Add docs code testing & documentation syntax changes (#196)

- Creates testing files `md_testing.py` and `md_testing.js` for testing
python and nodejs code in markdown files in the documentation
This listens for HTML tags as well: `<!--[language] code code
code...-->` will create a set-up file to create some mock tables or to
fulfill some assumptions in the documentation.
- Creates a github action workflow that triggers every push/pr to
`docs/**`
- Modifies documentation so tests run (mostly indentation, some small
syntax errors and some missing imports)

A list of excluded files that we need to take a closer look at later on:
```javascript
const excludedFiles = [
  "../src/fts.md",
  "../src/embedding.md",
  "../src/examples/serverless_lancedb_with_s3_and_lambda.md",
  "../src/examples/serverless_qa_bot_with_modal_and_langchain.md",
  "../src/examples/youtube_transcript_bot_with_nodejs.md",
];
```
Many of them can't be done because we need the OpenAI API key :(.
`fts.md` has some issues with the library, I believe this is still
experimental?

Closes #170

---------

Co-authored-by: Will Jones <willjones127@gmail.com>
This commit is contained in:
Tevin Wang
2023-06-28 11:07:26 -07:00
committed by GitHub
parent 0f58bd7af2
commit b731a6aed9
16 changed files with 1173 additions and 49 deletions

96
.github/workflows/docs_test.yml vendored Normal file
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@@ -0,0 +1,96 @@
name: Documentation Code Testing
on:
push:
branches:
- main
paths:
- docs/**
- .github/workflows/docs_test.yml
pull_request:
paths:
- docs/**
- .github/workflows/docs_test.yml
# Allows you to run this workflow manually from the Actions tab
workflow_dispatch:
env:
# Disable full debug symbol generation to speed up CI build and keep memory down
# "1" means line tables only, which is useful for panic tracebacks.
RUSTFLAGS: "-C debuginfo=1"
RUST_BACKTRACE: "1"
jobs:
test-python:
name: Test doc python code
runs-on: ${{ matrix.os }}
strategy:
matrix:
python-minor-version: [ "11" ]
os: ["ubuntu-22.04"]
steps:
- name: Checkout
uses: actions/checkout@v3
- name: Set up Python
uses: actions/setup-python@v4
with:
python-version: 3.${{ matrix.python-minor-version }}
cache: "pip"
cache-dependency-path: "docs/test/requirements.txt"
- name: Build Python
working-directory: docs/test
run:
python -m pip install -r requirements.txt
- name: Create test files
run: |
cd docs/test
python md_testing.py
- name: Test
run: |
cd docs/test/python
for d in *; do cd "$d"; echo "$d".py; python "$d".py; cd ..; done
test-node:
name: Test doc nodejs code
runs-on: ${{ matrix.os }}
strategy:
matrix:
node-version: [ "18" ]
os: ["ubuntu-22.04"]
steps:
- name: Checkout
uses: actions/checkout@v3
with:
fetch-depth: 0
lfs: true
- name: Set up Node
uses: actions/setup-node@v3
with:
node-version: ${{ matrix.node-version }}
cache: 'npm'
cache-dependency-path: docs/test/package-lock.json
- name: Install dependecies needed for ubuntu
if: ${{ matrix.os == 'ubuntu-22.04' }}
run: |
sudo apt install -y protobuf-compiler libssl-dev
- name: Install node dependencies
run: |
cd docs/test
npm cache clean --force
npm ci
- name: Rust cache
uses: swatinem/rust-cache@v2
- name: Install LanceDB
run: |
cd docs/test/node_modules/vectordb
npm ci
npm run build
npm run tsc
- name: Create test files
run: |
cd docs/test
node md_testing.js
- name: Test
run: |
cd docs/test/node
for d in *; do cd "$d"; echo "$d".js; node "$d".js; cd ..; done

