feat(python): knowledge refactoring [WIP]

examples/004/images/main/main.py

@@ -1,4 +1,4 @@
-from ockam import Agent, Knowledge, Model, Node
+from ockam import Agent, Memory, Model, Node
 
 
 async def main(node):
@@ -11,9 +11,9 @@ async def main(node):
         "sec-330e": "https://raw.githubusercontent.com/AlextheYounga/us-federal-code/refs/heads/master/usc/title-15-commerce-and-trade/chapter-9a-weather-modification-activities-or-attempts%3B-reporting-requirement/sec-330e.md",
     }
 
-    us_federal_code = Knowledge("us_federal_code")
+    us_federal_code = StaticKnowledgeProvider("us_federal_code")
     for name, url in docs.items():
-        await us_federal_code.add_document(name, url, content_type="text/markdown")
+        await us_federal_code.add(name, url, content_type="text/markdown")
 
     await Agent.start(
         node=node,

examples/005/images/main/main.py

@@ -1,9 +1,9 @@
-from ockam import Agent, Knowledge, Model, Node
+from ockam import Agent, Memory, Model, Node
 
 
 async def main(node):
-    pioneer_docs = Knowledge("pioneer_ai_documents")
-    await pioneer_docs.add_document(
+    pioneer_docs = Memory("pioneer_ai_documents")
+    await pioneer_docs.add(
         "Ownership in Pioneer.ai",
         "http://localhost:5555/ownership.md",
         content_type="text/markdown",

implementations/python/examples/13.py

@@ -1,44 +1,48 @@
-from ockam import Agent, Model, Node, SearchableKnowledge
+from ockam import Agent, Model, Node, Memory
+from ockam.knowledge.protocol import Document
 
 """
     This example shows how a model can be enriched with knowledge coming from inlined documents.
 """
 
 
 async def main(node):
-    restaurants = SearchableKnowledge(
-        "restaurants",
-        model=Model("ollama/nomic-embed-text"),
-    )
-    await restaurants.add_text(
-        "Tony's Pizzeria Menu",
-        """
-        1. Margherita - $10
-        2. Pepperoni - $12
-        3. Hawaiian - $11
-        4. Veggie - $9
-        5. Four Cheese - $13
-    """,
+    restaurants = Memory("restaurants")
+
+    await restaurants.add(
+        Document(
+            "Tony's Pizzeria Menu",
+            """
+            1. Margherita - $10
+            2. Pepperoni - $12
+            3. Hawaiian - $11
+            4. Veggie - $9
+            5. Four Cheese - $13
+            """,
+            content_type="text/plain",
+        )
     )
 
-    await restaurants.add_text(
-        "Diner Menu",
-        """
-        1. Caesar Salad - $8
-        2. Vegan Burger - $14
-        3. Chicken - $15
-        4. Shrimp Tacos - $16
-        5. Chocolate Lava Cake - $7
-    """,
+    await restaurants.add(
+        Document.inline(
+            "Diner Menu",
+            """
+            1. Caesar Salad - $8
+            2. Vegan Burger - $14
+            3. Chicken - $15
+            4. Shrimp Tacos - $16
+            5. Chocolate Lava Cake - $7
+            """,
+            content_type="text/plain",
+        )
     )
 
     agent = await Agent.start(
         node=node,
         name="Assistant",
         instructions="Assistant to solve some complex task ...",
         model=Model(name="ollama_chat/llama3.2"),
-        knowledge=restaurants,
-        max_knowledge_size=4096,
+        memory=restaurants,
     )
 
     reply = await agent.send("What's the price of a pepperoni pizza?", scope="a", conversation="1")

implementations/python/examples/14.py

@@ -1,15 +1,12 @@
-from ockam import Agent, Model, Node, SearchableKnowledge
+from ockam import Agent, Model, Node, Memory, Document, SEARCHABLE
 
 """
     This example shows how a model can be enriched with knowledge coming from documents retrieved online.
 """
 
 
 async def main(node):
-    ockam_documentation = SearchableKnowledge(
-        "ockam_documentation",
-        model=Model("ollama/nomic-embed-text"),
-    )
+    ockam_documentation = Memory("ockam_documentation")
 
     base_url = "https://raw.githubusercontent.com/build-trust/ockam-documentation/refs/heads/main"
     documents = [
@@ -22,19 +19,21 @@ async def main(node):
     ]
 
     for document in documents:
-        await ockam_documentation.add_document(
-            document,
-            f"{base_url}/{document}",
-            content_type="text/markdown",
+        await ockam_documentation.add(
+            Document(
+                name=document,
+                url=f"{base_url}/{document}",
+                content_type="text/markdown",
+            ),
+            retrieval=SEARCHABLE
         )
 
     agent = await Agent.start(
         node=node,
         name="Assistant",
         instructions="Assistant to solve some complex task ...",
         model=Model(name="ollama_chat/llama3.2"),
-        knowledge=ockam_documentation,
-        max_knowledge_size=4096,
+        memory=ockam_documentation,
     )
 
     reply = await agent.send("What's ockam?", scope="a", conversation="1")

implementations/python/python/ockam/__init__.py

@@ -1,8 +1,9 @@
 from .agents import Agent, AgentReference, HttpServer, Repl
 from .clusters import Cluster, Zone
 from .flows import Flow, FlowOperation, START, END
+from .knowledge.protocol import Document
 from .nodes.message import FlowReference
-from .memory import Memory
+from .history import ConversationHistory
 from .logging import info, warning, error, debug, set_log_levels, get_logger
 from .models import Model
 from .nodes import Node, RemoteNode, LocalNode, LocalNodeProtocol, MailboxProtocol, WorkerProtocol, ContextProtocol
@@ -12,23 +13,17 @@
 from .squads import Squad
 from .tools import McpTool, Tool
 from .knowledge import (
-    TextPiece,
-    SearchHit,
-    SearchResults,
-    Knowledge,
-    SearchableKnowledge,
-    InMemory,
-    Database,
-    KnowledgeProvider,
-    KnowledgeAggregator,
-    TextExtractor,
-    Chunker,
-    NaiveChunker,
+    Memory,
+    Retrieval,
 )
 from .gather import gather
+from .knowledge.memory import Retrieval
 
 from .ockam_in_rust_for_python import Mailbox, McpClient, McpServer
 
+SEARCHABLE = Retrieval.SEARCHABLE
+WHOLE = Retrieval.WHOLE
+
 __doc__ = ""
 __all__ = [
     # from .agents
@@ -59,8 +54,8 @@
     # from .tools
     "McpTool",
     "Tool",
-    # from .memory
-    "Memory",
+    # from .history
+    "ConversationHistory",
     # from .models
     "Model",
     # from .nodes
@@ -80,22 +75,12 @@
     "DynamicPlanner",
     # from .squads
     "Squad",
-    # from .knowledge
-    "RemoteManager",
-    "Node",
-    "Tool",
-    "KnowledgeProvider",
-    "KnowledgeAggregator",
-    "Knowledge",
-    "SearchableKnowledge",
-    "TextPiece",
-    "SearchHit",
-    "SearchResults",
-    "InMemory",
-    "Database",
-    "TextExtractor",
-    "Chunker",
-    "NaiveChunker",
+    # from .memory
+    "Memory",
+    "Retrieval",
+    "Document",
+    "SEARCHABLE",
+    "WHOLE",
     # from .gather
     "gather",
 ]