first commit

sage.py

@@ -1,24 +1,44 @@
 
-from langchain_community.vectorstores import FAISS
+import os
+import time
+from dotenv import load_dotenv
+from operator import itemgetter
+from typing_extensions import TypedDict
+from typing import List
+
+from langchain.prompts import PromptTemplate
+from langchain_core.output_parsers import JsonOutputParser
+from langchain_core.output_parsers import StrOutputParser
 
+from langchain_community.vectorstores import FAISS
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain_community.document_loaders import PyMuPDFLoader
 from langchain_community.vectorstores import FAISS
 from langchain_community.embeddings.fastembed import FastEmbedEmbeddings
-import os
-from dotenv import load_dotenv
+from langchain.retrievers import ContextualCompressionRetriever
+from langchain.retrievers.document_compressors import FlashrankRerank
+from langchain.schema import Document
+from langgraph.graph import END, StateGraph
+
+from groq import Groq
+from langchain_groq import ChatGroq
+
+from utils import get_payroll_api_schema, dummy_payroll_api_call
+
 load_dotenv()
 
+# Setup the models
 embed_model = FastEmbedEmbeddings(model_name="snowflake/snowflake-arctic-embed-m")
 
-from groq import Groq
-from langchain_groq import ChatGroq
 
 
 llm = ChatGroq(temperature=0,
                       model_name="Llama3-8b-8192",
                       api_key=os.getenv("GROQ_API_KEY"),)
 
+
+
+# Load the documents
 loader = PyMuPDFLoader("https://home.synise.com/HRUtility/Documents/HRA/UmaP/Synise%20Handbook.pdf")
 documents = loader.load()
 
@@ -29,21 +49,15 @@ doc_splits = text_splitter.split_documents(documents)
 
 vectorstore = FAISS.from_documents(documents=doc_splits,embedding=embed_model)
 
-from langchain.retrievers import ContextualCompressionRetriever
-from langchain.retrievers.document_compressors import FlashrankRerank
-
+# Setup the retriever
 compressor = FlashrankRerank()
 retriever = vectorstore.as_retriever(search_type="similarity", search_kwargs={"k": 20})
 compression_retriever = ContextualCompressionRetriever(
     base_compressor=compressor, base_retriever=retriever
 )
 
-from operator import itemgetter
-from langchain.prompts import PromptTemplate
-from langchain.schema.runnable import RunnablePassthrough
-from langchain_core.output_parsers import StrOutputParser
-
 
+# Define RAG Chain
 RAG_PROMPT_TEMPLATE = """
 <|begin_of_text|><|start_header_id|>system<|end_header_id|>
 
@@ -71,260 +85,7 @@ response_chain = (rag_prompt
 
 )
 
