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Merge pull request #379 from trustgraph-ai/release/v0.23

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Release/v0.23 to master
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32
.github/workflows/release.yaml vendored
View file

@ -71,35 +71,3 @@ jobs:
- name: Push containers
run: make push VERSION=${{ steps.version.outputs.VERSION }}
release-bundle:
name: Upload release bundle
runs-on: ubuntu-24.04
permissions:
contents: write
id-token: write
environment:
name: release
steps:
- name: Checkout
uses: actions/checkout@v4
- name: Install build dependencies
run: pip3 install jsonnet
- name: Get version
id: version
run: echo VERSION=$(git describe --exact-match --tags | sed 's/^v//') >> $GITHUB_OUTPUT
- name: Create deploy bundle
run: templates/generate-all deploy.zip ${{ steps.version.outputs.VERSION }}
- uses: ncipollo/release-action@v1
with:
artifacts: deploy.zip
generateReleaseNotes: true
makeLatest: false
prerelease: true
skipIfReleaseExists: true

863
LICENSE
View file

@ -1,660 +1,203 @@
# GNU AFFERO GENERAL PUBLIC LICENSE
Version 3, 19 November 2007
Copyright (C) 2007 Free Software Foundation, Inc.
<https://fsf.org/>
Everyone is permitted to copy and distribute verbatim copies of this
license document, but changing it is not allowed.
## Preamble
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### 8. Termination.
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### 12. No Surrender of Others' Freedom.
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### 13. Remote Network Interaction; Use with the GNU General Public License.
Notwithstanding any other provision of this License, if you modify the
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### 14. Revised Versions of this License.
The Free Software Foundation may publish revised and/or new versions
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If the Program specifies that a proxy can decide which future versions
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### 15. Disclaimer of Warranty.
THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
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HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT
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DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING, REPAIR OR
CORRECTION.
### 16. Limitation of Liability.
IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
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CONVEYS THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES,
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LOSSES SUSTAINED BY YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM
TO OPERATE WITH ANY OTHER PROGRAMS), EVEN IF SUCH HOLDER OR OTHER
PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES.
### 17. Interpretation of Sections 15 and 16.
If the disclaimer of warranty and limitation of liability provided
above cannot be given local legal effect according to their terms,
reviewing courts shall apply local law that most closely approximates
an absolute waiver of all civil liability in connection with the
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END OF TERMS AND CONDITIONS
## How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest
possible use to the public, the best way to achieve this is to make it
free software which everyone can redistribute and change under these
terms.
To do so, attach the following notices to the program. It is safest to
attach them to the start of each source file to most effectively state
the exclusion of warranty; and each file should have at least the
"copyright" line and a pointer to where the full notice is found.
<one line to give the program's name and a brief idea of what it does.>
Copyright (C) <year> <name of author>
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as
published by the Free Software Foundation, either version 3 of the
License, or (at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU Affero General Public License for more details.
You should have received a copy of the GNU Affero General Public License
along with this program. If not, see <https://www.gnu.org/licenses/>.
Also add information on how to contact you by electronic and paper
mail.
If your software can interact with users remotely through a computer
network, you should also make sure that it provides a way for users to
get its source. For example, if your program is a web application, its
interface could display a "Source" link that leads users to an archive
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You should also get your employer (if you work as a programmer) or
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the GNU AGPL, see <https://www.gnu.org/licenses/>.
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To apply the Apache License to your work, attach the following
boilerplate notice, with the fields enclosed by brackets "[]"
replaced with your own identifying information. (Don't include
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16
Makefile
View file

@ -60,6 +60,22 @@ container: update-package-versions
${DOCKER} build -f containers/Containerfile.ocr \
-t ${CONTAINER_BASE}/trustgraph-ocr:${VERSION} .
some-containers:
${DOCKER} build -f containers/Containerfile.base \
-t ${CONTAINER_BASE}/trustgraph-base:${VERSION} .
${DOCKER} build -f containers/Containerfile.flow \
-t ${CONTAINER_BASE}/trustgraph-flow:${VERSION} .
# ${DOCKER} build -f containers/Containerfile.vertexai \
# -t ${CONTAINER_BASE}/trustgraph-vertexai:${VERSION} .
# ${DOCKER} build -f containers/Containerfile.bedrock \
# -t ${CONTAINER_BASE}/trustgraph-bedrock:${VERSION} .
basic-containers: update-package-versions
${DOCKER} build -f containers/Containerfile.base \
-t ${CONTAINER_BASE}/trustgraph-base:${VERSION} .
${DOCKER} build -f containers/Containerfile.flow \
-t ${CONTAINER_BASE}/trustgraph-flow:${VERSION} .
container.ocr:
${DOCKER} build -f containers/Containerfile.ocr \
-t ${CONTAINER_BASE}/trustgraph-ocr:${VERSION} .

17
README.md
View file

@ -282,7 +282,22 @@ The default Grafana dashboard tracks the following:
[Developing for TrustGraph](docs/README.development.md)
## 📄 License
**TrustGraph** is licensed under [AGPL-3.0](https://www.gnu.org/licenses/agpl-3.0.en.html).
**TrustGraph** is licensed under [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0).
Copyright 2024-2025 TrustGraph
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
## 📞 Support & Community
- Bug Reports & Feature Requests: [Discord](https://discord.gg/sQMwkRz5GX)

125
templates/README.md
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@ -1,125 +0,0 @@
# TrustGraph template generation
There are two utilities here:
- `generate`: Generates a single Docker Compose launch configuration
based on configuration you provide.
- `generate-all`: Generates the release bundle for releases. You won't
need to use this unless you are managing releases.
## `generate-all`
Previously, this generates a full set of all vector DB / triple store / LLM
combinations, and put them in a single ZIP file. But this got out of
hand, so at the time of writing, this generates a single configuraton
using Qdrant vector DB, Ollama LLM support and Cassandra for a triple store.
The combinations are contained withing the code, it takes two arguments:
- output ZIP file (is over-written)
- TrustGraph version number
```
templates/generate-all output.zip 0.18.11
```
## `generate`
This utility takes a configuration file describing the components to bundle,
and outputs a Docker Compose YAML file.
### Input configuration
The input configuration is a JSON file, an array of components to pull into
the configuration. For each component, there is a name and a (possibly empty)
object describing addtional parameters for that component.
Example:
```
[
{
"name": "cassandra",
"parameters": {}
},
{
"name": "pulsar",
"parameters": {}
},
{
"name": "qdrant",
"parameters": {}
},
{
"name": "embeddings-hf",
"parameters": {}
},
{
"name": "graph-rag",
"parameters": {}
},
{
"name": "grafana",
"parameters": {}
},
{
"name": "trustgraph",
"parameters": {}
},
{
"name": "googleaistudio",
"parameters": {
"googleaistudio-temperature": 0.3,
"googleaistudio-max-output-tokens": 2048,
"googleaistudio-model": "gemini-1.5-pro-002"
}
},
{
"name": "prompt-template",
"parameters": {}
},
{
"name": "override-recursive-chunker",
"parameters": {
"chunk-size": 1000,
"chunk-overlap": 50
}
},
{
"name": "workbench-ui",
"parameters": {}
},
{
"name": "agent-manager-react",
"parameters": {}
}
]
```
If you want to make your own configuration you could try changing the
configuration above:
- Components which are essential: pulsar, trustgraph, graph-rag, grafana,
agent-manager-react
- You need a triple store, one of: cassandra, memgraph, falkordb, neo4j
- You need a vector store, one of: qdrant, pinecone
- You need an LLM, one of: azure, azure-openai, bedrock, claude, cohere,
llamafile, ollama, openai, vertexai.
- You need an embeddings implementation, one of: embeddings-hf,
embeddings-ollama
- Optionally add the Workbench tool: workbench-ui
Components have over-ridable parameters, look in the component definition
in `templates/components/` to see what you can override.
### Invocation
Two parameters:
- The output ZIP file
- The version number
The configuration file described above is provided on standard input
```
templates/generate out.zip 0.18.9 < config.json
```

31
templates/all-patterns.jsonnet
View file

@ -1,31 +0,0 @@
[
import "patterns/document-rag.jsonnet",
import "patterns/embeddings-hf.jsonnet",
import "patterns/embeddings-ollama.jsonnet",
import "patterns/grafana.jsonnet",
import "patterns/triple-store-cassandra.jsonnet",
import "patterns/triple-store-neo4j.jsonnet",
import "patterns/triple-store-falkordb.jsonnet",
import "patterns/graph-rag.jsonnet",
import "patterns/llm-azure.jsonnet",
import "patterns/llm-azure-openai.jsonnet",
import "patterns/llm-bedrock.jsonnet",
import "patterns/llm-claude.jsonnet",
import "patterns/llm-cohere.jsonnet",
import "patterns/llm-llamafile.jsonnet",
import "patterns/llm-mistral.jsonnet",
import "patterns/llm-ollama.jsonnet",
import "patterns/llm-openai.jsonnet",
import "patterns/llm-vertexai.jsonnet",
import "patterns/override-recursive-chunker.jsonnet",
import "patterns/prompt-template-definitions.jsonnet",
import "patterns/prompt-template-document-query.jsonnet",
import "patterns/prompt-template-kg-query.jsonnet",
import "patterns/prompt-template-relationships.jsonnet",
import "patterns/prompt-template-rows-template.jsonnet",
import "patterns/pulsar-manager.jsonnet",
import "patterns/pulsar.jsonnet",
import "patterns/trustgraph-base.jsonnet",
import "patterns/vector-store-milvus.jsonnet",
import "patterns/vector-store-qdrant.jsonnet",
]

3
templates/base/base.jsonnet
View file

@ -1,3 +0,0 @@
{
restart: "on-failure:100",
}

82
templates/components.jsonnet
View file

@ -1,82 +0,0 @@
{
// Essentials
"trustgraph-base": import "components/trustgraph.jsonnet",
"pulsar": import "components/pulsar.jsonnet",
// LLMs
"azure": import "components/azure.jsonnet",
"azure-openai": import "components/azure-openai.jsonnet",
"bedrock": import "components/bedrock.jsonnet",
"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",
"vertexai": import "components/vertexai.jsonnet",
// LLMs for RAG
"azure-rag": import "components/azure-rag.jsonnet",
"azure-openai-rag": import "components/azure-openai-rag.jsonnet",
"bedrock-rag": import "components/bedrock-rag.jsonnet",
"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",
"vertexai-rag": import "components/vertexai-rag.jsonnet",
// Embeddings
"embeddings-ollama": import "components/embeddings-ollama.jsonnet",
"embeddings-hf": import "components/embeddings-hf.jsonnet",
"embeddings-fastembed": import "components/embeddings-fastembed.jsonnet",
// Processing pipelines
"graph-rag": import "components/graph-rag.jsonnet",
"document-rag": import "components/document-rag.jsonnet",
// OCR options
"ocr": import "components/ocr.jsonnet",
"mistral-ocr": import "components/mistral-ocr.jsonnet",
// Librarian - document management
"librarian": import "components/librarian.jsonnet",
// Vector stores
"vector-store-milvus": import "components/milvus.jsonnet",
"vector-store-qdrant": import "components/qdrant.jsonnet",
"vector-store-pinecone": import "components/pinecone.jsonnet",
// Triples stores
"triple-store-cassandra": import "components/cassandra.jsonnet",
"triple-store-neo4j": import "components/neo4j.jsonnet",
"triple-store-falkordb": import "components/falkordb.jsonnet",
"triple-store-memgraph": import "components/memgraph.jsonnet",
// Observability support
"grafana": import "components/grafana.jsonnet",
// Pulsar manager is a UI for Pulsar. Uses a LOT of memory
"pulsar-manager": import "components/pulsar-manager.jsonnet",
"llamafile": import "components/llamafile.jsonnet",
"override-recursive-chunker": import "components/chunker-recursive.jsonnet",
// The prompt manager
"prompt-template": import "components/prompt-template.jsonnet",
"prompt-overrides": import "components/prompt-overrides.jsonnet",
// ReAct agent
"agent-manager-react": import "components/agent-manager-react.jsonnet",
// Optional UI
"workbench-ui": import "components/workbench-ui.jsonnet",
// Does nothing. But, can be a hack to overwrite parameters
"null": {},
}

64
templates/components/agent-manager-react.jsonnet
View file

@ -1,64 +0,0 @@
local base = import "base/base.jsonnet";
local images = import "values/images.jsonnet";
local url = import "values/url.jsonnet";
local prompts = import "prompts/mixtral.jsonnet";
local default_prompts = import "prompts/default-prompts.jsonnet";
{
tools:: [],
"agent-manager" +: {
create:: function(engine)
local container =
engine.container("agent-manager")
.with_image(images.trustgraph_flow)
.with_command([
"agent-manager-react",
"-p",
url.pulsar,
"--prompt-request-queue",
"non-persistent://tg/request/prompt-rag",
"--prompt-response-queue",
"non-persistent://tg/response/prompt-rag",
"--tool-type",
] + [
tool.id + "=" + tool.type
for tool in $.tools
] + [
"--tool-description"
] + [
tool.id + "=" + tool.description
for tool in $.tools
] + [
"--tool-argument"
] + [
"%s=%s:%s:%s" % [
tool.id, arg.name, arg.type, arg.description
]
for tool in $.tools
for arg in tool.arguments
]
)
.with_limits("0.5", "128M")
.with_reservations("0.1", "128M");
local containerSet = engine.containers(
"agent-manager", [ container ]
);
local service =
engine.internalService(containerSet)
.with_port(8000, 8000, "metrics");
engine.resources([
containerSet,
service,
])
},
} + default_prompts

61
templates/components/azure-openai-rag.jsonnet
View file

@ -1,61 +0,0 @@
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 + {
["ollama-rag-" + key]:: value,
},
"azure-openai-rag-model":: "GPT-3.5-Turbo",
"azure-openai-rag-max-output-tokens":: 4192,
"azure-openai-rag-temperature":: 0.0,
"text-completion-rag" +: {
create:: function(engine)
local envSecrets = engine.envSecrets("azure-openai-credentials")
.with_env_var("AZURE_TOKEN", "azure-token");
local containerRag =
engine.container("text-completion-rag")
.with_image(images.trustgraph_flow)
.with_command([
"text-completion-azure",
"-p",
url.pulsar,
"-x",
std.toString($["azure-openai-rag-max-output-tokens"]),
"-t",
"%0.3f" % $["azure-openai-rag-temperature"],
"-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(8000, 8000, "metrics");
engine.resources([
envSecrets,
containerSetRag,
serviceRag,
])
},
} + prompts

59
templates/components/azure-openai.jsonnet
View file

@ -1,59 +0,0 @@
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 + {
["azure-openai-" + key]:: value,
},
"azure-openai-model":: "GPT-3.5-Turbo",
"azure-openai-max-output-tokens":: 4192,
"azure-openai-temperature":: 0.0,
"text-completion" +: {
create:: function(engine)
local envSecrets = engine.envSecrets("azure-openai-credentials")
.with_env_var("AZURE_TOKEN", "azure-token");
local container =
engine.container("text-completion")
.with_image(images.trustgraph_flow)
.with_command([
"text-completion-azure-openai",
"-p",
url.pulsar,
"-m",
$["azure-openai-model"],
"-x",
std.toString($["azure-openai-max-output-tokens"]),
"-t",
"%0.3f" % $["azure-openai-temperature"],
])
.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(8000, 8000, "metrics");
engine.resources([
envSecrets,
containerSet,
service,
])
},
} + prompts

