lambdadb 0.8.1

The Official LambdaDB Python SDK.

LambdaDB Python SDK

Developer-friendly & type-safe Python SDK specifically catered to leverage LambdaDB API.

Summary

LambdaDB API: LambdaDB Open API Spec

Table of Contents

• LambdaDB Python SDK
  • SDK Installation
  • Qdrant Compatibility
  • IDE Support
  • SDK Example Usage
  • Authentication
  • Available Resources and Operations
  • Retries
  • Error Handling
  • Server Selection
  • Custom HTTP Client
  • Resource Management
  • Debugging
• Development
  • Maturity
  • Contributions

SDK Installation

[!NOTE] Python version upgrade policy

Once a Python version reaches its official end of life date, a 3-month grace period is provided for users to upgrade. Following this grace period, the minimum python version supported in the SDK will be updated.

The SDK currently supports Python >=3.9.2,<3.14.

The SDK can be installed with uv, pip, or poetry package managers.

uv

uv is a fast Python package installer and resolver, designed as a drop-in replacement for pip and pip-tools. It's recommended for its speed and modern Python tooling capabilities.

uv add lambdadb

PIP

PIP is the default package installer for Python, enabling easy installation and management of packages from PyPI via the command line.

pip install lambdadb

Poetry

Poetry is a modern tool that simplifies dependency management and package publishing by using a single pyproject.toml file to handle project metadata and dependencies.

poetry add lambdadb

Shell and script usage with uv

You can use this SDK in a Python shell with uv and the uvx command that comes with it like so:

uvx --from lambdadb python

It's also possible to write a standalone Python script without needing to set up a whole project like so:

#!/usr/bin/env -S uv run --script
# /// script
# requires-python = ">=3.9"
# dependencies = [
#     "lambdadb",
# ]
# ///

from lambdadb import LambdaDB

client = LambdaDB(
    project_api_key="<YOUR_PROJECT_API_KEY>",
    base_url="https://api.lambdadb.ai",
    project_name="playground",
)

# Rest of script here...

Once that is saved to a file, you can run it with uv run script.py where script.py can be replaced with the actual file name.

Qdrant Compatibility

LambdaDB includes explicit compatibility clients for migrating common vector database workflows with minimal application changes. The first supported compatibility layer is Qdrant:

from lambdadb.compat.qdrant import QdrantCompatClient, models

client = QdrantCompatClient(
    project_api_key="<YOUR_PROJECT_API_KEY>",
    base_url="https://api.lambdadb.ai",
    project_name="playground",
)

See Qdrant compatibility for supported APIs, payload index behavior, data mapping, limitations, and live test setup.

IDE Support

PyCharm

Generally, the SDK will work well with most IDEs out of the box. However, when using PyCharm, you can enjoy much better integration with Pydantic by installing an additional plugin.

• PyCharm Pydantic Plugin

SDK Example Usage

Recommended: collection-scoped API

Use base_url and project_name (defaults: https://api.lambdadb.ai, playground) and the collection-scoped API for the best experience:

from lambdadb import LambdaDB

with LambdaDB(
    project_api_key="<YOUR_PROJECT_API_KEY>",
    base_url="https://api.lambdadb.ai",  # optional, this is the default
    project_name="playground",            # optional, this is the default
) as client:
    # Collection-scoped: no need to pass collection_name to every call
    coll = client.collection("my_collection")
    list_res = coll.docs.list()
    items = list_res.results          # or list_res.documents for doc bodies only
    res = coll.query(query={"queryString": {"query": "some text"}})
    docs_only = res.documents         # document bodies; use res.results for score/metadata
    coll.docs.upsert(docs=[{"id": "1", "text": "hello"}])

Create a collection with managed embeddings

Managed embedding vector fields set managedEmbedding=True and put provider/model/source settings under embedding. Do not set top-level dimensions or similarity on managed embedding vector fields. For unmanaged vector fields, keep using top-level dimensions.

from lambdadb import LambdaDB, models

with LambdaDB(
    project_api_key="<YOUR_PROJECT_API_KEY>",
    base_url="https://api.lambdadb.ai",
    project_name="playground",
) as client:
    client.collections.create(
        collection_name="articles",
        index_configs={
            "body": {
                "type": models.TypeText.TEXT,
                "analyzers": [models.Analyzer.ENGLISH],
            },
            "bodyEmbedding": {
                "type": models.TypeVector.VECTOR,
                "managedEmbedding": True,
                "embedding": {
                    "provider": models.Provider.OPENAI,
                    "model": "text-embedding-3-small",
                    "sourceField": "body",
                },
            },
        },
    )