-def dummy_payroll_api_call(employee_id, month, year):
-
-  data = {
-    2023: {
-        "MAY": {
-            "employeeDetails": {
-                "employeeId": "E2468",
-                "firstName": "Sarah",
-                "lastName": "Thompson",
-                "designation": "Product Manager"
-            },
-            "paymentDetails": {
-                "year": 2023,
-                "month": "JAN",
-                "basicSalary": 5500,
-                "allowances": [
-                    {
-                        "type": "Housing Allowance",
-                        "amount": 1500
-                    },
-                    {
-                        "type": "Travel Allowance",
-                        "amount": 800
-                    }
-                ],
-                "deductions": [
-                    {
-                        "type": "Provident Fund",
-                        "amount": 650
-                    },
-                    {
-                        "type": "Health Insurance",
-                        "amount": 300
-                    }
-                ],
-                "taxes": [
-                    {
-                        "type": "Income Tax",
-                        "amount": 1300
-                    }
-                ],
-                "grossSalary": 7800,
-                "totalDeductions": 2250,
-                "netSalary": 6650
-            },
-            "companyDetails": {
-                "companyName": "Tech Solutions Ltd.",
-                "address": "789 Maple Avenue, City"
-            }
-        }
-    },
-    2024: {
-        "JAN": {
-            "employeeDetails": {
-                "employeeId": "E2468",
-                "firstName": "Sarah",
-                "lastName": "Thompson",
-                "designation": "Product Manager"
-            },
-            "paymentDetails": {
-                "year": 2024,
-                "month": "JAN",
-                "basicSalary": 6500,
-                "allowances": [
-                    {
-                        "type": "Housing Allowance",
-                        "amount": 1500
-                    },
-                    {
-                        "type": "Travel Allowance",
-                        "amount": 800
-                    }
-                ],
-                "deductions": [
-                    {
-                        "type": "Provident Fund",
-                        "amount": 650
-                    },
-                    {
-                        "type": "Health Insurance",
-                        "amount": 300
-                    }
-                ],
-                "taxes": [
-                    {
-                        "type": "Income Tax",
-                        "amount": 1300
-                    }
-                ],
-                "grossSalary": 8800,
-                "totalDeductions": 2250,
-                "netSalary": 6550
-            },
-            "companyDetails": {
-                "companyName": "Tech Solutions Ltd.",
-                "address": "789 Maple Avenue, City"
-            }
-        },
-        "FEB": {
-            "employeeDetails": {
-                "employeeId": "E2468",
-                "firstName": "Sarah",
-                "lastName": "Thompson",
-                "designation": "Product Manager"
-            },
-            "paymentDetails": {
-                "year": 2024,
-                "month": "FEB",
-                "basicSalary": 6500,
-                "allowances": [
-                    {
-                        "type": "Housing Allowance",
-                        "amount": 1500
-                    },
-                    {
-                        "type": "Travel Allowance",
-                        "amount": 800
-                    }
-                ],
-                "deductions": [
-                    {
-                        "type": "Provident Fund",
-                        "amount": 650
-                    },
-                    {
-                        "type": "Health Insurance",
-                        "amount": 300
-                    }
-                ],
-                "taxes": [
-                    {
-                        "type": "Income Tax",
-                        "amount": 1300
-                    }
-                ],
-                "grossSalary": 8800,
-                "totalDeductions": 2250,
-                "netSalary": 6550
-            },
-            "companyDetails": {
-                "companyName": "Tech Solutions Ltd.",
-                "address": "789 Maple Avenue, City"
-            }
-        },
-                "MAY": {
-            "employeeDetails": {
-                "employeeId": "E2468",
-                "firstName": "Sarah",
-                "lastName": "Thompson",
-                "designation": "Product Manager"
-            },
-            "paymentDetails": {
-                "year": 2024,
-                "month": "MAY",
-                "basicSalary": 6500,
-                "allowances": [
-                    {
-                        "type": "Housing Allowance",
-                        "amount": 1500
-                    },
-                    {
-                        "type": "Travel Allowance",
-                        "amount": 800
-                    }
-                ],
-                "deductions": [
-                    {
-                        "type": "Provident Fund",
-                        "amount": 650
-                    },
-                    {
-                        "type": "Health Insurance",
-                        "amount": 300
-                    }
-                ],
-                "taxes": [
-                    {
-                        "type": "Income Tax",
-                        "amount": 1500
-                    }
-                ],
-                "grossSalary": 8800,
-                "totalDeductions": 2450,
-                "netSalary": 6350
-            },
-            "companyDetails": {
-                "companyName": "Tech Solutions Ltd.",
-                "address": "789 Maple Avenue, City"
-            }
-        },
-        "APR": {
-            "employeeDetails": {
-                "employeeId": "E2468",
-                "firstName": "Sarah",
-                "lastName": "Thompson",
-                "designation": "Product Manager"
-            },
-            "paymentDetails": {
-                "year": 2024,
-                "month": "APR",
-                "basicSalary": 6500,
-                "allowances": [
-                    {
-                        "type": "Housing Allowance",
-                        "amount": 1500
-                    },
-                    {
-                        "type": "Travel Allowance",
-                        "amount": 800
-                    }
-                ],
-                "deductions": [
-                    {
-                        "type": "Provident Fund",
-                        "amount": 650
-                    },
-                    {
-                        "type": "Health Insurance",
-                        "amount": 300
-                    }
-                ],
-                "taxes": [
-                    {
-                        "type": "Income Tax",
-                        "amount": 1500
-                    }
-                ],
-                "grossSalary": 8800,
-                "totalDeductions": 2450,
-                "netSalary": 6350
-            },
-            "companyDetails": {
-                "companyName": "Tech Solutions Ltd.",
-                "address": "789 Maple Avenue, City"
-            }
-        }
-    }
-}
-  year= 2024 if year == "CUR" else year
-  year= 2023 if year == "PREV" else year
-
-  month= "MAY" if month == "CUR" else month
-  month= "APR" if month == "PREV" else month
-
-
-  return data[year][month]
-
-# print(dummy_payroll_api_call(1234, 'CUR', 2024))
-
-import time
-from langchain.prompts import PromptTemplate
-from langchain_core.output_parsers import JsonOutputParser
-from langchain_core.output_parsers import StrOutputParser
-
+# Setup Router Chain
 ROUTER_AGENT_PROMPT_TEMPLATE = """
 <|begin_of_text|><|start_header_id|>system<|end_header_id|>
 
