Merge branch 'main' of https://github.com/agno-agi/agno into feat/sta…

…teless-models

cookbook/agent_concepts/knowledge/custom/retriever.py

@@ -0,0 +1,80 @@
+from typing import Optional
+
+from agno.agent import Agent
+from agno.embedder.openai import OpenAIEmbedder
+from agno.knowledge.pdf_url import PDFUrlKnowledgeBase
+from agno.vectordb.qdrant import Qdrant
+from qdrant_client import QdrantClient
+
+# ---------------------------------------------------------
+# This section loads the knowledge base. Skip if your knowledge base was populated elsewhere.
+# Define the embedder
+embedder = OpenAIEmbedder(id="text-embedding-3-small")
+# Initialize vector database connection
+vector_db = Qdrant(collection="thai-recipes", path="tmp/qdrant", embedder=embedder)
+# Load the knowledge base
+knowledge_base = PDFUrlKnowledgeBase(
+    urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
+    vector_db=vector_db,
+)
+
+# Load the knowledge base
+# knowledge_base.load(recreate=True)  # Comment out after first run
+# Knowledge base is now loaded
+# ---------------------------------------------------------
+
+
+# Define the custom retriever
+# This is the function that the agent will use to retrieve documents
+def retriever(
+    query: str, agent: Optional[Agent] = None, num_documents: int = 5, **kwargs
+) -> Optional[list[dict]]:
+    """
+    Custom retriever function to search the vector database for relevant documents.
+
+    Args:
+        query (str): The search query string
+        agent (Agent): The agent instance making the query
+        num_documents (int): Number of documents to retrieve (default: 5)
+        **kwargs: Additional keyword arguments
+
+    Returns:
+        Optional[list[dict]]: List of retrieved documents or None if search fails
+    """
+    try:
+        qdrant_client = QdrantClient(path="tmp/qdrant")
+        query_embedding = embedder.get_embedding(query)
+        results = qdrant_client.query_points(
+            collection_name="thai-recipes",
+            query=query_embedding,
+            limit=num_documents,
+        )
+        results_dict = results.model_dump()
+        if "points" in results_dict:
+            return results_dict["points"]
+        else:
+            return None
+    except Exception as e:
+        print(f"Error during vector database search: {str(e)}")
+        return None
+
+
+def main():
+    """Main function to demonstrate agent usage."""
+    # Initialize agent with custom retriever
+    # Remember to set search_knowledge=True to use agentic_rag or add_reference=True for traditional RAG
+    # search_knowledge=True is default when you add a knowledge base but is needed here
+    agent = Agent(
+        retriever=retriever,
+        search_knowledge=True,
+        instructions="Search the knowledge base for information",
+        show_tool_calls=True,
+    )
+
+    # Example query
+    query = "List down the ingredients to make Massaman Gai"
+    agent.print_response(query, markdown=True)
+
+
+if __name__ == "__main__":
+    main()

cookbook/agent_concepts/knowledge/vector_dbs/weaviate_db.py

@@ -34,7 +34,7 @@
     search_type=SearchType.hybrid,
     vector_index=VectorIndex.HNSW,
     distance=Distance.COSINE,
-    local=True, # Set to False if using Weaviate Cloud and True if using local instance
+    local=True,  # Set to False if using Weaviate Cloud and True if using local instance
 )
 # Create knowledge base
 knowledge_base = PDFUrlKnowledgeBase(

cookbook/examples/apps/chess_team/README.md

@@ -27,7 +27,7 @@ pip install -r cookbook/examples/apps/chess_team/requirements.txt
 Create a `.envrc` file or export your API keys:
 
 ```shell
-export ANTHROPIC_API_KEY=your_api_key_here
+export OPENAI_API_KEY=your_api_key_here
 ```
 
 ### 4. Run the application

libs/agno/agno/agent/agent.py

@@ -1330,7 +1330,7 @@ def get_tools(self) -> Optional[List[Union[Toolkit, Callable, Function]]]:
             agent_tools.append(self.update_memory)
 
         # Add tools for accessing knowledge
-        if self.knowledge is not None:
+        if self.knowledge is not None or self.retriever is not None:
             if self.search_knowledge:
                 agent_tools.append(self.search_knowledge_base)
             if self.update_knowledge:
@@ -2425,9 +2425,19 @@ def get_relevant_docs_from_knowledge(
         """Return a list of references from the knowledge base"""
         from agno.document import Document
 
-        if self.retriever is not None:
-            retriever_kwargs = {"agent": self, "query": query, "num_documents": num_documents, **kwargs}
-            return self.retriever(**retriever_kwargs)
+        if self.retriever is not None and callable(self.retriever):
+            from inspect import signature
+
+            try:
+                sig = signature(self.retriever)
+                retriever_kwargs: Dict[str, Any] = {}
+                if "agent" in sig.parameters:
+                    retriever_kwargs = {"agent": self}
+                retriever_kwargs.update({"query": query, "num_documents": num_documents, **kwargs})
+                return self.retriever(**retriever_kwargs)
+            except Exception as e:
+                logger.warning(f"Retriever failed: {e}")
+                return None
 
         if self.knowledge is None:
             return None