### Install Weaviate Python Client

Source: https://github.com/weaviate/weaviate-python-client/blob/main/docs/index.md

Install the Weaviate Python client using pip. This is the standard installation for basic usage.

```bash
pip install -U weaviate-client
```

--------------------------------

### Install Weaviate Python Client with Agent Support

Source: https://github.com/weaviate/weaviate-python-client/blob/main/docs/index.md

Install the Weaviate Python client with optional agent support using the `[agents]` extra. This enables integration with Weaviate Agents for LLM-related tasks.

```bash
pip install -U "weaviate-client[agents]"
```

--------------------------------

### gRPC Configuration Example

Source: https://github.com/weaviate/weaviate-python-client/blob/main/docs/weaviate.classes.md

Example of how to configure gRPC channel options and credentials for the Weaviate client.

```python
from grpc import ssl_channel_credentials
import weaviate.classes as wvc

conf = wvc.init.GrpcConfig(
  channel_options=[
    ("grpc.keepalive_time_ms", 10000),
    ("grpc.keepalive_timeout_ms", 5000),
  ],
  credentials=ssl_channel_credentials(openssl_cafile=None, ssl_context=None, key_file=None, cert_file=None)

)
```

--------------------------------

### Pre-build hook for cloning and installing the agents client

Source: https://github.com/weaviate/weaviate-python-client/blob/main/docs/PUBLISHING.md

This YAML snippet shows how the weaviate-agents-python-client is cloned and installed as a pre-build step in Read the Docs.

```yaml
build:
  jobs:
    pre_build:
      - git clone -b main --depth=1 https://github.com/weaviate/weaviate-agents-python-client.git docs/weaviate-agents-python-client
      - python -m pip install -e ./docs/weaviate-agents-python-client
```

--------------------------------

### Initialize BM25 Query and Generate Classes

Source: https://github.com/weaviate/weaviate-python-client/blob/main/docs/weaviate.collections.queries.bm25.md

Example demonstrating the constructor signature for the internal BM25 query and generation classes. These classes require a connection object, collection name, and optional configuration for consistency, multi-tenancy, and data mapping.

```python
from weaviate.collections.queries.bm25 import _BM25Query, _BM25Generate

# Example instantiation pattern for internal BM25 classes
query_instance = _BM25Query(
    connection=connection,
    name="MyCollection",
    consistency_level=None,
    tenant=None,
    properties=None,
    references=None,
    validate_arguments=True
)

generate_instance = _BM25Generate(
    connection=connection,
    name="MyCollection",
    consistency_level=None,
    tenant=None,
    properties=None,
    references=None,
    validate_arguments=True
)
```

--------------------------------

### Manage Embedded Database Lifecycle

Source: https://github.com/weaviate/weaviate-python-client/blob/main/docs/weaviate.md

Demonstrates how to initialize and control the lifecycle of an embedded Weaviate database instance using the EmbeddedV3 or EmbeddedV4 classes. Methods include checking connectivity and starting the service.

```python
from weaviate.embedded import EmbeddedV4, EmbeddedOptions

options = EmbeddedOptions(port=8079)
embedded_db = EmbeddedV4(options=options)

if not embedded_db.is_listening():
    embedded_db.start()
```

--------------------------------

### Create Weaviate Collection from Dictionary (Async)

Source: https://github.com/weaviate/weaviate-python-client/blob/main/docs/weaviate.collections.collections.md

Create a new collection in Weaviate using a configuration dictionary and immediately get a collection object. This is useful for v3 to v4 migrations and experimental features. Requires a successful connection to Weaviate.

```python
from weaviate.collections.collection import CollectionAsync

# Assuming 'connection_async' is an initialized ConnectionAsync object
collections_async = _CollectionsAsync(connection_async)

collection_config_dict = {
    "class": "MyNewCollection",
    "description": "A new collection created from a dictionary",
    "properties": [
        {
            "name": "title",
            "dataType": ["text"]
        }
    ]
}

# The return type is CollectionAsync, but without generic types specified here
new_collection: CollectionAsync = await collections_async.create_from_dict(config=collection_config_dict)
```

--------------------------------

### Initialize and Connect to Local Weaviate Async Client

Source: https://github.com/weaviate/weaviate-python-client/blob/main/docs/weaviate.md

