Retrieval & Query

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Query libraries with mix of text, semantic, hybrid, metadata, and custom filters. The retrieval.py module implements the Query class, which is the primary way that search and retrieval is performed. Each Query object, when constructed, requires that a Library is passed as a mandatory parameter in the constructor. The Query object will operate against that Library, and have access to all of Library’s specific attributes, metadata and methods.

Retrievals in llmware leverage the Library abstraction as the primary unit against which a particular query or retrieval is executed. This provides the ability to have multiple distinct knowledge-bases, potentially aligned to different use cases, and/or users, accounts and permissions.

Executing Queries

from llmware.retrieval import Query
from llmware.library import Library

#   step 1 - load a previously created library
lib = Library().load_library("my_library")

#   step 2 - create a query object
q = Query(lib)

#    step 3 - run lots of different queries  (many other options in the examples)

#    basic text query
results1 = q.text_query("text query", result_count=20, exact_mode=False)

#    semantic query
results2 = q.semantic_query("semantic query", result_count=10)

#    combining a text query restricted to only certain documents in the library and "exact" match to the query
results3 = q.text_query_with_document_filter("new query", {"file_name": "selected file name"}, exact_mode=True)

#   to apply a specific embedding (if multiple on library), pass the names when creating the query object
q2 = Query(lib, embedding_model_name="mini_lm_sbert", vector_db="milvus")
results4 = q2.semantic_query("new semantic query")

Need help or have questions?

Check out the llmware videos and GitHub repository.

Reach out to us on GitHub Discussions.

About the project

llmware is © 2023-2024 by AI Bloks.

Contributing

Please first discuss any change you want to make publicly, for example on GitHub via raising an issue or starting a new discussion. You can also write an email or start a discussion on our Discrod channel. Read more about becoming a contributor in the GitHub repo.

Code of conduct

We welcome everyone into the llmware community. View our Code of Conduct in our GitHub repository.

llmware and AI Bloks

llmware is an open source project from AI Bloks - the company behind llmware. The company offers a Software as a Service (SaaS) Retrieval Augmented Generation (RAG) service. AI Bloks was founded by Namee Oberst and Darren Oberst in Oktober 2022.

License

llmware is distributed by an Apache-2.0 license.

Thank you to the contributors of llmware!

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