mirror of
https://github.com/trustgraph-ai/trustgraph.git
synced 2026-04-25 00:16:23 +02:00
Google AI Studio (#110)
* Testing GoogleAIStudio support --------- Co-authored-by: JackColquitt <daniel@kalntera.ai>
This commit is contained in:
cybermaggedon
GitHub
parent
aea30ae0d3
commit
942eedb7b0
6 changed files with 231 additions and 1 deletions
|
|
@ -13,7 +13,7 @@ RUN dnf install -y python3 python3-pip python3-wheel python3-aiohttp \
|
|||
|
||||
RUN pip3 install torch --index-url https://download.pytorch.org/whl/cpu
|
||||
|
||||
RUN pip3 install anthropic boto3 cohere openai google-cloud-aiplatform ollama \
|
||||
RUN pip3 install anthropic boto3 cohere openai google-cloud-aiplatform ollama google-generativeai \
|
||||
langchain langchain-core langchain-huggingface langchain-text-splitters \
|
||||
langchain-community pymilvus sentence-transformers transformers \
|
||||
huggingface-hub pulsar-client cassandra-driver pyarrow pyyaml \
|
||||
|
|
|
|||
6
trustgraph-flow/scripts/text-completion-googleaistudio
Executable file
6
trustgraph-flow/scripts/text-completion-googleaistudio
Executable file
|
|
@ -0,0 +1,6 @@
|
|||
#!/usr/bin/env python3
|
||||
|
||||
from trustgraph.model.text_completion.googleaistudio import run
|
||||
|
||||
run()
|
||||
|
||||
|
|
@ -55,6 +55,7 @@ setuptools.setup(
|
|||
"openai",
|
||||
"neo4j",
|
||||
"tiktoken",
|
||||
"google-generativeai",
|
||||
],
|
||||
scripts=[
|
||||
"scripts/chunker-recursive",
|
||||
|
|
@ -86,6 +87,7 @@ setuptools.setup(
|
|||
"scripts/text-completion-azure-openai",
|
||||
"scripts/text-completion-claude",
|
||||
"scripts/text-completion-cohere",
|
||||
"scripts/text-completion-googleaistudio",
|
||||
"scripts/text-completion-llamafile",
|
||||
"scripts/text-completion-ollama",
|
||||
"scripts/text-completion-openai",
|
||||
|
|
|
|||
|
|
@ -0,0 +1,3 @@
|
|||
|
||||
from . llm import *
|
||||
|
||||
|
|
@ -0,0 +1,7 @@
|
|||
#!/usr/bin/env python3
|
||||
|
||||
from . llm import run
|
||||
|
||||
if __name__ == '__main__':
|
||||
run()
|
||||
|
||||
|
|
@ -0,0 +1,212 @@
|
|||
|
||||
"""
|
||||
Simple LLM service, performs text prompt completion using GoogleAIStudio.
|
||||
Input is prompt, output is response.
|
||||
"""
|
||||
|
||||
import google.generativeai as genai
|
||||
from prometheus_client import Histogram
|
||||
|
||||
from .... schema import TextCompletionRequest, TextCompletionResponse, Error
|
||||
from .... schema import text_completion_request_queue
|
||||
from .... schema import text_completion_response_queue
|
||||
from .... log_level import LogLevel
|
||||
from .... base import ConsumerProducer
|
||||
from .... exceptions import TooManyRequests
|
||||
|
||||
module = ".".join(__name__.split(".")[1:-1])
|
||||
|
||||
default_input_queue = text_completion_request_queue
|
||||
default_output_queue = text_completion_response_queue
|
||||
default_subscriber = module
|
||||
default_model = 'gemini-1.5-flash-002'
|
||||
default_temperature = 0.0
|
||||
default_max_output = 8192
|
||||
|
||||
class Processor(ConsumerProducer):
|
||||
|
||||
def __init__(self, **params):
|
||||
|
||||
input_queue = params.get("input_queue", default_input_queue)
|
||||
output_queue = params.get("output_queue", default_output_queue)
|
||||
subscriber = params.get("subscriber", default_subscriber)
|
||||
model = params.get("model", default_model)
|
||||
api_key = params.get("api_key")
|
||||
temperature = params.get("temperature", default_temperature)
|
||||
max_output = params.get("max_output", default_max_output)
|
||||
|
||||
super(Processor, self).__init__(
|
||||
**params | {
|
||||
"input_queue": input_queue,
|
||||
"output_queue": output_queue,
|
||||
"subscriber": subscriber,
|
||||
"input_schema": TextCompletionRequest,
|
||||
"output_schema": TextCompletionResponse,
|
||||
"model": model,
|
||||
"temperature": temperature,
|
||||
"max_output": max_output,
|
||||
}
|
||||
)
|
||||
|
||||
if not hasattr(__class__, "text_completion_metric"):
|
||||
__class__.text_completion_metric = Histogram(
|
||||
'text_completion_duration',
|
||||
'Text completion duration (seconds)',
|
||||
buckets=[
|
||||
0.25, 0.5, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0,
|
||||
8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0,
|
||||
