Objective

To build a complete embedding pipeline for Jarvid by:

1. Generating embeddings for processed employee profiles.

2. Building a vector database using FAISS.

3. Performing similarity searches to retrieve contextually relevant profiles.

Step-by-Step Solution

Step 1: Generate Embeddings

Load the Processed Dataset

Ensure the cleaned dataset (employees_cleaned.csv) is available.

Code Example:

python

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import pandas as pd

# Load the dataset

file_path = "employees_cleaned.csv"

df = pd.read_csv(file_path)

# Display the first few rows

print("Dataset Preview:")

print(df.head())

Generate Embeddings

Use a pre-trained model or API to generate embeddings for relevant columns (e.g., role, department).

Option 1: Using OpenAI API

python

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import openai

# Set OpenAI API key

openai.api_key = "your_openai_api_key"

# Function to generate embeddings

def generate_embedding(text):

    response = openai.Embedding.create(

        input=text,

        model="text-embedding-ada-002"

    )

    return response['data'][0]['embedding']

# Apply the function to generate embeddings

df['role_embedding'] = df['role'].apply(generate_embedding)

df['department_embedding'] = df['department'].apply(generate_embedding)

print("Embeddings generated successfully!")

Option 2: Using HuggingFace Transformers

python

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from transformers import AutoTokenizer, AutoModel

import torch

# Load HuggingFace model and tokenizer

model_name = "sentence-transformers/all-MiniLM-L6-v2"

tokenizer = AutoTokenizer.from_pretrained(model_name)

model = AutoModel.from_pretrained(model_name)

# Function to generate embeddings

def generate_embedding(text):

    tokens = tokenizer(text, return_tensors="pt")

    with torch.no_grad():

        output = model(**tokens)

    return output.last_hidden_state.mean(dim=1).squeeze().numpy()

# Apply the function to generate embeddings

df['role_embedding'] = df['role'].apply(generate_embedding)

df['department_embedding'] = df['department'].apply(generate_embedding)

print("Embeddings generated successfully!")

Step 2: Build a Vector Database

Combine Embeddings

Combine embeddings for role and department into a single vector.

Code Example:

python

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import numpy as np

# Combine embeddings

df['combined_embedding'] = df.apply(

    lambda row: np.concatenate((row['role_embedding'], row['department_embedding'])),

    axis=1

)

# Convert to a NumPy array

embeddings = np.array(df['combined_embedding'].tolist())

print("Combined Embeddings Shape:", embeddings.shape)

Build a FAISS Index

Create a vector database using FAISS for efficient similarity searches.

Code Example:

python

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import faiss

# Get the dimensionality of the embeddings

dimension = embeddings.shape[1]

# Create a FAISS FlatL2 index

index = faiss.IndexFlatL2(dimension)

# Add embeddings to the index

index.add(embeddings)

# Verify the number of vectors in the index

print("Number of vectors in the FAISS index:", index.ntotal)

Step 3: Perform Similarity Searches

Search for Similar Profiles

Use a query vector to find the most similar profiles.

Code Example:

python

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# Select a query vector (e.g., the first embedding)

query_vector = embeddings[0].reshape(1, -1)

# Perform a similarity search for the top 5 nearest neighbors

k = 5

distances, indices = index.search(query_vector, k)

# Display the results

print("Distances:", distances)

print("Indices:", indices)

Retrieve Metadata

Use the indices from the search results to retrieve corresponding employee details.

Code Example:

python

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# Load the metadata

metadata = df[['name', 'role', 'department']]

# Display the closest neighbors

print("Closest Profiles:")

for idx in indices[0]:

    print(metadata.iloc[idx])

Step 4: Save and Document

Save the Index

Save the FAISS index for future use.

Code Example:

python

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faiss.write_index(index, "jarvid_vector_index.faiss")

Document the Process

Prepare documentation detailing:

1. Steps to generate embeddings.

2. FAISS index creation and configuration.

3. Querying process with sample outputs.

Example Structure:

• Introduction: Overview of the Jarvid embedding pipeline.

• Embedding Generation: Steps and tools used (e.g., OpenAI API or HuggingFace).

• Vector Database: Explanation of FAISS setup and configurations.

• Similarity Search: Methodology for querying and retrieving results.

Expected Outcomes

1. • Python Script:

     1. Generates embeddings, builds a FAISS index, and performs similarity searches.

   • Functional FAISS Index:

     1. Contains employee embeddings, ready for efficient querying.

   • Documentation:

     1. Detailed steps, methodologies, and tools used, along with sample outputs.