Add deprecation warnings for embeddings (#2244)

Add deprecation warnings for embeddings

Author: rhlbhatnagar

docs/extra/components/choose_evaluator_llm.md

@@ -15,11 +15,13 @@
 
     ```python
     from ragas.llms import LangchainLLMWrapper
-    from ragas.embeddings import LangchainEmbeddingsWrapper
     from langchain_openai import ChatOpenAI
-    from langchain_openai import OpenAIEmbeddings
+    from ragas.embeddings import OpenAIEmbeddings
+    import openai
+    
     evaluator_llm = LangchainLLMWrapper(ChatOpenAI(model="gpt-4o"))
-    evaluator_embeddings = LangchainEmbeddingsWrapper(OpenAIEmbeddings())
+    openai_client = openai.OpenAI()
+    evaluator_embeddings = OpenAIEmbeddings(client=openai_client)
     ```
 
 

docs/extra/components/choose_generator_llm.md

@@ -16,11 +16,13 @@
 
     ```python
     from ragas.llms import LangchainLLMWrapper
-    from ragas.embeddings import LangchainEmbeddingsWrapper
     from langchain_openai import ChatOpenAI
-    from langchain_openai import OpenAIEmbeddings
+    from ragas.embeddings import OpenAIEmbeddings
+    import openai
+    
     generator_llm = LangchainLLMWrapper(ChatOpenAI(model="gpt-4o"))
-    generator_embeddings = LangchainEmbeddingsWrapper(OpenAIEmbeddings())
+    openai_client = openai.OpenAI()
+    generator_embeddings = OpenAIEmbeddings(client=openai_client)
     ```
 
 

docs/getstarted/rag_eval.md

@@ -9,9 +9,12 @@ We will use `langchain_openai` to set the LLM and embedding model for building o
 
 ```python
 from langchain_openai import ChatOpenAI
-from langchain_openai import OpenAIEmbeddings
+from ragas.embeddings import OpenAIEmbeddings
+import openai
+
 llm = ChatOpenAI(model="gpt-4o")
-embeddings = OpenAIEmbeddings()
+openai_client = openai.OpenAI()
+embeddings = OpenAIEmbeddings(client=openai_client)
 ```
 
 ### Build a Simple RAG System
@@ -30,8 +33,10 @@ To build a simple RAG system, we need to define the following components:
 
     class RAG:
         def __init__(self, model="gpt-4o"):
+            import openai
             self.llm = ChatOpenAI(model=model)
-            self.embeddings = OpenAIEmbeddings()
+            openai_client = openai.OpenAI()
+            self.embeddings = OpenAIEmbeddings(client=openai_client)
             self.doc_embeddings = None
             self.docs = None
 

docs/howtos/applications/compare_embeddings.md

@@ -31,7 +31,9 @@ For this tutorial notebook, I am using papers from Semantic Scholar that is rela
 from llama_index.core import download_loader
 from ragas.testset.evolutions import simple, reasoning, multi_context
 from ragas.testset.generator import TestsetGenerator
-from langchain_openai import ChatOpenAI, OpenAIEmbeddings
+from langchain_openai import ChatOpenAI
+from ragas.embeddings import OpenAIEmbeddings
+import openai
 
 SemanticScholarReader = download_loader("SemanticScholarReader")
 loader = SemanticScholarReader()
@@ -41,7 +43,8 @@ documents = loader.load_data(query=query_space, limit=100)
 # generator with openai models
 generator_llm = ChatOpenAI(model="gpt-4o-mini")
 critic_llm = ChatOpenAI(model="gpt-4o")
-embeddings = OpenAIEmbeddings()
+openai_client = openai.OpenAI()
+embeddings = OpenAIEmbeddings(client=openai_client)
 
 generator = TestsetGenerator.from_langchain(
     generator_llm,
@@ -83,7 +86,9 @@ Here I am using llama-index to build a basic RAG pipeline with my documents. The
 
 import nest_asyncio
 from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, ServiceContext
-from langchain.embeddings import HuggingFaceEmbeddings, OpenAIEmbeddings
+from langchain.embeddings import HuggingFaceEmbeddings
+from ragas.embeddings import OpenAIEmbeddings
+import openai
 import pandas as pd
 
 nest_asyncio.apply()

docs/howtos/applications/singlehop_testset_gen.md

@@ -37,12 +37,14 @@ docs = loader.load()
 
 ```python
 from ragas.llms import LangchainLLMWrapper
-from ragas.embeddings import LangchainEmbeddingsWrapper
-from langchain_openai import ChatOpenAI, OpenAIEmbeddings
+from ragas.embeddings import OpenAIEmbeddings
+from langchain_openai import ChatOpenAI
+import openai
 
 
 generator_llm = LangchainLLMWrapper(ChatOpenAI(model="gpt-4o-mini"))
-generator_embeddings = LangchainEmbeddingsWrapper(OpenAIEmbeddings(model="text-embedding-3-small"))
+openai_client = openai.OpenAI()
+generator_embeddings = OpenAIEmbeddings(client=openai_client, model="text-embedding-3-small")
 ```
 
