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/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,
+            },
+        )