added tool for docling support

tests/knowledge/knowledge_test.py

@@ -11,6 +11,7 @@ from crewai.knowledge.source.json_knowledge_source import JSONKnowledgeSource
 from crewai.knowledge.source.pdf_knowledge_source import PDFKnowledgeSource
 from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
 from crewai.knowledge.source.text_file_knowledge_source import TextFileKnowledgeSource
+from crewai.knowledge.source.docling_source import DoclingSource
 
 
 @pytest.fixture(autouse=True)
@@ -200,7 +201,7 @@ def test_single_short_file(mock_vector_db, tmpdir):
         f.write(content)
 
     file_source = TextFileKnowledgeSource(
-        file_path=file_path, metadata={"preference": "personal"}
+        file_paths=[file_path], metadata={"preference": "personal"}
     )
     mock_vector_db.sources = [file_source]
     mock_vector_db.query.return_value = [{"context": content, "score": 0.9}]
@@ -242,7 +243,7 @@ def test_single_2k_character_file(mock_vector_db, tmpdir):
         f.write(content)
 
     file_source = TextFileKnowledgeSource(
-        file_path=file_path, metadata={"preference": "personal"}
+        file_paths=[file_path], metadata={"preference": "personal"}
     )
     mock_vector_db.sources = [file_source]
     mock_vector_db.query.return_value = [{"context": content, "score": 0.9}]
@@ -279,7 +280,7 @@ def test_multiple_short_files(mock_vector_db, tmpdir):
         file_paths.append((file_path, item["metadata"]))
 
     file_sources = [
-        TextFileKnowledgeSource(file_path=path, metadata=metadata)
+        TextFileKnowledgeSource(file_paths=[path], metadata=metadata)
         for path, metadata in file_paths
     ]
     mock_vector_db.sources = file_sources
@@ -352,7 +353,7 @@ def test_multiple_2k_character_files(mock_vector_db, tmpdir):
         file_paths.append(file_path)
 
     file_sources = [
-        TextFileKnowledgeSource(file_path=path, metadata={"preference": "personal"})
+        TextFileKnowledgeSource(file_paths=[path], metadata={"preference": "personal"})
         for path in file_paths
     ]
     mock_vector_db.sources = file_sources
@@ -399,7 +400,7 @@ def test_hybrid_string_and_files(mock_vector_db, tmpdir):
         file_paths.append(file_path)
 
     file_sources = [
-        TextFileKnowledgeSource(file_path=path, metadata={"preference": "personal"})
+        TextFileKnowledgeSource(file_paths=[path], metadata={"preference": "personal"})
         for path in file_paths
     ]
 
@@ -424,7 +425,7 @@ def test_pdf_knowledge_source(mock_vector_db):
 
     # Create a PDFKnowledgeSource
     pdf_source = PDFKnowledgeSource(
-        file_path=pdf_path, metadata={"preference": "personal"}
+        file_paths=[pdf_path], metadata={"preference": "personal"}
     )
     mock_vector_db.sources = [pdf_source]
     mock_vector_db.query.return_value = [
@@ -461,7 +462,7 @@ def test_csv_knowledge_source(mock_vector_db, tmpdir):
 
     # Create a CSVKnowledgeSource
     csv_source = CSVKnowledgeSource(
-        file_path=csv_path, metadata={"preference": "personal"}
+        file_paths=[csv_path], metadata={"preference": "personal"}
     )
     mock_vector_db.sources = [csv_source]
     mock_vector_db.query.return_value = [
@@ -496,7 +497,7 @@ def test_json_knowledge_source(mock_vector_db, tmpdir):
 
     # Create a JSONKnowledgeSource
     json_source = JSONKnowledgeSource(
-        file_path=json_path, metadata={"preference": "personal"}
+        file_paths=[json_path], metadata={"preference": "personal"}
     )
     mock_vector_db.sources = [json_source]
     mock_vector_db.query.return_value = [
@@ -529,7 +530,7 @@ def test_excel_knowledge_source(mock_vector_db, tmpdir):
 
     # Create an ExcelKnowledgeSource
     excel_source = ExcelKnowledgeSource(
-        file_path=excel_path, metadata={"preference": "personal"}
+        file_paths=[excel_path], metadata={"preference": "personal"}
     )
     mock_vector_db.sources = [excel_source]
     mock_vector_db.query.return_value = [
@@ -543,3 +544,23 @@ def test_excel_knowledge_source(mock_vector_db, tmpdir):
     # Assert that the correct information is retrieved
     assert any("30" in result["context"] for result in results)
     mock_vector_db.query.assert_called_once()
+
+
+def test_docling_source(mock_vector_db):
+    docling_source = DoclingSource(
+        file_paths=[
+            "https://lilianweng.github.io/posts/2024-11-28-reward-hacking/",
+        ],
+    )
+    mock_vector_db.sources = [docling_source]
+    mock_vector_db.query.return_value = [
+        {
+            "context": "Reward hacking is a technique used to improve the performance of reinforcement learning agents.",
+            "score": 0.9,
+        }
+    ]
+    # Perform a query
+    query = "What is reward hacking?"
+    results = mock_vector_db.query(query)
+    assert any("reward hacking" in result["context"].lower() for result in results)
+    mock_vector_db.query.assert_called_once()