Refactor: Update AWS Bedrock Naming to Amazon Bedrock Across Docs & Repo Structure (#1982)

- Changed all instances of “AWS Bedrock” to “Amazon Bedrock” in URLs,
sidebars, and text within our documentation.

Author: sahusiddharth

docs/howtos/integrations/aws_bedrock.md renamed to docs/howtos/integrations/amazon_bedrock.md

@@ -1,7 +1,7 @@
 
-# Create and Evaluate an Agent Integrated with Bedrock Knowledge Bases and Attached Action Group
+# Create and Evaluate an Amazon Bedrock Agent Integrated with an Amazon Bedrock Knowledge Base and Action Groups
 
-In this notebook, you will learn how to evaluate an Amazon Bedrock Agent. The agent we'll evaluate is a restaurant agent whose tasks include providing clients with information about adult and children's menus and managing the table booking system. This agent is inspired by a [features example notebooks](https://github.com/aws-samples/amazon-bedrock-samples/tree/main/agents-and-function-calling/bedrock-agents/features-examples/05-create-agent-with-knowledge-base-and-action-group) of [Amazon Bedrock Agents](https://aws.amazon.com/bedrock/agents/) with minor changes. You can learn more about the agent creation process [here](https://github.com/aws-samples/amazon-bedrock-samples/tree/main/agents-and-function-calling/bedrock-agents/features-examples/05-create-agent-with-knowledge-base-and-action-group).
+In this notebook, you will learn how to evaluate an Amazon Bedrock Agent. The agent we'll evaluate is a restaurant agent that provides clients with information about adult and children's menus and manages the table booking system. This agent is inspired by a [features example notebooks](https://github.com/aws-samples/amazon-bedrock-samples/tree/main/agents-and-function-calling/bedrock-agents/features-examples/05-create-agent-with-knowledge-base-and-action-group) of [Amazon Bedrock Agents](https://aws.amazon.com/bedrock/agents/) with minor changes. You can learn more about the agent creation process [here](https://github.com/aws-samples/amazon-bedrock-samples/tree/main/agents-and-function-calling/bedrock-agents/features-examples/05-create-agent-with-knowledge-base-and-action-group).
 
 The architecture is illustrated below:
 
@@ -22,7 +22,7 @@ The steps covered in this notebook include:
 
 
     ```python
-    %pip install --upgrade -q boto3 opensearch-py botocore awscli retrying ragas
+    %pip install --upgrade -q boto3 opensearch-py botocore awscli retrying ragas langchain-aws
     ```
 
 
@@ -112,8 +112,7 @@ The steps covered in this notebook include:
     ```
 
     ### Upload the Dataset to Amazon S3
-
-    Now that we have created the knowledge base, let’s populate it with the restaurant menus dataset. In this example, we will use the [boto3 abstraction](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/bedrock-agent/client/start_ingestion_job.html) of the API, via our helper classe. 
+    Now that we have created the knowledge base, let's populate it with the restaurant menus dataset. In this example, we will use [boto3 abstraction](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/bedrock-agent/client/start_ingestion_job.html) of the API, via our helper classes.
 
     Let’s first upload the menu data available in the dataset folder to Amazon S3.
 
@@ -503,9 +502,10 @@ The steps covered in this notebook include:
     time.sleep(30)
     ```
 
+The `invokeAgent` function sends a user query to the Bedrock agent and returns both the agent’s response and trace data. It processes the event stream, capturing trace information for evaluation purposes.
 
 ```python
-def invokeAgent(query, session_id, enable_trace=True, session_state=dict()):
+def invokeAgent(query, session_id, session_state=dict()):
     end_session: bool = False
 
     # invoke the agent API
@@ -514,7 +514,7 @@ def invokeAgent(query, session_id, enable_trace=True, session_state=dict()):
         agentId=agent_id,
         agentAliasId=alias_id,
         sessionId=session_id,
-        enableTrace=enable_trace,
+        enableTrace=True,
         endSession=end_session,
         sessionState=session_state,
     )
@@ -560,24 +560,13 @@ Ragas includes metrics suited to such evaluations, and we will explore some of t
 
 
 ```python
-import getpass
-import os
-
-if "OPENAI_API_KEY" not in os.environ:
-    os.environ["OPENAI_API_KEY"] = getpass.getpass("Enter your OpenAI API key: ")
-```
+from langchain_aws import ChatBedrock
 
