Tutorials

Use multiple AI models in one application. Combine the strengths of different models. Model Selection Strategy Simple tasks: Use cheaper models Complex reasoning: Use GPT-4 or Claude Code: Use DeepSeek Architecture class MultiModelLLM: def __init__(self): self.models = { “fast”: OpenAI(model=”gpt-3.5-turbo”), “smart”: OpenAI(model=”gpt-4″), “cheap”: DeepSeek() } Conclusion Multi-model apps optimize cost and quality!

Use Claude API from Anthropic. Integrate Claude for long-context applications. Features ✅ 200K context window ✅ Excellent reasoning ✅ Safe outputs Installation pip install anthropic Example from anthropic import Anthropic client = Anthropic(api_key=”your-key”) message = client.messages.create( model=”claude-3-opus”, max_tokens=1024, messages=[{“role”: “user”, “content”: “Hello”}] ) Conclusion Claude excels at long-context tasks!

Use DeepSeek API for cost-effective AI. Integrate DeepSeek into your applications. API Endpoint https://api.deepseek.com Example import requests response = requests.post( “https://api.deepseek.com/chat/completions”, headers={“Authorization”: “Bearer YOUR_KEY”}, json={ “model”: “deepseek-chat”, “messages”: [{“role”: “user”, “content”: “Hello”}] } ) Pricing Input: $0.14/1M tokens Output: $0.28/1M tokens Conclusion DeepSeek offers great value!

Master the OpenAI API for your applications. Complete guide to using OpenAI’s powerful models. Getting Started 1. Create OpenAI account 2. Generate API key 3. Install the SDK 4. Make your first call Installation pip install openai Basic Usage from openai import OpenAI client = OpenAI(api_key=”your-key”) response = client.chat.completions.create( model=”gpt-4″, messages=[{“role”: “user”, “content”: “Hello!”}] ) … Read more

Build semantic search that understands meaning. Create search that finds relevant results by meaning, not keywords. Architecture 1. Document ingestion 2. Embedding generation 3. Vector storage 4. Query embedding 5. Similarity search Code Example def semantic_search(query, vectorstore, k=5): query_embedding = embeddings.embed_query(query) results = vectorstore.similarity_search_by_vector(query_embedding, k=k) return results Benefits ✅ Better relevance ✅ Handles synonyms ✅ … Read more

Qdrant is a high-performance vector database written in Rust. Fast, reliable, and easy to deploy. Features ✅ Written in Rust ✅ High performance ✅ Rich filtering ✅ Easy deployment Docker Setup docker run -p 6333:6333 qdrant/qdrant Python Client from qdrant_client import QdrantClient client = QdrantClient(host=”localhost”, port=6333) Conclusion Qdrant is fast and reliable!

Milvus is a highly scalable vector database. Handle massive vector datasets efficiently. Features ✅ Highly scalable ✅ GPU acceleration ✅ Multiple index types ✅ Cloud-native Docker Setup docker run -p 19530:19530 milvusdb/milvus Python Client from pymilvus import connections, Collection connections.connect(“default”, host=”localhost”, port=”19530″) Conclusion Milvus scales to billions of vectors!

Weaviate is a vector database with GraphQL API. Build semantic search with rich querying. Features ✅ GraphQL interface ✅ Built-in vectorization ✅ Multi-tenancy ✅ Hybrid search Docker Setup docker run -p 8080:8080 semitechnologies/weaviate Python Client import weaviate client = weaviate.Client(“http://localhost:8080”) Conclusion Weaviate offers rich querying capabilities!

FAISS is Facebook’s library for efficient similarity search. Search billions of vectors efficiently. Installation pip install faiss-cpu Example import faiss import numpy as np dimension = 1536 index = faiss.IndexFlatL2(dimension) vectors = np.random.random((1000, dimension)).astype(‘float32’) index.add(vectors) D, I = index.search(query_vector, k=5) Features ✅ Extremely fast ✅ GPU support ✅ Scalable to billions Conclusion FAISS is the … Read more

Chroma is an open-source embedding database. Run vector search locally or in the cloud. Installation pip install chromadb Quick Start import chromadb client = chromadb.Client() collection = client.create_collection(“my-collection”) collection.add(documents=[“doc1”, “doc2”], ids=[“id1”, “id2”]) results = collection.query(query_texts=[“search”], n_results=3) Features ✅ Open source ✅ Easy to use ✅ Persistent storage ✅ Built-in embeddings Conclusion Chroma is perfect for … Read more