deepsearch-glm

import os from deepsearch.glm.core.glm import GraphLanguageModel from deepsearch.glm.queries import GlmQuery from deepsearch.documents.core.models import Document # Note: The deepsearch-glm-base model is large (~1.5GB) and will be downloaded on first run. # This example requires deepsearch-toolkit to implicitly process documents into a graph. try: # 1. Initialize the GraphLanguageModel (this will download a model if not cached) # This might take a while and uses significant memory/disk space. print("Loading GraphLanguageModel... (first run may download ~1.5GB model)") glm_model = GraphLanguageModel.from_pretrained("deepsearch-ai/deepsearch-glm-base") print("GraphLanguageModel loaded.") # 2. Prepare a dummy document. In a real scenario, this would be processed # by deepsearch-toolkit to extract a knowledge graph before querying. doc_content = "The DeepSearch AI platform helps enterprises extract insights from unstructured data using advanced AI." dummy_document = Document(name="deepsearch_info.txt", content=doc_content) # 3. Create a query question = "What is the DeepSearch AI platform used for?" query = GlmQuery( model=glm_model, documents=[dummy_document], # Provide documents directly for implicit graph creation query_text=question ) # 4. Run the query print(f"\nQuerying: {question}") result = query.run() # 5. Print the result print("\nQuery Result:") print(result.answer) except ImportError as e: print(f"Error: {e}. Ensure all core and optional dependencies (like 'transformers', 'torch' or 'tensorflow') are installed.") print("Try: pip install deepsearch-glm[all]") except Exception as e: print(f"An error occurred during execution: {e}") print("Common issues include insufficient disk space, network problems during model download, or missing ML backend (torch/tensorflow).")