🔥 chore(ai): remove legacy batch-processing helpers from documentation

## Python API

```python

from tdoc_crawler.ai import (

    process_tdoc,

    process_all,

    get_status,

    query_embeddings,

    query_graph,

    create_workspace,

    get_workspace,

)

# Create workspace

workspace = create_workspace("my-project")

# Process single TDoc

status = process_tdoc("SP-240001", "/path/to/checkout", workspace="my-project")

# Batch processing

results = process_all(

    ["SP-240001", "SP-240002"],

    "/base/checkout/path",

    workspace="my-project"

)

# Get status

status = get_status("SP-240001")

# Semantic search

results = query_embeddings("5G architecture", top_k=5, workspace="my-project")

# Query knowledge graph

graph_data = query_graph("evolution of 5G NR", workspace="my-project")

```

### Models

```python

from tdoc_crawler.ai import (

    ProcessingStatus,

    PipelineStage,

    DocumentClassification,

    DocumentSummary,

    DocumentChunk,

    Workspace,

)

```

## Pipeline Stages

The AI processing pipeline consists of these stages:

1. **CLASSIFY** - Identify main document among multiple files

1. **EXTRACT** - Convert DOCX/PDF to Markdown (via Kreuzberg)

1. **EMBED** - Generate vector embeddings

1. **SUMMARIZE** - Create AI summaries

1. **GRAPH** - Build knowledge graph relationships

Legacy batch-processing helpers are removed. Use the LightRAG interfaces exposed by the

`threegpp_ai` package for workspace processing and querying.

## Supported File Types