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change meddic call to return json response
Browse files
__pycache__/utils_opportunity_review.cpython-311.pyc
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__pycache__/utils_prep.cpython-311.pyc
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reports/HSBC Opportunity Review Report.md
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**Analysis Results**
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**Summary:** The opportunity
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**Score:**
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**MEDDIC Evaluation:**
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**Metrics:**
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**Decision
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**Champion:** 65
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**Next Steps**
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Schedule a meeting with
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**Talking Points:**
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**Analysis Results**
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**Summary:** The opportunity involves replacing HSBC's current analytics system for their loan origination process with our more flexible and faster solution. The decision is time-sensitive due to an impending renewal with their existing vendor, and there has been strong engagement from key stakeholders, including the VP of IT and the CFO.
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**Score:** 80
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**MEDDIC Evaluation:**
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**Metrics:** 7 - The potential ROI and performance improvements were discussed, but specific quantified metrics on cost savings or efficiency gains were not detailed.
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**Economic Buyer:** 8 - The involvement of the CFO in the proposal review suggests access to the economic buyer, but further direct engagement may be needed to solidify alignment.
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**Decision Criteria:** 7 - HSBC is evaluating the proposal based on implementation scenarios and pricing structure, indicating some understanding of their decision criteria, but more clarity is needed.
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**Decision Process:** 8 - HSBC has shown a structured decision-making process with multiple meetings and a review phase, but exact timelines and steps need more definition.
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**Identify Pain:** 9 - The pain point of a slow and inflexible current system is well understood and articulated, with urgency due to contract renewal.
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**Champion:** 8 - John Smith, the VP of IT, has been consistently involved and appears to be a strong advocate, but further validation of his influence in the final decision is necessary.
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**Next Steps**
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Schedule a meeting with the CFO to directly address economic concerns and solidify alignment with the economic buyer, ensuring full understanding of the ROI and financial benefits of the proposed solution.
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**Talking Points:**
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1. Highlight the specific ROI and cost savings that HSBC can expect by switching to our analytics system, providing detailed metrics and case studies to illustrate the potential financial impact
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2. Discuss the clear advantages of our system in terms of flexibility and speed, emphasizing how these improvements directly address HSBC's current pain points and align with their decision criteria
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3. Outline a clear implementation plan with defined timelines and steps to reassure HSBC of a smooth transition process, addressing any concerns about the decision-making process and renewal urgency
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utils_opportunity_review.py
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import json
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import os
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from langchain.document_loaders import CSVLoader, PyPDFLoader, Docx2txtLoader
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from langgraph.graph import StateGraph, END
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from langchain.prompts import PromptTemplate
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@@ -134,7 +135,8 @@ def agent_2() -> Dict[str, Any]:
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Assign an overall opportunity score (1-100) with 100 means that the opportunity is a sure win.
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Provide a Summary of the opportunity.
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Evaluate the opportunity based on each MEDDIC criterion and assign a score for each criterion:
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1. Metrics
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@@ -168,10 +170,13 @@ def agent_2() -> Dict[str, Any]:
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else:
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raise ValueError(f"Unexpected response type: {type(response)}")
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# Parse the response content
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lines = response_content.split('\n')
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summary = next((line.split('Summary:')[1].strip() for line in lines if line.startswith('Summary:')), 'N/A')
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score = next((int(line.split('Score:')[1].strip()) for line in lines if line.startswith('Score:')), 0)
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meddic_eval = {}
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current_criterion = None
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for line in lines:
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'meddic_evaluation': str(e)
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}
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def agent_3(meddic_evaluation: Dict[str, Any]) -> Dict[str, Any]:
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"""Agent 3: Suggest next best action and talking points."""
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try:
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@@ -250,7 +357,7 @@ def process_document(state: State) -> State:
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def evaluate_opportunity(state: State) -> State:
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print("Agent 2: Evaluating opportunity...")
