Pattern 1: RAG with LangChain
```python
from langchain.chains import RetrievalQA
from langchain.document_loaders import TextLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Chroma
from langchain.embeddings import OpenAIEmbeddings
# Load and process documents
loader = TextLoader('documents.txt')
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
texts = text_splitter.split_documents(documents)
# Create vector store
embeddings = OpenAIEmbeddings()
vectorstore = Chroma.from_documents(texts, embeddings)
# Create retrieval chain
qa_chain = RetrievalQA.from_chain_type(
llm=llm,
chain_type="stuff",
retriever=vectorstore.as_retriever(),
return_source_documents=True
)
# Query
result = qa_chain({"query": "What is the main topic?"})
```
Pattern 2: Custom Agent with Tools
```python
from langchain.agents import Tool, AgentExecutor
from langchain.agents.react.base import ReActDocstoreAgent
from langchain.tools import tool
@tool
def search_database(query: str) -> str:
"""Search internal database for information."""
# Your database search logic
return f"Results for: {query}"
@tool
def send_email(recipient: str, content: str) -> str:
"""Send an email to specified recipient."""
# Email sending logic
return f"Email sent to {recipient}"
tools = [search_database, send_email]
agent = initialize_agent(
tools,
llm,
agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
verbose=True
)
```
Pattern 3: Multi-Step Chain
```python
from langchain.chains import LLMChain, SequentialChain
from langchain.prompts import PromptTemplate
# Step 1: Extract key information
extract_prompt = PromptTemplate(
input_variables=["text"],
template="Extract key entities from: {text}\n\nEntities:"
)
extract_chain = LLMChain(llm=llm, prompt=extract_prompt, output_key="entities")
# Step 2: Analyze entities
analyze_prompt = PromptTemplate(
input_variables=["entities"],
template="Analyze these entities: {entities}\n\nAnalysis:"
)
analyze_chain = LLMChain(llm=llm, prompt=analyze_prompt, output_key="analysis")
# Step 3: Generate summary
summary_prompt = PromptTemplate(
input_variables=["entities", "analysis"],
template="Summarize:\nEntities: {entities}\nAnalysis: {analysis}\n\nSummary:"
)
summary_chain = LLMChain(llm=llm, prompt=summary_prompt, output_key="summary")
# Combine into sequential chain
overall_chain = SequentialChain(
chains=[extract_chain, analyze_chain, summary_chain],
input_variables=["text"],
output_variables=["entities", "analysis", "summary"],
verbose=True
)
```
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