cr

docs/examples/agents/react.ipynb

@@ -12,7 +12,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 6,
    "id": "4e272b47",
    "metadata": {},
    "outputs": [],
@@ -32,13 +32,13 @@
     "    )\n",
     "]\n",
     "\n",
-    "llm = OpenAI(temperature=0)\n",
+    "llm = OpenAI(temperature=0, model_name=\"text-davinci-002\")\n",
     "react = initialize_agent(tools, llm, agent=\"react-docstore\", verbose=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "8078c8f1",
    "metadata": {},
    "outputs": [
@@ -46,9 +46,31 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new ReActDocstoreAgent chain...\u001b[0m\n",
       "Author David Chanoff has collaborated with a U.S. Navy admiral who served as the ambassador to the United Kingdom under which President?\n",
-      "Thought 1:"
+      "Thought 1:\u001b[32;1m\u001b[1;3m I need to search David Chanoff and find the U.S. Navy admiral he collaborated\n",
+      "with.\n",
+      "Action 1: Search[David Chanoff]\u001b[0m\n",
+      "Observation 1: \u001b[36;1m\u001b[1;3mDavid Chanoff is a noted author of non-fiction work. His work has typically involved collaborations with the principal protagonist of the work concerned. His collaborators have included; Augustus A. White, Joycelyn Elders, Đoàn Văn Toại, William J. Crowe, Ariel Sharon, Kenneth Good and Felix Zandman. He has also written about a wide range of subjects including literary history, education and foreign for The Washington Post, The New Republic and The New York Times Magazine. He has published more than twelve books.\u001b[0m\n",
+      "Thought 2:\u001b[32;1m\u001b[1;3m The U.S. Navy admiral David Chanoff collaborated with is William J. Crowe.\n",
+      "Action 2: Search[William J. Crowe]\u001b[0m\n",
+      "Observation 2: \u001b[36;1m\u001b[1;3mWilliam James Crowe Jr. (January 2, 1925 – October 18, 2007) was a United States Navy admiral and diplomat who served as the 11th chairman of the Joint Chiefs of Staff under Presidents Ronald Reagan and George H. W. Bush, and as the ambassador to the United Kingdom and Chair of the Intelligence Oversight Board under President Bill Clinton.\u001b[0m\n",
+      "Thought 3:\u001b[32;1m\u001b[1;3m William J. Crowe served as the ambassador to the United Kingdom under President Bill Clinton.\n",
+      "Action 3: Finish[Bill Clinton]\u001b[0m\n",
+      "\u001b[1m> Finished ReActDocstoreAgent chain.\u001b[0m\n"
      ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'Bill Clinton'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -81,7 +103,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.10.8"
   }
  },
  "nbformat": 4,

langchain/__init__.py

@@ -13,6 +13,7 @@ from langchain.chains import (
 )
 from langchain.docstore import InMemoryDocstore, Wikipedia
 from langchain.llms import Cohere, HuggingFaceHub, OpenAI
+from langchain.logger import BaseLogger
 from langchain.prompts import (
     BasePromptTemplate,
     FewShotPromptTemplate,
@@ -23,6 +24,8 @@ from langchain.serpapi import SerpAPIChain, SerpAPIWrapper
 from langchain.sql_database import SQLDatabase
 from langchain.vectorstores import FAISS, ElasticVectorSearch
 
+logger = BaseLogger()
+
 __all__ = [
     "LLMChain",
     "LLMMathChain",

langchain/agents/agent.py

@@ -2,24 +2,18 @@
 from __future__ import annotations
 
 from abc import ABC, abstractmethod
-from typing import Any, ClassVar, Dict, List, NamedTuple, Optional, Tuple
+from typing import Any, ClassVar, Dict, List, Optional, Tuple
 
 from pydantic import BaseModel
 
+from langchain.agents.input import ChainedInput
 from langchain.agents.tools import Tool
 from langchain.chains.base import Chain
 from langchain.chains.llm import LLMChain
-from langchain.input import ChainedInput, get_color_mapping
+from langchain.input import get_color_mapping
 from langchain.llms.base import LLM
 from langchain.prompts.base import BasePromptTemplate
-
-
-class Action(NamedTuple):
-    """Action to take."""
-
-    tool: str
-    tool_input: str
-    log: str
+from langchain.schema import AgentAction
 
 
 class Agent(Chain, BaseModel, ABC):
@@ -99,7 +93,7 @@ class Agent(Chain, BaseModel, ABC):
         llm_chain = LLMChain(llm=llm, prompt=cls.create_prompt(tools))
         return cls(llm_chain=llm_chain, tools=tools, **kwargs)
 
-    def get_action(self, text: str) -> Action:
+    def get_action(self, text: str) -> AgentAction:
         """Given input, decided what to do.
 
