feat[Agent]: add agent conversation code

examples/agent.py

@@ -0,0 +1,37 @@
+import pandas as pd
+from pandasai import Agent
+
+from pandasai.llm.openai import OpenAI
+
+employees_data = {
+    "EmployeeID": [1, 2, 3, 4, 5],
+    "Name": ["John", "Emma", "Liam", "Olivia", "William"],
+    "Department": ["HR", "Sales", "IT", "Marketing", "Finance"],
+}
+
+salaries_data = {
+    "EmployeeID": [1, 2, 3, 4, 5],
+    "Salary": [5000, 6000, 4500, 7000, 5500],
+}
+
+employees_df = pd.DataFrame(employees_data)
+salaries_df = pd.DataFrame(salaries_data)
+
+
+llm = OpenAI("OPEN_API_KEY")
+agent = Agent([employees_df, salaries_df], config={"llm": llm}, memory_size=10)
+
+# Chat with the agent
+response = agent.chat("Who gets paid the most?")
+print(response)
+
+
+# # Get Clarification Questions
+questions = agent.clarification_questions()
+
+for question in questions:
+    print(question)
+
+# Explain how the chat response is generated
+response = agent.explain()
+print(response)

pandasai/__init__.py

@@ -44,6 +44,7 @@
 from .callbacks.base import BaseCallback
 from .schemas.df_config import Config
 from .helpers.cache import Cache
+from .agent import Agent
 
 __version__ = importlib.metadata.version(__package__ or __name__)
 
@@ -257,4 +258,4 @@ def clear_cache(filename: str = None):
     cache.clear()
 
 
-__all__ = ["PandasAI", "SmartDataframe", "SmartDatalake", "clear_cache"]
+__all__ = ["PandasAI", "SmartDataframe", "SmartDatalake", "Agent", "clear_cache"]

pandasai/agent/__init__.py

@@ -0,0 +1,106 @@
+import json
+from typing import Union, List, Optional
+from pandasai.helpers.df_info import DataFrameType
+from pandasai.helpers.logger import Logger
+from pandasai.prompts.clarification_questions_prompt import ClarificationQuestionPrompt
+from pandasai.prompts.explain_prompt import ExplainPrompt
+from pandasai.schemas.df_config import Config
+from pandasai.smart_datalake import SmartDatalake
+
+
+class Agent:
+    """
+    Agent class to improve the conversational experience in PandasAI
+    """
+
+    _lake: SmartDatalake = None
+    _logger: Optional[Logger] = None
+    _memory_size: int = None
+
+    def __init__(
+        self,
+        dfs: Union[DataFrameType, List[DataFrameType]],
+        config: Optional[Union[Config, dict]] = None,
+        logger: Optional[Logger] = None,
+        memory_size: int = 1,
+    ):
+        """
+        Args:
+            df (Union[SmartDataframe, SmartDatalake]): _description_
+            memory_size (int, optional): _description_. Defaults to 1.
+        """
+
+        if not isinstance(dfs, list):
+            dfs = [dfs]
+
+        self._lake = SmartDatalake(dfs, config, logger)
+        self._logger = self._lake.logger
+        self._memory_size = memory_size
+
+    def chat(self, query: str, output_type: Optional[str] = None):
+        """
+        Simulate a chat interaction with the assistant on Dataframe.
+        """
+        try:
+            result = self._lake.chat(
+                query,
+                output_type=output_type,
+                start_conversation=self._lake._memory.get_conversation(
+                    self._memory_size
+                ),
+            )
+            return result
+        except Exception as exception:
+            return (
+                "Unfortunately, I was not able to get your answers, "
+                "because of the following error:\n"
+                f"\n{exception}\n"
+            )
+
+    def clarification_questions(self) -> List[str]:
+        """
+        Generate clarification questions based on the data
+        """
+        try:
+            prompt = ClarificationQuestionPrompt(
+                self._lake.dfs, self._lake._memory.get_conversation(self._memory_size)
+            )
+
+            result = self._lake.llm.call(prompt)
+            self._logger.log(
+                f"""Clarification Questions:  {result}
+                """
+            )
+            questions: list[str] = json.loads(result)
+            return questions[:3]
+
+        except Exception as exception:
+            raise exception
+
+    def start_new_conversation(self):
+        """
+        Clears the previous conversation
+        """
+        self._lake._memory.clear()
+
+    def explain(self) -> str:
+        """
+        Returns the explanation of the code how it reached to the solution
+        """
+        try:
+            prompt = ExplainPrompt(
+                self._lake._memory.get_conversation(self._memory_size),
+                self._lake.last_code_executed,
+            )
+            response = self._lake.llm.call(prompt)
+            self._logger.log(
+                f"""Explaination:  {response}
+                """
+            )
+            return response
+        except Exception as exception:
+            return (
+                "Unfortunately, I was not able to explain, "
+                "because of the following error:\n"
+                f"\n{exception}\n"
+            )