List all collections (sync / async)

# Synchronous
from lambdadb import LambdaDB

with LambdaDB(
    project_api_key="<YOUR_PROJECT_API_KEY>",
    base_url="https://api.lambdadb.ai",
    project_name="playground",
) as client:
    res = client.collections.list()
    print(res)

# Asynchronous
import asyncio
from lambdadb import LambdaDB

async def main():
    async with LambdaDB(
        project_api_key="<YOUR_PROJECT_API_KEY>",
        base_url="https://api.lambdadb.ai",
        project_name="playground",
    ) as client:
        res = await client.collections.list_async()
        print(res)

asyncio.run(main())

Authentication

Per-Client Security Schemes

This SDK supports the following security scheme globally:

Name	Type	Scheme	Environment Variable
project_api_key	apiKey	API key	LAMBDADB_PROJECT_API_KEY

To authenticate with the API the project_api_key parameter must be set when initializing the SDK client instance. Use base_url and project_name for the API endpoint (defaults: https://api.lambdadb.ai, playground). For example:

from lambdadb import LambdaDB

with LambdaDB(
    project_api_key="<YOUR_PROJECT_API_KEY>",
    base_url="https://api.lambdadb.ai",
    project_name="playground",
) as client:
    res = client.collections.list()
    print(res)

Available Resources and Operations

Recommended: Use the collection-scoped API: client.collection("name").docs.list(), .docs.fetch(), .docs.upsert(), etc., and client.collection("name").query() for search. This matches the REST API structure and avoids repeating the collection name.

• Response access: List, query, and fetch responses expose .results (full result items, with score/metadata when applicable) and .documents (document bodies only). When the API returns is_docs_inline: false with a presigned docs_url, the SDK automatically fetches from that URL so response.results and response.documents are always populated when using coll.query() and coll.docs.fetch().
• Pagination: Use coll.docs.list_pages(size=10) to iterate pages of up to size documents, or coll.docs.iter_all(page_size=100) to iterate over all documents.
• Advanced options: Pass options=RequestOptions(timeout_ms=..., http_headers=...) to any docs or query call; import with from lambdadb import RequestOptions. For delete by filter, prefer query_filter=... over filter_=.... Response types such as ListDocsResponse, QueryCollectionResponse, and FetchDocsResponse are also exported from lambdadb for type hints.

Available methods

Collections

• list - List all collections in an existing project.
• create - Create a collection.
• delete - Delete an existing collection.
• get - Get metadata of an existing collection.
• update - Configure a collection.
• query - Search a collection with a query and return the most similar documents.

Collections.Docs

• list_docs - List documents in a collection.
• list_pages - Iterate pages of up to size documents each.
• iter_all - Iterate over all documents (handles pagination).
• upsert - Upsert documents into a collection. Note that the maximum supported payload size is 6MB.
• get_bulk_upsert - Request required info to upload documents.
• bulk_upsert - Bulk upsert documents into a collection. Note that the maximum supported object size is 200MB.
• bulk_upsert_docs - One-step bulk upsert: upload a list of documents without handling presigned URL or S3 yourself.
• update - Update documents in a collection. Note that the maximum supported payload size is 6MB.
• delete - Delete documents by document IDs or query filter from a collection.
• fetch - Lookup and return documents by document IDs from a collection.

Retries

Some of the endpoints in this SDK support retries. If you use the SDK without any configuration, it will fall back to the default retry strategy provided by the API. However, the default retry strategy can be overridden on a per-operation basis, or across the entire SDK.

To change the default retry strategy for a single API call, simply provide a RetryConfig object to the call:

from lambdadb import LambdaDB
from lambdadb.utils import BackoffStrategy, RetryConfig

with LambdaDB(
    project_api_key="<YOUR_PROJECT_API_KEY>",
    base_url="https://api.lambdadb.ai",
    project_name="playground",
) as client:
    res = client.collections.list(
        retries=RetryConfig("backoff", BackoffStrategy(1, 50, 1.1, 100), False)
    )
    print(res)