         Args:
@@ -119,7 +113,7 @@ class Agent(Chain, BaseModel, ABC):
             full_output += output
             parsed_output = self._extract_tool_and_input(full_output)
         tool, tool_input = parsed_output
-        return Action(tool, tool_input, full_output)
+        return AgentAction(tool, tool_input, full_output)
 
     def _call(self, inputs: Dict[str, str]) -> Dict[str, str]:
         """Run text through and get agent response."""
@@ -145,7 +139,7 @@ class Agent(Chain, BaseModel, ABC):
             # Call the LLM to see what to do.
             output = self.get_action(chained_input.input)
             # Add the log to the Chained Input.
-            chained_input.add(output.log, color="green")
+            chained_input.add_action(output, color="green")
             # If the tool chosen is the finishing tool, then we end and return.
             if output.tool == self.finish_tool_name:
                 return {self.output_key: output.tool_input}
@@ -154,8 +148,9 @@ class Agent(Chain, BaseModel, ABC):
             # We then call the tool on the tool input to get an observation
             observation = chain(output.tool_input)
             # We then log the observation
-            chained_input.add(f"\n{self.observation_prefix}")
-            chained_input.add(observation, color=color_mapping[output.tool])
-            # We then add the LLM prefix into the prompt to get the LLM to start
-            # thinking, and start the loop all over.
-            chained_input.add(f"\n{self.llm_prefix}")
+            chained_input.add_observation(
+                observation,
+                self.observation_prefix,
+                self.llm_prefix,
+                color=color_mapping[output.tool],
+            )

langchain/agents/input.py

@@ -0,0 +1,44 @@
+"""Input manager for agents."""
+from typing import Optional
+
+import langchain
+from langchain.schema import AgentAction
+
+
+class ChainedInput:
+    """Class for working with input that is the result of chains."""
+
+    def __init__(self, text: str, verbose: bool = False):
+        """Initialize with verbose flag and initial text."""
+        self._verbose = verbose
+        if self._verbose:
+            langchain.logger.log_agent_start(text)
+        self._input = text
+
+    def add_action(self, action: AgentAction, color: Optional[str] = None) -> None:
+        """Add text to input, print if in verbose mode."""
+        if self._verbose:
+            langchain.logger.log_agent_action(action, color=color)
+        self._input += action.log
+
+    def add_observation(
+        self,
+        observation: str,
+        observation_prefix: str,
+        llm_prefix: str,
+        color: Optional[str],
+    ) -> None:
+        """Add observation to input, print if in verbose mode."""
+        if self._verbose:
+            langchain.logger.log_agent_observation(
+                observation,
+                color=color,
+                observation_prefix=observation_prefix,
+                llm_prefix=llm_prefix,
+            )
+        self._input += f"\n{observation_prefix}{observation}\n{llm_prefix}"
+
+    @property
+    def input(self) -> str:
+        """Return the accumulated input."""
+        return self._input

langchain/chains/api/base.py

@@ -80,7 +80,7 @@ class APIChain(Chain, BaseModel):
             print_text(api_url, color="green", end="\n")
         api_response = self.requests_wrapper.run(api_url)
         if self.verbose:
-            print_text(api_url, color="yellow", end="\n")
+            print_text(api_response, color="yellow", end="\n")
         answer = self.api_answer_chain.predict(
             question=question,
             api_docs=self.api_docs,

langchain/chains/llm.py

@@ -3,8 +3,8 @@ from typing import Any, Dict, List, Union
 
 from pydantic import BaseModel, Extra
 
+import langchain
 from langchain.chains.base import Chain
-from langchain.input import print_text
 from langchain.llms.base import LLM
 from langchain.prompts.base import BasePromptTemplate
 
