spRAG is a high-performance RAG framework for unstructured data. It is especially good at handling complex queries over dense text, like financial reports and legal documents.

spRAG achieves substantially higher accuracy than vanilla RAG baselines on complex open-book question answering tasks. On one especially challenging benchmark, FinanceBench, spRAG gets accurate answers 83% of the time, compared to the vanilla RAG baseline which only gets 19% of questions correct.

There are two key methods used to improve performance over vanilla RAG systems:

1. AutoContext
2. Relevant Segment Extraction (RSE)

AutoContext automatically injects document-level context into individual chunks prior to embedding them. This gives the embeddings a much more accurate and complete representation of the content and meaning of the text. In our testing, this feature leads to a dramatic improvement in retrieval quality. In addition to increasing the rate at which the correct information is retrieved, AutoContext also substantially reduces the rate at which irrelevant results show up in the search results. This reduces the rate at which the LLM misinterprets a piece of text in downstream chat and generation applications.

The implementation of AutoContext is fairly straightforward. All we do is generate a 1-2 sentence summary of the document, add the file name to it, and then prepend that to each chunk prior to embedding it.

Relevant Segment Extraction (RSE) is a post-processing step that takes clusters of relevant chunks and intelligently combines them into longer sections of text that we call segments. These segments provide better context to the LLM than any individual chunk can. For simple factual questions, the answer is usually contained in a single chunk; but for more complex questions, the answer usually spans a longer section of text. The goal of RSE is to intelligently identify the section(s) of text that provide the most relevant information, without being constrained to fixed length chunks.

For example, suppose you have a bunch of SEC filings in a knowledge base and you ask “What were Apple’s key financial results in the most recent fiscal year?” RSE will identify the most relevant segment as the entire “Consolidated Statement of Operations” section, which will be 5-10 chunks long. Whereas if you ask “Who is Apple’s CEO?” the most relevant segment will be identified as a single chunk that mentions “Tim Cook, CEO.”

The easiest way to install spRAG is to use the Python package: pip install sprag

By default, spRAG uses OpenAI for embeddings, Claude 3 Haiku for AutoContext, and Cohere for reranking, so to run the code below you'll need to make sure you have API keys for those providers set as environmental variables with the following names: OPENAI_API_KEY, ANTHROPIC_API_KEY, and CO_API_KEY.

You can create a new KnowledgeBase directly from a file using the create_kb_from_file function:

from sprag.create_kb import create_kb_from_file

file_path = "spRAG/tests/data/levels_of_agi.pdf"
kb_id = "levels_of_agi"
kb = create_kb_from_file(kb_id, file_path)

KnowledgeBase objects persist to disk automatically, so you don't need to explicitly save it at this point.

Now you can load the KnowledgeBase by its kb_id (only necessary if you run this from a separate script) and query it using the query method:

from sprag.knowledge_base import KnowledgeBase

kb = KnowledgeBase("levels_of_agi")
search_queries = ["What are the levels of AGI?", "What is the highest level of AGI?"]
results = kb.query(search_queries)
for segment in results:
    print(segment)

Now let's look at an example of how we can customize the configuration of a KnowledgeBase. In this case, we'll customize it so that it only uses OpenAI (useful if you don't have API keys for Anthropic and Cohere). To do so, we need to pass in a subclass of LLM and a subclass of Reranker. We'll use gpt-3.5-turbo for the LLM (this is what gets used for document summarization in AutoContext) and since OpenAI doesn't offer a reranker, we'll use the NoReranker class for that.

from sprag.llm import OpenAIChatAPI
from sprag.reranker import NoReranker

llm = OpenAIChatAPI(model='gpt-3.5-turbo')
reranker = NoReranker()

kb = KnowledgeBase(kb_id="levels_of_agi", reranker=reranker, auto_context_model=llm)

Now we can add documents to this KnowledgeBase using the add_document method. Note that the add_document method takes in raw text, not files, so we'll have to extract the text from our file first. There are some utility functions for doing this in the document_parsing.py file.

from sprag.document_parsing import extract_text_from_pdf

file_path = "spRAG/tests/data/levels_of_agi.pdf"
text = extract_text_from_pdf(file_path)
kb.add_document(doc_id=file_path, text=text)

A KnowledgeBase object takes in documents (in the form of raw text) and does chunking and embedding on them, along with a few other preprocessing operations. Then at query time you feed in queries and it returns the most relevant segments of text.

KnowledgeBase objects are persistent by default. The full configuration needed to reconstruct the object gets saved as a JSON file upon creation and updating.

There are five key components that define the configuration of a KnowledgeBase, each of which are customizable:

1. VectorDB
2. ChunkDB
3. Embedding
4. Reranker
5. LLM

There are defaults for each of these components, as well as alternative options included in the repo. You can also define fully custom components by subclassing the base classes and passing in an instance of that subclass to the KnowledgeBase constructor.

The VectorDB component stores the embedding vectors, as well as a small amount of metadata.

The ChunkDB stores the content of text chunks in a nested dictionary format, keyed on doc_id and chunk_index. This is used by RSE to retrieve the full text associated with specific chunks.

The Embedding component defines the embedding model.

The Reranker components define the reranker. This is used after the vector database search (and before RSE) to provide a more accurate ranking of chunks.

This defines the LLM to be used for document summarization, which is only used in AutoContext.

Documents -> chunking -> embedding -> chunk and vector database upsert

Queries -> vector database search -> reranking -> RSE -> results

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