docker-compose.yaml

services:
  agent-manager:
    command:
    - agent-manager-react
    - -p
    - pulsar://pulsar:6650
    - --tool-type
    - uk-legislation=knowledge-query
    - general-knowledge=text-completion
    - primary-legislation-schedule=knowledge-query
    - --tool-description
    - uk-legislation=Query a knowledge base for information about UK legislation
      passed in 2024.  The query should be a simple natural language question.
    - general-knowledge=Query a general database of knowledge.  This will not
      provide domain-specific knowledge.  The query should be a simple natural
      language question.
    - primary-legislation-schedule=Query a knowledge base about the schedule of
      primary legilsation coming from UK parliament.  The query should be
      a simple natural language question.
    - --tool-argument
    - uk-legislation=query:string:Describe the search query here.
    - general-knowledge=query:string:Describe the search query here.
    - primary-legislation-schedule=query:string:Describe the search query here.
    deploy:
      resources:
        limits:
          cpus: '0.5'
          memory: 128M
        reservations:
          cpus: '0.1'
          memory: 128M
    image: docker.io/trustgraph/trustgraph-flow:0.17.6
    restart: on-failure:100
  api-gateway:
    command:
    - api-gateway
    - -p
    - pulsar://pulsar:6650
    - --timeout
    - '600'
    - --port
    - '8088'
    deploy:
      resources:
        limits:
          cpus: '0.5'
          memory: 256M
        reservations:
          cpus: '0.1'
          memory: 256M
    environment:
      GATEWAY_SECRET: ${GATEWAY_SECRET}
    image: docker.io/trustgraph/trustgraph-flow:0.17.6
    ports:
    - 8000:8000
    - 8088:8088
    restart: on-failure:100
  chunker:
    command:
    - chunker-recursive
    - -p
    - pulsar://pulsar:6650
    - --chunk-size
    - '1000'
    - --chunk-overlap
    - '50'
    deploy:
      resources:
        limits:
          cpus: '0.5'
          memory: 128M
        reservations:
          cpus: '0.1'
          memory: 128M
    image: docker.io/trustgraph/trustgraph-flow:0.17.6
    restart: on-failure:100
  embeddings:
    command:
    - embeddings-hf
    - -p
    - pulsar://pulsar:6650
    - -m
    - all-MiniLM-L6-v2
    deploy:
      resources:
        limits:
          cpus: '1.0'
          memory: 400M
        reservations:
          cpus: '0.5'
          memory: 400M
    image: docker.io/trustgraph/trustgraph-flow:0.17.6
    restart: on-failure:100
  grafana:
    deploy:
      resources:
        limits:
          cpus: '1.0'
          memory: 256M
        reservations:
          cpus: '0.5'
          memory: 256M
    environment:
      GF_ORG_NAME: trustgraph.ai
    image: docker.io/grafana/grafana:11.1.4
    ports:
    - 3000:3000
    restart: on-failure:100
    volumes:
    - grafana-storage:/var/lib/grafana
    - ./grafana/provisioning/:/etc/grafana/provisioning/dashboards/
    - ./grafana/provisioning/:/etc/grafana/provisioning/datasources/
    - ./grafana/dashboards/:/var/lib/grafana/dashboards/
  graph-rag:
    command:
    - graph-rag
    - -p
    - pulsar://pulsar:6650
    - --prompt-request-queue
    - non-persistent://tg/request/prompt-rag
    - --prompt-response-queue
    - non-persistent://tg/response/prompt-rag
    - --entity-limit
    - '50'
    - --triple-limit
    - '30'
    - --max-subgraph-size
    - '3000'
    deploy:
      resources:
        limits:
          cpus: '0.5'
          memory: 128M
        reservations:
          cpus: '0.1'
          memory: 128M
    image: docker.io/trustgraph/trustgraph-flow:0.17.6
    restart: on-failure:100
  init-pulsar:
    command:
    - tg-init-pulsar
    - -p
    - http://pulsar:8080
    deploy:
      resources:
        limits:
          cpus: '1'
          memory: 128M
        reservations:
          cpus: '0.1'
          memory: 128M
    image: docker.io/trustgraph/trustgraph-flow:0.17.6
    restart: on-failure:100
  kg-extract-definitions:
    command:
    - kg-extract-definitions
    - -p
    - pulsar://pulsar:6650
    deploy:
      resources:
        limits:
          cpus: '0.5'
          memory: 128M
        reservations:
          cpus: '0.1'
          memory: 128M
    image: docker.io/trustgraph/trustgraph-flow:0.17.6
    restart: on-failure:100
  kg-extract-relationships:
    command:
    - kg-extract-relationships
    - -p
    - pulsar://pulsar:6650
    deploy:
      resources:
        limits:
          cpus: '0.5'
          memory: 128M
        reservations:
          cpus: '0.1'
          memory: 128M
    image: docker.io/trustgraph/trustgraph-flow:0.17.6
    restart: on-failure:100
  kg-extract-topics:
    command:
    - kg-extract-topics
    - -p
    - pulsar://pulsar:6650
    deploy:
      resources:
        limits:
          cpus: '0.5'
          memory: 128M
        reservations:
          cpus: '0.1'
          memory: 128M
    image: docker.io/trustgraph/trustgraph-flow:0.17.6
    restart: on-failure:100
  lab:
    deploy:
      resources:
        limits:
          cpus: '1.0'
          memory: 512M
        reservations:
          cpus: '0.5'
          memory: 512M
    environment:
      QUICK_CONNECT_MG_HOST: memgraph
      QUICK_CONNECT_MG_PORT: '7687'
    image: docker.io/memgraph/lab:2.19.1
    ports:
    - 3010:3000
    restart: on-failure:100
  memgraph:
    deploy:
      resources:
        limits:
          cpus: '1.0'
          memory: 1000M
        reservations:
          cpus: '0.5'
          memory: 1000M
    image: docker.io/memgraph/memgraph-mage:1.22-memgraph-2.22
    ports:
    - 7474:7474
    - 7687:7687
    restart: on-failure:100
  metering:
    command:
    - metering
    - -p
    - pulsar://pulsar:6650
    deploy:
      resources:
        limits:
          cpus: '0.5'
          memory: 128M
        reservations:
          cpus: '0.1'
          memory: 128M
    image: docker.io/trustgraph/trustgraph-flow:0.17.6
    restart: on-failure:100
  metering-rag:
    command:
    - metering
    - -p
    - pulsar://pulsar:6650
    - -i
    - non-persistent://tg/response/text-completion-rag
    deploy:
      resources:
        limits:
          cpus: '0.5'
          memory: 128M
        reservations:
          cpus: '0.1'
          memory: 128M
    image: docker.io/trustgraph/trustgraph-flow:0.17.6
    restart: on-failure:100
  pdf-decoder:
    command:
    - pdf-decoder
    - -p
    - pulsar://pulsar:6650
    deploy:
      resources:
        limits:
          cpus: '0.5'
          memory: 128M
        reservations:
          cpus: '0.1'
          memory: 128M
    image: docker.io/trustgraph/trustgraph-flow:0.17.6
    restart: on-failure:100
  prometheus:
    deploy:
      resources:
        limits:
          cpus: '0.5'
          memory: 128M
        reservations:
          cpus: '0.1'
          memory: 128M
    image: docker.io/prom/prometheus:v2.53.2
    ports:
    - 9090:9090
    restart: on-failure:100
    volumes:
    - ./prometheus:/etc/prometheus/
    - prometheus-data:/prometheus
  prompt:
    command:
    - prompt-template
    - -p
    - pulsar://pulsar:6650
    - --text-completion-request-queue
    - non-persistent://tg/request/text-completion
    - --text-completion-response-queue
    - non-persistent://tg/response/text-completion
    - --system-prompt
    - 'You are a helpful assistant that performs NLP, Natural Language Processing,
      tasks.

