Building a production-grade Retrieval Augmented Generation (RAG) based AI system involves several critical components that require continuous tuning and optimization. Below are the key aspects and some practical commands and code snippets to help you get started.
Retrieval
1. Chunking
- Small vs. Large Chunks: Depending on the data, you may need to experiment with different chunk sizes.
- Sliding or Tumbling Window: Use sliding windows for overlapping chunks or tumbling windows for non-overlapping chunks.
- Retrieve Parent or Linked Chunks: Decide whether to retrieve parent or linked chunks for better context.
Python Code for Chunking:
from langchain.text_splitter import RecursiveCharacterTextSplitter text_splitter = RecursiveCharacterTextSplitter( chunk_size=500, chunk_overlap=50 ) chunks = text_splitter.split_text(your_text_data)
2. Embedding Model
- Choose an embedding model like OpenAI’s `text-embedding-ada-002` or Sentence Transformers.
- Consider contextual embeddings for better performance.
Python Code for Embedding:
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('all-MiniLM-L6-v2')
embeddings = model.encode(chunks)
3. Vector Database
- Database Choice: Options include Pinecone, Weaviate, or FAISS.
- Hosting: Decide between cloud-hosted or self-hosted solutions.
- Metadata Storage: Store metadata alongside embeddings for better retrieval.
- Indexing Strategy: Use HNSW or IVF for efficient indexing.
Python Code for FAISS:
import faiss import numpy as np dimension = 768 # Dimension of embeddings index = faiss.IndexFlatL2(dimension) index.add(np.array(embeddings))
4. Vector Search
- Similarity Measure: Use cosine similarity or dot product.
- Query Path: Choose between metadata-first or ANN-first search.
- Hybrid Search: Combine keyword and vector search for better results.
Python Code for Hybrid Search:
from sklearn.metrics.pairwise import cosine_similarity query_embedding = model.encode(["your query"]) similarities = cosine_similarity(query_embedding, embeddings)
5. Heuristics
- Time Importance: Prioritize recent data.
- Reranking: Use algorithms like BM25 for reranking.
- Duplicate Context: Apply diversity ranking to avoid redundancy.
- Source Retrieval: Ensure the source of the data is reliable.
- Conditional Document Preprocessing: Preprocess documents based on specific conditions.
Generation
1. LLM Selection
- Choose between proprietary models like GPT-4 or open-source models like LLaMA.
- Consider self-hosting for better control.
Python Code for LLM:
from transformers import pipeline
generator = pipeline('text-generation', model='gpt-4')
response = generator("Your prompt here")
2. Prompt Engineering
- Craft prompts carefully to align the system’s output with your desired results.
- Prevent jailbreak scenarios by testing various prompts.
Example
[plaintext]
“Given the context, provide a detailed answer to the following question: [Your Question]”
[/plaintext]
Observation, Evaluation, Monitoring, and Security
- Monitoring: Use tools like Prometheus and Grafana for real-time monitoring.
- Evaluation: Continuously evaluate the system’s performance using metrics like BLEU or ROUGE.
- Security: Implement security measures to protect your AI system from attacks.
Linux Command for Monitoring:
top -b -n 1 > system_monitor.log
Windows Command for Monitoring:
[cmd]
typeperf “\Processor(_Total)\% Processor Time” -sc 1 > system_monitor.log
[/cmd]
What Undercode Say
Building a production-grade RAG-based AI system is a complex but rewarding task. The key components include effective chunking strategies, choosing the right embedding model, and selecting an appropriate vector database. The retrieval process involves careful consideration of chunking methods, embedding models, and vector search techniques. On the generation side, selecting the right LLM and engineering effective prompts are crucial. Additionally, monitoring, evaluating, and securing your system are often overlooked but essential aspects of maintaining a robust AI system.
To further enhance your system, consider using Linux commands like `top` for real-time system monitoring or `grep` for log analysis. On Windows, commands like `typeperf` can help monitor system performance. For embedding and retrieval, Python libraries like `sentence-transformers` and `faiss` are invaluable. Always remember to continuously evaluate and fine-tune your system to ensure it meets the desired performance standards.
For more detailed insights, you can refer to the following resources:
– Sentence Transformers Documentation
– FAISS GitHub Repository
– LangChain Documentation
By following these guidelines and utilizing the provided code snippets and commands, you can build a robust and efficient RAG-based AI system that meets production-grade standards.
References:
Hackers Feeds, Undercode AI