View File

@@ -65,7 +65,7 @@ pip install lancedb
```python
import lancedb
uri = "/tmp/lancedb"
uri = "data/sample-lancedb"
db = lancedb.connect(uri)
table = db.create_table("my_table",
data=[{"vector": [3.1, 4.1], "item": "foo", "price": 10.0},

View File

@@ -23,7 +23,7 @@ In the future we will look to automatically create and configure the ANN index.
# Create 10,000 sample vectors
data = [{"vector": row, "item": f"item {i}"}
for i, row in enumerate(np.random.random((10_000, 768)).astype('float32'))]
for i, row in enumerate(np.random.random((10_000, 1536)).astype('float32'))]
# Add the vectors to a table
tbl = db.create_table("my_vectors", data=data)
@@ -41,8 +41,8 @@ In the future we will look to automatically create and configure the ANN index.
for (let i = 0; i < 10_000; i++) {
data.push({vector: Array(1536).fill(i), id: `${i}`, content: "", longId: `${i}`},)
}
const table = await db.createTable('vectors', data)
await table.create_index({ type: 'ivf_pq', column: 'vector', num_partitions: 256, num_sub_vectors: 96 })
const table = await db.createTable('my_vectors', data)
await table.createIndex({ type: 'ivf_pq', column: 'vector', num_partitions: 256, num_sub_vectors: 96 })
```
Since `create_index` has a training step, it can take a few minutes to finish for large tables. You can control the index
@@ -73,12 +73,13 @@ There are a couple of parameters that can be used to fine-tune the search:
=== "Python"
```python
tbl.search(np.random.random((768))) \
tbl.search(np.random.random((1536))) \
.limit(2) \
.nprobes(20) \
.refine_factor(10) \
.to_df()
```
```
vector item score
0 [0.44949695, 0.8444449, 0.06281311, 0.23338133... item 1141 103.575333
1 [0.48587373, 0.269207, 0.15095535, 0.65531915,... item 3953 108.393867
@@ -86,8 +87,8 @@ There are a couple of parameters that can be used to fine-tune the search:
=== "Javascript"
```javascript
const results = await table
.search(Array(768).fill(1.2))
const results_1 = await table
.search(Array(1536).fill(1.2))
.limit(2)
.nprobes(20)
.refineFactor(10)
@@ -104,14 +105,14 @@ You can further filter the elements returned by a search using a where clause.
=== "Python"
```python
tbl.search(np.random.random((768))).where("item != 'item 1141'").to_df()
tbl.search(np.random.random((1536))).where("item != 'item 1141'").to_df()
```
=== "Javascript"
```javascript
const results = await table
const results_2 = await table
.search(Array(1536).fill(1.2))
.where("item != 'item 1141'")
.where("id != '1141'")
.execute()
```
@@ -121,7 +122,9 @@ You can select the columns returned by the query using a select clause.
=== "Python"
```python
tbl.search(np.random.random((768))).select(["vector"]).to_df()
tbl.search(np.random.random((1536))).select(["vector"]).to_df()
```
```
vector score
0 [0.30928212, 0.022668175, 0.1756372, 0.4911822... 93.971092
1 [0.2525465, 0.01723831, 0.261568, 0.002007689,... 95.173485
@@ -130,7 +133,7 @@ You can select the columns returned by the query using a select clause.
=== "Javascript"
```javascript
const results = await table
const results_3 = await table
.search(Array(1536).fill(1.2))
.select(["id"])
.execute()

