New Crash Course Promises to Help You Develop AI Applications with LangChain

Introduction Large Language Models (LLMs) have revolutionized the way developers build AI-powered applications, from chatbots to intelligent search systems. However, managing LLM interactions effectively—structuring prompts, handling memory, and integrating external tools—can be complex. This is where LangChain comes in. LangChain is an open-source framework designed to simplify working with LLMs, enabling developers to create powerful AI applications with ease. By providing a modular approach, LangChain allows you to compose prompt templates, chains, memory, and agents to build flexible and scalable solutions. In this guide, we'll introduce you to LangChain and its companion libraries, including: langchain_community: A collection of core integrations and utilities. langchain_openai: A dedicated library for working with OpenAI models. We'll walk you through key LangChain concepts, installation steps, and practical code examples to help you get started. Whether you're looking to build chatbots, AI-powered search engines, or decision-making agents, this guide will give you the foundation you need to start developing with LangChain. What is LangChain? LangChain is an open-source framework that simplifies building applications powered by Large Language Models (LLMs). Instead of manually handling prompts, API calls, and responses, LangChain provides a structured way to chain together different components such as prompts, memory, and external tools. Why Use LangChain? Without LangChain, interacting with an LLM typically involves: Formatting a prompt manually. Sending the request to an API (e.g., OpenAI, Cohere). Parsing the response and deciding the next action. LangChain automates and streamlines these steps, making it easier to build complex AI applications with minimal effort. Key Use Cases LangChain is widely used for: Chatbots & Virtual Assistants – Retaining conversation context and improving responses. Retrieval-Augmented Generation (RAG) – Enhancing LLM responses by fetching external data sources. Data Processing & Summarization – Analyzing and summarizing large documents. AI Agents – Creating autonomous agents that interact with external APIs and databases. By leveraging LangChain’s modular architecture, you can integrate various models, tools, and memory mechanisms to build dynamic AI-driven applications. Core Concepts in LangChain LangChain is built around a modular architecture that allows developers to compose different components into a pipeline. Here are some of the key concepts you need to understand when working with LangChain: 1. Prompt Templates Prompt templates help structure the input given to an LLM. Instead of writing static prompts, you can create dynamic templates that format user inputs into well-structured queries. Example: from langchain.prompts import PromptTemplate template = PromptTemplate( input_variables=["topic"], template="Explain {topic} in simple terms." ) formatted_prompt = template.format(topic="LangChain") print(formatted_prompt) This ensures that every input follows a structured format before being passed to the model. 2. LLMs and Model Wrappers LangChain provides an easy way to interface with different LLM providers like OpenAI, Hugging Face, and more. Example: from langchain_openai import OpenAI llm = OpenAI(api_key="your_api_key") response = llm("What is LangChain?") print(response) This allows you to seamlessly query the LLM without worrying about API details. 3. Chains Chains allow you to combine multiple components (e.g., a prompt template and an LLM) into a single workflow. Example: from langchain.chains import LLMChain llm_chain = LLMChain(llm=llm, prompt=template) response = llm_chain.run("machine learning") print(response) Here, the prompt is formatted and automatically passed to the LLM, reducing boilerplate code. 4. Memory Memory allows your application to retain context between interactions, which is crucial for chatbots and multi-turn conversations. Example: from langchain.memory import ConversationBufferMemory memory = ConversationBufferMemory() memory.save_context({"input": "Hello"}, {"output": "Hi, how can I help you?"}) print(memory.load_memory_variables({})) With memory, LangChain can track past interactions and use them to generate more coherent responses. 