@@ -55,12 +55,13 @@ class LLMChain(Chain, BaseModel):
         selected_inputs = {k: inputs[k] for k in self.prompt.input_variables}
         prompt = self.prompt.format(**selected_inputs)
         if self.verbose:
-            print("Prompt after formatting:")
-            print_text(prompt, color="green", end="\n")
+            langchain.logger.log_llm_inputs(selected_inputs, prompt)
         kwargs = {}
         if "stop" in inputs:
             kwargs["stop"] = inputs["stop"]
         response = self.llm(prompt, **kwargs)
+        if self.verbose:
+            langchain.logger.log_llm_response(response)
         return {self.output_key: response}
 
     def predict(self, **kwargs: Any) -> str:

langchain/input.py

@@ -27,25 +27,3 @@ def print_text(text: str, color: Optional[str] = None, end: str = "") -> None:
     else:
         color_str = _TEXT_COLOR_MAPPING[color]
         print(f"\u001b[{color_str}m\033[1;3m{text}\u001b[0m", end=end)
-
-
-class ChainedInput:
-    """Class for working with input that is the result of chains."""
-
-    def __init__(self, text: str, verbose: bool = False):
-        """Initialize with verbose flag and initial text."""
-        self._verbose = verbose
-        if self._verbose:
-            print_text(text, color=None)
-        self._input = text
-
-    def add(self, text: str, color: Optional[str] = None) -> None:
-        """Add text to input, print if in verbose mode."""
-        if self._verbose:
-            print_text(text, color=color)
-        self._input += text
-
-    @property
-    def input(self) -> str:
-        """Return the accumulated input."""
-        return self._input

langchain/logger.py

@@ -0,0 +1,56 @@
+from typing import Any, Optional
+
+from langchain.input import print_text
+from langchain.schema import AgentAction
+
+
+class BaseLogger:
+    def log_agent_start(self, text: str, **kwargs: Any):
+        pass
+
+    def log_agent_end(self, text: str, **kwargs: Any):
+        pass
+
+    def log_agent_action(self, action: AgentAction, **kwargs: Any):
+        pass
+
+    def log_agent_observation(self, observation: str, **kwargs: Any):
+        pass
+
+    def log_llm_inputs(self, inputs: dict, prompt: str, **kwargs):
+        pass
+
+    def log_llm_response(self, output: str, **kwargs):
+        pass
+
+
+class StOutLogger(BaseLogger):
+    def log_agent_start(self, text: str, **kwargs: Any):
+        print_text(text)
+
+    def log_agent_end(self, text: str, **kwargs: Any):
+        pass
+
+    def log_agent_action(
+        self, action: AgentAction, color: Optional[str] = None, **kwargs: Any
+    ):
+        print_text(action.log, color=color)
+
+    def log_llm_inputs(self, inputs: dict, prompt: str, **kwargs):
+        print("Prompt after formatting:")
+        print_text(prompt, color="green", end="\n")
+
+    def log_llm_response(self, output: str, **kwargs):
+        pass
+
+    def log_agent_observation(
+        self,
+        observation: str,
+        color: Optional[str] = None,
+        observation_prefix: Optional[str] = None,
+        llm_prefix: Optional[str] = None,
+        **kwargs: Any,
+    ):
+        print_text(f"\n{observation_prefix}")
+        print_text(observation, color=color)
+        print_text(f"\n{llm_prefix}")

langchain/schema.py

@@ -0,0 +1,11 @@
+from __future__ import annotations
+
+from typing import NamedTuple
+
+
+class AgentAction(NamedTuple):
+    """Agent's action to take."""
+
+    tool: str
+    tool_input: str
+    log: str

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langchain"
-version = "0.0.28"
+version = "0.0.29"
 description = "Building applications with LLMs through composability"
 authors = []
 license = "MIT"
@@ -21,7 +21,6 @@ manifest-ml = {version = "^0.0.1", optional = true}
 spacy = {version = "^3", optional = true}
 nltk = {version = "^3", optional = true}
 transformers = {version = "^4", optional = true}
-types-toml = "^0.10.8.1"
 
 [tool.poetry.group.test.dependencies]
 pytest = "^7.2.0"
@@ -37,6 +36,7 @@ flake8 = "^6.0.0"
 mypy = "^0.991"
 types-pyyaml = "^6.0.12.2"
 types-requests = "^2.28.11.5"
+types-toml = "^0.10.8.1"
 
 [tool.poetry.group.dev]
 optional = true

tests/unit_tests/test_input.py

@@ -3,7 +3,8 @@
 import sys
 from io import StringIO
 
-from langchain.input import ChainedInput, get_color_mapping
+from langchain.agents.input import ChainedInput
+from langchain.input import get_color_mapping
 
 
 def test_chained_input_not_verbose() -> None: