Harrison/get rid of prints (#490)

deprecate all prints in favor of callback_manager.on_text (open to
better naming)

langchain/agents/agent.py

@@ -218,7 +218,7 @@ class AgentExecutor(Chain, BaseModel):
             # If the tool chosen is the finishing tool, then we end and return.
             if isinstance(output, AgentFinish):
                 if self.verbose:
-                    self.callback_manager.on_agent_end(output.log, color="green")
+                    self.callback_manager.on_text(output.log, color="green")
                 final_output = output.return_values
                 if self.return_intermediate_steps:
                     final_output["intermediate_steps"] = intermediate_steps

langchain/callbacks/base.py

@@ -55,7 +55,7 @@ class BaseCallbackHandler(ABC):
         """Run when tool errors."""
 
     @abstractmethod
-    def on_agent_end(self, log: str, **kwargs: Any) -> None:
+    def on_text(self, text: str, **kwargs: Any) -> None:
         """Run when agent ends."""
 
 
@@ -132,10 +132,10 @@ class CallbackManager(BaseCallbackManager):
         for handler in self.handlers:
             handler.on_tool_error(error)
 
-    def on_agent_end(self, log: str, **kwargs: Any) -> None:
+    def on_text(self, text: str, **kwargs: Any) -> None:
         """Run when agent ends."""
         for handler in self.handlers:
-            handler.on_agent_end(log, **kwargs)
+            handler.on_text(text, **kwargs)
 
     def add_handler(self, handler: BaseCallbackHandler) -> None:
         """Add a handler to the callback manager."""

langchain/callbacks/shared.py

@@ -88,10 +88,10 @@ class SharedCallbackManager(Singleton, BaseCallbackManager):
         with self._lock:
             self._callback_manager.on_tool_error(error)
 
-    def on_agent_end(self, log: str, **kwargs: Any) -> None:
+    def on_text(self, text: str, **kwargs: Any) -> None:
         """Run when agent ends."""
         with self._lock:
-            self._callback_manager.on_agent_end(log, **kwargs)
+            self._callback_manager.on_text(text, **kwargs)
 
     def add_handler(self, callback: BaseCallbackHandler) -> None:
         """Add a callback to the callback manager."""

langchain/callbacks/stdout.py

@@ -67,8 +67,12 @@ class StdOutCallbackHandler(BaseCallbackHandler):
         """Do nothing."""
         pass
 
-    def on_agent_end(
-        self, log: str, color: Optional[str] = None, **kwargs: Any
+    def on_text(
+        self,
+        text: str,
+        color: Optional[str] = None,
+        end: str = "",
+        **kwargs: Optional[str],
     ) -> None:
         """Run when agent ends."""
-        print_text(log, color=color)
+        print_text(text, color=color, end=end)

langchain/chains/api/base.py

@@ -8,7 +8,6 @@ from pydantic import BaseModel, root_validator
 from langchain.chains.api.prompt import API_RESPONSE_PROMPT, API_URL_PROMPT
 from langchain.chains.base import Chain
 from langchain.chains.llm import LLMChain
-from langchain.input import print_text
 from langchain.llms.base import BaseLLM
 from langchain.requests import RequestsWrapper
 
@@ -67,10 +66,10 @@ class APIChain(Chain, BaseModel):
             question=question, api_docs=self.api_docs
         )
         if self.verbose:
-            print_text(api_url, color="green", end="\n")
+            self.callback_manager.on_text(api_url, color="green", end="\n")
         api_response = self.requests_wrapper.run(api_url)
         if self.verbose:
-            print_text(api_response, color="yellow", end="\n")
+            self.callback_manager.on_text(api_response, color="yellow", end="\n")
         answer = self.api_answer_chain.predict(
             question=question,
             api_docs=self.api_docs,

langchain/chains/llm_bash/base.py

@@ -6,7 +6,6 @@ from pydantic import BaseModel, Extra
 from langchain.chains.base import Chain
 from langchain.chains.llm import LLMChain
 from langchain.chains.llm_bash.prompt import PROMPT
-from langchain.input import print_text
 from langchain.llms.base import BaseLLM
 from langchain.utilities.bash import BashProcess
 
@@ -52,11 +51,11 @@ class LLMBashChain(Chain, BaseModel):
         llm_executor = LLMChain(prompt=PROMPT, llm=self.llm)
         bash_executor = BashProcess()
         if self.verbose:
-            print_text(inputs[self.input_key])
+            self.callback_manager.on_text(inputs[self.input_key])
 
         t = llm_executor.predict(question=inputs[self.input_key])
         if self.verbose:
-            print_text(t, color="green")
+            self.callback_manager.on_text(t, color="green")
 
         t = t.strip()
         if t.startswith("```bash"):
@@ -69,8 +68,8 @@ class LLMBashChain(Chain, BaseModel):
             output = bash_executor.run(command_list)
 
             if self.verbose:
-                print_text("\nAnswer: ")
-                print_text(output, color="yellow")
+                self.callback_manager.on_text("\nAnswer: ")
+                self.callback_manager.on_text(output, color="yellow")
 
         else:
             raise ValueError(f"unknown format from LLM: {t}")

langchain/chains/llm_math/base.py

@@ -6,7 +6,6 @@ from pydantic import BaseModel, Extra
 from langchain.chains.base import Chain
 from langchain.chains.llm import LLMChain
 from langchain.chains.llm_math.prompt import PROMPT
-from langchain.input import print_text
 from langchain.llms.base import BaseLLM
 from langchain.python import PythonREPL
 
