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Merged
merged 1 commit into from
Dec 16, 2024
Merged

Error handling for model format in backend & frontend env #946

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210 changes: 112 additions & 98 deletions backend/src/llm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -16,95 +16,106 @@

def get_llm(model: str):
"""Retrieve the specified language model based on the model name."""
env_key = "LLM_MODEL_CONFIG_" + model
model = model.lower().strip()
env_key = f"LLM_MODEL_CONFIG_{model}"
env_value = os.environ.get(env_key)
logging.info("Model: {}".format(env_key))

if not env_value:
err = f"Environment variable '{env_key}' is not defined as per format or missing"
logging.error(err)
raise Exception(err)

if "gemini" in model:
model_name = env_value
credentials, project_id = google.auth.default()
llm = ChatVertexAI(
model_name=model_name,
#convert_system_message_to_human=True,
credentials=credentials,
project=project_id,
temperature=0,
safety_settings={
HarmCategory.HARM_CATEGORY_UNSPECIFIED: HarmBlockThreshold.BLOCK_NONE,
HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE,
HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_NONE,
HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_NONE,
HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_NONE,
},
)
elif "openai" in model:
model_name, api_key = env_value.split(",")
llm = ChatOpenAI(
api_key=api_key,
model=model_name,
temperature=0,
)
logging.info("Model: {}".format(env_key))
try:
if "gemini" in model:
model_name = env_value
credentials, project_id = google.auth.default()
llm = ChatVertexAI(
model_name=model_name,
#convert_system_message_to_human=True,
credentials=credentials,
project=project_id,
temperature=0,
safety_settings={
HarmCategory.HARM_CATEGORY_UNSPECIFIED: HarmBlockThreshold.BLOCK_NONE,
HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE,
HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_NONE,
HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_NONE,
HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_NONE,
},
)
elif "openai" in model:
model_name, api_key = env_value.split(",")
llm = ChatOpenAI(
api_key=api_key,
model=model_name,
temperature=0,
)

elif "azure" in model:
model_name, api_endpoint, api_key, api_version = env_value.split(",")
llm = AzureChatOpenAI(
api_key=api_key,
azure_endpoint=api_endpoint,
azure_deployment=model_name, # takes precedence over model parameter
api_version=api_version,
temperature=0,
max_tokens=None,
timeout=None,
)
elif "azure" in model:
model_name, api_endpoint, api_key, api_version = env_value.split(",")
llm = AzureChatOpenAI(
api_key=api_key,
azure_endpoint=api_endpoint,
azure_deployment=model_name, # takes precedence over model parameter
api_version=api_version,
temperature=0,
max_tokens=None,
timeout=None,
)

elif "anthropic" in model:
model_name, api_key = env_value.split(",")
llm = ChatAnthropic(
api_key=api_key, model=model_name, temperature=0, timeout=None
)
elif "anthropic" in model:
model_name, api_key = env_value.split(",")
llm = ChatAnthropic(
api_key=api_key, model=model_name, temperature=0, timeout=None
)

elif "fireworks" in model:
model_name, api_key = env_value.split(",")
llm = ChatFireworks(api_key=api_key, model=model_name)

elif "groq" in model:
model_name, base_url, api_key = env_value.split(",")
llm = ChatGroq(api_key=api_key, model_name=model_name, temperature=0)

elif "bedrock" in model:
model_name, aws_access_key, aws_secret_key, region_name = env_value.split(",")
bedrock_client = boto3.client(
service_name="bedrock-runtime",
region_name=region_name,
aws_access_key_id=aws_access_key,
aws_secret_access_key=aws_secret_key,
)
elif "fireworks" in model:
model_name, api_key = env_value.split(",")
llm = ChatFireworks(api_key=api_key, model=model_name)

elif "groq" in model:
model_name, base_url, api_key = env_value.split(",")
llm = ChatGroq(api_key=api_key, model_name=model_name, temperature=0)

