Updated README

README.md

@@ -1,15 +1,74 @@
-Welcome to your new dbt project!
+# dbt-fuzzy-text
 
-### Using the starter project
+Macros that help with fuzzy text matching, with the aim of keeping dbt models agnostic of data warehouses.
 
-Try running the following commands:
-- dbt run
-- dbt test
+Current coverage:
+| Algorithm | Snowflake | BigQuery |
+| :--- | :----: | ---: |
+| _Edit distance based_ |
+| Levenshtein Distance | ✔️ | ✔️ |
+| Jaro-Winkler Similarity | ✔️ | ❌ |
 
+# Installation instructions
 
-### Resources:
-- Learn more about dbt [in the docs](https://docs.getdbt.com/docs/introduction)
-- Check out [Discourse](https://discourse.getdbt.com/) for commonly asked questions and answers
-- Join the [chat](https://community.getdbt.com/) on Slack for live discussions and support
-- Find [dbt events](https://events.getdbt.com) near you
-- Check out [the blog](https://blog.getdbt.com/) for the latest news on dbt's development and best practices
+New to dbt packages? Read more about them [here](https://docs.getdbt.com/docs/building-a-dbt-project/package-management/).
+
+1. Include this package in your `packages.yml` file — check [here](https://hub.getdbt.com/dbt-labs/calum-mcg/latest/) for the latest version number:
+
+```yml
+packages:
+  - package: calum-mcg/dbt-fuzzy-text
+    version: X.X.X ## update to latest version here
+```
+
+2. Run `dbt deps` to install the package.
+
+# Macros
+
+## levenshtein_distance ([source](macros/levenshtein.sql))
+
+This macro generates the levenshtein distance between two strings.
+
+### Arguments
+
+- `str1` (required): First string to compare
+- `str2` (required): Second string to compare
+- `max` (optional, default=none): Maximum distance to compute (integer)
+
+### Usage:
+
+Copy the macro into a statement tab in the dbt Cloud IDE, or into a model, and compile your code
+
+```
+ ... {{ fuzzy_text.levenshtein_distance('input_string_column', 'comparison_string_column') }} as levenshtein_distance ...
+```
+
+## jaro_winkler ([source](macros/jaro_winkler.sql))
+
+This macro generates the Jaro-Winkler similarity between two strings.
+
+### Arguments
+
+- `str1` (required): First string to compare
+- `str2` (required): Second string to compare
+
+### Usage:
+
+Copy the macro into a statement tab in the dbt Cloud IDE, or into a model, and compile your code
+
+```
+ ... {{ fuzzy_text.jaro_winkler('input_string_column', 'comparison_string_column') }} as jaro_winkler ...
+```
+
+## Contribution Guidelines
+
+Pull requests are the best way to propose changes to the codebase. Steps required:
+
+1. Create an issue in the repo with a description of the problem / bug / improvement required
+2. Clone the `main` branch with a suitable branch name, e.g. `feature/add-cool-thing`
+3. Add tests for supported adaptors in the `integration_tests` folder
+4. If required, update the README documentation to include usage and an example.
+5. Issue a pull request, provide:
+   - a description of changes
+   - add a reviewer
+   - reference original issue (from step 1)