60
templates/components/azure-rag.jsonnet
View file

@ -1,60 +0,0 @@
local images = import "values/images.jsonnet";
local url = import "values/url.jsonnet";
local prompts = import "prompts/mixtral.jsonnet";
{
with:: function(key, value)
self + {
["azure-rag-" + key]:: value,
},
"azure-rag-max-output-tokens":: 4096,
"azure-rag-temperature":: 0.0,
"text-completion-rag" +: {
create:: function(engine)
local envSecrets = engine.envSecrets("azure-credentials")
.with_env_var("AZURE_TOKEN", "azure-token")
.with_env_var("AZURE_ENDPOINT", "azure-endpoint");
local containerRag =
engine.container("text-completion-rag")
.with_image(images.trustgraph_flow)
.with_command([
"text-completion-azure",
"-p",
url.pulsar,
"-x",
std.toString($["azure-rag-max-output-tokens"]),
"-t",
"%0.3f" % $["azure-rag-temperature"],
"-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(8000, 8000, "metrics");
engine.resources([
envSecrets,
containerSetRag,
serviceRag,
])
}
} + prompts

56
templates/components/azure.jsonnet
View file

@ -1,56 +0,0 @@
local images = import "values/images.jsonnet";
local url = import "values/url.jsonnet";
local prompts = import "prompts/mixtral.jsonnet";
{
with:: function(key, value)
self + {
["azure-" + key]:: value,
},
"azure-max-output-tokens":: 4096,
"azure-temperature":: 0.0,
"text-completion" +: {
create:: function(engine)
local envSecrets = engine.envSecrets("azure-credentials")
.with_env_var("AZURE_TOKEN", "azure-token")
.with_env_var("AZURE_ENDPOINT", "azure-endpoint");
local container =
engine.container("text-completion")
.with_image(images.trustgraph_flow)
.with_command([
"text-completion-azure",
"-p",
url.pulsar,
"-x",
std.toString($["azure-max-output-tokens"]),
"-t",
"%0.3f" % $["azure-temperature"],
])
.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(8000, 8000, "metrics");
engine.resources([
envSecrets,
containerSet,
service,
])
}
} + prompts

66
templates/components/bedrock-rag.jsonnet
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@ -1,66 +0,0 @@
local base = import "base/base.jsonnet";
local images = import "values/images.jsonnet";
local url = import "values/url.jsonnet";
local prompts = import "prompts/mixtral.jsonnet";
local chunker = import "chunker-recursive.jsonnet";
{
with:: function(key, value)
self + {
["bedrock-rag-" + key]:: value,
},
"bedrock-rag-max-output-tokens":: 4096,
"bedrock-rag-temperature":: 0.0,
"bedrock-rag-model":: "mistral.mixtral-8x7b-instruct-v0:1",
"text-completion-rag" +: {
create:: function(engine)
local envSecrets = engine.envSecrets("bedrock-credentials")
.with_env_var("AWS_ACCESS_KEY_ID", "aws-id-key")
.with_env_var("AWS_SECRET_ACCESS_KEY", "aws-secret")
.with_env_var("AWS_DEFAULT_REGION", "aws-region");
local containerRag =
engine.container("text-completion-rag")
.with_image(images.trustgraph_bedrock)
.with_command([
"text-completion-bedrock",
"-p",
url.pulsar,
"-x",
std.toString($["bedrock-rag-max-output-tokens"]),
"-t",
"%0.3f" % $["bedrock-rag-temperature"],
"-m",
$["bedrock-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(8000, 8000, "metrics");
engine.resources([
envSecrets,
containerSetRag,
serviceRag,
])
},
} + prompts + chunker

62
templates/components/bedrock.jsonnet
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@ -1,62 +0,0 @@
local base = import "base/base.jsonnet";
local images = import "values/images.jsonnet";
local url = import "values/url.jsonnet";
local prompts = import "prompts/mixtral.jsonnet";
local chunker = import "chunker-recursive.jsonnet";
{
with:: function(key, value)
self + {
["bedrock-" + key]:: value,
},
"bedrock-max-output-tokens":: 4096,
"bedrock-temperature":: 0.0,
"bedrock-model":: "mistral.mixtral-8x7b-instruct-v0:1",
"text-completion" +: {
create:: function(engine)
local envSecrets = engine.envSecrets("bedrock-credentials")
.with_env_var("AWS_ACCESS_KEY_ID", "aws-id-key")
.with_env_var("AWS_SECRET_ACCESS_KEY", "aws-secret")
.with_env_var("AWS_DEFAULT_REGION", "aws-region");
local container =
engine.container("text-completion")
.with_image(images.trustgraph_bedrock)
.with_command([
"text-completion-bedrock",
"-p",
url.pulsar,
"-x",
std.toString($["bedrock-max-output-tokens"]),
"-t",
"%0.3f" % $["bedrock-temperature"],
"-m",
$["bedrock-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(8000, 8000, "metrics");
engine.resources([
envSecrets,
containerSet,
service,
])
},
} + prompts + chunker

74
templates/components/cassandra.jsonnet
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@ -1,74 +0,0 @@
local base = import "base/base.jsonnet";
local images = import "values/images.jsonnet";
local url = import "values/url.jsonnet";
local cassandra_hosts = "cassandra";
local cassandra = import "stores/cassandra.jsonnet";
cassandra + {
"store-triples" +: {
create:: function(engine)
local container =
engine.container("store-triples")
.with_image(images.trustgraph_flow)
.with_command([
"triples-write-cassandra",
"-p",
url.pulsar,
"-g",
cassandra_hosts,
])
.with_limits("0.5", "128M")
.with_reservations("0.1", "128M");
local containerSet = engine.containers(
"store-triples", [ container ]
);
local service =
engine.internalService(containerSet)
.with_port(8080, 8080, "metrics");
engine.resources([
containerSet,
service,
])
},
"query-triples" +: {
create:: function(engine)
local container =
engine.container("query-triples")
.with_image(images.trustgraph_flow)
.with_command([
"triples-query-cassandra",
"-p",
url.pulsar,
"-g",
cassandra_hosts,
])
.with_limits("0.5", "512M")
.with_reservations("0.1", "512M");
local containerSet = engine.containers(
"query-triples", [ container ]
);
local service =
engine.internalService(containerSet)
.with_port(8080, 8080, "metrics");
engine.resources([
containerSet,
service,
])
}
}

46
templates/components/chunker-recursive.jsonnet
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@ -1,46 +0,0 @@
local base = import "base/base.jsonnet";
local images = import "values/images.jsonnet";
local url = import "values/url.jsonnet";
local prompts = import "prompts/mixtral.jsonnet";
{
"chunk-size":: 2000,
"chunk-overlap":: 100,
"chunker" +: {
create:: function(engine)
local container =
engine.container("chunker")
.with_image(images.trustgraph_flow)
.with_command([
"chunker-recursive",
"-p",
url.pulsar,
"--chunk-size",
std.toString($["chunk-size"]),
"--chunk-overlap",
std.toString($["chunk-overlap"]),
])
.with_limits("0.5", "128M")
.with_reservations("0.1", "128M");
local containerSet = engine.containers(
"chunker", [ container ]
);
local service =
engine.internalService(containerSet)
.with_port(8000, 8000, "metrics");
engine.resources([
containerSet,
service,
])
},
}

63
templates/components/claude-rag.jsonnet
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@ -1,63 +0,0 @@
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 + {
["claude-rag-" + key]:: value,
},
"claude-rag-model":: "claude-3-sonnet-20240229",
"claude-rag-max-output-tokens":: 4096,
"claude-rag-temperature":: 0.0,
"text-completion-rag" +: {
create:: function(engine)
local envSecrets = engine.envSecrets("claude-credentials")
.with_env_var("CLAUDE_KEY", "claude-key");
local containerRag =
engine.container("text-completion-rag")
.with_image(images.trustgraph_flow)
.with_command([
"text-completion-claude",
"-p",
url.pulsar,
"-x",
std.toString($["claude-rag-max-output-tokens"]),
"-m",
$["claude-rag-model"],
"-t",
"%0.3f" % $["claude-rag-temperature"],
"-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(8000, 8000, "metrics");
engine.resources([
envSecrets,
containerSetRag,
serviceRag,
])
},
} + prompts

59
templates/components/claude.jsonnet
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@ -1,59 +0,0 @@
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 + {
["claude-" + key]:: value,
},
"claude-model":: "claude-3-sonnet-20240229",
"claude-max-output-tokens":: 4096,
"claude-temperature":: 0.0,
"text-completion" +: {
create:: function(engine)
local envSecrets = engine.envSecrets("claude-credentials")
.with_env_var("CLAUDE_KEY", "claude-key");
local container =
engine.container("text-completion")
.with_image(images.trustgraph_flow)
.with_command([
"text-completion-claude",
"-p",
url.pulsar,
"-x",
std.toString($["claude-max-output-tokens"]),
"-m",
$["claude-model"],
"-t",
"%0.3f" % $["claude-temperature"],
])
.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(8000, 8000, "metrics");
engine.resources([
envSecrets,
containerSet,
service,
])
},
} + prompts

56
templates/components/cohere-rag.jsonnet
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@ -1,56 +0,0 @@
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 + {
["cohere-rag-" + key]:: value,
},
"cohere-rag-temperature":: 0.0,
"text-completion-rag" +: {
create:: function(engine)
local envSecrets = engine.envSecrets("cohere-credentials")
.with_env_var("COHERE_KEY", "cohere-key");
local containerRag =
engine.container("text-completion-rag")
.with_image(images.trustgraph_flow)
.with_command([
"text-completion-cohere",
"-p",
url.pulsar,
"-t",
"%0.3f" % $["cohere-rag-temperature"],
"-i",
"non-persistent://tg/request/text-completion-rag",
"-o",
"non-persistent://tg/response/text-completion-rag",
])
.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(8000, 8000, "metrics");
engine.resources([
envSecrets,
containerSetRag,
serviceRag,
])
},
} + prompts

52
templates/components/cohere.jsonnet
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@ -1,52 +0,0 @@
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 + {
["cohere-" + key]:: value,
},
"cohere-temperature":: 0.0,
"text-completion" +: {
create:: function(engine)
local envSecrets = engine.envSecrets("cohere-credentials")
.with_env_var("COHERE_KEY", "cohere-key");
local container =
engine.container("text-completion")
.with_image(images.trustgraph_flow)
.with_command([
"text-completion-cohere",
"-p",
url.pulsar,
"-t",
"%0.3f" % $["cohere-temperature"],
])
.with_limits("0.5", "128M")
.with_reservations("0.1", "128M");
local containerSet = engine.containers(
"text-completion", [ container ]
);
local service =
engine.internalService(containerSet)
.with_port(8000, 8000, "metrics");
engine.resources([
envSecrets,
containerSet,
service,
])
},
} + prompts

77
templates/components/document-rag.jsonnet
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@ -1,77 +0,0 @@
local base = import "base/base.jsonnet";
local images = import "values/images.jsonnet";
local url = import "values/url.jsonnet";
local prompts = import "prompts/mixtral.jsonnet";
{
"document-rag-doc-limit":: 20,
"document-rag" +: {
create:: function(engine)
local container =
engine.container("document-rag")
.with_image(images.trustgraph_flow)
.with_command([
"document-rag",
"-p",
url.pulsar,
"--doc-limit",
std.toString($["document-rag-doc-limit"]),
"--prompt-request-queue",
"non-persistent://tg/request/prompt-rag",
"--prompt-response-queue",
"non-persistent://tg/response/prompt-rag",
])
.with_limits("0.5", "128M")
.with_reservations("0.1", "128M");
local containerSet = engine.containers(
"document-rag", [ container ]
);
local service =
engine.internalService(containerSet)
.with_port(8000, 8000, "metrics");
engine.resources([
containerSet,
service,
])
},
"document-embeddings" +: {
create:: function(engine)
local container =
engine.container("document-embeddings")
.with_image(images.trustgraph_flow)
.with_command([
"document-embeddings",
"-p",
url.pulsar,
])
.with_limits("1.0", "512M")
.with_reservations("0.5", "512M");
local containerSet = engine.containers(
"document-embeddings", [ container ]
);
local service =
engine.internalService(containerSet)
.with_port(8000, 8000, "metrics");
engine.resources([
containerSet,
service,
])
},
}

43
templates/components/embeddings-fastembed.jsonnet
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@ -1,43 +0,0 @@
local base = import "base/base.jsonnet";
local images = import "values/images.jsonnet";
local url = import "values/url.jsonnet";
local prompts = import "prompts/mixtral.jsonnet";
{
"embeddings-model":: "sentence-transformers/all-MiniLM-L6-v2",
embeddings +: {
create:: function(engine)
local container =
engine.container("embeddings")
.with_image(images.trustgraph_flow)
.with_command([
"embeddings-fastembed",
"-p",
url.pulsar,
"-m",
$["embeddings-model"],
])
.with_limits("1.0", "400M")
.with_reservations("0.5", "400M");
local containerSet = engine.containers(
"embeddings", [ container ]
);
local service =
engine.internalService(containerSet)
.with_port(8000, 8000, "metrics");
engine.resources([
containerSet,
service,
])
},
}

43
templates/components/embeddings-hf.jsonnet
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@ -1,43 +0,0 @@
local base = import "base/base.jsonnet";
local images = import "values/images.jsonnet";
local url = import "values/url.jsonnet";
local prompts = import "prompts/mixtral.jsonnet";
{
"embeddings-model":: "all-MiniLM-L6-v2",
embeddings +: {
create:: function(engine)
local container =
engine.container("embeddings")
.with_image(images.trustgraph_hf)
.with_command([
"embeddings-hf",
"-p",
url.pulsar,
"-m",
$["embeddings-model"],
])
.with_limits("1.0", "400M")
.with_reservations("0.5", "400M");
local containerSet = engine.containers(
"embeddings", [ container ]
);
local service =
engine.internalService(containerSet)
.with_port(8000, 8000, "metrics");
engine.resources([
containerSet,
service,
])
},
}

45
templates/components/embeddings-ollama.jsonnet
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@ -1,45 +0,0 @@
local base = import "base/base.jsonnet";
local images = import "values/images.jsonnet";
local url = import "values/url.jsonnet";
{
"embeddings-model":: "mxbai-embed-large",
"ollama-url":: "${OLLAMA_HOST}",
embeddings +: {
create:: function(engine)
local container =
engine.container("embeddings")
.with_image(images.trustgraph_flow)
.with_command([
"embeddings-ollama",
"-p",
url.pulsar,
"-m",
$["embeddings-model"],
"-r",
$["ollama-url"],
])
.with_limits("0.5", "128M")
.with_reservations("0.1", "128M");
local containerSet = engine.containers(
"embeddings", [ container ]
);
local service =
engine.internalService(containerSet)
.with_port(8000, 8000, "metrics");
engine.resources([
containerSet,
service,
])
},
}

76
templates/components/falkordb.jsonnet
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@ -1,76 +0,0 @@
local base = import "base/base.jsonnet";
local images = import "values/images.jsonnet";
local url = import "values/url.jsonnet";
local falkordb = import "stores/falkordb.jsonnet";
falkordb + {
"falkordb-url":: "falkor://falkordb:6379",
"store-triples" +: {
create:: function(engine)
local container =
engine.container("store-triples")
.with_image(images.trustgraph_flow)
.with_command([
"triples-write-falkordb",
"-p",
url.pulsar,
"-g",
$["falkordb-url"],
])
.with_limits("0.5", "128M")
.with_reservations("0.1", "128M");
local containerSet = engine.containers(
"store-triples", [ container ]
);
local service =
engine.internalService(containerSet)
.with_port(8080, 8080, "metrics");
engine.resources([
containerSet,
service,
])
},
"query-triples" +: {
create:: function(engine)
local container =
engine.container("query-triples")
.with_image(images.trustgraph_flow)
.with_command([
"triples-query-falkordb",
"-p",
url.pulsar,
"-g",
$["falkordb-url"],
])
.with_limits("0.5", "128M")
.with_reservations("0.1", "128M");
local containerSet = engine.containers(
"query-triples", [ container ]
);
local service =
engine.internalService(containerSet)
.with_port(8080, 8080, "metrics");
engine.resources([
containerSet,
service,
])
}
}

