cr

docs/examples/agents/mrkl.ipynb

@@ -32,7 +32,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 2,
    "id": "07e96d99",
    "metadata": {},
    "outputs": [],
@@ -63,7 +63,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 3,
    "id": "a069c4b6",
    "metadata": {},
    "outputs": [],
@@ -73,7 +73,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 4,
    "id": "e603cd7d",
    "metadata": {},
    "outputs": [
@@ -121,7 +121,7 @@
        "\"Harry Styles, Olivia Wilde's boyfriend, is 28 years old and his age raised to the 0.23 power is 2.1520202182226886.\""
       ]
      },
-     "execution_count": 29,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -132,7 +132,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 5,
    "id": "a5c07010",
    "metadata": {},
    "outputs": [
@@ -170,7 +170,7 @@
        "\"Alanis Morissette's album 'Jagged Little Pill' is in the FooBar database.\""
       ]
      },
-     "execution_count": 25,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -182,7 +182,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "3f13b1c3",
+   "id": "af016a70",
    "metadata": {},
    "outputs": [],
    "source": []

langchain/agents/agent.py

@@ -4,7 +4,7 @@ from __future__ import annotations
 from abc import ABC, abstractmethod
 from typing import Any, ClassVar, Dict, List, Optional, Tuple
 
-from pydantic import BaseModel
+from pydantic import BaseModel, root_validator
 
 from langchain.agents.input import ChainedInput
 from langchain.agents.tools import Tool
@@ -41,6 +41,16 @@ class Agent(Chain, BaseModel, ABC):
         """
         return [self.output_key]
 
+    @root_validator()
+    def validate_prompt(cls, values: Dict) -> Dict:
+        """Validate that prompt matches format."""
+        prompt = values["llm_chain"].prompt
+        if "agent_scratchpad" not in prompt.input_variables:
+            raise ValueError(
+                "`agent_scratchpad` should be a variable in prompt.input_variables"
+            )
+        return values
+
     @property
     @abstractmethod
     def observation_prefix(self) -> str:

langchain/agents/react/textworld_prompt.py

@@ -47,4 +47,6 @@ Action 4: Finish[yes]
 SUFFIX = """\n\nSetup: {input}
 {agent_scratchpad}"""
 
-TEXTWORLD_PROMPT = PromptTemplate.from_examples(EXAMPLES, SUFFIX, ["input", "agent_scratchpad"])
+TEXTWORLD_PROMPT = PromptTemplate.from_examples(
+    EXAMPLES, SUFFIX, ["input", "agent_scratchpad"]
+)

langchain/agents/react/wiki_prompt.py

@@ -110,4 +110,6 @@ Action 3: Finish[yes]""",
 SUFFIX = """\n\nQuestion: {input}
 {agent_scratchpad}"""
 
-WIKI_PROMPT = PromptTemplate.from_examples(EXAMPLES, SUFFIX, ["input", "agent_scratchpad"])
+WIKI_PROMPT = PromptTemplate.from_examples(
+    EXAMPLES, SUFFIX, ["input", "agent_scratchpad"]
+)

langchain/agents/self_ask_with_search/prompt.py

@@ -39,4 +39,6 @@ So the final answer is: No
 
 Question: {input}
 {agent_scratchpad}"""
-PROMPT = PromptTemplate(input_variables=["input", "agent_scratchpad"], template=_DEFAULT_TEMPLATE)
+PROMPT = PromptTemplate(
+    input_variables=["input", "agent_scratchpad"], template=_DEFAULT_TEMPLATE
+)

tests/unit_tests/agents/test_react.py

@@ -56,7 +56,7 @@ def test_predict_until_observation_normal() -> None:
         Tool("Lookup", lambda x: x),
     ]
     agent = ReActDocstoreAgent.from_llm_and_tools(fake_llm, tools)
-    output = agent.get_action("")
+    output = agent.get_action("", {"input": ""})
     assert output.log == outputs[0]
     assert output.tool == "Search"
     assert output.tool_input == "foo"
@@ -71,7 +71,7 @@ def test_predict_until_observation_repeat() -> None:
         Tool("Lookup", lambda x: x),
     ]
     agent = ReActDocstoreAgent.from_llm_and_tools(fake_llm, tools)
-    output = agent.get_action("")
+    output = agent.get_action("", {"input": ""})
     assert output.log == "foo\nAction 1: Search[foo]"
     assert output.tool == "Search"
     assert output.tool_input == "foo"