langchain-reference-architecture skill

---
name: langchain-reference-architecture
description: |
  Implement LangChain reference architecture patterns for production.
  Use when designing LangChain systems, implementing scalable patterns,
  or architecting enterprise LLM applications.
  Trigger with phrases like "langchain architecture", "langchain design",
  "langchain scalable", "langchain enterprise", "langchain patterns".
allowed-tools: Read, Write, Edit
version: 1.0.0
license: MIT
author: Jeremy Longshore <[email protected]>
---

# LangChain Reference Architecture

## Overview
Production-ready architectural patterns for building scalable, maintainable LangChain applications.

## Prerequisites
- Understanding of LangChain fundamentals
- Experience with software architecture
- Knowledge of cloud infrastructure

## Architecture Patterns

### Pattern 1: Layered Architecture
```
src/
├── api/                    # API layer (FastAPI/Flask)
│   ├── __init__.py
│   ├── routes/
│   │   ├── chat.py
│   │   └── agents.py
│   └── middleware/
│       ├── auth.py
│       └── rate_limit.py
├── core/                   # Business logic layer
│   ├── __init__.py
│   ├── chains/
│   │   ├── __init__.py
│   │   ├── chat_chain.py
│   │   └── rag_chain.py
│   ├── agents/
│   │   ├── __init__.py
│   │   └── research_agent.py
│   └── tools/
│       ├── __init__.py
│       └── search.py
├── infrastructure/         # Infrastructure layer
│   ├── __init__.py
│   ├── llm/
│   │   ├── __init__.py
│   │   └── provider.py
│   ├── vectorstore/
│   │   └── pinecone.py
│   └── cache/
│       └── redis.py
├── config/                 # Configuration
│   ├── __init__.py
│   └── settings.py
└── main.py
```

### Pattern 2: Provider Abstraction
```python
# infrastructure/llm/provider.py
from abc import ABC, abstractmethod
from langchain_core.language_models import BaseChatModel
from langchain_openai import ChatOpenAI
from langchain_anthropic import ChatAnthropic

class LLMProvider(ABC):
    """Abstract LLM provider."""

    @abstractmethod
    def get_chat_model(self, **kwargs) -> BaseChatModel:
        pass

class OpenAIProvider(LLMProvider):
    def get_chat_model(self, model: str = "gpt-4o-mini", **kwargs) -> BaseChatModel:
        return ChatOpenAI(model=model, **kwargs)

class AnthropicProvider(LLMProvider):
    def get_chat_model(self, model: str = "claude-3-5-sonnet-20241022", **kwargs) -> BaseChatModel:
        return ChatAnthropic(model=model, **kwargs)

class LLMFactory:
    """Factory for creating LLM instances."""

    _providers = {
        "openai": OpenAIProvider(),
        "anthropic": AnthropicProvider(),
    }

    @classmethod
    def create(cls, provider: str = "openai", **kwargs) -> BaseChatModel:
        if provider not in cls._providers:
            raise ValueError(f"Unknown provider: {provider}")
        return cls._providers[provider].get_chat_model(**kwargs)

# Usage
llm = LLMFactory.create("openai", model="gpt-4o-mini")
```

### Pattern 3: Chain Registry
```python
# core/chains/__init__.py
from typing import Dict, Type
from langchain_core.runnables import Runnable

class ChainRegistry:
    """Registry for managing chains."""

    _chains: Dict[str, Runnable] = {}

    @classmethod
    def register(cls, name: str, chain: Runnable) -> None:
        cls._chains[name] = chain

    @classmethod
    def get(cls, name: str) -> Runnable:
        if name not in cls._chains:
            raise ValueError(f"Chain '{name}' not found")
        return cls._chains[name]

    @classmethod
    def list_chains(cls) -> list:
        return list(cls._chains.keys())

# Register chains at startup
from core.chains.chat_chain import create_chat_chain
from core.chains.rag_chain import create_rag_chain

ChainRegistry.register("chat", create_chat_chain())
ChainRegistry.register("rag", create_rag_chain())

# Usage in API
@app.post("/invoke/{chain_name}")
async def invoke_chain(chain_name: str, request: InvokeRequest):
    chain = ChainRegistry.get(chain_name)
    return await chain.ainvoke(request.input)
```

### Pattern 4: RAG Architecture
```python
# core/chains/rag_chain.py
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough
from langchain_pinecone import PineconeVectorStore

def create_rag_chain(
    llm: BaseChatModel = None,
    vectorstore: VectorStore = None
) -> Runnable:
    """Create a RAG chain with retrieval."""

    llm = llm or ChatOpenAI(model="gpt-4o-mini")
    vectorstore = vectorstore or PineconeVectorStore.from_existing_index(
        index_name="knowledge-base",
        embedding=OpenAIEmbeddings()
    )

    retriever = vectorstore.as_retriever(
        search_type="similarity",
        search_kwargs={"k": 5}
    )

    prompt = ChatPromptTemplate.from_template("""
    Answer the question based on the following context:

    Context:
    {context}

    Question: {question}

    Answer:
    """)

    def format_docs(docs):
        return "\n\n".join(doc.page_content for doc in docs)

    chain = (
        {"context": retriever | format_docs, "question": RunnablePassthrough()}
        | prompt
        | llm
    )

    return chain
```

### Pattern 5: Multi-Agent System
```python
# core/agents/orchestrator.py
from langchain_core.runnables import RunnableLambda
from typing import Dict, Any

class AgentOrchestrator:
    """Orchestrate multiple specialized agents."""

    def __init__(self):
        self.agents = {}
        self.router = None

    def register_agent(self, name: str, agent: Runnable) -> None:
        self.agents[name] = agent

    def set_router(self, router: Runnable) -> None:
        """Set routing logic for agent selection."""
        self.router = router

    async def route_and_execute(self, input_data: Dict[str, Any]) -> Any:
        """Route input to appropriate agent and execute."""
        # Determine which agent to use
        agent_name = await self.router.ainvoke(input_data)

        if agent_name not in self.agents:
            raise ValueError(f"Agent '{agent_name}' not found")

        # Execute with selected agent
        agent = self.agents[agent_name]
        return await agent.ainvoke(input_data)

# Setup
orchestrator = AgentOrchestrator()
orchestrator.register_agent("research", research_agent)
orchestrator.register_agent("coding", coding_agent)
orchestrator.register_agent("general", general_agent)

# Router uses LLM to classify request
router_prompt = ChatPromptTemplate.from_template("""
Classify this request into one of: research, coding, general

Request: {input}

Classification:
""")
orchestrator.set_router(router_prompt | llm | StrOutputParser())
```

### Pattern 6: Configuration-Driven Design
```python
# config/settings.py
from pydantic_settings import BaseSettings
from pydantic import Field

class LLMSettings(BaseSettings):
    provider: str = "openai"
    model: str = "gpt-4o-mini"
    temperature: float = 0.7
    max_tokens: int = 4096
    max_retries: int = 3

class VectorStoreSettings(BaseSettings):
    provider: str = "pinecone"
    index_name: str = "default"
    embedding_model: str = "text-embedding-3-small"

class Settings(BaseSettings):
    llm: LLMSettings = Field(default_factory=LLMSettings)
    vectorstore: VectorStoreSettings = Field(default_factory=VectorStoreSettings)
    redis_url: str = "redis://localhost:6379"
    log_level: str = "INFO"

    class Config:
        env_file = ".env"
        env_nested_delimiter = "__"

settings = Settings()

# Usage
llm = LLMFactory.create(
    settings.llm.provider,
    model=settings.llm.model,
    temperature=settings.llm.temperature
)
```

## Architecture Diagram
```
                    ┌─────────────────┐
                    │   API Gateway   │
                    └────────┬────────┘
                             │
              ┌──────────────┼──────────────┐
              │              │              │
    ┌─────────▼─────┐ ┌──────▼──────┐ ┌─────▼─────────┐
    │  Chat Chain   │ │  RAG Chain  │ │ Agent System  │
    └───────┬───────┘ └──────┬──────┘ └───────┬───────┘
            │                │                │
            └────────────────┼────────────────┘
                             │
              ┌──────────────┼──────────────┐
              │              │              │
    ┌─────────▼─────┐ ┌──────▼──────┐ ┌─────▼─────────┐
    │  LLM Provider │ │ VectorStore │ │    Cache      │
    └───────────────┘ └─────────────┘ └───────────────┘
```

## Output
- Layered architecture with clear separation
- Provider abstraction for LLM flexibility
- Chain registry for runtime management
- Multi-agent orchestration pattern

## Resources
- [LangChain Architecture Guide](https://python.langchain.com/docs/concepts/architecture/)
- [Clean Architecture](https://blog.cleancoder.com/uncle-bob/2012/08/13/the-clean-architecture.html)
- [Domain-Driven Design](https://martinfowler.com/bliki/DomainDrivenDesign.html)

## Next Steps
Use `langchain-multi-env-setup` for environment management.