View File

@@ -23,7 +23,7 @@ We'll cover the basics of using LanceDB on your local machine in this section.
=== "Python"
```python
import lancedb
uri = "~/.lancedb"
uri = "data/sample-lancedb"
db = lancedb.connect(uri)
```
@@ -35,7 +35,7 @@ We'll cover the basics of using LanceDB on your local machine in this section.
```javascript
const lancedb = require("vectordb");
const uri = "~./lancedb";
const uri = "data/sample-lancedb";
const db = await lancedb.connect(uri);
```
@@ -102,7 +102,7 @@ Once created, you can open a table using the following code:
If you forget the name of your table, you can always get a listing of all table names:
```javascript
console.log(db.tableNames());
console.log(await db.tableNames());
```
## How to add data to a table
@@ -118,7 +118,7 @@ After a table has been created, you can always add more data to it using
=== "Javascript"
```javascript
await tbl.add([vector: [1.3, 1.4], item: "fizz", price: 100.0},
await tbl.add([{vector: [1.3, 1.4], item: "fizz", price: 100.0},
{vector: [9.5, 56.2], item: "buzz", price: 200.0}])
```

View File

@@ -98,7 +98,7 @@ You can also use an external API like OpenAI to generate embeddings
embededings for your data.
```javascript
const db = await lancedb.connect("/tmp/lancedb");
const db = await lancedb.connect("data/sample-lancedb");
const data = [
{ text: 'pepperoni' },
{ text: 'pineapple' }

View File

@@ -18,6 +18,20 @@ Assume:
1. `table` is a LanceDB Table
2. `text` is the name of the Table column that we want to index
For example,
```python
import lancedb
uri = "data/sample-lancedb"
db = lancedb.connect(uri)
table = db.create_table("my_table",
data=[{"vector": [3.1, 4.1], "text": "Frodo was a happy puppy"},
{"vector": [5.9, 26.5], "text": "There are several kittens playing"}])
```
To create the index:
```python

View File

@@ -28,7 +28,7 @@ LanceDB's core is written in Rust 🦀 and is built using <a href="https://githu
```python
import lancedb
uri = "/tmp/lancedb"
uri = "data/sample-lancedb"
db = lancedb.connect(uri)
table = db.create_table("my_table",
data=[{"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
@@ -44,7 +44,7 @@ LanceDB's core is written in Rust 🦀 and is built using <a href="https://githu
```javascript
const lancedb = require("vectordb");
const uri = "/tmp/lancedb";
const uri = "data/sample-lancedb";
const db = await lancedb.connect(uri);
const table = await db.createTable("my_table",
[{ id: 1, vector: [3.1, 4.1], item: "foo", price: 10.0 },

View File

@@ -6,11 +6,11 @@ Built on top of Apache Arrow, `LanceDB` is easy to integrate with the Python eco
First, we need to connect to a `LanceDB` database.
``` py
```py
import lancedb
db = lancedb.connect("/tmp/lancedb")
db = lancedb.connect("data/sample-lancedb")
```
And write a `Pandas DataFrame` to LanceDB directly.
@@ -79,7 +79,7 @@ We will re-use the dataset created previously
```python
import lancedb
db = lancedb.connect("/tmp/lancedb")
db = lancedb.connect("data/sample-lancedb")
table = db.open_table("pd_table")
arrow_table = table.to_arrow()
```
@@ -87,8 +87,12 @@ arrow_table = table.to_arrow()
`DuckDB` can directly query the `arrow_table`:
```python
In [15]: duckdb.query("SELECT * FROM t")
Out[15]:
import duckdb
duckdb.query("SELECT * FROM arrow_table")
```
```
┌─────────────┬─────────┬────────┐
│ vector │ item │ price │
│ float[] │ varchar │ double │
@@ -96,8 +100,12 @@ Out[15]:
│ [3.1, 4.1] │ foo │ 10.0 │
│ [5.9, 26.5] │ bar │ 20.0 │
└─────────────┴─────────┴────────┘
```
```python
duckdb.query("SELECT mean(price) FROM arrow_table")
```
In [16]: duckdb.query("SELECT mean(price) FROM t")
```
Out[16]:
┌─────────────┐
│ mean(price) │

