[Graph RAG] Init Commit with GraphRag interfaces

autogen/agentchat/contrib/graph_rag/document.py

@@ -0,0 +1,24 @@
+from dataclasses import dataclass
+from enum import Enum, auto
+from typing import Optional
+
+
+class DocumentType(Enum):
+    """
+    Enum for supporting document type.
+    """
+
+    TEXT = auto()
+    HTML = auto()
+    PDF = auto()
+
+
+@dataclass
+class Document:
+    """
+    A wrapper of graph store query results.
+    """
+
+    doctype: DocumentType
+    data: Optional[object] = None
+    path_or_url: Optional[str] = ""

autogen/agentchat/contrib/graph_rag/graph_query_engine.py

@@ -0,0 +1,51 @@
+from dataclasses import dataclass, field
+from typing import List, Optional, Protocol
+
+from .document import Document
+
+
+@dataclass
+class GraphStoreQueryResult:
+    """
+    A wrapper of graph store query results.
+
+    answer: human readable answer to question/query.
+    results: intermediate results to question/query, e.g. node entities.
+    """
+
+    answer: Optional[str] = None
+    results: list = field(default_factory=list)
+
+
+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.
+    """
+
+    def init_db(self, input_doc: List[Document] | None = None):
+        """
+        This method initializes graph database with the input documents or records.
+        Usually, it takes the following steps,
+        1. connecting to a graph database.
+        2. extract graph nodes, edges based on input data, graph schema and etc.
+        3. build indexes etc.
+
+        Args:
+        input_doc: a list of input documents that are used to build the graph in database.
+
+        Returns: GraphStore
+        """
+        pass
+
+    def add_records(self, new_records: List) -> bool:
+        """
+        Add new records to the underlying database and add to the graph if required.
+        """
+        pass
+
+    def query(self, question: str, n_results: int = 1, **kwargs) -> GraphStoreQueryResult:
+        """
+        This method transform a string format question into database query and return the result.
+        """
+        pass

autogen/agentchat/contrib/graph_rag/graph_rag_capability.py

@@ -0,0 +1,56 @@
+from autogen.agentchat.contrib.capabilities.agent_capability import AgentCapability
+from autogen.agentchat.conversable_agent import ConversableAgent
+
+from .graph_query_engine import GraphQueryEngine
+
+
+class GraphRagCapability(AgentCapability):
+    """
+    A graph rag capability uses a graph query engine to give a conversable agent the graph rag ability.
+
+    An agent class with graph 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.
+
+    For example,
+    graph_query_engine = GraphQueryEngine(...)
+    graph_query_engine.init_db([Document(doc1), Document(doc2), ...])
+
+    graph_rag_agent = ConversableAgent(
+        name="graph_rag_agent",
+        max_consecutive_auto_reply=3,
+        ...
+    )
+    graph_rag_capability = GraphRagCapbility(graph_query_engine)
+    graph_rag_capability.add_to_agent(graph_rag_agent)
+
+    user_proxy = UserProxyAgent(
+        name="user_proxy",
+        code_execution_config=False,
+        is_termination_msg=lambda msg: "TERMINATE" in msg["content"],
+        human_input_mode="ALWAYS",
+    )
+    user_proxy.initiate_chat(graph_rag_agent, message="Name a few actors who've played in 'The Matrix'")
+
+    # ChatResult(
+        # chat_id=None,
+        # chat_history=[
+            # {'content': 'Name a few actors who've played in \'The Matrix\'', 'role': 'graph_rag_agent'},
+            # {'content': 'A few actors who have played in The Matrix are:
+            #   - Keanu Reeves
+            #   - Laurence Fishburne
+            #   - Carrie-Anne Moss
+            #   - Hugo Weaving',
+            #   'role': 'user_proxy'},
+        # ...)
+
+    """
+
+    def __init__(self, query_engine: GraphQueryEngine):
+        """
+        initialize graph rag capability with a graph query engine
+        """
+        ...
+
+    def add_to_agent(self, agent: ConversableAgent): ...

notebook/agentchat_nested_sequential_chats.ipynb

@@ -811,7 +811,9 @@
   "front_matter": {
    "description": "Solve complex tasks with one or more sequence chats nested as inner monologue.",
    "tags": [
-    "nested chat", "sequential chats", "orchestration"
+    "nested chat",
+    "sequential chats",
+    "orchestration"
    ]
   },
   "kernelspec": {

test/agentchat/contrib/graph_rag/test_graph_rag_basic.py

@@ -0,0 +1,17 @@
+from unittest.mock import Mock
+
+from autogen.agentchat.contrib.graph_rag.graph_query_engine import GraphQueryEngine
+from autogen.agentchat.contrib.graph_rag.graph_rag_capability import GraphRagCapability
+from autogen.agentchat.conversable_agent import ConversableAgent
+
+
+def test_dry_run():
+    """Dry run for basic graph rag objects."""
+    mock_graph_query_engine = Mock(spec=GraphQueryEngine)
+
+    graph_rag_agent = ConversableAgent(
+        name="graph_rag_agent",
+        max_consecutive_auto_reply=3,
+    )
+    graph_rag_capability = GraphRagCapability(mock_graph_query_engine)
+    graph_rag_capability.add_to_agent(graph_rag_agent)