65
templates/components/googleaistudio-rag.jsonnet
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@ -1,65 +0,0 @@
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 + {
["googleaistudio-rag-" + key]:: value,
},
"googleaistudio-rag-max-output-tokens":: 4096,
"googleaistudio-rag-temperature":: 0.0,
"googleaistudio-rag-model":: "gemini-1.5-flash-002",
"text-completion-rag" +: {
create:: function(engine)
local envSecrets = engine.envSecrets("googleaistudio-credentials")
.with_env_var("GOOGLE_AI_STUDIO_KEY", "googleaistudio-key");
local containerRag =
engine.container("text-completion-rag")
.with_image(images.trustgraph_flow)
.with_command([
"text-completion-googleaistudio",
"-p",
url.pulsar,
"-x",
std.toString(
$["googleaistudio-rag-max-output-tokens"]
),
"-t",
"%0.3f" % $["googleaistudio-rag-temperature"],
"-m",
$["googleaistudio-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(8000, 8000, "metrics");
engine.resources([
envSecrets,
containerSetRag,
serviceRag,
])
},
} + prompts

59
templates/components/googleaistudio.jsonnet
View file

@ -1,59 +0,0 @@
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 + {
["googleaistudio-" + key]:: value,
},
"googleaistudio-max-output-tokens":: 4096,
"googleaistudio-temperature":: 0.0,
"googleaistudio-model":: "gemini-1.5-flash-002",
"text-completion" +: {
create:: function(engine)
local envSecrets = engine.envSecrets("googleaistudio-credentials")
.with_env_var("GOOGLE_AI_STUDIO_KEY", "googleaistudio-key");
local container =
engine.container("text-completion")
.with_image(images.trustgraph_flow)
.with_command([
"text-completion-googleaistudio",
"-p",
url.pulsar,
"-x",
std.toString($["googleaistudio-max-output-tokens"]),
"-t",
"%0.3f" % $["googleaistudio-temperature"],
"-m",
$["googleaistudio-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(8000, 8000, "metrics");
engine.resources([
envSecrets,
containerSet,
service,
])
},
} + prompts

122
templates/components/grafana.jsonnet
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@ -1,122 +0,0 @@
local base = import "base/base.jsonnet";
local images = import "values/images.jsonnet";
{
"prometheus" +: {
create:: function(engine)
local vol = engine.volume("prometheus-data").with_size("20G");
local cfgVol = engine.configVolume(
"prometheus-cfg", "prometheus",
{
"prometheus.yml": importstr "prometheus/prometheus.yml",
}
);
local container =
engine.container("prometheus")
.with_image(images.prometheus)
.with_limits("0.5", "128M")
.with_reservations("0.1", "128M")
.with_port(9090, 9090, "http")
.with_volume_mount(cfgVol, "/etc/prometheus/")
.with_volume_mount(vol, "/prometheus");
local containerSet = engine.containers(
"prometheus", [ container ]
);
local service =
engine.service(containerSet)
.with_port(9090, 9090, "http");
engine.resources([
cfgVol,
vol,
containerSet,
service,
])
},
"grafana" +: {
create:: function(engine)
local vol = engine.volume("grafana-storage").with_size("20G");
local provDashVol = engine.configVolume(
"prov-dash", "grafana/provisioning/",
{
"dashboard.yml":
importstr "grafana/provisioning/dashboard.yml",
}
);
local provDataVol = engine.configVolume(
"prov-data", "grafana/provisioning/",
{
"datasource.yml":
importstr "grafana/provisioning/datasource.yml",
}
);
local dashVol = engine.configVolume(
"dashboards", "grafana/dashboards/",
{
"dashboard.json":
importstr "grafana/dashboards/dashboard.json",
}
);
local container =
engine.container("grafana")
.with_image(images.grafana)
.with_environment({
// GF_AUTH_ANONYMOUS_ORG_ROLE: "Admin",
// GF_AUTH_ANONYMOUS_ENABLED: "true",
// GF_ORG_ROLE: "Admin",
GF_ORG_NAME: "trustgraph.ai",
// GF_SERVER_ROOT_URL: "https://example.com",
})
.with_limits("1.0", "256M")
.with_reservations("0.5", "256M")
.with_port(3000, 3000, "cassandra")
.with_volume_mount(vol, "/var/lib/grafana")
.with_volume_mount(
provDashVol, "/etc/grafana/provisioning/dashboards/"
)
.with_volume_mount(
provDataVol, "/etc/grafana/provisioning/datasources/"
)
.with_volume_mount(
dashVol, "/var/lib/grafana/dashboards/"
);
local containerSet = engine.containers(
"grafana", [ container ]
);
local service =
engine.service(containerSet)
.with_port(3000, 3000, "http");
engine.resources([
vol,
provDashVol,
provDataVol,
dashVol,
containerSet,
service,
])
},
}

175
templates/components/graph-rag.jsonnet
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@ -1,175 +0,0 @@
local base = import "base/base.jsonnet";
local images = import "values/images.jsonnet";
local url = import "values/url.jsonnet";
{
"graph-rag-entity-limit":: 50,
"graph-rag-triple-limit":: 30,
"graph-rag-max-subgraph-size":: 400,
"graph-rag-max-path-length":: 2,
"kg-extract-definitions" +: {
create:: function(engine)
local container =
engine.container("kg-extract-definitions")
.with_image(images.trustgraph_flow)
.with_command([
"kg-extract-definitions",
"-p",
url.pulsar,
])
.with_limits("0.5", "128M")
.with_reservations("0.1", "128M");
local containerSet = engine.containers(
"kg-extract-definitions", [ container ]
);
local service =
engine.internalService(containerSet)
.with_port(8000, 8000, "metrics");
engine.resources([
containerSet,
service,
])
},
"kg-extract-relationships" +: {
create:: function(engine)
local container =
engine.container("kg-extract-relationships")
.with_image(images.trustgraph_flow)
.with_command([
"kg-extract-relationships",
"-p",
url.pulsar,
])
.with_limits("0.5", "128M")
.with_reservations("0.1", "128M");
local containerSet = engine.containers(
"kg-extract-relationships", [ container ]
);
local service =
engine.internalService(containerSet)
.with_port(8000, 8000, "metrics");
engine.resources([
containerSet,
service,
])
},
"kg-extract-topics" +: {
create:: function(engine)
local container =
engine.container("kg-extract-topics")
.with_image(images.trustgraph_flow)
.with_command([
"kg-extract-topics",
"-p",
url.pulsar,
])
.with_limits("0.5", "128M")
.with_reservations("0.1", "128M");
local containerSet = engine.containers(
"kg-extract-topics", [ container ]
);
local service =
engine.internalService(containerSet)
.with_port(8000, 8000, "metrics");
engine.resources([
containerSet,
service,
])
},
"graph-rag" +: {
create:: function(engine)
local container =
engine.container("graph-rag")
.with_image(images.trustgraph_flow)
.with_command([
"graph-rag",
"-p",
url.pulsar,
"--prompt-request-queue",
"non-persistent://tg/request/prompt-rag",
"--prompt-response-queue",
"non-persistent://tg/response/prompt-rag",
"--entity-limit",
std.toString($["graph-rag-entity-limit"]),
"--triple-limit",
std.toString($["graph-rag-triple-limit"]),
"--max-subgraph-size",
std.toString($["graph-rag-max-subgraph-size"]),
"--max-path-length",
std.toString($["graph-rag-max-path-length"]),
])
.with_limits("0.5", "128M")
.with_reservations("0.1", "128M");
local containerSet = engine.containers(
"graph-rag", [ container ]
);
local service =
engine.internalService(containerSet)
.with_port(8000, 8000, "metrics");
engine.resources([
containerSet,
service,
])
},
"graph-embeddings" +: {
create:: function(engine)
local container =
engine.container("graph-embeddings")
.with_image(images.trustgraph_flow)
.with_command([
"graph-embeddings",
"-p",
url.pulsar,
])
.with_limits("1.0", "512M")
.with_reservations("0.5", "512M");
local containerSet = engine.containers(
"graph-embeddings", [ container ]
);
local service =
engine.internalService(containerSet)
.with_port(8000, 8000, "metrics");
engine.resources([
containerSet,
service,
])
},
}

43
templates/components/librarian.jsonnet
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@ -1,43 +0,0 @@
local base = import "base/base.jsonnet";
local images = import "values/images.jsonnet";
local url = import "values/url.jsonnet";
local minio = import "stores/minio.jsonnet";
local cassandra = import "stores/cassandra.jsonnet";
{
"librarian" +: {
create:: function(engine)
local container =
engine.container("librarian")
.with_image(images.trustgraph_flow)
.with_command([
"librarian",
"-p",
url.pulsar,
])
.with_limits("0.5", "256M")
.with_reservations("0.1", "256M");
local containerSet = engine.containers(
"librarian", [ container ]
);
local service =
engine.internalService(containerSet)
.with_port(8000, 8000, "metrics");
engine.resources([
containerSet,
service,
])
},
}
// Minio and Cassandra are used by the Librarian
+ minio + cassandra

57
templates/components/llamafile-rag.jsonnet
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@ -1,57 +0,0 @@
local base = import "base/base.jsonnet";
local images = import "values/images.jsonnet";
local url = import "values/url.jsonnet";
local prompts = import "prompts/slm.jsonnet";
{
with:: function(key, value)
self + {
["llamafile-rag-" + key]:: value,
},
"llamafile-rag-model":: "LLaMA_CPP",
"text-completion-rag" +: {
create:: function(engine)
local envSecrets = engine.envSecrets("llamafile-credentials")
.with_env_var("LLAMAFILE_URL", "llamafile-url");
local containerRag =
engine.container("text-completion-rag")
.with_image(images.trustgraph_flow)
.with_command([
"text-completion-llamafile",
"-p",
url.pulsar,
"-m",
$["llamafile-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

53
templates/components/llamafile.jsonnet
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@ -1,53 +0,0 @@
local base = import "base/base.jsonnet";
local images = import "values/images.jsonnet";
local url = import "values/url.jsonnet";
local prompts = import "prompts/slm.jsonnet";
{
with:: function(key, value)
self + {
["llamafile-" + key]:: value,
},
"llamafile-model":: "LLaMA_CPP",
"text-completion" +: {
create:: function(engine)
local envSecrets = engine.envSecrets("llamafile-credentials")
.with_env_var("LLAMAFILE_URL", "llamafile-url");
local container =
engine.container("text-completion")
.with_image(images.trustgraph_flow)
.with_command([
"text-completion-llamafile",
"-p",
url.pulsar,
"-m",
$["llamafile-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

63
templates/components/lmstudio-rag.jsonnet
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@ -1,63 +0,0 @@
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
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@ -1,59 +0,0 @@
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

81
templates/components/memgraph.jsonnet
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@ -1,81 +0,0 @@
local base = import "base/base.jsonnet";
local images = import "values/images.jsonnet";
local url = import "values/url.jsonnet";
local memgraph = import "stores/memgraph.jsonnet";
memgraph + {
"memgraph-url":: "bolt://memgraph:7687",
"memgraph-database":: "memgraph",
"store-triples" +: {
create:: function(engine)
local container =
engine.container("store-triples")
.with_image(images.trustgraph_flow)
.with_command([
"triples-write-memgraph",
"-p",
url.pulsar,
"-g",
$["memgraph-url"],
"--database",
$["memgraph-database"],
])
.with_limits("0.5", "128M")
.with_reservations("0.1", "128M");
local containerSet = engine.containers(
"store-triples", [ container ]
);
local service =
engine.internalService(containerSet)
.with_port(8080, 8080, "metrics");
engine.resources([
containerSet,
service,
])
},
"query-triples" +: {
create:: function(engine)
local container =
engine.container("query-triples")
.with_image(images.trustgraph_flow)
.with_command([
"triples-query-memgraph",
"-p",
url.pulsar,
"-g",
$["memgraph-url"],
"--database",
$["memgraph-database"],
])
.with_limits("0.5", "128M")
.with_reservations("0.1", "128M");
local containerSet = engine.containers(
"query-triples", [ container ]
);
local service =
engine.internalService(containerSet)
.with_port(8080, 8080, "metrics");
engine.resources([
containerSet,
service,
])
}
}

139
templates/components/milvus.jsonnet
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@ -1,139 +0,0 @@
local base = import "base/base.jsonnet";
local images = import "values/images.jsonnet";
local url = import "values/url.jsonnet";
local cassandra_hosts = "cassandra";
local milvus = import "stores/milvus.jsonnet";
milvus + {
"store-graph-embeddings" +: {
create:: function(engine)
local container =
engine.container("store-graph-embeddings")
.with_image(images.trustgraph_flow)
.with_command([
"ge-write-milvus",
"-p",
url.pulsar,
"-t",
url.milvus,
])
.with_limits("0.5", "128M")
.with_reservations("0.1", "128M");
local containerSet = engine.containers(
"store-graph-embeddings", [ container ]
);
local service =
engine.internalService(containerSet)
.with_port(8000, 8000, "metrics");
engine.resources([
containerSet,
service,
])
},
"query-graph-embeddings" +: {
create:: function(engine)
local container =
engine.container("query-graph-embeddings")
.with_image(images.trustgraph_flow)
.with_command([
"ge-query-milvus",
"-p",
url.pulsar,
"-t",
url.milvus,
])
.with_limits("0.5", "128M")
.with_reservations("0.1", "128M");
local containerSet = engine.containers(
"query-graph-embeddings", [ container ]
);
local service =
engine.internalService(containerSet)
.with_port(8000, 8000, "metrics");
engine.resources([
containerSet,
service,
])
},
"store-doc-embeddings" +: {
create:: function(engine)
local container =
engine.container("store-doc-embeddings")
.with_image(images.trustgraph_flow)
.with_command([
"de-write-milvus",
"-p",
url.pulsar,
"-t",
url.milvus,
])
.with_limits("0.5", "128M")
.with_reservations("0.1", "128M");
local containerSet = engine.containers(
"store-doc-embeddings", [ container ]
);
local service =
engine.internalService(containerSet)
.with_port(8000, 8000, "metrics");
engine.resources([
containerSet,
service,
])
},
"query-doc-embeddings" +: {
create:: function(engine)
local container =
engine.container("query-doc-embeddings")
.with_image(images.trustgraph_flow)
.with_command([
"de-query-milvus",
"-p",
url.pulsar,
"-t",
url.milvus,
])
.with_limits("0.5", "128M")
.with_reservations("0.1", "128M");
local containerSet = engine.containers(
"query-doc-embeddings", [ container ]
);
local service =
engine.internalService(containerSet)
.with_port(8000, 8000, "metrics");
engine.resources([
containerSet,
service,
])
}
}

