LangChain is a powerful framework designed for building applications powered by language models. One of its standout features is the ability to perform Document Chaining. This allows developers to link various documents together, enabling more complex and informed interactions with the language model.
Document chaining can significantly enhance the contextual understanding of the model by creating a flow of information from one document to another. This can be particularly useful in applications like chatbots, where context from previous interactions is necessary to maintain a coherent conversation.
Here’s a simple example showcasing how to implement document chaining with LangChain:
from langchain.chains import SimpleSequentialChain
from langchain.prompts import PromptTemplate
# Define prompt templates for two documents
first_prompt = PromptTemplate(
input_variables=["text"],
template="Given the text: {text}, summarize the main points."
)
second_prompt = PromptTemplate(
input_variables=["summary"],
template="Based on the summary: {summary}, provide additional insights."
)
# Create a sequential chain
chain = SimpleSequentialChain(chain=[first_prompt, second_prompt])
# Sample text input
input_text = "LangChain provides structured tools to facilitate language model applications."
# Generate the output
output = chain.run(input_text)
print(output)
This code snippet demonstrates a simple sequential chain where the output of the first prompt (a summary of the input text) is fed into the second prompt to extract additional insights. By utilizing document chaining, you can build more sophisticated applications that leverage the full potential of language models.
For more information and advanced features, check out the LangChain documentation.