Retrieval - Introduction by Examples

We introduce llmware through self-contained examples.

SEMANTIC Retrieval Example

"""
This 'getting started' example demonstrates how to use basic semantic retrieval with the Query class
      1. Create a sample library
      2. Run a basic semantic query
      3. View the results
"""

import os
from llmware.library import Library
from llmware.retrieval import Query
from llmware.setup import Setup
from llmware.configs import LLMWareConfig


def create_fin_docs_sample_library(library_name):

    print(f"update: creating library - {library_name}")

    library = Library().create_new_library(library_name)
    sample_files_path = Setup().load_sample_files(over_write=False)
    ingestion_folder_path = os.path.join(sample_files_path, "FinDocs")
    parsing_output = library.add_files(ingestion_folder_path)

    print(f"update: building embeddings - may take a few minutes the first time")

    #   note: if you have installed Milvus or another vector DB, please feel free to substitute
    #   note: if you have any memory constraints on laptop:
    #       (1) reduce embedding batch_size or ...
    #       (2) substitute "mini-lm-sbert" as embedding model

    library.install_new_embedding(embedding_model_name="industry-bert-sec", vector_db="chromadb",batch_size=200)

    return library


def basic_semantic_retrieval_example (library):

    # Create a Query instance
    q = Query(library)

    # Set the keys that should be returned - optional - full set of keys will be returned by default
    q.query_result_return_keys = ["distance","file_source", "page_num", "text"]

    # perform a simple query
    my_query = "ESG initiatives"
    query_results1 = q.semantic_query(my_query, result_count=20)

    # Iterate through query_results, which is a list of result dicts
    print(f"\nQuery 1 -  {my_query}")
    for i, result in enumerate(query_results1):
        print("results - ", i, result)

    # perform another query
    my_query2 = "stock performance"
    query_results2 = q.semantic_query(my_query2, result_count=10)

    print(f"\nQuery 2 - {my_query2}")
    for i, result in enumerate(query_results2):
        print("results - ", i, result)

    # perform another query
    my_query3 = "cloud computing"

    # note: use of embedding_distance_threshold will cap results with distance < 1.0
    query_results3 = q.semantic_query(my_query3, result_count=50, embedding_distance_threshold=1.0)

    print(f"\nQuery 3 - {my_query3}")
    for i, result in enumerate(query_results3):
        print("result - ", i, result)

    return [query_results1, query_results2, query_results3]


if __name__ == "__main__":

    print(f"Example - Running a Basic Semantic Query")

    LLMWareConfig().set_active_db("sqlite")

    # step 1- will create library + embeddings with Financial Docs
    lib = create_fin_docs_sample_library("lib_semantic_query_1")

    # step 2- run query against the library and embeddings
    my_results = basic_semantic_retrieval_example(lib)

For more examples, see the [retrieval examples]((https://www.github.com/llmware-ai/llmware/tree/main/examples/Retrieval/) in the main repo.

Check back often - we are updating these examples regularly - and many of these examples have companion videos as well.

More information about the project - see main repository

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 October 2022.

License

llmware is distributed by an Apache-2.0 license.

Thank you to the contributors of llmware!

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