File size: 20,145 Bytes
7781557 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 |
import json
import os
import re
from langchain_community.document_loaders import CSVLoader, PyPDFLoader, Docx2txtLoader
from langgraph.graph import StateGraph, END
from langchain.prompts import PromptTemplate
from langchain.schema import Document, AIMessage
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from pathlib import Path
from pydantic import BaseModel, Field
from qdrant_client import QdrantClient
from qdrant_client.models import Distance, VectorParams, PointStruct
from typing import List, Dict, Any
from pydantic import BaseModel, Field
from typing import Dict, Any
llm = ChatOpenAI(model_name="gpt-4o")
embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
qdrant = QdrantClient(":memory:") # In-memory Qdrant instance
# Create collection
qdrant.create_collection(
collection_name="opportunities",
vectors_config=VectorParams(size=1536, distance=Distance.COSINE),
)
class State(BaseModel):
file_path: str
document_processed: str = ""
opportunity_evaluation: Dict[str, Any] = Field(default_factory=dict)
next_action: Dict[str, Any] = Field(default_factory=dict)
def dict_representation(self) -> Dict[str, Any]:
return {
"file_path": self.file_path,
"document_processed": self.document_processed,
"opportunity_evaluation": self.opportunity_evaluation,
"next_action": self.next_action
}
async def prep_opportunity_review(session_state):
file_path = prep_document()
structured_results = run_analysis(file_path)
opportunity_review_report = create_opportunity_review_report(structured_results)
session_state.opportunity_review_results = structured_results
session_state.opportunity_review_report = opportunity_review_report
def prep_document():
file_path = "data/HSBC Opportunity Information.docx"
path = Path(file_path)
if path.exists():
if path.is_file():
print(f"File found: {path}")
print(f"File size: {path.stat().st_size / 1024:.2f} KB")
print(f"Last modified: {path.stat().st_mtime}")
print("File is ready for processing.")
if os.access(path, os.R_OK):
print("File is readable.")
else:
print("Warning: File exists but may not be readable. Check permissions.")
else:
print(f"Error: {path} exists but is not a file. It might be a directory.")
else:
print(f"Error: File not found at {path}")
print("Please check the following:")
print("1. Ensure the file path is correct.")
print("2. Verify that the file exists in the specified location.")
print("3. Check if you have the necessary permissions to access the file.")
parent = path.parent
if not parent.exists():
print(f"Note: The directory {parent} does not exist.")
elif not parent.is_dir():
print(f"Note: {parent} exists but is not a directory.")
file_path_for_processing = str(path)
return file_path_for_processing
def load_and_chunk_document(file_path: str) -> List[Document]:
"""Load and chunk the document based on file type."""
if not os.path.exists(file_path):
raise FileNotFoundError(f"File not found: {file_path}")
_, file_extension = os.path.splitext(file_path.lower())
if file_extension == '.csv':
loader = CSVLoader(file_path)
elif file_extension == '.pdf':
loader = PyPDFLoader(file_path)
elif file_extension == '.docx':
loader = Docx2txtLoader(file_path)
else:
raise ValueError(f"Unsupported file type: {file_extension}")
documents = loader.load()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
return text_splitter.split_documents(documents)
def agent_1(file_path: str) -> str:
"""Agent 1: Load, chunk, embed, and store document in Qdrant."""
try:
chunks = load_and_chunk_document(file_path)
points = []
for i, chunk in enumerate(chunks):
vector = embeddings.embed_query(chunk.page_content)
points.append(PointStruct(id=i, vector=vector, payload={"text": chunk.page_content}))
qdrant.upsert(
collection_name="opportunities",
points=points
)
return f"Document processed and stored in Qdrant. {len(chunks)} chunks created."
except Exception as e:
print(f"Error in agent_1: {str(e)}")
return f"Error processing document: {str(e)}"
def agent_2() -> Dict[str, Any]:
"""Agent 2: Evaluate opportunity based on MEDDIC criteria."""
try:
results = qdrant.scroll(collection_name="opportunities", limit=100)
if not results or len(results[0]) == 0:
raise ValueError("No documents found in Qdrant")
full_text = " ".join([point.payload.get("text", "") for point in results[0]])
meddic_template = """
Analyze the following opportunity information using the MEDDIC sales methodology:
{opportunity_info}
Assign an overall opportunity score (1-100) with 100 means that the opportunity is a sure win.