47
templates/components/mistral-ocr.jsonnet
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@ -1,47 +0,0 @@
local images = import "values/images.jsonnet";
local url = import "values/url.jsonnet";
{
with:: function(key, value)
self + {
["mistral-" + key]:: value,
},
"pdf-decoder" +: {
create:: function(engine)
local envSecrets = engine.envSecrets("mistral-credentials")
.with_env_var("MISTRAL_TOKEN", "mistral-token");
local container =
engine.container("mistral-ocr")
.with_image(images.trustgraph_flow)
.with_command([
"pdf-ocr-mistral",
"-p",
url.pulsar,
])
.with_env_var_secrets(envSecrets)
.with_limits("0.5", "128M")
.with_reservations("0.1", "128M");
local containerSet = engine.containers(
"mistral-ocr", [ container ]
);
local service =
engine.internalService(containerSet)
.with_port(8080, 8080, "metrics");
engine.resources([
envSecrets,
containerSet,
service,
])
},
}

63
templates/components/mistral-rag.jsonnet
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@ -1,63 +0,0 @@
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 + {
["mistral-rag-" + key]:: value,
},
"mistral-rag-max-output-tokens":: 4096,
"mistral-rag-temperature":: 0.0,
"mistral-rag-model":: "ministral-8b-latest",
"text-completion-rag" +: {
create:: function(engine)
local envSecrets = engine.envSecrets("mistral-credentials")
.with_env_var("MISTRAL_TOKEN", "mistral-token");
local containerRag =
engine.container("text-completion-rag")
.with_image(images.trustgraph_flow)
.with_command([
"text-completion-mistral",
"-p",
url.pulsar,
"-x",
std.toString($["mistral-rag-max-output-tokens"]),
"-t",
"%0.3f" % $["mistral-rag-temperature"],
"-m",
$["mistral-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/mistral.jsonnet
View file

@ -1,59 +0,0 @@
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 + {
["mistral-" + key]:: value,
},
"mistral-max-output-tokens":: 4096,
"mistral-temperature":: 0.0,
"mistral-model":: "ministral-8b-latest",
"text-completion" +: {
create:: function(engine)
local envSecrets = engine.envSecrets("mistral-credentials")
.with_env_var("MISTRAL_TOKEN", "mistral-token");
local container =
engine.container("text-completion")
.with_image(images.trustgraph_flow)
.with_command([
"text-completion-mistral",
"-p",
url.pulsar,
"-x",
std.toString($["mistral-max-output-tokens"]),
"-t",
"%0.3f" % $["mistral-temperature"],
"-m",
$["mistral-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

76
templates/components/neo4j.jsonnet
View file

@ -1,76 +0,0 @@
local base = import "base/base.jsonnet";
local images = import "values/images.jsonnet";
local url = import "values/url.jsonnet";
local neo4j = import "stores/neo4j.jsonnet";
neo4j + {
"neo4j-url":: "bolt://neo4j:7687",
"store-triples" +: {
create:: function(engine)
local container =
engine.container("store-triples")
.with_image(images.trustgraph_flow)
.with_command([
"triples-write-neo4j",
"-p",
url.pulsar,
"-g",
$["neo4j-url"],
])
.with_limits("0.5", "128M")
.with_reservations("0.1", "128M");
local containerSet = engine.containers(
"store-triples", [ container ]
);
local service =
engine.internalService(containerSet)
.with_port(8080, 8080, "metrics");
engine.resources([
containerSet,
service,
])
},
"query-triples" +: {
create:: function(engine)
local container =
engine.container("query-triples")
.with_image(images.trustgraph_flow)
.with_command([
"triples-query-neo4j",
"-p",
url.pulsar,
"-g",
$["neo4j-url"],
])
.with_limits("0.5", "128M")
.with_reservations("0.1", "128M");
local containerSet = engine.containers(
"query-triples", [ container ]
);
local service =
engine.internalService(containerSet)
.with_port(8080, 8080, "metrics");
engine.resources([
containerSet,
service,
])
}
}

2
templates/components/null.jsonnet
View file

@ -1,2 +0,0 @@
{
}

37
templates/components/ocr.jsonnet
View file

@ -1,37 +0,0 @@
local images = import "values/images.jsonnet";
local url = import "values/url.jsonnet";
{
"pdf-decoder" +: {
create:: function(engine)
local container =
engine.container("pdf-ocr")
.with_image(images.trustgraph_ocr)
.with_command([
"pdf-ocr",
"-p",
url.pulsar,
])
.with_limits("1.0", "512M")
.with_reservations("0.1", "512M");
local containerSet = engine.containers(
"pdf-ocr", [ container ]
);
local service =
engine.internalService(containerSet)
.with_port(8080, 8080, "metrics");
engine.resources([
containerSet,
service,
])
},
}

57
templates/components/ollama-rag.jsonnet
View file

@ -1,57 +0,0 @@
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 + {
["ollama-rag-" + key]:: value,
},
"ollama-rag-model":: "gemma2:9b",
"text-completion-rag" +: {
create:: function(engine)
local envSecrets = engine.envSecrets("ollama-credentials")
.with_env_var("OLLAMA_HOST", "ollama-host");
local containerRag =
engine.container("text-completion-rag")
.with_image(images.trustgraph_flow)
.with_command([
"text-completion-ollama",
"-p",
url.pulsar,
"-m",
$["ollama-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

53
templates/components/ollama.jsonnet
View file

@ -1,53 +0,0 @@
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 + {
["ollama-" + key]:: value,
},
"ollama-model":: "gemma2:9b",
"text-completion" +: {
create:: function(engine)
local envSecrets = engine.envSecrets("ollama-credentials")
.with_env_var("OLLAMA_HOST", "ollama-host");
local container =
engine.container("text-completion")
.with_image(images.trustgraph_flow)
.with_command([
"text-completion-ollama",
"-p",
url.pulsar,
"-m",
$["ollama-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

63
templates/components/openai-rag.jsonnet
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@ -1,63 +0,0 @@
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 + {
["openai-rag-" + key]:: value,
},
"openai-rag-max-output-tokens":: 4096,
"openai-rag-temperature":: 0.0,
"openai-rag-model":: "GPT-3.5-Turbo",
"text-completion-rag" +: {
create:: function(engine)
local envSecrets = engine.envSecrets("openai-credentials")
.with_env_var("OPENAI_TOKEN", "openai-token");
local containerRag =
engine.container("text-completion-rag")
.with_image(images.trustgraph_flow)
.with_command([
"text-completion-openai",
"-p",
url.pulsar,
"-x",
std.toString($["openai-rag-max-output-tokens"]),
"-t",
"%0.3f" % $["openai-rag-temperature"],
"-m",
$["openai-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/openai.jsonnet
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@ -1,59 +0,0 @@
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 + {
["openai-" + key]:: value,
},
"openai-max-output-tokens":: 4096,
"openai-temperature":: 0.0,
"openai-model":: "GPT-3.5-Turbo",
"text-completion" +: {
create:: function(engine)
local envSecrets = engine.envSecrets("openai-credentials")
.with_env_var("OPENAI_TOKEN", "openai-token");
local container =
engine.container("text-completion")
.with_image(images.trustgraph_flow)
.with_command([
"text-completion-openai",
"-p",
url.pulsar,
"-x",
std.toString($["openai-max-output-tokens"]),
"-t",
"%0.3f" % $["openai-temperature"],
"-m",
$["openai-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

153
templates/components/pinecone.jsonnet
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@ -1,153 +0,0 @@
local base = import "base/base.jsonnet";
local images = import "values/images.jsonnet";
local url = import "values/url.jsonnet";
local cassandra_hosts = "cassandra";
{
"pinecone-cloud":: "aws",
"pinecone-region":: "us-east-1",
"store-graph-embeddings" +: {
create:: function(engine)
local envSecrets = engine.envSecrets("pinecone-api-key")
.with_env_var("PINECONE_API_KEY", "pinecone-api-key");
local container =
engine.container("store-graph-embeddings")
.with_image(images.trustgraph_flow)
.with_command([
"ge-write-pinecone",
"-p",
url.pulsar,
])
.with_env_var_secrets(envSecrets)
.with_limits("0.5", "128M")
.with_reservations("0.1", "128M");
local containerSet = engine.containers(
"store-graph-embeddings", [ container ]
);
local service =
engine.internalService(containerSet)
.with_port(8080, 8080, "metrics");
engine.resources([
envSecrets,
containerSet,
service,
])
},
"query-graph-embeddings" +: {
create:: function(engine)
local envSecrets = engine.envSecrets("pinecone-api-key")
.with_env_var("PINECONE_API_KEY", "pinecone-api-key");
local container =
engine.container("query-graph-embeddings")
.with_image(images.trustgraph_flow)
.with_command([
"ge-query-pinecone",
"-p",
url.pulsar,
])
.with_env_var_secrets(envSecrets)
.with_limits("0.5", "128M")
.with_reservations("0.1", "128M");
local containerSet = engine.containers(
"query-graph-embeddings", [ container ]
);
local service =
engine.internalService(containerSet)
.with_port(8080, 8080, "metrics");
engine.resources([
envSecrets,
containerSet,
service,
])
},
"store-doc-embeddings" +: {
create:: function(engine)
local envSecrets = engine.envSecrets("pinecone-api-key")
.with_env_var("PINECONE_API_KEY", "pinecone-api-key");
local container =
engine.container("store-doc-embeddings")
.with_image(images.trustgraph_flow)
.with_command([
"de-write-pinecone",
"-p",
url.pulsar,
])
.with_env_var_secrets(envSecrets)
.with_limits("0.5", "128M")
.with_reservations("0.1", "128M");
local containerSet = engine.containers(
"store-doc-embeddings", [ container ]
);
local service =
engine.internalService(containerSet)
.with_port(8080, 8080, "metrics");
engine.resources([
envSecrets,
containerSet,
service,
])
},
"query-doc-embeddings" +: {
create:: function(engine)
local envSecrets = engine.envSecrets("pinecone-api-key")
.with_env_var("PINECONE_API_KEY", "pinecone-api-key");
local container =
engine.container("query-doc-embeddings")
.with_image(images.trustgraph_flow)
.with_command([
"de-query-pinecone",
"-p",
url.pulsar,
])
.with_env_var_secrets(envSecrets)
.with_limits("0.5", "128M")
.with_reservations("0.1", "128M");
local containerSet = engine.containers(
"query-doc-embeddings", [ container ]
);
local service =
engine.internalService(containerSet)
.with_port(8080, 8080, "metrics");
engine.resources([
envSecrets,
containerSet,
service,
])
}
}

24
templates/components/prompt-overrides.jsonnet
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@ -1,24 +0,0 @@
local default_prompts = import "prompts/default-prompts.jsonnet";
{
with:: function(key, value)
if (key == "system-template") then
self + {
prompts +:: {
"system-template": value,
}
}
else
self + {
prompts +:: {
templates +:: {
[key] +:: {
prompt: value
}
}
}
},
} + default_prompts

125
templates/components/prompt-template.jsonnet
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@ -1,125 +0,0 @@
local base = import "base/base.jsonnet";
local images = import "values/images.jsonnet";
local url = import "values/url.jsonnet";
local prompts = import "prompts/mixtral.jsonnet";
local default_prompts = import "prompts/default-prompts.jsonnet";
{
prompts:: default_prompts,
local prompt_template_args = [ "--prompt" ] + [
p.key + "=" + p.value.prompt,
for p in std.objectKeysValuesAll($.prompts.templates)
],
local prompt_response_type_args = [ "--prompt-response-type" ] + [
p.key + "=" + p.value["response-type"],
for p in std.objectKeysValuesAll($.prompts.templates)
if std.objectHas(p.value, "response-type")
],
local prompt_schema_args = [ "--prompt-schema" ] + [
(
p.key + "=" +
std.manifestJsonMinified(p.value["schema"])
)
for p in std.objectKeysValuesAll($.prompts.templates)
if std.objectHas(p.value, "schema")
],
local prompt_term_args = [ "--prompt-term" ] + [
p.key + "=" + t.key + ":" + t.value
for p in std.objectKeysValuesAll($.prompts.templates)
if std.objectHas(p.value, "terms")
for t in std.objectKeysValuesAll(p.value.terms)
],
local prompt_args = prompt_template_args + prompt_response_type_args +
prompt_schema_args + prompt_term_args,
"prompt" +: {
create:: function(engine)
local container =
engine.container("prompt")
.with_image(images.trustgraph_flow)
.with_command([
"prompt-template",
"-p",
url.pulsar,
"--text-completion-request-queue",
"non-persistent://tg/request/text-completion",
"--text-completion-response-queue",
"non-persistent://tg/response/text-completion",
"--system-prompt",
$["prompts"]["system-template"],
] + prompt_args
)
.with_limits("0.5", "128M")
.with_reservations("0.1", "128M");
local containerSet = engine.containers(
"prompt", [ container ]
);
local service =
engine.internalService(containerSet)
.with_port(8080, 8080, "metrics");
engine.resources([
containerSet,
service,
])
},
"prompt-rag" +: {
create:: function(engine)
local container =
engine.container("prompt-rag")
.with_image(images.trustgraph_flow)
.with_command([
"prompt-template",
"-p",
url.pulsar,
"-i",
"non-persistent://tg/request/prompt-rag",
"-o",
"non-persistent://tg/response/prompt-rag",
"--text-completion-request-queue",
"non-persistent://tg/request/text-completion-rag",
"--text-completion-response-queue",
"non-persistent://tg/response/text-completion-rag",
"--system-prompt",
$["prompts"]["system-template"],
] + prompt_args
)
.with_limits("0.5", "128M")
.with_reservations("0.1", "128M");
local containerSet = engine.containers(
"prompt-rag", [ container ]
);
local service =
engine.internalService(containerSet)
.with_port(8080, 8080, "metrics");
engine.resources([
containerSet,
service,
])
},
} + default_prompts

41
templates/components/pulsar-manager.jsonnet
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@ -1,41 +0,0 @@
local base = import "base/base.jsonnet";
local images = import "values/images.jsonnet";
{
"pulsar" +: {
create:: function(engine)
// FIXME: Should persist something?
// local volume = engine.volume(...)
local container =
engine.container("pulsar")
.with_image(images.pulsar_manager)
.with_environment({
SPRING_CONFIGURATION_FILE: "/pulsar-manager/pulsar-manager/application.properties",
})
.with_limits("0.5", "1.4G")
.with_reservations("0.1", "1.4G")
.with_port(9527, 9527, "api")
.with_port(7750, 7750, "api2");
local containerSet = engine.containers(
"pulsar", [ container ]
);
local service =
engine.service(containerSet)
.with_port(9527, 9527, "api")
.with_port(7750, 7750, "api2);
engine.resources([
containerSet,
service,
])
}
}