5. Agents and Tools Agents allow an LLM to make decisions dynamically. Instead of following a predefined sequence, an agent determines which tool to call based on the user’s query. Example: from langchain.agents import initialize_agent, AgentType from langchain.tools import Tool def add_numbers(a, b): return a + b tool = Tool( name="Calculator", func=add_numbers, description="Adds two numbers." ) agent = initialize_agent( tools=[tool], llm=llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION ) response = agent.run("What is 3 + 5?") print(response) This enables an LLM to call functions, fetch data, or interact with APIs to generate more intelligent responses. By understanding these core concepts, you can start building more structured and powerful AI applications with LangChain. In the next section, we’ll set up LangChain and its companion libraries to start developing real-world applications. Installing LangChain and Companion Libraries Before we start building with LangChain, we need to install the necessary packages. LangChain is modular, meaning that different functionalities are split across separate libraries. The main ones you'll need are: langchain – The core LangChain library. langchain_community – A collection of integrations for third-party tools and services. langchain_openai – A dedicated package for working with OpenAI models. openai – The OpenAI Python SDK for API access. 1. Installing LangChain and Dependencies You can install the required libraries using pip: pip install langchain langchain_community langchain_openai openai This command will install the core LangChain framework along with the OpenAI integration. 2. Setting Up an OpenAI API Key If you plan to use OpenAI models, you’ll need an API key. Follow these steps: Sign up at OpenAI. Navigate to your API settings and generate an API key. Store your API key securely. You can set your API key in an environment variable: export OPENAI_API_KEY="your_api_key_here" Or pass it directly in your code: import os os.environ["OPENAI_API_KEY"] = "your_api_key_here" 3. Verifying the Installation To test if everything is installed correctly, run the following Python script: from langchain_openai import OpenAI llm = OpenAI(api_key="your_api_key_here") response = llm("Say hello in French.") print(response) If you see the response "Bonjour!", then your setup is working properly. 4. Understanding the Role of Companion Libraries langchain_community: Contains integrations for databases, vector stores, and APIs. langchain_openai: A streamlined package for interacting with OpenAI's models. Other integrations: LangChain supports many LLM providers (Cohere, Hugging Face, etc.), which can be installed separately. With LangChain and its dependencies installed, you're ready to start building AI-powered applications. In the next section, we'll explore how to use LangChain with OpenAI models and create structured workflows. Setting Up and Using LangChain Now that we have LangChain installed, let's explore how to use it for interacting with LLMs, structuring prompts, and building simple AI workflows. 1. Connecting to an OpenAI Model The first step in using LangChain is to connect to an LLM. We'll start by using OpenAI's models. Example: Basic Query to an OpenAI Model from langchain_openai import OpenAI llm = OpenAI(api_key="your_api_key_here") response = llm.invoke("What is LangChain?") print(response) This sends a query to OpenAI and prints the response. The invoke method is the recommended way to interact with LLMs in LangChain. 2. Working with Prompt Templates A prompt template ensures that user input is formatted consistently before being sent to an LLM. This is useful when you need structured responses. Example: Creating and Using a Prompt Template from langchain.prompts import PromptTemplate template = PromptTemplate( input_variables=["topic"], template="Explain {topic} in simple terms." ) formatted_prompt = template.format(topic="machine learning") print(formatted_prompt) This generates a properly structured prompt: "Explain machine learning in simple terms." You can pass this formatted prompt to an LLM for processing. 3. Building a Basic Chain A chain connects multiple components, such as prompts and LLMs, to automate workflows. Example: Using a Chain to Generate Responses from langchain.chains import LLMChain llm_chain = LLMChain(llm=llm, prompt=template) response = llm_chain.run("data science") print(response) Here, LangChain automatically formats the prompt and sends it to the LLM, reducing manual effort. 4. Using Memory to Maintain Context By default, LLMs don’t remember past interactions. LangChain provides memory components to store and retrieve conversation history. Example: Storing Conversation History from langchain.memory import ConversationBufferMemory memory = ConversationBufferMemory() # Simulating a conversation memory.save_context({"input": "Hello"}, {"output": "Hi, how can I help you?"}) memory.save_context({"input": "What is LangChain?"}, {"output": "LangChain is a framework for working with LLMs."}) # Retrieving stored interactions print(memory.load_memory_variables({})) This ensures that previous interactions can be referenced in future queries. 