+model_id = "us.amazon.nova-pro-v1:0"   # Choose your desired model
+region_name = "us-east-1"              # Choose your desired AWS region
 
-```python
-from ragas.llms import LangchainLLMWrapper
-from ragas.embeddings import LangchainEmbeddingsWrapper
-from langchain_openai import ChatOpenAI, OpenAIEmbeddings
-
-
-evaluator_llm = LangchainLLMWrapper(ChatOpenAI(model="gpt-4o-mini"))
-evaluator_embeddings = LangchainEmbeddingsWrapper(
-    OpenAIEmbeddings(model="text-embedding-3-small")
-)
+bedrock_llm = ChatBedrock(model_id=model_id, region_name=region_name)
+evaluator_llm = LangchainLLMWrapper(bedrock_llm)
 ```
 
 
@@ -591,8 +580,7 @@ rubrics = {
         "The item requested by the customer is not present in the menu and no recommendations were made."
     ),
     "score0_description": (
-        "Either the item requested by the customer is present in the menu, or the conversation does not include any food or menu inquiry (e.g., booking, cancellation), "
-        "regardless of whether any recommendation was provided."
+        "Either the item requested by the customer is present in the menu, or the conversation does not include any food or menu inquiry (e.g., booking, cancellation). This score applies regardless of whether any recommendation was provided."
     ),
     "score1_description": (
         "The item requested by the customer is not present in the menu and a recommendation was provided."
@@ -625,7 +613,7 @@ brand_tone = AspectCritic(
 
 ## Evaluating Agent with Ragas
 
-In order to perform evaluations using Ragas, the traces need to be converted into the format recognized by Ragas. To convert an AWS Bedrock agent trace into a format suitable for Ragas evaluation, Ragas provides the function [convert_to_ragas_messages][ragas.integrations.aws_bedrock.convert_to_ragas_messages], which can be used to transform AWS Bedrock messages into the format expected by Ragas. You can read more about it [here](../../concepts/components/eval_dataset.md).
+In order to perform evaluations using Ragas, the traces need to be converted into the format recognized by Ragas. To convert an Amazon Bedrock agent trace into a format suitable for Ragas evaluation, Ragas provides the function [convert_to_ragas_messages][ragas.integrations.amazon_bedrock.convert_to_ragas_messages], which can be used to transform Amazon Bedrock messages into the format expected by Ragas. You can read more about it [here](../../concepts/components/eval_dataset.md).
 
 
 ```python
@@ -643,7 +631,7 @@ Your booking for 2 people at 7pm on the 5th of May 2025 has been successfully cr
 ```
 
 ```python
-query = "Can you check if my previous booking? can you please delete the booking"
+query = "Can you check my previous booking? Can you please delete the booking?"
 agent_answer, traces_2 = invokeAgent(query, session_id)
 
 print(agent_answer)
@@ -654,10 +642,10 @@ Your reservation was found and has been successfully canceled.
 ```
 
 ```python
-from ragas.integrations.aws_bedrock import convert_to_ragas_messages
+from ragas.integrations.amazon_bedrock import convert_to_ragas_messages
 
-# Convert AWS traces to messages accepted by RAGAS.
-# The convert_to_ragas_messages function transforms AWS-specific trace data 
+# Convert Amazon Bedrock traces to messages accepted by RAGAS.
+# The convert_to_ragas_messages function transforms Bedrock-specific trace data 
 # into a format that RAGAS can process as conversation messages.
 ragas_messages_trace_1 = convert_to_ragas_messages(traces_1)
 ragas_messages_trace_2 = convert_to_ragas_messages(traces_2)
@@ -782,7 +770,7 @@ I could not find Indian food on our menu. However, we offer a variety of other c
 ```
 
 ```python
-from ragas.integrations.aws_bedrock import convert_to_ragas_messages
+from ragas.integrations.amazon_bedrock import convert_to_ragas_messages
 
 ragas_messages_trace_3 = convert_to_ragas_messages(traces_3)
 ragas_messages_trace_4 = convert_to_ragas_messages(traces_4)
@@ -845,7 +833,7 @@ Evaluating: 100%|██████████| 3/3 [00:00<?, ?it/s]
 
 
 
-For the Recommendation metric, the chicken wings inquiry scored 0 since the item was available, while both the chocolate truffle cake and Indian food inquiries scored 1 because the requested items were not on the menu and alternative recommendations were provided.
+For the Recommendation metric, the chicken wings inquiry scored 0 since the item was available. Both the chocolate truffle cake and Indian food inquiries scored 1 because the requested items were not on the menu and alternative recommendations were provided.
 
