wehub-resource-sync
168 lines
6.2 KiB
Markdown
168 lines
6.2 KiB
Markdown
---
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layout: default
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title: Retrieval
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parent: Examples
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nav_order: 7
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description: overview of the major modules and classes of LLMWare
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permalink: /examples/retrieval
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---
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# Retrieval - Introduction by Examples
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We introduce ``llmware`` through self-contained examples.
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# SEMANTIC Retrieval Example
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```python
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"""
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This 'getting started' example demonstrates how to use basic semantic retrieval with the Query class
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1. Create a sample library
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2. Run a basic semantic query
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3. View the results
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"""
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import os
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from llmware.library import Library
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from llmware.retrieval import Query
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from llmware.setup import Setup
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from llmware.configs import LLMWareConfig
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def create_fin_docs_sample_library(library_name):
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print(f"update: creating library - {library_name}")
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library = Library().create_new_library(library_name)
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sample_files_path = Setup().load_sample_files(over_write=False)
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ingestion_folder_path = os.path.join(sample_files_path, "FinDocs")
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parsing_output = library.add_files(ingestion_folder_path)
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print(f"update: building embeddings - may take a few minutes the first time")
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# note: if you have installed Milvus or another vector DB, please feel free to substitute
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# note: if you have any memory constraints on laptop:
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# (1) reduce embedding batch_size or ...
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# (2) substitute "mini-lm-sbert" as embedding model
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library.install_new_embedding(embedding_model_name="industry-bert-sec", vector_db="chromadb",batch_size=200)
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return library
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def basic_semantic_retrieval_example (library):
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# Create a Query instance
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q = Query(library)
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# Set the keys that should be returned - optional - full set of keys will be returned by default
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q.query_result_return_keys = ["distance","file_source", "page_num", "text"]
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# perform a simple query
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my_query = "ESG initiatives"
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query_results1 = q.semantic_query(my_query, result_count=20)
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# Iterate through query_results, which is a list of result dicts
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print(f"\nQuery 1 - {my_query}")
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for i, result in enumerate(query_results1):
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print("results - ", i, result)
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# perform another query
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my_query2 = "stock performance"
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query_results2 = q.semantic_query(my_query2, result_count=10)
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print(f"\nQuery 2 - {my_query2}")
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for i, result in enumerate(query_results2):
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print("results - ", i, result)
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# perform another query
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my_query3 = "cloud computing"
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# note: use of embedding_distance_threshold will cap results with distance < 1.0
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query_results3 = q.semantic_query(my_query3, result_count=50, embedding_distance_threshold=1.0)
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print(f"\nQuery 3 - {my_query3}")
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for i, result in enumerate(query_results3):
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print("result - ", i, result)
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return [query_results1, query_results2, query_results3]
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if __name__ == "__main__":
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print(f"Example - Running a Basic Semantic Query")
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LLMWareConfig().set_active_db("sqlite")
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# step 1- will create library + embeddings with Financial Docs
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lib = create_fin_docs_sample_library("lib_semantic_query_1")
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# step 2- run query against the library and embeddings
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my_results = basic_semantic_retrieval_example(lib)
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```
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For more examples, see the [retrieval examples]((https://www.github.com/llmware-ai/llmware/tree/main/examples/Retrieval/) in the main repo.
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Check back often - we are updating these examples regularly - and many of these examples have companion videos as well.
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# More information about the project - [see main repository](https://www.github.com/llmware-ai/llmware.git)
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# About the project
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`llmware` is © 2023-{{ "now" | date: "%Y" }} by [AI Bloks](https://www.aibloks.com/home).
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## Contributing
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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).
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You can also write an email or start a discussion on our Discrod channel.
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Read more about becoming a contributor in the [GitHub repo](https://github.com/llmware-ai/llmware/blob/main/CONTRIBUTING.md).
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## Code of conduct
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We welcome everyone into the ``llmware`` community.
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[View our Code of Conduct](https://github.com/llmware-ai/llmware/blob/main/CODE_OF_CONDUCT.md) in our GitHub repository.
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## ``llmware`` and [AI Bloks](https://www.aibloks.com/home)
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``llmware`` is an open source project from [AI Bloks](https://www.aibloks.com/home) - the company behind ``llmware``.
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The company offers a Software as a Service (SaaS) Retrieval Augmented Generation (RAG) service.
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[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 October 2022.
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## License
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`llmware` is distributed by an [Apache-2.0 license](https://www.github.com/llmware-ai/llmware/blob/main/LICENSE).
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## Thank you to the contributors of ``llmware``!
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<ul class="list-style-none">
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{% for contributor in site.github.contributors %}
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<li class="d-inline-block mr-1">
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<a href="{{ contributor.html_url }}">
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<img src="{{ contributor.avatar_url }}" width="32" height="32" alt="{{ contributor.login }}">
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</a>
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</li>
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{% endfor %}
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</ul>
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---
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<ul class="list-style-none">
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<li class="d-inline-block mr-1">
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<a href="https://discord.gg/MhZn5Nc39h"><span><i class="fa-brands fa-discord"></i></span></a>
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</li>
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<li class="d-inline-block mr-1">
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<a href="https://www.youtube.com/@llmware"><span><i class="fa-brands fa-youtube"></i></span></a>
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</li>
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<li class="d-inline-block mr-1">
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<a href="https://huggingface.co/llmware"><span><img src="assets/images/hf-logo.svg" alt="Hugging Face" class="hugging-face-logo"/></span></a>
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</li>
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<li class="d-inline-block mr-1">
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<a href="https://www.linkedin.com/company/aibloks/"><span><i class="fa-brands fa-linkedin"></i></span></a>
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</li>
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<li class="d-inline-block mr-1">
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<a href="https://twitter.com/AiBloks"><span><i class="fa-brands fa-square-x-twitter"></i></span></a>
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</li>
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<li class="d-inline-block mr-1">
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<a href="https://www.instagram.com/aibloks/"><span><i class="fa-brands fa-instagram"></i></span></a>
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</li>
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</ul>
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---
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