 ## Create Knowledge Graph

docs/howtos/customizations/testgenerator/_language_adaptation.md

@@ -1,4 +1,5 @@
 ## Synthetic test generation from non-english corpus
+## Synthetic test generation from non-english corpus
 
 In this notebook, you'll learn how to adapt synthetic test data generation to non-english corpus settings. For the sake of this tutorial, I am generating queries in Spanish from Spanish wikipedia articles. 
 
@@ -48,12 +49,13 @@ len(docs)
 
 ```python
 from ragas.llms import LangchainLLMWrapper
-from ragas.embeddings import LangchainEmbeddingsWrapper
+from ragas.embeddings import OpenAIEmbeddings
 from langchain_openai import ChatOpenAI
-from langchain_openai import OpenAIEmbeddings
+import openai
 
 generator_llm = LangchainLLMWrapper(ChatOpenAI(model="gpt-4o-mini"))
-generator_embeddings = LangchainEmbeddingsWrapper(OpenAIEmbeddings())
+openai_client = openai.OpenAI()
+generator_embeddings = OpenAIEmbeddings(client=openai_client)
 ```
 
     /opt/homebrew/Caskroom/miniforge/base/envs/ragas/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html

docs/howtos/customizations/testgenerator/_testgen-custom-single-hop.md

@@ -45,10 +45,12 @@ You may use any of [your choice](./../../customizations/customize_models.md), he
 
 ```python
 from ragas.llms.base import llm_factory
-from ragas.embeddings.base import embedding_factory
+from ragas.embeddings import OpenAIEmbeddings
+import openai
 
 llm = llm_factory()
-embedding = embedding_factory()
+openai_client = openai.OpenAI()
+embedding = OpenAIEmbeddings(client=openai_client)
 ```
 
 ### Setup the transforms

docs/howtos/customizations/testgenerator/_testgen-customisation.md

@@ -48,10 +48,12 @@ You may use any of [your choice](./../../customizations/customize_models.md), he
 
 ```python
 from ragas.llms.base import llm_factory
-from ragas.embeddings.base import embedding_factory
+from ragas.embeddings import OpenAIEmbeddings
+import openai
 
 llm = llm_factory()
-embedding = embedding_factory()
+openai_client = openai.OpenAI()
+embedding = OpenAIEmbeddings(client=openai_client)
 ```
 
 ### Setup Extractors and Relationship builders

docs/howtos/customizations/testgenerator/language_adaptation.ipynb

@@ -89,7 +89,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -102,13 +102,15 @@
     }
    ],
    "source": [
-    "from langchain_openai import ChatOpenAI, OpenAIEmbeddings\n",
+    "import openai\n",
+    "from langchain_openai import ChatOpenAI\n",
     "\n",
-    "from ragas.embeddings import LangchainEmbeddingsWrapper\n",
+    "from ragas.embeddings import OpenAIEmbeddings\n",
     "from ragas.llms import LangchainLLMWrapper\n",
     "\n",
     "generator_llm = LangchainLLMWrapper(ChatOpenAI(model=\"gpt-4o-mini\"))\n",
-    "generator_embeddings = LangchainEmbeddingsWrapper(OpenAIEmbeddings())"
+    "openai_client = openai.OpenAI()\n",
+    "generator_embeddings = OpenAIEmbeddings(client=openai_client)"
    ]
   },
   {

docs/howtos/customizations/testgenerator/testgen-custom-single-hop.ipynb

@@ -83,16 +83,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "52f6d1ae-c9ed-4d82-99d7-d130a36e41e8",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from ragas.embeddings.base import embedding_factory\n",
+    "import openai\n",
+    "\n",
+    "from ragas.embeddings import OpenAIEmbeddings\n",
     "from ragas.llms.base import llm_factory\n",
     "\n",
     "llm = llm_factory()\n",
-    "embedding = embedding_factory()"
+    "openai_client = openai.OpenAI()\n",
+    "embedding = OpenAIEmbeddings(client=openai_client)"
    ]
   },
   {