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result =
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return State(file_path=state.file_path, document_processed=state.document_processed, opportunity_evaluation=result)
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def suggest_next_action(state: State) -> State:
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import json
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import os
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import re
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from langchain.document_loaders import CSVLoader, PyPDFLoader, Docx2txtLoader
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from langgraph.graph import StateGraph, END
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from langchain.prompts import PromptTemplate
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Assign an overall opportunity score (1-100) with 100 means that the opportunity is a sure win.
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Provide a Summary of the opportunity. There must always be a summary. Ensure the summary is on the
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same line as the Summary: title
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Evaluate the opportunity based on each MEDDIC criterion and assign a score for each criterion:
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1. Metrics
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else:
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raise ValueError(f"Unexpected response type: {type(response)}")
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print(response_content)
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# Parse the response content
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lines = response_content.split('\n')
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summary = next((line.split('Summary:')[1].strip() for line in lines if line.startswith('Summary:')), 'N/A')
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print(summary)
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score = next((int(line.split('Score:')[1].strip()) for line in lines if line.startswith('Score:')), 0)
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print(score)
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meddic_eval = {}
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current_criterion = None
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for line in lines:
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'meddic_evaluation': str(e)
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}
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def clean_and_parse_json(json_string):
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# Remove triple backticks and "json" label if present
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json_string = re.sub(r'^```json\s*', '', json_string)
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json_string = re.sub(r'\s*```$', '', json_string)
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# Remove any leading/trailing whitespace
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json_string = json_string.strip()
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# Parse the cleaned JSON string
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try:
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return json.loads(json_string)
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except json.JSONDecodeError as e:
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print(f"Error parsing JSON: {e}")
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return None
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def agent_2_json() -> Dict[str, Any]:
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"""Agent 2: Evaluate opportunity based on MEDDIC criteria."""
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try:
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results = qdrant.scroll(collection_name="opportunities", limit=100)
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if not results or len(results[0]) == 0:
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raise ValueError("No documents found in Qdrant")
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full_text = " ".join([point.payload.get("text", "") for point in results[0]])
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meddic_template = """
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Analyze the following opportunity information using the MEDDIC sales methodology:
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{opportunity_info}
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Assign an overall opportunity score (1-100) with 100 means that the opportunity is a sure win.
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Provide a Summary of the opportunity. There must always be a summary. Ensure the summary is on the
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same line as the Summary: title
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Evaluate the opportunity based on each MEDDIC criterion and assign a score for each criterion:
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1. Metrics
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2. Economic Buyer
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3. Decision Criteria
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4. Decision Process
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5. Identify Pain
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6. Champion
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Your response must be in JSON format.
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Use the following keys in the JSON:
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Summary: Opportunity Summary
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Score: Overall Opportunity Score between 1 to 100 based on MEDDIC criteria
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Metrics Score: MEDDIC score on Metrics
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Metrics Evaluation: Evaluation on MEDDIC Metrics criterion
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Economic Buyer Score: MEDDIC Score on Economic Buyer
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Economic Buyer Evaluation: Evaluation on MEDDIC Economic Buyer criterion
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Decision Criteria Score: MEDDIC Score on Decision Criteria
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Decision Criteria Evaluation: Evaluation on MEDDIC Decision Criteria criterion
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Decision Process Score: MEDDIC Score on Decision Process
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Decision Process Evaluation: Evaluation on MEDDIC Decision Process criterion
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Identify Pain Score: MEDDIC Score on Identify Pain
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Identify Pain Evaluation: Evaluation on MEDDIC Identify Pain criterion
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Champion Score: MEDDIC Score on Champion
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Champion Evaluation: Evaluation on MEDDIC Champion criterion
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"""
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meddic_prompt = PromptTemplate(template=meddic_template, input_variables=["opportunity_info"])
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meddic_chain = meddic_prompt | llm
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response = meddic_chain.invoke({"opportunity_info": full_text})