pandasai/prompts/clarification_questions_prompt.py

@@ -0,0 +1,51 @@
+""" Prompt to get clarification questions
+You are provided with the following pandas DataFrames:
+
+<dataframe>
+{dataframe}
+</dataframe>
+
+<conversation>
+{conversation}
+</conversation>
+
+Based on the conversation, are there any clarification questions that a senior data scientist would ask? These are questions for non technical people, only ask for questions they could ask given low tech expertise and no knowledge about how the dataframes are structured.
+
+Return the JSON array of the clarification questions. If there is no clarification question, return an empty array.
+
+Json:
+"""  # noqa: E501
+
+
+from .base import Prompt
+
+
+class ClarificationQuestionPrompt(Prompt):
+    """Prompt to get clarification questions"""
+
+    text: str = """
+You are provided with the following pandas DataFrames:
+
+<dataframe>
+{dataframes}
+</dataframe>
+
+<conversation>
+{conversation}
+</conversation>
+
+Based on the conversation, are there any clarification questions 
+that a senior data scientist would ask? These are questions for non technical people, 
+only ask for questions they could ask given low tech expertise and 
+no knowledge about how the dataframes are structured.
+
+Return the JSON array of the clarification questions. 
+
+If there is no clarification question, return an empty array.
+
+Json:
+"""
+
+    def __init__(self, dataframes, conversation):
+        self.set_var("dataframes", dataframes)
+        self.set_var("conversation", conversation)

pandasai/prompts/explain_prompt.py

@@ -0,0 +1,34 @@
+""" Prompt to explain solution generated
+Based on the last conversation you generated the code. 
+Can you explain briefly for non technical person on how you came up with code 
+without explaining pandas library?
+"""
+
+
+from .base import Prompt
+
+
+class ExplainPrompt(Prompt):
+    """Prompt to get clarification questions"""
+
+    text: str = """
+The previous conversation we had
+
+<Conversation>
+{conversation}
+</Conversation>
+
+Based on the last conversation you generated the following code:
+
+<Code>
+{code}
+</Code
+
+Explain how you came up with code for non-technical people without 
+mentioning technical details or mentioning the libraries used?
+
+"""
+
+    def __init__(self, conversation, code):
+        self.set_var("conversation", conversation)
+        self.set_var("code", code)

pandasai/smart_datalake/__init__.py

@@ -255,7 +255,12 @@ def _get_cache_key(self) -> str:
 
         return cache_key
 
-    def chat(self, query: str, output_type: Optional[str] = None):
+    def chat(
+        self,
+        query: str,
+        output_type: Optional[str] = None,
+        start_conversation: Optional[str] = None,
+    ):
         """
         Run a query on the dataframe.
 
@@ -305,6 +310,9 @@ def chat(self, query: str, output_type: Optional[str] = None):
                     "save_charts_path": self._config.save_charts_path.rstrip("/"),
                     "output_type_hint": output_type_helper.template_hint,
                 }
+                if start_conversation is not None:
+                    default_values["conversation"] = start_conversation
+
                 generate_python_code_instruction = self._get_prompt(
                     "generate_python_code",
                     default_prompt=GeneratePythonCodePrompt,
@@ -623,7 +631,7 @@ def last_code_generated(self):
 
     @last_code_generated.setter
     def last_code_generated(self, last_code_generated: str):
-        self._code_manager._last_code_generated = last_code_generated
+        self._last_code_generated = last_code_generated
 
     @property
     def last_code_executed(self):
@@ -644,3 +652,11 @@ def last_error(self):
     @last_error.setter
     def last_error(self, last_error: str):
         self._last_error = last_error
+
+    @property
+    def dfs(self):
+        return self._dfs
+
+    @property
+    def memory(self):
+        return self._memory