Add new MCP tools for knowledge management

.github/workflows/coverage.yml

@@ -15,7 +15,7 @@ jobs:
       options: --security-opt seccomp=unconfined
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v2
+        uses: actions/checkout@v4
 
       - name: Generate code coverage
         run: |
@@ -29,7 +29,7 @@ jobs:
           CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
 
       - name: Archive code coverage results
-        uses: actions/upload-artifact@v1
+        uses: actions/upload-artifact@v3
         with:
           name: code-coverage-report
           path: tarpaulin-report.html

Cargo.toml

@@ -20,6 +20,11 @@ uuid = { version = "1.3", features = ["v4", "serde"] }
 qdrant-client = "1.4"
 toml = "0.8"
 dirs = "5.0"
+deadpool = "0.9"
+backoff = { version = "0.4", features = ["tokio"] }
+async-trait = "0.1"
+regex = "1.10"
+lazy_static = "1.4"
 
 [dev-dependencies]
 tempfile = "3.5"

src/lib.rs

@@ -4,6 +4,8 @@ pub mod api;
 pub mod vector_store;
 pub mod config;
 pub mod app;
+pub mod mcp;
+pub mod text_processing;
 
 pub use server::Server;
 pub use cli::{Cli, Args};

src/mcp/mock.rs

@@ -0,0 +1,47 @@
+use crate::vector_store::{Document, SearchQuery, SearchResult, VectorStore, VectorStoreError};
+use async_trait::async_trait;
+
+/// Mock implementation of the EmbeddedQdrantConnector for testing
+pub struct MockQdrantConnector;
+
+impl MockQdrantConnector {
+    /// Create a new mock connector
+    pub fn new() -> Self {
+        Self
+    }
+}
+
+#[async_trait]
+impl VectorStore for MockQdrantConnector {
+    async fn test_connection(&self) -> Result<(), VectorStoreError> {
+        Ok(())
+    }
+
+    async fn create_collection(&self, _name: &str, _vector_size: usize) -> Result<(), VectorStoreError> {
+        Ok(())
+    }
+
+    async fn delete_collection(&self, _name: &str) -> Result<(), VectorStoreError> {
+        Ok(())
+    }
+
+    async fn insert_document(&self, _collection: &str, _document: Document) -> Result<(), VectorStoreError> {
+        Ok(())
+    }
+
+    async fn search(&self, _collection: &str, _query: SearchQuery) -> Result<Vec<SearchResult>, VectorStoreError> {
+        // Return a mock result
+        let doc = Document {
+            id: "test-id".to_string(),
+            content: "Test document".to_string(),
+            embedding: vec![0.0; 384],
+        };
+
+        let result = SearchResult {
+            document: doc,
+            score: 0.95,
+        };
+
+        Ok(vec![result])
+    }
+}