional
+from pathlib import Path
+import logging
+from pydantic import BaseModel, Field
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_community.vectorstores import Chroma
+from langchain.schema import Document
+
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+
+class EmbeddingConfig(BaseModel):
+    """Configuration for embeddings."""
+    model_name: str = Field(default="all-MiniLM-L6-v2", description="The name of the HuggingFace model to use")
+    device: str = Field(default="cpu", description="Device to use for embedding generation (cpu or cuda)")
+    normalize_embeddings: bool = Field(default=True, description="Whether to normalize embeddings")
+
+class AristaDocumentQuery(BaseModel):
+    """Query interface for Arista AVD documentation."""
+    persist_directory: str = Field(default="./chroma_db", description="Directory containing the vector store")
+    embedding_config: EmbeddingConfig = Field(default_factory=EmbeddingConfig, description="Configuration for embeddings")
+    
+    # These will be initialized in __init__
+    embeddings: Any = Field(default=None, exclude=True)
+    vector_store: Any = Field(default=None, exclude=True)
+    
+    class Config:
+        arbitrary_types_allowed = True
+    
+    def __init__(self, **data):
+        super().__init__(**data)
+        self.embeddings = HuggingFaceEmbeddings(
+            model_name=self.embedding_config.model_name,
+            model_kwargs={'device': self.embedding_config.device},
+            encode_kwargs={'normalize_embeddings': self.embedding_config.normalize_embeddings}
+        )
+        self.vector_store = self._load_vector_store()
+        
+    def _load_vector_store(self) -> Chroma:
+        """Load the existing vector store."""
+        try:
+            vector_store = Chroma(
+                persist_directory=self.persist_directory,
+                embedding_function=self.embeddings
+            )
+            logger.info(f"Loaded vector store from {self.persist_directory}")
+            return vector_store
+        except Exception as e:
+            logger.error(f"Error loading vector store: {e}")
+            raise
+    
+    def similarity_search(self, query: str, k: int = 5, filter_dict: Optional[Dict] = None) -> List[Document]:
+        """Perform similarity search on the vector store."""
+        try:
+            if filter_dict:
+                results = self.vector_store.similarity_search(
+                    query=query,
+                    k=k,
+                    filter=filter_dict
+                )
+            else:
+                results = self.vector_store.similarity_search(
+                    query=query,
+                    k=k
+                )
+            return results
+        except Exception as e:
+            logger.error(f"Error during similarity search: {e}")
+            return []
+    
+    def search_by_category(self, query: str, category: str, k: int = 5) -> List[Document]:
+        """Search documents within a specific category."""
+        filter_dict = {"category": category}
+        return self.similarity_search(query, k=k, filter_dict=filter_dict)
+    
+    def search_by_type(self, query: str, doc_type: str, k: int = 5) -> List[Document]:
+        """Search documents of a specific type (markdown/csv)."""
+        filter_dict = {"type": doc_type}
+        return self.similarity_search(query, k=k, filter_dict=filter_dict)
+    
+    def get_categories(self) -> List[str]:
+        """Get all available categories in the vector store."""
+        # This is a simplified version - in a real implementation,
+        # you might want to query the metadata directly from ChromaDB
+        categories = [
+            'device_configuration',
+            'fabric_documentation',
+            'testing',
+            'netbox_integration',
+            'arista_cloud_test',
+            'avd_design',
+            'api_usage',
+            'workflow',
+            'infoblox_integration',
+            'network_testing',
+            'general_documentation',
+            'project_documentation'
+        ]
+        return categories
+    
+    def format_results(self, results: List[Document], verbose: bool = False) -> str:
+        """Format search results for display."""
+        output = []
+        
+        for i, doc in enumerate(results, 1):
+            output.append(f"\n{'='*80}")
+            output.append(f"Result {i}:")
+            output.append(f"Source: {doc.metadata.get('source', 'Unknown')}")
+            output.append(f"Category: {doc.metadata.get('category', 'Unknown')}")
+            output.append(f"Type: {doc.metadata.get('type', 'Unknown')}")
+            
+            if doc.metadata.get('type') == 'csv':
+                output.append(f"Columns: {doc.metadata.get('columns', 'Unknown')}")
+                output.append(f"Rows: {doc.metadata.get('rows', 'Unknown')}")
+            
+            output.append(f"\nContent Preview:")
+            content_preview = doc.page_content[:500] + "..." if len(doc.page_content) > 500 else doc.page_content
+            output.append(content_preview)
+            
+            if verbose:
+                output.append(f"\nFull Content:")
+                output.append(doc.page_content)
+        
+        return "\n".join(output)
+    
+    def export_results(self, results: List[Document], output_file: str) -> None:
+        """Export search results to a JSON file."""
+        data = []
+        for doc in results:
+            data.append({
+                'content': doc.page_content,
+                'metadata': doc.metadata
+            })
+        
+        with open(output_file, 'w') as f:
+            json.dump(data, f, indent=2)
+        
+        logger.info(f"Results exported to {output_file}")
+
+
+def main():
+    """Main function for command-line interface."""
+    parser = argparse.ArgumentParser(description="Query Arista AVD documentation vector database")
+    parser.add_argument("query", nargs="?", help="Search query")
+    parser.add_argument("-k", "--top-k", type=int, default=5, help="Number of results to return (default: 5)")
+    parser.add_argument("-c", "--category", help="Filter by category")
+    parser.add_argument("-t", "--type", choices=['markdown', 'csv'], help="Filter by document type")
+    parser.add_argument("-v", "--verbose", action="store_true", help="Show full content")
+    parser.add_argument("-e", "--export", help="Export results to JSON file")
+    parser.add_argument("--list-categories", action="store_true", help="List available categories")
+    
+    args = parser.parse_args()
+    
+    # Initialize query interface
+    query_interface = AristaDocumentQuery()
+    
+    # List categories if requested
+    if args.list_categories:
+        categories = query_interface.get_categories()
+        print("Available categories:")
+        for cat in categories:
+            print(f"  - {cat}")
+        return
+    
+    # Ensure query is provided if not listing categories
+    if not args.query:
+        parser.error("Query is required unless using --list-categories")
+    
+    # Perform search
+    if args.category:
+        results = query_interface.search_by_category(args.query, args.category, k=args.top_k)
+    elif args.type:
+        results = query_interface.search_by_type(args.query, args.type, k=args.top_k)
+    else:
+        results = query_interface.similarity_search(args.query, k=args.top_k)
+    
+    # Display results
+    if results:
+        formatted_results = query_interface.format_results(results, verbose=args.verbose)
+        print(formatted_results)
+        
+        # Export if requested
+        if args.export:
+            query_interface.export_results(results, args.export)
+    else:
+        print("No results found.")
+
+
+if __name__ == "__main__":
+    main()

requirements.txt

@@ -0,0 +1,6 @@
+gradio[mcp]
+langchain_community
+chromadb
+huggingface_hub
+sentence_transformers
+pydantic