Add New Modules: GraphCypherQAChain. Add New Preprocess modules: embedding and nodes retrival. Added New Config: OpenAi key retrieve

embedding.py
graph-preprocess-simpl.py
neo4j_import.py
openai_key.py
job_recommendation_system.py
neo4j_model.py
recommendation_generator.py
resume_processor.py
retrieval_engine.py
verify.py
view.py

Author: andyasdd1

be_repo/modules/job_recommendation_system.py

@@ -1,57 +1,75 @@
 # job_recommendation_system.py
 
-import logging
+from neo4j_model import Neo4jModel
+from resume_processor import ResumeProcessor
+from retrieval_engine import RetrievalEngine
+from recommendation_generator import RecommendationGenerator
+from view import CLIView
+import sys
 
-from .neo4j_model import Neo4jModel
-from .recommendation_generator import RecommendationGenerator
-from .resume_processor import ResumeProcessor
-from .retrieval_engine import RetrievalEngine
-from .view import CLIView
+def main():
+    
+
+    # Redirect standard output to a file
+    sys.stdout = open('output.log', 'w')
+
+    # Your code here
+    print("Lots of output")
 
 
-def job_recommend(resume_text, user_id):
     # Setup Logging
+    import logging
     logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
     logger = logging.getLogger(__name__)
-
-    # Get Resume Input from User
-    if not resume_text.strip():
-        logger.error(f'No resume text provided, user_id: {user_id}.')
-        return 'Error: No resume text provided.'
-
+    
     # Neo4j Connection Details
     NEO4J_URI = "neo4j+ssc://7bf5a48e.databases.neo4j.io"  # Replace with your Neo4j URI
-    NEO4J_USERNAME = "neo4j"  # Replace with your Neo4j username
-    NEO4J_PASSWORD = "oxsK7V5_86emZlYQlvCfQHfVWS95wXz29OhtU8GAdFc"  # Replace with your Neo4j password
+    NEO4J_USERNAME = "neo4j"             # Replace with your Neo4j username
+    NEO4J_PASSWORD = "oxsK7V5_86emZlYQlvCfQHfVWS95wXz29OhtU8GAdFc"          # Replace with your Neo4j password
 
     # Initialize Model
     neo4j_model = Neo4jModel(
         uri=NEO4J_URI,
         username=NEO4J_USERNAME,
         password=NEO4J_PASSWORD
     )
-
-    node_label = "JTitle"  # Adjust as needed; could be dynamic based on user input or other criteria
-
+    
     # Initialize Controller Components
     resume_processor = ResumeProcessor()
     retrieval_engine = RetrievalEngine(resume_processor, neo4j_model)
     recommendation_generator = RecommendationGenerator()
-
+    
     # Initialize View
     view = CLIView()
-
-    # Perform Mixed Retrieval
+    
+    # Get Resume Input from User
+    resume_text = view.get_resume_input()
+    
+    if not resume_text.strip():
+        logger.error("No resume text provided.")
+        print("Error: No resume text provided.")
+        return
+    
+    # Perform Mixed Retrieval for 'JD' Node Label
+    node_label = "JD"  # Adjust as needed; could be dynamic based on user input or other criteria
     similar_docs, graph_results = retrieval_engine.perform_mixed_retrieval(resume_text, node_label=node_label)
-
+    
     if not similar_docs and not graph_results:
-        return 'No job recommendations found based on your resume.'
-
+        print("No job recommendations found based on your resume.")
+        return
+    
     # Generate Recommendations
     try:
         recommendations = recommendation_generator.generate_recommendations(similar_docs, graph_results)
     except Exception as e:
-        return 'Error: Failed to generate job recommendations.'
-
+        print("Error: Failed to generate job recommendations.")
+        return
+    
     # Display Recommendations
-    return view.display_recommendations(recommendations)
+    view.display_recommendations(recommendations)
+
+    # Close the file
+    sys.stdout.close()
+
+if __name__ == "__main__":
+    main()

be_repo/modules/retrieval_engine.py

@@ -1,10 +1,12 @@
 # retrieval_engine.py
 
+from langchain_neo4j import GraphCypherQAChain
+from langchain_openai import ChatOpenAI
+from langchain.chains.retrieval import create_retrieval_chain 
 from langchain.chains.combine_documents import create_stuff_documents_chain
-from langchain.chains.retrieval import create_retrieval_chain
+from configs.openai_key import get_openai_api_key  # New import
 from langchain.prompts import PromptTemplate
 
-
 class RetrievalEngine:
     def __init__(self, resume_processor, neo4j_model):
         """
@@ -19,7 +21,7 @@ def __init__(self, resume_processor, neo4j_model):
 
         # Initialize Language Model (already initialized in Neo4jModel)
         self.llm = self.neo4j_model.llm
-
+        
         # Initialize GraphCypherQAChain (already initialized in Neo4jModel)
         self.graph_chain = self.neo4j_model.get_graph_chain()
 
@@ -51,12 +53,12 @@ def __init__(self, resume_processor, neo4j_model):
             {context}
             \"\"\"
             """,
-            input_variables=["input"]
+            input_variables=["input"]  
         )
 
-        # Create a documents chain
+         # Create a documents chain
         self.combine_docs_chain = create_stuff_documents_chain(self.llm, prompt=prompt)
-
+        
         # Initialize Retrieval Chain
         # Default node_label is 'JD'; can be adjusted as needed
         self.retrieval_chain = create_retrieval_chain(
@@ -77,13 +79,13 @@ def perform_mixed_retrieval(self, resume_text, node_label="JD"):
         """
         # Process resume into a Document
         doc = self.resume_processor.process_resume(resume_text)
-
+        
         if not doc:
             return [], {}
-
+        
         # Store the Document in the appropriate vector store
         self.neo4j_model.store_documents([doc], node_label=node_label)
-
+        
         # Access the schema property correctly
         schema = self.neo4j_model.graph.get_schema
 
@@ -92,15 +94,16 @@ def perform_mixed_retrieval(self, resume_text, node_label="JD"):
         similar_docs = similar_docs_result.get("output", [])
         print("similar_docs_result:", similar_docs_result)
         print("Keys in similar_docs_result:", similar_docs_result.keys())
+        
+
 
         for doc in similar_docs:
             print("Document Metadata:", doc.metadata)
 
-        query = (f"Based on the following resume, recommend relevant job positions based on skills and experience, "
-                 f"while ignoring the location: {resume_text}")
+        query = f"Based on the following resume, recommend relevant job positions: {resume_text}"
         graph_response = self.graph_chain.invoke({"query": query, "schema": schema})
         # After graph query
         print("Graph Response:")
         print(graph_response)
-
-        return similar_docs, graph_response
+       
+        return similar_docs, graph_response

be_repo/modules/view.py

@@ -28,8 +28,8 @@ def display_recommendations(self, recommendations):
         Display job recommendations to the user.
         """
         if not recommendations:
-            return 'No job recommendations found based on your resume.'
-        res = '\nRecommended Jobs for You:\n'
+            print("No job recommendations found based on your resume.")
+            return
+        print("\nRecommended Jobs for You:")
         for idx, job in enumerate(recommendations, start=1):
-            res += f'{idx}. {job}\n'
-        return res
+            print(f"{idx}. {job}")