Tell about the entire pipeline of the RAG system: what documents were used, how they were loaded and processed?

RAG (Retrieval-Augmented Generation) is an architecture that combines generative models with document retrieval to provide more accurate and substantiated answers.

The pipeline of a RAG system typically includes the following stages:

1. Collection and preparation of documents:

   • Sources can vary: articles, books, knowledge bases, web pages.
   • Documents are loaded from files (PDF, TXT), databases, or APIs.

2. Preprocessing:

   • Cleaning text from unnecessary characters, splitting into segments (paragraphs, sentences).
   • Tokenization and normalization.

3. Vectorization:

   • Each segment is transformed into a vector using embeddings (e.g., Sentence-BERT, OpenAI Embeddings).

4. Indexing:

   • Vectors are loaded into a vector database (FAISS, Pinecone, Milvus) for fast search.

5. Query and search:

   • When a query is received, it is also vectorized.
   • Search for the most semantically similar documents or segments in the index.

6. Answer generation:

   • The found documents are passed to a generative model (e.g., GPT), which formulates an answer considering the retrieved context.

Example of loading and processing documents in Python:

from langchain.document_loaders import TextLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import FAISS

# Load documents
loader = TextLoader('documents/article.txt')
docs = loader.load()

# Split into segments
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
doc_chunks = text_splitter.split_documents(docs)

# Create embeddings
embeddings = OpenAIEmbeddings()

# Indexing
vectorstore = FAISS.from_documents(doc_chunks, embeddings)

# Search by query
query = "What is RAG?"
query_embedding = embeddings.embed_query(query)
results = vectorstore.similarity_search(query)

# Pass results to a generative model for response