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
Pinecone is a fully managed vector database. Learn to use Pinecone for semantic search and RAG. Getting Started 1. Create Pinecone account 2. Get API key 3. Create an index 4. Start adding vectors Installation pip install pinecone-client Example import pinecone pinecone.init(api_key=”your-key”, environment=”us-west1″) pinecone.create_index(“my-index”, dimension=1536) index = pinecone.Index(“my-index”) index.upsert([(“id1”, embedding1, {“text”: “hello”})]) results = index.query(vector=query_embedding, … Read more
LangChain integrates with multiple LLM providers. Switch between models easily. Supported Providers ✅ OpenAI ✅ DeepSeek ✅ Anthropic ✅ HuggingFace ✅ Local models Using DeepSeek from langchain_openai import OpenAI llm = OpenAI( openai_api_key=”your-deepseek-key”, openai_api_base=”https://api.deepseek.com” ) Model Switching Change the model without rewriting code. Conclusion LangChain provides unified API for all LLMs!
Streaming provides real-time LLM responses. Implement streaming for better user experience. Streaming with Callbacks from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler llm = OpenAI(streaming=True, callbacks=[StreamingStdOutCallbackHandler()]) llm.invoke(“Tell me a story”) Async Streaming Use async callbacks for web applications. Benefits ✅ Faster perceived response ✅ Better UX ✅ Progressive display Conclusion Streaming improves user experience!
Callbacks monitor and log LangChain operations. Track tokens, costs, and performance. Callback Types ✅ StdOutCallbackHandler: Print to console ✅ FileCallbackHandler: Log to file ✅ Custom handlers Example from langchain.callbacks import StdOutCallbackHandler handler = StdOutCallbackHandler() chain = LLMChain(llm=llm, prompt=prompt, callbacks=[handler]) Custom Callback class MyCallback(BaseCallbackHandler): def on_llm_start(self, serialized, prompts): print(f”LLM started with {len(prompts)} prompts”) Conclusion Callbacks provide … Read more
Embeddings convert text into numerical vectors. Understand and use embeddings for semantic search. Embedding Models ✅ OpenAI Embeddings ✅ HuggingFace Embeddings ✅ Cohere Embeddings ✅ Local models Example from langchain.embeddings import OpenAIEmbeddings embeddings = OpenAIEmbeddings() vector = embeddings.embed_query(“Hello world”) vectors = embeddings.embed_documents([“doc1”, “doc2”]) Similarity Calculation Use cosine similarity to compare embeddings. Conclusion Embeddings enable semantic … Read more