wehub-resource-sync
73 lines
2.5 KiB
Python
73 lines
2.5 KiB
Python
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""" This example shows the general recipe for creating an embedding. This scenario uses ChromaDB in local
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file mode for no-install laptop deployment.
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NOTE: you may need to install separately: pip3 install chromadb.
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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 importlib import util
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from llmware.configs import LLMWareConfig
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if not util.find_spec("chromadb"):
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print("\nto run this example with chromadb, you need to install the chromadb python sdk: pip3 install chromadb")
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def embeddings_fast_start (library_name, vector_db="chromadb"):
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# Create and populate a library
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print (f"\nstep 1 - creating and populating 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()
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library.add_files(input_folder_path=os.path.join(sample_files_path, "AgreementsLarge"))
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# To create vector embeddings you just need to specify the embedding model and the vector embedding DB
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# For examples of using HuggingFace and SentenceTransformer models, see those examples in this same folder
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embedding_model = "mini-lm-sbert"
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print (f"\n > Generating embedding vectors and storing in '{vector_db}'...")
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library.install_new_embedding(embedding_model_name=embedding_model, vector_db=vector_db)
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# Then when doing semantic queries, the most recent vector DB used for embeddings will be used.
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# We just find the best 3 hits for "Salary"
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q = Query(library)
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print (f"\n > Running a query for 'Salary'...")
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query_results = q.semantic_query(query="Salary", result_count=10, results_only=True)
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for i, entries in enumerate(query_results):
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# each query result is a dictionary with many useful keys
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text = entries["text"]
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document_source = entries["file_source"]
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page_num = entries["page_num"]
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vector_distance = entries["distance"]
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# for display purposes only, we will only show the first 100 characters of the text
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if len(text) > 125: text = text[0:125] + " ... "
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print("\nupdate: query results - {} - document - {} - page num - {} - distance - {} "
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.format( i, document_source, page_num, vector_distance))
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print("update: text sample - ", text)
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return query_results
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if __name__ == "__main__":
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LLMWareConfig().set_active_db("sqlite")
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# set to 'chromadb' local file storage for no-install fast start
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db = "chromadb"
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embeddings_fast_start("embedding_test_1", vector_db=db)
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