llmware

🧰🛠️ Unified framework for building knowledge-based local, private, secure LLM-based applications

llmware is optimized for AI PC and local laptop, edge and self-hosted deployment across a wide range of Windows, Mac and Linux platforms, with support for GGUF, OpenVINO, ONNXRuntime, ONNXRuntime-QNN (Qualcomm), WindowsLocalFoundry, and Pytorch, providing a high-level interface that makes it easy to leverage the right inferencing technology optimized for the target platform.

llmware has two main components:

1. Model catalog with 300+ models - models prepackaged in quantized, optimized formats, to leverage on device GPU and NPU capabilities, with support for major open source model families and 50+ llmware finetuned SLIM, Bling, Dragon and Industry-Bert models specialized for key tasks in enterprise process automation. Also supports leading cloud models from OpenAI, Anthropic and Google.

2. RAG Pipeline - integrated components for the full lifecycle of connecting knowledge sources to generative AI models with wide range of document parsing and ingestion capabilities, and the ability to create scalable knowledge bases.

By bringing together both of these components, llmware offers a comprehensive set of tools to rapidly build knowledge-based enterprise LLM applications.

Our vision is that AI should be sustainable, accurate, and cost-effective, using the smallest possible compute footprint to get the job done.

Virtually all of our examples and models can be run on device - get started right away on your laptop.

Join us on Discord | Watch Youtube Tutorials | Explore our Model Families on Huggingface

🎯 Key features

Writing code withllmware is based on a few main concepts:

<details> <summary><b>Model Catalog</b>: Access all models the same way with easy lookup, regardless of underlying implementation. </summary>

#   300+ Models in Catalog with 50+ RAG-optimized BLING, DRAGON and Industry BERT models
#   Full support for GGUF, OpenVINO, Onnxruntime, HuggingFace, Sentence Transformers and major API-based models
#   Easy to extend to add custom models - see examples

from llmware.models import ModelCatalog
from llmware.prompts import Prompt

#   all models accessed through the ModelCatalog
models = ModelCatalog().list_all_models()

#   to use any model in the ModelCatalog - "load_model" method and pass the model_name parameter
my_model = ModelCatalog().load_model("llmware/bling-phi-3-gguf")

#   call model with: inference 
output = my_model.inference("what is the future of AI?", add_context="Here is the article to read")

#   call model with: stream
for token in my_model.stream("What is the future of AI?"):
    print(token, end="")

#   to integrate model into a Prompt
prompter = Prompt().load_model("llmware/bling-tiny-llama-v0")
response = prompter.prompt_main("what is the future of AI?", context="Insert Sources of information")

</details> <details> <summary><b>Library</b>: ingest, organize and index a collection of knowledge at scale - Parse, Text Chunk and Embed. </summary>

from llmware.library import Library

#   to parse and text chunk a set of documents (pdf, pptx, docx, xlsx, txt, csv, md, json/jsonl, wav, png, jpg, html)  

#   step 1 - create a library, which is the 'knowledge-base container' construct
#          - libraries have both text collection (DB) resources, and file resources (e.g., llmware_data/accounts/{library_name})
#          - embeddings and queries are run against a library

lib = Library().create_new_library("my_library")

#    step 2 - add_files is the universal ingestion function - point it at a local file folder with mixed file types
#           - files will be routed by file extension to the correct parser, parsed, text chunked and indexed in text collection DB

lib.add_files("/folder/path/to/my/files")

#   to install an embedding on a library - pick an embedding model and vector_db
lib.install_new_embedding(embedding_model_name="mini-lm-sbert", vector_db="milvus", batch_size=500)

#   to add a second embedding to the same library (mix-and-match models + vector db)  
lib.install_new_embedding(embedding_model_name="industry-bert-sec", vector_db="chromadb", batch_size=100)

#   easy to create multiple libraries for different projects and groups

finance_lib = Library().create_new_library("finance_q4_2023")
finance_lib.add_files("/finance_folder/")

hr_lib = Library().create_new_library("hr_policies")
hr_lib.add_files("/hr_folder/")

#    pull library card with key metadata - documents, text chunks, images, tables, embedding record
lib_card = Library().get_library_card("my_library")

#   see all libraries
all_my_libs = Library().get_all_library_cards()

</details> <details> <summary><b>Query</b>: query libraries with mix of text, semantic, hybrid, metadata, and custom filters. </summary>

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

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

#   step 2 - create a query object and pass the library
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")

</details> <details> <summary><b>Prompt with Sources</b>: the easiest way to combine knowledge retrieval with a LLM inference. </summary>

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

#   build a prompt
prompter = Prompt().load_model("llmware/bling-tiny-llama-v0")

#   add a file -> file is parsed, text chunked, filtered by query, and then packaged as model-ready context,
#   including in batches, if needed, to fit the model context window

source = prompter.add_source_document("/folder/to/one/doc/", "filename", query="fast query")

#   attach query results (from a Query) into a Prompt
my_lib = Library().load_library("my_library")
results = Query(my_lib).query("my query")
source2 = prompter.add_source_query_results(results)

#   run a new query against a library and load directly into a prompt
source3 = prompter.add_source_new_query(my_lib, query="my new query", query_type="semantic", result_count=15)

#   to run inference with 'prompt with sources'
responses = prompter.prompt_with_source("my query")

#   to run fact-checks - post inference
fact_check = prompter.evidence_check_sources(responses)

#   to view source materials (batched 'model-ready' and attached to prompt)
source_materials = prompter.review_sources_summary()

#   to see the full prompt history
prompt_history = prompter.get_current_history()

</details> <details> <summary><b>RAG-Optimized Models</b> - 1-7B parameter models designed for RAG workflow integration and running locally. </summary>

""" This 'Hello World' example demonstrates how to get started using local BLING models with provided context, using both
Pytorch and GGUF versions. """

import time
from llmware.prompts import Prompt


def hello_world_questions():

    test_list = [

    {"query": "What is the total amount of the invoice?",
     "answer": "$22,500.00",
     "context": "Services Vendor Inc. \n100 Elm Street Pleasantville, NY \nTO Alpha Inc. 5900 1st Street "
                "Los Angeles, CA \nDescription Front End Engineering Service $5000.00 \n Back End Engineering"
                " Service $7500.00 \n Quality Assurance Manager $10,000.00 \n Total Amount $22,500.00 \n"
                "Make all checks payable to Services Vendor Inc. Payment is due within 30 days."
                "If you have any questions concerning this invoice, contact Bia Hermes. "
                "THANK YOU FOR YOUR BUSINESS!  INVOICE INVOICE # 0001 DATE 01/01/2022 FOR Alpha Project P.O. # 1000"},

    {"query": "What was the amount of the trade surplus?",
     "answer": "62.4 billion yen ($416.6 million)",
     "context": "Japan’s September trade balance swings into surplus, surprising expectations"
                "Japan recorded a trade surplus of 62.4 billion yen ($416.6 million) for September, "
                "beating expectations from economists polled by Reuters for a trade deficit of 42.5 "
                "billion yen. Data from Japan’s customs agency revealed that exports in September "
                "increased 4.3% year on year, while imports slid 16.3% compared to the same period "
                "last year. According to FactSet, exports to Asia fell for the ninth straight month, "
                "which reflected ongoing China weakness. Exports were supported by shipments to "
                "Western markets, FactSet added. — Lim Hui Jie"},

    {"query": "When did the LISP machine market colla