5. Implementing an Agent with Tools An agent allows LLMs to dynamically decide which tool to use for a given query. For example, we can create an agent that uses a calculator tool. Example: Creating an Agent to Perform Calculations from langchain.agents import initialize_agent, AgentType from langchain.tools import Tool # Defining a simple addition function def add_numbers(a, b): return a + b tool = Tool( name="Calculator", func=add_numbers, description="Adds two numbers." ) # Creating an agent with the tool agent = initialize_agent( tools=[tool], llm=llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION ) # Running the agent response = agent.run("What is 5 + 7?") print(response) This enables the LLM to recognize when to use the calculator tool instead of responding based purely on its pre-trained knowledge. What’s Next? Now that we've covered basic LangChain functionalities, you can start experimenting with more advanced features like: Retrieval-Augmented Generation (RAG) – Enhancing LLMs with external knowledge sources. Vector Databases – Storing and retrieving information efficiently. Custom Tools and APIs – Expanding agents to interact with real-world data. In the next section, we'll discuss best practices for using LangChain efficiently and how to scale applications for production use. Best Practices and Next Steps Now that you understand the basics of LangChain—connecting to LLMs, structuring prompts, using chains, memory, and agents—let’s discuss some best practices for building efficient and scalable applications. 1. Optimize Prompt Engineering Use clear and structured prompt templates to get better responses from LLMs. Experiment with few-shot learning by providing example inputs and outputs. Keep prompts concise to reduce token usage and improve performance. Example: Few-Shot Prompting from langchain.prompts import PromptTemplate template = PromptTemplate( input_variables=["word"], template="Convert the following word into plural form: {word}\n\nExample:\n- dog -> dogs\n- cat -> cats\n- book -> ?" ) print(template.format(word="tree")) Providing examples improves the model's accuracy. 2. Use Memory Efficiently Only use conversation memory when necessary (e.g., chatbots). Choose the right memory type: ConversationBufferMemory – Stores all conversation history. ConversationSummaryMemory – Summarizes past interactions. ConversationKGMemory – Extracts key facts from a conversation. Example: Using Summary Memory from langchain.memory import ConversationSummaryMemory from langchain_openai import OpenAI llm = OpenAI(api_key="your_api_key") memory = ConversationSummaryMemory(llm=llm) memory.save_context({"input": "I love pizza."}, {"output": "Pizza is a great choice!"}) summary = memory.load_memory_variables({}) print(summary) This helps reduce storage while maintaining context. 3. Handle API Costs and Rate Limits Use token-efficient prompts to reduce API costs. Implement batch processing for multiple queries. Monitor API usage with OpenAI’s rate limits in mind. Example: Monitoring Token Usage from langchain_openai import OpenAI llm = OpenAI(api_key="your_api_key", model="gpt-4", max_tokens=100) response = llm("Summarize the history of AI in 50 words.") print(response) Setting max_tokens prevents excessive token consumption. 4. Enhance LLMs with External Knowledge (RAG) Retrieval-Augmented Generation (RAG) improves LLM responses by fetching external data instead of relying solely on pre-trained knowledge. Use vector databases like Pinecone or FAISS for document search. Fetch real-time data from APIs. Example: Querying an External Document from langchain.vectorstores import FAISS from langchain.embeddings.openai import OpenAIEmbeddings # Load and embed documents embeddings = OpenAIEmbeddings(api_key="your_api_key") vectorstore = FAISS.load_local("faiss_index", embeddings) # Query the knowledge base docs = vectorstore.similarity_search("What is LangChain?", k=2) print(docs) This retrieves relevant documents to supplement the LLM’s response. 5. Scale Applications for Production When moving from prototyping to production, consider: Caching responses to avoid redundant API calls. Logging interactions for debugging and improvement. Implementing user authentication for secured access. Example: Implementing Response Caching from langchain.cache import InMemoryCache from langchain.chains import LLMChain llm_chain = LLMChain(llm=llm, prompt=template) llm_chain.cache = InMemoryCache() # Enable caching response1 = llm_chain.run("machine learning") response2 = llm_chain.run("machine learning") # Cached response print(response2) Caching reduces API calls, improving performance and cost-efficiency. Conclusion LangChain provides a powerful framework for building AI applications that leverage Large Language Models (LLMs). By combining prompt engineering, chains, memory, and agents, LangChain simplifies the development process, making it easier to create chatbots, AI assistants, and retrieval-augmented generation (RAG) applications. In this guide, we covered: What LangChain is and why it’s useful. Core concepts like prompt templates, chains, memory, and agents. How to install and set up LangChain along with langchain_openai and langchain_community. Practical code examples for using LangChain with OpenAI models. Best practices for optimizing prompts, managing