elif "bedrock" in model:
model_name, aws_access_key, aws_secret_key, region_name = env_value.split(",")
bedrock_client = boto3.client(
service_name="bedrock-runtime",
region_name=region_name,
aws_access_key_id=aws_access_key,
aws_secret_access_key=aws_secret_key,
)

llm = ChatBedrock(
client=bedrock_client, model_id=model_name, model_kwargs=dict(temperature=0)
)
llm = ChatBedrock(
client=bedrock_client, model_id=model_name, model_kwargs=dict(temperature=0)
)

elif "ollama" in model:
model_name, base_url = env_value.split(",")
llm = ChatOllama(base_url=base_url, model=model_name)
elif "ollama" in model:
model_name, base_url = env_value.split(",")
llm = ChatOllama(base_url=base_url, model=model_name)

elif "diffbot" in model:
#model_name = "diffbot"
model_name, api_key = env_value.split(",")
llm = DiffbotGraphTransformer(
diffbot_api_key=api_key,
extract_types=["entities", "facts"],
)

else:
model_name, api_endpoint, api_key = env_value.split(",")
llm = ChatOpenAI(
api_key=api_key,
base_url=api_endpoint,
model=model_name,
temperature=0,
)

elif "diffbot" in model:
#model_name = "diffbot"
model_name, api_key = env_value.split(",")
llm = DiffbotGraphTransformer(
diffbot_api_key=api_key,
extract_types=["entities", "facts"],
)

else:
model_name, api_endpoint, api_key = env_value.split(",")
llm = ChatOpenAI(
api_key=api_key,
base_url=api_endpoint,
model=model_name,
temperature=0,
)
except Exception as e:
err = f"Error while creating LLM '{model}': {str(e)}"
logging.error(err)
raise Exception(err)

logging.info(f"Model created - Model Version: {model}")
return llm, model_name

Expand Down Expand Up @@ -179,21 +190,24 @@ async def get_graph_document_list(


async def get_graph_from_llm(model, chunkId_chunkDoc_list, allowedNodes, allowedRelationship):

llm, model_name = get_llm(model)
combined_chunk_document_list = get_combined_chunks(chunkId_chunkDoc_list)
#combined_chunk_document_list = get_chunk_id_as_doc_metadata(chunkId_chunkDoc_list)

if allowedNodes is None or allowedNodes=="":
allowedNodes =[]
else:
allowedNodes = allowedNodes.split(',')
if allowedRelationship is None or allowedRelationship=="":
allowedRelationship=[]
else:
allowedRelationship = allowedRelationship.split(',')
try:
llm, model_name = get_llm(model)
combined_chunk_document_list = get_combined_chunks(chunkId_chunkDoc_list)

graph_document_list = await get_graph_document_list(
llm, combined_chunk_document_list, allowedNodes, allowedRelationship
)
return graph_document_list
if allowedNodes is None or allowedNodes=="":
allowedNodes =[]
else:
allowedNodes = allowedNodes.split(',')
if allowedRelationship is None or allowedRelationship=="":
allowedRelationship=[]
else:
allowedRelationship = allowedRelationship.split(',')

graph_document_list = await get_graph_document_list(
llm, combined_chunk_document_list, allowedNodes, allowedRelationship
)
return graph_document_list
except Exception as e:
err = f"Error during extracting graph with llm: {e}"
logging.error(err)
raise
1 change: 1 addition & 0 deletions backend/src/ragas_eval.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,6 +17,7 @@
load_dotenv()

EMBEDDING_MODEL = os.getenv("RAGAS_EMBEDDING_MODEL")
logging.info(f"Loading embedding model '{EMBEDDING_MODEL}' for ragas evaluation")
EMBEDDING_FUNCTION, _ = load_embedding_model(EMBEDDING_MODEL)

def get_ragas_metrics(question: str, context: list, answer: list, model: str):
Expand Down
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