LangChain & LlamaIndex: Orchestration Frameworks
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Compare LangChain and LlamaIndex orchestration libraries with clear Python code examples.
#ai-engineering#langchain#llamaindex#orchestration#python
Choosing Orchestration Frameworks
LLM orchestration frameworks simplify building AI pipelines:
• LangChain: Highly modular and action-oriented. Best for building conversational chat agents, complex pipelines, and multi-step tool integrations. • LlamaIndex: Data-oriented. Built specifically for ingestion, advanced indexing, and structured retrieval, making it the preferred choice for complex RAG architectures.
LangChain Prompt Template & LLM Chain (Python)
python
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from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
# Instantiate LLM
llm = ChatOpenAI(model="gpt-4o", temperature=0.2)
# Define template
prompt = ChatPromptTemplate.from_messages([
("system", "You are a tech interview coach. Answer concisely."),
("user", "Explain the concept of {concept}")
])
# Combine using LangChain Expression Language (LCEL)
chain = prompt | llm
response = chain.invoke({"concept": "Vector Embeddings"})
print(response.content)Discussion
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