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78 lines
2.4 KiB
Python
78 lines
2.4 KiB
Python
# Copyright (c) Microsoft. All rights reserved.
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"""Shows how to generate embeddings using the Mistral AI embedding client.
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Requires ``MISTRAL_API_KEY`` and ``MISTRAL_EMBEDDING_MODEL`` environment variables.
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"""
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import asyncio
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from dotenv import load_dotenv
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from agent_framework_mistral import MistralEmbeddingClient
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load_dotenv()
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async def basic_embedding_example() -> None:
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"""Generate embeddings for a list of texts."""
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print("=== Basic Embedding Generation ===")
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# 1. Create the embedding client (uses MISTRAL_API_KEY and MISTRAL_EMBEDDING_MODEL env vars).
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client = MistralEmbeddingClient()
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# 2. Generate embeddings for multiple texts.
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texts = ["Hello, world!", "How are you?", "Agent Framework with Mistral AI"]
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result = await client.get_embeddings(texts)
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# 3. Print results.
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print(f"Generated {len(result)} embeddings")
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for i, embedding in enumerate(result):
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print(f" Text {i + 1}: dimensions={embedding.dimensions}, vector={embedding.vector[:5]}...")
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if result.usage:
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print(
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f" Usage: {result.usage['input_token_count']} input tokens, "
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f"{result.usage['total_token_count']} total tokens"
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)
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async def embedding_with_options_example() -> None:
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"""Generate embeddings with custom dimensions."""
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print("\n=== Embedding with Custom Dimensions ===")
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from agent_framework_mistral import MistralEmbeddingOptions
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client = MistralEmbeddingClient()
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# Request a specific output dimension (model must support it).
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options: MistralEmbeddingOptions = {"dimensions": 256}
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result = await client.get_embeddings(["Dimensionality reduction example"], options=options)
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print(f" Dimensions: {result[0].dimensions}")
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print(f" Vector (first 5): {result[0].vector[:5]}...")
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async def main() -> None:
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"""Run embedding examples."""
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await basic_embedding_example()
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await embedding_with_options_example()
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if __name__ == "__main__":
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asyncio.run(main())
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"""
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Sample output:
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=== Basic Embedding Generation ===
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Generated 3 embeddings
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Text 1: dimensions=1024, vector=[0.0123, -0.0456, 0.0789, -0.0012, 0.0345]...
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Text 2: dimensions=1024, vector=[0.0234, -0.0567, 0.0891, -0.0023, 0.0456]...
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Text 3: dimensions=1024, vector=[0.0345, -0.0678, 0.0912, -0.0034, 0.0567]...
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Usage: 15 input tokens, 15 total tokens
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=== Embedding with Custom Dimensions ===
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Dimensions: 256
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Vector (first 5): [0.0456, -0.0789, 0.0123, -0.0456, 0.0789]...
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"""
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