Merge remote-tracking branch 'origin/main' into swarmsysmsgfunc

.github/workflows/contrib-graph-rag-tests.yml

@@ -43,7 +43,7 @@ jobs:
         run: |
           python -m pip install --upgrade pip wheel
           pip install pytest
-      - name: Install Falkor DB SDK when on linux
+      - name: Install FalkorDB SDK when on linux
         run: |
           pip install -e .[graph_rag_falkor_db]
       - name: Set AUTOGEN_USE_DOCKER based on OS

MAINTAINERS.md

@@ -20,6 +20,7 @@
 | Aaron Ward     | [AaronWard](https://github.com/AaronWard)                | yappstore.ai                | all                      |
 | Rudy Wu     | [rudyalways](https://github.com/rudyalways)                | Google                | all, group chats, sequential chats                |
 | Haiyang Li  | [ohdearquant](https://github.com/ohdearquant)               | -               | all, sequential chats, structured output, low-level|
+| Eric Moore  | [emooreatx](https://github.com/emooreatx)               | IBM               | all|
 
 **Pending Maintainers list (Marked with \*, Waiting for explicit approval from the maintainers)**
 | Name            | GitHub Handle                                              | Organization           | Features                                |

README.md

@@ -1,5 +1,6 @@
 <a name="readme-top"></a>
 
+![Pypi Downloads](https://img.shields.io/pypi/dm/pyautogen?label=PyPI%20downloads)
 [![PyPI version](https://badge.fury.io/py/autogen.svg)](https://badge.fury.io/py/autogen)
 [![Build](https://github.com/ag2ai/ag2/actions/workflows/python-package.yml/badge.svg)](https://github.com/ag2ai/ag2/actions/workflows/python-package.yml)
 ![Python Version](https://img.shields.io/badge/3.8%20%7C%203.9%20%7C%203.10%20%7C%203.11%20%7C%203.12-blue)
@@ -8,24 +9,20 @@
 
 <!-- [![NuGet version](https://badge.fury.io/nu/AutoGen.Core.svg)](https://badge.fury.io/nu/AutoGen.Core) -->
 
-
-<a name="readme-top"></a>
-
-<div align="center">
-<img src="./website/static/img/ag2.png" alt="AutoGen Logo" width="100">
-
-
-</div>
+# [AG2](https://github.com/ag2ai/ag2)
 
 [📚 Cite paper](#related-papers).
 <!-- <p align="center">
     <img src="https://github.com/ag2ai/ag2/blob/main/website/static/img/flaml.svg"  width=200>
     <br>
 </p> -->
 
-> [!IMPORTANT]
+> **:tada: IMPORTANT**
+>
+> :fire: :tada: **Nov 11, 2024:** We are evolving AutoGen into **AG2**!
+> A new organization [AG2ai](https://github.com/ag2ai) is created to host the development of AG2 and related projects with open governance. Check [AG2's new look](https://ag2.ai/).
 >
-> :fire: :tada: Nov 11, 2024: We are evolving AutoGen into AG2! A new organization [ag2ai](https://github.com/ag2ai) is created to host the development of AG2 and related projects with open governance. We invite collaborators from all organizations and individuals to join the development.
+> We invite collaborators from all organizations and individuals to join the development.
 
 
 :fire: :tada: AG2 is available via `ag2` (or its alias  `autogen` or  `pyautogen`)  on PyPI! Starting with version 0.3.2, you can now install AG2 using:
@@ -98,8 +95,7 @@ AG2 (formerly AutoGen) is an open-source programming framework for building AI a
 The project is currently maintained by a [dynamic group of volunteers](MAINTAINERS.md) from several organizations. Contact project administrators Chi Wang and Qingyun Wu via [support@ag2.ai](mailto:support@ag2.ai) if you are interested in becoming a maintainer.
 
 
-![AutoGen Overview](./website/static/img/autogen_agentchat.png)
-
+![AutoGen Overview](https://media.githubusercontent.com/media/ag2ai/ag2/refs/heads/main/website/static/img/autogen_agentchat.png)
 
 
 <p align="right" style="font-size: 14px; color: #555; margin-top: 20px;">
@@ -199,7 +195,7 @@ python test/twoagent.py
 After the repo is cloned.
 The figure below shows an example conversation flow with AG2.
 