memory, and reducing API costs. How to scale LangChain applications for production using caching and external knowledge retrieval. By applying these concepts, you can start building custom AI-powered solutions with real-world impact. Where to Go from Here? If you're ready to take the next step, consider: Building a LangChain Project – Try creating a chatbot, document summarizer, or an AI-driven search engine. Exploring Vector Databases – Learn how to integrate Pinecone, FAISS, or ChromaDB for RAG applications. Joining the Community – Engage with other developers on LangChain's GitHub or Discord. LangChain is continuously evolving, and staying updated with the latest features will help you build more advanced and efficient AI applications. Start experimenting and bring your AI ideas to life! Introduction Large Language Models (LLMs) have revolutionized the way developers build AI-powered applications, from chatbots to intelligent search systems. However, managing LLM interactions effectively—structuring prompts, handling memory, and integrating external tools—can be complex. This is where LangChain comes in. LangChain LangChain is an open-source framework designed to simplify working with LLMs, enabling developers to create powerful AI applications with ease. By providing a modular approach, LangChain allows you to compose prompt templates, chains, memory, and agents to build flexible and scalable solutions. prompt templates, chains, memory, and agents In this guide, we'll introduce you to LangChain and its companion libraries, including: LangChain langchain_community: A collection of core integrations and utilities. langchain_openai: A dedicated library for working with OpenAI models. langchain_community : A collection of core integrations and utilities. langchain_community langchain_openai : A dedicated library for working with OpenAI models. langchain_openai We'll walk you through key LangChain concepts, installation steps, and practical code examples to help you get started. Whether you're looking to build chatbots, AI-powered search engines, or decision-making agents, this guide will give you the foundation you need to start developing with LangChain. What is LangChain? LangChain is an open-source framework that simplifies building applications powered by Large Language Models (LLMs). Instead of manually handling prompts, API calls, and responses, LangChain provides a structured way to chain together different components such as prompts, memory, and external tools. chain together different components Why Use LangChain? Without LangChain, interacting with an LLM typically involves: Formatting a prompt manually. Sending the request to an API (e.g., OpenAI, Cohere). Parsing the response and deciding the next action. Formatting a prompt manually. Sending the request to an API (e.g., OpenAI, Cohere). Parsing the response and deciding the next action. LangChain automates and streamlines these steps, making it easier to build complex AI applications with minimal effort. Key Use Cases LangChain is widely used for: Chatbots & Virtual Assistants – Retaining conversation context and improving responses. Retrieval-Augmented Generation (RAG) – Enhancing LLM responses by fetching external data sources. Data Processing & Summarization – Analyzing and summarizing large documents. AI Agents – Creating autonomous agents that interact with external APIs and databases. Chatbots & Virtual Assistants – Retaining conversation context and improving responses. Chatbots & Virtual Assistants Retrieval-Augmented Generation (RAG) – Enhancing LLM responses by fetching external data sources. Retrieval-Augmented Generation (RAG) Data Processing & Summarization – Analyzing and summarizing large documents. Data Processing & Summarization AI Agents – Creating autonomous agents that interact with external APIs and databases. AI Agents By leveraging LangChain’s modular architecture, you can integrate various models, tools, and memory mechanisms to build dynamic AI-driven applications. models, tools, and memory mechanisms Core Concepts in LangChain LangChain is built around a modular architecture that allows developers to compose different components into a pipeline. Here are some of the key concepts you need to understand when working with LangChain: 1. Prompt Templates Prompt Templates Prompt templates help structure the input given to an LLM. Instead of writing static prompts, you can create dynamic templates that format user inputs into well-structured queries. Example: from langchain.prompts import PromptTemplate template = PromptTemplate( input_variables=["topic"], template="Explain {topic} in simple terms." ) formatted_prompt = template.format(topic="LangChain") print(formatted_prompt) from langchain.prompts import PromptTemplate template = PromptTemplate( input_variables=["topic"], template="Explain {topic} in simple terms." ) formatted_prompt = template.format(topic="LangChain") print(formatted_prompt) This ensures that every input follows a structured format before being passed to the model. 