Provide a Summary of the opportunity. There must always be a summary. Ensure the summary is on the
same line as the Summary: title
Evaluate the opportunity based on each MEDDIC criterion and assign a score for each criterion:
1. Metrics
2. Economic Buyer
3. Decision Criteria
4. Decision Process
5. Identify Pain
6. Champion
Format your response as follows:
Summary: [Opportunity Summary]
Score: [Overall Opportunity Score between 1 to 100 based on MEDDIC criteria]
MEDDIC Evaluation:
- Metrics: [Score on Metrics, Evaluation on Metrics criterion]
- Economic Buyer: [Score on Economic Buyer, Evaluation on Economic Buyer criterion]
- Decision Criteria: [Score on Decision Criteria, Evaluation on Decision Criteria criterion]
- Decision Process: [Score on Decision Process, Evaluation on Decision Process criterion]
- Identify Pain: [Score on Identify Pain, Evaluation on Identify Pain criterion]
- Champion: [Score on Champion, Evaluation on Champion criterion]
"""
meddic_prompt = PromptTemplate(template=meddic_template, input_variables=["opportunity_info"])
meddic_chain = meddic_prompt | llm
response = meddic_chain.invoke({"opportunity_info": full_text})
if isinstance(response, AIMessage):
response_content = response.content
elif isinstance(response, str):
response_content = response
else:
raise ValueError(f"Unexpected response type: {type(response)}")
print(response_content)
# Parse the response content
lines = response_content.split('\n')
summary = next((line.split('Summary:')[1].strip() for line in lines if line.startswith('Summary:')), 'N/A')
print(summary)
score = next((int(line.split('Score:')[1].strip()) for line in lines if line.startswith('Score:')), 0)
print(score)
meddic_eval = {}
current_criterion = None
for line in lines:
if line.strip().startswith('-'):
parts = line.split(':', 1)
if len(parts) == 2:
current_criterion = parts[0].strip('- ')
meddic_eval[current_criterion] = parts[1].strip()
elif current_criterion and line.strip():
meddic_eval[current_criterion] += ' ' + line.strip()
return {
'summary': summary,
'score': score,
'meddic_evaluation': meddic_eval
}
except Exception as e:
print(f"Error in agent_2: {str(e)}")
return {
'summary': "Error occurred during evaluation",
'score': 0,
'meddic_evaluation': str(e)
}
def clean_and_parse_json(json_string):
# Remove triple backticks and "json" label if present
json_string = re.sub(r'^```json\s*', '', json_string)
json_string = re.sub(r'\s*```$', '', json_string)
# Remove any leading/trailing whitespace
json_string = json_string.strip()
# Parse the cleaned JSON string
try:
return json.loads(json_string)
except json.JSONDecodeError as e:
print(f"Error parsing JSON: {e}")
return None
def agent_2_json() -> Dict[str, Any]:
"""Agent 2: Evaluate opportunity based on MEDDIC criteria."""
try:
results = qdrant.scroll(collection_name="opportunities", limit=100)
if not results or len(results[0]) == 0:
raise ValueError("No documents found in Qdrant")
full_text = " ".join([point.payload.get("text", "") for point in results[0]])
meddic_template = """
Analyze the following opportunity information using the MEDDIC sales methodology:
{opportunity_info}
Assign an overall opportunity score (1-100) with 100 means that the opportunity is a sure win.
Provide a Summary of the opportunity. There must always be a summary. Ensure the summary is on the
same line as the Summary: title
Evaluate the opportunity based on each MEDDIC criterion and assign a score for each criterion:
1. Metrics
2. Economic Buyer
3. Decision Criteria
4. Decision Process
5. Identify Pain
6. Champion
Your response must be in JSON format.
