Ai Rag Pipeline

Ai Rag Pipeline is a community skill for building retrieval-augmented generation systems, covering document ingestion, embedding generation, vector storage, retrieval strategies, and response synthesis for knowledge-grounded AI applications.

What Is This?

Overview

Ai Rag Pipeline provides patterns for constructing end-to-end RAG systems that ground language model responses in specific document collections. It covers document loading and chunking strategies for splitting source material, embedding generation using configurable model providers, vector database integration for similarity search, retrieval ranking that selects the most relevant chunks for a query, and response synthesis that combines retrieved context with model generation. The skill enables developers to build AI applications that answer questions accurately from private knowledge bases.

Who Should Use This

This skill serves developers building question-answering systems over private document collections, teams creating internal knowledge assistants for enterprise documentation, and engineers designing customer support bots that answer from product documentation.

Why Use It?

Problems It Solves

Language models hallucinate answers when asked about information not in their training data. Stuffing entire documents into the prompt exceeds context window limits and wastes tokens. Keyword search misses semantically related content when users phrase questions differently from the source text. Without retrieval ranking, irrelevant chunks dilute context and degrade answers.

Core Highlights

Document chunking splits source material at semantic boundaries to preserve meaning within each chunk. Embedding generation converts text chunks into vector representations for similarity comparison. Vector search retrieves the most relevant chunks based on query embedding distance. Response synthesis combines retrieved context with model generation for grounded answers.

How to Use It?

Basic Usage

from dataclasses import dataclass, field
import hashlib

@dataclass
class DocumentChunk:
    text: str
    source: str
    chunk_id: str = ""
    embedding: list[float] = field(default_factory=list)

    def __post_init__(self):
        if not self.chunk_id:
            self.chunk_id = hashlib.md5(
                self.text[:100].encode()).hexdigest()[:12]

class DocumentChunker:
    def __init__(self, chunk_size: int = 500,
                 overlap: int = 50):
        self.chunk_size = chunk_size
        self.overlap = overlap

    def chunk(self, text: str,
             source: str = "") -> list[DocumentChunk]:
        words = text.split()
        chunks = []
        start = 0
        while start < len(words):
            end = min(start + self.chunk_size, len(words))
            chunk_text = " ".join(words[start:end])
            chunks.append(DocumentChunk(
                text=chunk_text, source=source))
            start += self.chunk_size - self.overlap
        return chunks

Real-World Examples

from dataclasses import dataclass, field
import math

class VectorStore:
    def __init__(self):
        self.chunks: list[DocumentChunk] = []

    def add(self, chunk: DocumentChunk):
        self.chunks.append(chunk)

    def _cosine_sim(self, a: list[float],
                    b: list[float]) -> float:
        dot = sum(x * y for x, y in zip(a, b))
        mag_a = math.sqrt(sum(x * x for x in a))
        mag_b = math.sqrt(sum(x * x for x in b))
        if mag_a == 0 or mag_b == 0:
            return 0.0
        return dot / (mag_a * mag_b)

    def search(self, query_embedding: list[float],
              top_k: int = 3) -> list[DocumentChunk]:
        scored = []
        for chunk in self.chunks:
            score = self._cosine_sim(
                query_embedding, chunk.embedding)
            scored.append((score, chunk))
        scored.sort(key=lambda x: x[0], reverse=True)
        return [c for _, c in scored[:top_k]]

class RAGPipeline:
    def __init__(self, store: VectorStore,
                 embed_fn=None, generate_fn=None):
        self.store = store
        self.embed_fn = embed_fn
        self.generate_fn = generate_fn

    def query(self, question: str,
             top_k: int = 3) -> dict:
        q_embed = (self.embed_fn(question)
                   if self.embed_fn else [])
        results = self.store.search(q_embed, top_k)
        context = "\n\n".join(
            r.text for r in results)
        prompt = (f"Context:\n{context}\n\n"
                  f"Question: {question}")
        answer = (self.generate_fn(prompt)
                  if self.generate_fn else prompt)
        return {"answer": answer,
                "sources": [r.source for r in results]}

Advanced Tips

Experiment with chunk sizes and overlap to find the balance between context completeness and retrieval precision for your document type. Use hybrid search that combines vector similarity with keyword matching for better retrieval across different query styles. Re-rank retrieved chunks with a cross-encoder before generation.

When to Use It?

Use Cases

Build a documentation assistant that answers developer questions from API reference documents. Create an internal knowledge base search that retrieves relevant policy documents for employee queries. Implement a customer support bot that grounds responses in product manuals and FAQ collections.

Related Topics

Vector databases, embedding models, semantic search, document processing pipelines, and knowledge-grounded generation.

Important Notes

Requirements

An embedding model API for generating vector representations. A vector database or in-memory store for chunk storage and retrieval. A language model for synthesizing answers from retrieved context.

Usage Recommendations

Do: tune chunk size based on the document type and the typical query length for your use case. Include source references in responses so users can verify answers against original documents. Re-index documents when source content is updated to keep the knowledge base current.

Don't: use excessively large chunks that exceed the model context window when combined. Skip overlap between chunks, which can split important information across boundaries. Trust RAG answers without source attribution, as retrieval errors can surface irrelevant content.

Limitations

Retrieval quality depends on embedding model alignment with the document domain. Chunking strategies effective for one document type may underperform on another. Large document collections require vector database infrastructure.