Supercharge Your LLM Projects with LangChain

Introduction

Building language model (LLM) applications can feel like navigating a maze. Between managing prompts, processing outputs, and creating seamless workflows, it’s easy to get lost. That’s where LangChain comes in.

LangChain is an open-source framework designed to streamline LLM application development. Whether you're creating a chatbot or building automated translation tools, LangChain’s modular approach makes your life easier.

LangChain and Use cases

Why Use LangChain?

Imagine you receive a customer email written in British English, but you need to respond in American English. Instead of crafting the response from scratch, you can use LangChain to handle this conversion with a single prompt.

LangChain abstracts essential steps like input generation and output parsing, helping developers focus on building rather than getting stuck in repetitive tasks. Here’s a sneak peek of how it works:

customer_email = """
Arrr, I be fuming that me blender lid flew off and splattered me kitchen walls with smoothie!...
"""
style = "American English in a calm and respectful tone"

prompt = f"""Translate the text delimited by triple backticks into a style that is {style}.
text: ```{customer_email}```
"""
response = get_completion(prompt)
print(response)

Instead of reinventing the wheel for every LLM task, LangChain handles it seamlessly.

Going Beyond Basics: Output Parsing

If your application involves structured data, parsing LLM responses into a readable format is crucial. LangChain’s output parsers can transform raw text responses into JSON or other structured formats.

For instance, imagine you receive a customer review about a product:

customer_review = """This leaf blower is pretty amazing.I bought for my wife's birthday

she was thrilled to receive it, Though it took 7 days to get delivered. But the price 

was reasonable so all in all a great product!"""

review_template = """
For the following text, extract the following information:
- gift: Was the item purchased as a gift?
- delivery_days: Delivery time in days
- price_value: Sentences about value or price
text: {text}
"""

LangChain takes the guesswork out of parsing by providing standardized output formats. This way, you don’t just get answers; you get answers you can use.

Types of Memory in LangChain

LangChain recognizes that LLMs are inherently stateless, meaning that each transaction is independent. To simulate memory, chatbots need to provide the entire conversation as context, which can quickly become expensive as the chat length increases. To address this, LangChain provides several types of memory.

ConversationBufferMemory stores the entire conversation history, ideal for applications that need complete recall. 

ConversationBufferWindowMemory stores only a limited number of the most recent interactions, useful when maintaining short-term context without ballooning memory. 

ConversationTokenBufferMemory imposes a token limit to manage costs, retaining only recent exchanges that fit within the specified limit. 

Finally, ConversationSummaryMemory leverages LLMs to summarize and condense previous interactions, maintaining essential context without storing raw dialogue.

By utilizing these different memory types, LangChain allows chatbots to balance memory efficiency with conversational fluidity, making human-AI interactions more coherent and cost-effective.

Evaluation in LangChain

Evaluating the performance of Large Language Models (LLMs) is essential to ensure they generate accurate, relevant, and high-quality responses. LangChain provides several evaluation mechanisms, which can be categorized into:

1. Automatic Evaluation

   • Accuracy & Correctness: Measures whether the LLM's output matches expected answers (e.g., exact string matching, semantic similarity using embeddings).

   • Relevance: Assesses if the response is contextually appropriate (e.g., using cosine similarity between query and response embeddings).

   • Fluency & Coherence: Checks if the response is well-structured and grammatically correct (often using secondary LLMs as judges).

   • Custom Metrics: Developers can define task-specific evaluation criteria (e.g., fact-checking against a knowledge base).

2. Human Evaluation

   • Since some aspects (like nuance and creativity) are hard to automate, human reviewers may manually score responses based on predefined rubrics.

3. Evaluation Frameworks in LangChain

   • LangChain Evaluation Modules: Built-in tools like CriteriaEvalChain for checking responses against specific criteria (e.g., helpfulness, conciseness).

   • Integration with Third-Party Tools: LangChain supports integrations with platforms like Weights & Biases (W&B) and MLflow for tracking and comparing model performance.

4. Benchmarking & A/B Testing

• Compare different prompts, models, or chains by running them on the same dataset and analyzing metrics.

Agents In LangChain

Agents are intelligent systems that dynamically decide which actions to take (e.g., calling an LLM, using a tool, or retrieving external data). They enhance flexibility by selecting the best approach for a given task.

1. Components of an Agent

   • LLM Core: The brain of the agent (e.g., GPT-4, Claude, or local models).

   • Tools: External functions the agent can use (e.g., search APIs, calculators, databases).

   • Memory: Retains context across interactions (short-term or long-term).

   • Decision Logic: Determines the next action based on the input (e.g., ReAct, Plan-and-Execute).

2. How Agents Work

   • Step 1: Receive Input – The agent takes a user query (e.g., "What's the weather in Tokyo?").

   • Step 2: Plan & Decide – The LLM decides whether to:

     • Generate an answer directly (if it knows).

     • Use a tool (e.g., call a weather API).

     • Break the task into subtasks (e.g., first find Tokyo's location, then fetch weather).

   • Step 3: Execute & Iterate – The agent performs actions, observes results, and refines its approach if needed.

   • Step 4: Return Output – The final response is compiled and returned to the user.

3. Types of Agents in LangChain

   • Zero-shot ReAct Agent: Uses reasoning (ReAct framework) to decide actions without prior examples.

   • Plan-and-Execute Agent: Breaks complex tasks into smaller steps before execution.

   • Self-Ask Agent: Asks follow-up questions to itself to gather more information.

   • Custom Agents: Developers can build specialized agents with unique decision-making logic.

4. Benefits of Using Agents

   • Dynamic Model Selection: Switches between different LLMs based on task complexity (e.g., GPT-4 for reasoning, a smaller model for simple queries).

   • Tool Integration: Can leverage APIs, databases, and code execution for accurate responses.

   • Adaptability: Improves over time with memory and feedback loops

Final Thoughts

LangChain doesn’t just simplify LLM application development; it empowers you to build smarter, more efficient solutions. By leveraging its modular structure, you can focus on creating applications rather than wrestling with code.

Give LangChain a try, and see how it transforms your approach to LLMs! 

You can also use offline LLM by using Ollama, guide is here.