@@ -52,17 +51,17 @@ class LLMMathChain(Chain, BaseModel):
         llm_executor = LLMChain(prompt=PROMPT, llm=self.llm)
         python_executor = PythonREPL()
         if self.verbose:
-            print_text(inputs[self.input_key])
+            self.callback_manager.on_text(inputs[self.input_key])
         t = llm_executor.predict(question=inputs[self.input_key], stop=["```output"])
         if self.verbose:
-            print_text(t, color="green")
+            self.callback_manager.on_text(t, color="green")
         t = t.strip()
         if t.startswith("```python"):
             code = t[9:-4]
             output = python_executor.run(code)
             if self.verbose:
-                print_text("\nAnswer: ")
-                print_text(output, color="yellow")
+                self.callback_manager.on_text("\nAnswer: ")
+                self.callback_manager.on_text(output, color="yellow")
             answer = "Answer: " + output
         elif t.startswith("Answer:"):
             answer = t

langchain/chains/pal/base.py

@@ -12,7 +12,6 @@ from langchain.chains.base import Chain
 from langchain.chains.llm import LLMChain
 from langchain.chains.pal.colored_object_prompt import COLORED_OBJECT_PROMPT
 from langchain.chains.pal.math_prompt import MATH_PROMPT
-from langchain.input import print_text
 from langchain.llms.base import BaseLLM
 from langchain.prompts.base import BasePromptTemplate
 from langchain.python import PythonREPL
@@ -53,7 +52,7 @@ class PALChain(Chain, BaseModel):
         llm_chain = LLMChain(llm=self.llm, prompt=self.prompt)
         code = llm_chain.predict(stop=[self.stop], **inputs)
         if self.verbose:
-            print_text(code, color="green", end="\n")
+            self.callback_manager.on_text(code, color="green", end="\n")
         repl = PythonREPL()
         res = repl.run(code + f"\n{self.get_answer_expr}")
         return {self.output_key: res.strip()}

langchain/chains/sequential.py

@@ -5,7 +5,7 @@ from typing import Dict, List
 from pydantic import BaseModel, Extra, root_validator
 
 from langchain.chains.base import Chain
-from langchain.input import get_color_mapping, print_text
+from langchain.input import get_color_mapping
 
 
 class SequentialChain(Chain, BaseModel):
@@ -133,5 +133,7 @@ class SimpleSequentialChain(Chain, BaseModel):
             if self.strip_outputs:
                 _input = _input.strip()
             if self.verbose:
-                print_text(_input, color=color_mapping[str(i)], end="\n")
+                self.callback_manager.on_text(
+                    _input, color=color_mapping[str(i)], end="\n"
+                )
         return {self.output_key: _input}

langchain/chains/sql_database/base.py

@@ -6,7 +6,6 @@ from pydantic import BaseModel, Extra
 from langchain.chains.base import Chain
 from langchain.chains.llm import LLMChain
 from langchain.chains.sql_database.prompt import PROMPT
-from langchain.input import print_text
 from langchain.llms.base import BaseLLM
 from langchain.sql_database import SQLDatabase
 
@@ -55,7 +54,7 @@ class SQLDatabaseChain(Chain, BaseModel):
         llm_chain = LLMChain(llm=self.llm, prompt=PROMPT)
         input_text = f"{inputs[self.input_key]} \nSQLQuery:"
         if self.verbose:
-            print_text(input_text)
+            self.callback_manager.on_text(input_text)
         llm_inputs = {
             "input": input_text,
             "dialect": self.database.dialect,
@@ -64,15 +63,15 @@ class SQLDatabaseChain(Chain, BaseModel):
         }
         sql_cmd = llm_chain.predict(**llm_inputs)
         if self.verbose:
-            print_text(sql_cmd, color="green")
+            self.callback_manager.on_text(sql_cmd, color="green")
         result = self.database.run(sql_cmd)
         if self.verbose:
-            print_text("\nSQLResult: ")
-            print_text(result, color="yellow")
-            print_text("\nAnswer:")
+            self.callback_manager.on_text("\nSQLResult: ")
+            self.callback_manager.on_text(result, color="yellow")
+            self.callback_manager.on_text("\nAnswer:")
         input_text += f"{sql_cmd}\nSQLResult: {result}\nAnswer:"
         llm_inputs["input"] = input_text
         final_result = llm_chain.predict(**llm_inputs)
         if self.verbose:
-            print_text(final_result, color="green")
+            self.callback_manager.on_text(final_result, color="green")
         return {self.output_key: final_result}

tests/unit_tests/callbacks/fake_callback_handler.py

@@ -59,6 +59,6 @@ class FakeCallbackHandler(BaseCallbackHandler):
         """Run when tool errors."""
         self.errors += 1
 
-    def on_agent_end(self, log: str, **kwargs: Any) -> None:
+    def on_text(self, text: str, **kwargs: Any) -> None:
         """Run when agent is ending."""
         self.ends += 1