      '
    - --prompt
    - "agent-react=Answer the following questions as best you can. You have\naccess\
      \ to the following functions:\n\n{% for tool in tools %}{\n    \"function\"\
      : \"{{ tool.name }}\",\n    \"description\": \"{{ tool.description }}\",\n \
      \   \"arguments\": [\n{% for arg in tool.arguments %}        {\n           \
      \ \"name\": \"{{ arg.name }}\",\n            \"type\": \"{{ arg.type }}\",\n\
      \            \"description\": \"{{ arg.description }}\",\n        }\n{% endfor\
      \ %}\n    ]\n}\n{% endfor %}\n\nYou can either choose to call a function to\
      \ get more information, or\nreturn a final answer.\n    \nTo call a function,\
      \ respond with a JSON object of the following format:\n\n{\n    \"thought\"\
      : \"your thought about what to do\",\n    \"action\": \"the action to take,\
      \ should be one of [{{tool_names}}]\",\n    \"arguments\": {\n        \"argument1\"\
      : \"argument_value\",\n        \"argument2\": \"argument_value\"\n    }\n}\n\
      \nTo provide a final answer, response a JSON object of the following format:\n\
      \n{\n  \"thought\": \"I now know the final answer\",\n  \"final-answer\": \"\
      the final answer to the original input question\"\n}\n\nPrevious steps are included\
      \ in the input.  Each step has the following\nformat in your output:\n\n{\n\
      \  \"thought\": \"your thought about what to do\",\n  \"action\": \"the action\
      \ taken\",\n  \"arguments\": {\n      \"argument1\": action argument,\n    \
      \  \"argument2\": action argument2\n  },\n  \"observation\": \"the result of\
      \ the action\",\n}\n\nRespond by describing either one single thought/action/arguments\
      \ or\nthe final-answer.  Pause after providing one action or final-answer.\n\
      \n{% if context %}Additional context has been provided:\n{{context}}{% endif\
      \ %}\n\nQuestion: {{question}}\n\nInput:\n    \n{% for h in history %}\n{\n\
      \    \"action\": \"{{h.action}}\",\n    \"arguments\": [\n{% for k, v in h.arguments.items()\
      \ %}        {\n            \"{{k}}\": \"{{v}}\",\n{%endfor%}        }\n    ],\n\
      \    \"observation\": \"{{h.observation}}\"\n}\n{% endfor %}"
    - 'document-prompt=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.


