Fix: update simple RAG init to use embed_text(s) (docs)

docs/getstarted/install.md

@@ -17,4 +17,10 @@ If you're planning to contribute and make modifications to the code, ensure that
 ```bash
 git clone https://github.com/explodinggradients/ragas.git 
 pip install -e .
-```
+```
+
+!!! note on "LangChain OpenAI dependency versions"
+    If you use `langchain_openai` (e.g., `ChatOpenAI`), install `langchain-core` and `langchain-openai` explicitly to avoid version mismatches. You can adjust bounds to match your environment, but installing both explicitly helps prevent strict dependency conflicts.
+    ```bash
+    pip install -U "langchain-core>=0.2,<0.3" "langchain-openai>=0.1,<0.2" openai
+    ```

docs/getstarted/rag_eval.md

@@ -17,6 +17,9 @@ openai_client = openai.OpenAI()
 embeddings = OpenAIEmbeddings(client=openai_client)
 ```
 
+!!! note "OpenAI Embeddings API"
+    `ragas.embeddings.OpenAIEmbeddings` exposes `embed_text` (single) and `embed_texts` (batch), not `embed_query`/`embed_documents` like some LangChain wrappers. The example below uses `embed_texts` for documents and `embed_text` for the query. Please refer to [OpenAI embeddings implementation](https://docs.ragas.io/en/stable/references/embeddings/\#ragas.embeddings.OpenAIEmbeddings)
+
 ### Build a Simple RAG System
 
 To build a simple RAG system, we need to define the following components:
@@ -43,14 +46,14 @@ To build a simple RAG system, we need to define the following components:
         def load_documents(self, documents):
             """Load documents and compute their embeddings."""
             self.docs = documents
-            self.doc_embeddings = self.embeddings.embed_documents(documents)
+            self.doc_embeddings = self.embeddings.embed_texts(documents)
 
         def get_most_relevant_docs(self, query):
             """Find the most relevant document for a given query."""
             if not self.docs or not self.doc_embeddings:
                 raise ValueError("Documents and their embeddings are not loaded.")
 
-            query_embedding = self.embeddings.embed_query(query)
+            query_embedding = self.embeddings.embed_text(query)
             similarities = [
                 np.dot(query_embedding, doc_emb)
                 / (np.linalg.norm(query_embedding) * np.linalg.norm(doc_emb))
@@ -197,4 +200,4 @@ If you want help with improving and scaling up your AI application using evals.
 
 ## Up Next
 
-- [Generate test data for evaluating RAG](rag_testset_generation.md)
+- [Generate test data for evaluating RAG](rag_testset_generation.md)