@@ -348,145 +109,10 @@ router_prompt = PromptTemplate(
 
 router_chain = router_prompt | llm | JsonOutputParser()
 
-# print(router_chain.invoke({"question":"What is my salary on 6 2024 ?"}))
-
-# print(router_chain.invoke({"question":"What is leave policy ?"}))
-
-payroll_schema= {
-  "$schema": "http://json-schema.org/draft-07/schema#",
-  "title": "Monthly Payslip",
-  "description": "A schema for a monthly payslip",
-  "type": "object",
-  "properties": {
-    "employeeDetails": {
-      "type": "object",
-      "properties": {
-        "employeeId": {
-          "type": "string",
-          "description": "Unique identifier for the employee"
-        },
-        "firstName": {
-          "type": "string",
-          "description": "First name of the employee"
-        },
-        "lastName": {
-          "type": "string",
-          "description": "Last name of the employee"
-        },
-        "designation": {
-          "type": "string",
-          "description": "Designation or job title of the employee"
-        }
-      },
-      "required": ["employeeId", "firstName", "lastName", "designation"]
-    },
-    "paymentDetails": {
-      "type": "object",
-      "properties": {
-        "year": {
-          "type": "integer",
-          "description": "Year of the pay period"
-        },
-        "month": {
-          "type": "string",
-          "enum": ["JAN", "FEB", "MAR", "APR", "MAY", "JUN", "JUL", "AUG", "SEP", "OCT", "NOV", "DEC"],
-          "description": "Month of the pay period"
-        },
-        "basicSalary": {
-          "type": "number",
-          "description": "Basic salary of the employee"
-        },
-        "allowances": {
-          "type": "array",
-          "items": {
-            "type": "object",
-            "properties": {
-              "type": {
-                "type": "string",
-                "description": "Type of allowance"
-              },
-              "amount": {
-                "type": "number",
-                "description": "Amount of the allowance"
-              }
-            },
-            "required": ["type", "amount"]
-          }
-        },
-        "deductions": {
-          "type": "array",
-          "items": {
-            "type": "object",
-            "properties": {
-              "type": {
-                "type": "string",
-                "description": "Type of deduction"
-              },
-              "amount": {
-                "type": "number",
-                "description": "Amount of the deduction"
-              }
-            },
-            "required": ["type", "amount"]
-          }
-        },
-        "taxes": {
-          "type": "array",
-          "items": {
-            "type": "object",
-            "properties": {
-              "type": {
-                "type": "string",
-                "description": "Type of tax"
-              },
-              "amount": {
-                "type": "number",
-                "description": "Amount of the tax"
-              }
-            },
-            "required": ["type", "amount"]
-          }
-        },
-        "grossSalary": {
-          "type": "number",
-          "description": "Gross salary (basic salary + allowances)"
-        },
-        "totalDeductions": {
-          "type": "number",
-          "description": "Total deductions (including taxes)"
-        },
-        "netSalary": {
-          "type": "number",
-          "description": "Net salary (gross salary - total deductions)"
-        }
-      },
-      "required": ["year", "month", "basicSalary", "allowances", "deductions", "taxes", "grossSalary", "totalDeductions", "netSalary"]
-    },
-    "companyDetails": {
-      "type": "object",
-      "properties": {
-        "companyName": {
-          "type": "string",
-          "description": "Name of the company"
-        },
-        "address": {
-          "type": "string",
-          "description": "Address of the company"
-        }
-      },
-      "required": ["companyName", "address"]
-    }
-  },
-  "required": ["employeeDetails", "paymentDetails", "companyDetails"]
-}
-
-# print(str(payroll_schema))
+payroll_schema = get_payroll_api_schema()
 
-import time
-from langchain.prompts import PromptTemplate
-from langchain_core.output_parsers import JsonOutputParser
-from langchain_core.output_parsers import StrOutputParser
 
+# Define Filter Extraction Chain
 FILTER_EXTTRACTION_PROMPT = """<|begin_of_text|><|start_header_id|>system<|end_header_id|>
 Extract the month and year from a given user question about payroll. Use the following schema instructions to guide your extraction.
 
