Langchain Workaround for with_structured_output with ChatBedrock
Problem: Langchain's with_structured_output feature, designed to streamline the extraction of structured data from LLM responses, currently lacks direct support for Amazon's ChatBedrock. This presents a challenge for users seeking to leverage the powerful capabilities of ChatBedrock for structured data retrieval.
Simplified Explanation: Imagine you want to extract information like contact details or product specifications from a chat conversation using Amazon's ChatBedrock. Langchain's with_structured_output feature would make this extraction process much easier, but unfortunately, it doesn't work directly with ChatBedrock yet.
Scenario and Original Code:
Let's consider a scenario where we want to extract product details from a customer query:
from langchain.llms import Bedrock
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
# Define the prompt
template = """Extract the product name, price, and availability from the following customer query: {query}"""
prompt = PromptTemplate(template=template, input_variables=["query"])
# Initialize the ChatBedrock LLM
llm = Bedrock(model_id="amazon.titan-xl", task="text-generation")
# Create the LLM chain
chain = LLMChain(llm=llm, prompt=prompt)
# Pass a customer query
query = "I'm interested in the new Galaxy S23 Ultra, what's the price and is it available?"
response = chain.run(query)
# **Challenge:** How to extract structured data (product name, price, availability) from the response?
print(response)
Analysis and Workaround:
Currently, Langchain's with_structured_output functionality does not directly integrate with ChatBedrock. To achieve structured data extraction, we need to implement a workaround:
-
Prompt Engineering: Design your prompt to guide the LLM towards providing the output in a structured format. This could involve using specific keywords or templates to indicate the desired output structure.
-
Post-Processing: After receiving the response from the LLM, use Python's string processing capabilities to extract the required information. This can be done using techniques like regular expressions or libraries like
jsonorpandasfor parsing structured data.
Example with Post-Processing:
import re
# ... (Existing code) ...
# Post-processing to extract structured data
match = re.search(r"Product Name: (.+), Price: (.+), Availability: (.+)", response)
if match:
product_name = match.group(1)
price = match.group(2)
availability = match.group(3)
print(f"Product Name: {product_name}")
print(f"Price: {price}")
print(f"Availability: {availability}")
else:
print("Could not extract structured data")
Optimization and Readability:
- Prompt Engineering: Invest in designing prompts that maximize the chance of structured output. Experiment with different prompts and analyze their effectiveness.
- Robust Post-Processing: Implement thorough validation and error handling in your post-processing logic to ensure data integrity.
- Modularization: Break down your code into smaller, reusable functions to improve maintainability and readability.
Additional Value:
- Explore external libraries like
jsonorpandasfor robust structured data handling. - Consider using other LLM models like Amazon's "amazon.bedrock-lambda-invoke" which allows for running custom code within the prompt for more complex extraction.
- Keep an eye on Langchain's development, as future versions may incorporate direct support for ChatBedrock and
with_structured_output.
References:
Conclusion:
While Langchain's with_structured_output doesn't directly integrate with ChatBedrock, creative prompt engineering and post-processing strategies can achieve the desired structured data extraction. As the LLM ecosystem evolves, we can expect more seamless integration and improved tools for working with structured data.
