Reorganize agents and chains #56
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franciscodr
approved these changes
May 12, 2023
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| "LLMAgent should return a prediction with just the output" { | ||
| val template = "Tell me {foo}." | ||
| either { | ||
| val promptTemplate = PromptTemplate(template, listOf("foo")) | ||
| val chain = LLMAgent(testLLM, promptTemplate, model) | ||
| with (chain) { call(mapOf("foo" to "a joke")) } | ||
| } shouldBeRight listOf("I'm not good at jokes") | ||
| } | ||
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| "LLMAgent should return a prediction with both output and inputs" { | ||
| val template = "Tell me {foo}." | ||
| either { | ||
| val prompt = PromptTemplate(template, listOf("foo")) | ||
| val chain = LLMAgent(testLLM, prompt, model) | ||
| with (chain) { call(mapOf("foo" to "a joke")) } | ||
| } shouldBeRight listOf("I'm not good at jokes") | ||
| } |
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Those tests check the same thing, aren't they?
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You're right, I've removed one
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This PR moves into the direction of a simpler interface, but with the nice composition abilities from the DSL. In particular there are no more chains/agents/..., everything is an
Agent, which should provide a function of the form.I've re-implemented the
promptfunctionality fromAIScopeinto two newAgents, namelyLLMAgentandDeserializerLLMAgent(which just wraps the former with deserialization capabilities). As a result, theAIScopeis much more contained: it only calls other agents, but doesn't perform the calls to OpenAI itself. Note that barely any change was required in the examples, but we've gained the ability to useLLMAgentas an agent incontext.A side effect is that the entire
chainspackage is gone: instead of creating aSequenceChain, the idea is that developers will manipulate the passing of information between agents themselves.@raulraja @nomisRev @realdavidvega any feedback is welcome :)