      Here is the context:

      {{documents}}


      Use only the provided knowledge statements to respond to the following:

      {{query}}

      '
    - 'extract-definitions=Study the following text and derive definitions for any
      discovered entities. Do not provide definitions for entities whose definitions
      are incomplete or unknown. Output relationships in JSON format as an array of
      objects with keys:

      - entity: the name of the entity

      - definition: English text which defines the entity


      Here is the text:

      {{text}}


      Requirements:

      - Do not provide explanations.

      - Do not use special characters in the response text.

      - The response will be written as plain text.

      - Do not include null or unknown definitions.

      - The response shall use the following JSON schema structure:


      ```json

      [{"entity": string, "definition": string}]

      ```'
    - 'extract-relationships=Study the following text and derive entity relationships.  For
      each relationship, derive the subject, predicate and object of the relationship.
      Output relationships in JSON format as an array of objects with keys:

      - subject: the subject of the relationship

      - predicate: the predicate

      - object: the object of the relationship

      - object-entity: FALSE if the object is a simple data type and TRUE if the object
      is an entity


      Here is the text:

      {{text}}


      Requirements:

      - You will respond only with well formed JSON.

      - Do not provide explanations.

      - Respond only with plain text.

      - Do not respond with special characters.

      - The response shall use the following JSON schema structure:


      ```json

      [{"subject": string, "predicate": string, "object": string, "object-entity":
      boolean}]

      ```

      '
    - 'extract-rows=<instructions>

      Study the following text and derive objects which match the schema provided.


      You must output an array of JSON objects for each object you discover

      which matches the schema.  For each object, output a JSON object whose fields

      carry the name field specified in the schema.

      </instructions>


      <schema>

      {{schema}}

      </schema>


      <text>

      {{text}}

      </text>


      <requirements>

      You will respond only with raw JSON format data. Do not provide

      explanations. Do not add markdown formatting or headers or prefixes.

      </requirements>'
    - "extract-topics=Read the provided text carefully. You will identify topics and\
      \ their definitions found in the provided text. Topics are intangible concepts.\n\
      \nReading Instructions:\n- Ignore document formatting in the provided text.\n\
      - Study the provided text carefully for intangible concepts.\n\nHere is the\
      \ text:\n{{text}}\n\nResponse Instructions: \n- Do not respond with special\
      \ characters.\n- Return only topics that are concepts and unique to the provided\
      \ text.\n- Respond only with well-formed JSON.\n- The JSON response shall be\
      \ an array of objects with keys \"topic\" and \"definition\". \n- The response\
      \ shall use the following JSON schema structure:\n\n```json\n[{\"topic\": string,\
      \ \"definition\": string}]\n```\n\n- Do not write any additional text or explanations."
    - 'kg-prompt=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.