@@ -509,12 +135,8 @@ filter_extraction_prompt = PromptTemplate(
 
 fiter_extraction_chain = filter_extraction_prompt | llm | JsonOutputParser()
 
-# print(fiter_extraction_chain.invoke({"question":"What is my salary on 6 2024 ?"}))
 
-import time
-from langchain.prompts import PromptTemplate
-from langchain_core.output_parsers import JsonOutputParser
-from langchain_core.output_parsers import StrOutputParser
+# Define Payroll QA Chain
 
 PAYROLL_QA_PROMPT = """<|begin_of_text|><|start_header_id|>system<|end_header_id|>
 
@@ -536,120 +158,13 @@ payroll_qa_prompt = PromptTemplate(
     template=PAYROLL_QA_PROMPT, input_variables=["question", "data", "schema"]
 )
 
-payroll_qa_chain = payroll_qa_prompt | llm | StrOutputParser()
-
-result = fiter_extraction_chain.invoke({"question":"What is my salary on jan 2024 ?"})
-
-result
-
-api_result = dummy_payroll_api_call(1234, result["month"], result["year"])
-
-api_result
-
-payroll_qa_chain.invoke({"question":"What is my salary on jan 2024 ?", "data":api_result, "schema":payroll_schema})
-
-
-
-
-### Retrieval Grader
-
-from langchain.prompts import PromptTemplate
-from langchain_community.chat_models import ChatOllama
-from langchain_core.output_parsers import JsonOutputParser
-
-
-RETREIVAL_GRADER_PROMPT = PromptTemplate(
-    template="""<|begin_of_text|><|start_header_id|>system<|end_header_id|>
-    You are a grader assessing relevance of a retrieved document to a user question. \n 
-    Here is the retrieved document: \n\n {document} \n\n
-    Here is the user question: {question} \n
-    If the document contains keywords related to the user question, grade it as relevant. \n
-    It does not need to be a stringent test. The goal is to filter out erroneous retrievals. \n
-    Give a binary score 'yes' or 'no' score to indicate whether the document is relevant to the question. \n
-    Provide the binary score as a JSON with a single key 'score',
-    Do not include any preamble, explanation, or additional text
-    <|eot_id|><|start_header_id|>assistant<|end_header_id|>""",
-    input_variables=["question", "document"],
-)
-
-retrieval_grader = RETREIVAL_GRADER_PROMPT | llm | JsonOutputParser()
-
-# question = "agent memory"
-# docs = retriever.get_relevant_documents(question)
-# doc_txt = docs[1].page_content
-# print(retrieval_grader.invoke({"question": question, "document": doc_txt}))
-
-
-### Hallucination Grader
-
-# Prompt
-HALLUCINATION_GRADER_PROMPT = PromptTemplate(
-    template="""<|begin_of_text|><|start_header_id|>system<|end_header_id|>
-    You are a grader assessing whether an answer is grounded in / supported by a set of facts. \n 
-    Here are the facts:
-    \n ------- \n
-    {documents} 
-    \n ------- \n
-    Here is the answer: {answer}
-    Give a binary score 'yes' or 'no' score to indicate whether the answer is grounded in / supported by a set of facts. \n
-    Provide the binary score as a JSON with a single key 'score'.
-    Do not include any preamble, explanation, or additional text
-    <|eot_id|><|start_header_id|>assistant<|end_header_id|>""",
-    input_variables=["answer", "documents"],
-)
-
-hallucination_grader = HALLUCINATION_GRADER_PROMPT | llm | JsonOutputParser()
-# hallucination_grader.invoke({"documents": docs, "generation": generation})
-
-
-### Answer Grader
-
-# Prompt
-ANSWER_GRADER_PROMPT = PromptTemplate(
-    template="""<|begin_of_text|><|start_header_id|>system<|end_header_id|>
-    You are a grader assessing whether an answer is useful to resolve a question. \n 
-    Here is the answer:
-    \n ------- \n
-    {answer} 
-    \n ------- \n
-    Here is the question: {question}
-    Give a binary score 'yes' or 'no' to indicate whether the answer is useful to resolve a question. \n
-    Provide the binary score as a JSON with a single key 'score'.
-     Do not include any preamble, explanation, or additional text
-    <|eot_id|><|start_header_id|>assistant<|end_header_id|>""",
-    input_variables=["answer", "question"],
-)
-
-answer_grader = ANSWER_GRADER_PROMPT | llm | JsonOutputParser()
-
-## Question Re-writer
-
-# Prompt
-REWRITER_PROMPT = PromptTemplate(
-    template="""<|begin_of_text|><|start_header_id|>system<|end_header_id|>
-    You a question re-writer that converts an input question to a better version that is optimized \n 
-     for vectorstore retrieval. Look at the initial and formulate an improved question. \n
-     Here is the initial question: \n\n {question}. Improved question with no preamble: \n 
-     <|eot_id|><|start_header_id|>assistant<|end_header_id|>""",
-    input_variables=["answer", "question"],
-)
-
-question_rewriter = REWRITER_PROMPT | llm | StrOutputParser()
-# question_rewriter.invoke({"question": question})
-
-# answer_grader.invoke({"question": question, "generation": generation})
-
-########### Create Nodes and Actions ###########
-from typing_extensions import TypedDict
-from typing import List
+########### Create Nodes Actions ###########
 