If you'd like to override the default retry strategy for all operations that support retries, you can use the retry_config optional parameter when initializing the SDK:

from lambdadb import LambdaDB
from lambdadb.utils import BackoffStrategy, RetryConfig

with LambdaDB(
    project_api_key="<YOUR_PROJECT_API_KEY>",
    base_url="https://api.lambdadb.ai",
    project_name="playground",
    retry_config=RetryConfig("backoff", BackoffStrategy(1, 50, 1.1, 100), False),
) as client:
    res = client.collections.list()
    print(res)

Error Handling

LambdaDBError is the base class for all HTTP error responses. It has the following properties:

Property	Type	Description
err.message	str	Error message
err.status_code	int	HTTP response status code eg 404
err.headers	httpx.Headers	HTTP response headers
err.body	str	HTTP body. Can be empty string if no body is returned.
err.raw_response	httpx.Response	Raw HTTP response
err.data		Optional. Some errors may contain structured data. See Error Classes.

Example

from lambdadb import LambdaDB, errors

with LambdaDB(
    project_api_key="<YOUR_PROJECT_API_KEY>",
    base_url="https://api.lambdadb.ai",
    project_name="playground",
) as client:
    res = None
    try:
        res = client.collections.list()
        print(res)
    except errors.LambdaDBError as e:
        # The base class for HTTP error responses
        print(e.message)
        print(e.status_code)
        print(e.body)
        print(e.headers)
        print(e.raw_response)

        # Depending on the method different errors may be thrown
        if isinstance(e, errors.UnauthenticatedError):
            print(e.data.message)  # Optional[str]

Error Classes

Primary errors:

• LambdaDBError: The base class for HTTP error responses.
  • UnauthenticatedError: Unauthenticated. Status code 401.
  • TooManyRequestsError: Too many requests. Status code 429.
  • InternalServerError: Internal server error. Status code 500.
  • ResourceNotFoundError: Resource not found. Status code 404. *

Less common errors (7)

Network errors:

• httpx.RequestError: Base class for request errors.
  • httpx.ConnectError: HTTP client was unable to make a request to a server.
  • httpx.TimeoutException: HTTP request timed out.

Inherit from LambdaDBError:

• BadRequestError: Bad request. Status code 400. Applicable to 9 of 13 methods.*
• ResourceAlreadyExistsError: Resource already exists. Status code 409. Applicable to 1 of 13 methods.*
• ResponseValidationError: Type mismatch between the response data and the expected Pydantic model. Provides access to the Pydantic validation error via the cause attribute.

* Check the method documentation to see if the error is applicable.

Server Selection

Recommended: base_url and project_name

Use base_url and project_name so the client uses the REST path {base_url}/projects/{project_name} (e.g. https://api.lambdadb.ai/projects/playground).

Parameter	Default	Description
base_url: str	"https://api.lambdadb.ai"	API base URL.
project_name: str	"playground"	Project name (path segment).

from lambdadb import LambdaDB

with LambdaDB(
    project_api_key="<YOUR_PROJECT_API_KEY>",
    base_url="https://api.lambdadb.ai",
    project_name="playground",
) as client:
    res = client.collections.list()
    print(res)

Legacy (deprecated)

server_url and project_host are deprecated and will be removed in the next major version. Prefer base_url and project_name above.

Variable	Parameter	Default (legacy)	Description
projectHost	project_host: str	"api.lambdadb.com/projects/default"	The project URL of the API

# Deprecated: use base_url + project_name instead
with LambdaDB(
    project_host="api.lambdadb.com/projects/default",
    project_api_key="<YOUR_PROJECT_API_KEY>",
) as lambda_db:
    res = lambda_db.collections.list()

# Deprecated: use base_url + project_name instead
with LambdaDB(
    server_url="https://api.lambdadb.com/projects/default",
    project_api_key="<YOUR_PROJECT_API_KEY>",
) as lambda_db:
    res = lambda_db.collections.list()

Custom HTTP Client

The Python SDK makes API calls using the httpx HTTP library. In order to provide a convenient way to configure timeouts, cookies, proxies, custom headers, and other low-level configuration, you can initialize the SDK client with your own HTTP client instance. Depending on whether you are using the sync or async version of the SDK, you can pass an instance of HttpClient or AsyncHttpClient respectively, which are Protocol's ensuring that the client has the necessary methods to make API calls. This allows you to wrap the client with your own custom logic, such as adding custom headers, logging, or error handling, or you can just pass an instance of httpx.Client or httpx.AsyncClient directly.