Demonstrates how to initialize a local Weaviate async client. It shows both the manual approach using connect/close methods and the recommended approach using an async context manager for automatic resource cleanup.

```python
import weaviate

# Without Context Manager
client = weaviate.use_async_with_local(
    host="localhost",
    port=8080,
    grpc_port=50051,
)
await client.connect()
# ... perform operations ...
await client.close()

# With Context Manager
async with weaviate.use_async_with_local(
    host="localhost",
    port=8080,
    grpc_port=50051,
) as client:
    is_ready = await client.is_ready()
    print(is_ready)
```

--------------------------------

### Initialize and Connect to Embedded Weaviate Async Client

Source: https://github.com/weaviate/weaviate-python-client/blob/main/docs/weaviate.md

Demonstrates how to initialize an embedded async client, connect to it, and verify readiness. It also shows the recommended approach using an async context manager for automatic resource cleanup.

```python
import weaviate

# Manual connection management
client = weaviate.use_async_with_embedded(
    port=8080,
    grpc_port=50051
)
await client.connect()
ready = await client.is_ready()

# Using context manager for automatic cleanup
async with weaviate.use_async_with_embedded(
    port=8080,
    grpc_port=50051
) as client:
    ready = await client.is_ready()
```

--------------------------------

### CrossReferenceAnnotation Example

Source: https://github.com/weaviate/weaviate-python-client/blob/main/docs/weaviate.classes.md

Example demonstrating how to use CrossReferenceAnnotation to include vectors and metadata when querying cross-referenced objects.

```python
>>> import typing
>>> import weaviate.classes as wvc
>>> 
>>> class One(typing.TypedDict):
...     prop: str
>>> 
>>> class Two(typing.TypedDict):
...     one: typing.Annotated[
...         wvc.CrossReference[One],
...         wvc.CrossReferenceAnnotation(include_vector=True)
...     ]
```

--------------------------------

### Async Batch Wrapper Get Shards Readiness

Source: https://github.com/weaviate/weaviate-python-client/blob/main/docs/weaviate.collections.batch.md

Internal method to get shard readiness status asynchronously.

```APIDOC
## async _BatchWrapperAsync__get_shards_readiness

### Description
Internal method to get shard readiness status asynchronously.

### Method
ASYNC

### Parameters
#### Path Parameters
- **shard** (Shard) - Required - The shard to check the status of.

### Return type
List[bool]
```

--------------------------------

### Initialize Collection Configuration Classes

Source: https://github.com/weaviate/weaviate-python-client/blob/main/docs/weaviate.collections.config.md

Demonstrates the instantiation of synchronous and asynchronous configuration objects for a Weaviate collection. These classes require a connection object and the collection name, with an optional tenant identifier.

```python
from weaviate.collections.config import _ConfigCollection, _ConfigCollectionAsync

# Synchronous configuration access
sync_config = _ConfigCollection(connection=conn, name="MyCollection", tenant="tenant_a")

# Asynchronous configuration access
async_config = _ConfigCollectionAsync(connection=conn, name="MyCollection", tenant="tenant_a")
```

--------------------------------

### CrossReference Type Hint Example

Source: https://github.com/weaviate/weaviate-python-client/blob/main/docs/weaviate.collections.classes.md

Example of how to use CrossReference for type hinting within a generic data model.

```python
>>> import typing
>>> import weaviate.classes as wvc
>>> 
>>> class One(typing.TypedDict):
...     prop: str
>>> 
>>> class Two(typing.TypedDict):
...     one: wvc.CrossReference[One]
```

--------------------------------

### Initialize Collection Backup Classes

Source: https://github.com/weaviate/weaviate-python-client/blob/main/docs/weaviate.collections.backups.md

Demonstrates how to instantiate the synchronous and asynchronous backup handlers. These classes require a connection object and the name of the backup to operate.

```python
from weaviate.collections.backups import _CollectionBackup, _CollectionBackupAsync

# Synchronous initialization
sync_backup = _CollectionBackup(connection=my_connection, name="backup_name")

# Asynchronous initialization
async_backup = _CollectionBackupAsync(connection=my_connection, name="backup_name")
```

--------------------------------

### Connect to Embedded Weaviate Instance

Source: https://github.com/weaviate/weaviate-python-client/blob/main/docs/weaviate.md

Demonstrates how to initialize an embedded Weaviate client using standard connection parameters. It shows both manual connection management and the recommended context manager pattern to ensure resources are properly released.

```python
import weaviate

# Manual connection management
client = weaviate.connect_to_embedded(
    port=8080,
    grpc_port=50051,
)
print(client.is_ready())
client.close()

# Using a context manager for automatic resource cleanup
with weaviate.connect_to_embedded(
    port=8080,
    grpc_port=50051,
) as client:
    print(client.is_ready())
```