 class AgentState(TypedDict):
     question : str
     answer : str
     documents : List[str]
 
-import logging as log
-
 def route_question(state):
     """
     Route question to payroll_agent or policy_agent to retrieve reevant data
@@ -660,18 +175,16 @@ def route_question(state):
     Returns:
         str: Next node to call
     """
-
+    print("---ROUTING---")
     question = state["question"]
     result = router_chain.invoke({"question": question})
 
-    log.debug('Routing to {}....'.format(result["agent"]))
-
     return result["agent"]
 
 state = AgentState(question="What is my salary on jan 2024 ?", answer="", documents=None)
 route_question(state)
 
-from langchain.schema import Document
+
 def retrieve(state):
     """
     Retrieve documents from vectorstore
@@ -709,137 +222,6 @@ def generate(state):
 
     return {"documents": documents, "question": question, "answer": answer}
 
-# state = AgentState(question="What is leave policy?", answer="", documents=[Document(page_content="According to leave policy, there are two types of leaves 1: PL 2: CL")])
-# generate_answer(state)
-
-def grade_documents(state):
-    """
-    Determines whether the retrieved documents are relevant to the question.
-
-    Args:
-        state (dict): The current graph state
-
-    Returns:
-        state (dict): Updates documents key with only filtered relevant documents
-    """
-
-    print("---CHECK DOCUMENT RELEVANCE TO QUESTION---")
-    question = state["question"]
-    documents = state["documents"]
-
-    # Score each doc
-    filtered_docs = []
-    for d in documents:
-        score = retrieval_grader.invoke(
-            {"question": question, "document": d.page_content}
-        )
-        grade = score["score"]
-        if grade == "yes":
-            print("---GRADE: DOCUMENT RELEVANT---")
-            filtered_docs.append(d)
-        else:
-            print("---GRADE: DOCUMENT NOT RELEVANT---")
-            continue
-    return {"documents": filtered_docs, "question": question}
-
-def transform_query(state):
-    """
-    Transform the query to produce a better question.
-
-    Args:
-        state (dict): The current graph state
-
-    Returns:
-        state (dict): Updates question key with a re-phrased question
-    """
-
-    print("---TRANSFORM QUERY---")
-    question = state["question"]
-    documents = state["documents"]
-
-    # Re-write question
-    better_question = question_rewriter.invoke({"question": question})
-    return {"documents": documents, "question": better_question}
-
-def decide_to_generate(state):
-    """
-    Determines whether to generate an answer, or re-generate a question.
-
-    Args:
-        state (dict): The current graph state
-
-    Returns:
-        str: Binary decision for next node to call
-    """
-
-    print("---ASSESS GRADED DOCUMENTS---")
-    question = state["question"]
-    filtered_documents = state["documents"]
-
-    if not filtered_documents:
-        # All documents have been filtered check_relevance
-        # We will re-generate a new query
-        print(
-            "---DECISION: ALL DOCUMENTS ARE NOT RELEVANT TO QUESTION, TRANSFORM QUERY---"
-        )
-        return "transform_query"
-    else:
-        # We have relevant documents, so generate answer
-        print("---DECISION: GENERATE---")
-        return "generate"
-    
-def fallback(state):
-    """
-    Fallback to default answer.
-
-    Args:
-        state (dict): The current graph state
-
-    Returns:
-        str: Decision for next node to call
-    """
-
-   
-    return {"answer": "Sorry,I don't know the answer to this question."}
-    
-def grade_generation_v_documents_and_question(state):
-    """
-    Determines whether the generation is grounded in the document and answers question.
-
-    Args:
-        state (dict): The current graph state
-
-    Returns:
-        str: Decision for next node to call
-    """
-
-    print("---CHECK HALLUCINATIONS---")
-    question = state["question"]
-    documents = state["documents"]
-    answer = state["answer"]
-
-    score = hallucination_grader.invoke(
-        {"documents": documents, "answer": answer}
-    )
-    grade = score["score"]
-
-    # Check hallucination
-    if grade == "yes":
-        print("---DECISION: GENERATION IS GROUNDED IN DOCUMENTS---")
-        # Check question-answering
-        print("---GRADE GENERATION vs QUESTION---")
-        score = answer_grader.invoke({"question": question, "answer": answer})
-        grade = score["score"]
-        if grade == "yes":
-            print("---DECISION: GENERATION ADDRESSES QUESTION---")
-            return "useful"
-        else:
-            print("---DECISION: GENERATION DOES NOT ADDRESS QUESTION---")
-            return "not useful"
-    else:
-        print("---DECISION: GENERATION IS NOT GROUNDED IN DOCUMENTS, RE-TRY---")
-        return "not supported"
-
 def payroll(state):
     """
     Query payroll api to retrieve payroll data
@@ -854,7 +236,7 @@ def payroll(state):
     print("---QUERY PAYROLL API---")
     question = state["question"]
     payroll_query_filters = fiter_extraction_chain.invoke({"question":question})
-    payroll_api_query_results = dummy_payroll_api_call(1234, result["month"], result["year"])
+    payroll_api_query_results = dummy_payroll_api_call(1234, payroll_query_filters["month"], payroll_query_filters["year"])
 
