Aryankb commented on issue #183:
URL:
https://github.com/apache/incubator-hugegraph-ai/issues/183#issuecomment-2691519490
> For example, suppose the user is already using `AutoGen` to orchestrate
their Agent system, and now assuming that the data in the graph has been
extracted (skipping the extraction step), how can they better and faster
integrate it into their original system, while maintaining high performance and
simplicity as much as possible
> They(Devs) can directly modify our pipeline/workflow code, instead of
requiring us to provide a fixed "local/global" mode like Microsoft GraphRAG
that is not easy to adjust.
@imbajin As per my understanding of 2, we need a way to integrate hugegraph
agents with any other existing framework. And we will provide an http api layer
abstraction or python SDK for hugegraph agents.
For example. Someone working on some projects involving AI agents. They
thought that they needs to use GRAPH RAG for some use case. Now for that, they
will be able to import HG agentic library say HG_agentic. Now using this
library, they can create agents using plane english (what the agent needs to
do, given some input and required output format.) Each agent will have the
information about graph from which information needs to be extracted. And it
will have some inputs - (output from previous agents, like node names ,
relationship names , similarity search results, etc), the agent will convert
txt2gql according to the given prompt and graph structure knowledge. This way
(Devs) can create agents and orchestrate them using our library HG_orchestrator
by just passing all created agents in a data structure.
HG_orchestrator will handle the order of agent execution. User can specify
if he want sequential in case next agent needs outputs from any previous agent
, parallel otherwise.
If we need this, we need a framework having really good `WORKFLOW FEATURES`.
Because then we can be really flexible. (We can manage the flow of data between
agents, as specified by user, also we can add if-else validation condition,
deligation steps like deligate to previous agent with some modified input.)
Eg :-
`HG_orchestrate([agent1, agent2, agent3], fllow="sequential", data_flow:
{agent1 : [agent2, agent3], agent2 : [agent3]}, deligation: [{from:agent3,
to:agent1 , condition :"some_condition", modification :
"required_modification"}]`
One naive approach can be to do everything using simple python while loop,
here we don't need to manage different dependency conflicts. If (devs) use
AutoGen, and if we create HG_agentic SDK using crewai. Then there might be
dependency conflicts.
If not, then we can follow the below priority order.
Priority suggestion for workflow :-
- 1. Llamaindex (have workflow features like checkpoint, trigger, retries,
streaming, context across runs, with really good example as per our needs)
- 2. pydantic AI
- 3. CrewFlow
- 4. Agno (don't have workflow features)
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