The sauce: chunking, embedding + similarity search, and prompt construction

 Great question! The next highest-value thing to work on for your MVP, given your current setup, is:

Implement Document Chunking + Embedding + Similarity Search

Why?
Because this is the core of effective Retrieval-Augmented Generation. Without chunking and similarity search, your queries will either:

• Use the entire document as context (too large, noisy, or truncated)

• Or only a limited set of documents that might be irrelevant

Chunking + embeddings let you filter and focus on the most relevant text for each query — massively improving answer accuracy and efficiency.

---

What that means concretely:

1. Chunking

   • Take your parsed text from each document

   • Split it into manageable pieces (e.g., 500 tokens or ~1000 characters)

   • Each chunk should be semantically coherent (paragraph, sentence group, etc.)

2. Embedding

   • Call OpenAI or other embedding API to get a vector for each chunk

   • Store these embeddings in your database alongside chunk text and metadata

3. Similarity Search

   • On each query, embed the question

   • Find top N most similar chunks via cosine similarity or approximate nearest neighbors

   • Pass only those chunks as context to the LLM for answer generation

---

Why skip other features for now?

• User management, roles, paid plans, UI polish — all valuable but they don't improve the core function or accuracy of your app.

• Focusing on RAG pipeline basics first lets you deliver strong value with simpler architecture.

---

Quick next steps for chunking + embedding:

• Write or integrate a chunking utility for your parsed text

• Add embedding calls inside your worker after parsing, store results in embeddings table

• Implement simple cosine similarity in your query handler to pick top chunks

• Build your prompt using those chunks + question, then call LLM

---

If you want, I can help you draft the chunking function or an example of cosine similarity search in SQLite or Python.

---

Bottom line: Get chunking + embedding + similarity search working smoothly — it’s the biggest bang for your buck next. Everything else flows from there!

1. Document Chunking — How to split your documents for best results

Goal: Split your input text files into chunks that are:

• Small enough to fit easily in model context windows (e.g., 1000 tokens max)

• Large enough to keep coherent meaning (so chunks are useful)

• Optionally overlapping, to avoid missing info between chunks

Recommended approach:

• Use a token-based chunker, since GPT counts tokens, not characters.

• Target ~500 to 1000 tokens per chunk (about 300–700 words).

• Use some overlap (e.g., 50–100 tokens) between chunks to preserve context at boundaries.

• If you don’t want to use a library, you can split on paragraphs or sentences and accumulate until token limit.

Example with Python & tiktoken tokenizer:

import tiktoken

tokenizer = tiktoken.get_encoding("cl100k_base")

def chunk_text(text, max_tokens=800, overlap=100):
    tokens = tokenizer.encode(text)
    chunks = []
    start = 0
    while start < len(tokens):
        end = min(start + max_tokens, len(tokens))
        chunk_tokens = tokens[start:end]
        chunk_text = tokenizer.decode(chunk_tokens)
        chunks.append(chunk_text)
        start += max_tokens - overlap
    return chunks

---

2. Embeddings and similarity search — How to find relevant chunks at query time

Goal: For every chunk, generate a vector embedding that captures its meaning. When a user asks a question:

• Embed the question into the same vector space

• Find chunks with embeddings most similar (e.g., cosine similarity) to the question

• Pass those relevant chunks to the LLM

Recommended approach:

• Use OpenAI’s text-embedding-ada-002 or similar for embeddings.

• Store embeddings in a vector database (e.g., FAISS, Chroma, Pinecone) for fast similarity search.

• At query time, embed question, then query the vector DB for top N closest chunks.

Example using OpenAI Python SDK + FAISS:

from openai import OpenAI
import faiss
import numpy as np

client = OpenAI(api_key="your_api_key")

def get_embedding(text):
    response = client.embeddings.create(input=text, model="text-embedding-ada-002")
    return np.array(response.data[0].embedding)

# Assume you have a list of chunk texts
chunk_texts = [...]

# Create FAISS index
dimension = 1536  # embedding size for ada-002
index = faiss.IndexFlatL2(dimension)

# Build index
chunk_embeddings = [get_embedding(text) for text in chunk_texts]
index.add(np.array(chunk_embeddings))

# Query
query = "What are the todo items?"
query_embedding = get_embedding(query)
k = 5  # top 5 closest chunks
D, I = index.search(np.array([query_embedding]), k)

# Retrieve chunks
relevant_chunks = [chunk_texts[i] for i in I[0]]

---

3. Prompt construction — How to build the prompt for OpenAI API

Goal: Give the LLM clear instructions + relevant info so it can answer well.

Recommended approach:

• Use a system prompt setting behavior and role

• Use a user prompt containing the relevant chunks and question

• Format clearly: label sections, keep chunk info concise but complete

• Add instructions to answer only from given docs and say “I don’t know” if uncertain

Example prompt template:

System prompt:
You are a helpful assistant that answers questions ONLY using the provided documents. 
If the answer is not in the documents, say "I don't know."

User prompt:
DOCUMENTS:
[chunk 1]
[chunk 2]
...
[chunk N]

QUESTION:
{user question}

ANSWER:

In code (OpenAI chat completions):

messages = [
    {"role": "system", "content": "You are a helpful assistant that answers questions ONLY using the provided documents. If the answer is not in the documents, say \"I don't know.\""},
    {"role": "user", "content": f"DOCUMENTS:\n\n{'\n\n'.join(relevant_chunks)}\n\nQUESTION:\n{user_question}\n\nANSWER:"}
]

response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=messages,
    max_tokens=256,
    temperature=0
)
answer = response.choices[0].message.content.strip()

---

Summary

Step	Key Tips	Tools / Libraries
Chunking	Token-based, overlap for context	tiktoken, sentence splitting
Embeddings & Search	Use ada-002 embeddings, FAISS or Chroma DB	OpenAI embeddings, faiss, chromadb
Prompt construction	Clear system & user prompts, instructions	OpenAI Chat API, custom templates