updates

willemcdejongh · willemcdejongh · commit 528b0350ba22 · 2025-11-05T11:12:59.000+02:00
diff --git a/cookbook/knowledge/filters/async_filtering.py b/cookbook/knowledge/filters/async_filtering.py
@@ -114,7 +114,7 @@
     # Query for Jordan Mitchell's experience and skills
     asyncio.run(
         agent.aprint_response(
-            "Tell me about the candidate's experience and skills",
+            "Search the knowledge base for the candidate's experience and skills",
             knowledge_filters={"user_id": "jordan_mitchell"},
             markdown=True,
         )
diff --git a/cookbook/knowledge/filters/filtering_with_conditions_on_agent.py b/cookbook/knowledge/filters/filtering_with_conditions_on_agent.py
@@ -6,12 +6,12 @@
 """
 
 from agno.agent import Agent
-from agno.utils.search_filters import AND, EQ, IN, NOT
 from agno.knowledge.knowledge import Knowledge
 from agno.utils.media import (
     SampleDataFileExtension,
     download_knowledge_filters_sample_data,
 )
+from agno.utils.search_filters import AND, EQ, IN, NOT
 from agno.vectordb.pgvector import PgVector
 
 # Download all sample sales documents and get their paths
@@ -88,7 +88,7 @@
 sales_agent.print_response(
     "Describe revenue performance for the region",
     # knowledge_filters=[{"region": "north_america", "data_type": "sales"}],
-    knowledge_filters=[(IN("region", ["north_america", "europe"]))],
+    knowledge_filters=[(IN("region", ["north_america"]))],
     markdown=True,
 )
 
diff --git a/cookbook/knowledge/filters/filtering_with_conditions_on_team.py b/cookbook/knowledge/filters/filtering_with_conditions_on_team.py
@@ -6,7 +6,6 @@
 """
 
 from agno.agent import Agent
-from agno.utils.search_filters import AND, EQ, IN, NOT, OR
 from agno.knowledge.knowledge import Knowledge
 from agno.knowledge.reader.pdf_reader import PDFReader
 from agno.models.openai import OpenAIChat
@@ -15,6 +14,7 @@
     SampleDataFileExtension,
     download_knowledge_filters_sample_data,
 )
+from agno.utils.search_filters import AND, EQ, IN, NOT, OR
 from agno.vectordb.pgvector import PgVector
 
 # Download all sample CVs and get their paths
@@ -34,51 +34,51 @@
 )
 
 # Add documents with metadata for filtering
-# knowledge_base.add_contents(
-#     [
-#         {
-#             "path": downloaded_cv_paths[0],
-#             "metadata": {
-#                 "user_id": "jordan_mitchell",
-#                 "document_type": "cv",
-#                 "year": 2025,
-#             },
-#         },
-#         {
-#             "path": downloaded_cv_paths[1],
-#             "metadata": {
-#                 "user_id": "taylor_brooks",
-#                 "document_type": "cv",
-#                 "year": 2025,
-#             },
-#         },
-#         {
-#             "path": downloaded_cv_paths[2],
-#             "metadata": {
-#                 "user_id": "morgan_lee",
-#                 "document_type": "cv",
-#                 "year": 2025,
-#             },
-#         },
-#         {
-#             "path": downloaded_cv_paths[3],
-#             "metadata": {
-#                 "user_id": "casey_jordan",
-#                 "document_type": "cv",
-#                 "year": 2020,
-#             },
-#         },
-#         {
-#             "path": downloaded_cv_paths[4],
-#             "metadata": {
-#                 "user_id": "alex_rivera",
-#                 "document_type": "cv",
-#                 "year": 2020,
-#             },
-#         },
-#     ],
-#     reader=PDFReader(chunk=True),
-# )
+knowledge_base.add_contents(
+    [
+        {
+            "path": downloaded_cv_paths[0],
+            "metadata": {
+                "user_id": "jordan_mitchell",
+                "document_type": "cv",
+                "year": 2025,
+            },
+        },
+        {
+            "path": downloaded_cv_paths[1],
+            "metadata": {
+                "user_id": "taylor_brooks",
+                "document_type": "cv",
+                "year": 2025,
+            },
+        },
+        {
+            "path": downloaded_cv_paths[2],
+            "metadata": {
+                "user_id": "morgan_lee",
+                "document_type": "cv",
+                "year": 2025,
+            },
+        },
+        {
+            "path": downloaded_cv_paths[3],
+            "metadata": {
+                "user_id": "casey_jordan",
+                "document_type": "cv",
+                "year": 2020,
+            },
+        },
+        {
+            "path": downloaded_cv_paths[4],
+            "metadata": {
+                "user_id": "alex_rivera",
+                "document_type": "cv",
+                "year": 2020,
+            },
+        },
+    ],
+    reader=PDFReader(chunk=True),
+)
 
 # Create knowledge search agent
 web_agent = Agent(
@@ -102,54 +102,53 @@
     markdown=True,
 )
 
-# print("--------------------------------")
-# print("Using IN operator")
-# team_with_knowledge.print_response(
-#     "Tell me about the candidate's work and experience",
-#     knowledge_filters=[
-#         (
-#             IN(
-#                 "user_id",
-#                 [
-#                     "jordan_mitchell",
-#                     "taylor_brooks",
-#                     "morgan_lee",
-#                     "casey_jordan",
-#                     "alex_rivera",
-#                 ],
-#             )
-#         )
-#     ],
-#     markdown=True,
-# )
+print("--------------------------------")
+print("Using IN operator")
+team_with_knowledge.print_response(
+    "Tell me about the candidate's work and experience",
+    knowledge_filters=[
+        (
+            IN(
+                "user_id",
+                [
+                    "jordan_mitchell",
+                    "taylor_brooks",
+                    "morgan_lee",
+                    "casey_jordan",
+                    "alex_rivera",
+                ],
+            )
+        )
+    ],
+    markdown=True,
+)
 
