Flock v0.5.0

Author: anasdorbani

extensions/flockmtl/description.yml extensions/flock/description.ymlextensions/flockmtl/description.yml renamed to extensions/flock/description.yml

@@ -1,7 +1,7 @@
 extension:
-  name: flockmtl
+  name: flock
   description: LLM & RAG extension to combine analytics and semantic analysis
-  version: 0.4.0
+  version: 0.5.0
   language: SQL & C++
   build: cmake
   license: MIT
@@ -12,14 +12,14 @@ extension:
     - queryproc
 
 repo:
-  github: dais-polymtl/flockmtl
-  ref: 0b0e4ddd6d98ac4192f44148bae0bce3012a9123
+  github: dais-polymtl/flock
+  ref: 7f1c36abe481b97c9e2c6e7303f36005c8d242fa
 
 docs:
   hello_world: |
     -- After loading, any function call will throw an error if the provider's secret doesn't exist
 
-    -- Create your provider secret by following the [documentation](https://dais-polymtl.github.io/flockmtl/docs/what-is-flockmtl/). For example, you can create a default OpenAI API key as follows:
+    -- Create your provider secret by following the [documentation](https://dais-polymtl.github.io/flock/docs/what-is-flock/). For example, you can create a default OpenAI API key as follows:
     D CREATE SECRET (TYPE OPENAI, API_KEY 'your-api-key');
 
     -- Call an OpenAI model with a predefined prompt ('Tell me hello world') and default model ('gpt-4o-mini')
@@ -45,12 +45,12 @@ docs:
     D SELECT llm_complete({'model_name': 'summarizer-model'}, {'prompt_name': 'summarize','context_columns': [{'data': 'We support more functions and approaches to combine relational analytics and semantic analysis. Check our repo for documentation and examples.'}}]);
 
   extended_description: |
-    **FlockMTL** is an experimental DuckDB extension that enables seamless integration of large language models (LLMs) and retrieval-augmented generation (RAG) directly within SQL.
+    **Flock** is an experimental DuckDB extension that enables seamless integration of large language models (LLMs) and retrieval-augmented generation (RAG) directly within SQL.
 
     It introduces `MODEL` and `PROMPT` objects as first-class SQL entities, making it easy to define, manage, and reuse LLM interactions. Core functions like `llm_complete`, `llm_filter`, and `llm_rerank` allow you to perform generation, semantic filtering, and ranking—all from SQL.
 
-    FlockMTL is designed for rapid prototyping of LLM-based analytics and is optimized with batching and caching features for better performance.
+    Flock is designed for rapid prototyping of LLM-based analytics and is optimized with batching and caching features for better performance.
 
-    📄 For more details and examples, see the [FlockMTL documentation](https://dais-polymtl.github.io/flockmtl/docs/what-is-flockmtl).
+    📄 For more details and examples, see the [Flock documentation](https://dais-polymtl.github.io/flock/docs/what-is-flock).
 
-    > *Note:* FlockMTL is part of ongoing research by the [Data & AI Systems (DAIS) Laboratory @ Polytechnique Montréal](https://dais-polymtl.github.io/). It is under active development, and some features may evolve. Feedback and contributions are welcome!
+    > *Note:* Flock is part of ongoing research by the [Data & AI Systems (DAIS) Laboratory @ Polytechnique Montréal](https://dais-polymtl.github.io/). It is under active development, and some features may evolve. Feedback and contributions are welcome!