Output Formats

know supports three output formats for search results: rich terminal UI (default), plain text, and JSON. Choose the format that best fits your workflow.

Rich Format (Default)

The rich format uses the Rich library to display beautifully formatted results in your terminal.

# Default: rich terminal UI
know search "error handling"

# Explicitly specify rich output (same as default)
know search "database queries"

Rich Output Features

Results Table
Top Match Panel
Styling

The results are displayed in a table with:

Rank number (1-5 by default)
Score (distance/BM25 score/RRF)
Filename (highlighted in green)
Path (dimmed, with middle truncation for long paths)
Snippet (preview of content)

# From src/output.py:89-109
table = Table(
    title=result.title,
    box=box.SIMPLE_HEAVY,
    show_lines=True,
    pad_edge=False,
)
table.add_column("#", style="dim", width=3, justify="right")
table.add_column(result.score_label, style="cyan", width=9, justify="right")
table.add_column("File", style="green", overflow="fold")
table.add_column("Path", style="dim", overflow="fold", max_width=40)
table.add_column("Snippet", style="white", overflow="fold", max_width=60)

Below the table, a panel shows the top result with:

Full file path (with home directory as ~)
Chunk index and file size
Extended preview (up to 800 characters)

# From src/output.py:112-126
top_doc = result.items[0].doc
top_meta = result.items[0].meta
meta_line = Text(
    f"{_display_path(top_meta['path'], 100)}  ·  "
    f"chunk {top_meta.get('chunk_index', 0)}  ·  "
    f"{top_meta.get('size_bytes', 0)} bytes",
    style="dim",
)
console.print(
    Panel(
        Group(meta_line, Text(_preview_text(top_doc, 800))),
        title=f"[bold]Top match:[/] {top_meta['filename']}",
        border_style="green",
    )
)

Rich format includes:

Colors: Green for filenames, cyan for scores, dim for metadata
Box drawing: SIMPLE_HEAVY style with line separators
Text wrapping: Automatic folding for long content
Path display: Home directory shown as ~, long paths truncated in middle

Example path display:

# From src/output.py:20-36
def _shorten_middle(text: str, max_len: int) -> str:
    if len(text) <= max_len:
        return text
    keep = max_len - 3
    head = keep // 2
    tail = keep - head
    return f"{text[:head]}...{text[-tail:]}"

def _display_path(path: str, max_len: int = 60) -> str:
    p = Path(path)
    home = Path.home()
    if p.is_absolute() and home in p.parents:
        display = f"~/{p.relative_to(home)}"
    else:
        display = str(p)
    return _shorten_middle(display, max_len)

Visual Example:

┏━━━┳━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━┓
┃ # ┃ Distance┃ File         ┃ Path                ┃ Snippet            ┃
┡━━━╇━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━┩
│ 1 │  0.4521 │ db.py        │ ~/workspace/src/... │ def search(query...│
│ 2 │  0.5234 │ retrieval.py │ ~/workspace/src/... │ def rrf_fuse(res...│
└───┴─────────┴──────────────┴─────────────────────┴────────────────────┘

╭─ Top match: db.py ──────────────────────────────────────────────────────╮
│ ~/workspace/src/db.py  ·  chunk 12  ·  18432 bytes                      │
│                                                                          │
│ def search(query: str, limit: int = 5, ...) -> ResultSet:               │
│     Dense vector search uses ChromaDB embeddings to find semantically...│
╰──────────────────────────────────────────────────────────────────────────╯

Implementation reference: src/output.py:84-126

Plain Text Format

Plain text output is simple, unformatted text suitable for piping to other commands or logging.

# Use plain text output
know search "api endpoints" --plain

# Pipe to other commands
know search "config" --plain | grep -i database

# Save to file
know search "migration" --plain > results.txt

Plain Output Structure

# From src/output.py:65-81
def _render_plain(result: ResultSet) -> None:
    if not result.items:
        print("No results found")
        return
    print(result.title)
    for i, item in enumerate(result.items, 1):
        print(
            f"{i}. {result.score_label}={item.distance:.4f} | "
            f"{item.meta.get('filename')} | {item.meta.get('path')}"
        )
        print(f"    {_preview_text(item.doc, 200)}")
    top = result.items[0]
    print(
        f"Top match: {top.meta.get('filename')} | {top.meta.get('path')} | "
        f"chunk {top.meta.get('chunk_index', 0)} | {top.meta.get('size_bytes', 0)} bytes"
    )
    print(_preview_text(top.doc, 800))

Example output:

Results for: error handling
1. Distance=0.4521 | db.py | /home/user/workspace/src/db.py
    def search(query: str, limit: int = 5, include_globs: list[str] | None = None...
2. Distance=0.5234 | retrieval.py | /home/user/workspace/src/retrieval.py
    def rrf_fuse(result_lists: Iterable[list[SearchItem]], k: int = 60, limit: int...
Top match: db.py | /home/user/workspace/src/db.py | chunk 12 | 18432 bytes
def search(query: str, limit: int = 5, ...) -> ResultSet: Dense vector search...

