Optimizing AI Workflows with LangChain - A Practical Introduction
LangChain is a framework for developing applications powered by large language models (LLMs). It helps in simplifying the stages of the LLM application lifecycle - provides a structured approach to integrate LLMs with other data sources and tools, making it easier to build complex multi-step and context-aware applications. Also?supports third-party integrations and partner packages, enabling developers to tailor the framework to their specific needs. Ideal for both basic and advanced LLM-driven applications - A good use case of LangChain is Retrieval-Augmented Generation (RAG), where external knowledge sources such as documents and databases can enhance accuracy.?
[Features]?
[1] Chains - Sequences of operations that integrate various components. E.g. a chain might query a knowledge base, retrieve information, and format a response.??
[2] Agents – Enable dynamic decision making within the application. E.g. an agent can decide which tool to call next based on intermediate results.??
[3] Memory – Enables applications to retain conversation history or past interactions, making it invaluable for chatbots and personalized AI tools. E.g. include conversation history storage or embeddings-based memory for semantic recall.??
[4] Tools – Functions that help the models in task completion. E.g. supports integration with tools like Python functions, APIs, calculators, and custom logic for extended capabilities.?
[5] Evaluation - Acts like quality control, ensuring that outputs meet the desired standards and providing insights to improve workflows.?
LangChain consists of a number of packages,?
Now that we’ve explored core features, let’s delve into one of its most basic but important components - Prompt Template, and how it simplifies user interactions. ?
[Prompt Template - HumanMessage]?
Prompt templates help to structure conversations with AI. HumanMessage brings structure and clarity to the model's input which comes from the users. By defining a consistent and well-crafted template. By leveraging templates developers can standardize complex workflows, avoid unnecessary formatting issues, templates can become an important component in AI pipelines. Let’s see a basic example of HumanMessage ?templating ?and how it can be used for summarizing tasks.??
[1] Install necessary Python libraries and add imports.?
[2] Store your API key - set the Google API Key as an environment variable for Gemini model access. Utilize the ChatGoogleGenerativeAI class to create an instance of the desired Gemini model.??
[3] Define or import input text??
[4] Prepare the template for prompt using LangChain framework and invoke the model to get the response. Here we are using the HumanMessage class to format the input as a human-like message, which is then passed to the LLM using the invoke method. The core functionality is to provide a template that constructs a prompt, including instructions, context (film title and film text), and the desired task (summarization). ?
By integrating with the Google Gemini model, LangChain facilitates the generation of a concise film summary based on the given input. This showcases how LangChain acts as an intermediary, enabling structured communication and task delegation to language models for text summarizing.?
Generated Summary: "Kal Ho Naa Ho" is a poignant Bollywood romance set in New York City. Naina, a pessimistic young woman grappling with family issues, finds her life transformed by her cheerful neighbor, Aman. She falls in love with him, unaware that he harbors a life-threatening secret. Aman, determined to see Naina happy, encourages her relationship with her best friend, Rohit, who also loves her. As Aman's health declines, he orchestrates Naina and Rohit's love story, sacrificing his own happiness for hers. The film explores themes of love, sacrifice, and living life to the fullest, underscored by the title's message, "Tomorrow May Never Come." With memorable performances by Shah Rukh Khan, Preity Zinta, and Saif Ali Khan, and a soulful soundtrack, "Kal Ho Naa Ho" remains a beloved classic.?
HumanMessage here enables chat-based interactions, if offers a standardized format for user messages. The “template” in the framework offers?placeholders for film Title and film Text.?Similarly, we can also add SystemMessagePromptTemplate for more context. After structuring input with prompt templates, the next step is to combine multiple operations seamlessly using chains, enabling more sophisticated workflows. Let’s go through the Chaining and how it works. ?
[Chaining]?
Chaining is a central concept that allows us to combine multiple operations. It enables the creation of structured workflows for using LLMs in complex applications. ?
A chain in LangChain is a sequence of operations where:?
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?Chains can range from simple single-step workflows to multi-step pipelines involving dynamic inputs, multiple LLM queries. Let's see one basic chain example for question answer type applications.???
[1] Install necessary Python libraries and add imports (Install and GenAI components were already added).?
[2] Define the prompt template to create a structured format for the input question. It can contain a placeholder {question} to dynamically insert the user's query and explicitly instructs the model to avoid markdown formatting in the answer (surprisingly sometimes you might get that).?
[3] Create the Chain to connect the above prompt template with the LLM
[4] Run the Chain using chain.run() method is used to pass the query "How do we become life long learners?" to the chain. The language model processes this input and would return the response. Then you can print the response. ?
Response from Gemini Pro, ?
Becoming a lifelong learner involves cultivating curiosity, embracing challenges, and actively seeking knowledge and new experiences throughout life. It's a mindset shift more than a specific set of actions. Here are some key aspects: ?
1. Cultivate Curiosity: Ask questions constantly. Don't accept things at face value. Wonder about the world around you and how things work. ?
2. Be Open to New Experiences: Step outside your comfort zone. Try new hobbies, travel to new places, talk to people with different backgrounds and perspectives. ?
3. Embrace Challenges: Don't shy away from difficult tasks or subjects. View challenges as opportunities to learn and grow.?
4. Be Proactive in Seeking Knowledge: Don't wait for learning opportunities to come to you. Actively seek them out. Read books, take online courses, attend workshops, listen to podcasts.?
5. Reflect on Your Learning: Take time to think about what you've learned and how you can apply?
Both these examples look simple and easy to use, and you might think why can’t we directly invoke chat methods with LLMs – sure you can do that, however LangChain framework goes way behind these simple examples and in more complex LLM application you can realize the potential of these methods. In upcoming articles, we will cover more of such scenarios.? For now, let’s go through this example from Insurance domain and try to relate how chaining will potentially work. ?
Chain 1 - Extract critical information from claim description.?
Chain 2 - Categorizing the claim based on the extracted information.?
Chain 3 - Generating a concise summary of the claim for internal review?
Combine the chains using SimpleSequentialChain to execute the chains together and achieve the large objective. And the run the combined chain with user input and run the combined chain with chain run method. ?
Hopefully with the example above you get a better understanding of components and how they can help you in bigger implementations. ?
[Summary]
A framework designed to streamline the development of complex LLM applications. It simplifies development by utilizing data sources, tools, and workflows. The?approach makes it ideal for both simple and complex tasks, such as RAG - where external knowledge sources enhance the model’s accuracy. The Prompt Template feature, demonstrated through the HumanMessage or SystemMessage methods, is helpful for structuring inputs. It standardizes workflows, avoids formatting issues, and ensures the model understands user intent, resulting in reliable outputs. A practical example includes summarizing a film by dynamically inserting context like title and description into the prompt. Chaining is a key concept - we saw how?chains can process claims by extracting information, categorizing claims, and generating summaries for review. By combining chains, developers can automate intricate processes and achieve larger objectives efficiently.?
At the heart of LangChain are components like Chains, Agents, Parsers, Loaders, Memory, Tools, and Evaluation. At high level Chains allow users to build workflows by combining multiple tasks into a structured sequence, enabling dynamic input handling and multi-step reasoning. Agents introduce decision-making capabilities, while Memory ensures context retention, critical for chatbots or extended interactions. It also offers deployment and debugging tools like LangServe and LangSmith ensuring scalability and ease of use, more on these later. Whether you’re building a chatbot or a complex automation system, it provides the tools to make applications smarter and more efficient. ?