Here are a few catchy titles, under 50 characters, for the provided content: * **Build a RAG Pipeline: A Guide** * **RAG with Embeddings: Step-by-Step** * **Custom RAG: From Docs to Answers**

• Text extraction libraries (e.g., PyPDF2, Beautiful Soup)
• Text splitting libraries (e.g., LangChain, spaCy)

          
import PyPDF2
from langchain.text_splitter import RecursiveCharacterTextSplitter

def load_pdf(file_path):
    with open(file_path, 'rb') as file:
        reader = PyPDF2.PdfReader(file)
        text = ""
        for page in reader.pages:
            text += page.extract_text()
    return text

def chunk_text(text, chunk_size=1000, chunk_overlap=100):
    text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap)
    chunks = text_splitter.split_text(text)
    return chunks

# Example usage:
pdf_text = load_pdf("your_document.pdf")
chunks = chunk_text(pdf_text)
print(f"Number of chunks: {len(chunks)}")
          
        

• Embedding models (e.g., OpenAI's embeddings, Sentence Transformers)
• API keys (for cloud-based embedding models)
• Libraries for interacting with embedding models (e.g., openai, sentence-transformers)

          
from sentence_transformers import SentenceTransformer

# Load a pre-trained model (e.g., all-MiniLM-L6-v2)
model = SentenceTransformer('all-MiniLM-L6-v2')

def generate_embeddings(texts):
    embeddings = model.encode(texts)
    return embeddings

# Example usage:
embeddings = generate_embeddings(chunks)
print(f"Shape of the embeddings: {embeddings.shape}") # (number_of_chunks, embedding_dimension)
          
        

• Vector database (e.g., FAISS, Pinecone, ChromaDB)
• API keys/credentials (for cloud-based vector databases)
• Libraries for interacting with the vector database (e.g., faiss-cpu, pinecone-client, chromadb)

          
import faiss
import numpy as np

# Assuming you have embeddings and chunks from previous steps

# Convert embeddings to numpy array if needed
embeddings_np = np.array(embeddings)

# Build the FAISS index (dimension of the embeddings)
dimension = embeddings_np.shape[1]
index = faiss.IndexFlatL2(dimension) # Using L2 distance for similarity

# Add the embeddings to the index
index.add(embeddings_np)

# (Optional) Save the index to disk
# faiss.write_index(index, "my_faiss_index.bin")

# Load the index
# index = faiss.read_index("my_faiss_index.bin")
          
        

• Embedding model (same as step 2)
• Vector database query functionality

          
# Assuming you have the index and embedding model

def get_relevant_chunks(query, index, model, chunks, top_k=3):
    query_embedding = model.encode([query])
    query_embedding_np = np.array(query_embedding)

    # Perform the search
    distances, indices = index.search(query_embedding_np, top_k) # returns distances and indices of closest vectors

    relevant_chunks = [chunks[i] for i in indices[0]]
    return relevant_chunks

# Example Usage:
query = "What is the main topic of the document?"
relevant_chunks = get_relevant_chunks(query, index, model, chunks)
print(f"Relevant chunks:/n{relevant_chunks}")
          
        

• Large Language Model (e.g., GPT-3.5, GPT-4, Llama 2)
• API keys/credentials (for cloud-based LLMs)
• Prompt engineering techniques
• Libraries for interacting with LLMs (e.g., openai, transformers)

          
from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain

# Replace with your OpenAI API key
import os
os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_API_KEY"

# Define the prompt template
template = """
You are a helpful assistant. Answer the question based on the context provided below.
Context:
{context}

Question: {question}

Answer:
"""
prompt = PromptTemplate(template=template, input_variables=["context", "question"])

# Initialize the LLM (e.g., OpenAI's GPT-3.5)
llm = OpenAI(model_name="gpt-3.5-turbo")

# Create the LLM chain
def generate_answer(question, relevant_chunks, llm, prompt):
    context = "/n".join(relevant_chunks)
    llm_chain = LLMChain(prompt=prompt, llm=llm)
    answer = llm_chain.run(context=context, question=question)
    return answer

# Example usage
question = "What are the key findings of the document?"
answer = generate_answer(question, relevant_chunks, llm, prompt)
print(f"Answer: {answer}")
          
        

• Evaluation metrics (e.g., accuracy, relevance, coherence scores)
• Human evaluation
• Tools for automated evaluation (e.g., RAGAS, RAGatouille)

          
# Examples of evaluation
# 1. Human evaluation: Have human evaluators assess the generated answers.

# 2. Automated evaluation (e.g., using RAGAS)

# 3. Consider using metrics to evaluate the quality of the context returned

# 4. Analyze the results and iterate on your RAG pipeline.