Implementation reference: src/output.py:65-81

JSON Format

JSON output provides structured data for programmatic access and integration with other tools.

# Output JSON to stdout
know search "database" --json

# Save JSON to file
know search "authentication" --json-out results.json

# Both stdout and file
know search "config" --json --json-out backup.json

# Parse with jq
know search "api" --json | jq '.results[0].filename'

JSON Schema

{
  "query": "error handling",
  "mode": "dense",
  "title": "Results for: error handling",
  "results": [
    {
      "rank": 1,
      "score": "0.4521",
      "filename": "db.py",
      "path": "/home/user/workspace/src/db.py",
      "snippet": "def search(query: str, limit: int = 5...",
      "chunk_index": 12,
      "size_bytes": 18432
    },
    {
      "rank": 2,
      "score": "0.5234",
      "filename": "retrieval.py",
      "path": "/home/user/workspace/src/retrieval.py",
      "snippet": "def rrf_fuse(result_lists...",
      "chunk_index": 3,
      "size_bytes": 8421
    }
  ]
}

JSON Generation

# From src/output.py:39-56
def _result_set_json(result: ResultSet) -> dict:
    return {
        "query": result.query,
        "mode": result.mode,
        "title": result.title,
        "results": [
            {
                "rank": i,
                "score": f"{item.distance:.4f}" if item.distance is not None else "n/a",
                "filename": item.meta.get("filename"),
                "path": item.meta.get("path"),
                "snippet": _preview_text(item.doc, 200),
                "chunk_index": item.meta.get("chunk_index", 0),
                "size_bytes": item.meta.get("size_bytes", 0),
            }
            for i, item in enumerate(result.items, 1)
        ],
    }

Implementation reference: src/output.py:39-63, src/output.py:129-160

Benchmark JSON

When using --benchmark, JSON includes both dense and BM25 results:

{
  "query": "error handling",
  "dense": [
    {"rank": 1, "score": "0.4521", "filename": "db.py", ...},
    {"rank": 2, "score": "0.5234", "filename": "retrieval.py", ...}
  ],
  "bm25": [
    {"rank": 1, "score": "8.2341", "filename": "error_handler.py", ...},
    {"rank": 2, "score": "6.1283", "filename": "exceptions.py", ...}
  ]
}

# From src/output.py:134-149
if isinstance(response, BenchmarkResult):
    payload = {
        "query": response.query,
        "dense": _result_set_json(response.dense)["results"],
        "bm25": _result_set_json(response.bm25)["results"],
    }
    _write_json_file(payload, json_out)
    if output == "json":
        print(json.dumps(payload, ensure_ascii=True))
        return

Implementation reference: src/output.py:134-149

Format Comparison

Rich (Default)
Plain
JSON

Best for:

Interactive terminal use
Quick visual scanning
Exploring results
Daily workflow

Features:

✅ Beautiful formatting
✅ Color highlighting
✅ Easy to read
✅ Extended preview panel

Example:

know search "api endpoints"

Best for:

Piping to other commands
Simple text processing
Logging and archiving
Scripts without color support

Features:

✅ No formatting codes
✅ Easy to parse with grep/sed
✅ Works in any terminal
✅ Redirects cleanly

Example:

know search "config" --plain | grep database

Best for:

Programmatic access
Integration with tools
Structured data processing
Building custom workflows

Features:

✅ Structured data
✅ Easy to parse (jq, Python)
✅ Machine-readable
✅ Complete metadata

Example:

know search "api" --json | jq -r '.results[].path'

Text Previews

All formats include text previews with varying lengths:

# From src/output.py:15-17
def _preview_text(text: str, limit_chars: int) -> str:
    cleaned = text.replace("\n", " ")
    return cleaned[:limit_chars] + ("..." if len(cleaned) > limit_chars else "")