191
templates/components/pulsar.jsonnet
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@ -1,191 +0,0 @@
local base = import "base/base.jsonnet";
local images = import "values/images.jsonnet";
local url = import "values/url.jsonnet";
// This is a Pulsar configuration. Non-standalone mode so we deploy
// individual components: bookkeeper, broker and zookeeper.
//
// This also deploys the TrustGraph 'admin' container which initialises
// TrustGraph-specific namespaces etc.
{
"pulsar" +: {
create:: function(engine)
// Zookeeper volume
local zkVolume = engine.volume("zookeeper").with_size("1G");
// Zookeeper container
local zkContainer =
engine.container("zookeeper")
.with_image(images.pulsar)
.with_command([
"bash",
"-c",
"bin/apply-config-from-env.py conf/zookeeper.conf && bin/generate-zookeeper-config.sh conf/zookeeper.conf && exec bin/pulsar zookeeper"
])
.with_limits("1", "400M")
.with_reservations("0.05", "400M")
.with_user("0:1000")
.with_volume_mount(zkVolume, "/pulsar/data/zookeeper")
.with_environment({
"metadataStoreUrl": "zk:zookeeper:2181",
"PULSAR_MEM": "-Xms256m -Xmx256m -XX:MaxDirectMemorySize=256m",
})
.with_port(2181, 2181, "zookeeper")
.with_port(2888, 2888, "zookeeper2")
.with_port(3888, 3888, "zookeeper3");
// Pulsar cluster init container
local initContainer =
engine.container("pulsar-init")
.with_image(images.pulsar)
.with_command([
"bash",
"-c",
"sleep 10 && bin/pulsar initialize-cluster-metadata --cluster cluster-a --zookeeper zookeeper:2181 --configuration-store zookeeper:2181 --web-service-url http://pulsar:8080 --broker-service-url pulsar://pulsar:6650",
])
.with_limits("1", "512M")
.with_reservations("0.05", "512M")
.with_environment({
"PULSAR_MEM": "-Xms256m -Xmx256m -XX:MaxDirectMemorySize=256m",
});
// Bookkeeper volume
local bookieVolume = engine.volume("bookie").with_size("20G");
// Bookkeeper container
local bookieContainer =
engine.container("bookie")
.with_image(images.pulsar)
.with_command([
"bash",
"-c",
"bin/apply-config-from-env.py conf/bookkeeper.conf && exec bin/pulsar bookie"
// false ^ causes this to be a 'failure' exit.
])
.with_limits("1", "800M")
.with_reservations("0.1", "800M")
.with_user("0:1000")
.with_volume_mount(bookieVolume, "/pulsar/data/bookkeeper")
.with_environment({
"clusterName": "cluster-a",
"zkServers": "zookeeper:2181",
"bookieId": "bookie",
"metadataStoreUri": "metadata-store:zk:zookeeper:2181",
"advertisedAddress": "bookie",
"BOOKIE_MEM": "-Xms512m -Xmx512m -XX:MaxDirectMemorySize=256m",
})
.with_port(3181, 3181, "bookie");
// Pulsar broker, stateless (uses ZK and Bookkeeper for state)
local brokerContainer =
engine.container("pulsar")
.with_image(images.pulsar)
.with_command([
"bash",
"-c",
"bin/apply-config-from-env.py conf/broker.conf && exec bin/pulsar broker"
])
.with_limits("1", "800M")
.with_reservations("0.1", "800M")
.with_environment({
"metadataStoreUrl": "zk:zookeeper:2181",
"zookeeperServers": "zookeeper:2181",
"clusterName": "cluster-a",
"managedLedgerDefaultEnsembleSize": "1",
"managedLedgerDefaultWriteQuorum": "1",
"managedLedgerDefaultAckQuorum": "1",
"advertisedAddress": "pulsar",
"advertisedListeners": "external:pulsar://pulsar:6650,localhost:pulsar://localhost:6650",
"PULSAR_MEM": "-Xms512m -Xmx512m -XX:MaxDirectMemorySize=256m",
})
.with_port(6650, 6650, "pulsar")
.with_port(8080, 8080, "admin");
// Trustgraph Pulsar initialisation
local adminContainer =
engine.container("init-trustgraph")
.with_image(images.trustgraph_flow)
.with_command([
"tg-init-pulsar",
"-p",
url.pulsar_admin,
])
.with_limits("1", "128M")
.with_reservations("0.1", "128M");
// Container sets
local zkContainerSet = engine.containers(
"zookeeper",
[
zkContainer,
]
);
local initContainerSet = engine.containers(
"init-pulsar",
[
initContainer,
]
);
local bookieContainerSet = engine.containers(
"bookie",
[
bookieContainer,
]
);
local brokerContainerSet = engine.containers(
"pulsar",
[
brokerContainer,
]
);
local adminContainerSet = engine.containers(
"init-pulsar",
[
adminContainer
]
);
// Zookeeper service
local zkService =
engine.service(zkContainerSet)
.with_port(2181, 2181, "zookeeper")
.with_port(2888, 2888, "zookeeper2")
.with_port(3888, 3888, "zookeeper3");
// Bookkeeper service
local bookieService =
engine.service(bookieContainerSet)
.with_port(3181, 3181, "bookie");
// Pulsar broker service
local brokerService =
engine.service(brokerContainerSet)
.with_port(6650, 6650, "pulsar")
.with_port(8080, 8080, "admin");
engine.resources([
zkVolume,
bookieVolume,
zkContainerSet,
initContainerSet,
bookieContainerSet,
brokerContainerSet,
adminContainerSet,
zkService,
bookieService,
brokerService,
])
}
}

139
templates/components/qdrant.jsonnet
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@ -1,139 +0,0 @@
local base = import "base/base.jsonnet";
local images = import "values/images.jsonnet";
local url = import "values/url.jsonnet";
local cassandra_hosts = "cassandra";
local qdrant = import "stores/qdrant.jsonnet";
qdrant + {
"store-graph-embeddings" +: {
create:: function(engine)
local container =
engine.container("store-graph-embeddings")
.with_image(images.trustgraph_flow)
.with_command([
"ge-write-qdrant",
"-p",
url.pulsar,
"-t",
url.qdrant,
])
.with_limits("0.5", "128M")
.with_reservations("0.1", "128M");
local containerSet = engine.containers(
"store-graph-embeddings", [ container ]
);
local service =
engine.internalService(containerSet)
.with_port(8080, 8080, "metrics");
engine.resources([
containerSet,
service,
])
},
"query-graph-embeddings" +: {
create:: function(engine)
local container =
engine.container("query-graph-embeddings")
.with_image(images.trustgraph_flow)
.with_command([
"ge-query-qdrant",
"-p",
url.pulsar,
"-t",
url.qdrant,
])
.with_limits("0.5", "128M")
.with_reservations("0.1", "128M");
local containerSet = engine.containers(
"query-graph-embeddings", [ container ]
);
local service =
engine.internalService(containerSet)
.with_port(8080, 8080, "metrics");
engine.resources([
containerSet,
service,
])
},
"store-doc-embeddings" +: {
create:: function(engine)
local container =
engine.container("store-doc-embeddings")
.with_image(images.trustgraph_flow)
.with_command([
"de-write-qdrant",
"-p",
url.pulsar,
"-t",
url.qdrant,
])
.with_limits("0.5", "128M")
.with_reservations("0.1", "128M");
local containerSet = engine.containers(
"store-doc-embeddings", [ container ]
);
local service =
engine.internalService(containerSet)
.with_port(8080, 8080, "metrics");
engine.resources([
containerSet,
service,
])
},
"query-doc-embeddings" +: {
create:: function(engine)
local container =
engine.container("query-doc-embeddings")
.with_image(images.trustgraph_flow)
.with_command([
"de-query-qdrant",
"-p",
url.pulsar,
"-t",
url.qdrant,
])
.with_limits("0.5", "128M")
.with_reservations("0.1", "128M");
local containerSet = engine.containers(
"query-doc-embeddings", [ container ]
);
local service =
engine.internalService(containerSet)
.with_port(8080, 8080, "metrics");
engine.resources([
containerSet,
service,
])
}
}

184
templates/components/trustgraph.jsonnet
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@ -1,184 +0,0 @@
local base = import "base/base.jsonnet";
local images = import "values/images.jsonnet";
local url = import "values/url.jsonnet";
{
"api-gateway-port":: 8088,
"api-gateway-timeout":: 600,
"chunk-size":: 250,
"chunk-overlap":: 15,
"api-gateway" +: {
create:: function(engine)
local envSecrets = engine.envSecrets("gateway-secret")
.with_env_var("GATEWAY_SECRET", "gateway-secret");
local port = $["api-gateway-port"];
local container =
engine.container("api-gateway")
.with_image(images.trustgraph_flow)
.with_command([
"api-gateway",
"-p",
url.pulsar,
"--timeout",
std.toString($["api-gateway-timeout"]),
"--port",
std.toString(port),
])
.with_env_var_secrets(envSecrets)
.with_limits("0.5", "256M")
.with_reservations("0.1", "256M")
.with_port(8000, 8000, "metrics")
.with_port(port, port, "api");
local containerSet = engine.containers(
"api-gateway", [ container ]
);
local service =
engine.internalService(containerSet)
.with_port(8000, 8000, "metrics")
.with_port(port, port, "api");
engine.resources([
envSecrets,
containerSet,
service,
])
},
"chunker" +: {
create:: function(engine)
local container =
engine.container("chunker")
.with_image(images.trustgraph_flow)
.with_command([
"chunker-token",
"-p",
url.pulsar,
"--chunk-size",
std.toString($["chunk-size"]),
"--chunk-overlap",
std.toString($["chunk-overlap"]),
])
.with_limits("0.5", "128M")
.with_reservations("0.1", "128M");
local containerSet = engine.containers(
"chunker", [ container ]
);
local service =
engine.internalService(containerSet)
.with_port(8000, 8000, "metrics");
engine.resources([
containerSet,
service,
])
},
"pdf-decoder" +: {
create:: function(engine)
local container =
engine.container("pdf-decoder")
.with_image(images.trustgraph_flow)
.with_command([
"pdf-decoder",
"-p",
url.pulsar,
])
.with_limits("0.5", "128M")
.with_reservations("0.1", "128M");
local containerSet = engine.containers(
"pdf-decoder", [ container ]
);
local service =
engine.internalService(containerSet)
.with_port(8000, 8000, "metrics");
engine.resources([
containerSet,
service,
])
},
"metering" +: {
create:: function(engine)
local container =
engine.container("metering")
.with_image(images.trustgraph_flow)
.with_command([
"metering",
"-p",
url.pulsar,
])
.with_limits("0.5", "128M")
.with_reservations("0.1", "128M");
local containerSet = engine.containers(
"metering", [ container ]
);
local service =
engine.internalService(containerSet)
.with_port(8000, 8000, "metrics");
engine.resources([
containerSet,
service,
])
},
"metering-rag" +: {
create:: function(engine)
local container =
engine.container("metering-rag")
.with_image(images.trustgraph_flow)
.with_command([
"metering",
"-p",
url.pulsar,
"-i",
"non-persistent://tg/response/text-completion-rag",
])
.with_limits("0.5", "128M")
.with_reservations("0.1", "128M");
local containerSet = engine.containers(
"metering-rag", [ container ]
);
local service =
engine.internalService(containerSet)
.with_port(8000, 8000, "metrics");
engine.resources([
containerSet,
service,
])
},
}

74
templates/components/vertexai-rag.jsonnet
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@ -1,74 +0,0 @@
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 + {
["vertexai-rag-" + key]:: value,
},
"vertexai-rag-model":: "gemini-1.0-pro-001",
"vertexai-rag-private-key":: "/vertexai/private.json",
"vertexai-rag-region":: "us-central1",
"vertexai-rag-max-output-tokens":: 4096,
"vertexai-rag-temperature":: 0.0,
"text-completion-rag" +: {
create:: function(engine)
local cfgVol = engine.secretVolume(
"vertexai-creds",
"./vertexai",
{
"private.json": importstr "vertexai/private.json",
}
);
local container =
engine.container("text-completion-rag")
.with_image(images.trustgraph_vertexai)
.with_command([
"text-completion-vertexai",
"-p",
url.pulsar,
"-k",
$["vertexai-rag-private-key"],
"-r",
$["vertexai-rag-region"],
"-x",
std.toString($["vertexai-rag-max-output-tokens"]),
"-t",
"%0.3f" % $["vertexai-rag-temperature"],
"-m",
$["vertexai-rag-model"],
"-i",
"non-persistent://tg/request/text-completion-rag",
"-o",
"non-persistent://tg/response/text-completion-rag",
])
.with_limits("0.5", "256M")
.with_reservations("0.1", "256M")
.with_volume_mount(cfgVol, "/vertexai");
local containerSet = engine.containers(
"text-completion-rag", [ container ]
);
local service =
engine.internalService(containerSet)
.with_port(8000, 8000, "metrics");
engine.resources([
cfgVol,
containerSet,
service,
])
}
} + prompts

70
templates/components/vertexai.jsonnet
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@ -1,70 +0,0 @@
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 + {
["vertexai-" + key]:: value,
},
"vertexai-model":: "gemini-1.0-pro-001",
"vertexai-private-key":: "/vertexai/private.json",
"vertexai-region":: "us-central1",
"vertexai-max-output-tokens":: 4096,
"vertexai-temperature":: 0.0,
"text-completion" +: {
create:: function(engine)
local cfgVol = engine.secretVolume(
"vertexai-creds",
"./vertexai",
{
"private.json": importstr "vertexai/private.json",
}
);
local container =
engine.container("text-completion")
.with_image(images.trustgraph_vertexai)
.with_command([
"text-completion-vertexai",
"-p",
url.pulsar,
"-k",
$["vertexai-private-key"],
"-r",
$["vertexai-region"],
"-x",
std.toString($["vertexai-max-output-tokens"]),
"-t",
"%0.3f" % $["vertexai-temperature"],
"-m",
$["vertexai-model"],
])
.with_limits("0.5", "256M")
.with_reservations("0.1", "256M")
.with_volume_mount(cfgVol, "/vertexai");
local containerSet = engine.containers(
"text-completion", [ container ]
);
local service =
engine.internalService(containerSet)
.with_port(8000, 8000, "metrics");
engine.resources([
cfgVol,
containerSet,
service,
])
},
} + prompts

32
templates/components/workbench-ui.jsonnet
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@ -1,32 +0,0 @@
local images = import "values/images.jsonnet";
{
"workbench-ui" +: {
create:: function(engine)
local container =
engine.container("workbench-ui")
.with_image(images["workbench-ui"])
.with_limits("0.1", "256M")
.with_reservations("0.1", "256M")
.with_port(8888, 8888, "ui");
local containerSet = engine.containers(
"workbench-ui", [ container ]
);
local service =
engine.internalService(containerSet)
.with_port(8888, 8888, "ui");
engine.resources([
containerSet,
service,
])
},
}

16
templates/config-to-aks-k8s.jsonnet
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@ -1,16 +0,0 @@
local engine = import "engine/aks-k8s.jsonnet";
local decode = import "util/decode-config.jsonnet";
local components = import "components.jsonnet";
// Import config
local config = import "config.json";
// Produce patterns from config
local patterns = decode(config);
// Extract resources usnig the engine
local resourceList = engine.package(patterns);
resourceList

20
templates/config-to-docker-compose.jsonnet
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@ -1,20 +0,0 @@
local engine = import "engine/docker-compose.jsonnet";
local decode = import "util/decode-config.jsonnet";
local components = import "components.jsonnet";
// Import config
local config = import "config.json";
// Produce patterns from config
local patterns = decode(config);
// Extract resources usnig the engine
local resources = std.foldl(
function(state, p) state + p.create(engine),
std.objectValues(patterns),
{}
);
resources

16
templates/config-to-gcp-k8s.jsonnet
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@ -1,16 +0,0 @@
local engine = import "engine/gcp-k8s.jsonnet";
local decode = import "util/decode-config.jsonnet";
local components = import "components.jsonnet";
// Import config
local config = import "config.json";
// Produce patterns from config
local patterns = decode(config);
// Extract resources usnig the engine
local resourceList = engine.package(patterns);
resourceList

26
templates/config-to-minikube-k8s.jsonnet
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@ -1,26 +0,0 @@
local engine = import "engine/minikube-k8s.jsonnet";
local decode = import "util/decode-config.jsonnet";
local components = import "components.jsonnet";
// Import config
local config = import "config.json";
// Produce patterns from config
local patterns = decode(config);
local ns = {
apiVersion: "v1",
kind: "Namespace",
metadata: {
name: "trustgraph",
},
"spec": {
},
};
// Extract resources using the engine
local resourceList = engine.package(patterns);
resourceList

