docs: Update customizations how-to guides to use collections API and LLM factory

.cursor/worktrees.json

@@ -0,0 +1,7 @@
+{
+  "setup-worktree": [
+    "cp $ROOT_WORKTREE_PATH/.env .env",
+    "make install-minimal",
+    "make check"
+  ]
+}

docs/howtos/customizations/customize_models.md

@@ -1,170 +1,142 @@
 ## Customize Models
 
-Ragas may use a LLM and or Embedding for evaluation and synthetic data generation. Both of these models can be customised according to you availability.
+Ragas may use a LLM and or Embedding for evaluation and synthetic data generation. Both of these models can be customised according to your availability.
 
-!!! note
-    Ragas supports all the [LLMs](https://python.langchain.com/docs/integrations/chat/) and [Embeddings](https://python.langchain.com/docs/integrations/text_embedding/) available in LangChain
+Ragas provides factory functions (`llm_factory` and `embedding_factory`) that support multiple providers:
 
-- `BaseRagasLLM` and `BaseRagasEmbeddings` are the base classes Ragas uses internally for LLMs and Embeddings. Any custom LLM or Embeddings should be a subclass of these base classes.
+- **Direct provider support**: OpenAI, Anthropic, Google 
+- **Other providers via LiteLLM**: Azure OpenAI, AWS Bedrock, Google Vertex AI, and 100+ other providers
 
-- If you are using LangChain, you can pass the LangChain LLM and Embeddings directly and Ragas will wrap it with `LangchainLLMWrapper` or `LangchainEmbeddingsWrapper` as needed.
+The factory functions use the [Instructor](https://python.useinstructor.com/) library for structured outputs and [LiteLLM](https://docs.litellm.ai/) for unified access to multiple LLM providers.
 
 ## Examples
 
-- [Azure OpenAI](#azure-openai)
-- [Google Vertex](#google-vertex)
-- [AWS Bedrock](#aws-bedrock)
+- [Customize Models](#customize-models)
+- [Examples](#examples)
+  - [Azure OpenAI](#azure-openai)
+  - [Google Vertex](#google-vertex)
+  - [AWS Bedrock](#aws-bedrock)
 
 
 ### Azure OpenAI
 
 ```bash
-pip install langchain_openai
+pip install litellm
 ```
 
 ```python
-
-from langchain_openai.chat_models import AzureChatOpenAI
-from langchain_openai.embeddings import AzureOpenAIEmbeddings
-from ragas.llms import LangchainLLMWrapper
-from ragas.embeddings import LangchainEmbeddingsWrapper
+import litellm
+from ragas.llms import llm_factory
+from ragas.embeddings.base import embedding_factory
 
 azure_configs = {
-    "base_url": "https://<your-endpoint>.openai.azure.com/",
+    "api_base": "https://<your-endpoint>.openai.azure.com/",
+    "api_key": "your-api-key",
+    "api_version": "2024-02-15-preview",
     "model_deployment": "your-deployment-name",
-    "model_name": "your-model-name",
-    "embedding_deployment": "your-deployment-name",
-    "embedding_name": "text-embedding-ada-002",  # most likely
+    "embedding_deployment": "your-embedding-deployment-name",
 }
 
-
-azure_llm = AzureChatOpenAI(
-    openai_api_version="2023-05-15",
-    azure_endpoint=azure_configs["base_url"],
-    azure_deployment=azure_configs["model_deployment"],
-    model=azure_configs["model_name"],
-    validate_base_url=False,
+# Configure LiteLLM for Azure OpenAI
+litellm.api_base = azure_configs["api_base"]
+litellm.api_key = azure_configs["api_key"]
+litellm.api_version = azure_configs["api_version"]
+
+# Create LLM using llm_factory with litellm provider
+# Note: Use deployment name, not model name for Azure
+azure_llm = llm_factory(
+    f"azure/{azure_configs['model_deployment']}",
+    provider="litellm",
+    client=litellm,
 )
 
-# init the embeddings for answer_relevancy, answer_correctness and answer_similarity
-azure_embeddings = AzureOpenAIEmbeddings(
-    openai_api_version="2023-05-15",
-    azure_endpoint=azure_configs["base_url"],
-    azure_deployment=azure_configs["embedding_deployment"],
-    model=azure_configs["embedding_name"],
+# Create embeddings using embedding_factory
+# Note: Embeddings use the global litellm configuration set above
+azure_embeddings = embedding_factory(
+    "litellm",
+    model=f"azure/{azure_configs['embedding_deployment']}",
 )
-
-azure_llm = LangchainLLMWrapper(azure_llm)
-azure_embeddings = LangchainEmbeddingsWrapper(azure_embeddings)
 ```
 Yay! Now you are ready to use ragas with Azure OpenAI endpoints
 
