--- layout: default title: Query parent: Components nav_order: 8 description: overview of the major modules and classes of LLMWare permalink: /components/query --- # Retrieval & Query --- 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 ```python 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](https://www.youtube.com/@llmware) and [GitHub repository](https://github.com/llmware-ai/llmware). Reach out to us on [GitHub Discussions](https://github.com/llmware-ai/llmware/discussions). # About the project `llmware` is © 2023-{{ "now" | date: "%Y" }} by [AI Bloks](https://www.aibloks.com/home). ## Contributing Please first discuss any change you want to make publicly, for example on GitHub via raising an [issue](https://github.com/llmware-ai/llmware/issues) or starting a [new discussion](https://github.com/llmware-ai/llmware/discussions). You can also write an email or start a discussion on our Discrod channel. Read more about becoming a contributor in the [GitHub repo](https://github.com/llmware-ai/llmware/blob/main/CONTRIBUTING.md). ## Code of conduct We welcome everyone into the ``llmware`` community. [View our Code of Conduct](https://github.com/llmware-ai/llmware/blob/main/CODE_OF_CONDUCT.md) in our GitHub repository. ## ``llmware`` and [AI Bloks](https://www.aibloks.com/home) ``llmware`` is an open source project from [AI Bloks](https://www.aibloks.com/home) - the company behind ``llmware``. The company offers a Software as a Service (SaaS) Retrieval Augmented Generation (RAG) service. [AI Bloks](https://www.aibloks.com/home) was founded by [Namee Oberst](https://www.linkedin.com/in/nameeoberst/) and [Darren Oberst](https://www.linkedin.com/in/darren-oberst-34a4b54/) in Oktober 2022. ## License `llmware` is distributed by an [Apache-2.0 license](https://github.com/llmware-ai/llmware/blob/main/LICENSE). ## Thank you to the contributors of ``llmware``! --- ---