45
templates/engine/aks-k8s.jsonnet
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@ -1,45 +0,0 @@
local k8s = import "k8s.jsonnet";
local ns = {
apiVersion: "v1",
kind: "Namespace",
metadata: {
name: "trustgraph",
},
"spec": {
},
};
local sc = {
apiVersion: "storage.k8s.io/v1",
kind: "StorageClass",
metadata: {
name: "tg",
},
provisioner: "disk.csi.azure.com",
parameters: {
// Standard disks (spinning magnetic), Locally Redundant Storage
// Cheapest, basically
skuName: "Standard_LRS",
},
reclaimPolicy: "Delete",
volumeBindingMode: "WaitForFirstConsumer",
};
k8s + {
// Extract resources usnig the engine
package:: function(patterns)
local resources = [sc, ns] + std.flattenArrays([
p.create(self) for p in std.objectValues(patterns)
]);
local resourceList = {
apiVersion: "v1",
kind: "List",
items: [ns, sc] + resources,
};
resourceList
}

237
templates/engine/docker-compose.jsonnet
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@ -1,237 +0,0 @@
{
// Extract resources usnig the engine
package:: function(patterns)
std.foldl(
function(state, p) state + p.create(self),
std.objectValues(patterns),
{}
),
container:: function(name)
{
local container = self,
name: name,
limits: {},
reservations: {},
ports: [],
volumes: [],
environment: {},
with_image:: function(x) self + { image: x },
with_user:: function(x) self + { user: x },
with_command:: function(x) self + { command: x },
with_environment:: function(x) self + {
environment: super.environment + x,
},
with_limits:: function(c, m) self + { limits: { cpus: c, memory: m } },
with_reservations::
function(c, m) self + { reservations: { cpus: c, memory: m } },
with_volume_mount::
function(vol, mnt)
self + {
volumes: super.volumes + [{
volume: vol, mount: mnt
}]
},
with_port::
function(src, dest, name) self + {
ports: super.ports + [
{ src: src, dest: dest, name : name }
]
},
with_env_var_secrets::
function(vars)
std.foldl(
function(obj, x) obj.with_environment(
{ [x]: "${" + x + "}" }
),
vars.variables,
self
),
add:: function() {
services +: {
[container.name]: {
image: container.image,
deploy: {
resources: {
limits: container.limits,
reservations: container.reservations,
}
},
restart: "on-failure:100",
} +
(if std.objectHas(container, "command") then
{ command: container.command }
else {}) +
(if std.objectHas(container, "user") then
{ user: container.user }
else {}) +
(if ! std.isEmpty(container.environment) then
{ environment: container.environment }
else {}) +
(if std.length(container.ports) > 0 then
{
ports: [
"%d:%d" % [port.src, port.dest]
for port in container.ports
]
}
else {}) +
(if std.length(container.volumes) > 0 then
{
volumes: [
"%s:%s" % [vol.volume.volid, vol.mount]
for vol in container.volumes
]
}
else {})
}
}
},
internalService:: function(containers)
{
local service = self,
name: containers.name,
with_port:: function(src, dest, name)
self + { port: [src, dest] },
add:: function() {
}
},
service:: function(containers)
{
local service = self,
name: containers.name,
with_port:: function(src, dest, name)
self + { port: [src, dest] },
add:: function() {
}
},
volume:: function(name)
{
local volume = self,
name: name,
volid:: name,
with_size:: function(size) self + { size: size },
add:: function() {
volumes +: {
[volume.name]: {}
}
}
},
configVolume:: function(name, dir, parts)
{
local volume = self,
name: dir,
volid:: "./" + dir,
with_size:: function(size) self + { size: size },
add:: function() {
}
},
secretVolume:: function(name, dir, parts)
{
local volume = self,
name: dir,
volid:: dir,
with_size:: function(size) self + { size: size },
add:: function() {
}
},
envSecrets:: function(name)
{
local volume = self,
name: name,
volid:: name,
variables:: [],
with_env_var::
function(name, key) self + {
variables: super.variables + [name],
},
add:: function() {
}
},
containers:: function(name, containers)
{
local cont = self,
name: name,
containers: containers,
add:: function() std.foldl(
function(state, c) state + c.add(),
cont.containers,
{}
),
},
resources:: function(res)
std.foldl(
function(state, c) state + c.add(),
res,
{}
),
}

44
templates/engine/gcp-k8s.jsonnet
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@ -1,44 +0,0 @@
local k8s = import "k8s.jsonnet";
local ns = {
apiVersion: "v1",
kind: "Namespace",
metadata: {
name: "trustgraph",
},
"spec": {
},
};
local sc = {
apiVersion: "storage.k8s.io/v1",
kind: "StorageClass",
metadata: {
name: "tg",
},
provisioner: "pd.csi.storage.gke.io",
parameters: {
type: "pd-balanced",
"csi.storage.k8s.io/fstype": "ext4",
},
reclaimPolicy: "Delete",
volumeBindingMode: "WaitForFirstConsumer",
};
k8s + {
// Extract resources usnig the engine
package:: function(patterns)
local resources = [sc, ns] + std.flattenArrays([
p.create(self) for p in std.objectValues(patterns)
]);
local resourceList = {
apiVersion: "v1",
kind: "List",
items: [ns, sc] + resources,
};
resourceList
}

358
templates/engine/k8s.jsonnet
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@ -1,358 +0,0 @@
{
container:: function(name)
{
local container = self,
name: name,
limits: {},
reservations: {},
ports: [],
volumes: [],
environment: [],
with_image:: function(x) self + { image: x },
with_user:: function(x) self + { user: x },
with_command:: function(x) self + { command: x },
with_environment:: function(x) self + {
environment: super.environment + [
{
name: v.key, value: v.value
}
for v in std.objectKeysValues(x)
],
},
with_limits:: function(c, m) self + { limits: { cpu: c, memory: m } },
with_reservations::
function(c, m) self + { reservations: { cpu: c, memory: m } },
with_volume_mount::
function(vol, mnt)
self + {
volumes: super.volumes + [{
volume: vol, mount: mnt
}]
},
with_port::
function(src, dest, name) self + {
ports: super.ports + [
{ src: src, dest: dest, name : name }
]
},
with_env_var_secrets::
function(vars)
std.foldl(
function(obj, x) obj + {
environment: super.environment + [{
name: x,
valueFrom: {
secretKeyRef: {
name: vars.name,
key: vars.keyMap[x],
}
}
}]
},
vars.variables,
self
),
add:: function() [
{
apiVersion: "apps/v1",
kind: "Deployment",
metadata: {
name: container.name,
namespace: "trustgraph",
labels: {
app: container.name
}
},
spec: {
replicas: 1,
selector: {
matchLabels: {
app: container.name,
}
},
template: {
metadata: {
labels: {
app: container.name,
}
},
spec: {
containers: [
{
name: container.name,
image: container.image,
// FIXME: Make everything run as
// root. Needed to get filesystems
// to be accessible. There's a
// better way of doing this?
securityContext: {
runAsUser: 0,
runAsGroup: 0,
},
resources: {
requests: container.reservations,
limits: container.limits
},
} + (
if std.length(container.ports) > 0 then
{
ports: [
{
hostPort: port.src,
containerPort: port.dest,
}
for port in container.ports
]
} else
{}) +
(if std.objectHas(container, "command") then
{ command: container.command }
else {}) +
(if ! std.isEmpty(container.environment) then
{
env: container.environment,
}
else {}) +
(if std.length(container.volumes) > 0 then
{
volumeMounts: [
{
mountPath: vol.mount,
name: vol.volume.name,
}
for vol in container.volumes
]
}
else
{}
)
],
volumes: [
vol.volume.volRef()
for vol in container.volumes
]
}
},
} + {}
}
]
},
// Just an alias
internalService:: self.service,
service:: function(containers)
{
local service = self,
name: containers.name,
ports: [],
with_port::
function(src, dest, name)
self + {
ports: super.ports + [
{ src: src, dest: dest, name: name }
]
},
add:: function() [
{
apiVersion: "v1",
kind: "Service",
metadata: {
name: service.name,
namespace: "trustgraph",
},
spec: {
selector: {
app: service.name,
},
ports: [
{
port: port.src,
targetPort: port.dest,
name: port.name,
}
for port in service.ports
],
}
}
],
},
volume:: function(name)
{
local volume = self,
name: name,
with_size:: function(size) self + { size: size },
add:: function() [
{
apiVersion: "v1",
kind: "PersistentVolumeClaim",
metadata: {
name: volume.name,
namespace: "trustgraph",
},
spec: {
storageClassName: "tg",
accessModes: [ "ReadWriteOnce" ],
resources: {
requests: {
storage: volume.size,
}
},
}
}
],
volRef:: function() {
name: volume.name,
persistentVolumeClaim: { claimName: volume.name },
}
},
configVolume:: function(name, dir, parts)
{
local volume = self,
name: name,
with_size:: function(size) self + { size: size },
add:: function() [
{
apiVersion: "v1",
kind: "ConfigMap",
metadata: {
name: volume.name,
namespace: "trustgraph",
},
data: parts
},
],
volRef:: function() {
name: volume.name,
configMap: { name: volume.name },
}
},
secretVolume:: function(name, dir, parts)
{
local volume = self,
name: name,
with_size:: function(size) self + { size: size },
add:: function() [
{
apiVersion: "v1",
kind: "Secret",
metadata: {
name: volume.name,
namespace: "trustgraph",
},
data: {
[item.key]: std.base64(item.value)
for item in std.objectKeysValues(parts)
}
},
],
volRef:: function() {
name: volume.name,
secret: { secretName: volume.name },
}
},
envSecrets:: function(name)
{
local volume = self,
name: name,
variables: [],
keyMap: {},
with_size:: function(size) self + { size: size },
add:: function() [
],
volRef:: function() {
name: volume.name,
secret: { secretName: volume.name },
},
with_env_var::
function(name, key) self + {
variables: super.variables + [name],
keyMap: super.keyMap + { [name]: key },
},
},
containers:: function(name, containers)
{
local cont = self,
name: name,
containers: containers,
add:: function() std.flattenArrays(
[ c.add() for c in cont.containers ]
),
},
resources:: function(res)
std.flattenArrays(
[ c.add() for c in res ]
),
}

115
templates/engine/minikube-k8s.jsonnet
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@ -1,115 +0,0 @@
local k8s = import "k8s.jsonnet";
local ns = {
apiVersion: "v1",
kind: "Namespace",
metadata: {
name: "trustgraph",
},
"spec": {
},
};
k8s + {
// Extract resources usnig the engine
package:: function(patterns)
local resources = [ns] + std.flattenArrays([
p.create(self) for p in std.objectValues(patterns)
]);
local resourceList = {
apiVersion: "v1",
kind: "List",
items: resources,
};
resourceList,
volume:: function(name)
{
local volume = self,
name: name,
with_size:: function(size) self + { size: size },
add:: function() [
{
apiVersion: "v1",
kind: "PersistentVolume",
metadata: {
name: volume.name,
},
spec: {
accessModes: [ "ReadWriteOnce" ],
capacity: {
storage: volume.size,
},
persistentVolumeReclaimPolicy: "Delete",
hostPath: {
path: "/data/pv-" + volume.name,
},
}
},
{
apiVersion: "v1",
kind: "PersistentVolumeClaim",
metadata: {
name: volume.name,
namespace: "trustgraph",
},
spec: {
accessModes: [ "ReadWriteOnce" ],
resources: {
requests: {
storage: volume.size,
}
},
}
}
],
volRef:: function() {
name: volume.name,
persistentVolumeClaim: { claimName: volume.name },
}
},
service:: function(containers)
{
local service = self,
name: containers.name,
ports: [],
with_port::
function(src, dest, name)
self + {
ports: super.ports + [
{ src: src, dest: dest, name: name }
]
},
add:: function() [
{
apiVersion: "v1",
kind: "Service",
metadata: {
name: service.name,
namespace: "trustgraph",
},
spec: {
selector: {
app: service.name,
},
type: "LoadBalancer",
ports: [
{
port: port.src,
targetPort: port.dest,
name: port.name,
}
for port in service.ports
],
}
}
],
},
}

235
templates/generate
View file

@ -1,235 +0,0 @@
#!/usr/bin/env python3
import _jsonnet as j
import json
import yaml
import logging
import os
import sys
import zipfile
import pathlib
from io import BytesIO
logger = logging.getLogger("generate")
logging.basicConfig(level=logging.INFO, format='%(message)s')
private_json = "Put your GCP private.json here"
class Generator:
def __init__(
self, config, templates="./templates/", resources="./resources",
version="0.0.0",
):
self.templates = pathlib.Path(templates)
self.resources = pathlib.Path(resources)
self.config = config
self.version = f"\"{version}\"".encode("utf-8")
def process(self, config):
res = j.evaluate_snippet("config", config, import_callback=self.load)
return json.loads(res)
def load(self, dir, filename):
logger.debug("Request jsonnet: %s %s", dir, filename)
if filename == "config.json" and dir == "":
path = os.path.join(".", dir, filename)
return str(path), self.config
if filename == "version.jsonnet" and dir == "templates/values/":
path = os.path.join(".", dir, filename)
return str(path), self.version
if dir:
candidates = [
self.templates.joinpath(dir, filename),
self.templates.joinpath(filename),
self.resources.joinpath(dir, filename),
self.resources.joinpath(filename),
pathlib.Path(dir).joinpath(filename),
]
else:
candidates = [
self.templates.joinpath(filename),
pathlib.Path(dir).joinpath(filename),
pathlib.Path(filename),
]
try:
if filename == "vertexai/private.json":
return str(candidates[0]), private_json.encode("utf-8")
for c in candidates:
logger.debug("Try: %s", c)
if os.path.isfile(c):
with open(c, "rb") as f:
logger.debug("Loading: %s", c)
return str(c), f.read()
raise RuntimeError(
f"Could not load file={filename} dir={dir}"
)
except:
path = os.path.join(self.templates, filename)
logger.debug("Try: %s", path)
with open(path, "rb") as f:
logger.debug("Loaded: %s", path)
return str(path), f.read()
class Packager:
def __init__(self):
self.templates = pathlib.Path("./templates")
self.resources = pathlib.Path("./")
def process(
self, config, version="0.0.0", platform="docker-compose",
):
config = config.encode("utf-8")
gen = Generator(
config, templates=self.templates, resources=self.resources,
version=version
)
path = self.templates.joinpath(
f"config-to-{platform}.jsonnet"
)
wrapper = path.read_text()
processed = gen.process(wrapper)
return processed
def generate(self, config, version, platform):
logger.info(f"Generating for platform={platform} version={version}")
try:
if platform in set(["docker-compose", "podman-compose"]):
return self.generate_docker_compose(
"docker-compose", version, config
)
elif platform in set(["minikube-k8s", "gcp-k8s", "aks-k8s"]):
return self.generate_k8s(
platform, version, config
)
else:
raise RuntimeError("Bad configuration")
except Exception as e:
logging.error(f"Exception: {e}")
raise e
def generate_docker_compose(self, platform, version, config):
processed = self.process(
config, platform=platform, version=version
)
y = yaml.dump(processed)
mem = BytesIO()
with zipfile.ZipFile(mem, mode='w') as out:
def output(name, content):
logger.info(f"Adding {name}...")
out.writestr(name, content)
fname = "docker-compose.yaml"
output(fname, y)
# Grafana config
path = self.resources.joinpath(
"grafana/dashboards/dashboard.json"
)
res = path.read_text()
output("grafana/dashboards/dashboard.json", res)
path = self.resources.joinpath(
"grafana/provisioning/dashboard.yml"
)
res = path.read_text()
output("grafana/provisioning/dashboard.yml", res)
path = self.resources.joinpath(
"grafana/provisioning/datasource.yml"
)
res = path.read_text()
output("grafana/provisioning/datasource.yml", res)
# Prometheus config
path = self.resources.joinpath(
"prometheus/prometheus.yml"
)
res = path.read_text()
output("prometheus/prometheus.yml", res)
logger.info("Generation complete.")
return mem.getvalue()
def generate_k8s(self, platform, version, config):
processed = self.process(
config, platform=platform, version=version
)
y = yaml.dump(processed)
mem = BytesIO()
with zipfile.ZipFile(mem, mode='w') as out:
def output(name, content):
logger.info(f"Adding {name}...")
out.writestr(name, content)
fname = "resources.yaml"
output(fname, y)
logger.info("Generation complete.")
return mem.getvalue()
def main():
if len(sys.argv) != 4:
print()
print("Usage:")
print(" generate <outfile> <version> <platform> < input.json")
print()
sys.exit(1)
outfile = sys.argv[1]
version = sys.argv[2]
platform = sys.argv[3]
cfg = sys.stdin.read()
logger.info(f"Outputting to {outfile}...")
p = Packager()
resp = p.generate(cfg, version, platform)
with open(outfile, "wb") as f:
f.write(resp)
return
main()

