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@@ -0,0 +1,101 @@ +# Retrieval Augmented Generation + +## Plan + +- [ ] Architecture + - [ ] Vector store + - [ ] which one? FAISS? + - [ ] Build index of the document + - [ ] Embedding model (mxbai-embed-large) + - [ ] LLM (Dolphin) +- [ ] Gather some documents +- [ ] Create a prompt for the query + + +### Pre-Processing of Document +1. Use langchain document loader and splitter + ```python + from langchain_community.document_loaders import PyPDFLoader + from langchain.text_splitter import RecursiveCharacterTextSplitter + ``` + +2. Generate embeddings with mxbai, example: +```python +from sentence_transformers import SentenceTransformer +from sentence_transformers.util import cos_sim + +# 1. load model +model = SentenceTransformer("mixedbread-ai/mxbai-embed-large-v1") + +# For retrieval you need to pass this prompt. +query = 'Represent this sentence for searching relevant passages: A man is eating a piece of bread' + +docs = [ + query, + "A man is eating food.", + "A man is eating pasta.", + "The girl is carrying a baby.", + "A man is riding a horse.", +] + +# 2. Encode +embeddings = model.encode(docs) + +# 3. Calculate cosine similarity +similarities = cos_sim(embeddings[0], embeddings[1:]) +``` +But we will use ollama... + +(otherwise install `sentence-transformers`) + +3. Create vector store +```python +import numpy as np +d = 64 # dimension +nb = 100000 # database size +nq = 10000 # nb of queries +np.random.seed(1234) # make reproducible +xb = np.random.random((nb, d)).astype('float32') +xb[:, 0] += np.arange(nb) / 1000. +xq = np.random.random((nq, d)).astype('float32') +xq[:, 0] += np.arange(nq) / 1000. + +import faiss # make faiss available +index = faiss.IndexFlatL2(d) # build the index +print(index.is_trained) +index.add(xb) # add vectors to the index +print(index.ntotal) + +k = 4 # we want to see 4 nearest neighbors +D, I = index.search(xb[:5], k) # sanity check +print(I) +print(D) +D, I = index.search(xq, k) # actual search +print(I[:5]) # neighbors of the 5 first queries +print(I[-5:]) # neighbors of the 5 last queries +``` + +I need to figure out the vector dim of the mxbai model. + +4. Use Postgres as a persisted kv-store + +Save index of chunk as key and value as paragraph. + +5. Create user input pipeline +5.1 Create search prompt for document retrieval +5.2 Fetch nearest neighbors as context +5.3 Retrieve the values from the document db +5.4 Add paragraphs as context to the query +5.5 Send query to LLM +5.6 Return output +5.7 .... +5.8 Profit + +### Frontend (Low priority) + +[streamlit](https://github.com/streamlit/streamlit) + + +### Tutorial + +[link](https://medium.com/@solidokishore/building-rag-application-using-langchain-openai-faiss-3b2af23d98ba) |