📝 docs(ai): update documentation with new query and workspace features

```bash

#Create a new workspace for your project

tdoc-crawler ai workspace create my-project --auto-build

tdoc-crawler ai workspace create my-project

```

### 4. Query Your Knowledge Base

Once processed, query your documents using semantic search or graph queries:

Once processed, query your documents using semantic search and knowledge graph (RAG + GraphRAG):

```bash

# Semantic search

tdoc-crawler ai query --workspace my-project --query "5G NR architecture" --top-k 5

# Knowledge graph query

tdoc-crawler ai graph --workspace my-project --query "evolution of 5G standards"

tdoc-crawler ai query --workspace my-project "your query here"

```

### 5. Check Status

Monitor processing status:

Once processed, query your documents using semantic search and knowledge graph (RAG + GraphRAG):

```bash

# Check status of specific TDoc

tdoc-crawler ai status --tdoc-id SP-240001

# List all processed documents

tdoc-crawler ai status --workspace my-project

tdoc-crawler ai query --workspace my-project "your query here"

```

______________________________________________________________________

```bash

# Create a new workspace

tdoc-crawler ai workspace create <name>

tdoc-crawler ai workspace create <name> [--auto-build]

Options:

- `name`: Workspace name

- `--auto-build`: Automatically process documents when added to workspace

# List all workspaces

tdoc-crawler ai workspace list

tdoc-crawler ai workspace delete <name>

```

### Document Processing

### Querying

Query the knowledge graph using semantic embeddings and knowledge graph (RAG + GraphRAG).

```bash

# Process single TDoc

tdoc-crawler ai process --tdoc-id <TDOC_ID> --checkout-path <PATH>

tdoc-crawler ai query --workspace <workspace_name> "your query here"

```

# Process all TDocs in workspace

tdoc-crawler ai process-all --workspace <NAME>

Note: `--workspace` is required. This command uses both vector embeddings (RAG) and the knowledge graph (GraphRAG) to provide comprehensive results.

# Force re-processing

tdoc-crawler ai process --tdoc-id <TDOC_ID> --checkout-path <PATH> --force

```

### Single TDoc Operations

### Querying

#### Summarize a TDoc

```bash

# Semantic search

tdoc-crawler ai query --workspace <NAME> --query "<SEARCH_QUERY>" --top-k 5

Summarize a single TDoc with specified word count.

# Knowledge graph query

tdoc-crawler ai graph --workspace <NAME> --query "<GRAPH_QUERY>"

```bash

tdoc-crawler ai summarize <tdoc_id> [--words N] [--format markdown|json|yaml] [--json-output]

```

### Status

Options:

```bash

# Check processing status

tdoc-crawler ai status --tdoc-id <TDOC_ID>

- `tdoc_id`: TDoc identifier (e.g., "RP-240001")

- `--words N`: Target word count for summary (default: 200)

- `--format`: Output format - markdown (default), json, or yaml

- `--json-output`: Output raw JSON

# List all statuses in workspace

tdoc-crawler ai status --workspace <NAME>

#### Convert a TDoc

# Output as JSON

tdoc-crawler ai status --tdoc-id <TDOC_ID> --json

Convert a single TDoc to markdown format.

```bash

tdoc-crawler ai convert <tdoc_id> [--output FILE.md] [--json-output]

```

Options:

- `tdoc_id`: TDoc identifier

- `--output FILE.md`: Write output to file (prints to stdout if not specified)

- `--json-output`: Output raw JSON

______________________________________________________________________

## Model Providers