From aadb9e0da97affdb8208e73e88eb35b6868abacf Mon Sep 17 00:00:00 2001
From: Gustaf Rydholm <gustaf.rydholm@gmail.com>
Date: Thu, 4 Apr 2024 01:09:18 +0200
Subject: Add game plan

---
 README.md | 101 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 101 insertions(+)

(limited to 'README.md')

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+# 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)
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