Add Enum for agent types (#2321)

This pull request adds an enum class for the various types of agents
used in the project, located in the `agent_types.py` file. Currently,
the project is using hardcoded strings for the initialization of these
agents, which can lead to errors and make the code harder to maintain.
With the introduction of the new enums, the code will be more readable
and less error-prone.

The new enum members include:

- ZERO_SHOT_REACT_DESCRIPTION
- REACT_DOCSTORE
- SELF_ASK_WITH_SEARCH
- CONVERSATIONAL_REACT_DESCRIPTION
- CHAT_ZERO_SHOT_REACT_DESCRIPTION
- CHAT_CONVERSATIONAL_REACT_DESCRIPTION

In this PR, I have also replaced the hardcoded strings with the
appropriate enum members throughout the codebase, ensuring a smooth
transition to the new approach.

docs/ecosystem/aim_tracking.ipynb

@@ -205,7 +205,8 @@
    },
    "outputs": [],
    "source": [
-    "from langchain.agents import initialize_agent, load_tools"
+    "from langchain.agents import initialize_agent, load_tools\n",
+    "from langchain.agents.agent_types import AgentType"
    ]
   },
   {
@@ -252,7 +253,7 @@
     "agent = initialize_agent(\n",
     "    tools,\n",
     "    llm,\n",
-    "    agent=\"zero-shot-react-description\",\n",
+    "    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,\n",
     "    callback_manager=manager,\n",
     "    verbose=True,\n",
     ")\n",

docs/ecosystem/clearml_tracking.ipynb

@@ -520,13 +520,14 @@
    ],
    "source": [
     "from langchain.agents import initialize_agent, load_tools\n",
+    "from langchain.agents.agent_types import AgentType\n",
     "\n",
     "# SCENARIO 2 - Agent with Tools\n",
     "tools = load_tools([\"serpapi\", \"llm-math\"], llm=llm, callback_manager=manager)\n",
     "agent = initialize_agent(\n",
     "    tools,\n",
     "    llm,\n",
-    "    agent=\"zero-shot-react-description\",\n",
+    "    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,\n",
     "    callback_manager=manager,\n",
     "    verbose=True,\n",
     ")\n",

docs/ecosystem/google_serper.md

@@ -23,6 +23,7 @@ You can use it as part of a Self Ask chain:
 from langchain.utilities import GoogleSerperAPIWrapper
 from langchain.llms.openai import OpenAI
 from langchain.agents import initialize_agent, Tool
+from langchain.agents.agent_types import AgentType
 
 import os
 
@@ -39,7 +40,7 @@ tools = [
     )
 ]
 
-self_ask_with_search = initialize_agent(tools, llm, agent="self-ask-with-search", verbose=True)
+self_ask_with_search = initialize_agent(tools, llm, agent=AgentType.SELF_ASK_WITH_SEARCH, verbose=True)
 self_ask_with_search.run("What is the hometown of the reigning men's U.S. Open champion?")
 ```
 

docs/ecosystem/wandb_tracking.ipynb

@@ -505,7 +505,8 @@
    },
    "outputs": [],
    "source": [
-    "from langchain.agents import initialize_agent, load_tools"
+    "from langchain.agents import initialize_agent, load_tools\n",
+    "from langchain.agents.agent_types import AgentType"
    ]
   },
   {
@@ -580,7 +581,7 @@
     "agent = initialize_agent(\n",
     "    tools,\n",
     "    llm,\n",
-    "    agent=\"zero-shot-react-description\",\n",
+    "    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,\n",
     "    callback_manager=manager,\n",
     "    verbose=True,\n",
     ")\n",