View File

@@ -16,9 +16,11 @@ npm install vectordb
```javascript
const lancedb = require('vectordb');
const db = lancedb.connect('<PATH_TO_LANCEDB_DATASET>');
const table = await db.openTable('my_table');
const query = await table.search([0.1, 0.3]).limit(20).execute();
const db = await lancedb.connect('data/sample-lancedb');
const table = await db.createTable("my_table",
[{ id: 1, vector: [0.1, 1.0], item: "foo", price: 10.0 },
{ id: 2, vector: [3.9, 0.5], item: "bar", price: 20.0 }])
const results = await table.search([0.1, 0.3]).limit(20).execute();
console.log(results);
```

View File

@@ -17,7 +17,7 @@ Currently, we support the following metrics:
| Metric | Description |
| ----------- | ------------------------------------ |
| `L2` | [Euclidean / L2 distance](https://en.wikipedia.org/wiki/Euclidean_distance) |
| `Cosine` | [Cosine Similarity](https://en.wikipedia.org/wiki/Cosine_similarity)|
<!-- | `Cosine` | [Cosine Similarity](https://en.wikipedia.org/wiki/Cosine_similarity)| -->
## Search
@@ -28,16 +28,44 @@ Currently, we support the following metrics:
If there is no [vector index is created](ann_indexes.md), LanceDB will just brute-force scan
the vector column and compute the distance.
<!-- Setup Code
```python
import lancedb
import numpy as np
uri = "data/sample-lancedb"
db = lancedb.connect(uri)
data = [{"vector": row, "item": f"item {i}"}
for i, row in enumerate(np.random.random((10_000, 1536)).astype('float32'))]
db.create_table("my_vectors", data=data)
```
-->
<!-- Setup Code
```javascript
const vectordb_setup = require('vectordb')
const db_setup = await vectordb_setup.connect('data/sample-lancedb')
let data = []
for (let i = 0; i < 10_000; i++) {
data.push({vector: Array(1536).fill(i), id: `${i}`, content: "", longId: `${i}`},)
}
await db_setup.createTable('my_vectors', data)
```
-->
=== "Python"
```python
import lancedb
import numpy as np
db = lancedb.connect("data/sample-lancedb")
tbl = db.open_table("my_vectors")
df = tbl.search(np.random.random((768)))
.limit(10)
df = tbl.search(np.random.random((1536))) \
.limit(10) \
.to_df()
```
@@ -47,38 +75,41 @@ the vector column and compute the distance.
const vectordb = require('vectordb')
const db = await vectordb.connect('data/sample-lancedb')
tbl = db.open_table("my_vectors")
const tbl = await db.openTable("my_vectors")
const results = await tbl.search(Array(768))
const results_1 = await tbl.search(Array(1536).fill(1.2))
.limit(20)
.execute()
```
<!-- Commenting out for now since metricType fails for JS on Ubuntu 22.04.
By default, `l2` will be used as `Metric` type. You can customize the metric type
as well.
-->
<!--
=== "Python"
```python
df = tbl.search(np.random.random((768)))
.metric("cosine")
.limit(10)
-->
<!-- ```python
df = tbl.search(np.random.random((1536))) \
.metric("cosine") \
.limit(10) \
.to_df()
```
-->
<!--
=== "JavaScript"
-->
```javascript
const vectordb = require('vectordb')
const db = await vectordb.connect('data/sample-lancedb')
tbl = db.open_table("my_vectors")
const results = await tbl.search(Array(768))
.metric("cosine")
<!-- ```javascript
const results_2 = await tbl.search(Array(1536).fill(1.2))
.metricType("cosine")
.limit(20)
.execute()
```
-->
### Search with Vector Index.

View File

@@ -15,11 +15,35 @@ Currently, Lance supports a growing list of expressions.
* ``regexp_match(column, pattern)``
For example, the following filter string is acceptable:
<!-- Setup Code
```python
import lancedb
import numpy as np
uri = "data/sample-lancedb"
db = lancedb.connect(uri)
data = [{"vector": row, "item": f"item {i}"}
for i, row in enumerate(np.random.random((10_000, 2)).astype('int'))]
tbl = db.create_table("my_vectors", data=data)
```
-->
<!-- Setup Code
```javascript
const vectordb = require('vectordb')
const db = await vectordb.connect('data/sample-lancedb')
let data = []
for (let i = 0; i < 10_000; i++) {
data.push({vector: Array(1536).fill(i), id: `${i}`, content: "", longId: `${i}`},)
}
const tbl = await db.createTable('my_vectors', data)
```
-->
=== "Python"
```python
tbl.search([100, 102])
tbl.search([100, 102]) \
.where("""(
(label IN [10, 20])
AND

