contextcrunch-langchain 1.0.0

LangChain Integration for ContextCrunch prompt compression.

ContextCrunch-LangChain-python

Integration for ContextCrunch in a LangChain pipeline.

Usage

The ContextCruncher is a Runnable Lambda that takes in 2 inputs (as an input dictionary):

• context: This could be your conversation history, or retrieved information from RAG
• question: The relevant query to find in the data. ContextCrunch uses this narrow down the context to only the most essential parts.

When initializing the ContextCruncher() there is also an optional compression_ratio parameter that controls how aggresively the algorithm should compress. The general trend is the higher the compression ratio, the less information is retained. Generally, a compression ratio of 0.9 is a good start, though for small contexts, the algorithm may compress less than requested compression ratio.

Return

ContextCruncher returns a dictionary with:

• context: The updated (compressed) context.
• question: The original question (for later uses in a chain).

Quickstart

Install this package with pip install contextcrunch-langchain.

RAG

You can easily modify an existing RAG pipeline by simply applying a ContextCruncher() to the context before filling the prompt template.

For example, if you are using this example from the LangChain docs, the modified pipeline becomes:

rag_chain = (
    {"context": retriever | format_docs, "question": RunnablePassthrough()}
    | ContextCruncher(compression_ratio=0.95)
    | prompt
    | llm
    | StrOutputParser()
)