--------------------------------

### Build HTML Documentation Locally

Source: https://github.com/weaviate/weaviate-python-client/blob/main/docs/README.rst

Commands to build the HTML documentation locally from the 'docs' directory and open the generated index file.

```bash
make html
```

```bash
open _build/html/index.html
```

--------------------------------

### GET number_errors

Source: https://github.com/weaviate/weaviate-python-client/blob/main/docs/weaviate.collections.batch.md

Retrieves the count of errors encountered in the current batch.

```APIDOC
## GET number_errors

### Description
Get the number of errors in the current batch.

### Response
- **Returns** (int) - The number of errors in the current batch.
```

--------------------------------

### GET /batch/errors

Source: https://github.com/weaviate/weaviate-python-client/blob/main/docs/weaviate.collections.batch.md

Retrieves the count of errors encountered in the current batch.

```APIDOC
## GET /batch/errors

### Description
Get the number of errors in the current batch.

### Response
#### Success Response (200)
- **count** (int) - The number of errors in the current batch.
```

--------------------------------

### Get Batch Results

Source: https://github.com/weaviate/weaviate-python-client/blob/main/docs/weaviate.collections.batch.md

Retrieves the overall results of the batch operation.

```APIDOC
## property results

### Description
Get the results of the batch operation.

### Returns
The results of the batch operation.

### Return type
BatchResult
```

--------------------------------

### Connect to Weaviate Cloud Instance

Source: https://github.com/weaviate/weaviate-python-client/blob/main/docs/weaviate.md

Demonstrates how to initialize a connection to a Weaviate Cloud cluster using API key authentication. It shows both the manual approach requiring a close() call and the recommended context manager pattern.

```python
import weaviate

# Manual connection management
client = weaviate.connect_to_weaviate_cloud(
    cluster_url="rAnD0mD1g1t5.something.weaviate.cloud",
    auth_credentials=weaviate.classes.init.Auth.api_key("my-api-key"),
)
client.is_ready()
client.close()

# Using context manager for automatic cleanup
with weaviate.connect_to_weaviate_cloud(
    cluster_url="rAnD0mD1g1t5.something.weaviate.cloud",
    auth_credentials=weaviate.classes.init.Auth.api_key("my-api-key"),
) as client:
    client.is_ready()
```

--------------------------------

### GET /v1/nodes

Source: https://github.com/weaviate/weaviate-python-client/blob/main/docs/modules.md

Checks the status and readiness of the Weaviate cluster nodes.

```APIDOC
## GET /v1/nodes

### Description
Returns the status of the nodes in the cluster, indicating if they are ready to accept requests.

### Method
GET

### Endpoint
/v1/nodes

### Parameters
None

### Request Example
GET /v1/nodes

### Response
#### Success Response (200)
- **nodes** (array) - List of node status objects.

#### Response Example
{
  "nodes": [
    {
      "name": "node-1",
      "status": "HEALTHY"
    }
  ]
}
```

--------------------------------

### Connect to a local Weaviate instance

Source: https://github.com/weaviate/weaviate-python-client/blob/main/docs/weaviate.md

Demonstrates how to connect to a local Weaviate instance using the Python client. It shows both the manual approach requiring a call to .close() and the recommended context manager approach for automatic resource management.

```python
import weaviate

# Approach 1: Manual connection management
client = weaviate.connect_to_local(
    host="localhost",
    port=8080,
    grpc_port=50051
)
client.is_ready()
client.close()

# Approach 2: Using a context manager (Recommended)
with weaviate.connect_to_local(
    host="localhost",
    port=8080,
    grpc_port=50051
) as client:
    client.is_ready()
```

--------------------------------

### Get Number of Errors in Batch

Source: https://github.com/weaviate/weaviate-python-client/blob/main/docs/weaviate.collections.batch.md

Retrieves the number of errors encountered in the current batch.

```APIDOC
## GET /batch/errors/count

### Description
Retrieves the number of errors encountered in the current batch.

### Method
GET

### Endpoint
/batch/errors/count

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
None

### Request Example
```json
{} 
```

### Response
#### Success Response (200)
- **count** (int) - The number of errors in the current batch.

#### Response Example
```json
{
  "count": 0
}
```
```

--------------------------------

### Get Failed References

Source: https://github.com/weaviate/weaviate-python-client/blob/main/docs/weaviate.collections.batch.md

