Nova models addition (#1006)

* amazon nova models added, titan embeddings added

* example env added with nova model config

Author: kaustubh-darekar

backend/example.env

@@ -45,3 +45,7 @@ YOUTUBE_TRANSCRIPT_PROXY="https://user:pass@domain:port"
 EFFECTIVE_SEARCH_RATIO=5
 GRAPH_CLEANUP_MODEL="openai_gpt_4o"
 CHUNKS_TO_BE_PROCESSED="50"
+BEDROCK_EMBEDDING_MODEL="model_name,aws_access_key,aws_secret_key,region_name"                       #model_name="amazon.titan-embed-text-v1"
+LLM_MODEL_CONFIG_bedrock_nova_micro_v1="model_name,aws_access_key,aws_secret_key,region_name"        #model_name="amazon.nova-micro-v1:0"
+LLM_MODEL_CONFIG_bedrock_nova_lite_v1="model_name,aws_access_key,aws_secret_key,region_name"         #model_name="amazon.nova-lite-v1:0"
+LLM_MODEL_CONFIG_bedrock_nova_pro_v1="model_name,aws_access_key,aws_secret_key,region_name"          #model_name="amazon.nova-pro-v1:0"

backend/src/llm.py

@@ -89,7 +89,7 @@ def get_llm(model: str):
             )
 
             llm = ChatBedrock(
-                client=bedrock_client, model_id=model_name, model_kwargs=dict(temperature=0)
+                client=bedrock_client,region_name=region_name, model_id=model_name, model_kwargs=dict(temperature=0)
             )
 
         elif "ollama" in model:

backend/src/shared/common_fn.py

@@ -11,7 +11,8 @@
 import os
 from pathlib import Path
 from urllib.parse import urlparse
-
+import boto3
+from langchain_community.embeddings import BedrockEmbeddings
 
 def check_url_source(source_type, yt_url:str=None, wiki_query:str=None):
     language=''
@@ -77,6 +78,10 @@ def load_embedding_model(embedding_model_name: str):
         )
         dimension = 768
         logging.info(f"Embedding: Using Vertex AI Embeddings , Dimension:{dimension}")
+    elif embedding_model_name == "titan":
+        embeddings = get_bedrock_embeddings()
+        dimension = 1536
+        logging.info(f"Embedding: Using bedrock titan Embeddings , Dimension:{dimension}")
     else:
         embeddings = HuggingFaceEmbeddings(
             model_name="all-MiniLM-L6-v2"#, cache_folder="/embedding_model"
@@ -164,4 +169,38 @@ def last_url_segment(url):
   parsed_url = urlparse(url)
   path = parsed_url.path.strip("/")  # Remove leading and trailing slashes
   last_url_segment = path.split("/")[-1] if path else parsed_url.netloc.split(".")[0]
-  return last_url_segment
+  return last_url_segment
+
+def get_bedrock_embeddings():
+   """
+   Creates and returns a BedrockEmbeddings object using the specified model name.
+   Args:
+       model (str): The name of the model to use for embeddings.
+   Returns:
+       BedrockEmbeddings: An instance of the BedrockEmbeddings class.
+   """
+   try:
+       env_value = os.getenv("BEDROCK_EMBEDDING_MODEL")
+       if not env_value:
+           raise ValueError("Environment variable 'BEDROCK_EMBEDDING_MODEL' is not set.")
+       try:
+           model_name, aws_access_key, aws_secret_key, region_name = env_value.split(",")
+       except ValueError:
+           raise ValueError(
+               "Environment variable 'BEDROCK_EMBEDDING_MODEL' is improperly formatted. "
+               "Expected format: 'model_name,aws_access_key,aws_secret_key,region_name'."
+           )
+       bedrock_client = boto3.client(
+               service_name="bedrock-runtime",
+               region_name=region_name.strip(),
+               aws_access_key_id=aws_access_key.strip(),
+               aws_secret_access_key=aws_secret_key.strip(),
+           )
+       bedrock_embeddings = BedrockEmbeddings(
+           model_id=model_name.strip(),
+           client=bedrock_client
+       )
+       return bedrock_embeddings
+   except Exception as e:
+       print(f"An unexpected error occurred: {e}")
+       raise