Add Langchain Agent wrapper with OpenAI Function / Self-ask agent support (#82)

* update Param() type hint in MVP

* update default embedding endpoint

* update Langchain agent wrapper

* update langchain agent

knowledgehub/pipelines/agents/__init__.py

@@ -1,5 +1,6 @@
-from .base import BaseAgent
+from .base import AgentType, BaseAgent
+from .langchain import LangchainAgent
 from .react.agent import ReactAgent
 from .rewoo.agent import RewooAgent
 
-__all__ = ["BaseAgent", "ReactAgent", "RewooAgent"]
+__all__ = ["BaseAgent", "ReactAgent", "RewooAgent", "LangchainAgent", "AgentType"]

knowledgehub/pipelines/agents/base.py

@@ -1,8 +1,6 @@
 from enum import Enum
 from typing import Dict, List, Optional, Union
 
-from pydantic import BaseModel
-
 from kotaemon.llms import PromptTemplate
 from kotaemon.llms.chats.base import ChatLLM
 from kotaemon.llms.completions.base import LLM
@@ -17,10 +15,12 @@ class AgentType(Enum):
     """
 
     openai = "openai"
+    openai_multi = "openai_multi"
+    openai_tool = "openai_tool"
+    self_ask = "self_ask"
     react = "react"
     rewoo = "rewoo"
     vanilla = "vanilla"
-    openai_memory = "openai_memory"
 
     @staticmethod
     def get_agent_class(_type: "AgentType"):
@@ -37,16 +37,6 @@ class AgentType(Enum):
             raise ValueError(f"Unknown agent type: {_type}")
 
 
-class AgentOutput(BaseModel):
-    """
-    Pydantic model for agent output.
-    """
-
-    output: str
-    cost: float
-    token_usage: int
-
-
 class BaseAgent(BaseTool):
     name: str
     """Name of the agent."""
@@ -62,6 +52,10 @@ class BaseAgent(BaseTool):
     prompt_template: Optional[Union[PromptTemplate, Dict[str, PromptTemplate]]]
     """A prompt template or a dict to supply different prompt to the agent
     """
-    plugins: List[BaseTool]
+    plugins: List[BaseTool] = []
     """List of plugins / tools to be used in the agent
     """
+
+    def add_tools(self, tools: List[BaseTool]) -> None:
+        """Helper method to add tools and update agent state if needed"""
+        self.plugins.extend(tools)

knowledgehub/pipelines/agents/langchain.py

@@ -0,0 +1,85 @@
+from typing import List, Optional, Type
+
+from langchain.agents import AgentType as LCAgentType
+from langchain.agents import initialize_agent
+from langchain.agents.agent import AgentExecutor as LCAgentExecutor
+from pydantic import BaseModel, create_model
+
+from kotaemon.base.schema import Document
+from kotaemon.llms.chats.base import ChatLLM
+from kotaemon.llms.completions.base import LLM
+from kotaemon.pipelines.tools import BaseTool
+
+from .base import AgentType, BaseAgent
+
+
+class LangchainAgent(BaseAgent):
+    """Wrapper for Langchain Agent"""
+
+    name: str = "LangchainAgent"
+    agent_type: AgentType
+    description: str = "LangchainAgent for answering multi-step reasoning questions"
+    args_schema: Optional[Type[BaseModel]] = create_model(
+        "LangchainArgsSchema", instruction=(str, ...)
+    )
+    AGENT_TYPE_MAP = {
+        AgentType.openai: LCAgentType.OPENAI_FUNCTIONS,
+        AgentType.openai_multi: LCAgentType.OPENAI_MULTI_FUNCTIONS,
+        AgentType.react: LCAgentType.ZERO_SHOT_REACT_DESCRIPTION,
+        AgentType.self_ask: LCAgentType.SELF_ASK_WITH_SEARCH,
+    }
+    agent: Optional[LCAgentExecutor] = None
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        if self.agent_type not in self.AGENT_TYPE_MAP:
+            raise NotImplementedError(
+                f"AgentType {self.agent_type } not supported by Langchain wrapper"
+            )
+        self.update_agent_tools()
+
+    def update_agent_tools(self):
+        assert isinstance(self.llm, (ChatLLM, LLM))
+        langchain_plugins = [tool.to_langchain_format() for tool in self.plugins]
+
+        # a fix for search_doc tool name:
+        # use "Intermediate Answer" for self-ask agent
+        found_search_tool = False
+        if self.agent_type == AgentType.self_ask:
+            for plugin in langchain_plugins:
+                if plugin.name == "search_doc":
+                    plugin.name = "Intermediate Answer"
+                    langchain_plugins = [plugin]
+                    found_search_tool = True
+                    break
+
+        if self.agent_type != AgentType.self_ask or found_search_tool:
+            # reinit Langchain AgentExecutor
+            self.agent = initialize_agent(
+                langchain_plugins,
+                self.llm.agent,
+                agent=self.AGENT_TYPE_MAP[self.agent_type],
+                handle_parsing_errors=True,
+                verbose=True,
+            )
+
+    def add_tools(self, tools: List[BaseTool]) -> None:
+        super().add_tools(tools)
+        self.update_agent_tools()
+        return
+
+    def _run_tool(self, instruction: str) -> Document:
+        assert (
+            self.agent is not None
+        ), "Lanchain AgentExecutor is not correclty initialized"
+        # Langchain AgentExecutor call
+        output = self.agent(instruction)["output"]
+        return Document(
+            text=output,
+            metadata={
+                "agent": "langchain",
+                "cost": 0.0,
+                "usage": 0,
+            },
+        )