 
     context = context = 'PAYROLL DATA SCHEMA: \n {payroll_schema} \n PAYROLL DATA: {payroll_api_query_results}'.format(
@@ -863,21 +245,13 @@ def payroll(state):
     documents = [Document(page_content=context)]
     return {"documents": documents, "question": question}
 
-# state = AgentState(question="Tell me salary for Jan 2024?", answer="", documents=None)
-# query_payroll(state)
-
-
 ########### Build Execution Graph ###########
-from langgraph.graph import END, StateGraph
 workflow = StateGraph(AgentState)
 
 # Define the nodes
 workflow.add_node("payroll", payroll)
 workflow.add_node("retrieve", retrieve)
 workflow.add_node("generate", generate)
-# workflow.add_node("grade_documents", grade_documents)  # grade documents
-# workflow.add_node("transform_query", transform_query)  # transform_query
-# workflow.add_node("fallback", fallback)
 
 workflow.set_conditional_entry_point(
     route_question,
@@ -887,27 +261,7 @@ workflow.set_conditional_entry_point(
     },
 )
 workflow.add_edge("payroll", "generate")
-# workflow.add_edge("retrieve", "generate")
-# workflow.add_edge("generate", END)
 workflow.add_edge("retrieve", "generate")
-# workflow.add_conditional_edges(
-#     "grade_documents",
-#     decide_to_generate,
-#     {
-#         "transform_query": "transform_query",
-#         "generate": "generate",
-#     },
-# )
-# workflow.add_edge("transform_query", "retrieve")
-# workflow.add_conditional_edges(
-#     "generate",
-#     grade_generation_v_documents_and_question,
-#     {
-#         "not supported": "generate",
-#         "useful": END,
-#         "not useful": "fallback",
-#     },
-# )
 workflow.add_edge("generate", END)
 
 app = workflow.compile()

