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LLM dynamic settings, using the llm-model and llm-rag-model paramters to a flow (#531)

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* Ported LLMs to dynamic models
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cybermaggedon 2025-09-24 16:36:25 +01:00 • committed by GitHub
parent 9a34ab1b93
commit 7a3bfad826
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15 changed files with 266 additions and 143 deletions
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55
trustgraph-bedrock/trustgraph/model/text_completion/bedrock/llm.py
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@ -183,13 +183,13 @@ class Processor(LlmService):
}
)
self.model = model
# Store default configuration
self.default_model = model
self.temperature = temperature
self.max_output = max_output
self.variant = self.determine_variant(self.model)()
self.variant.set_temperature(temperature)
self.variant.set_max_output(max_output)
# Cache for model variants to avoid re-initialization
self.model_variants = {}
self.session = boto3.Session(
aws_access_key_id=aws_access_key_id,
@ -208,47 +208,66 @@ class Processor(LlmService):
# FIXME: Missing, Amazon models, Deepseek
# This set of conditions deals with normal bedrock on-demand usage
if self.model.startswith("mistral"):
if model.startswith("mistral"):
return Mistral
elif self.model.startswith("meta"):
elif model.startswith("meta"):
return Meta
elif self.model.startswith("anthropic"):
elif model.startswith("anthropic"):
return Anthropic
elif self.model.startswith("ai21"):
elif model.startswith("ai21"):
return Ai21
elif self.model.startswith("cohere"):
elif model.startswith("cohere"):
return Cohere
# The inference profiles
if self.model.startswith("us.meta"):
if model.startswith("us.meta"):
return Meta
elif self.model.startswith("us.anthropic"):
elif model.startswith("us.anthropic"):
return Anthropic
elif self.model.startswith("eu.meta"):
elif model.startswith("eu.meta"):
return Meta
elif self.model.startswith("eu.anthropic"):
elif model.startswith("eu.anthropic"):
return Anthropic
return Default
async def generate_content(self, system, prompt):
def _get_or_create_variant(self, model_name):
"""Get cached model variant or create new one"""
if model_name not in self.model_variants:
logger.info(f"Creating model variant for '{model_name}'")
variant_class = self.determine_variant(model_name)
variant = variant_class()
variant.set_temperature(self.temperature)
variant.set_max_output(self.max_output)
self.model_variants[model_name] = variant
return self.model_variants[model_name]
async def generate_content(self, system, prompt, model=None):
# Use provided model or fall back to default
model_name = model or self.default_model
logger.debug(f"Using model: {model_name}")
try:
# Get the appropriate variant for this model
variant = self._get_or_create_variant(model_name)
promptbody = self.variant.encode_request(system, prompt)
promptbody = variant.encode_request(system, prompt)
accept = 'application/json'
contentType = 'application/json'
response = self.bedrock.invoke_model(
body=promptbody,
modelId=self.model,
modelId=model_name,
accept=accept,
contentType=contentType
)
# Response structure decode
outputtext = self.variant.decode_response(response)
outputtext = variant.decode_response(response)
metadata = response['ResponseMetadata']['HTTPHeaders']
inputtokens = int(metadata['x-amzn-bedrock-input-token-count'])
@ -262,7 +281,7 @@ class Processor(LlmService):
text = outputtext,
in_token = inputtokens,
out_token = outputtokens,
model = self.model
model = model_name
)
return resp