knowledgehub/pipelines/agents/rewoo/agent.py

@@ -9,7 +9,7 @@ from kotaemon.base.schema import Document
 from kotaemon.llms import LLM, ChatLLM, PromptTemplate
 from kotaemon.pipelines.citation import CitationPipeline
 
-from ..base import AgentOutput, AgentType, BaseAgent, BaseLLM, BaseTool
+from ..base import AgentType, BaseAgent, BaseLLM, BaseTool
 from ..output.base import BaseScratchPad
 from ..utils import get_plugin_response_content
 from .planner import Planner
@@ -28,7 +28,9 @@ class RewooAgent(BaseAgent):
         str, PromptTemplate
     ] = dict()  # {"Planner": xxx, "Solver": xxx}
     plugins: List[BaseTool] = list()
-    examples: Dict[str, Union[str, List[str]]] = dict()
+    examples: Dict[
+        str, Union[str, List[str]]
+    ] = dict()  # {"Planner": xxx, "Solver": xxx}
     args_schema: Optional[Type[BaseModel]] = create_model(
         "RewooArgsSchema", instruction=(str, ...)
     )
@@ -156,10 +158,6 @@ class RewooAgent(BaseAgent):
                 if selected_plugin is None:
                     raise ValueError("Invalid plugin detected")
                 tool_response = selected_plugin(tool_input)
-                # cumulate agent-as-plugin costs and tokens.
-                if isinstance(tool_response, AgentOutput):
-                    result["plugin_cost"] = tool_response.cost
-                    result["plugin_token"] = tool_response.token_usage
                 result["evidence"] = get_plugin_response_content(tool_response)
             except ValueError:
                 result["evidence"] = "No evidence found."

knowledgehub/pipelines/agents/rewoo/planner.py

@@ -73,6 +73,7 @@ class Planner(BaseComponent):
         output.debug(f"Prompt: {prompt}")
         try:
             response = self.model(prompt)
+            self.log_progress(".planner", response=response)
             output.info("Planner run successful.")
         except ValueError as e:
             output.error("Planner failed to retrieve response from LLM")

knowledgehub/pipelines/agents/utils.py

@@ -1,12 +1,12 @@
-from .base import AgentOutput
+from ...base import Document
 
 
 def get_plugin_response_content(output) -> str:
     """
     Wrapper for AgentOutput content return
     """
-    if isinstance(output, AgentOutput):
-        return output.output
+    if isinstance(output, Document):
+        return output.text
     else:
         return str(output)
 

knowledgehub/pipelines/ingest.py

@@ -43,14 +43,14 @@ class ReaderIndexingPipeline(BaseComponent):
     reader_name: str = "normal"  # "normal", "mathpix" or "ocr"
     chunk_size: int = 1024
     chunk_overlap: int = 256
-    vector_store: _[BaseVectorStore] = _(InMemoryVectorStore)
-    doc_store: _[BaseDocumentStore] = _(InMemoryDocumentStore)
+    vector_store: BaseVectorStore = _(InMemoryVectorStore)
+    doc_store: BaseDocumentStore = _(InMemoryDocumentStore)
     doc_parsers: List[DocParser] = []
 