-![Agent Chat Example](./website/static/img/chat_example.png)
+![Agent Chat Example](https://media.githubusercontent.com/media/ag2ai/ag2/refs/heads/main/website/static/img/chat_example.png)
 
 
 Alternatively, the [sample code](https://github.com/ag2ai/build-with-ag2/blob/main/samples/simple_chat.py) here allows a user to chat with an AG2 agent in ChatGPT style.
@@ -252,8 +248,6 @@ In addition, you can find:
 
 - [Contributing guide](https://ag2ai.github.io/ag2/docs/Contribute)
 
-- [Roadmap](https://ag2.ai/#roadmap) and [Roadmap Issues](https://github.com/ag2ai/ag2/issues?q=is%3Aopen+is%3Aissue+label%3Aroadmap)
-
 <p align="right" style="font-size: 14px; color: #555; margin-top: 20px;">
   <a href="#readme-top" style="text-decoration: none; color: blue; font-weight: bold;">
     ↑ Back to Top ↑

autogen/agentchat/contrib/graph_rag/falkor_graph_query_engine.py

@@ -1,27 +1,24 @@
 # Copyright (c) 2023 - 2024, Owners of https://github.com/ag2ai
 #
 # SPDX-License-Identifier: Apache-2.0
-#
-# Portions derived from https://github.com/microsoft/autogen are under the MIT License.
-# SPDX-License-Identifier: MIT
+
 import os
-from dataclasses import field
+from dataclasses import dataclass, field
 from typing import List
 
 from graphrag_sdk import KnowledgeGraph, Source
-from graphrag_sdk.schema import Schema
+from graphrag_sdk.model_config import KnowledgeGraphModelConfig
+from graphrag_sdk.models import GenerativeModel
+from graphrag_sdk.models.openai import OpenAiGenerativeModel
+from graphrag_sdk.ontology import Ontology
 
 from .document import Document
 from .graph_query_engine import GraphStoreQueryResult
 
 
-class FalkorGraphQueryResult(GraphStoreQueryResult):
-    messages: list = field(default_factory=list)
-
-
 class FalkorGraphQueryEngine:
     """
-    This is a wrapper for Falkor DB KnowledgeGraph.
+    This is a wrapper for FalkorDB KnowledgeGraph.
     """
 
     def __init__(
@@ -31,11 +28,11 @@ def __init__(
         port: int = 6379,
         username: str | None = None,
         password: str | None = None,
-        model: str = "gpt-4-1106-preview",
-        schema: Schema | None = None,
+        model: GenerativeModel = OpenAiGenerativeModel("gpt-4o"),
+        ontology: Ontology | None = None,
     ):
         """
-        Initialize a Falkor DB knowledge graph.
+        Initialize a FalkorDB knowledge graph.
         Please also refer to https://github.com/FalkorDB/GraphRAG-SDK/blob/main/graphrag_sdk/kg.py
 
         Args:
@@ -44,11 +41,18 @@ def __init__(
             port (int): FalkorDB port number.
             username (str|None): FalkorDB username.
             password (str|None): FalkorDB password.
-            model (str): OpenAI model to use for Falkor DB to build and retrieve from the graph.
-            schema: Falkor DB knowledge graph schema (ontology), https://github.com/FalkorDB/GraphRAG-SDK/blob/main/graphrag_sdk/schema/schema.py
-                    If None, Falkor DB will auto generate a schema from the input docs.
+            model (GenerativeModel): LLM model to use for FalkorDB to build and retrieve from the graph, default to use OAI gpt-4o.
+            ontology: FalkorDB knowledge graph schema/ontology, https://github.com/FalkorDB/GraphRAG-SDK/blob/main/graphrag_sdk/ontology.py
+                If None, FalkorDB will auto generate an ontology from the input docs.
         """
-        self.knowledge_graph = KnowledgeGraph(name, host, port, username, password, model, schema)
+        self.name = name
+        self.host = host
+        self.port = port
+        self.username = username
+        self.password = password
+        self.model = model
+        self.model_config = KnowledgeGraphModelConfig.with_model(model)
+        self.ontology = ontology
 
     def init_db(self, input_doc: List[Document] | None):
         """
@@ -60,14 +64,33 @@ def init_db(self, input_doc: List[Document] | None):
                 sources.append(Source(doc.path_or_url))
 
         if sources:
+            # Auto generate graph ontology if not created by user.
+            if self.ontology is None:
+                self.ontology = Ontology.from_sources(
+                    sources=sources,
+                    model=self.model,
+                )
+
+            self.knowledge_graph = KnowledgeGraph(
+                name=self.name,
+                host=self.host,
+                port=self.port,
+                username=self.username,
+                password=self.password,
+                model_config=KnowledgeGraphModelConfig.with_model(self.model),
+                ontology=self.ontology,
+            )
+
+            # Establish a chat session, this will maintain the history
+            self._chat_session = self.knowledge_graph.chat_session()
             self.knowledge_graph.process_sources(sources)
 
     def add_records(self, new_records: List) -> bool:
-        raise NotImplementedError("This method is not supported by Falkor DB SDK yet.")
+        raise NotImplementedError("This method is not supported by FalkorDB SDK yet.")
 
