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backend/analysis.py

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+import os
+from dotenv import load_dotenv
+from langchain_groq import ChatGroq
+from langchain_core.prompts import PromptTemplate
+
+# Set up Groq API key
+load_dotenv()  # Load environment variables from .env file
+os.environ["GROQ_API_KEY"] = os.getenv("GROQ_API_KEY")  # Set the Groq API key from environment variables
+
+# Initialize the ChatGroq model with the specified model name
+llm = ChatGroq(model_name="mixtral-8x7b-32768")
+
+def analyze_resume(full_resume, job_description):
+    # Template for analyzing the resume against the job description
+    template = """
+    You are an AI assistant specialized in resume analysis and recruitment. Analyze the given resume and compare it with the job description. 
+    
+    Example Response Structure:
+    
+    **OVERVIEW**:
+    - **Match Percentage**: [Calculate overall match percentage between the resume and job description]
+    - **Matched Skills**: [List the skills in job description that match the resume]
+    - **Unmatched Skills**: [List the skills in the job description that are missing in the resume]
+
+    **DETAILED ANALYSIS**:
+    Provide a detailed analysis about:
+    1. Overall match percentage between the resume and job description
+    2. List of skills from the job description that match the resume
+    3. List of skills from the job description that are missing in the resume
+    
+    **Additional Comments**:
+    Additional comments about the resume and suggestions for the recruiter or HR manager.
+
+    Resume: {resume}
+    Job Description: {job_description}
+
+    Analysis:
+    """
+    prompt = PromptTemplate(  # Create a prompt template with input variables
+        input_variables=["resume", "job_description"],
+        template=template
+    )
+
+    # Create a chain combining the prompt and the language model
+    chain = prompt | llm
+
+    # Invoke the chain with input data
+    response = chain.invoke({"resume": full_resume, "job_description": job_description})
+
+    # Return the content of the response
+    return response.content

backend/vector_store.py

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+from langchain_huggingface import HuggingFaceEmbeddings
+from langchain_community.vectorstores import FAISS
+
+# Initialize the Hugging Face embedding model
+embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
+
+def create_vector_store(chunks):
+    # Store embeddings into the vector store
+    vector_store = FAISS.from_documents(
+        documents=chunks,  # Input chunks to the vector store
+        embedding=embeddings  # Use the initialized embeddings model
+    )
+    return vector_store