Building AI-Driven Applications with LangChain: A Developer’s Guide（1）
Welcome to a developer-centric exploration of LangChain, where we not only dive into the concepts but also roll up our sleeves to code. LangChain, with its suite of tools, makes it easier to incorporate large language models into your projects, and here’s how you can get started.
Setting Up Your Development Environment
Before we embark on our coding adventure, ensure you have Python and the necessary packages installed:
pip install langchain openai
Grab your OpenAI API key as we’ll be using OpenAI’s models through LangChain for various tasks like summarization and question answering.
Summarizing Documents with LangChain
Summarization is a breeze with LangChain. Let’s start by summarizing a text document. First, we need to segment the document into manageable parts:
from langchain.document_loaders import TextLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
# Load your document
loader = TextLoader(‘path/to/your/document.txt’)
document = loader.load()
# Split document into smaller segments
text_splitter = RecursiveCharacterTextSplitter(chunk_size=5000, chunk_overlap=350)
docs = text_splitter.split_documents(document)
Next, we utilize LangChain’s summarization chain for our segmented text:
from langchain.llms import OpenAI
from langchain.chains.summarize import load_summarize_chain
# Initialize LLM with OpenAI
llm = OpenAI(model_name=’text-davinci-003', openai_api_key=’your_openai_api_key’)
# Summarize the document
chain = load_summarize_chain(llm=llm, chain_type=’map_reduce’)
summary = chain.run(docs)
print(「Summary:」, summary)
Question Answering Over Segmented Documents
LangChain shines in extracting answers from large documents. After segmenting and embedding our document, we can query it efficiently:
from langchain.vectorstores import FAISS
from langchain.embeddings import OpenAIEmbeddings
from langchain.chains import RetrievalQA
# Prepare embeddings
embeddings = OpenAIEmbeddings(openai_api_key=’your_openai_api_key’)
docsearch = FAISS.from_documents(docs, embeddings)
# Initialize RetrievalQA
qa = RetrievalQA.from_chain_type(llm=llm, retriever=docsearch.as_retriever())
# Querying the document
question = 「What’s the main idea?」
answer = qa.run(question)
print(「Answer:」, answer)
Data Extraction: Structuring Unstructured Text
Extracting structured data from text is another LangChain forte. Let’s say we have a chatbot response that we want to parse into structured data:
from langchain.schema import ResponseSchema
from langchain.output_parsers import StructuredOutputParser
response_schemas = [
. ResponseSchema(name=」date」, description=」The date of the event」),
. ResponseSchema(name=」event」, description=」The event name」)
]
output_parser = StructuredOutputParser.from_response_schemas(response_schemas)
chat_response = 「The event, LangChain Con, is on October 10th.」
parsed_data = output_parser.parse(chat_response)
print(「Parsed Data:」, parsed_data)
Conclusion: Building Dynamic and Scalable AI Applications
Through these examples, we’ve seen how LangChain not only simplifies the interaction with LLMs but also empowers developers to build sophisticated AI-driven applications. From summarizing lengthy documents and answering complex questions to extracting structured data from unstructured text, LangChain equips you with the tools necessary for a wide range of applications.
Remember, the examples provided are just the tip of the iceberg. LangChain’s modular architecture and extensive component library allow for deep customization and optimization, ensuring your applications are not only functional but also efficient and scalable.
Dive deeper into each component, explore advanced features, and join the LangChain community for more insights and collaboration opportunities. With experimentation and creativity, you’ll unlock the full potential of LangChain in your AI projects.
Advancd part🤔
For developers seeking to push the boundaries of what’s possible with LangChain, diving into advanced use cases and leveraging custom components is a thrilling journey. This section will guide you through an advanced scenario, demonstrating how to create a custom agent that makes decisions based on user input and external data, complete with code examples to get you started.
Advanced Scenario: Building a Custom Agent with LangChain
Imagine you’re developing a chatbot that helps users find books based on their preferences. This bot needs to understand user queries, access an external book database, and make recommendations. Here’s how you can achieve this with LangChain.
Step 1: Setting Up Your Environment
Ensure you have all necessary packages installed. If you haven’t set up LangChain yet, refer to the initial setup section.
Step 2: Define Your Custom Agent
LangChain allows for the creation of agents that can interact with external APIs, make decisions, and provide tailored responses. Let’s start by defining our custom agent:
from langchain.agents import BaseAgent
from langchain.llms import OpenAI
from langchain.tools import ExternalAPI
class BookFinderAgent(BaseAgent):
. def __init__(self, llm, book_api):
. super().__init__(llm=llm)
. self.book_api = book_api
. def decide_action(self, input_text):
. # Use LLM to understand user query and decide on action
. processed_query = self.llm(input_text)
. action = 「search_books」 # Simplified for example
. return action, processed_query
. def perform_action(self, action, processed_query):
. if action == 「search_books」:
. books = self.book_api.search(processed_query)
. return books
. return None
In this example, BookFinderAgent uses an LLM to process user queries, decides to search for books, and interacts with an external book API to fetch recommendations.
Step 3: Integrate an External Book API
Let’s mock an external API tool for searching books. In practice, you’d replace this with actual API calls to a book database:
class BookAPI(ExternalAPI):
. def search(self, query):
. # Mock API call
. return f」Found books related to {query}」
Step 4: Bringing It All Together
Now, let’s create instances of our OpenAI model, the mock BookAPI, and our custom BookFinderAgent. Then, we’ll ask the agent to find a book based on a user query:
llm = OpenAI(model_name=’text-davinci-003', openai_api_key=’your_openai_api_key’)
book_api = BookAPI()
agent = BookFinderAgent(llm=llm, book_api=book_api)
# Simulating user input
user_input = 「I’m looking for books on artificial intelligence」
action, processed_query = agent.decide_action(user_input)
books = agent.perform_action(action, processed_query)
print(books)
Conclusion: Leveraging LangChain for Complex AI Applications
This advanced example showcases LangChain’s power in creating complex, decision-making agents capable of interfacing with external data sources. By defining custom actions and integrating LangChain with external APIs, you can build sophisticated AI-driven applications that go beyond simple text generation or processing tasks.
Remember, this is srill just a starting point. Explore LangChain’s extensive documentation, experiment with different components, and don’t hesitate to create custom modules that fit your unique requirements. The possibilities are endless, and with LangChain, you have the tools to explore them all. Happy coding!