2. LLMs and Model Wrappers LangChain provides an easy way to interface with different LLM providers like OpenAI, Hugging Face, and more. Example: from langchain_openai import OpenAI llm = OpenAI(api_key="your_api_key") response = llm("What is LangChain?") print(response) from langchain_openai import OpenAI llm = OpenAI(api_key="your_api_key") response = llm("What is LangChain?") print(response) This allows you to seamlessly query the LLM without worrying about API details. 3. Chains Chains allow you to combine multiple components (e.g., a prompt template and an LLM) into a single workflow. Example: from langchain.chains import LLMChain llm_chain = LLMChain(llm=llm, prompt=template) response = llm_chain.run("machine learning") print(response) from langchain.chains import LLMChain llm_chain = LLMChain(llm=llm, prompt=template) response = llm_chain.run("machine learning") print(response) Here, the prompt is formatted and automatically passed to the LLM, reducing boilerplate code. 4. Memory Memory allows your application to retain context between interactions, which is crucial for chatbots and multi-turn conversations. Example: from langchain.memory import ConversationBufferMemory memory = ConversationBufferMemory() memory.save_context({"input": "Hello"}, {"output": "Hi, how can I help you?"}) print(memory.load_memory_variables({})) from langchain.memory import ConversationBufferMemory memory = ConversationBufferMemory() memory.save_context({"input": "Hello"}, {"output": "Hi, how can I help you?"}) print(memory.load_memory_variables({})) With memory, LangChain can track past interactions and use them to generate more coherent responses. 5. Agents and Tools Agents allow an LLM to make decisions dynamically. Instead of following a predefined sequence, an agent determines which tool to call based on the user’s query. Example: from langchain.agents import initialize_agent, AgentType from langchain.tools import Tool def add_numbers(a, b): return a + b tool = Tool( name="Calculator", func=add_numbers, description="Adds two numbers." ) agent = initialize_agent( tools=[tool], llm=llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION ) response = agent.run("What is 3 + 5?") print(response) from langchain.agents import initialize_agent, AgentType from langchain.tools import Tool def add_numbers(a, b): return a + b tool = Tool( name="Calculator", func=add_numbers, description="Adds two numbers." ) agent = initialize_agent( tools=[tool], llm=llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION ) response = agent.run("What is 3 + 5?") print(response) This enables an LLM to call functions, fetch data, or interact with APIs to generate more intelligent responses. By understanding these core concepts, you can start building more structured and powerful AI applications with LangChain. In the next section, we’ll set up LangChain and its companion libraries to start developing real-world applications. Installing LangChain and Companion Libraries Before we start building with LangChain, we need to install the necessary packages. LangChain is modular, meaning that different functionalities are split across separate libraries. The main ones you'll need are: langchain – The core LangChain library. langchain_community – A collection of integrations for third-party tools and services. langchain_openai – A dedicated package for working with OpenAI models. openai – The OpenAI Python SDK for API access. langchain – The core LangChain library. langchain langchain_community – A collection of integrations for third-party tools and services. langchain_community langchain_openai – A dedicated package for working with OpenAI models. langchain_openai openai – The OpenAI Python SDK for API access. openai 1. Installing LangChain and Dependencies 1. Installing LangChain and Dependencies You can install the required libraries using pip : pip pip install langchain langchain_community langchain_openai openai pip install langchain langchain_community langchain_openai openai This command will install the core LangChain framework along with the OpenAI integration. 2. Setting Up an OpenAI API Key If you plan to use OpenAI models, you’ll need an API key. Follow these steps: Sign up at OpenAI. Navigate to your API settings and generate an API key. Store your API key securely. Sign up at OpenAI. Navigate to your API settings and generate an API key. Store your API key securely. You can set your API key in an environment variable: export OPENAI_API_KEY="your_api_key_here" export OPENAI_API_KEY="your_api_key_here" Or pass it directly in your code: import os os.environ["OPENAI_API_KEY"] = "your_api_key_here" import os os.environ["OPENAI_API_KEY"] = "your_api_key_here" 3. Verifying the Installation To test if everything is installed correctly, run the following Python script: from langchain_openai import OpenAI llm = OpenAI(api_key="your_api_key_here") response = llm("Say hello in French.") print(response) from langchain_openai import OpenAI llm = OpenAI(api_key="your_api_key_here") response = llm("Say hello in French.") print(response) If you see the response "Bonjour!", then your setup is working properly. 