For example, you could specify a header for every request that this sdk makes as follows:

from lambdadb import LambdaDB
import httpx

http_client = httpx.Client(headers={"x-custom-header": "someValue"})
client = LambdaDB(
    project_api_key="<YOUR_PROJECT_API_KEY>",
    base_url="https://api.lambdadb.ai",
    project_name="playground",
    client=http_client,
)

or you could wrap the client with your own custom logic:

from lambdadb import LambdaDB
from lambdadb.httpclient import AsyncHttpClient
import httpx

class CustomClient(AsyncHttpClient):
    client: AsyncHttpClient

    def __init__(self, client: AsyncHttpClient):
        self.client = client

    async def send(
        self,
        request: httpx.Request,
        *,
        stream: bool = False,
        auth: Union[
            httpx._types.AuthTypes, httpx._client.UseClientDefault, None
        ] = httpx.USE_CLIENT_DEFAULT,
        follow_redirects: Union[
            bool, httpx._client.UseClientDefault
        ] = httpx.USE_CLIENT_DEFAULT,
    ) -> httpx.Response:
        request.headers["Client-Level-Header"] = "added by client"

        return await self.client.send(
            request, stream=stream, auth=auth, follow_redirects=follow_redirects
        )

    def build_request(
        self,
        method: str,
        url: httpx._types.URLTypes,
        *,
        content: Optional[httpx._types.RequestContent] = None,
        data: Optional[httpx._types.RequestData] = None,
        files: Optional[httpx._types.RequestFiles] = None,
        json: Optional[Any] = None,
        params: Optional[httpx._types.QueryParamTypes] = None,
        headers: Optional[httpx._types.HeaderTypes] = None,
        cookies: Optional[httpx._types.CookieTypes] = None,
        timeout: Union[
            httpx._types.TimeoutTypes, httpx._client.UseClientDefault
        ] = httpx.USE_CLIENT_DEFAULT,
        extensions: Optional[httpx._types.RequestExtensions] = None,
    ) -> httpx.Request:
        return self.client.build_request(
            method,
            url,
            content=content,
            data=data,
            files=files,
            json=json,
            params=params,
            headers=headers,
            cookies=cookies,
            timeout=timeout,
            extensions=extensions,
        )

client = LambdaDB(
    project_api_key="<YOUR_PROJECT_API_KEY>",
    base_url="https://api.lambdadb.ai",
    project_name="playground",
    async_client=CustomClient(httpx.AsyncClient()),
)

Resource Management

The LambdaDB class implements the context manager protocol and registers a finalizer function to close the underlying sync and async HTTPX clients it uses under the hood. This will close HTTP connections, release memory and free up other resources held by the SDK. In short-lived Python programs and notebooks that make a few SDK method calls, resource management may not be a concern. However, in longer-lived programs, it is beneficial to create a single SDK instance via a context manager and reuse it across the application.

from lambdadb import LambdaDB

def main():
    with LambdaDB(
        project_api_key="<YOUR_PROJECT_API_KEY>",
        base_url="https://api.lambdadb.ai",
        project_name="playground",
    ) as client:
        # Rest of application here...
        coll = client.collection("my_collection")
        coll.docs.list()

# Or when using async:
async def amain():
    async with LambdaDB(
        project_api_key="<YOUR_PROJECT_API_KEY>",
        base_url="https://api.lambdadb.ai",
        project_name="playground",
    ) as client:
        # Rest of application here...

Debugging

You can setup your SDK to emit debug logs for SDK requests and responses.

You can pass your own logger class directly into your SDK.

from lambdadb import LambdaDB
import logging

logging.basicConfig(level=logging.DEBUG)
client = LambdaDB(
    project_api_key="<YOUR_PROJECT_API_KEY>",
    base_url="https://api.lambdadb.ai",
    project_name="playground",
    debug_logger=logging.getLogger("lambdadb"),
)

You can also enable a default debug logger by setting an environment variable LAMBDADB_DEBUG to true.

Development

Maturity

This SDK is in beta, and there may be breaking changes between versions without a major version update. Therefore, we recommend pinning usage to a specific package version. This way, you can install the same version each time without breaking changes unless you are intentionally looking for the latest version.

Contributions

While we value open-source contributions to this SDK, this library is generated programmatically. Any manual changes added to internal files will be overwritten on the next generation. We look forward to hearing your feedback. Feel free to open a PR or an issue with a proof of concept and we'll do our best to include it in a future release.