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Documentation Index

Fetch the complete documentation index at: https://notes.kodekloud.com/llms.txt

Use this file to discover all available pages before exploring further.

Before diving into the runnable interface and pass-through components, this lesson reviews three primary ways to invoke a chain in LangChain:
  • Synchronous invocation (invoke)
  • Streaming output (stream)
  • Batch processing (batch)

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1. Synchronous Invocation (invoke)

Execute the entire chain in one call and receive the complete response: 
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser

# 1. Construct prompt, LLM, and parser
prompt = ChatPromptTemplate.from_template(
    "You are a helpful assistant.\nAnswer the following question: {question}"
)
llm = ChatOpenAI()
output_parser = StrOutputParser()

# 2. Build the chain
chain = prompt | llm | output_parser

# 3. Invoke synchronously
result = chain.invoke({"question": "Tell me about The Godfather Movie"})
print(result)
chain.invoke(...) will block until the LLM has generated the full response. Use this for simpler or short-prompt scenarios.

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2. Streaming Output (stream)

When you need a “typing” effect or to process tokens on the fly, use streaming: 
for chunk in chain.stream({"question": "Tell me about The Godfather Movie"}):
    # Print each chunk without extra newlines
    print(chunk, end="")
Example of streamed chunks: 
The God
father is
a classic
American
crime
film
directed
by Francis...
Streaming large models can generate many tokens. Monitor your usage and consider rate limits.

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3. Batch Processing (batch)

Process multiple inputs in parallel to improve throughput: 
questions = [
    {"question": "What is LangChain?"},
    {"question": "Explain chain-of-thought prompting."},
    {"question": "How does streaming work?"}
]
results = chain.batch(questions)
for response in results:
    print(response)
Batch execution is ideal for high-volume workloads and will be explored further alongside parallelism patterns.

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Invocation Methods Comparison

MethodDescriptionExample
invokeSynchronous, returns full resultchain.invoke({...})
streamYields tokens/chunks as they’re generatedfor chunk in chain.stream(...)
batchRuns multiple inputs concurrentlychain.batch([{…}, {…}])

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What’s Next?

In the following sections, we’ll explore:
  • The Runnable Interface: How to wrap custom logic into reusable components
  • Pass-Through Components: Techniques for chaining without transforming the data

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