Retrieves a list of all references that failed during the batch import process.

```APIDOC
## property failed_references

### Description
Get all failed references from the batch manager.

### Returns
A list of all the failed references from the batch.

### Return type
List[ErrorReference]
```

--------------------------------

### Initialize Near Image Query Classes

Source: https://github.com/weaviate/weaviate-python-client/blob/main/docs/weaviate.collections.queries.near_image.md

Demonstrates the instantiation of the internal _NearImageQuery and _NearImageGenerate classes. These classes require a connection object, collection name, and optional configuration for consistency, multi-tenancy, and property mapping.

```python
from weaviate.collections.queries.near_image import _NearImageQuery, _NearImageGenerate

# Example instantiation of the query class
near_image_query = _NearImageQuery(
    connection=connection,
    name="MyCollection",
    consistency_level=None,
    tenant=None,
    properties=None,
    references=None,
    validate_arguments=True
)

# Example instantiation of the generative query class
near_image_gen = _NearImageGenerate(
    connection=connection,
    name="MyCollection",
    consistency_level=None,
    tenant=None,
    properties=None,
    references=None,
    validate_arguments=True
)
```

--------------------------------

### Get Failed Objects

Source: https://github.com/weaviate/weaviate-python-client/blob/main/docs/weaviate.collections.batch.md

Retrieves a list of all objects that failed during the batch import process.

```APIDOC
## property failed_objects

### Description
Get all failed objects from the batch manager.

### Returns
A list of all the failed objects from the batch.

### Return type
List[ErrorObject]
```

--------------------------------

### Create Async Weaviate Client with Custom Parameters (Python)

Source: https://github.com/weaviate/weaviate-python-client/blob/main/docs/weaviate.md

This snippet demonstrates how to initialize an asynchronous Weaviate client with specific HTTP and gRPC connection details. It shows both manual connection management (connect/close) and the preferred method using an async context manager for automatic connection handling.

```python
import weaviate

# Without Context Manager
client = weaviate.use_async_with_custom(
    http_host="localhost",
    http_port=8080,
    http_secure=False,
    grpc_host="localhost",
    grpc_port=50051,
    grpc_secure=False,
)
# Manual connection and closing
# await client.connect()
# await client.is_ready()
# await client.close()

# With Async Context Manager
async with weaviate.use_async_with_custom(
    http_host="localhost",
    http_port=8080,
    http_secure=False,
    grpc_host="localhost",
    grpc_port=50051,
    grpc_secure=False,
) as client:
    await client.is_ready()
# Connection is automatically closed here
```

--------------------------------

### Get Shard Readiness Status

Source: https://github.com/weaviate/weaviate-python-client/blob/main/docs/weaviate.collections.batch.md

Asynchronously checks the readiness status of a given shard.

```APIDOC
## async _get_shards_readiness

### Description
Checks the readiness status of a given shard.

### Method
ASYNC

### Parameters
#### Path Parameters
- **shard** (Shard) - Required - The shard to check the status of.

### Return type
List[bool]
```

--------------------------------

### weaviate.use_async_with_custom

Source: https://github.com/weaviate/weaviate-python-client/blob/main/docs/weaviate.md

Initializes an asynchronous Weaviate client with specific host, port, and security configurations.

```APIDOC
## METHOD weaviate.use_async_with_custom

### Description
Creates an async client object ready to connect to a Weaviate instance with custom connection parameters. This method is useful for custom infrastructure setups where standard connection helpers are insufficient.

### Parameters
- **http_host** (str) - Required - The host for REST and GraphQL API calls.
- **http_port** (int) - Required - The port for REST and GraphQL API calls.
- **http_secure** (bool) - Required - Whether to use https.
- **grpc_host** (str) - Required - The host for the gRPC API.
- **grpc_port** (int) - Required - The port for the gRPC API.
- **grpc_secure** (bool) - Required - Whether to use a secure gRPC channel.
- **headers** (Dict[str, str]) - Optional - Additional request headers.
- **additional_config** (AdditionalConfig) - Optional - Advanced configuration options.
- **auth_credentials** (Auth) - Optional - Authentication credentials (API Key, Bearer Token, etc.).
- **skip_init_checks** (bool) - Optional - Whether to skip initialization checks.

### Request Example
```python
import weaviate
client = weaviate.use_async_with_custom(
    http_host="localhost",
    http_port=8080,
    http_secure=False,
    grpc_host="localhost",
    grpc_port=50051,
    grpc_secure=False
)
await client.connect()
```

### Response
- **Returns** (WeaviateAsyncClient) - An instance of the WeaviateAsyncClient ready for connection.
```