195
templates/generate-all
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@ -1,195 +0,0 @@
#!/usr/bin/env python3
import _jsonnet as j
import json
import yaml
import logging
import os
import sys
import zipfile
logger = logging.getLogger("generate")
logging.basicConfig(level=logging.INFO, format='%(message)s')
private_json = "Put your GCP private.json here"
class Generator:
def __init__(self, config, base="./templates/", version="0.0.0"):
self.jsonnet_base = base
self.config = config
self.version = f"\"{version}\"".encode("utf-8")
def process(self, config):
res = j.evaluate_snippet("config", config, import_callback=self.load)
return json.loads(res)
def load(self, dir, filename):
logger.debug("Request jsonnet: %s %s", dir, filename)
if filename == "config.json" and dir == "":
path = os.path.join(".", dir, filename)
return str(path), self.config
if filename == "version.jsonnet" and dir == "./templates/values/":
path = os.path.join(".", dir, filename)
return str(path), self.version
if dir:
candidates = [
os.path.join(".", dir, filename),
os.path.join(".", filename)
]
else:
candidates = [
os.path.join(".", filename)
]
try:
if filename == "vertexai/private.json":
return candidates[0], private_json.encode("utf-8")
for c in candidates:
logger.debug("Try: %s", c)
if os.path.isfile(c):
with open(c, "rb") as f:
logger.debug("Loading: %s", c)
return str(c), f.read()
raise RuntimeError(
f"Could not load file={filename} dir={dir}"
)
except:
path = os.path.join(self.jsonnet_base, filename)
logger.debug("Try: %s", path)
with open(path, "rb") as f:
logger.debug("Loaded: %s", path)
return str(path), f.read()
def config_object(items):
return [
{ "name": v, "parameters": {} }
for v in items
]
def full_config_object(
vector_store="qdrant", embeddings="embeddings-hf",
graph_store="cassandra", llm="vertexai",
):
return config_object([
"triple-store-" + graph_store,
"pulsar",
"vector-store-" + vector_store,
embeddings,
"graph-rag",
"grafana",
"trustgraph-base",
llm,
llm + "-rag",
"workbench-ui",
"prompt-template",
"agent-manager-react",
])
def generate_config(
vector_store="qdrant", embeddings="embeddings-hf",
graph_store="cassandra", llm="vertexai",
platform="docker-compose",
version="0.0.0"
):
config = full_config_object(
vector_store=vector_store,
embeddings=embeddings,
graph_store=graph_store,
llm=llm,
)
with open(f"./templates/config-to-{platform}.jsonnet", "r") as f:
wrapper = f.read()
gen = Generator(json.dumps(config).encode("utf-8"), version=version)
processed = gen.process(wrapper)
y = yaml.dump(processed)
return y
def generate_all(output, version):
for platform in [
"docker-compose",
# "minikube-k8s", "gcp-k8s"
]:
for model in [
# "azure", "azure-openai", "bedrock", "claude", "cohere",
# "googleaistudio", "llamafile", "mistral",
"ollama",
# "openai", "vertexai",
]:
for graph in [
"cassandra",
# "neo4j", "falkordb"
]:
y = generate_config(
llm=model, graph_store=graph, platform=platform,
version=version
)
fname = f"{platform}/tg-{model}-{graph}.yaml"
output(fname, y)
if len(sys.argv) < 3:
raise RuntimeError("Usage: generate-all <file> <version>")
outfile = sys.argv[1]
version = sys.argv[2]
logger.info(f"Outputting to {outfile}...")
with zipfile.ZipFile(outfile, mode='w') as out:
def output(name, content):
logger.info(f"Adding {name}...")
out.writestr(name, content)
generate_all(output=output, version=version)
# Placeholder for the private.json file. Won't put actual credentials
# here.
output("docker-compose/vertexai/private.json", private_json)
# Grafana config
with open("grafana/dashboards/dashboard.json") as f:
output("docker-compose/grafana/dashboards/dashboard.json", f.read())
with open("grafana/provisioning/dashboard.yml") as f:
output("docker-compose/grafana/provisioning/dashboard.yml", f.read())
with open("grafana/provisioning/datasource.yml") as f:
output("docker-compose/grafana/provisioning/datasource.yml", f.read())
# Prometheus config
with open("prometheus/prometheus.yml") as f:
output("docker-compose/prometheus/prometheus.yml", f.read())
# A README
with open("templates/zip-readme.md") as f:
output("README.md", f.read())
logger.info("Output file written.")

9
templates/module-list.jsonnet
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@ -1,9 +0,0 @@
local all = import "all-patterns.jsonnet";
std.foldl(
function(m, p) m + { [p.pattern.name]: p.module},
all,
{}
)

21
templates/opts-to-docker-compose.jsonnet
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@ -1,21 +0,0 @@
local engine = import "engine/docker-compose.jsonnet";
local components = import "components.jsonnet";
// Options
local options = std.split(std.extVar("options"), ",");
// Produce patterns from config
local patterns = std.foldl(
function(state, p) state + components[p],
options,
{}
);
// Extract resources usnig the engine
local resources = engine.package(patterns);
resources

19
templates/opts-to-gcp-k8s.jsonnet
View file

@ -1,19 +0,0 @@
local engine = import "engine/docker-compose.jsonnet";
local components = import "components.jsonnet";
// Options
local options = std.split(std.extVar("options"), ",");
// Produce patterns from config
local patterns = std.foldl(
function(state, p) state + components[p],
options,
{}
);
// Extract resources usnig the engine
local resources = engine.package(patterns);
std.manifestYamlDoc(resources)

19
templates/opts-to-minikube-k8s.jsonnet
View file

@ -1,19 +0,0 @@
local engine = import "engine/minikube-k8s.jsonnet";
local components = import "components.jsonnet";
// Options
local options = std.split(std.extVar("options"), ",");
// Produce patterns from config
local patterns = std.foldl(
function(state, p) state + components[p],
options,
{}
);
// Extract resources usnig the engine
local resources = engine.package(patterns);
resources

14
templates/patterns/document-rag.jsonnet
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@ -1,14 +0,0 @@
{
pattern: {
name: "document-rag",
icon: "🤝😂",
title: "Add DocumentRAG processing flow",
description: "This pattern adds DocumentRAG components for extracting and querying documents based on document embeddings. You should make sure a vector store is included in your plan.",
requires: ["pulsar", "trustgraph", "llm"],
features: ["document-rag"],
args: [
],
category: [ "processing" ],
},
module: "components/document-rag.jsonnet",
}

26
templates/patterns/embeddings-hf.jsonnet
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@ -1,26 +0,0 @@
{
pattern: {
name: "embeddings-hf",
icon: "🤖💬",
title: "Add embeddings model which uses HuggingFace models",
description: "This pattern integrates an embeddings model based on HuggingFace sentence-transformer library.",
requires: ["pulsar", "trustgraph"],
features: ["llm"],
args: [
{
name: "embeddings-model",
label: "Embeddings model",
type: "select",
description: "Embeddings model for sentence analysis",
options: [
{ id: "all-MiniLM-L6-v2", description: "all-MiniLM-L6-v2" },
{ id: "mixedbread-ai/mxbai-embed-large-v1", description: "mxbai-embed-large-v1" },
],
default: "all-MiniLM-L6-v2",
required: true,
},
],
category: [ "embeddings" ],
},
module: "components/embeddings-hf.jsonnet",
}

34
templates/patterns/embeddings-ollama.jsonnet
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@ -1,34 +0,0 @@
{
pattern: {
name: "embeddings-ollama",
icon: "🤖💬",
title: "Add embeddings model hosted on Ollama",
description: "This pattern integrates an embeddings model based on HuggingFace sentence-transformer library.",
requires: ["pulsar", "trustgraph"],
features: ["llm"],
args: [
{
name: "embeddings-model",
label: "Embeddings model",
type: "select",
description: "Embeddings model for sentence analysis",
options: [
{ id: "mxbai-embed-large", description: "mxbai-embed-large" },
],
default: "mxbai-embed-large",
required: true,
},
{
name: "ollama-url",
label: "URL",
type: "text",
width: 120,
description: "URL of the Ollama service",
default: "http://ollama:11434",
required: true,
},
],
category: [ "embeddings" ],
},
module: "components/embeddings-hf.jsonnet",
}

14
templates/patterns/grafana.jsonnet
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@ -1,14 +0,0 @@
{
pattern: {
name: "grafana",
icon: "📈🧯",
title: "Add Prometheus and Grafana for monitoring and dashboards",
description: "System monitoring and dashboarding using Grafana and Prometheus",
requires: ["pulsar", "trustgraph"],
features: ["prometheus", "grafana"],
args: [
],
category: ["monitoring"],
},
module: "components/grafana.jsonnet",
}

38
templates/patterns/graph-rag.jsonnet
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@ -1,38 +0,0 @@
{
pattern: {
name: "graph-rag",
icon: "🤝😂",
title: "Add GraphRAG processing flow",
description: "This pattern adds GraphRAG components for extracting and querying graph edges. You should make sure a triple store and vector store are included in your plan.",
requires: ["pulsar", "trustgraph", "llm"],
features: ["graph-rag"],
args: [
{
name: "graph-rag-entity-limit",
label: "GraphRAG entity query limit",
type: "integer",
description: "Limit on entities to fetch from vector store",
default: 50,
required: true,
},
{
name: "graph-rag-triple-limit",
label: "GraphRAG triple query limit",
type: "integer",
description: "Limit on triples to fetch from triple store",
default: 30,
required: true,
},
{
name: "graph-rag-max-subgraph-size",
label: "GraphRAG maximum subgraph size",
type: "integer",
description: "Limit on size of subgraph to present to text-completion model",
default: 3000,
required: true,
},
],
category: [ "processing" ],
},
module: "components/trustgraph.jsonnet",
}

32
templates/patterns/llm-azure-openai.jsonnet
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@ -1,32 +0,0 @@
{
pattern: {
name: "azure-openai",
icon: "🤖💬",
title: "Add Azure OpenAI LLM endpoint for text completion",
description: "This pattern integrates an Azure OpenAI LLM endpoint hosted in the Azure cloud for text completion operations. You need an Azure subscription to be able to use this service.",
requires: ["pulsar", "trustgraph"],
features: ["llm"],
args: [
{
name: "azure-openai-max-output-tokens",
label: "Maximum output tokens",
type: "integer",
description: "Limit on number tokens to generate",
default: 4096,
required: true,
},
{
name: "azure-openai-temperature",
label: "Temperature",
type: "slider",
description: "Controlling predictability / creativity balance",
min: 0,
max: 1,
step: 0.05,
default: 0.5,
},
],
category: [ "llm" ],
},
module: "components/azure.jsonnet",
}

32
templates/patterns/llm-azure.jsonnet
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@ -1,32 +0,0 @@
{
pattern: {
name: "azure",
icon: "🤖💬",
title: "Add Azure LLM endpoint for text completion",
description: "This pattern integrates an Azure LLM endpoint hosted in the Azure cloud for text completion operations. You need an Azure subscription and to have an endpoint deployed to be able to use this service.",
requires: ["pulsar", "trustgraph"],
features: ["llm"],
args: [
{
name: "azure-max-output-tokens",
label: "Maximum output tokens",
type: "integer",
description: "Limit on number tokens to generate",
default: 4096,
required: true,
},
{
name: "azure-temperature",
label: "Temperature",
type: "slider",
description: "Controlling predictability / creativity balance",
min: 0,
max: 1,
step: 0.05,
default: 0.5,
},
],
category: [ "llm" ],
},
module: "components/azure.jsonnet",
}

32
templates/patterns/llm-bedrock.jsonnet
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@ -1,32 +0,0 @@
{
pattern: {
name: "bedrock",
icon: "🤖💬",
title: "Add AWS Bedrock for text completion",
description: "This pattern integrates an AWS Bedrock LLM service hosted in the AWS cloud for text completion operations. You need an AWS cloud subscription and to have Bedrock configured to be able to use this service.",
requires: ["pulsar", "trustgraph"],
features: ["llm"],
args: [
{
name: "bedrock-max-output-tokens",
label: "Maximum output tokens",
type: "integer",
description: "Limit on number tokens to generate",
default: 4096,
required: true,
},
{
name: "bedrock-temperature",
label: "Temperature",
type: "slider",
description: "Controlling predictability / creativity balance",
min: 0,
max: 1,
step: 0.05,
default: 0.5,
},
],
category: [ "llm" ],
},
module: "components/bedrock.jsonnet",
}

32
templates/patterns/llm-claude.jsonnet
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@ -1,32 +0,0 @@
{
pattern: {
name: "claude",
icon: "🤖💬",
title: "Add Anthropic Claude for text completion",
description: "This pattern integrates an Anthropic Claude LLM service for text completion operations. You need a Claude subscription to be able to use this service.",
requires: ["pulsar", "trustgraph"],
features: ["llm"],
args: [
{
name: "claude-max-output-tokens",
label: "Maximum output tokens",
type: "integer",
description: "Limit on number tokens to generate",
default: 4096,
required: true,
},
{
name: "claude-temperature",
label: "Temperature",
type: "slider",
description: "Controlling predictability / creativity balance",
min: 0,
max: 1,
step: 0.05,
default: 0.5,
},
],
category: [ "llm" ],
},
module: "components/claude.jsonnet",
}

32
templates/patterns/llm-cohere.jsonnet
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@ -1,32 +0,0 @@
{
pattern: {
name: "cohere",
icon: "🤖💬",
title: "Add Cohere LLM endpoint for text completion",
description: "This pattern integrates the Cohere LLM service for text completion operations. You need a Cohere subscription and API keys to be able to use this service.",
requires: ["pulsar", "trustgraph"],
features: ["llm"],
args: [
{
name: "cohere-max-output-tokens",
label: "Maximum output tokens",
type: "integer",
description: "Limit on number tokens to generate",
default: 4096,
required: true,
},
{
name: "cohere-temperature",
label: "Temperature",
type: "slider",
description: "Controlling predictability / creativity balance",
min: 0,
max: 1,
step: 0.05,
default: 0.5,
},
],
category: [ "llm" ],
},
module: "components/cohere.jsonnet",
}

