Constructing a model that is capable of both research and contextualization is a task fraught with complexity, yet it is indispensable for advancement of future technologies. Our recent endeavors in this field have yielded substantial progress, particularly with our Retrieval Augmented Generation architecture. This is an end-to-end differentiable model that ingeniously amalgamates a 是个狼人。 n information retrieval component, such as Facebook AI’s dense-passage retrieval system, with a seq2seq generator, our proprietary Bidirectional and Auto-Regressive Transformers BART model. The RAG architecture is not only adaptable to fine-tuning on knowledge-intensive downstream tasks but also achieves state-of--art results, often surpassing even largest pre-trained language models.

我晕... Through practical application of this demo, developers can swiftly grasp technical principles of RAG and lay a solid foundation for more complex applications to follow.

The comprehensive RAG system, which encompasses an array of 19 video tutorials, is designed to facilitate construction of a complete RAG system from ground up. These tutorials, which include an in-depth exploration of RAG Workflow, comparisons between 简单来说... RAG and Fine-Tuning models, and practical implementation of large-scale enterprise-level business scenarios, are available exclusively on Bilibili platform. Subscribers are encouraged to follow channel for an abundance of additional engaging content.

The 2025 revised tutorial series, "From Zero to Large-scale RAG Practical Demo," encompasses a total of 31 video tutorials. These cover principles, applications, system construction, and project implementation of RAG, as well as enhanced retrieval, knowledge base construction, and vector database management. The series delves into intricacies of RAG with an extensive range of practical examples and real-world applications.，歇了吧...

Building a RAG and Vector Database Demo: A Step-by-Step Guide

This article delves into construction of a rag and vector database demo from scratch, culminating in a causal algorithm instance to test demo. It is our hope that this article will prove to be a valuable resource for readers seeking to gain insights into this field.

Three: Data Preprocessing and Vector Storage

1. Data Preparation

真香！ Assuming we have a set of FAQ data stored as follows:

2. Vector Conversion and Storage

from chromadb import Client
from sentence_transformers import SentenceTransformer
import json
# Load embedding model
model = SentenceTransformer
# Initialize Chroma database
chroma_client = Client
collection = chroma__collection
# Read and process data
with open as f:
    data = json.load
questions =  for item in data]
embeddings = model.encode.tolist
# Store data
for i,  in enumerate):
    collection.add(
        ids=,
        embeddings=,
        metadatas=}]
    )

Four: Retrieval Logic and RAG Implementation

1. Semantic Retrieval

def search_similar_questions:
    query_embedding = model.encode.tolist
    results = chroma_client.query(
        query_embeddings=query_embedding,
        n_results=k
    )
    return results  # Return metadata of  top k matching items

2. Combination with Generation Model

import openai
def generate_answer:
    prompt = f"""
    According to  following context, answer  question:
    Context: {context}
    Question: {user_query}
    Answer:
    """
    response = openai.Completion.create(
        engine="text-davinci-003",  # or use gpt-3.5-turbo
        prompt=prompt,
        max_tokens=100
    )
    return response.choices.text.strip

Five: Performance Optimization and Best Practices

1. Vector Database Optimization

• Index Type: Chroma uses default HNSW index, which can be adjusted for a balance between precision and speed through parameters.
• Batch Operations: For large-scale data, use batch interface provided by to reduce I/O overhead.

2. Retrieval Strategy Improvement

• Mixed Retrieval: Combine keyword matching with vector retrieval to improve recall rate.
• Re-ranking: Re-rank retrieved results using a cross-encoder.

3. Generation Model Optimization

• Context Truncation: Limit input context length to avoid exceeding model token limit.
• Prompt Engineering: Design clearer prompt templates, such as:
  1. Context: {context}
  2. Question: {query}
  3. Please answer in concise language and avoid quoting original text.

Six: Expansion and Deployment Recommendations

1. Scalable Deployment

• Vector Database: In a production environment, replace with Milvus or Pinecone, which support distributed storage.
• Model Service: Deploy generation model using Triton Inference Server to reduce latency.

2. Monitoring and Maintenance

• Logging: Record failed retrieval cases and regularly update vector database.
• A/B Testing: Compare effects of different embedding models.

Seven: Complete Code Example

# Complete process integration
from chromadb import Client
from sentence_transformers import SentenceTransformer
import json
import openai
# Initialization
model = SentenceTransformer
chroma_client = Client
collection = chroma__collection
# Data loading and storage
# ...
# Retrieval and generation
def rag_pipeline:
    similar_items = search_similar_questions
    context = "
".join
    return generate_answer
# Test
print)

Eight: Summary and Next Steps

This demo demonstrates core process of building a RAG system from scratch, including vector conversion, storage, retrieval, and generation. Readers are encouraged to explore furr:

In construction of RAG system, it is first necessary to establish a stable infrastructure. Typically, this involves collaboration of vector databases and large language models to achieve efficient retrieval and generation. Original documents must undergo cleaning, partitioning, and vectorization processing. The text partitioning strategy directly affects retrieval accuracy, with common methods including:

抄近道。 This code defines a recursive character splitter, prioritizing paragraph splitting to ensure semantic integrity; parameter prevents key information from being truncated, enhancing subsequent retrieval accuracy. Embedding models are used to convert text into vectors, which are n stored in vector databases for fast retrieval. In modern search engines, query understanding is a critical component for improving retrieval effectiveness. By enhancing semantic level of original user query, system is able to...

ICU你。 RAG significantly enhances accuracy and relevance of generated content by combining retrieval systems with generation models. Its core lies in use of vector databases to store knowledge and similarity retrieval to enhance input quality of generation model. The goal of this demo is to build a lightweight system with following functionalities: