diff --git a/CHANGELOG.md b/CHANGELOG.md
index 330a773..513fc90 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -6,6 +6,16 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## [Unreleased]
 
+## [0.1.13] - 2023-12-06
+### Added
+- Bedrock plugin updates to support new text models - #22
+  - amazon.titan-text-lite-v1
+  - anthropic.claude-v2:1
+  - cohere.command-text-v14
+  - cohere.command-light-text-v14
+  - meta.llama2-13b-chat-v1
+  - meta.llama2-70b-chat-v1
+
 ## [0.1.12] - 2023-11-29
 ### Added
 - Amazon Q, your business expert now integrates with QnABot as a fallback answer source, using QnAbot's using Lambda hooks with CustomNoMatches/no_hits. For more information see: [QnABot LambdaHook for Amazon Q, your business expert (preview)](./lambdas/qna_bot_qbusiness_lambdahook/README.md)
@@ -65,7 +75,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 ### Added
 - Initial release
 
-[Unreleased]: https://github.com/aws-samples/qnabot-on-aws-plugin-samples/compare/v0.1.12...develop
+[Unreleased]: https://github.com/aws-samples/qnabot-on-aws-plugin-samples/compare/v0.1.13...develop
+[0.1.13]: https://github.com/aws-samples/qnabot-on-aws-plugin-samples/releases/tag/v0.1.13
 [0.1.12]: https://github.com/aws-samples/qnabot-on-aws-plugin-samples/releases/tag/v0.1.12
 [0.1.11]: https://github.com/aws-samples/qnabot-on-aws-plugin-samples/releases/tag/v0.1.11
 [0.1.10]: https://github.com/aws-samples/qnabot-on-aws-plugin-samples/releases/tag/v0.1.10
diff --git a/lambdas/bedrock-embeddings-and-llm/src/lambdahook.py b/lambdas/bedrock-embeddings-and-llm/src/lambdahook.py
index 1ffa5b0..be1efaf 100644
--- a/lambdas/bedrock-embeddings-and-llm/src/lambdahook.py
+++ b/lambdas/bedrock-embeddings-and-llm/src/lambdahook.py
@@ -40,6 +40,18 @@ def get_request_body(modelId, parameters, prompt):
             "inputText": prompt,
             "textGenerationConfig": textGenerationConfig
         }
+    elif provider == "cohere":
+        request_body = {
+            "prompt": prompt,
+            "max_tokens": DEFAULT_MAX_TOKENS
+        }
+        request_body.update(parameters)
+    elif provider == "meta":
+        request_body = {
+            "prompt": prompt,
+            "max_gen_len": DEFAULT_MAX_TOKENS
+        }
+        request_body.update(parameters)
     else:
         raise Exception("Unsupported provider: ", provider)
     return request_body
@@ -56,6 +68,12 @@ def get_generate_text(modelId, response):
     elif provider == "amazon":
         response_body = json.loads(response.get("body").read())
         generated_text = response_body.get("results")[0].get("outputText")
+    elif provider == "cohere":
+        response_body = json.loads(response.get('body').read())
+        generated_text = response_body.get("generations")[0].get("text")
+    elif provider == "meta":
+        response_body = json.loads(response.get('body').read())
+        generated_text = response_body.get("generation")
     else:
         raise Exception("Unsupported provider: ", provider)
     return generated_text
diff --git a/lambdas/bedrock-embeddings-and-llm/src/llm.py b/lambdas/bedrock-embeddings-and-llm/src/llm.py
index 1b6e66b..156ad8f 100644
--- a/lambdas/bedrock-embeddings-and-llm/src/llm.py
+++ b/lambdas/bedrock-embeddings-and-llm/src/llm.py
@@ -40,6 +40,18 @@ def get_request_body(modelId, parameters, prompt):
             "inputText": prompt,
             "textGenerationConfig": textGenerationConfig
         }
+    elif provider == "cohere":
+        request_body = {
+            "prompt": prompt,
+            "max_tokens": DEFAULT_MAX_TOKENS
+        }
+        request_body.update(parameters)
+    elif provider == "meta":
+        request_body = {
+            "prompt": prompt,
+            "max_gen_len": DEFAULT_MAX_TOKENS
+        }
+        request_body.update(parameters)
     else:
         raise Exception("Unsupported provider: ", provider)
     return request_body
@@ -56,6 +68,12 @@ def get_generate_text(modelId, response):
     elif provider == "amazon":
         response_body = json.loads(response.get("body").read())
         generated_text = response_body.get("results")[0].get("outputText")
+    elif provider == "cohere":
+        response_body = json.loads(response.get('body').read())
+        generated_text = response_body.get("generations")[0].get("text")
+    elif provider == "meta":
+        response_body = json.loads(response.get('body').read())
+        generated_text = response_body.get("generation")
     else:
         raise Exception("Unsupported provider: ", provider)
     return generated_text
diff --git a/lambdas/bedrock-embeddings-and-llm/src/settings.py b/lambdas/bedrock-embeddings-and-llm/src/settings.py
index 6acc8c3..ce4dda3 100644
--- a/lambdas/bedrock-embeddings-and-llm/src/settings.py
+++ b/lambdas/bedrock-embeddings-and-llm/src/settings.py
@@ -8,6 +8,10 @@
 ANTHROPIC_QA_PROMPT_TEMPLATE = AMAZON_QA_PROMPT_TEMPLATE
 AI21_GENERATE_QUERY_PROMPT_TEMPATE = ANTHROPIC_GENERATE_QUERY_PROMPT_TEMPLATE
 AI21_QA_PROMPT_TEMPLATE = """The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know. Documents: {context} Instruction: Based on the above documents, provide a detailed answer for {query} Answer "don't know" if not present in the document. Solution:"""
+COHERE_GENERATE_QUERY_PROMPT_TEMPLATE = AMAZON_GENERATE_QUERY_PROMPT_TEMPLATE
+COHERE_QA_PROMPT_TEMPLATE = AMAZON_QA_PROMPT_TEMPLATE
+META_GENERATE_QUERY_PROMPT_TEMPLATE = AMAZON_GENERATE_QUERY_PROMPT_TEMPLATE
+META_QA_PROMPT_TEMPLATE = AMAZON_QA_PROMPT_TEMPLATE
 
 def getEmbeddingSettings(modelId):
     provider = modelId.split(".")[0]
@@ -51,6 +55,16 @@ def getModelSettings(modelId):
         'LLM_GENERATE_QUERY_PROMPT_TEMPLATE': AMAZON_GENERATE_QUERY_PROMPT_TEMPLATE,
         'LLM_QA_PROMPT_TEMPLATE': AMAZON_QA_PROMPT_TEMPLATE
         })
+    elif provider == "cohere":
+        settings.update({
+        'LLM_GENERATE_QUERY_PROMPT_TEMPLATE': COHERE_GENERATE_QUERY_PROMPT_TEMPLATE,
+        'LLM_QA_PROMPT_TEMPLATE': COHERE_QA_PROMPT_TEMPLATE
+        })
+    elif provider == "meta":
+        settings.update({
+        'LLM_GENERATE_QUERY_PROMPT_TEMPLATE': META_GENERATE_QUERY_PROMPT_TEMPLATE,
+        'LLM_QA_PROMPT_TEMPLATE': META_QA_PROMPT_TEMPLATE
+        })
     else:
         raise Exception("Unsupported provider: ", provider)
     return settings
diff --git a/lambdas/bedrock-embeddings-and-llm/template.yml b/lambdas/bedrock-embeddings-and-llm/template.yml
index 631eb12..977ab79 100644
--- a/lambdas/bedrock-embeddings-and-llm/template.yml
+++ b/lambdas/bedrock-embeddings-and-llm/template.yml
@@ -1,5 +1,5 @@
 AWSTemplateFormatVersion: "2010-09-09"
-Description: QnABot on AWS LLM Plugin for Bedrock - v0.1.11
+Description: QnABot on AWS LLM Plugin for Bedrock - v0.1.13
 
 Parameters:
 
@@ -15,11 +15,17 @@ Parameters:
     Default: anthropic.claude-instant-v1
     AllowedValues:
       - amazon.titan-text-express-v1
+      - amazon.titan-text-lite-v1
       - ai21.j2-ultra-v1
       - ai21.j2-mid-v1
       - anthropic.claude-v1
       - anthropic.claude-instant-v1
       - anthropic.claude-v2
+      - anthropic.claude-v2:1
+      - cohere.command-text-v14
+      - cohere.command-light-text-v14
+      - meta.llama2-13b-chat-v1
+      - meta.llama2-70b-chat-v1
     Description: Bedrock LLM ModelId
 
 Resources:
@@ -72,18 +78,6 @@ Resources:
       Role: !GetAtt 'BedrockBoto3ZipFunctionRole.Arn'
       Timeout: 60
       MemorySize: 512
-      Environment:
-        Variables:
-          BOTO3_BUCKET: !Ref BedrockBoto3Bucket
-      Code:
-        ZipFile: |
-    Type: AWS::Lambda::Function
-    Properties:
-      Handler: index.handler
-      Runtime: python3.11
-      Role: !GetAtt 'BedrockBoto3ZipFunctionRole.Arn'
-      Timeout: 60
-      MemorySize: 512
       Environment:
         Variables:
           BOTO3_BUCKET: !Ref BedrockBoto3Bucket