zhongwei/gh-oceanbase-ecology-plugins-seekdb-claudecode-plugin

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Zhongwei Li eb309b7b59 Initial commit

2025-11-30 08:44:54 +08:00

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/hybrid-search-of-api

hybrid_search - Hybrid search

hybrid_search() combines full-text search and vector similarity search with ranking.

:::info

This API is only available when using the Client. For more information about the Client, see Client.

:::

Prerequisites

You have installed pyseekdb. For more information about how to install pyseekdb, see Get Started.
You have connected to the database. For more information about how to connect to the database, see Client.
You have created a collection and inserted data. For more information about how to create a collection and insert data, see create_collection - Create a collection and add - Insert Data.

Request parameters

hybrid_search(
    query={
        "where_document": ,
        "where": ,
        "n_results": 
    },
    knn={
        "query_texts": 
        "where":
        "n_results": 
    },
    rank=,  
    n_results=,
    include=
)

query: full-text search configuration, including the following parameters:

Parameter	Type	Required	Description	Example value
`where`	dict	Optional	Metadata filter conditions.	`{"category": {"$eq": "AI"}}`
`where_document`	dict	Optional	Document filter conditions.	`{"$contains": "machine"}`
`n_results`	int	Yes	Number of results for full-text search.

knn: vector search configuration, including the following parameters:

Parameter	Type	Required	Description	Example value
`query_embeddings`	List[float] or List[List[float]]	Yes	A single vector or list of vectors for batch queries; if provided, it will be used directly (ignoring `embedding_function`); if not provided, `query_text` must be provided, and the `collection` must have an `embedding_function`	[1.0, 2.0, 3.0]
`query_texts`	str or List[str]	Optional	A single vector or list of vectors; if provided, it will be used directly (ignoring `embedding_function`); if not provided, `documents` must be provided, and the `collection` must have an `embedding_function`	["my query text"]
`where`	dict	Optional	Metadata filter conditions.	`{"category": {"$eq": "AI"}}`
`n_results`	int	Yes	Number of results for vector search.

Other parameters are as follows:

|Parameter|Type|Required|Description|Example value| |rank|dict |Optional|Ranking configuration, for example: {"rrf": {"rank_window_size": 60, "rank_constant": 60}}|{"category": {"$eq": "AI"}}| |n_results|int|Yes|Number of similar results to return. Default value is 10|3| |include|List[str]|Optional|List of fields to include: ["documents", "metadatas", "embeddings"].|["documents", "metadatas", "embeddings"]|

:::info

The embedding_function used is associated with the collection (set during create_collection() or get_collection()). You cannot override it for each operation.

:::

Request example

import pyseekdb

# Create a client
client = pyseekdb.Client()

collection = client.get_collection("my_collection")
collection1 = client.get_collection("my_collection1")

# Hybrid search with query_embeddings (embedding_function not used)
results = collection.hybrid_search(
    query={
        "where_document": {"$contains": "machine learning"},
        "n_results": 10
    },
    knn={
        "query_embeddings": [[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]],  # Used directly
        "n_results": 10
    },
    rank={"rrf": {}},
    n_results=5
)

# Hybrid search with both full-text and vector search (using query_texts)
results = collection1.hybrid_search(
    query={
        "where_document": {"$contains": "machine learning"},
        "where": {"category": {"$eq": "science"}},
        "n_results": 10
    },
    knn={
        "query_texts": ["AI research"],  # Will be embedded automatically
        "where": {"year": {"$gte": 2020}},
        "n_results": 10
    },
    rank={"rrf": {}},  # Reciprocal Rank Fusion
    n_results=5,
    include=["documents", "metadatas", "embeddings"]
)

# Hybrid search with multiple query texts (batch)
results = collection1.hybrid_search(
    query={
        "where_document": {"$contains": "AI"},
        "n_results": 10
    },
    knn={
        "query_texts": ["machine learning", "neural networks"],  # Multiple queries
        "n_results": 10
    },
    rank={"rrf": {}},
    n_results=5
)

Return parameters

A dictionary containing search results, including ID, distances, metadatas, document, etc.

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