4. Understanding the Role of Companion Libraries langchain_community: Contains integrations for databases, vector stores, and APIs. langchain_openai: A streamlined package for interacting with OpenAI's models. Other integrations: LangChain supports many LLM providers (Cohere, Hugging Face, etc.), which can be installed separately. langchain_community : Contains integrations for databases, vector stores, and APIs. langchain_community langchain_openai : A streamlined package for interacting with OpenAI's models. langchain_openai Other integrations : LangChain supports many LLM providers (Cohere, Hugging Face, etc.), which can be installed separately. Other integrations With LangChain and its dependencies installed, you're ready to start building AI-powered applications. In the next section, we'll explore how to use LangChain with OpenAI models and create structured workflows. Setting Up and Using LangChain Now that we have LangChain installed, let's explore how to use it for interacting with LLMs, structuring prompts, and building simple AI workflows. 1. Connecting to an OpenAI Model 1. Connecting to an OpenAI Model The first step in using LangChain is to connect to an LLM. We'll start by using OpenAI's models. Example: Basic Query to an OpenAI Model Example: Basic Query to an OpenAI Model from langchain_openai import OpenAI llm = OpenAI(api_key="your_api_key_here") response = llm.invoke("What is LangChain?") print(response) from langchain_openai import OpenAI llm = OpenAI(api_key="your_api_key_here") response = llm.invoke("What is LangChain?") print(response) This sends a query to OpenAI and prints the response. The invoke method is the recommended way to interact with LLMs in LangChain. 2. Working with Prompt Templates A prompt template ensures that user input is formatted consistently before being sent to an LLM. This is useful when you need structured responses. Example: Creating and Using a Prompt Template from langchain.prompts import PromptTemplate template = PromptTemplate( input_variables=["topic"], template="Explain {topic} in simple terms." ) formatted_prompt = template.format(topic="machine learning") print(formatted_prompt) from langchain.prompts import PromptTemplate template = PromptTemplate( input_variables=["topic"], template="Explain {topic} in simple terms." ) formatted_prompt = template.format(topic="machine learning") print(formatted_prompt) This generates a properly structured prompt: "Explain machine learning in simple terms." You can pass this formatted prompt to an LLM for processing. 3. Building a Basic Chain A chain connects multiple components, such as prompts and LLMs, to automate workflows. Example: Using a Chain to Generate Responses from langchain.chains import LLMChain llm_chain = LLMChain(llm=llm, prompt=template) response = llm_chain.run("data science") print(response) from langchain.chains import LLMChain llm_chain = LLMChain(llm=llm, prompt=template) response = llm_chain.run("data science") print(response) Here, LangChain automatically formats the prompt and sends it to the LLM, reducing manual effort. 4. Using Memory to Maintain Context By default, LLMs don’t remember past interactions. LangChain provides memory components to store and retrieve conversation history. Example: Storing Conversation History from langchain.memory import ConversationBufferMemory memory = ConversationBufferMemory() # Simulating a conversation memory.save_context({"input": "Hello"}, {"output": "Hi, how can I help you?"}) memory.save_context({"input": "What is LangChain?"}, {"output": "LangChain is a framework for working with LLMs."}) # Retrieving stored interactions print(memory.load_memory_variables({})) from langchain.memory import ConversationBufferMemory memory = ConversationBufferMemory() # Simulating a conversation memory.save_context({"input": "Hello"}, {"output": "Hi, how can I help you?"}) memory.save_context({"input": "What is LangChain?"}, {"output": "LangChain is a framework for working with LLMs."}) # Retrieving stored interactions print(memory.load_memory_variables({})) This ensures that previous interactions can be referenced in future queries. 