delete hybrid example

Author: staoxiao

FlagEmbedding/BGE_M3/README.md

@@ -260,105 +260,7 @@ If you have no enough resource to fine-tuning model with long text, the method i
 Refer to our [report](https://arxiv.org/pdf/2402.03216.pdf) for more details. 
 
 
-## Examples of hybrid retrieval
 
-### Milvus
-
-```python
-# A demo showing semantic search with dense and sparse vectors, implemented
-# with BGE-M3 embedding model and the Milvus vector database.
-
-# The overall steps are as follows:
-# 1. embed the text as dense and sparse vectors using BGE-M3 model
-# 2. setup a Milvus collection to store the dense and sparse vectors
-# 3. insert the data to Milvus
-# 4. search and inspect the result!
-
-# 1. prepare a small corpus to search
-docs = [
-    "Artificial intelligence was founded as an academic discipline in 1956.",
-    "Alan Turing was the first person to conduct substantial research in AI.",
-    "Born in Maida Vale, London, Turing was raised in southern England.",
-]
-query = "Who started AI research?"
-
-# BGE-M3 model can embed texts as dense and sparse vectors.
-# It is included in the optional `model` module in pymilvus, to install it,
-# simply run "pip install pymilvus[model]".
-from pymilvus.model.hybrid import BGEM3EmbeddingFunction
-
-bge_m3_ef = BGEM3EmbeddingFunction(use_fp16=False, device="cpu")
-
-docs_embeddings = bge_m3_ef(docs)
-query_embeddings = bge_m3_ef([query])
-
-# 2. setup Milvus collection and index
-from pymilvus import (
-    utility,
-    FieldSchema, CollectionSchema, DataType,
-    Collection, AnnSearchRequest, RRFRanker, connections,
-)
-connections.connect("default", host="localhost", port="19530")
-
-# Specify the data schema for the new Collection.
-fields = [
-    # Use auto generated id as primary key
-    FieldSchema(name="pk", dtype=DataType.VARCHAR,
-                is_primary=True, auto_id=True, max_length=100),
-    # Store the original text to retrieve based on semantically distance
-    FieldSchema(name="text", dtype=DataType.VARCHAR, max_length=512),
-    # Milvus now supports both sparse and dense vectors, we can store each in
-    # a separate field to conduct hybrid search on both vectors.
-    FieldSchema(name="sparse_vector", dtype=DataType.SPARSE_FLOAT_VECTOR),
-    FieldSchema(name="dense_vector", dtype=DataType.FLOAT_VECTOR,
-                dim=bge_m3_ef.dim["dense"]),
-]
-schema = CollectionSchema(fields, "")
-col_name = 'hybrid_demo'
-# Now we can create the new collection with above name and schema.
-col = Collection(col_name, schema, consistency_level="Strong")
-
-# We need to create indices for the vector fields. The indices will be loaded
-# into memory for efficient search.
-sparse_index = {"index_type": "SPARSE_INVERTED_INDEX", "metric_type": "IP"}
-col.create_index("sparse_vector", sparse_index)
-dense_index = {"index_type": "FLAT", "metric_type": "L2"}
-col.create_index("dense_vector", dense_index)
-col.load()
-
-# 3. insert text and sparse/dense vector representations into the collection
-entities = [docs, docs_embeddings["sparse"], docs_embeddings["dense"]]
-col.insert(entities)
-col.flush()
-
-# 4. search and inspect the result!
-k = 2 # we want to get the top 2 docs closest to the query
-
-# Prepare the search requests for both vector fields
-sparse_search_params = {"metric_type": "IP"}
-sparse_req = AnnSearchRequest(query_embeddings["sparse"],
-                              "sparse_vector", sparse_search_params, limit=k)
-dense_search_params = {"metric_type": "L2"}
-dense_req = AnnSearchRequest(query_embeddings["dense"],
-                             "dense_vector", dense_search_params, limit=k)
-
-# Search topK docs based on dense and sparse vectors and rerank with RRF.
-res = col.hybrid_search([sparse_req, dense_req], rerank=RRFRanker(),
-                        limit=k, output_fields=['text'])
-
-# Currently Milvus only support 1 query in the same hybrid search request, so
-# we inspect res[0] directly. In future release Milvus will accept batch
-# hybrid search queries in the same call.
-for hit in res[0]:
-    # print out the data of topK search results
-    print(f'text: {hit.fields["text"]} distance {hit.distance}')
-# Output is:
-# text: Alan Turing was the first person to conduct substantial research in AI. distance 0.032786883413791656
-# text: Artificial intelligence was founded as an academic discipline in 1956. distance 0.016129031777381897
-
-# Drop the collection to clean up the data.
-utility.drop_collection(col_name)
-```