3.1 KiB
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