Update README.md

README.md

@@ -8,29 +8,20 @@
 [![codecov](https://codecov.io/gh/Florents-Tselai/tsellm/branch/main/graph/badge.svg)](https://codecov.io/gh/Florents-Tselai/tsellm)
 [![License](https://img.shields.io/badge/BSD%20license-blue.svg)](https://github.com/Florents-Tselai/tsellm/blob/main/LICENSE)
 
-**tsellm** is the easiest way to access LLMs through your SQLite or DuckDB database.
+**tsellm** is the easiest way to access LLMs from SQLite or DuckDB.
 
 ```shell
 pip install tsellm
 ```
 
-```shell
-usage: tsellm [-h] [--sqlite | --duckdb] [-v] [filename] [sql]
-
-Use LLMs in SQLite and DuckDB
-
-positional arguments:
-  filename       SQLite/DuckDB database to open (defaults to SQLite ':memory:').
-                 A new database is created if the file does not previously exist.
-  sql            An SQL query to execute. Any returned rows are printed to
-                 stdout.
-
-options:
-  -h, --help     show this help message and exit
-  --sqlite       SQLite mode
-  --duckdb       DuckDB mode
-  -v, --version  Print tsellm version
-
+```bash
+cat <<EOF | tee >(sqlite3 prompts.sqlite3) | duckdb prompts.duckdb
+CREATE TABLE prompts ( p TEXT);
+INSERT INTO prompts VALUES('hello world!');
+INSERT INTO prompts VALUES('how are you?');
+INSERT INTO prompts VALUES('is this real life?');
+INSERT INTO prompts VALUES('1+1=?');
+EOF
 ```
 
 Behind the scenes, **tsellm** is based on the beautiful [llm](https://llm.datasette.io) library,
@@ -42,95 +33,56 @@ For example, to access `gpt4all` models
 
 ```shell
 llm install llm-gpt4all
-# Then pick any gpt4all (it will be downloaded automatically the first time you use any model
-tsellm :memory: "select prompt('What is the capital of Greece?', 'orca-mini-3b-gguf2-q4_0')"
-tsellm :memory: "select prompt('What is the capital of Greece?', 'orca-2-7b')"
+```
+
+```sql
+tsellm prompts.duckdb "select prompt(p, 'orca-mini-3b-gguf2-q4_0') from prompts"
+tsellm prompts.sqlite3 "select prompt(p, 'orca-2-7b') from prompts"
 ```
 
 ## Embeddings
 
 ```shell
 llm install llm-sentence-transformers
 llm sentence-transformers register all-MiniLM-L12-v2
-tsellm :memory: "select embed('Hello', 'sentence-transformers/all-MiniLM-L12-v2')"
+```
+
+```sql
+tsellm prompts.sqlite3 "select embed(p, 'sentence-transformers/all-MiniLM-L12-v2')"
 ```
 
 ### Embeddings for binary (`BLOB`) columns
 
 ```shell
 wget https://tselai.com/img/flo.jpg
-sqlite3 images.db <<EOF
+sqlite3 images.sqlite3 <<EOF
 CREATE TABLE images(name TEXT, type TEXT, img BLOB);
 INSERT INTO images(name,type,img) VALUES('flo','jpg',readfile('flo.jpg'));
 EOF
 ```
 
 ```shell
 llm install llm-clip
-tsellm images.db "select embed(img, 'clip') from images"
-```
-
-## Examples
-
-Things get more interesting if you
-combine models in your standard queries.
-
-First, create a db with some data.
-You can easily toggle between SQLite and DuckDB,
-and **tsellm** will pick this up automatically.
-
-### SQLite
-```bash
-sqlite3 prompts.db <<EOF
-CREATE TABLE prompts (
-   p TEXT
-);
-INSERT INTO prompts VALUES('hello world!');
-INSERT INTO prompts VALUES('how are you?');
-INSERT INTO prompts VALUES('is this real life?');
-INSERT INTO prompts VALUES('1+1=?');
-EOF
 ```
 
-With a single query you can access get prompt 
-responses from different LLMs:
-
 ```sql
-tsellm prompts.db "
-        select p,
-        prompt(p, 'orca-2-7b'),
-        prompt(p, 'orca-mini-3b-gguf2-q4_0'),
-        embed(p, 'sentence-transformers/all-MiniLM-L12-v2') 
-        from prompts"
+tsellm images.sqlite3 "select embed(img, 'clip') from images"
 ```
 
-### DuckDB
+### Multiple Prompts
 
-```bash
-duckdb prompts.duckdb <<EOF
-CREATE TABLE prompts (
-   p TEXT
-);
-INSERT INTO prompts VALUES('hello world!');
-INSERT INTO prompts VALUES('how are you?');
-INSERT INTO prompts VALUES('is this real life?');
-INSERT INTO prompts VALUES('1+1=?');
-EOF
-```
-
-With a single query you can access get prompt 
+With a single query you can easily access get prompt 
 responses from different LLMs:
 
 ```sql
-tsellm prompts.duckdb "
+tsellm prompts.sqlite3 "
         select p,
         prompt(p, 'orca-2-7b'),
         prompt(p, 'orca-mini-3b-gguf2-q4_0'),
         embed(p, 'sentence-transformers/all-MiniLM-L12-v2') 
         from prompts"
 ```
 
-
 ## Interactive Shell
 
 If you don't provide an SQL query,