5. Implementing an Agent with Tools An agent allows LLMs to dynamically decide which tool to use for a given query. For example, we can create an agent that uses a calculator tool. Example: Creating an Agent to Perform Calculations from langchain.agents import initialize_agent, AgentType from langchain.tools import Tool # Defining a simple addition function def add_numbers(a, b): return a + b tool = Tool( name="Calculator", func=add_numbers, description="Adds two numbers." ) # Creating an agent with the tool agent = initialize_agent( tools=[tool], llm=llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION ) # Running the agent response = agent.run("What is 5 + 7?") print(response) from langchain.agents import initialize_agent, AgentType from langchain.tools import Tool # Defining a simple addition function def add_numbers(a, b): return a + b tool = Tool( name="Calculator", func=add_numbers, description="Adds two numbers." ) # Creating an agent with the tool agent = initialize_agent( tools=[tool], llm=llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION ) # Running the agent response = agent.run("What is 5 + 7?") print(response) This enables the LLM to recognize when to use the calculator tool instead of responding based purely on its pre-trained knowledge. What’s Next? Now that we've covered basic LangChain functionalities, you can start experimenting with more advanced features like: Retrieval-Augmented Generation (RAG) – Enhancing LLMs with external knowledge sources. Vector Databases – Storing and retrieving information efficiently. Custom Tools and APIs – Expanding agents to interact with real-world data. Retrieval-Augmented Generation (RAG) – Enhancing LLMs with external knowledge sources. Retrieval-Augmented Generation (RAG) – Vector Databases – Storing and retrieving information efficiently. Vector Databases – Custom Tools and APIs – Expanding agents to interact with real-world data. Custom Tools and APIs – In the next section, we'll discuss best practices for using LangChain efficiently and how to scale applications for production use. Best Practices and Next Steps Now that you understand the basics of LangChain—connecting to LLMs, structuring prompts, using chains, memory, and agents—let’s discuss some best practices for building efficient and scalable applications. 1. Optimize Prompt Engineering 1. Optimize Prompt Engineering Use clear and structured prompt templates to get better responses from LLMs. Experiment with few-shot learning by providing example inputs and outputs. Keep prompts concise to reduce token usage and improve performance. Use clear and structured prompt templates to get better responses from LLMs. clear and structured prompt templates Experiment with few-shot learning by providing example inputs and outputs. few-shot learning Keep prompts concise to reduce token usage and improve performance. concise Example: Few-Shot Prompting Example: Few-Shot Prompting from langchain.prompts import PromptTemplate template = PromptTemplate( input_variables=["word"], template="Convert the following word into plural form: {word}\n\nExample:\n- dog -> dogs\n- cat -> cats\n- book -> ?" ) print(template.format(word="tree")) from langchain.prompts import PromptTemplate template = PromptTemplate( input_variables=["word"], template="Convert the following word into plural form: {word}\n\nExample:\n- dog -> dogs\n- cat -> cats\n- book -> ?" ) print(template.format(word="tree")) Providing examples improves the model's accuracy. 2. Use Memory Efficiently Only use conversation memory when necessary (e.g., chatbots). Choose the right memory type: Choose the right memory type: ConversationBufferMemory – Stores all conversation history. ConversationSummaryMemory – Summarizes past interactions. ConversationKGMemory – Extracts key facts from a conversation. ConversationBufferMemory – Stores all conversation history. ConversationBufferMemory – ConversationSummaryMemory – Summarizes past interactions. ConversationSummaryMemory – ConversationKGMemory – Extracts key facts from a conversation. ConversationKGMemory – Example: Using Summary Memory from langchain.memory import ConversationSummaryMemory from langchain_openai import OpenAI llm = OpenAI(api_key="your_api_key") memory = ConversationSummaryMemory(llm=llm) memory.save_context({"input": "I love pizza."}, {"output": "Pizza is a great choice!"}) summary = memory.load_memory_variables({}) print(summary) from langchain.memory import ConversationSummaryMemory from langchain_openai import OpenAI llm = OpenAI(api_key="your_api_key") memory = ConversationSummaryMemory(llm=llm) memory.save_context({"input": "I love pizza."}, {"output": "Pizza is a great choice!"}) summary = memory.load_memory_variables({}) print(summary) This helps reduce storage while maintaining context. 3. Handle API Costs and Rate Limits Use token-efficient prompts to reduce API costs. Implement batch processing for multiple queries. Monitor API usage with OpenAI’s rate limits in mind. Example: Monitoring Token Usage from langchain_openai import OpenAI llm = OpenAI(api_key="your_api_key", model="gpt-4", max_tokens=100) response = llm("Summarize the history of AI in 50 words.") print(response) from langchain_openai import OpenAI llm = OpenAI(api_key="your_api_key", model="gpt-4", max_tokens=100) response = llm("Summarize the history of AI in 50 words.") print(response) Setting max_tokens prevents excessive token consumption. 