# Getting Started with LangChain
LangChain is a powerful framework for developing applications powered by language models. In this comprehensive guide, we'll walk through building your first AI application.
## What is LangChain?
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LangChain is a framework designed to simplify the creation of applications using large language models (LLMs). It provides a standard interface for chains, lots of integrations with other tools, and end-to-end chains for common applications.
## Key Features
- **Modular Components**: Build complex applications from simple, reusable components
- **Chain Management**: Easily create sequences of calls to LLMs and other tools
- **Memory Systems**: Maintain context across multiple interactions
- **Agent Framework**: Build autonomous agents that can use tools and make decisions
## Building Your First Application
Let's create a simple question-answering application:
```python
from langchain.llms import OpenAI
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
# Initialize the LLM
llm = OpenAI(temperature=0.7)
# Create a prompt template
template = "Answer the following question: {question}"
prompt = PromptTemplate(template=template, input_variables=["question"])
# Create a chain
chain = LLMChain(llm=llm, prompt=prompt)
# Run the chain
response = chain.run("What is artificial intelligence?")
print(response)
```
## Advanced Patterns
Once you've mastered the basics, you can explore:
- **Document Loading**: Process and query large documents
- **Vector Stores**: Implement semantic search capabilities
- **Agents**: Create autonomous AI assistants
- **Custom Chains**: Build specialized workflows for your use case
## Best Practices
1. **Start Simple**: Begin with basic chains before moving to complex agents
2. **Test Thoroughly**: LLM outputs can be unpredictable, so test extensively
3. **Monitor Costs**: Keep track of API usage to manage expenses
4. **Handle Errors**: Implement robust error handling for production applications
## Conclusion
LangChain opens up endless possibilities for AI application development. Start experimenting today and see what you can build!
## Key Features
- **Modular Components**: Build complex applications from simple, reusable components
- **Chain Management**: Easily create sequences of calls to LLMs and other tools
- **Memory Systems**: Maintain context across multiple interactions
- **Agent Framework**: Build autonomous agents that can use tools and make decisions
## Building Your First Application
Let's create a simple question-answering application:
```python
from langchain.llms import OpenAI
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
# Initialize the LLM
llm = OpenAI(temperature=0.7)
# Create a prompt template
template = "Answer the following question: {question}"
prompt = PromptTemplate(template=template, input_variables=["question"])
# Create a chain
chain = LLMChain(llm=llm, prompt=prompt)
# Run the chain
response = chain.run("What is artificial intelligence?")
print(response)
```
## Advanced Patterns
Once you've mastered the basics, you can explore:
- **Document Loading**: Process and query large documents
- **Vector Stores**: Implement semantic search capabilities
- **Agents**: Create autonomous AI assistants
- **Custom Chains**: Build specialized workflows for your use case
## Best Practices
1. **Start Simple**: Begin with basic chains before moving to complex agents
2. **Test Thoroughly**: LLM outputs can be unpredictable, so test extensively
3. **Monitor Costs**: Keep track of API usage to manage expenses
4. **Handle Errors**: Implement robust error handling for production applications
## Conclusion
LangChain opens up endless possibilities for AI application development. Start experimenting today and see what you can build!
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