Use the following keys in the JSON:
Summary: Opportunity Summary
Score: Overall Opportunity Score between 1 to 100 based on MEDDIC criteria
Metrics Score: MEDDIC score on Metrics
Metrics Evaluation: Evaluation on MEDDIC Metrics criterion
Economic Buyer Score: MEDDIC Score on Economic Buyer
Economic Buyer Evaluation: Evaluation on MEDDIC Economic Buyer criterion
Decision Criteria Score: MEDDIC Score on Decision Criteria
Decision Criteria Evaluation: Evaluation on MEDDIC Decision Criteria criterion
Decision Process Score: MEDDIC Score on Decision Process
Decision Process Evaluation: Evaluation on MEDDIC Decision Process criterion
Identify Pain Score: MEDDIC Score on Identify Pain
Identify Pain Evaluation: Evaluation on MEDDIC Identify Pain criterion
Champion Score: MEDDIC Score on Champion
Champion Evaluation: Evaluation on MEDDIC Champion criterion
"""
meddic_prompt = PromptTemplate(template=meddic_template, input_variables=["opportunity_info"])
meddic_chain = meddic_prompt | llm
response = meddic_chain.invoke({"opportunity_info": full_text})
if isinstance(response, AIMessage):
response_content = response.content
elif isinstance(response, str):
response_content = response
else:
raise ValueError(f"Unexpected response type: {type(response)}")
print(response_content)
meddic_data = clean_and_parse_json(response_content)
print("jsonified")
print(meddic_data)
# Create the output structure
output = {
'summary': meddic_data.get('Summary', 'N/A'),
'score': meddic_data.get('Score', 0),
'meddic_evaluation': {
'Metrics': f"{meddic_data.get('Metrics Score', 'N/A')} - {meddic_data.get('Metrics Evaluation', 'N/A')}",
'Economic Buyer': f"{meddic_data.get('Economic Buyer Score', 'N/A')} - {meddic_data.get('Economic Buyer Evaluation', 'N/A')}",
'Decision Criteria': f"{meddic_data.get('Decision Criteria Score', 'N/A')} - {meddic_data.get('Decision Criteria Evaluation', 'N/A')}",
'Decision Process': f"{meddic_data.get('Decision Process Score', 'N/A')} - {meddic_data.get('Decision Process Evaluation', 'N/A')}",
'Identify Pain': f"{meddic_data.get('Identify Pain Score', 'N/A')} - {meddic_data.get('Identify Pain Evaluation', 'N/A')}",
'Champion': f"{meddic_data.get('Champion Score', 'N/A')} - {meddic_data.get('Champion Evaluation', 'N/A')}"
}
}
print("output")
print(output)
return output
except Exception as e:
print(f"Error in agent_2_json: {str(e)}")
return {
'summary': "Error occurred during evaluation",
'score': 0,
'meddic_evaluation': str(e)
}
def agent_3(meddic_evaluation: Dict[str, Any]) -> Dict[str, Any]:
"""Agent 3: Suggest next best action and talking points."""
try:
next_action_template = """
Based on the following MEDDIC evaluation of an opportunity:
{meddic_evaluation}
Suggest the next best action for the upcoming customer meeting and provide the top 3 talking points.
Format your response as follows:
Next Action: [Your suggested action]
Talking Points:
1. [First talking point]
2. [Second talking point]
3. [Third talking point]
"""
next_action_prompt = PromptTemplate(template=next_action_template, input_variables=["meddic_evaluation"])
next_action_chain = next_action_prompt | llm
response = next_action_chain.invoke({"meddic_evaluation": json.dumps(meddic_evaluation)})
if isinstance(response, AIMessage):
response_content = response.content
elif isinstance(response, str):
response_content = response
else:
raise ValueError(f"Unexpected response type: {type(response)}")
# Parse the response content
lines = response_content.split('\n')
next_action = next((line.split('Next Action:')[1].strip() for line in lines if line.startswith('Next Action:')), 'N/A')
talking_points = [line.split('.')[1].strip() for line in lines if line.strip().startswith(('1.', '2.', '3.'))]
return {
'next_action': next_action,
'talking_points': talking_points
}
except Exception as e:
print(f"Error in agent_3: {str(e)}")
return {
'next_action': "Error occurred while suggesting next action",
'talking_points': [str(e)]
}
def process_document(state: State) -> State:
print("Agent 1: Processing document...")
file_path = state.file_path
result = agent_1(file_path)
return State(file_path=state.file_path, document_processed=result)
def evaluate_opportunity(state: State) -> State:
print("Agent 2: Evaluating opportunity...")
result = agent_2_json()
return State(file_path=state.file_path, document_processed=state.document_processed, opportunity_evaluation=result)
def suggest_next_action(state: State) -> State:
print("Agent 3: Suggesting next actions...")
