Tutorials

Chains allow you to combine multiple LLM operations. Build complex workflows with LangChain chains. Chain Types SimpleChain: Single input/output SequentialChain: Multiple steps in sequence RouterChain: Conditional routing Sequential Chain Example from langchain.chains import SequentialChain chain1 = LLMChain(llm=llm, prompt=prompt1) chain2 = LLMChain(llm=llm, prompt=prompt2) overall_chain = SequentialChain(chains=[chain1, chain2]) Best Practices ✅ Keep chains focused ✅ Handle errors … Read more

Tools give LLMs access to external functions and APIs. Create and use custom tools in LangChain. Built-in Tools ✅ Web Search ✅ Calculator ✅ Python REPL ✅ File operations Custom Tool Example from langchain.tools import BaseTool class WeatherTool(BaseTool): name = “weather” description = “Get weather for a location” def _run(self, location: str): # API call … Read more

RAG combines document retrieval with LLM generation. Build powerful document-based Q&A systems. RAG Components 1. Document Loader 2. Text Splitter 3. Embeddings 4. Vector Store 5. Retriever Implementation from langchain.document_loaders import TextLoader from langchain.text_splitter import CharacterTextSplitter from langchain.embeddings import OpenAIEmbeddings from langchain.vectorstores import Chroma loader = TextLoader(“document.txt”) documents = loader.load() text_splitter = CharacterTextSplitter(chunk_size=1000) texts = … Read more

Agents use LLMs to decide which actions to take. Build autonomous AI assistants with LangChain agents. Agent Types Zero-shot Agent: Decides without examples Conversational Agent: For chat applications ReAct Agent: Reasoning and acting Creating an Agent from langchain.agents import initialize_agent, Tool from langchain.tools import DuckDuckGoSearchRun search = DuckDuckGoSearchRun() tools = [Tool(name=”Search”, func=search.run, description=”Search the web”)] … Read more

Memory allows LLMs to remember previous interactions. Learn to implement different memory types in LangChain. Memory Types ConversationBufferMemory: Stores all messages ConversationSummaryMemory: Summarizes conversations VectorStoreMemory: Uses vector similarity Implementation from langchain.memory import ConversationBufferMemory from langchain.chains import ConversationChain memory = ConversationBufferMemory() conversation = ConversationChain(llm=llm, memory=memory) conversation.predict(input=”Hi, I’m learning LangChain”) conversation.predict(input=”What did I say my name was?”) … Read more

Create engaging multi-turn chatbot conversations. Implement conversation memory and context. Conversation Memory Store previous messages to maintain context. Implementation messages = [ {‘role’: ‘system’, ‘content’: ‘You are a helpful assistant’}, {‘role’: ‘user’, ‘content’: ‘Hello’}, {‘role’: ‘assistant’, ‘content’: ‘Hi! How can I help?’} ] Memory Management Trim old messages to stay within token limits. Conclusion Multi-turn … Read more

Create autonomous AI agents using DeepSeek models. Build agents that can plan, reason, and execute tasks. What are AI Agents? Autonomous systems that use LLMs to reason, plan, and take actions. Agent Architecture 1. Perception: Understand environment 2. Planning: Decide actions 3. Action: Execute tasks 4. Memory: Remember context Tools for Building Agents ✅ LangChain … Read more

Retrieval-Augmented Generation combines search with AI generation. Build powerful RAG systems with DeepSeek models. What is RAG? RAG retrieves relevant documents and uses them as context for LLM responses. RAG Architecture 1. Document ingestion 2. Embedding generation 3. Vector storage 4. Similarity search 5. Context assembly 6. LLM generation Tools for RAG ✅ LangChain ✅ … Read more

Fine-tuning allows you to customize AI models for your specific use case. Step-by-step guide to fine-tuning DeepSeek models. What is Fine-tuning? Training a pre-trained model on your specific data to improve performance. When to Fine-tune ✅ Domain-specific tasks ✅ Custom output formats ✅ Improved accuracy needs ✅ Reduced prompt engineering Preparing Data Format your data … Read more

Build a powerful chatbot using DeepSeek AI API. Step-by-step tutorial for creating your own AI chatbot. Prerequisites ✅ Python 3.8+ ✅ DeepSeek API key ✅ Basic Python knowledge Step 1: Install Dependencies pip install openai requests Step 2: Create Chatbot Class class DeepSeekChatbot: def __init__(self, api_key): self.api_key = api_key self.messages = [] Step 3: Add … Read more