Feat/sales assistant

backend/director/agents/sales_assistant.py

@@ -0,0 +1,238 @@
+import logging
+import json
+import os
+from director.agents.base import BaseAgent, AgentResponse, AgentStatus
+from director.core.session import (
+    Session, 
+    MsgStatus, 
+    TextContent,
+    ContextMessage,
+    RoleTypes
+
+)
+from director.llm import get_default_llm
+from director.llm.base import LLMResponseStatus
+
+from director.tools.videodb_tool import VideoDBTool
+from director.tools.composio_tool import composio_tool, ToolsType
+
+logger = logging.getLogger(__name__)
+
+SALES_ASSISTANT_PARAMETER = {
+    "type": "object",
+    "properties": {
+        "video_id": {
+            "type": "string",
+            "description": "The ID of the sales call video",
+        },
+        "collection_id": {
+            "type": "string",
+            "description": "The ID of the collection which the video belongs to"
+        },
+        "prompt": {
+            "type": "string",
+            "description": "Additional information/query given by the user to make the appropriate action to take place"
+        }
+    },
+    "required": ["video_id", "collection_id"]
+}
+
+
+SALES_ASSISTANT_PROMPT = """
+    Under "transcript", transcript of a sales call video is present.
+    Under "user prompt", the user has given additional context or information given
+    Generate a sales summary from it and generate answers from the transcript for the following properties
+
+    Following are the properties for which you need to find answers for and the Field type definition or the only possible answers to answer are given below
+
+    Each field has a predefined format or set of possible values and can also have **default** property which needs to be used if a field is missing in transcript and user prompt:  
+
+    #### **Fields & Expected Answers**  
+    Field: dealname
+    description: (The company or individuals name with whom we are making a deal with)
+    type: text (which says the name of person we are dealing with or the company)
+
+    Field: dealstage
+    Possible Answers: appointmentscheduled, qualifiedtobuy, presentationscheduled, decisionmakerboughtin, contractsent, closedwon, closedlost
+    default: appointmentscheduled
+
+    Field: budget
+    type: Multi line text (Around 150 words description)
+    description: 
+        The multi line text answer for this field must consist of a detailed analysis of the budget situation of the company. 
+        If numbers are mentioned, do include those details aswell.
+        If the deal is overpriced, underpriced or considered fair, should also be added if mentioned
+
+
+    Field: authority
+    type: Multi line text (Around 150 words description)
+    description: 
+        The multi line text answer for this field must consist of a detailed analysis of the authority the client possesses for the conclusion of the deal. 
+        If decision making powers are mentioned, do include those details.
+        If the client mention that they are the final signing authority, or any other details signifying their level of power in the deal. mention them
+
+
+    Field: need
+    type: Multi line text (Around 150 words description)
+    description: 
+        The multi line text answer for this field must consist of a detailed analysis of how much the client wants the product. 
+        Need can be found from the level or urgency, the depth or importance of problem they want to get solved or the amount of hurry they have
+
+
+    Field: timeline
+    type: Multi line text (Around 150 words description)
+    description: 
+        The multi line text answer for this field must consist of a detailed analysis of how the timeline of the project looks like
+        Mention when they need the product, when they want to test the product etc. Important details about the timelines must be added here.
+
+    Since this is a BANT analysis. Do not forget to generate a response for these properties.
+    ALL THE ABOVE FIELDS ARE MANDATORY TO BE GENERATED AN OUTPUT FOR.
+    AN ANALYSIS FOR budget, authority, need and timeline IS COMPULSORY
+
+    Only give answers to the field. Do not give any additional texts such as introduction, conclusion etc.
+"""
+
+
+class SalesAssistantAgent(BaseAgent):
+    def __init__(self, session: Session, **kwargs):
+        self.agent_name = "sales_assistant"
+        self.description = "This agent will transcribe, study and analyse sales calls, automatically create deal summaries & update CRM software like Salesforce & Hubspot"
+        self.parameters = SALES_ASSISTANT_PARAMETER
+        self.llm = get_default_llm()
+        super().__init__(session=session, **kwargs)
+
+
+    def _generate_prompt(self, transcript:str, prompt:str):
+        final_prompt = SALES_ASSISTANT_PROMPT
+
+        final_prompt += f"""
+            "transcript":
+            {transcript}
+
+            "user prompt":
+            {prompt}
+        """
+
+        return final_prompt
+
+    def run(self, 
+            video_id:str,
+            collection_id:str,
+            prompt="",
+            *args, 
+            **kwargs
+            ) -> AgentResponse:
+        """
+        Create deal summaries and update the users CRM software
+
+        :param str video_id: The sales call video ID
+        :param str collection_id: The videos collection ID
+        :param str prompt: Additional context or query given by the user for the task
+        :param args: Additional positional arguments.
+        :param kwargs: Additional keyword arguments.
+        :return: The response containing information about the sample processing operation.
+        :rtype: AgentResponse
+        """
+        try:
+            HUBSPOT_ACCESS_TOKEN = os.getenv("HUBSPOT_ACCESS_TOKEN")
+
+            if not HUBSPOT_ACCESS_TOKEN:
+                return AgentResponse(
+                    status=AgentStatus.ERROR,
+                    message="Hubspot token not present"
+                )
+
+            text_content = TextContent(
+                agent_name=self.agent_name,
+                status=MsgStatus.progress,
+                status_message="Making magic happen with VideoDB Director...",
+            )
+
+            self.output_message.content.append(text_content)
+            self.output_message.push_update()
+
+            videodb_tool = VideoDBTool(collection_id=collection_id)
+
+            self.output_message.actions.append("Extracting the transcript")
+            self.output_message.push_update()
+
+            try:
+                transcript_text = videodb_tool.get_transcript(video_id)
+            except Exception:
+                logger.error("Transcript not found. Indexing spoken words..")
+                self.output_message.actions.append("Indexing spoken words..")
+                self.output_message.push_update()
+                videodb_tool.index_spoken_words(video_id)
+                transcript_text = videodb_tool.get_transcript(video_id)
+
+            self.output_message.actions.append("Processing the transcript")
+            self.output_message.push_update()
+
+            sales_assist_llm_prompt = self._generate_prompt(transcript=transcript_text, prompt=prompt)
+            sales_assist_llm_message = ContextMessage(
+                content=sales_assist_llm_prompt, role=RoleTypes.user
+            )
+            llm_response = self.llm.chat_completions([sales_assist_llm_message.to_llm_msg()])
+
+            if not llm_response.status:
+                logger.error(f"LLM failed with {llm_response}")
+                text_content.status = MsgStatus.error
+                text_content.status_message = "Failed to generate the response."
+                self.output_message.publish()
+                return AgentResponse(
+                    status=AgentStatus.ERROR,
+                    message="Sales assistant failed due to LLM error.",
+                )
+
+            composio_prompt = f"""
+                Create a new deal in HubSpot with the following details:
+
+                ---
+                {llm_response.content}
+                ---
+
+                Use the HUBSPOT_CREATE_CRM_OBJECT_WITH_PROPERTIES action to accomplish this.
+                """
+
+            self.output_message.actions.append("Adding it into the Hubspot CRM")
+            self.output_message.push_update()
+
+            composio_response = composio_tool(
+                task=composio_prompt,
+                auth_data={
+                    "name": "HUBSPOT",
+                    "token": HUBSPOT_ACCESS_TOKEN
+                },
+                tools_type=ToolsType.actions
+            )
+
+            llm_prompt = (
+                f"User has asked to run a task: {composio_prompt} in Composio. \n"
+                "Dont mention the action name directly as is"
+                "Comment on the fact whether the composio call was sucessful or not"
+                "Make this message short and crisp"
+                f"{json.dumps(composio_response)}"
+                "If there are any errors or if it was not successful, do tell about that as well"
+                "If the response is successful, Show the details given to create a new deal in a markdown table format"
+            )
+            final_message = ContextMessage(content=llm_prompt, role=RoleTypes.user)
+            llm_response = self.llm.chat_completions([final_message.to_llm_msg()])
+            if llm_response.status == LLMResponseStatus.ERROR:
+                raise Exception(f"LLM Failed with error {llm_response.content}")
+
+            text_content.text = llm_response.content
+            text_content.status = MsgStatus.success
+            text_content.status_message = "Here's the response from your sales assistant"
+            self.output_message.publish()
+        except Exception as e:
+            logger.exception(f"Error in {self.agent_name}")
+            text_content.status = MsgStatus.error
+            text_content.status_message = "Error in sales assistant"
+            self.output_message.publish()
+            error_message = f"Agent failed with error {e}"
+            return AgentResponse(status=AgentStatus.ERROR, message=error_message)
+        return AgentResponse(
+            status=AgentStatus.SUCCESS,
+            message=f"Agent {self.name} completed successfully.",
+            data={},
+        )