 To evaluate how well our agent utilizes information retrieved from the knowledge base, we use the RAG evaluation metrics provided by Ragas. You can learn more about these metrics [here]().
 
@@ -868,7 +856,7 @@ metrics = [
 
 
 ```python
-from ragas.integrations.aws_bedrock import extract_kb_trace
+from ragas.integrations.amazon_bedrock import extract_kb_trace
 
 kb_trace_3 = extract_kb_trace(traces_3)
 kb_trace_4 = extract_kb_trace(traces_4)
@@ -884,7 +872,7 @@ trace_4_single_turn_sample = SingleTurnSample(
     user_input=kb_trace_4[0].get("user_input"),
     retrieved_contexts=kb_trace_4[0].get("retrieved_contexts"),
     response=kb_trace_4[0].get("response"),
-    reference="The desserts on the adult menu are:\n1. Classic New York Cheesecake\n2. Apple Pie à la Mode\n3. Chocolate Lava Cake\4. Pecan Pie Bars\n5. Banana Pudding Parfait",
+    reference="The desserts on the adult menu are:\n1. Classic New York Cheesecake\n2. Apple Pie à la Mode\n3. Chocolate Lava Cake\n4. Pecan Pie Bars\n5. Banana Pudding Parfait",
 )
 
 single_turn_samples = [trace_3_single_turn_sample, trace_4_single_turn_sample]
@@ -992,7 +980,7 @@ Here are the entrees available for children:
 ``` 
 
 ```python
-from ragas.integrations.aws_bedrock import convert_to_ragas_messages
+from ragas.integrations.amazon_bedrock import convert_to_ragas_messages
 
 ragas_messages_trace_6 = convert_to_ragas_messages(traces_6)
 
@@ -1092,7 +1080,7 @@ Evaluating: 100%|██████████| 1/1 [00:00<?, ?it/s]
 
 
 
-In both scenarios, the agent earned a score of 1 by comprehensively providing all available options—whether listing all children’s entrees.
+In both scenarios, the agent earned a score of 1 by comprehensively providing all available options—specifically by listing all children's entrees.
 
 ## Clean-up 
 Let's delete all the associated resources created to avoid unnecessary costs. 

docs/howtos/integrations/index.md

@@ -8,7 +8,7 @@ happy to look into it 🙂
 
 ## Frameworks
 
-- [AWS Bedrock](./aws_bedrock.md) - AWS Bedrock is a managed framework for building, deploying, and scaling intelligent agents and integrated AI solutions; more information can be found [here](https://aws.amazon.com/bedrock/).
+- [Amazon Bedrock](./amazon_bedrock.md) - Amazon Bedrock is a managed framework for building, deploying, and scaling intelligent agents and integrated AI solutions; more information can be found [here](https://aws.amazon.com/bedrock/).
 - [Langchain](./langchain.md) - Langchain is a framework for building LLM applications, more information can be found [here](https://www.langchain.com/).
 - [LlamaIndex](./_llamaindex.md) - LlamaIndex is a framework for building RAG applications, more information can be found [here](https://www.llamaindex.ai/).
 - [Haystack](./haystack.md) - Haystack is a LLM orchestration framework to build customizable, production-ready LLM applications, more information can be found [here](https://haystack.deepset.ai/).

mkdocs.yml

@@ -110,7 +110,7 @@ nav:
       - Integrations:
           - howtos/integrations/index.md
           - Arize: howtos/integrations/_arize.md
-          - AWS Bedrock: howtos/integrations/aws_bedrock.md
+          - Amazon Bedrock: howtos/integrations/amazon_bedrock.md
           - Haystack: howtos/integrations/haystack.md
           - LangChain: howtos/integrations/langchain.md
           - LangGraph: howtos/integrations/_langgraph_agent_evaluation.md

src/ragas/embeddings/base.py

@@ -244,10 +244,9 @@ def __post_init__(self):
                 self.model_name, **self.model_kwargs
             )
         else:
-            self.model = sentence_transformers.SentenceTransformer(
+            self.model = sentence_transformers.SentenceTransformer(  # type: ignore
                 self.model_name, cache_folder=self.cache_folder, **self.model_kwargs
             )
-
         # ensure outputs are tensors
         if "convert_to_tensor" not in self.encode_kwargs:
             self.encode_kwargs["convert_to_tensor"] = True