Integration Langchain

LangChain Integration

🎯 Trigger Conditions

Use when asked about integrating DSPy with LangChain, combining frameworks, or building hybrid pipelines.

📚 Prerequisites

• langchain package installed
• dspy package installed
• Understanding of both frameworks

🛠️ Integration Patterns

1. DSPy as LangChain LLM

from langchain.llms import LLM
import dspy

class DSPyLLM(LLM):
    def __init__(self, dspy_program):
        self.program = dspy_program
    
    @property
    def _identifying_params(self):
        return {"name": "dspy-program"}
    
    @property
    def _llm_type(self):
        return "dspy"
    
    def _call(self, prompt, stop=None):
        # Convert LangChain prompt to DSPy input
        result = self.program(question=prompt)
        return result.answer

# Use with LangChain
dspy_llm = DSPyLLM(your_dspy_program)
chain = dspy_llm | your_output_parser

2. LangChain as DSPy Tool

from langchain.tools import Tool
import dspy

# Create LangChain tool
def langchain_tool(query):
    # Use LangChain for specific task
    result = your_langchain_chain.run(query)
    return result

langchain_tool = Tool(
    name="langchain-tool",
    func=langchain_tool,
    description="Use LangChain for..."
)

# Use with DSPy
class HybridProgram(dspy.Module):
    def __init__(self):
        self.dspy_step = dspy.Predict("input -> intermediate")
        self.langchain_tool = langchain_tool
    
    def forward(self, input):
        intermediate = self.dspy_step(input=input).intermediate
        result = self.langchain_tool(intermediate)
        return result

3. Hybrid Pipeline

class HybridPipeline(dspy.Module):
    def __init__(self):
        self.dspy_retrieve = dspy.Retrieve(k=3)
        self.langchain_summarize = your_langchain_summarizer
        self.dspy_answer = dspy.Predict("context, question -> answer")
    
    def forward(self, question):
        # DSPy for retrieval
        context = self.dspy_retrieve(question).passages
        
        # LangChain for summarization
        summary = self.langchain_summarize(context)
        
        # DSPy for answer generation
        return self.dspy_answer(context=summary, question=question)

4. LangChain Chains with DSPy

from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate

# Create LangChain chain with DSPy
prompt = PromptTemplate(
    input_variables=["question"],
    template="Answer: {question}"
)

dspy_llm = DSPyLLM(your_program)
chain = LLMChain(llm=dspy_llm, prompt=prompt)

# Run chain
result = chain.run("Your question")

⚠️ Pitfalls

• Complexity: Combining frameworks increases complexity
• Performance: Additional abstraction layers add latency
• Compatibility: Not all components integrate smoothly
• Debugging: Cross-framework debugging is harder

📖 References

• LangChain Documentation
• DSPy Integration