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Merge branch 'release/v0.21'

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This commit is contained in:
Cyber MacGeddon 2025-03-25 21:13:31 +00:00
commit f8586dd9c3
7 changed files with 149 additions and 11 deletions
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2
templates/components.jsonnet
View file

@ -11,6 +11,7 @@
"claude": import "components/claude.jsonnet",
"cohere": import "components/cohere.jsonnet",
"googleaistudio": import "components/googleaistudio.jsonnet",
"lmstudio": import "components/lmstudio.jsonnet",
"mistral": import "components/mistral.jsonnet",
"ollama": import "components/ollama.jsonnet",
"openai": import "components/openai.jsonnet",
@ -23,6 +24,7 @@
"claude-rag": import "components/claude-rag.jsonnet",
"cohere-rag": import "components/cohere-rag.jsonnet",
"googleaistudio-rag": import "components/googleaistudio-rag.jsonnet",
"lmstudio-rag": import "components/lmstudio-rag.jsonnet",
"mistral-rag": import "components/mistral-rag.jsonnet",
"ollama-rag": import "components/ollama-rag.jsonnet",
"openai-rag": import "components/openai-rag.jsonnet",

63
templates/components/lmstudio-rag.jsonnet Normal file
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@ -0,0 +1,63 @@
local base = import "base/base.jsonnet";
local images = import "values/images.jsonnet";
local url = import "values/url.jsonnet";
local prompts = import "prompts/mixtral.jsonnet";
{
with:: function(key, value)
self + {
["lmstudio-rag-" + key]:: value,
},
"lmstudio-rag-max-output-tokens":: 4096,
"lmstudio-rag-temperature":: 0.0,
"lmstudio-rag-model":: "GPT-3.5-Turbo",
"text-completion-rag" +: {
create:: function(engine)
local envSecrets = engine.envSecrets("lmstudio-credentials")
.with_env_var("LMSTUDIO_URL", "lmstudio-url");
local containerRag =
engine.container("text-completion-rag")
.with_image(images.trustgraph_flow)
.with_command([
"text-completion-lmstudio",
"-p",
url.pulsar,
"-x",
std.toString($["lmstudio-rag-max-output-tokens"]),
"-t",
"%0.3f" % $["lmstudio-rag-temperature"],
"-m",
$["lmstudio-rag-model"],
"-i",
"non-persistent://tg/request/text-completion-rag",
"-o",
"non-persistent://tg/response/text-completion-rag",
])
.with_env_var_secrets(envSecrets)
.with_limits("0.5", "128M")
.with_reservations("0.1", "128M");
local containerSetRag = engine.containers(
"text-completion-rag", [ containerRag ]
);
local serviceRag =
engine.internalService(containerSetRag)
.with_port(8080, 8080, "metrics");
engine.resources([
envSecrets,
containerSetRag,
serviceRag,
])
},
} + prompts

59
templates/components/lmstudio.jsonnet Normal file
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@ -0,0 +1,59 @@
local base = import "base/base.jsonnet";
local images = import "values/images.jsonnet";
local url = import "values/url.jsonnet";
local prompts = import "prompts/mixtral.jsonnet";
{
with:: function(key, value)
self + {
["lmstudio-" + key]:: value,
},
"lmstudio-max-output-tokens":: 4096,
"lmstudio-temperature":: 0.0,
"lmstudio-model":: "GPT-3.5-Turbo",
"text-completion" +: {
create:: function(engine)
local envSecrets = engine.envSecrets("lmstudio-credentials")
.with_env_var("LMSTUDIO_URL", "lmstudio-url");
local container =
engine.container("text-completion")
.with_image(images.trustgraph_flow)
.with_command([
"text-completion-lmstudio",
"-p",
url.pulsar,
"-x",
std.toString($["lmstudio-max-output-tokens"]),
"-t",
"%0.3f" % $["lmstudio-temperature"],
"-m",
$["lmstudio-model"],
])
.with_env_var_secrets(envSecrets)
.with_limits("0.5", "128M")
.with_reservations("0.1", "128M");
local containerSet = engine.containers(
"text-completion", [ container ]
);
local service =
engine.internalService(containerSet)
.with_port(8080, 8080, "metrics");
engine.resources([
envSecrets,
containerSet,
service,
])
},
} + prompts

2
templates/components/mistral-ocr.jsonnet
View file

@ -43,5 +43,5 @@ local url = import "values/url.jsonnet";
},
} + prompts
}

3
templates/components/ocr.jsonnet
View file

@ -27,12 +27,11 @@ local url = import "values/url.jsonnet";
.with_port(8080, 8080, "metrics");
engine.resources([
envSecrets,
containerSet,
service,
])
},
} + prompts
}

18
trustgraph-flow/trustgraph/model/text_completion/mistral/llm.py
View file

@ -4,7 +4,7 @@ Simple LLM service, performs text prompt completion using Mistral.
Input is prompt, output is response.
"""
from mistralai import Mistral, RateLimitError
from mistralai import Mistral
from prometheus_client import Histogram
import os
@ -130,12 +130,18 @@ class Processor(ConsumerProducer):
print("Done.", flush=True)
# FIXME: Wrong exception, don't know what this LLM throws
# for a rate limit
except Mistral.RateLimitError:
# FIXME: Wrong exception. The MistralAI library has retry logic
# so retry-able errors are retried transparently. It means we
# don't get rate limit events.
# Leave rate limit retries to the base handler
raise TooManyRequests()
# We could choose to turn off retry and handle all that here
# or subclass BackoffStrategy to keep the retry logic, but
# get the events out.
# except Mistral.RateLimitError:
# # Leave rate limit retries to the base handler
# raise TooManyRequests()
except Exception as e:

13
trustgraph-flow/trustgraph/model/text_completion/openai/llm.py
View file

@ -24,6 +24,7 @@ default_model = 'gpt-3.5-turbo'
default_temperature = 0.0
default_max_output = 4096
default_api_key = os.getenv("OPENAI_TOKEN")
default_base_url = os.getenv("OPENAI_BASE_URL", None)
class Processor(ConsumerProducer):
@ -34,6 +35,7 @@ class Processor(ConsumerProducer):
subscriber = params.get("subscriber", default_subscriber)
model = params.get("model", default_model)
api_key = params.get("api_key", default_api_key)
base_url = params.get("base_url", default_base_url)
temperature = params.get("temperature", default_temperature)
max_output = params.get("max_output", default_max_output)
@ -50,6 +52,7 @@ class Processor(ConsumerProducer):
"model": model,
"temperature": temperature,
"max_output": max_output,
"base_url": base_url,
}
)
@ -69,7 +72,7 @@ class Processor(ConsumerProducer):
self.model = model
self.temperature = temperature
self.max_output = max_output
self.openai = OpenAI(api_key=api_key)
self.openai = OpenAI(base_url=base_url, api_key=api_key)
print("Initialised", flush=True)
@ -132,7 +135,7 @@ class Processor(ConsumerProducer):
# FIXME: Wrong exception, don't know what this LLM throws
# for a rate limit
except openai.RateLimitError:
except RateLimitError:
# Leave rate limit retries to the base handler
raise TooManyRequests()
@ -180,6 +183,12 @@ class Processor(ConsumerProducer):
help=f'OpenAI API key'
)
parser.add_argument(
'-u', '--url',
default=default_base_url,
help=f'OpenAI service base URL'
)
parser.add_argument(
'-t', '--temperature',
type=float,