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if isinstance(response, AIMessage):
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response_content = response.content
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elif isinstance(response, str):
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response_content = response
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else:
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raise ValueError(f"Unexpected response type: {type(response)}")
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print(response_content)
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meddic_data = clean_and_parse_json(response_content)
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print("jsonified")
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print(meddic_data)
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# Create the output structure
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output = {
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'summary': meddic_data.get('Summary', 'N/A'),
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'score': meddic_data.get('Score', 0),
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'meddic_evaluation': {
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'Metrics': f"{meddic_data.get('Metrics Score', 'N/A')} - {meddic_data.get('Metrics Evaluation', 'N/A')}",
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'Economic Buyer': f"{meddic_data.get('Economic Buyer Score', 'N/A')} - {meddic_data.get('Economic Buyer Evaluation', 'N/A')}",
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'Decision Criteria': f"{meddic_data.get('Decision Criteria Score', 'N/A')} - {meddic_data.get('Decision Criteria Evaluation', 'N/A')}",
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'Decision Process': f"{meddic_data.get('Decision Process Score', 'N/A')} - {meddic_data.get('Decision Process Evaluation', 'N/A')}",
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'Identify Pain': f"{meddic_data.get('Identify Pain Score', 'N/A')} - {meddic_data.get('Identify Pain Evaluation', 'N/A')}",
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'Champion': f"{meddic_data.get('Champion Score', 'N/A')} - {meddic_data.get('Champion Evaluation', 'N/A')}"
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}
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}
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print("output")
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print(output)
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return output
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except Exception as e:
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print(f"Error in agent_2_json: {str(e)}")
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return {
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'summary': "Error occurred during evaluation",
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'score': 0,
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'meddic_evaluation': str(e)
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}
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def agent_3(meddic_evaluation: Dict[str, Any]) -> Dict[str, Any]:
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"""Agent 3: Suggest next best action and talking points."""
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try:
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def evaluate_opportunity(state: State) -> State:
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print("Agent 2: Evaluating opportunity...")
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result = agent_2_json()
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return State(file_path=state.file_path, document_processed=state.document_processed, opportunity_evaluation=result)
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def suggest_next_action(state: State) -> State:
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utils_prep.py
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async def prep_opportunity_analysis():
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session_state = cl.user_session.get("session_state", None)
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opportunity_analysis_message = "
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await cl.Message(content=opportunity_analysis_message).send()
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if session_state.do_opportunity_analysis:
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agent_1_message = "*Retrieving and
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await cl.Message(content=agent_1_message).send()
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await prep_opportunity_review(session_state)
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report = session_state.opportunity_review_report
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await asyncio.sleep(2)
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if session_state.add_objections_to_analysis:
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agent_3_message = "*Evaluating opportunity and identifying risks ...*"
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await cl.Message(content=agent_3_message).send()
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session_state.objections = await create_objections(session_state)
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else:
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agent_2_message = "*Evaluating opportunity ...*"
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await cl.Message(content=agent_2_message).send()
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await asyncio.sleep(1.5)
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async def prep_opportunity_analysis():
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session_state = cl.user_session.get("session_state", None)
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opportunity_analysis_message = "Reviewing HSBC Opportunitiy - please wait..."
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await cl.Message(content=opportunity_analysis_message).send()
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if session_state.do_opportunity_analysis:
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agent_1_message = "*Retrieving and evaluating opportunity data from SalesForce CRM ...*"
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await cl.Message(content=agent_1_message).send()
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await prep_opportunity_review(session_state)
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report = session_state.opportunity_review_report
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await asyncio.sleep(2)
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if session_state.add_objections_to_analysis:
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agent_3_message = "*Evaluating opportunity data and identifying risks ...*"
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await cl.Message(content=agent_3_message).send()
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session_state.objections = await create_objections(session_state)
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else:
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agent_2_message = "*Evaluating opportunity data...*"
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await cl.Message(content=agent_2_message).send()
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await asyncio.sleep(1.5)
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