✨ add ICV RAG documentation

Author: HamedBabaei

docs/source/tutorials/rag.rst

@@ -96,3 +96,25 @@ This tutorial works based on FewShot RAG matching, an extension of the RAG model
 
 In-Context Vectors RAG
 ==================================
+This RAG variant performs ontology matching using ``ConceptRAGEncoder`` only. The In-Contect Vectors introduced by `[1]<https://github.com/shengliu66/ICV>`_ tackle in-context learning as in-context vectors (ICV). We used LLMs in this perspective in the RAG module. The workflow is the same as RAG or FewShot RAG with the following differences:
+
+
+1. Incorporate the ``ConceptRAGEncoder`` and also provide reference (or examples to build up the ICV vectors).
+
+.. code-block:: python
+
+    from ontoaligner.encoder import ConceptRAGEncoder
+    encoder_model = ConceptRAGEncoder()
+    encoded_ontology = encoder_model(source=dataset['source'], target=dataset['target'], reference=dataset['reference'])
+
+2. Next, import an ICVRAG model, here we use Falcon model:
+
+.. code-block:: python
+
+    from ontoaligner.ontology_matchers import FalconLLMBERTRetrieverICVRAG
+    model = FalconLLMBERTRetrieverICVRAG(**config)
+
+    model.load(llm_path="tiiuae/falcon-7b", ir_path="all-MiniLM-L6-v2")
+
+
+[1] Liu, S., Ye, H., Xing, L., & Zou, J. (2023). `In-context vectors: Making in context learning more effective and controllable through latent space steering<https://arxiv.org/abs/2311.06668>`_. arXiv preprint arXiv:2311.06668.