utils.py

@@ -0,0 +1,376 @@
+def dummy_payroll_api_call(employee_id, month, year):
+
+  data = {
+    2023: {
+        "MAY": {
+            "employeeDetails": {
+                "employeeId": "E2468",
+                "firstName": "Sarah",
+                "lastName": "Thompson",
+                "designation": "Product Manager"
+            },
+            "paymentDetails": {
+                "year": 2023,
+                "month": "JAN",
+                "basicSalary": 5500,
+                "allowances": [
+                    {
+                        "type": "Housing Allowance",
+                        "amount": 1500
+                    },
+                    {
+                        "type": "Travel Allowance",
+                        "amount": 800
+                    }
+                ],
+                "deductions": [
+                    {
+                        "type": "Provident Fund",
+                        "amount": 650
+                    },
+                    {
+                        "type": "Health Insurance",
+                        "amount": 300
+                    }
+                ],
+                "taxes": [
+                    {
+                        "type": "Income Tax",
+                        "amount": 1300
+                    }
+                ],
+                "grossSalary": 7800,
+                "totalDeductions": 2250,
+                "netSalary": 6650
+            },
+            "companyDetails": {
+                "companyName": "Tech Solutions Ltd.",
+                "address": "789 Maple Avenue, City"
+            }
+        }
+    },
+    2024: {
+        "JAN": {
+            "employeeDetails": {
+                "employeeId": "E2468",
+                "firstName": "Sarah",
+                "lastName": "Thompson",
+                "designation": "Product Manager"
+            },
+            "paymentDetails": {
+                "year": 2024,
+                "month": "JAN",
+                "basicSalary": 6500,
+                "allowances": [
+                    {
+                        "type": "Housing Allowance",
+                        "amount": 1500
+                    },
+                    {
+                        "type": "Travel Allowance",
+                        "amount": 800
+                    }
+                ],
+                "deductions": [
+                    {
+                        "type": "Provident Fund",
+                        "amount": 650
+                    },
+                    {
+                        "type": "Health Insurance",
+                        "amount": 300
+                    }
+                ],
+                "taxes": [
+                    {
+                        "type": "Income Tax",
+                        "amount": 1300
+                    }
+                ],
+                "grossSalary": 8800,
+                "totalDeductions": 2250,
+                "netSalary": 6550
+            },
+            "companyDetails": {
+                "companyName": "Tech Solutions Ltd.",
+                "address": "789 Maple Avenue, City"
+            }
+        },
+        "FEB": {
+            "employeeDetails": {
+                "employeeId": "E2468",
+                "firstName": "Sarah",
+                "lastName": "Thompson",
+                "designation": "Product Manager"
+            },
+            "paymentDetails": {
+                "year": 2024,
+                "month": "FEB",
+                "basicSalary": 6500,
+                "allowances": [
+                    {
+                        "type": "Housing Allowance",
+                        "amount": 1500
+                    },
+                    {
+                        "type": "Travel Allowance",
+                        "amount": 800
+                    }
+                ],
+                "deductions": [
+                    {
+                        "type": "Provident Fund",
+                        "amount": 650
+                    },
+                    {
+                        "type": "Health Insurance",
+                        "amount": 300
+                    }
+                ],
+                "taxes": [
+                    {
+                        "type": "Income Tax",
+                        "amount": 1300
+                    }
+                ],
+                "grossSalary": 8800,
+                "totalDeductions": 2250,
+                "netSalary": 6550
+            },
+            "companyDetails": {
+                "companyName": "Tech Solutions Ltd.",
+                "address": "789 Maple Avenue, City"
+            }
+        },
+                "MAY": {
+            "employeeDetails": {
+                "employeeId": "E2468",
+                "firstName": "Sarah",
+                "lastName": "Thompson",
+                "designation": "Product Manager"
+            },
+            "paymentDetails": {
+                "year": 2024,
+                "month": "MAY",
+                "basicSalary": 6500,
+                "allowances": [
+                    {
+                        "type": "Housing Allowance",
+                        "amount": 1500
+                    },
+                    {
+                        "type": "Travel Allowance",
+                        "amount": 800
+                    }
+                ],
+                "deductions": [
+                    {
+                        "type": "Provident Fund",
+                        "amount": 650
+                    },
+                    {
+                        "type": "Health Insurance",
+                        "amount": 300
+                    }
+                ],
+                "taxes": [
+                    {
+                        "type": "Income Tax",
+                        "amount": 1500
+                    }
+                ],