17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0, 25.0,
|
||||
30.0, 35.0, 40.0, 45.0, 50.0, 60.0, 80.0, 100.0,
|
||||
120.0
|
||||
]
|
||||
)
|
||||
|
||||
genai.configure(api_key=api_key)
|
||||
self.model = model
|
||||
self.temperature = temperature
|
||||
self.max_output = max_output
|
||||
|
||||
self.generation_config = {
|
||||
"temperature": temperature,
|
||||
"top_p": 1,
|
||||
"top_k": 40,
|
||||
"max_output_tokens": max_output,
|
||||
"response_mime_type": "text/plain",
|
||||
}
|
||||
|
||||
block_level = HarmBlockThreshold.BLOCK_ONLY_HIGH
|
||||
|
||||
self.safety_settings={
|
||||
HarmCategory.HARM_CATEGORY_HATE_SPEECH: block_level,
|
||||
HarmCategory.HARM_CATEGORY_HARASSMENT: block_level,
|
||||
HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: block_level,
|
||||
HarmCategory.HARM_CATEGORY_DANGEROUS: block_level,
|
||||
HarmCategory.HARM_CATEGORY_CIVIC_INTEGRITY: block_level,
|
||||
}
|
||||
|
||||
self.llm = genai.GenerativeModel(
|
||||
model_name=model,
|
||||
generation_config=self.generation_config,
|
||||
safety_settings=self.safety_settings,
|
||||
system_instruction="You are a helpful AI assistant.",
|
||||
)
|
||||
|
||||
print("Initialised", flush=True)
|
||||
|
||||
def handle(self, msg):
|
||||
|
||||
v = msg.value()
|
||||
|
||||
# Sender-produced ID
|
||||
|
||||
id = msg.properties()["id"]
|
||||
|
||||
print(f"Handling prompt {id}...", flush=True)
|
||||
|
||||
prompt = v.prompt
|
||||
|
||||
try:
|
||||
|
||||
# FIXME: Rate limits?
|
||||
|
||||
with __class__.text_completion_metric.time():
|
||||
|
||||
chat_session = self.llm.start_chat(
|
||||
history=[
|
||||
]
|
||||
)
|
||||
response = chat_session.send_message(prompt)
|
||||
|
||||
resp = response.text
|
||||
inputtokens = int(response.usage_metadata.prompt_token_count)
|
||||
outputtokens = int(response.usage_metadata.candidates_token_count)
|
||||
print(resp, flush=True)
|
||||
print(f"Input Tokens: {inputtokens}", flush=True)
|
||||
print(f"Output Tokens: {outputtokens}", flush=True)
|
||||
|
||||
print("Send response...", flush=True)
|
||||
r = TextCompletionResponse(response=resp, error=None, in_token=inputtokens, out_token=outputtokens, model=self.model)
|
||||
self.send(r, properties={"id": id})
|
||||
|
||||
print("Done.", flush=True)
|
||||
|
||||
# FIXME: Wrong exception, don't know what this LLM throws
|
||||
# for a rate limit
|
||||
except TooManyRequests:
|
||||
|
||||
print("Send rate limit response...", flush=True)
|
||||
|
||||
r = TextCompletionResponse(
|
||||
error=Error(
|
||||
type = "rate-limit",
|
||||
message = str(e),
|
||||
),
|
||||
response=None,
|
||||
in_token=None,
|
||||
out_token=None,
|
||||
model=None,
|
||||
)
|
||||
|
||||
self.producer.send(r, properties={"id": id})
|
||||
|
||||
self.consumer.acknowledge(msg)
|
||||
|
||||
except Exception as e:
|
||||
|
||||
print(f"Exception: {e}")
|
||||
|
||||
print("Send error response...", flush=True)
|
||||
|
||||
r = TextCompletionResponse(
|
||||
error=Error(
|
||||
type = "llm-error",
|
||||
message = str(e),
|
||||
),
|
||||
response=None,
|
||||
in_token=None,
|
||||
out_token=None,
|
||||
model=None,
|
||||
)
|
||||
|
||||
self.producer.send(r, properties={"id": id})
|
||||
|
||||
self.consumer.acknowledge(msg)
|
||||
|
||||
@staticmethod
|
||||
def add_args(parser):
|
||||
|
||||
ConsumerProducer.add_args(
|
||||
parser, default_input_queue, default_subscriber,
|
||||
default_output_queue,
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
'-m', '--model',
|
||||
default=default_model,
|
||||
help=f'LLM model (default: {default_model})'
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
'-k', '--api-key',
|
||||
help=f'GoogleAIStudio API key'
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
'-t', '--temperature',
|
||||
type=float,
|
||||
default=default_temperature,
|
||||
help=f'LLM temperature parameter (default: {default_temperature})'
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
'-x', '--max-output',
|
||||
type=int,
|
||||
default=default_max_output,
|
||||
help=f'LLM max output tokens (default: {default_max_output})'
|
||||
)
|
||||
|
||||
def run():
|
||||
|
||||
Processor.start(module, __doc__)
|
||||
|
||||
|
||||
Loading…
Add table
Add a link
Reference in a new issue