-    def query(self, question: str, n_results: int = 1, **kwargs) -> FalkorGraphQueryResult:
+    def query(self, question: str, n_results: int = 1, **kwargs) -> GraphStoreQueryResult:
         """
-        Query the knowledage graph with a question and optional message history.
+        Query the knowledge graph with a question and optional message history.
 
         Args:
         question: a human input question.
@@ -77,6 +100,12 @@ def query(self, question: str, n_results: int = 1, **kwargs) -> FalkorGraphQuery
 
         Returns: FalkorGraphQueryResult
         """
-        messages = kwargs.pop("messages", [])
-        answer, messages = self.knowledge_graph.ask(question, messages)
-        return FalkorGraphQueryResult(answer=answer, results=[], messages=messages)
+        if self.knowledge_graph is None:
+            raise ValueError("Knowledge graph is not created.")
+
+        response = self._chat_session.send_message(question)
+
+        # History will be considered when querying by setting the last_answer
+        self._chat_session.last_answer = response["response"]
+
+        return GraphStoreQueryResult(answer=response["response"], results=[])

autogen/agentchat/contrib/graph_rag/falkor_graph_rag_capability.py

@@ -0,0 +1,81 @@
+# Copyright (c) 2023 - 2024, Owners of https://github.com/ag2ai
+#
+# SPDX-License-Identifier: Apache-2.0
+
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+from autogen import Agent, ConversableAgent, UserProxyAgent
+
+from .falkor_graph_query_engine import FalkorGraphQueryEngine
+from .graph_query_engine import GraphStoreQueryResult
+from .graph_rag_capability import GraphRagCapability
+
+
+class FalkorGraphRagCapability(GraphRagCapability):
+    """
+    The FalkorDB GraphRAG capability integrate FalkorDB with graphrag_sdk version: 0.1.3b0.
+    Ref: https://github.com/FalkorDB/GraphRAG-SDK/tree/2-move-away-from-sql-to-json-ontology-detection
+
+    For usage, please refer to example notebook/agentchat_graph_rag_falkordb.ipynb
+    """
+
+    def __init__(self, query_engine: FalkorGraphQueryEngine):
+        """
+        initialize GraphRAG capability with a graph query engine
+        """
+        self.query_engine = query_engine
+
+    def add_to_agent(self, agent: UserProxyAgent):
+        """
+        Add FalkorDB GraphRAG capability to a UserProxyAgent.
+        The restriction to a UserProxyAgent to make sure the returned message does not contain information retrieved from the graph DB instead of any LLMs.
+        """
+        self.graph_rag_agent = agent
+
+        # Validate the agent config
+        if agent.llm_config not in (None, False):
+            raise Exception(
+                "Agents with GraphRAG capabilities do not use an LLM configuration. Please set your llm_config to None or False."
+            )
+
+        # Register method to generate the reply using a FalkorDB query
+        # All other reply methods will be removed
+        agent.register_reply(
+            [ConversableAgent, None], self._reply_using_falkordb_query, position=0, remove_other_reply_funcs=True
+        )
+
+    def _reply_using_falkordb_query(
+        self,
+        recipient: ConversableAgent,
+        messages: Optional[List[Dict]] = None,
+        sender: Optional[Agent] = None,
+        config: Optional[Any] = None,
+    ) -> Tuple[bool, Union[str, Dict, None]]:
+        """
+        Query FalkorDB and return the message. Internally, it utilises OpenAI to generate a reply based on the given messages.
+        The history with FalkorDB is also logged and updated.
+
+        If no results are found, a default message is returned: "I'm sorry, I don't have an answer for that."
+
+        Args:
+            recipient: The agent instance that will receive the message.
+            messages: A list of messages in the conversation history with the sender.
+            sender: The agent instance that sent the message.
+            config: Optional configuration for message processing.
+
+        Returns:
+            A tuple containing a boolean indicating success and the assistant's reply.
+        """
+        question = self._get_last_question(messages[-1])
+        result: GraphStoreQueryResult = self.query_engine.query(question)
+
+        return True, result.answer if result.answer else "I'm sorry, I don't have an answer for that."
+
+    def _get_last_question(self, message: Union[Dict, str]):
+        """Retrieves the last message from the conversation history."""
+        if isinstance(message, str):
+            return message
+        if isinstance(message, Dict):
+            if "content" in message:
+                return message["content"]
+        return None