Preview lengths:

Table snippets: 200 characters
Top match (rich/plain): 800 characters
JSON snippets: 200 characters

Implementation reference: src/output.py:15-17

Integration Examples

Using jq with JSON Output

# Extract just filenames
know search "auth" --json | jq -r '.results[].filename'

# Get top result path
know search "config" --json | jq -r '.results[0].path'

# Filter by score threshold
know search "error" --json | jq '.results[] | select(.score | tonumber < 0.5)'

# Extract all paths with scores
know search "db" --json | jq -r '.results[] | "\(.score)\t\(.path)"'

# Count results
know search "test" --json | jq '.results | length'

Python Integration

import json
import subprocess

# Run search and parse results
result = subprocess.run(
    ["know", "search", "error handling", "--json"],
    capture_output=True,
    text=True
)

data = json.loads(result.stdout)

# Process results
for item in data["results"]:
    print(f"{item['filename']}: {item['score']}")
    print(f"  Path: {item['path']}")
    print(f"  Snippet: {item['snippet'][:100]}...")
    print()

# Filter by criteria
top_matches = [r for r in data["results"] if float(r["score"]) < 0.5]
print(f"Found {len(top_matches)} high-quality matches")

Shell Scripting

#!/bin/bash

# Search and open top result in editor
top_file=$(know search "$1" --json | jq -r '.results[0].path')
if [ -n "$top_file" ]; then
    $EDITOR "$top_file"
else
    echo "No results found"
fi

#!/bin/bash

# Find all unique files matching a query
know search "$1" --json --limit 20 | \
    jq -r '.results[].path' | \
    sort -u

Output File Writing

Save JSON results to a file:

# Write JSON to file
know search "api" --json-out results.json

# View the file
cat results.json | jq .

# Combine with stdout (both render)
know search "config" --json --json-out backup.json

# From src/output.py:59-62
def _write_json_file(payload: dict, json_out: Path | None) -> None:
    if json_out is None:
        return
    json_out.write_text(json.dumps(payload, ensure_ascii=True))

Implementation reference: src/output.py:59-62

Format Selection Logic

# From src/know.py:131-136
output = "rich"
if plain:
    output = "plain"
if json_stdout:
    output = "json"

The format is selected based on flags:

Default: rich
If --plain: plain
If --json: json
--plain and --json together is an error

Implementation reference: src/know.py:127-136, src/output.py:129-160

Common Workflows

Interactive Exploration

# Use rich format (default)
know search "machine learning"

Best for browsing and discovery

Text Processing

# Use plain format
know search "error" --plain | \
  grep -i "critical"

Best for grep/sed/awk pipelines

Data Analysis

# Use JSON format
know search "api" --json | \
  jq '.results | group_by(.filename)'

Best for structured analysis

Archival

# Save to files
know search "migration" \
  --json-out "results-$(date +%F).json"

Best for keeping records

Next Steps

Search Modes

Learn about dense, BM25, and hybrid search

Filtering

Filter results by patterns and time

Get Started

Commands

Guides

Reference

Output Formats

Rich Format (Default)

Rich Output Features

Plain Text Format

Plain Output Structure

JSON Format

JSON Schema

JSON Generation

Benchmark JSON

Format Comparison

Text Previews

Integration Examples

Using jq with JSON Output

Python Integration

Shell Scripting

Output File Writing

Format Selection Logic

Common Workflows

Interactive Exploration

Text Processing

Data Analysis

Archival

Next Steps

Search Modes

Filtering

Build docs developers (and LLMs) love

Get Started

Commands

Guides

Reference

​Rich Format (Default)

​Rich Output Features

​Plain Text Format

​Plain Output Structure

​JSON Format

​JSON Schema

​JSON Generation

​Benchmark JSON

​Format Comparison

​Text Previews

​Integration Examples

​Using jq with JSON Output

​Python Integration

​Shell Scripting

​Output File Writing

​Format Selection Logic

​Common Workflows

Interactive Exploration

Text Processing

Data Analysis

Archival

​Next Steps

Search Modes

Filtering

Build docs developers (and LLMs) love

Rich Format (Default)

Rich Output Features

Plain Text Format

Plain Output Structure

JSON Format

JSON Schema

JSON Generation

Benchmark JSON

Format Comparison

Text Previews

Integration Examples

Using jq with JSON Output

Python Integration

Shell Scripting

Output File Writing

Format Selection Logic

Common Workflows

Next Steps