32
templates/patterns/llm-googleaistudio.jsonnet
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@ -1,32 +0,0 @@
{
pattern: {
name: "googleaistudio",
icon: "🤖💬",
title: "Add GoogleAIStudio for text completion",
description: "This pattern integrates a GoogleAIStudio LLM service for text completion operations. You need a GoogleAISTudio API key to be able to use this service.",
requires: ["pulsar", "trustgraph"],
features: ["llm"],
args: [
{
name: "googleaistudio-max-output-tokens",
label: "Maximum output tokens",
type: "integer",
description: "Limit on number tokens to generate",
default: 4096,
required: true,
},
{
name: "googleaistudio-temperature",
label: "Temperature",
type: "slider",
description: "Controlling predictability / creativity balance",
min: 0,
max: 1,
step: 0.05,
default: 0.5,
},
],
category: [ "llm" ],
},
module: "components/googleaistudio.jsonnet",
}

41
templates/patterns/llm-llamafile.jsonnet
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@ -1,41 +0,0 @@
{
pattern: {
name: "llamafile",
icon: "🤖💬",
title: "Add Llamafile-invoked LLMs for text completion",
description: "This pattern integrates a Llamafile service for text completion operations. You need to have a running Llamafile implementation executing the necessary model in order to be able to use this service.",
requires: ["pulsar", "trustgraph"],
features: ["llm"],
args: [
{
name: "llamafile-max-output-tokens",
label: "Maximum output tokens",
type: "integer",
description: "Limit on number tokens to generate",
default: 4096,
required: true,
},
{
name: "llamafile-temperature",
label: "Temperature",
type: "slider",
description: "Controlling predictability / creativity balance",
min: 0,
max: 1,
step: 0.05,
default: 0.5,
},
{
name: "llamafile-url",
label: "URL",
type: "text",
width: 120,
description: "URL of the Llamafile service",
default: "http://llamafile:8080",
required: true,
},
],
category: [ "llm" ],
},
module: "components/llamafile.jsonnet",
}

32
templates/patterns/llm-mistral.jsonnet
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@ -1,32 +0,0 @@
{
pattern: {
name: "mistral",
icon: "🤖💬",
title: "Add Mistral LLM endpoint for text completion",
description: "This pattern integrates a Mistral LLM service for text completion operations. You need a Mistral subscription and have an API key to be able to use this service.",
requires: ["pulsar", "trustgraph"],
features: ["llm"],
args: [
{
name: "mistral-max-output-tokens",
label: "Maximum output tokens",
type: "integer",
description: "Limit on number tokens to generate",
default: 4096,
required: true,
},
{
name: "mistral-temperature",
label: "Temperature",
type: "slider",
description: "Controlling predictability / creativity balance",
min: 0,
max: 1,
step: 0.05,
default: 0.5,
},
],
category: [ "llm" ],
},
module: "components/mistral.jsonnet",
}

41
templates/patterns/llm-ollama.jsonnet
View file

@ -1,41 +0,0 @@
{
pattern: {
name: "ollama",
icon: "🤖💬",
title: "Add Ollama LLM for text completion",
description: "This pattern integrates an Ollama service for text completion operations. You need to have a running Ollama service with the necessary models added in order to be able to use this service.",
requires: ["pulsar", "trustgraph"],
features: ["llm"],
args: [
{
name: "ollama-max-output-tokens",
label: "Maximum output tokens",
type: "integer",
description: "Limit on number tokens to generate",
default: 4096,
required: true,
},
{
name: "ollama-temperature",
label: "Temperature",
type: "slider",
description: "Controlling predictability / creativity balance",
min: 0,
max: 1,
step: 0.05,
default: 0.5,
},
{
name: "ollama-url",
label: "URL",
type: "text",
width: 120,
description: "URL of the Ollama service",
default: "http://ollama:11434",
required: true,
},
],
category: [ "llm" ],
},
module: "components/ollama.jsonnet",
}

32
templates/patterns/llm-openai.jsonnet
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@ -1,32 +0,0 @@
{
pattern: {
name: "openai",
icon: "🤖💬",
title: "Add OpenAI LLM endpoint for text completion",
description: "This pattern integrates an OpenAI LLM service for text completion operations. You need an OpenAI subscription and have an API key to be able to use this service.",
requires: ["pulsar", "trustgraph"],
features: ["llm"],
args: [
{
name: "openai-max-output-tokens",
label: "Maximum output tokens",
type: "integer",
description: "Limit on number tokens to generate",
default: 4096,
required: true,
},
{
name: "openai-temperature",
label: "Temperature",
type: "slider",
description: "Controlling predictability / creativity balance",
min: 0,
max: 1,
step: 0.05,
default: 0.5,
},
],
category: [ "llm" ],
},
module: "components/openai.jsonnet",
}

32
templates/patterns/llm-vertexai.jsonnet
View file

@ -1,32 +0,0 @@
{
pattern: {
name: "vertexai",
icon: "🤖💬",
title: "Add Google Cloud VertexAI LLM for text completion",
description: "This pattern integrates a VertexAI endpoint hosted in Google Cloud for text completion operations. You need a GCP subscription and to have VertexAI enabled to be able to use this service.",
requires: ["pulsar", "trustgraph"],
features: ["llm"],
args: [
{
name: "vertexai-max-output-tokens",
label: "Maximum output tokens",
type: "integer",
description: "Limit on number tokens to generate",
default: 4096,
required: true,
},
{
name: "vertexai-temperature",
label: "Temperature",
type: "slider",
description: "Controlling predictability / creativity balance",
min: 0,
max: 1,
step: 0.05,
default: 0.5,
},
],
category: [ "llm" ],
},
module: "components/vertexai.jsonnet",
}

28
templates/patterns/override-recursive-chunker.jsonnet
View file

@ -1,28 +0,0 @@
{
pattern: {
name: "override-recursive-chunker",
icon: "✂️🪚",
title: "Replace default chunker with recursive chunker",
description: "The default chunker used in Trustgraph core is a token-based chunker. This pattern replaces that with a recursive chunker, and allows ou to configure the chunking parameters.",
requires: ["pulsar", "trustgraph"],
features: [],
args: [
{
name: "chunk-size",
type: "integer",
description: "Chunk size value",
default: 2000,
required: true,
},
{
name: "chunk-overlap",
type: "integer",
description: "Overlap size value",
default: 100,
required: true,
}
],
category: [ "chunking" ],
},
module: "components/cassandra.jsonnet",
}

23
templates/patterns/prompt-template-definitions.jsonnet
View file

@ -1,23 +0,0 @@
{
pattern: {
name: "prompt-template-definitions",
icon: "📜️️💬",
title: "Override definition extraction prompt",
description: "This pattern overrides the default definition extraction LLM prompt allowing you to provide your own prompt.",
requires: ["pulsar", "trustgraph"],
features: ["extract-definition-prompt"],
args: [
{
name: "prompt-definition-template",
type: "multiline",
size: 2000,
rows: 10,
description: "Definition extraction prompt",
default: "<instructions>\nStudy the following text and derive definitions for any discovered entities.\nDo not provide definitions for entities whose definitions are incomplete\nor unknown.\nOutput relationships in JSON format as an arary of objects with fields:\n- entity: the name of the entity\n- definition: English text which defines the entity\n</instructions>\n\n<text>\n{text}\n</text>\n\n<requirements>\nYou will respond only with raw JSON format data. Do not provide\nexplanations. Do not use special characters in the abstract text. The\nabstract will be written as plain text. Do not add markdown formatting\nor headers or prefixes. Do not include null or unknown definitions.\n</requirements>",
required: true,
}
],
category: [ "prompting" ],
},
module: "components/null.jsonnet",
}

23
templates/patterns/prompt-template-document-query.jsonnet
View file

@ -1,23 +0,0 @@
{
pattern: {
name: "prompt-template-document-query",
icon: "📜️️💬",
title: "Override document query prompt",
description: "This pattern overrides the default document query prompt used for DocumentRAG allowing you to specify your own prompt.",
requires: ["pulsar", "trustgraph"],
features: ["document-query-prompt"],
args: [
{
name: "prompt-document-query-template",
type: "multiline",
size: 2000,
rows: 10,
description: "Document query prompt",
default: "Study the following context. Use only the information provided in the context in your response. Do not speculate if the answer is not found in the provided set of knowledge statements.\n\nHere is the context:\n{documents}\n\nUse only the provided knowledge statements to respond to the following:\n{query}\n",
required: true,
}
],
category: [ "prompting" ],
},
module: "components/null.jsonnet",
}

23
templates/patterns/prompt-template-kg-query.jsonnet
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@ -1,23 +0,0 @@
{
pattern: {
name: "prompt-template-kq-query",
icon: "📜️️💬",
title: "Override knowledge query prompt",
description: "This pattern overrides the default knowledge query LLM prompt allowing you to provide your own prompt.",
requires: ["pulsar", "trustgraph"],
features: ["kg-query-prompt"],
args: [
{
name: "prompt-knowledge-query-template",
type: "multiline",
size: 2000,
rows: 10,
description: "Knowledge graph extraction prompt",
default: "Study the following set of knowledge statements. The statements are written in Cypher format that has been extracted from a knowledge graph. Use only the provided set of knowledge statements in your response. Do not speculate if the answer is not found in the provided set of knowledge statements.\n\nHere's the knowledge statements:\n{graph}\n\nUse only the provided knowledge statements to respond to the following:\n{query}\n",
required: true,
}
],
category: [ "prompting" ],
},
module: "components/null.jsonnet",
}

23
templates/patterns/prompt-template-relationships.jsonnet
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@ -1,23 +0,0 @@
{
pattern: {
name: "prompt-template-relationships",
icon: "📜️️💬",
title: "Override relationship extraction prompt",
description: "This pattern overrides the default relationship extraction LLM prompt allowing you to provide your own prompt.",
requires: ["pulsar", "trustgraph"],
features: ["extract-relationship-prompt"],
args: [
{
name: "prompt-relationship-template",
type: "multiline",
size: 2000,
rows: 10,
description: "Relationship extraction prompt",
default: "<instructions>\nStudy the following text and derive entity relationships. For each\nrelationship, derive the subject, predicate and object of the relationship.\nOutput relationships in JSON format as an arary of objects with fields:\n- subject: the subject of the relationship\n- predicate: the predicate\n- object: the object of the relationship\n- object-entity: false if the object is a simple data type: name, value or date. true if it is an entity.\n</instructions>\n\n<text>\n{text}\n</text>\n\n<requirements>\nYou will respond only with raw JSON format data. Do not provide\nexplanations. Do not use special characters in the abstract text. The\nabstract must be written as plain text. Do not add markdown formatting\nor headers or prefixes.\n</requirements>",
required: true,
}
],
category: [ "prompting" ],
},
module: "components/null.jsonnet",
}

23
templates/patterns/prompt-template-rows-template.jsonnet
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@ -1,23 +0,0 @@
{
pattern: {
name: "prompt-template-rows-template",
icon: "📜️️💬",
title: "Override table/row extraction prompt",
description: "This pattern overrides the default table/row extraction prompt to be changed. This is used for DatabaseRAG pipelines.",
requires: ["pulsar", "trustgraph"],
features: ["extract-rows-prompt"],
args: [
{
name: "prompt-rows-template",
type: "multiline",
size: 2000,
rows: 10,
description: "Row data extraction prompt",
default: "<instructions>\nStudy the following text and derive objects which match the schema provided.\n\nYou must output an array of JSON objects for each object you discover\nwhich matches the schema. For each object, output a JSON object whose fields\ncarry the name field specified in the schema.\n</instructions>\n\n<schema>\n{schema}\n</schema>\n\n<text>\n{text}\n</text>\n\n<requirements>\nYou will respond only with raw JSON format data. Do not provide\nexplanations. Do not add markdown formatting or headers or prefixes.\n</requirements>",
required: true,
}
],
category: [ "prompting" ],
},
module: "components/null.jsonnet",
}

23
templates/patterns/pulsar-manager.jsonnet
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@ -1,23 +0,0 @@
{
pattern: {
name: "pulsar-manager",
icon: "🏻🛃",
title: "Add Pulsar Manager",
description: "Adds Pulsar Manager which provides a web interface to manage Pulsar. Pulsar Manager is a large container and deployment requiring over 1GB of RAM, so is not deployed by default. This is not a required component, it may be useful to help manage a large operational deployment.",
requires: ["pulsar"],
features: ["pulsar-manager"],
args: [
{
name: "initial-admin-password",
label: "Password",
type: "text",
width: 40,
description: "Admin password to apply at initialisation",
default: "pulsaradmin",
required: true,
},
],
category: [ "foundation" ],
},
module: "components/pulsar.jsonnet",
}

14
templates/patterns/pulsar.jsonnet
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@ -1,14 +0,0 @@
{
pattern: {
name: "pulsar",
icon: "🌟☄️",
title: "Deploy foundation messaging fabric",
description: "Deploy Pulsar as the inter-process messaging fabric. Pulsar is a Cloud-native, distributed messaging and Streaming. Apache Pulsar is an open-source, distributed messaging and streaming platform built for the cloud. Trustgraph uses Pulsar to manage the message flow between all components.",
requires: [],
features: ["pulsar"],
args: [
],
category: [ "foundation" ],
},
module: "components/pulsar.jsonnet",
}

13
templates/patterns/triple-store-cassandra.jsonnet
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@ -1,13 +0,0 @@
{
pattern: {
name: "triple-store-cassandra",
icon: "🖇️🙋‍♀️",
title: "Adds a Cassandra store configured to act as a triple store",
description: "GraphRAG processing needs a triple store. This pattern adds a Cassandra store, along with plumbing so that Cassandra is integrated with GraphRag indexing and querying.",
requires: ["pulsar", "trustgraph"],
features: ["cassandra", "triple-store"],
args: [],
category: ["knowledge-graph"],
},
module: "components/cassandra.jsonnet",
}

13
templates/patterns/triple-store-falkordb.jsonnet
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@ -1,13 +0,0 @@
{
pattern: {
name: "triple-store-falkordb",
icon: "🖇️🙋‍♀️",
title: "Adds a FalkorDB store configured to act as a triple store.",
description: "GraphRAG processing needs a triple store. This pattern adds a FalkorDB store, along with plumbing so that FalkorDB is integrated with GraphRag indexing and querying.",
requires: ["pulsar", "trustgraph"],
features: ["falkordb", "triple-store"],
args: [],
category: [ "knowledge-graph" ],
},
module: "components/falkordb.jsonnet",
}

13
templates/patterns/triple-store-neo4j.jsonnet
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@ -1,13 +0,0 @@
{
pattern: {
name: "triple-store-neo4j",
icon: "🖇️🙋‍♀️",
title: "Adds a Neo4j store configured to act as a triple store.",
description: "GraphRAG processing needs a triple store. This pattern adds a Neo4j store, along with plumbing so that Neo4j is integrated with GraphRag indexing and querying.",
requires: ["pulsar", "trustgraph"],
features: ["neo4j", "triple-store"],
args: [],
category: [ "knowledge-graph" ],
},
module: "components/neo4j.jsonnet",
}

14
templates/patterns/trustgraph-base.jsonnet
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{
pattern: {
name: "trustgraph-base",
icon: "🤝😂",
title: "Add Trustgraph base processing flows",
description: "This pattern adds a core set of Trustgraph flows, including PDF ingest, chunking, embeddings, and knowledge graph extraction. You should also consider adding an LLM and at least one RAG processing flow.",
requires: ["pulsar"],
features: ["trustgraph"],
args: [
],
category: [ "foundation" ],
},
module: "components/trustgraph.jsonnet",
}

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