      Here''s the knowledge statements:

      {% for edge in knowledge %}({{edge.s}})-[{{edge.p}}]->({{edge.o}})

      {%endfor%}


      Use only the provided knowledge statements to respond to the following:

      {{query}}

      '
    - question={{question}}
    - --prompt-response-type
    - agent-react=json
    - document-prompt=text
    - extract-definitions=json
    - extract-relationships=json
    - extract-rows=json
    - extract-topics=json
    - kg-prompt=text
    - --prompt-schema
    - extract-definitions={"items":{"properties":{"definition":{"type":"string"},"entity":{"type":"string"}},"required":["entity","definition"],"type":"object"},"type":"array"}
    - extract-relationships={"items":{"properties":{"object":{"type":"string"},"object-entity":{"type":"boolean"},"predicate":{"type":"string"},"subject":{"type":"string"}},"required":["subject","predicate","object","object-entity"],"type":"object"},"type":"array"}
    - extract-topics={"items":{"properties":{"definition":{"type":"string"},"topic":{"type":"string"}},"required":["topic","definition"],"type":"object"},"type":"array"}
    - --prompt-term
    deploy:
      resources:
        limits:
          cpus: '0.5'
          memory: 128M
        reservations:
          cpus: '0.1'
          memory: 128M
    image: docker.io/trustgraph/trustgraph-flow:0.17.6
    restart: on-failure:100
  prompt-rag:
    command:
    - prompt-template
    - -p
    - pulsar://pulsar:6650
    - -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
    - 'You are a helpful assistant that performs NLP, Natural Language Processing,
      tasks.

      '
    - --prompt
    - "agent-react=Answer the following questions as best you can. You have\naccess\
      \ to the following functions:\n\n{% for tool in tools %}{\n    \"function\"\
      : \"{{ tool.name }}\",\n    \"description\": \"{{ tool.description }}\",\n \
      \   \"arguments\": [\n{% for arg in tool.arguments %}        {\n           \
      \ \"name\": \"{{ arg.name }}\",\n            \"type\": \"{{ arg.type }}\",\n\
      \            \"description\": \"{{ arg.description }}\",\n        }\n{% endfor\
      \ %}\n    ]\n}\n{% endfor %}\n\nYou can either choose to call a function to\
      \ get more information, or\nreturn a final answer.\n    \nTo call a function,\
      \ respond with a JSON object of the following format:\n\n{\n    \"thought\"\
      : \"your thought about what to do\",\n    \"action\": \"the action to take,\
      \ should be one of [{{tool_names}}]\",\n    \"arguments\": {\n        \"argument1\"\
      : \"argument_value\",\n        \"argument2\": \"argument_value\"\n    }\n}\n\
      \nTo provide a final answer, response a JSON object of the following format:\n\
      \n{\n  \"thought\": \"I now know the final answer\",\n  \"final-answer\": \"\
      the final answer to the original input question\"\n}\n\nPrevious steps are included\
      \ in the input.  Each step has the following\nformat in your output:\n\n{\n\
      \  \"thought\": \"your thought about what to do\",\n  \"action\": \"the action\
      \ taken\",\n  \"arguments\": {\n      \"argument1\": action argument,\n    \
      \  \"argument2\": action argument2\n  },\n  \"observation\": \"the result of\
      \ the action\",\n}\n\nRespond by describing either one single thought/action/arguments\
      \ or\nthe final-answer.  Pause after providing one action or final-answer.\n\
      \n{% if context %}Additional context has been provided:\n{{context}}{% endif\
      \ %}\n\nQuestion: {{question}}\n\nInput:\n    \n{% for h in history %}\n{\n\
      \    \"action\": \"{{h.action}}\",\n    \"arguments\": [\n{% for k, v in h.arguments.items()\
      \ %}        {\n            \"{{k}}\": \"{{v}}\",\n{%endfor%}        }\n    ],\n\
      \    \"observation\": \"{{h.observation}}\"\n}\n{% endfor %}"
    - 'document-prompt=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.


      Here is the context:

      {{documents}}


      Use only the provided knowledge statements to respond to the following:

      {{query}}

      '
    - 'extract-definitions=Study the following text and derive definitions for any
      discovered entities. Do not provide definitions for entities whose definitions
      are incomplete or unknown. Output relationships in JSON format as an array of
      objects with keys:

      - entity: the name of the entity

      - definition: English text which defines the entity


      Here is the text:

      {{text}}


      Requirements:

      - Do not provide explanations.