-# print("--------------------------------")
-# print("Using NOT operator")
-# team_with_knowledge.print_response(
-#     "Tell me about the candidate's work and experience",
-#     knowledge_filters=[
-#         AND(
-#             IN("user_id", ["jordan_mitchell", "taylor_brooks"]),
-#             NOT(IN("user_id", ["morgan_lee", "casey_jordan", "alex_rivera"])),
-#         )
-#     ],
-#     markdown=True,
-# )
+print("--------------------------------")
+print("Using NOT operator")
+team_with_knowledge.print_response(
+    "Tell me about the candidate's work and experience",
+    knowledge_filters=[
+        AND(
+            IN("user_id", ["jordan_mitchell", "taylor_brooks"]),
+            NOT(IN("user_id", ["morgan_lee", "casey_jordan", "alex_rivera"])),
+        )
+    ],
+    markdown=True,
+)
 
-# print("--------------------------------")
-# print("Using OR operator")
-# team_with_knowledge.print_response(
-#     "Tell me about the candidate's work and experience",
-#     knowledge_filters=[
-#         OR(EQ("user_id", "this candidate does not exist"), EQ("year", 2020))
-#     ],
-#     markdown=True,
-# )
+print("--------------------------------")
+print("Using OR operator")
+team_with_knowledge.print_response(
+    "Tell me about the candidate's work and experience",
+    knowledge_filters=[
+        OR(EQ("user_id", "this candidate does not exist"), EQ("year", 2020))
+    ],
+    markdown=True,
+)
 
 
 team_with_knowledge.print_response(
     "Tell me about the candidate's work and experience",
     knowledge_filters=EQ("user_id", "jordan_mitchell"),  # Wrong: plain strings
     markdown=True,
 )
-
diff --git a/libs/agno/agno/agent/agent.py b/libs/agno/agno/agent/agent.py
@@ -5412,7 +5412,6 @@ async def aget_tools(
 
         # Consider both run_context.knowledge_filters and knowledge_filters (deprecated)
         run_context.knowledge_filters = run_context.knowledge_filters or knowledge_filters
-
         # Connect MCP tools
         await self._connect_mcp_tools()
 
@@ -7359,7 +7358,6 @@ def _get_user_message(
             dependencies = run_context.dependencies or dependencies
             metadata = run_context.metadata or metadata
             knowledge_filters = run_context.knowledge_filters or knowledge_filters
-
         # Get references from the knowledge base to use in the user message
         references = None
 
@@ -8047,7 +8045,7 @@ def get_relevant_docs_from_knowledge(
                 filters = valid_filters
                 if not filters:
                     log_warning("No valid filters remain after validation. Search will proceed without filters.")
-            
+
             if invalid_keys == [] and valid_filters == {}:
                 log_warning("No valid filters provided. Search will proceed without filters.")
                 filters = None
diff --git a/libs/agno/agno/knowledge/knowledge.py b/libs/agno/agno/knowledge/knowledge.py
@@ -1495,7 +1495,7 @@ def validate_filters(self, filters: Optional[Any]) -> Tuple[Any, List[str]]:
                 else:
                     invalid_keys.append(key)
                     log_debug(f"Invalid filter key: {key} - not present in knowledge base")
-        
+
         elif isinstance(filters, List):
             # Validate that list contains FilterExpr instances
             for i, filter_item in enumerate(filters):
@@ -1506,9 +1506,10 @@ def validate_filters(self, filters: Optional[Any]) -> Tuple[Any, List[str]]:
                         f"Use filter expressions like EQ('key', 'value'), IN('key', [values]), "
                         f"AND(...), OR(...), NOT(...) from agno.utils.search_filters"
                     )
+
             # Filter expressions are already validated, return empty dict/list
             # The actual filtering happens in the vector_db layer
-            return {}, []
+            return filters, []
 
         return valid_filters, invalid_keys
 
diff --git a/libs/agno/agno/team/team.py b/libs/agno/agno/team/team.py
@@ -8382,6 +8382,7 @@ def get_relevant_docs_from_knowledge(
     ) -> Optional[List[Union[Dict[str, Any], str]]]:
         """Return a list of references from the knowledge base"""
         from agno.knowledge.document import Document
+
         if num_documents is None and self.knowledge is not None:
             num_documents = self.knowledge.max_results
 
@@ -8399,7 +8400,7 @@ def get_relevant_docs_from_knowledge(
                 filters = valid_filters
                 if not filters:
                     log_warning("No valid filters remain after validation. Search will proceed without filters.")
-            
+
             if invalid_keys == [] and valid_filters == {}:
                 log_warning("No valid filters provided. Search will proceed without filters.")
                 filters = None
diff --git a/libs/agno/agno/utils/search_filters.py b/libs/agno/agno/utils/search_filters.py

Original file line number	Diff line number	Diff line change
`@@ -114,7 +114,7 @@`
`114`	`114`	`# Query for Jordan Mitchell's experience and skills`
`115`	`115`	`asyncio.run(`
`116`	`116`	`agent.aprint_response(`
`117`		`- "Tell me about the candidate's experience and skills",`
	`117`	`+ "Search the knowledge base for the candidate's experience and skills",`
`118`	`118`	`knowledge_filters={"user_id": "jordan_mitchell"},`
`119`	`119`	`markdown=True,`
`120`	`120`	`)`