 ### Google Vertex
 
 ```bash
-!pip install langchain_google_vertexai
+pip install litellm google-cloud-aiplatform
 ```
 
 ```python
-import google.auth
-from langchain_google_vertexai import ChatVertexAI, VertexAIEmbeddings
-from ragas.llms import LangchainLLMWrapper
-from ragas.embeddings import LangchainEmbeddingsWrapper
-from langchain_core.outputs import LLMResult, ChatGeneration
+import litellm
+import os
+from ragas.llms import llm_factory
+from ragas.embeddings.base import embedding_factory
 
 config = {
     "project_id": "<your-project-id>",
+    "location": "us-central1",  # e.g., "us-central1", "us-east1"
     "chat_model_id": "gemini-1.5-pro-002",
     "embedding_model_id": "text-embedding-005",
 }
 
-# authenticate to GCP
-creds, _ = google.auth.default(quota_project_id=config["project_id"])
+# Set environment variables for Vertex AI
+os.environ["VERTEXAI_PROJECT"] = config["project_id"]
+os.environ["VERTEXAI_LOCATION"] = config["location"]
 
-# create LangChain LLM and Embeddings
-vertextai_llm = ChatVertexAI(
-    credentials=creds,
-    model_name=config["chat_model_id"],
-)
-vertextai_embeddings = VertexAIEmbeddings(
-    credentials=creds, model_name=config["embedding_model_id"]
+# Create LLM using llm_factory with litellm provider
+vertex_llm = llm_factory(
+    f"vertex_ai/{config['chat_model_id']}",
+    provider="litellm",
+    client=litellm,
 )
 
-# Create a custom is_finished_parser to capture Gemini generation completion signals
-def gemini_is_finished_parser(response: LLMResult) -> bool:
-    is_finished_list = []
-    for g in response.flatten():
-        resp = g.generations[0][0]
-
-        # Check generation_info first
-        if resp.generation_info is not None:
-            finish_reason = resp.generation_info.get("finish_reason")
-            if finish_reason is not None:
-                is_finished_list.append(
-                    finish_reason in ["STOP", "MAX_TOKENS"]
-                )
-                continue
-
-        # Check response_metadata as fallback
-        if isinstance(resp, ChatGeneration) and resp.message is not None:
-            metadata = resp.message.response_metadata
-            if metadata.get("finish_reason"):
-                is_finished_list.append(
-                    metadata["finish_reason"] in ["STOP", "MAX_TOKENS"]
-                )
-            elif metadata.get("stop_reason"):
-                is_finished_list.append(
-                    metadata["stop_reason"] in ["STOP", "MAX_TOKENS"]
-                )
-
-        # If no finish reason found, default to True
-        if not is_finished_list:
-            is_finished_list.append(True)
-
-    return all(is_finished_list)
-
-
-vertextai_llm = LangchainLLMWrapper(vertextai_llm, is_finished_parser=gemini_is_finished_parser)
-vertextai_embeddings = LangchainEmbeddingsWrapper(vertextai_embeddings)
+# Create embeddings using embedding_factory
+# Note: Embeddings use the environment variables set above
+vertex_embeddings = embedding_factory(
+    "litellm",
+    model=f"vertex_ai/{config['embedding_model_id']}",
+)
 ```
 Yay! Now you are ready to use ragas with Google VertexAI endpoints
 
 ### AWS Bedrock
 
 ```bash
-pip install langchain_aws
+pip install litellm
 ```
 
 ```python
-from langchain_aws import ChatBedrockConverse
-from langchain_aws import BedrockEmbeddings
-from ragas.llms import LangchainLLMWrapper
-from ragas.embeddings import LangchainEmbeddingsWrapper
+import litellm
+import os
+from ragas.llms import llm_factory
+from ragas.embeddings.base import embedding_factory
 
 config = {
-    "credentials_profile_name": "your-profile-name",  # E.g "default"
-    "region_name": "your-region-name",  # E.g. "us-east-1"
-    "llm": "your-llm-model-id",  # E.g "anthropic.claude-3-5-sonnet-20241022-v2:0"
-    "embeddings": "your-embedding-model-id",  # E.g "amazon.titan-embed-text-v2:0"
+    "region_name": "us-east-1",  # E.g. "us-east-1"
+    "llm": "anthropic.claude-3-5-sonnet-20241022-v2:0",  # Your LLM model ID
+    "embeddings": "amazon.titan-embed-text-v2:0",  # Your embedding model ID
     "temperature": 0.4,
 }
 
-bedrock_llm = ChatBedrockConverse(
-    credentials_profile_name=config["credentials_profile_name"],
-    region_name=config["region_name"],
-    base_url=f"https://bedrock-runtime.{config['region_name']}.amazonaws.com",
-    model=config["llm"],
+# Set AWS credentials as environment variables
+# Option 1: Use AWS credentials file (~/.aws/credentials)
+# Option 2: Set environment variables directly
+os.environ["AWS_REGION_NAME"] = config["region_name"]
+# os.environ["AWS_ACCESS_KEY_ID"] = "your-access-key"
+# os.environ["AWS_SECRET_ACCESS_KEY"] = "your-secret-key"
+
+# Create LLM using llm_factory with litellm provider
+bedrock_llm = llm_factory(
+    f"bedrock/{config['llm']}",
+    provider="litellm",
+    client=litellm,
     temperature=config["temperature"],
 )
 
-# init the embeddings
-bedrock_embeddings = BedrockEmbeddings(
-    credentials_profile_name=config["credentials_profile_name"],
-    region_name=config["region_name"],
-    model_id=config["embeddings"],
+# Create embeddings using embedding_factory
+# Note: Embeddings use the environment variables set above
+bedrock_embeddings = embedding_factory(
+    "litellm",
+    model=f"bedrock/{config['embeddings']}",
 )
-
-bedrock_llm = LangchainLLMWrapper(bedrock_llm)
-bedrock_embeddings = LangchainEmbeddingsWrapper(bedrock_embeddings)
 ```
 Yay! Now you are ready to use ragas with AWS Bedrock endpoints