      - Do not use special characters in the response text.

      - The response will be written as plain text.

      - Do not include null or unknown definitions.

      - The response shall use the following JSON schema structure:


      ```json

      [{"entity": string, "definition": string}]

      ```'
    - 'extract-relationships=Study the following text and derive entity relationships.  For
      each relationship, derive the subject, predicate and object of the relationship.
      Output relationships in JSON format as an array of objects with keys:

      - subject: the subject of the relationship

      - predicate: the predicate

      - object: the object of the relationship

      - object-entity: FALSE if the object is a simple data type and TRUE if the object
      is an entity


      Here is the text:

      {{text}}


      Requirements:

      - You will respond only with well formed JSON.

      - Do not provide explanations.

      - Respond only with plain text.

      - Do not respond with special characters.

      - The response shall use the following JSON schema structure:


      ```json

      [{"subject": string, "predicate": string, "object": string, "object-entity":
      boolean}]

      ```

      '
    - 'extract-rows=<instructions>

      Study the following text and derive objects which match the schema provided.


      You must output an array of JSON objects for each object you discover

      which matches the schema.  For each object, output a JSON object whose fields

      carry the name field specified in the schema.

      </instructions>


      <schema>

      {{schema}}

      </schema>


      <text>

      {{text}}

      </text>


      <requirements>

      You will respond only with raw JSON format data. Do not provide

      explanations. Do not add markdown formatting or headers or prefixes.

      </requirements>'
    - "extract-topics=Read the provided text carefully. You will identify topics and\
      \ their definitions found in the provided text. Topics are intangible concepts.\n\
      \nReading Instructions:\n- Ignore document formatting in the provided text.\n\
      - Study the provided text carefully for intangible concepts.\n\nHere is the\
      \ text:\n{{text}}\n\nResponse Instructions: \n- Do not respond with special\
      \ characters.\n- Return only topics that are concepts and unique to the provided\
      \ text.\n- Respond only with well-formed JSON.\n- The JSON response shall be\
      \ an array of objects with keys \"topic\" and \"definition\". \n- The response\
      \ shall use the following JSON schema structure:\n\n```json\n[{\"topic\": string,\
      \ \"definition\": string}]\n```\n\n- Do not write any additional text or explanations."
    - 'kg-prompt=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.


      Here''s the knowledge statements:

      {% for edge in knowledge %}({{edge.s}})-[{{edge.p}}]->({{edge.o}})

      {%endfor%}


      Use only the provided knowledge statements to respond to the following:

      {{query}}

      '
    - question={{question}}
    - --prompt-response-type
    - agent-react=json
    - document-prompt=text
    - extract-definitions=json
    - extract-relationships=json
    - extract-rows=json
    - extract-topics=json
    - kg-prompt=text
    - --prompt-schema
    - extract-definitions={"items":{"properties":{"definition":{"type":"string"},"entity":{"type":"string"}},"required":["entity","definition"],"type":"object"},"type":"array"}
    - extract-relationships={"items":{"properties":{"object":{"type":"string"},"object-entity":{"type":"boolean"},"predicate":{"type":"string"},"subject":{"type":"string"}},"required":["subject","predicate","object","object-entity"],"type":"object"},"type":"array"}
    - extract-topics={"items":{"properties":{"definition":{"type":"string"},"topic":{"type":"string"}},"required":["topic","definition"],"type":"object"},"type":"array"}
    - --prompt-term
    deploy:
      resources:
        limits:
          cpus: '0.5'
          memory: 128M
        reservations:
          cpus: '0.1'
          memory: 128M
    image: docker.io/trustgraph/trustgraph-flow:0.17.6
    restart: on-failure:100
  pulsar:
    command:
    - bin/pulsar
    - standalone
    deploy:
      resources:
        limits:
          cpus: '2.0'
          memory: 1500M
        reservations:
          cpus: '1.0'
          memory: 1500M
    environment:
      PULSAR_MEM: -Xms600M -Xmx600M
    image: docker.io/apachepulsar/pulsar:3.3.1
    ports:
    - 6650:6650
    - 8080:8080
    restart: on-failure:100
    volumes:
    - pulsar-data:/pulsar/data
  query-doc-embeddings:
    command:
    - de-query-pinecone
    - -p
    - pulsar://pulsar:6650
    deploy:
      resources:
        limits:
          cpus: '0.5'
          memory: 128M
        reservations:
          cpus: '0.1'
          memory: 128M
    environment:
      PINECONE_API_KEY: ${PINECONE_API_KEY}
    image: docker.io/trustgraph/trustgraph-flow:0.17.6
    restart: on-failure:100
  query-graph-embeddings:
    command:
    - ge-query-pinecone
    - -p
    - pulsar://pulsar:6650
    deploy:
      resources:
        limits:
          cpus: '0.5'
          memory: 128M
        reservations:
          cpus: '0.1'
          memory: 128M
    environment:
      PINECONE_API_KEY: ${PINECONE_API_KEY}
    image: docker.io/trustgraph/trustgraph-flow:0.17.6
    restart: on-failure:100
  query-triples:
    command:
    - triples-query-neo4j
    - -p
    - pulsar://pulsar:6650
    - -g
    - bolt://memgraph:7687
    - --database
    - memgraph
    deploy:
      resources:
        limits:
          cpus: '0.5'
          memory: 128M
        reservations:
          cpus: '0.1'
          memory: 128M
    image: docker.io/trustgraph/trustgraph-flow:0.17.6
    restart: on-failure:100
  store-doc-embeddings:
    command:
    - de-write-pinecone
    - -p
    - pulsar://pulsar:6650
    deploy:
      resources:
        limits:
          cpus: '0.5'
          memory: 128M
        reservations:
          cpus: '0.1'
          memory: 128M
    environment:
      PINECONE_API_KEY: ${PINECONE_API_KEY}
    image: docker.io/trustgraph/trustgraph-flow:0.17.6
    restart: on-failure:100
  store-graph-embeddings:
    command:
    - ge-write-pinecone
    - -p
    - pulsar://pulsar:6650
    deploy:
      resources:
        limits:
          cpus: '0.5'
          memory: 128M
        reservations:
          cpus: '0.1'
          memory: 128M
    environment:
      PINECONE_API_KEY: ${PINECONE_API_KEY}
    image: docker.io/trustgraph/trustgraph-flow:0.17.6
    restart: on-failure:100
  store-triples:
    command:
    - triples-write-neo4j
    - -p
    - pulsar://pulsar:6650
    - -g
    - bolt://memgraph:7687
    - --database
    - memgraph
    deploy:
      resources:
        limits:
          cpus: '0.5'
          memory: 128M
        reservations:
          cpus: '0.1'
          memory: 128M
    image: docker.io/trustgraph/trustgraph-flow:0.17.6
    restart: on-failure:100
  text-completion:
    command:
    - text-completion-googleaistudio
    - -p
    - pulsar://pulsar:6650
    - -x
    - '8000'
    - -t
    - '0.170'
    - -m
    - gemini-1.5-pro-002
    deploy:
      resources:
        limits:
          cpus: '0.5'
          memory: 128M
        reservations:
          cpus: '0.1'
          memory: 128M
    environment:
      GOOGLE_AI_STUDIO_KEY: ${GOOGLE_AI_STUDIO_KEY}
    image: docker.io/trustgraph/trustgraph-flow:0.17.6
    restart: on-failure:100
  text-completion-rag:
    command:
    - text-completion-googleaistudio
    - -p
    - pulsar://pulsar:6650
    - -x
    - '8000'
    - -t
    - '0.170'
    - -m
    - gemini-1.5-pro-002
    - -i
    - non-persistent://tg/request/text-completion-rag
    - -o
    - non-persistent://tg/response/text-completion-rag
    deploy:
      resources:
        limits:
          cpus: '0.5'
          memory: 128M
        reservations:
          cpus: '0.1'
          memory: 128M
    environment:
      GOOGLE_AI_STUDIO_KEY: ${GOOGLE_AI_STUDIO_KEY}
    image: docker.io/trustgraph/trustgraph-flow:0.17.6
    restart: on-failure:100
  vectorize:
    command:
    - embeddings-vectorize
    - -p
    - pulsar://pulsar:6650
    deploy:
      resources:
        limits:
          cpus: '1.0'
          memory: 512M
        reservations:
          cpus: '0.5'
          memory: 512M
    image: docker.io/trustgraph/trustgraph-flow:0.17.6
    restart: on-failure:100
volumes:
  grafana-storage: {}
  prometheus-data: {}
  pulsar-data: {}