01
Gemini Embedding 2，突破在哪里？
02
多向量检索到底解决什么问题？
https://milvus.io/docs/hybrid_search_with_milvus.md
03
选型指南
04
如何快速在Milvus中使用Gemini Embedding 2
"""Prerequisites: pip install google-genai pymilvusSet environment variable: export GOOGLE_API_KEY="your-api-key""""import osimport structimport numpy as npfrom google import genaifrom google.genai import typesfrom pymilvus import MilvusClient, DataType# ── Config ───────────────────────────────────────────────────────────────COLLECTION_NAME = "gemini_multimodal_demo"MILVUS_URI = "http://localhost:19530" # Change to your Milvus addressDIM = 3072 # gemini-embedding-2-preview output dimensionGEMINI_MODEL = "gemini-embedding-2-preview"# ── Initialize clients ──────────────────────────────────────────────────gemini_client = genai.Client() # Uses GOOGLE_API_KEY env varmilvus_client = MilvusClient(MILVUS_URI)# ── Helper: generate embedding ──────────────────────────────────────────def embed_texts(texts: list[str], task_type: str = "SEMANTIC_SIMILARITY") -> list[list[float]]: """Embed a list of text strings.""" result = gemini_client.models.embed_content( model=GEMINI_MODEL, contents=texts, config=types.EmbedContentConfig(task_type=task_type), ) return [e.values for e in result.embeddings]def embed_image(image_path: str) -> list[float]: """Embed an image file.""" with open(image_path, "rb") as f: image_bytes = f.read() mime = "image/png" if image_path.endswith(".png") else "image/jpeg" result = gemini_client.models.embed_content( model=GEMINI_MODEL, contents=types.Part.from_bytes(data=image_bytes, mime_type=mime), ) return result.embeddings[0].valuesdef embed_audio(audio_path: str) -> list[float]: """Embed an audio file.""" with open(audio_path, "rb") as f: audio_bytes = f.read() mime_map = {".mp3": "audio/mpeg", ".wav": "audio/wav", ".flac": "audio/flac"} ext = os.path.splitext(audio_path)[1].lower() mime = mime_map.get(ext, "audio/mpeg") result = gemini_client.models.embed_content( model=GEMINI_MODEL, contents=types.Part.from_bytes(data=audio_bytes, mime_type=mime), ) return result.embeddings[0].values# ── 1. Create Milvus collection ─────────────────────────────────────────print("=== Creating collection ===")if milvus_client.has_collection(COLLECTION_NAME): milvus_client.drop_collection(COLLECTION_NAME)schema = milvus_client.create_schema()schema.add_field("id", DataType.INT64, is_primary=True, auto_id=True)schema.add_field("content", DataType.VARCHAR, max_length=2000) # description of the contentschema.add_field("modality", DataType.VARCHAR, max_length=20) # "text", "image", "audio"schema.add_field("vector", DataType.FLOAT_VECTOR, dim=DIM)index_params = milvus_client.prepare_index_params()index_params.add_index( field_name="vector", index_type="AUTOINDEX", metric_type="COSINE",)milvus_client.create_collection( COLLECTION_NAME, schema=schema, index_params=index_params, consistency_level="Strong",)print(f"Collection '{COLLECTION_NAME}' created (dim={DIM}, metric=COSINE)")# ── 2. Insert text embeddings ───────────────────────────────────────────print("n=== Inserting text embeddings ===")documents = [ "Artificial intelligence was founded as an academic discipline in 1956.", "The Mona Lisa is a half-length portrait painting by Leonardo da Vinci.", "Beethoven's Symphony No. 9 premiered in Vienna on May 7, 1824.", "The Great Wall of China stretches over 13,000 miles across northern China.", "Jazz music originated in the African-American communities of New Orleans.", "The Hubble Space Telescope was launched into orbit on April 24, 1990.", "Vincent van Gogh painted The Starry Night while in an asylum in Saint-Rémy.", "Machine learning is a subset of AI focused on learning from data.",]text_vectors = embed_texts(documents)text_rows = [ {"content": doc, "modality": "text", "vector": vec} for doc, vec in zip(documents, text_vectors)]milvus_client.insert(COLLECTION_NAME, text_rows)print(f"Inserted {len(text_rows)} text documents")# ── 3. (Optional) Insert image embeddings ───────────────────────────────# Uncomment and provide real image paths to test multimodal search## image_files = [# ("photo of the Mona Lisa painting", "mona_lisa.jpg"),# ("satellite photo of the Great Wall of China", "great_wall.png"),# ]# for desc, path in image_files:# if os.path.exists(path):# vec = embed_image(path)# milvus_client.insert(COLLECTION_NAME, [# {"content": desc, "modality": "image", "vector": vec}# ])# print(f"Inserted image: {desc}")# ── 4. (Optional) Insert audio embeddings ───────────────────────────────# Uncomment and provide real audio paths to test multimodal search## audio_files = [# ("Beethoven Symphony No.9 excerpt", "beethoven_9.mp3"),# ("jazz piano improvisation", "jazz_piano.mp3"),# ]# for desc, path in audio_files:# if os.path.exists(path):# vec = embed_audio(path)# milvus_client.insert(COLLECTION_NAME, [# {"content": desc, "modality": "audio", "vector": vec}# ])# print(f"Inserted audio: {desc}")# ── 5. Search ────────────────────────────────────────────────────────────print("n=== Searching ===")queries = [ "history of artificial intelligence", "famous Renaissance paintings", "classical music concerts",]query_vectors = embed_texts(queries, task_type="SEMANTIC_SIMILARITY")for query_text, query_vec in zip(queries, query_vectors): results = milvus_client.search( COLLECTION_NAME, data=[query_vec], limit=3, output_fields=["content", "modality"], search_params={"metric_type": "COSINE"}, ) print(f"nQuery: '{query_text}'") for hits in results: for rank, hit in enumerate(hits, 1): print(f" [{rank}] (score={hit['distance']:.4f}, modality={hit['entity']['modality']}) " f"{hit['entity']['content'][:80]}")# ── 6. Cross-modal search example (image query -> text results) ─────────# Uncomment to search text collection using an image as query## print("n=== Cross-modal search: image -> text ===")# query_image_vec = embed_image("query_image.jpg")# results = milvus_client.search(# COLLECTION_NAME,# data=[query_image_vec],# limit=3,# output_fields=["content", "modality"],# search_params={"metric_type": "COSINE"},# )# for hits in results:# for rank, hit in enumerate(hits, 1):# print(f" [{rank}] (score={hit['distance']:.4f}) {hit['entity']['content'][:80]}")# ── Cleanup ──────────────────────────────────────────────────────────────# milvus_client.drop_collection(COLLECTION_NAME)# print(f"nCollection '{COLLECTION_NAME}' dropped")print("nDone!")