autogen/agentchat/contrib/graph_rag/graph_query_engine.py

@@ -26,7 +26,7 @@ class GraphStoreQueryResult:
 class GraphQueryEngine(Protocol):
     """An abstract base class that represents a graph query engine on top of a underlying graph database.
 
-    This interface defines the basic methods for graph rag.
+    This interface defines the basic methods for graph-based RAG.
     """
 
     def init_db(self, input_doc: List[Document] | None = None):

autogen/agentchat/contrib/graph_rag/graph_rag_capability.py

@@ -12,9 +12,9 @@
 
 class GraphRagCapability(AgentCapability):
     """
-    A graph rag capability uses a graph query engine to give a conversable agent the graph rag ability.
+    A graph-based RAG capability uses a graph query engine to give a conversable agent the graph-based RAG ability.
 
-    An agent class with graph rag capability could
+    An agent class with graph-based RAG capability could
     1. create a graph in the underlying database with input documents.
     2. retrieved relevant information based on messages received by the agent.
     3. generate answers from retrieved information and send messages back.
@@ -55,8 +55,10 @@ class GraphRagCapability(AgentCapability):
 
     def __init__(self, query_engine: GraphQueryEngine):
         """
-        initialize graph rag capability with a graph query engine
+        Initialize graph-based RAG capability with a graph query engine
         """
         ...
 
-    def add_to_agent(self, agent: ConversableAgent): ...
+    def add_to_agent(self, agent: ConversableAgent):
+        """Add the capability to an agent"""
+        ...

autogen/agentchat/contrib/swarm_agent.py

@@ -1,6 +1,7 @@
 # Copyright (c) 2023 - 2024, Owners of https://github.com/ag2ai
 #
 # SPDX-License-Identifier: Apache-2.0
+import copy
 import json
 from dataclasses import dataclass
 from enum import Enum
@@ -441,16 +442,19 @@ def generate_swarm_tool_reply(
         message = messages[-1]
         if "tool_calls" in message:
 
-            tool_calls = len(message["tool_calls"])
+            tool_call_count = len(message["tool_calls"])
 
             # Loop through tool calls individually (so context can be updated after each function call)
             next_agent = None
             tool_responses_inner = []
             contents = []
-            for index in range(tool_calls):
+            for index in range(tool_call_count):
+
+                # Deep copy to ensure no changes to messages when we insert the context variables
+                message_copy = copy.deepcopy(message)
 
                 # 1. add context_variables to the tool call arguments
-                tool_call = message["tool_calls"][index]
+                tool_call = message_copy["tool_calls"][index]
 
                 if tool_call["type"] == "function":
                     function_name = tool_call["function"]["name"]
@@ -459,20 +463,16 @@ def generate_swarm_tool_reply(
                     if function_name in self._function_map:
                         func = self._function_map[function_name]  # Get the original function
 
-                        # Check if function has context_variables parameter
+                        # Inject the context variables into the tool call if it has the parameter
                         sig = signature(func)
                         if __CONTEXT_VARIABLES_PARAM_NAME__ in sig.parameters:
+
                             current_args = json.loads(tool_call["function"]["arguments"])
                             current_args[__CONTEXT_VARIABLES_PARAM_NAME__] = self._context_variables
-                            # Update the tool call with new arguments
                             tool_call["function"]["arguments"] = json.dumps(current_args)
 
-                # Copy the message
-                message_copy = message.copy()
-                tool_calls_copy = message_copy["tool_calls"]
-
-                # remove all the tool calls except the one at the index
-                message_copy["tool_calls"] = [tool_calls_copy[index]]
+                # Ensure we are only executing the one tool at a time
+                message_copy["tool_calls"] = [tool_call]
 
                 # 2. generate tool calls reply
                 _, tool_message = self.generate_tool_calls_reply([message_copy])