51
docs/test/md_testing.js Normal file
View File

@@ -0,0 +1,51 @@
const glob = require("glob");
const fs = require("fs");
const path = require("path");
const excludedFiles = [
"../src/fts.md",
"../src/embedding.md",
"../src/examples/serverless_lancedb_with_s3_and_lambda.md",
"../src/examples/serverless_qa_bot_with_modal_and_langchain.md",
"../src/examples/youtube_transcript_bot_with_nodejs.md",
];
const nodePrefix = "javascript";
const nodeFile = ".js";
const nodeFolder = "node";
const globString = "../src/**/*.md";
const asyncPrefix = "(async () => {\n";
const asyncSuffix = "})();";
function* yieldLines(lines, prefix, suffix) {
let inCodeBlock = false;
for (const line of lines) {
if (line.trim().startsWith(prefix + nodePrefix)) {
inCodeBlock = true;
} else if (inCodeBlock && line.trim().startsWith(suffix)) {
inCodeBlock = false;
yield "\n";
} else if (inCodeBlock) {
yield line;
}
}
}
const files = glob.sync(globString, { recursive: true });
for (const file of files.filter((file) => !excludedFiles.includes(file))) {
const lines = [];
const data = fs.readFileSync(file, "utf-8");
const fileLines = data.split("\n");
for (const line of yieldLines(fileLines, "```", "```")) {
lines.push(line);
}
if (lines.length > 0) {
const fileName = path.basename(file, ".md");
const outPath = path.join(nodeFolder, fileName, `${fileName}${nodeFile}`);
console.log(outPath)
fs.mkdirSync(path.dirname(outPath), { recursive: true });
fs.writeFileSync(outPath, asyncPrefix + "\n" + lines.join("\n") + asyncSuffix);
}
}

41
docs/test/md_testing.py Normal file
View File

@@ -0,0 +1,41 @@
import glob
from typing import Iterator
from pathlib import Path
excluded_files = [
"../src/fts.md",
"../src/embedding.md",
"../src/examples/serverless_lancedb_with_s3_and_lambda.md",
"../src/examples/serverless_qa_bot_with_modal_and_langchain.md",
"../src/examples/youtube_transcript_bot_with_nodejs.md"
]
python_prefix = "py"
python_file = ".py"
python_folder = "python"
glob_string = "../src/**/*.md"
def yield_lines(lines: Iterator[str], prefix: str, suffix: str):
in_code_block = False
# Python code has strict indentation
strip_length = 0
for line in lines:
if line.strip().startswith(prefix + python_prefix):
in_code_block = True
strip_length = len(line) - len(line.lstrip())
elif in_code_block and line.strip().startswith(suffix):
in_code_block = False
yield "\n"
elif in_code_block:
yield line[strip_length:]
for file in filter(lambda file: file not in excluded_files, glob.glob(glob_string, recursive=True)):
with open(file, "r") as f:
lines = list(yield_lines(iter(f), "```", "```"))
if len(lines) > 0:
out_path = Path(python_folder) / Path(file).name.strip(".md") / (Path(file).name.strip(".md") + python_file)
print(out_path)
out_path.parent.mkdir(exist_ok=True, parents=True)
with open(out_path, "w") as out:
out.writelines(lines)

