Mastering Retrieval-Augmented Generation (RAG) and Prompt Design 

Objective

By the end of this project, you will have developed a fully functional Retrieval-Augmented Generation (RAG) pipeline tailored for Jarvid. The pipeline will:

1. Integrate FAISS with an LLM API to retrieve relevant data.

2. Utilize prompt templates to effectively incorporate retrieved context.

3. Generate accurate, context-aware responses to sample queries.

Step-by-Step Implementation

Step 1: Environment Setup

Install Required Libraries: Ensure you have the necessary libraries installed.
bash
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pip install faiss-cpu openai numpy pandas

1. 
   

2. Setup OpenAI API:

   • Obtain an API key from OpenAI.

   • Save it in your script to access the LLM API.

Step 2: Initialize FAISS

Prepare Employee Data: Simulate a dataset of employee profiles with precomputed embeddings.
python
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import numpy as np

# Employee profiles

profiles = [

    {"name": "Alice", "role": "Python Developer", "skills": ["Python", "Machine Learning"], "department": "AI Development"},

    {"name": "Bob", "role": "Data Scientist", "skills": ["Data Analysis", "Python", "Statistics"], "department": "Data Analytics"},

    {"name": "Carol", "role": "Backend Engineer", "skills": ["API Development", "Database Management"], "department": "API Integrations"}

]

# Simulated embeddings (replace with actual embeddings)

embeddings = np.array([

    [0.1, 0.2, 0.3],

    [0.4, 0.5, 0.6],

    [0.7, 0.8, 0.9]

], dtype=np.float32)

Create and Populate FAISS Index:
python
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import faiss

# Define embedding dimension

dimension = embeddings.shape[1]

# Create FAISS index

index = faiss.IndexFlatL2(dimension)

# Add embeddings to the index

index.add(embeddings)

print(f"FAISS index created with {index.ntotal} vectors.")

Step 3: Query the FAISS Index

Generate Query Embedding: Use the OpenAI API to generate an embedding for a sample query.
python
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import openai

# Set OpenAI API key

openai.api_key = "your_openai_api_key"

# Function to generate query embedding

def generate_query_embedding(query):

    response = openai.Embedding.create(

        input=query,

        model="text-embedding-ada-002"

    )

    return np.array(response['data'][0]['embedding'], dtype=np.float32)

# Example query

query = "Who can help me with Python development?"

query_embedding = generate_query_embedding(query).reshape(1, -1)

Search the FAISS Index: Retrieve the most relevant profiles for the query.
python
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# Number of results to retrieve

top_k = 3

# Perform similarity search

distances, indices = index.search(query_embedding, top_k)

# Map indices to profiles

retrieved_profiles = [profiles[idx] for idx in indices[0]]

print("Retrieved Profiles:", retrieved_profiles)

Step 4: Design Prompt Templates

Prompt for Expertise Summaries:
python
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def expertise_summary_prompt(profiles):

    data = "\n".join(

        f"{profile['name']} is a {profile['role']} skilled in {', '.join(profile['skills'])} from {profile['department']}."

        for profile in profiles

    )

    prompt = f"Summarize the expertise of the following employees:\n\n{data}\n\nProvide a concise summary."

    return prompt

Prompt for Contextual QA:
python
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def contextual_qa_prompt(profiles, query):

    context = "\n".join(

        f"{profile['name']} is a {profile['role']} skilled in {', '.join(profile['skills'])} from {profile['department']}."

        for profile in profiles

    )

    prompt = f"Context:\n{context}\n\nQuestion: {query}\n\nAnswer the question using the context above."

    return prompt

Prompt for Recommendations:
python
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def recommendation_prompt(profiles, request):

    context = "\n".join(

        f"{profile['name']} is a {profile['role']} skilled in {', '.join(profile['skills'])} from {profile['department']}."

        for profile in profiles

    )

    prompt = f"Request: {request}\n\nBased on the profiles below, recommend the most suitable employee(s):\n\n{context}\n\nRecommendation:"

    return prompt

Step 5: Generate Responses

Generate LLM Response: Use the designed prompts with the OpenAI API to get answers.
python
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def generate_response(prompt):

    response = openai.Completion.create(

        engine="text-davinci-003",

        prompt=prompt,

        max_tokens=100

    )

    return response['choices'][0]['text'].strip()

# Example usage

prompt = contextual_qa_prompt(retrieved_profiles, query)

response = generate_response(prompt)

print("Generated Response:", response)

Step 6: Documentation

Create detailed documentation that includes:

• Pipeline Architecture:

  • How FAISS integrates with the LLM API.

• Code Explanation:

  • Annotate scripts for clarity.

• Prompt Library:

  • Include all prompt templates with example outputs.

• Evaluation:

  • Describe testing methodologies and results for sample queries.

Expected Outcomes

1. 1. Python Script:

      • A complete implementation of the RAG pipeline for Jarvid, integrating FAISS and LLM APIs.

   2. Prompt Library:

      • Templates tailored for tasks like expertise summaries, contextual QA, and recommendations.

   3. Documentation:

      • Comprehensive, user-friendly documentation detailing the pipeline setup, usage, and testing results.