What is LangChain and why should you care?
Langchain 🦜 has quickly grown in the open-source space, experiencing exponential growth. One of the major reasons behind this surge is the recent interest in Language Model Integrations (LLMs).
Let me explain it in simpler terms. Langchain provides a platform for developers to connect data to language models, such as GPT models from OpenAI and various others, through their API. It provides a collection of modular components and utilities that simplify the process of building applications that leverage the capabilities of LLMs.
Why do we need LangChain?
Developers often lack the necessary tools to deploy language models in real-world use cases as the ecosystem for building GenAI applications is still evolving. Starting scratch to work with LLMs directly can be a bit complex with tasks such as prompt engineering, data preprocessing, managing context and memory, and handling different model APIs and output formats. LangChain abstracts away much of this complexity, providing a higher-level interface for interacting with LLMs.
Many real-world applications require combining multiple operations or tasks involving LLMs, such as retrieving information from databases, processing text data, and generating outputs based on specific prompts. LangChain's concept of "chains" and "agents" makes it easier to create and manage these complex workflows.
The best part is that you can do all of this within a single interface. No more crazy scaling of code bases just to support different providers!
The community behind
When it comes to the future of any technology framework, one of the big factors is the community supporting it. And for projects like Langchain, this is even more crucial. You want to know you're not flying solo, right?
Langchain has got a massive fan base! As of today, it's got over 51k stars on GitHub, which is a pretty credible stat for its popularity in the open-source space.
It's racking up a million downloads every single month with some serious love from the developer community.
The community also maintains an active Discord channel. So, if you ever hit a blocker or just want to experiment around you know that you've got a place to hang out.
LangChain Community: Click Here
LangChain Documentation: Click Here
Bottom line: with that kind of following and engagement, you can bet Langchain is doing something right. Let's now try to understand what exactly all of this hype is about.
What does LangChain really do?
Complexity Abstraction
At its core, LangChain acts as an abstraction layer, allowing developers to interact with various LLMs through a consistent and user-friendly interface. This abstraction simplifies the process of working with different LLM providers, APIs, and models, enabling seamless model interoperability and facilitating the integration of multiple LLMs within a single application.
Chaining
Beyond abstraction, LangChain introduces the concept of "chains," which are sequences of operations that can be applied to inputs or outputs of LLMs. These chains can be combined and orchestrated to create more complex workflows, enabling developers to build sophisticated applications that leverage the strengths of LLMs in various capacities. For example, a chain could involve retrieving data from a database, processing and summarizing that data using an LLM, and then generating a natural language response based on the processed information.
For example, you can define a custom chain that retrieves relevant information from various data sources, processes it using an LLM, and generates a natural language response:
from langchain import OpenAI, GooglePalm, Anthropic
from langchain.chains import RetrievalQA
# load models
openai_llm = OpenAI(model_name="text-davinci-003")
palm_llm = GooglePalm(model_name="palm-2")
claude_llm = Anthropic(model_name="claude-v1")
# chain definition
chain = RetrievalQA(
retriever=..., # retriever for fetching relevant information
question_llm=openai_llm,
answer_llm=palm_llm,
feedback_llm=claude_llm,
...
)
# use the chain to answer questions
result = chain.run("What is the capital of India?")The above code is an example on how you can integrate and use multiple LLMs within the same application, without having to worry about the underlying complexities of interacting with each provider's API directly. The RetrievalQA chain abstraction handles the orchestration of different LLMs for different subtasks (generating questions, answers, and feedback).
Memory and Context Management
LangChain can manage memory and context effectively. Most LLMs often struggle to maintain coherent and consistent responses across multiple interactions due to their limited context retention capabilities. LangChain addresses this limitation by providing tools for managing and persisting context, allowing applications to maintain relevant information and provide more coherent and contextually-aware responses.
from langchain import OpenAI, ConversationBufferMemory
# initialize the LLM and memory
llm = OpenAI(temperature=0)
memory = ConversationBufferMemory()
# set up the conversation chain
from langchain.chains import ConversationChain
conversation = ConversationChain(llm=llm, memory=memory)
# start the conversation
print(conversation.predict(input="Hi there!"))
# -> 'Hi there! It's nice to meet you. How can I assist you today?'
print(conversation.predict(input="I'm looking for information about the French Revolution."))
# -> 'Sure, I'd be happy to help you with that. The French Revolution was a major event in European history...'
print(conversation.predict(input="What were some of the key causes?"))
# -> 'Some of the key causes of the French Revolution included...'
print(conversation.predict(input="And what were the major outcomes?"))