     embedding: AzureOpenAIEmbeddings = AzureOpenAIEmbeddings.withx(
         model="text-embedding-ada-002",
         deployment="dummy-q2-text-embedding",
-        azure_endpoint="https://bleh-dummy-2.openai.azure.com/",
+        azure_endpoint="https://bleh-dummy.openai.azure.com/",
         openai_api_key=os.environ.get("OPENAI_API_KEY", ""),
         chunk_size=16,
     )

knowledgehub/pipelines/qa.py

@@ -49,14 +49,14 @@ class QuestionAnsweringPipeline(BaseComponent):
         request_timeout=60,
     )
 
-    vector_store: _[BaseVectorStore] = _(InMemoryVectorStore)
-    doc_store: _[BaseDocumentStore] = _(InMemoryDocumentStore)
+    vector_store: BaseVectorStore = _(InMemoryVectorStore)
+    doc_store: BaseDocumentStore = _(InMemoryDocumentStore)
     rerankers: Sequence[BaseRerankingPipeline] = []
 
     embedding: AzureOpenAIEmbeddings = AzureOpenAIEmbeddings.withx(
         model="text-embedding-ada-002",
         deployment="dummy-q2-text-embedding",
-        azure_endpoint="https://bleh-dummy-2.openai.azure.com/",
+        azure_endpoint="https://bleh-dummy.openai.azure.com/",
         openai_api_key=os.environ.get("OPENAI_API_KEY", ""),
     )
 
@@ -137,8 +137,9 @@ class AgentQAPipeline(QuestionAnsweringPipeline):
             component=self.retrieving_pipeline,
         )
         if search_tool not in self.agent.plugins:
-            self.agent.plugins.append(search_tool)
+            self.agent.add_tools([search_tool])
 
     def run(self, question: str, use_citation: bool = False) -> Document:
-        answer = self.agent(question, use_citation=use_citation)
+        kwargs = {"use_citation": use_citation} if use_citation else {}
+        answer = self.agent(question, **kwargs)
         return answer

tests/test_agent.py

@@ -4,6 +4,8 @@ import pytest
 from openai.types.chat.chat_completion import ChatCompletion
 
 from kotaemon.llms.chats.openai import AzureChatOpenAI
+from kotaemon.pipelines.agents.base import AgentType
+from kotaemon.pipelines.agents.langchain import LangchainAgent
 from kotaemon.pipelines.agents.react import ReactAgent
 from kotaemon.pipelines.agents.rewoo import RewooAgent
 from kotaemon.pipelines.tools import (
@@ -13,7 +15,7 @@ from kotaemon.pipelines.tools import (
     WikipediaTool,
 )
 
-FINAL_RESPONSE_TEXT = "Hello Cinnamon AI!"
+FINAL_RESPONSE_TEXT = "Final Answer: Hello Cinnamon AI!"
 
 
 _openai_chat_completion_responses_rewoo = [
@@ -199,7 +201,7 @@ def test_react_agent_langchain(openai_completion, llm, mock_google_search):
     agent = initialize_agent(
         langchain_plugins,
         llm.agent,
-        agent=AgentType.OPENAI_FUNCTIONS,
+        agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
         verbose=True,
     )
     response = agent("Tell me about Cinnamon AI company")
@@ -207,6 +209,26 @@ def test_react_agent_langchain(openai_completion, llm, mock_google_search):
     assert response
 
 
+@patch(
+    "openai.resources.chat.completions.Completions.create",
+    side_effect=_openai_chat_completion_responses_react,
+)
+def test_wrapper_agent_langchain(openai_completion, llm, mock_google_search):
+    plugins = [
+        GoogleSearchTool(),
+        WikipediaTool(),
+        LLMTool(llm=llm),
+    ]
+    agent = LangchainAgent(
+        llm=llm,
+        plugins=plugins,
+        agent_type=AgentType.react,
+    )
+    response = agent("Tell me about Cinnamon AI company")
+    openai_completion.assert_called()
+    assert response
+
+
 @patch(
     "openai.resources.chat.completions.Completions.create",
     side_effect=_openai_chat_completion_responses_react_langchain_tool,