834
docs/test/package-lock.json generated Normal file
View File

@@ -0,0 +1,834 @@
{
"name": "lancedb-docs-test",
"version": "1.0.0",
"lockfileVersion": 3,
"requires": true,
"packages": {
"": {
"name": "lancedb-docs-test",
"version": "1.0.0",
"license": "ISC",
"dependencies": {
"fs": "^0.0.1-security",
"glob": "^10.2.7",
"path": "^0.12.7"
},
"devDependencies": {
"vectordb": "https://gitpkg.now.sh/lancedb/lancedb/node?main"
}
},
"node_modules/@apache-arrow/ts": {
"version": "12.0.1",
"resolved": "https://registry.npmjs.org/@apache-arrow/ts/-/ts-12.0.1.tgz",
"integrity": "sha512-oLL/la62/eff4/nTtU4mZzx/SuIIAjrFx6sTSRDZ65a1Ej84IIMptVb/i5rnp2kY14GFnyc928MyJexQmQc4zA==",
"dev": true,
"dependencies": {
"@types/command-line-args": "5.2.0",
"@types/command-line-usage": "5.0.2",
"@types/node": "18.14.5",
"@types/pad-left": "2.1.1",
"command-line-args": "5.2.1",
"command-line-usage": "6.1.3",
"flatbuffers": "23.3.3",
"json-bignum": "^0.0.3",
"pad-left": "^2.1.0",
"tslib": "^2.5.0"
}
},
"node_modules/@isaacs/cliui": {
"version": "8.0.2",
"resolved": "https://registry.npmjs.org/@isaacs/cliui/-/cliui-8.0.2.tgz",
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"dependencies": {
"string-width": "^5.1.2",
"string-width-cjs": "npm:string-width@^4.2.0",
"strip-ansi": "^7.0.1",
"strip-ansi-cjs": "npm:strip-ansi@^6.0.1",
"wrap-ansi": "^8.1.0",
"wrap-ansi-cjs": "npm:wrap-ansi@^7.0.0"
},
"engines": {
"node": ">=12"
}
},
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"resolved": "https://registry.npmjs.org/@pkgjs/parseargs/-/parseargs-0.11.0.tgz",
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"optional": true,
"engines": {
"node": ">=14"
}
},
"node_modules/@types/command-line-args": {
"version": "5.2.0",
"resolved": "https://registry.npmjs.org/@types/command-line-args/-/command-line-args-5.2.0.tgz",
"integrity": "sha512-UuKzKpJJ/Ief6ufIaIzr3A/0XnluX7RvFgwkV89Yzvm77wCh1kFaFmqN8XEnGcN62EuHdedQjEMb8mYxFLGPyA==",
"dev": true
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"resolved": "https://registry.npmjs.org/@types/command-line-usage/-/command-line-usage-5.0.2.tgz",
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"dev": true
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"resolved": "https://registry.npmjs.org/@types/node/-/node-18.14.5.tgz",
"integrity": "sha512-CRT4tMK/DHYhw1fcCEBwME9CSaZNclxfzVMe7GsO6ULSwsttbj70wSiX6rZdIjGblu93sTJxLdhNIT85KKI7Qw==",
"dev": true
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"version": "2.1.1",
"resolved": "https://registry.npmjs.org/@types/pad-left/-/pad-left-2.1.1.tgz",
"integrity": "sha512-Xd22WCRBydkGSApl5Bw0PhAOHKSVjNL3E3AwzKaps96IMraPqy5BvZIsBVK6JLwdybUzjHnuWVwpDd0JjTfHXA==",
"dev": true
},
"node_modules/ansi-regex": {
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15
docs/test/package.json Normal file
View File

@@ -0,0 +1,15 @@
{
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"version": "1.0.0",
"description": "",
"author": "",
"license": "ISC",
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}
}

View File

@@ -0,0 +1,5 @@
lancedb @ git+https://github.com/lancedb/lancedb.git#egg=subdir&subdirectory=python
numpy
pandas
pylance
duckdb