# LambdaDB Python SDK

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

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<!-- Start Summary [summary] -->
## Summary

LambdaDB API: LambdaDB Open API Spec
<!-- End Summary [summary] -->

<!-- Start Table of Contents [toc] -->
## Table of Contents
<!-- $toc-max-depth=2 -->
* [LambdaDB Python SDK](#lambdadb-python-sdk)
  * [SDK Installation](#sdk-installation)
  * [IDE Support](#ide-support)
  * [SDK Example Usage](#sdk-example-usage)
  * [Authentication](#authentication)
  * [Available Resources and Operations](#available-resources-and-operations)
  * [Retries](#retries)
  * [Error Handling](#error-handling)
  * [Server Selection](#server-selection)
  * [Custom HTTP Client](#custom-http-client)
  * [Resource Management](#resource-management)
  * [Debugging](#debugging)
* [Development](#development)
  * [Maturity](#maturity)
  * [Contributions](#contributions)

<!-- End Table of Contents [toc] -->

<!-- Start SDK Installation [installation] -->
## SDK Installation

> [!NOTE]
> **Python version upgrade policy**
>
> Once a Python version reaches its [official end of life date](https://devguide.python.org/versions/), 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 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.

```bash
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.

```bash
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.

```bash
poetry add lambdadb
```

### Shell and script usage with `uv`

You can use this SDK in a Python shell with [uv](https://docs.astral.sh/uv/) and the `uvx` command that comes with it like so:

```shell
uvx --from lambdadb python
```

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

```python
#!/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.
<!-- End SDK Installation [installation] -->

<!-- Start IDE Support [idesupport] -->
## 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](https://docs.pydantic.dev/latest/integrations/pycharm/)
<!-- End IDE Support [idesupport] -->

<!-- Start SDK Example Usage [usage] -->
## 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:

```python
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"}])
```

### List all collections (sync / async)

```python
# 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)
```

```python
# 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())
```
<!-- End SDK Example Usage [usage] -->

<!-- Start Authentication [security] -->
## 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:
```python
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)
```
<!-- End Authentication [security] -->

<!-- Start Available Resources and Operations [operations] -->
## 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.

<details open>
<summary>Available methods</summary>

### [Collections](docs/sdks/collections/README.md)

* [list](docs/sdks/collections/README.md#list) - List all collections in an existing project.
* [create](docs/sdks/collections/README.md#create) - Create a collection.
* [delete](docs/sdks/collections/README.md#delete) - Delete an existing collection.
* [get](docs/sdks/collections/README.md#get) - Get metadata of an existing collection.
* [update](docs/sdks/collections/README.md#update) - Configure a collection.
* [query](docs/sdks/collections/README.md#query) - Search a collection with a query and return the most similar documents.

#### [Collections.Docs](docs/sdks/docs/README.md)

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

</details>
<!-- End Available Resources and Operations [operations] -->

<!-- Start Retries [retries] -->
## 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:
```python
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:
```python
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)
```
<!-- End Retries [retries] -->

<!-- Start Error Handling [errors] -->
## Error Handling

[`LambdaDBError`](./src/lambdadb/errors/lambdadberror.py) 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](#error-classes). |

### Example
```python
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`](./src/lambdadb/errors/lambdadberror.py): The base class for HTTP error responses.
  * [`UnauthenticatedError`](./src/lambdadb/errors/unauthenticatederror.py): Unauthenticated. Status code `401`.
  * [`TooManyRequestsError`](./src/lambdadb/errors/toomanyrequestserror.py): Too many requests. Status code `429`.
  * [`InternalServerError`](./src/lambdadb/errors/internalservererror.py): Internal server error. Status code `500`.
  * [`ResourceNotFoundError`](./src/lambdadb/errors/resourcenotfounderror.py): Resource not found. Status code `404`. *

<details><summary>Less common errors (7)</summary>

<br />

**Network errors:**
* [`httpx.RequestError`](https://www.python-httpx.org/exceptions/#httpx.RequestError): Base class for request errors.
    * [`httpx.ConnectError`](https://www.python-httpx.org/exceptions/#httpx.ConnectError): HTTP client was unable to make a request to a server.
    * [`httpx.TimeoutException`](https://www.python-httpx.org/exceptions/#httpx.TimeoutException): HTTP request timed out.


**Inherit from [`LambdaDBError`](./src/lambdadb/errors/lambdadberror.py)**:
* [`BadRequestError`](./src/lambdadb/errors/badrequesterror.py): Bad request. Status code `400`. Applicable to 9 of 13 methods.*
* [`ResourceAlreadyExistsError`](./src/lambdadb/errors/resourcealreadyexistserror.py): Resource already exists. Status code `409`. Applicable to 1 of 13 methods.*
* [`ResponseValidationError`](./src/lambdadb/errors/responsevalidationerror.py): Type mismatch between the response data and the expected Pydantic model. Provides access to the Pydantic validation error via the `cause` attribute.

</details>

\* Check [the method documentation](#available-resources-and-operations) to see if the error is applicable.
<!-- End Error Handling [errors] -->

<!-- Start Server Selection [server] -->
## 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).   |

```python
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 |

```python
# 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()
```

```python
# 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()
```
<!-- End Server Selection [server] -->

<!-- Start Custom HTTP Client [http-client] -->
## Custom HTTP Client

The Python SDK makes API calls using the [httpx](https://www.python-httpx.org/) 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:
```python
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:
```python
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()),
)
```
<!-- End Custom HTTP Client [http-client] -->

<!-- Start Resource Management [resource-management] -->
## 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][context-manager] and reuse it across the application.

[context-manager]: https://docs.python.org/3/reference/datamodel.html#context-managers

```python
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...
```
<!-- End Resource Management [resource-management] -->

<!-- Start Debugging [debug] -->
## 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.
```python
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.
<!-- End Debugging [debug] -->

<!-- Placeholder for Future Speakeasy SDK Sections -->

# 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.