feat: [Ai] Add basic RAG capabilities, optimize Codegeex

Log: as title

assets/CMakeLists.txt

@@ -1,6 +1,8 @@
 # install templates.
 install(DIRECTORY templates/
  DESTINATION "${SOURCES_INSTALL_RPEFIX}/templates")
+install(DIRECTORY models/
+ DESTINATION "${SOURCES_INSTALL_RPEFIX}/models")
 
 find_package(Qt5 COMPONENTS LinguistTools)
 
@@ -45,4 +47,3 @@ file(GLOB ICON "${CMAKE_CURRENT_SOURCE_DIR}/configures/*.svg")
 install(FILES ${SUPPORTFILES} DESTINATION "${SOURCES_INSTALL_RPEFIX}/configures")
 install(FILES ${DESKTOPFILES} DESTINATION "/usr/share/applications")
 install(FILES ${ICON} DESTINATION "${SOURCES_INSTALL_RPEFIX}/configures/icons")
-

assets/models/README.md

@@ -0,0 +1 @@
+all-MiniLM-L6-v2 is the sentence transformers model used with transformers.js to locally generate codebase embeddings.

assets/models/all-MiniLM-L6-v2/README.md

@@ -0,0 +1,44 @@
+---
+library_name: "transformers.js"
+---
+
+https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2 with ONNX weights to be compatible with Transformers.js.
+
+## Usage (Transformers.js)
+
+If you haven't already, you can install the [Transformers.js](https://huggingface.co/docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@xenova/transformers) using:
+```bash
+npm i @xenova/transformers
+```
+
+You can then use the model to compute embeddings like this:
+
+```js
+import { pipeline } from '@xenova/transformers';
+
+// Create a feature-extraction pipeline
+const extractor = await pipeline('feature-extraction', 'Xenova/all-MiniLM-L6-v2');
+
+// Compute sentence embeddings
+const sentences = ['This is an example sentence', 'Each sentence is converted'];
+const output = await extractor(sentences, { pooling: 'mean', normalize: true });
+console.log(output);
+// Tensor {
+// dims: [ 2, 384 ],
+// type: 'float32',
+// data: Float32Array(768) [ 0.04592696577310562, 0.07328180968761444, ... ],
+// size: 768
+// }
+```
+
+You can convert this Tensor to a nested JavaScript array using `.tolist()`:
+```js
+console.log(output.tolist());
+// [
+// [ 0.04592696577310562, 0.07328180968761444, 0.05400655046105385, ... ],
+// [ 0.08188057690858841, 0.10760223120450974, -0.013241755776107311, ... ]
+// ]
+```
+
+
+Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).

assets/models/all-MiniLM-L6-v2/config.json

@@ -0,0 +1,23 @@
+{
+ "_name_or_path": "all-MiniLM-L6-v2",
+ "architectures": ["BertModel"],
+ "attention_probs_dropout_prob": 0.1,
+ "classifier_dropout": null,
+ "gradient_checkpointing": false,
+ "hidden_act": "gelu",
+ "hidden_dropout_prob": 0.1,
+ "hidden_size": 384,
+ "initializer_range": 0.02,
+ "intermediate_size": 1536,
+ "layer_norm_eps": 1e-12,
+ "max_position_embeddings": 512,
+ "model_type": "bert",
+ "num_attention_heads": 12,
+ "num_hidden_layers": 6,
+ "pad_token_id": 0,
+ "position_embedding_type": "absolute",
+ "transformers_version": "4.29.2",
+ "type_vocab_size": 2,
+ "use_cache": true,
+ "vocab_size": 30522
+}

assets/models/all-MiniLM-L6-v2/special_tokens_map.json

@@ -0,0 +1,7 @@
+{
+ "cls_token": "[CLS]",
+ "mask_token": "[MASK]",
+ "pad_token": "[PAD]",
+ "sep_token": "[SEP]",
+ "unk_token": "[UNK]"
+}