rule-based-retrieval 0.1.3

Python package for Rule-based Retrieval using RAG

Rule-based Retrieval

The Rule-based Retrieval package is a Python package that enables you to create and manage Retrieval Augmented Generation (RAG) applications with advanced filtering capabilities. It seamlessly integrates with OpenAI for text generation and Pinecone for efficient vector database management.

Installation

Prerequisites

• Python 3.10 or higher
• OpenAI API key
• Pinecone API key

Install from PyPI

You can install the package directly from PyPI using pip:

pip install rule-based-retrieval

export OPENAI_API_KEY=<your open ai api key>
export PINECONE_API_KEY=<your pinecone api key>

Install from GitHub

Alternatively, you can clone the repo and install the package:

git clone [e-mail]:whyhow-ai/rule-based-retrieval.git
cd rule-based-retrieval
pip install .

export OPENAI_API_KEY=<your open ai api key>
export PINECONE_API_KEY=<your pinecone api key>

Developer Install

For a developer installation, use an editable install and include the development dependencies:

pip install -e .[dev]

For ZSH:

pip install -e ".[dev]"

If you want to install the package directly without explicitly cloning yourself run

pip install git+ssh://[e-mail]/whyhow-ai/rule-based-retrieval

Documentation

Documentation can be found here.

To serve the docs locally run

pip install -e .[docs]
mkdocs serve

For ZSH:

pip install -e ".[docs]"
mkdocs serve

Navigate to http://127.0.0.1:8000/ in your browser to view the documentation.

Examples

Check out the examples/ directory for sample scripts demonstrating how to use the Rule-based Retrieval package.

How to

Demo

Create index & upload

from whyhow_rbr import Client

# Configure parameters
index_name = "whyhow-demo"
namespace = "demo"
pdfs = ["harry_potter_book_1.pdf"]

# Initialize client
client = Client()

# Create index
index = client.get_index(index_name)

# Upload, split, chunk, and vectorize documents in Pinecone
client.upload_documents(index=index, documents=pdfs, namespace=namespace)

Query with rules

from whyhow_rbr import Client, Rule

# Configure query parameters
index_name = "whyhow-demo"
namespace = "demo"
question = "What does Harry wear?"
top_k = 5

# Initialize client
client = Client()

# Create rules
rules = [
    Rule(
        filename="harry_potter_book_1.pdf",
        page_numbers=[21, 22, 23]
    ),
    Rule(
        filename="harry_potter_book_1.pdf",
        page_numbers=[151, 152, 153, 154]
    )
]

# Run query
result = client.query(
    question=question,
    index=index,
    namespace=namespace,
    rules=rules,
    top_k=top_k,
)

answer = result["answer"]
used_contexts = [
    result["matches"][i]["metadata"]["text"] for i in result["used_contexts"]
]
print(f"Answer: {answer}")
print(
    f"The model used {len(used_contexts)} chunk(s) from the DB to answer the question"
)

Query with keywords

from whyhow_rbr import Client, Rule

client = Client()

index = client.get_index("amazing-index")
namespace = "books"

question = "What does Harry Potter like to eat?"

rule = Rule(
    filename="harry-potter.pdf",
    keywords=["food", "favorite", "likes to eat"]
)

result = client.query(
    question=question,
    index=index,
    namespace=namespace,
    rules=[rule],
    keyword_trigger=True
)

print(result["answer"])
print(result["matches"])
print(result["used_contexts"])

Query each rule separately

from whyhow_rbr import Client, Rule

client = Client()

index = client.get_index("amazing-index")
namespace = "books"

question = "What is Harry Potter's favorite food?"

rule_1 = Rule(
    filename="harry-potter.pdf",
    page_numbers=[120, 121, 150]
)

rule_2 = Rule(
    filename="harry-potter-volume-2.pdf",
    page_numbers=[80, 81, 82]
)

result = client.query(
    question=question,
    index=index,
    namespace=namespace,
    rules=[rule_1, rule_2],
    process_rules_separately=True
)

print(result["answer"])
print(result["matches"])
print(result["used_contexts"])

Contributing

We welcome contributions to improve the Rule-based Retrieval package! If you have any ideas, bug reports, or feature requests, please open an issue on the GitHub repository.

If you'd like to contribute code, please follow these steps:

1. Fork the repository
2. Create a new branch for your feature or bug fix
3. Make your changes and commit them with descriptive messages
4. Push your changes to your forked repository
5. Open a pull request to the main repository

License

This project is licensed under the MIT License.

Support

WhyHow.AI is building tools to help developers bring more determinism and control to their RAG pipelines using graph structures. If you're thinking about, in the process of, or have already incorporated knowledge graphs in RAG, we’d love to chat at [e-mail], or follow our newsletter at WhyHow.AI. Join our discussions about rules, determinism and knowledge graphs in RAG on our newly-created Discord.