+                "grossSalary": 8800,
+                "totalDeductions": 2450,
+                "netSalary": 6350
+            },
+            "companyDetails": {
+                "companyName": "Tech Solutions Ltd.",
+                "address": "789 Maple Avenue, City"
+            }
+        },
+        "APR": {
+            "employeeDetails": {
+                "employeeId": "E2468",
+                "firstName": "Sarah",
+                "lastName": "Thompson",
+                "designation": "Product Manager"
+            },
+            "paymentDetails": {
+                "year": 2024,
+                "month": "APR",
+                "basicSalary": 6500,
+                "allowances": [
+                    {
+                        "type": "Housing Allowance",
+                        "amount": 1500
+                    },
+                    {
+                        "type": "Travel Allowance",
+                        "amount": 800
+                    }
+                ],
+                "deductions": [
+                    {
+                        "type": "Provident Fund",
+                        "amount": 650
+                    },
+                    {
+                        "type": "Health Insurance",
+                        "amount": 300
+                    }
+                ],
+                "taxes": [
+                    {
+                        "type": "Income Tax",
+                        "amount": 1500
+                    }
+                ],
+                "grossSalary": 8800,
+                "totalDeductions": 2450,
+                "netSalary": 6350
+            },
+            "companyDetails": {
+                "companyName": "Tech Solutions Ltd.",
+                "address": "789 Maple Avenue, City"
+            }
+        }
+    }
+}
+  year= 2024 if year == "CUR" else year
+  year= 2023 if year == "PREV" else year
+
+  month= "MAY" if month == "CUR" else month
+  month= "APR" if month == "PREV" else month
+
+
+  return data[year][month]
+
+def get_payroll_api_schema():
+    schema = {
+  "$schema": "http://json-schema.org/draft-07/schema#",
+  "title": "Monthly Payslip",
+  "description": "A schema for a monthly payslip",
+  "type": "object",
+  "properties": {
+    "employeeDetails": {
+      "type": "object",
+      "properties": {
+        "employeeId": {
+          "type": "string",
+          "description": "Unique identifier for the employee"
+        },
+        "firstName": {
+          "type": "string",
+          "description": "First name of the employee"
+        },
+        "lastName": {
+          "type": "string",
+          "description": "Last name of the employee"
+        },
+        "designation": {
+          "type": "string",
+          "description": "Designation or job title of the employee"
+        }
+      },
+      "required": ["employeeId", "firstName", "lastName", "designation"]
+    },
+    "paymentDetails": {
+      "type": "object",
+      "properties": {
+        "year": {
+          "type": "integer",
+          "description": "Year of the pay period"
+        },
+        "month": {
+          "type": "string",
+          "enum": ["JAN", "FEB", "MAR", "APR", "MAY", "JUN", "JUL", "AUG", "SEP", "OCT", "NOV", "DEC"],
+          "description": "Month of the pay period"
+        },
+        "basicSalary": {
+          "type": "number",
+          "description": "Basic salary of the employee"
+        },
+        "allowances": {
+          "type": "array",
+          "items": {
+            "type": "object",
+            "properties": {
+              "type": {
+                "type": "string",
+                "description": "Type of allowance"
+              },
+              "amount": {
+                "type": "number",
+                "description": "Amount of the allowance"
+              }
+            },
+            "required": ["type", "amount"]
+          }
+        },
+        "deductions": {
+          "type": "array",
+          "items": {
+            "type": "object",
+            "properties": {
+              "type": {
+                "type": "string",
+                "description": "Type of deduction"
+              },
+              "amount": {
+                "type": "number",
+                "description": "Amount of the deduction"
+              }
+            },
+            "required": ["type", "amount"]
+          }
+        },
+        "taxes": {
+          "type": "array",
+          "items": {
+            "type": "object",
+            "properties": {
+              "type": {
+                "type": "string",
+                "description": "Type of tax"
+              },
+              "amount": {
+                "type": "number",
+                "description": "Amount of the tax"
+              }
+            },
+            "required": ["type", "amount"]
+          }
+        },
+        "grossSalary": {
+          "type": "number",
+          "description": "Gross salary (basic salary + allowances)"
+        },
+        "totalDeductions": {
+          "type": "number",
+          "description": "Total deductions (including taxes)"
+        },
+        "netSalary": {
+          "type": "number",
+          "description": "Net salary (gross salary - total deductions)"
+        }
+      },
+      "required": ["year", "month", "basicSalary", "allowances", "deductions", "taxes", "grossSalary", "totalDeductions", "netSalary"]
+    },
+    "companyDetails": {
+      "type": "object",
+      "properties": {
+        "companyName": {
+          "type": "string",
+          "description": "Name of the company"
+        },
+        "address": {
+          "type": "string",
+          "description": "Address of the company"
+        }
+      },
+      "required": ["companyName", "address"]
+    }
+  },
+  "required": ["employeeDetails", "paymentDetails", "companyDetails"]
+}
+    return schema