result = agent_3(state.opportunity_evaluation)
return State(file_path=state.file_path, document_processed=state.document_processed, opportunity_evaluation=state.opportunity_evaluation, next_action=result)
def define_graph() -> StateGraph:
workflow = StateGraph(State)
workflow.add_node("process_document", process_document)
workflow.add_node("evaluate_opportunity", evaluate_opportunity)
workflow.add_node("suggest_next_action", suggest_next_action)
workflow.set_entry_point("process_document")
workflow.add_edge("process_document", "evaluate_opportunity")
workflow.add_edge("evaluate_opportunity", "suggest_next_action")
return workflow
def run_analysis(file_path: str) -> Dict[str, Any]:
if not os.path.exists(file_path):
return {"error": f"File not found: {file_path}"}
graph = define_graph()
initial_state = State(file_path=file_path)
try:
app = graph.compile()
final_state = app.invoke(initial_state)
# Convert the final state to a dictionary manually
structured_results = {
"file_path": final_state["file_path"],
"document_processed": final_state["document_processed"],
"opportunity_evaluation": final_state["opportunity_evaluation"],
"next_action": final_state["next_action"]
}
# Print a summary of the results
print("\n--- Analysis Results ---")
print(f"Document Processing: {'Successful' if 'Error' not in structured_results['document_processed'] else 'Failed'}")
print(f"Details: {structured_results['document_processed']}")
if isinstance(structured_results['opportunity_evaluation'], dict):
print("\nOpportunity Evaluation:")
print(f"Summary: {structured_results['opportunity_evaluation'].get('summary', 'N/A')}")
print(f"Score: {structured_results['opportunity_evaluation'].get('score', 'N/A')}")
print("MEDDIC Evaluation:")
for criterion, evaluation in structured_results['opportunity_evaluation'].get('meddic_evaluation', {}).items():
print(f"{criterion}: {evaluation}")
else:
print("\nOpportunity Evaluation:")
print(f"Error: {structured_results['opportunity_evaluation']}")
if isinstance(structured_results['next_action'], dict):
print("\nNext Action:")
print(f"Action: {structured_results['next_action'].get('next_action', 'N/A')}")
print("Talking Points:")
for i, point in enumerate(structured_results['next_action'].get('talking_points', []), 1):
print(f" {i}. {point}")
else:
print("\nNext Action:")
print(f"Error: {structured_results['next_action']}")
return structured_results
except Exception as e:
print(f"An error occurred during analysis: {str(e)}")
return {"error": str(e)}
def create_opportunity_review_report(structured_results):
opportunity_review_report = ""
opportunity_review_report += "**Analysis Results**\n\n"
if 'Error' in structured_results['document_processed']:
opportunity_review_report += f"Opportunity Analysis Failed\n"
else:
if isinstance(structured_results['opportunity_evaluation'], dict):
opportunity_review_report += f"**Summary:** {structured_results['opportunity_evaluation'].get('summary', 'N/A')}\n\n"
opportunity_review_report += f"**Score:** {structured_results['opportunity_evaluation'].get('score', 'N/A')}\n\n"
opportunity_review_report += "**MEDDIC Evaluation:**\n\n"
for criterion, evaluation in structured_results['opportunity_evaluation'].get('meddic_evaluation', {}).items():
opportunity_review_report += f"**{criterion}:** {evaluation}\n"
if isinstance(structured_results['next_action'], dict):
opportunity_review_report += "\n\n**Next Steps**\n\n"
opportunity_review_report += f"{structured_results['next_action'].get('next_action', 'N/A')}\n\n"
opportunity_review_report += "**Talking Points:**\n\n"
for i, point in enumerate(structured_results['next_action'].get('talking_points', []), 1):
opportunity_review_report += f" {i}. {point}\n"
file_path = "./reports/HSBC Opportunity Review Report.md"
save_md_file(file_path, opportunity_review_report)
return opportunity_review_report
def save_md_file(file_path, file_content):
try:
if os.path.exists(file_path):
os.remove(file_path)
print(f"Existing file deleted: {file_path}")
with open(file_path, 'w', encoding='utf-8') as md_file:
md_file.write(file_content)
print(f"File saved successfully: {file_path}")
except PermissionError:
print(f"Permission denied when trying to delete or save file: {file_path}")
return None
|