4. Enhance LLMs with External Knowledge (RAG) Retrieval-Augmented Generation (RAG) improves LLM responses by fetching external data instead of relying solely on pre-trained knowledge. Use vector databases like Pinecone or FAISS for document search. Fetch real-time data from APIs. Use vector databases like Pinecone or FAISS for document search. Fetch real-time data from APIs. Example: Querying an External Document from langchain.vectorstores import FAISS from langchain.embeddings.openai import OpenAIEmbeddings # Load and embed documents embeddings = OpenAIEmbeddings(api_key="your_api_key") vectorstore = FAISS.load_local("faiss_index", embeddings) # Query the knowledge base docs = vectorstore.similarity_search("What is LangChain?", k=2) print(docs) from langchain.vectorstores import FAISS from langchain.embeddings.openai import OpenAIEmbeddings # Load and embed documents embeddings = OpenAIEmbeddings(api_key="your_api_key") vectorstore = FAISS.load_local("faiss_index", embeddings) # Query the knowledge base docs = vectorstore.similarity_search("What is LangChain?", k=2) print(docs) This retrieves relevant documents to supplement the LLM’s response. 5. Scale Applications for Production When moving from prototyping to production, consider: Caching responses to avoid redundant API calls. Logging interactions for debugging and improvement. Implementing user authentication for secured access. Caching responses to avoid redundant API calls. Logging interactions for debugging and improvement. Implementing user authentication for secured access. Example: Implementing Response Caching from langchain.cache import InMemoryCache from langchain.chains import LLMChain llm_chain = LLMChain(llm=llm, prompt=template) llm_chain.cache = InMemoryCache() # Enable caching response1 = llm_chain.run("machine learning") response2 = llm_chain.run("machine learning") # Cached response print(response2) from langchain.cache import InMemoryCache from langchain.chains import LLMChain llm_chain = LLMChain(llm=llm, prompt=template) llm_chain.cache = InMemoryCache() # Enable caching response1 = llm_chain.run("machine learning") response2 = llm_chain.run("machine learning") # Cached response print(response2) Caching reduces API calls, improving performance and cost-efficiency. Conclusion LangChain provides a powerful framework for building AI applications that leverage Large Language Models (LLMs). By combining prompt engineering, chains, memory, and agents , LangChain simplifies the development process, making it easier to create chatbots, AI assistants, and retrieval-augmented generation (RAG) applications . prompt engineering, chains, memory, and agents chatbots, AI assistants, and retrieval-augmented generation (RAG) applications In this guide, we covered: What LangChain is and why it’s useful. Core concepts like prompt templates, chains, memory, and agents. How to install and set up LangChain along with langchain_openai and langchain_community. Practical code examples for using LangChain with OpenAI models. Best practices for optimizing prompts, managing memory, and reducing API costs. How to scale LangChain applications for production using caching and external knowledge retrieval. What LangChain is and why it’s useful. Core concepts like prompt templates, chains, memory, and agents . prompt templates, chains, memory, and agents How to install and set up LangChain along with langchain_openai and langchain_community . install and set up LangChain langchain_openai langchain_community Practical code examples for using LangChain with OpenAI models. code examples Best practices for optimizing prompts, managing memory, and reducing API costs . optimizing prompts, managing memory, and reducing API costs How to scale LangChain applications for production using caching and external knowledge retrieval. scale LangChain applications for production By applying these concepts, you can start building custom AI-powered solutions with real-world impact. custom AI-powered solutions Where to Go from Here? Where to Go from Here? If you're ready to take the next step, consider: Building a LangChain Project – Try creating a chatbot, document summarizer, or an AI-driven search engine. Exploring Vector Databases – Learn how to integrate Pinecone, FAISS, or ChromaDB for RAG applications. Joining the Community – Engage with other developers on LangChain's GitHub or Discord. Building a LangChain Project – Try creating a chatbot, document summarizer, or an AI-driven search engine. Building a LangChain Project Exploring Vector Databases – Learn how to integrate Pinecone, FAISS, or ChromaDB for RAG applications. Exploring Vector Databases Joining the Community – Engage with other developers on LangChain's GitHub or Discord . Joining the Community LangChain's GitHub Discord LangChain is continuously evolving, and staying updated with the latest features will help you build more advanced and efficient AI applications . Start experimenting and bring your AI ideas to life! more advanced and efficient AI applications