backend/director/handler.py

@@ -25,7 +25,7 @@
 from director.agents.transcription import TranscriptionAgent
 from director.agents.comparison import ComparisonAgent
 from director.agents.web_search_agent import WebSearchAgent
-
+from director.agents.sales_assistant import SalesAssistantAgent
 
 from director.core.session import Session, InputMessage, MsgStatus
 from director.core.reasoning import ReasoningEngine
@@ -67,6 +67,7 @@ def __init__(self, db, **kwargs):
             ComposioAgent,
             ComparisonAgent,
             WebSearchAgent,
+            SalesAssistantAgent
         ]
 
     def add_videodb_state(self, session):

backend/director/tools/composio_tool.py

@@ -3,8 +3,14 @@
 
 from director.llm.openai import OpenAIChatModel
 
+from enum import Enum
 
-def composio_tool(task: str):
+
+class ToolsType(str, Enum):
+    apps = "apps"
+    actions = "actions"
+
+def composio_tool(task: str, auth_data: dict = None, tools_type:ToolsType=ToolsType.apps):
     from composio_openai import ComposioToolSet
     from openai import OpenAI
 
@@ -18,8 +24,25 @@ def composio_tool(task: str):
     openai_client = OpenAI(api_key=key, base_url=base_url)
 
     toolset = ComposioToolSet(api_key=os.getenv("COMPOSIO_API_KEY"))
-    tools = toolset.get_tools(apps=json.loads(os.getenv("COMPOSIO_APPS")))
-    print(tools)
+
+    if auth_data and "name" in auth_data and "token" in auth_data:
+        toolset.add_auth(
+            app=auth_data["name"].upper(),
+            parameters=[
+                {
+                    "name": "Authorization",
+                    "in_": "header",
+                    "value": f"Bearer {auth_data['token']}"
+                }
+            ]
+        )
+
+    if tools_type == ToolsType.apps:
+        tools = toolset.get_tools(apps=json.loads(os.getenv("COMPOSIO_APPS")))
+
+    elif tools_type == ToolsType.actions:
+        tools = toolset.get_tools(actions=json.loads(os.getenv("COMPOSIO_ACTIONS")))
+
 
     response = openai_client.chat.completions.create(
         model=OpenAIChatModel.GPT4o,