llama-index-embeddings-ollama

import os from llama_index.embeddings.ollama import OllamaEmbedding # Pre-requisites: # 1. Ensure the Ollama server is running locally: `ollama serve` in your terminal. # 2. Pull the desired embedding model: `ollama pull llama2` (or another model like `nomic-embed-text`) try: # Initialize the Ollama Embedding model # Specify the model_name (must be pulled via Ollama) and optionally base_url embed_model = OllamaEmbedding( model_name="llama2", # Make sure this model is pulled base_url="http://localhost:11434" ) # Get an embedding for a piece of text text_to_embed = "This is an example sentence for LlamaIndex with Ollama embeddings." embedding_vector = embed_model.get_text_embedding(text_to_embed) print(f"Successfully generated embedding using OllamaEmbedding.") print(f"Embedding vector length: {len(embedding_vector)}") print(f"First 10 dimensions: {embedding_vector[:10]}") # You can also embed multiple texts in a batch texts_to_embed_batch = [ "LlamaIndex helps build LLM applications.", "Ollama runs large language models locally and efficiently.", ] embedding_vectors_batch = embed_model.get_text_embedding_batch(texts_to_embed_batch) print(f"\nGenerated {len(embedding_vectors_batch)} embeddings in batch.") print(f"Length of first batch embedding: {len(embedding_vectors_batch[0])}") except Exception as e: print(f"An error occurred: {e}") if "Connection refused" in str(e) or "Failed to connect to Ollama" in str(e): print("Hint: Ensure the Ollama server is running (run `ollama serve`).") elif "model not found" in str(e) or "no such model" in str(e): print(f"Hint: Ensure the model '{embed_model.model_name}' is pulled (run `ollama pull {embed_model.model_name}`).")