A search query, or query, is a request for information about data in LambdaDB collections.
LambdaDB supports several search methods:
- Search for exact values: search for exact values or ranges of numbers, dates, IPs, or strings.
- Full-text search: use full text queries to query unstructured textual data and find documents that best match query terms.
- Vector search: store vectors in LambdaDB and use approximate nearest neighbor (ANN) to find vectors that are similar, supporting use cases like semantic search.
Common parameters for the request body of a query are as follows:
| Parameter | Description | Type | Required | Default |
|---|
| size | The number of results to return for each query | integer | ✓ | |
| query | Query object (details in the subsections) | object | ✓ | |
| includeVectors | Indicates whether vector values are included in the response | boolean | | false |
| consistentRead | Determines the read consistency model: If set to true, then the operation uses strongly consistent reads; otherwise, the operation uses eventually consistent reads. | boolean | | false |
| fields | A list of field names to include and/or exclude in the result. Use dot notation (e.g., user.name) to specify nested fields. | object | | |
| sort | Specifies the sorting criteria for the results (details in the subsection) | object[] | | |
| partitionFilter | Partition filter | object | | |
Depending on your data and your query, you may get fewer than size results.
This happens when size is larger than the number of possible matching documents for your query.
Setting includeVectors=True will increase response size significantly, especially for high-dimensional vectors.
Use this option only when vector data is specifically needed for your application.
include is applied first, and than exclude is applied to the included fields when you both set include and exclude in fields parameter.
LambdaDB is eventually consistent by default, so there can be a slight delay before new or changed documents are visible to queries.
If your application requires strong (read-after-write) consistency, set consistentRead to true when query data from a collection, at the expense of potential high latency and cost.
| Field | Description | Type |
|---|
| took | Milliseconds it took LambdaDB to execute the request | long |
| maxScore | Highest returned document score | float |
| total | The total number of matching documents | long |
| docs | Contains returned documents and metadata | object[] |
Example
from lambdadb import LambdaDB
with LambdaDB(project_api_key="YOUR_API_KEY", base_url="YOUR_BASE_URL", project_name="YOUR_PROJECT_NAME") as client:
coll = client.collection("my_collection")
results = coll.query(query={"queryString": {"query": "*:*"}}, size=10)
# results.results or results.documents
{
"took": 76,
"maxScore": 1.0,
"total": 1,
"docs": [
{
"collection": "example_collection",
"score": 1.0,
"doc": {
"id": "33201222",
"url": "https://en.wikipedia.org/wiki/LambdaDB",
"title": "LambdaDB is awesome",
"text": null
}
}
],
"isDocsInline": true
}
score, and it is calculated based on BM25 algorithm for full-text search
and a configured similarity metric for vector search. 