# -> 'The major outcomes of the French Revolution were...'In this example, we first initialize an OpenAI LLM and a ConversationBufferMemory object, which will be used to store and manage the conversation context.
Next, we create a ConversationChain and pass it to the LLM and memory objects. This chain is designed to maintain and utilize the conversation history to provide more coherent and contextually-aware responses.
As we interact with the ConversationChain through the predict method, the chain stores the input prompts and generated responses in the ConversationBufferMemory. When a new input is provided, the chain retrieves the relevant context from the memory and prepends it to the prompt before sending it to the LLM.
You'll notice that in the example, even though the prompts "What were some of the key causes?" and "And what were the major outcomes?" don't explicitly mention the French Revolution, the LLM can still provide relevant responses based on the context maintained in the ConversationBufferMemory.
LangChain provides several different memory implementations, including ConversationBufferMemory, ConversationBufferWindowMemory (which limits the context to a specific number of interactions), and ConversationEntityMemory (which can extract and store specific entities from the conversation). You can also implement custom memory classes to suit your specific use case.
Model Agnostic
Different LLMs have different APIs, input/output formats, and capabilities. LangChain provides a unified interface for working with various LLMs, allowing developers to switch between models or use multiple models in the same application without significant code changes.
Data Ingestion & Monitoring
LangChain simplifies the process of ingesting and working with diverse data sources, such as text files, PDFs, websites, and databases. It provides utilities for preprocessing and formatting data, ensuring that it is compatible with the input requirements of LLMs. This data integration capability enables developers to leverage the power of LLMs with real-world data sources, unlocking new possibilities for processing and generating insights from complex and unstructured data.
It also offers utilities for evaluating and monitoring the performance of LLM-based applications, including tools for generating and analyzing metrics. This feature allows developers to assess the effectiveness of their applications, identify areas for improvement, and optimize their use of LLMs.
from langchain.document_loaders import TextLoader, PyPDFLoader, WebBaseLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Chroma
from langchain.chains import RetrievalQA
from langchain.llms import OpenAI
from langchain.evaluation import LoadAndEvaluateQA
# ingest the data
loaders = [
TextLoader('data/text_files/'),
PyPDFLoader('data/pdfs/'),
WebBaseLoader('https://example.com')
]
documents = []
for loader in loaders:
docs = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_documents(docs)
documents.extend(texts)
# create a vector store
vectorstore = Chroma.from_documents(documents, persist_directory='vectorstore')
# create a retrieval chain
llm = OpenAI(temperature=0)
qa = RetrievalQA.from_chain_type(
llm=llm,
chain_type="stuff",
retriever=vectorstore.as_retriever()
)
# evaluate and monitor
dataset = LoadAndEvaluateQA.load_from_file('data/qa_dataset.json')
metrics = LoadAndEvaluateQA.evaluate(qa, dataset)
print(metrics)In the above code example, we start by ingesting data from various sources: text files, PDFs, and a website. We use LangChain's TextLoader, PyPDFLoader, and WebBaseLoader to load these documents. We then split the documents into smaller chunks using the CharacterTextSplitter to ensure that they are compatible with the input requirements of the LLM.
Next, we create a vector store using Chroma, which is a tool for ingesting and storing documents in a format suitable for retrieval and querying.
With the vector store in place, we create a RetrievalQA chain, which combines a retriever (in this case, the vector store) with an LLM (OpenAI's GPT-3) to answer questions based on the ingested data.
Finally, we use LangChain's evaluation and monitoring tools to assess the performance of our question-answering application. We load a dataset of question-answer pairs (LoadAndEvaluateQA.load_from_file('data/qa_dataset.json')) and evaluate the RetrievalQA chain against this dataset using the LoadAndEvaluateQA.evaluate function. This function returns a dictionary of metrics, such as accuracy, precision, recall, and F1 score, which we can use to monitor and optimize the performance of our application.
Closing thoughts
LangChain has proven to be a valuable tool for working with large language models (LLMs) by simplifying the complexities involved. But to make a solid point - the APIs for these language model things are constantly evolving. Who knows, maybe down the line they'll just bake in a bunch of the functionality that LangChain provides right now. That could make LangChain a little redundant, or at least force it to switch things up a bit.
What I do think is that LangChain's model-agnostic approach could position it as a standard interface for LLMs across different platforms If it becomes like the go-to interface for working with any language model out there, that'd be a pretty sweet spot to be in.
Whichever way it shakes out, you have to appreciate the LangChain team for putting in the work to build something that's legitimately useful right now. They've helped a lot of folks to essentially build use case driven apps using LLMs. Even if LangChain ends up evolving or getting replaced down the line, their contribution to pushing this space forward is solid.