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* docs(docs): start implementing docs website * update video url * add autogenerated codebase docs for backend * precommit * update links * fix config and video * gh actions * rename * workdirs * path * path * fix doc1 * redo markdown * docs * change main folder name * simplify readme * add back architecture * Fix lint errors * lint * update poetry lock --------- Co-authored-by: Jim Su <jimsu@protonmail.com>
111 lines
5.2 KiB
Markdown
111 lines
5.2 KiB
Markdown
---
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sidebar_position: 3
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---
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# 🧠 Agents and Capabilities
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## Monologue Agent
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### Description
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The Monologue Agent utilizes long and short term memory to complete tasks.
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Long term memory is stored as a LongTermMemory object and the model uses it to search for examples from the past.
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Short term memory is stored as a Monologue object and the model can condense it as necessary.
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### Actions
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`Action`,
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`NullAction`,
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`CmdRunAction`,
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`FileWriteAction`,
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`FileReadAction`,
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`AgentRecallAction`,
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`BrowseURLAction`,
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`GithubPushAction`,
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`AgentThinkAction`
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### Observations
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`Observation`,
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`NullObservation`,
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`CmdOutputObservation`,
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`FileReadObservation`,
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`AgentRecallObservation`,
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`BrowserOutputObservation`
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### Methods
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| Method | Description |
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| --------------- | --------------------------------------------------------------------------------------------------------------------------------------------- |
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| `__init__` | Initializes the agent with a long term memory, and an internal monologue |
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| `_add_event` | Appends events to the monologue of the agent and condenses with summary automatically if the monologue is too long |
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| `_initialize` | Utilizes the `INITIAL_THOUGHTS` list to give the agent a context for its capabilities and how to navigate the `/workspace` |
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| `step` | Modifies the current state by adding the most recent actions and observations, then prompts the model to think about its next action to take. |
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| `search_memory` | Uses `VectorIndexRetriever` to find related memories within the long term memory. |
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## Planner Agent
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### Description
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The planner agent utilizes a special prompting strategy to create long term plans for solving problems.
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The agent is given its previous action-observation pairs, current task, and hint based on last action taken at every step.
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### Actions
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`NullAction`,
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`CmdRunAction`,
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`CmdKillAction`,
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`BrowseURLAction`,
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`GithubPushAction`,
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`FileReadAction`,
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`FileWriteAction`,
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`AgentRecallAction`,
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`AgentThinkAction`,
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`AgentFinishAction`,
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`AgentSummarizeAction`,
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`AddTaskAction`,
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`ModifyTaskAction`,
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### Observations
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`Observation`,
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`NullObservation`,
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`CmdOutputObservation`,
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`FileReadObservation`,
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`AgentRecallObservation`,
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`BrowserOutputObservation`
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### Methods
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| Method | Description |
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| --------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `__init__` | Initializes an agent with `llm` |
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| `step` | Checks to see if current step is completed, returns `AgentFinishAction` if True. Otherwise, creates a plan prompt and sends to model for inference, adding the result as the next action. |
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| `search_memory` | Not yet implemented |
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## CodeAct Agent
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### Description
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The Code Act Agent is a minimalist agent. The agent works by passing the model a list of action-observation pairs and prompting the model to take the next step.
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### Actions
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`Action`,
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`CmdRunAction`,
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`AgentEchoAction`,
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`AgentFinishAction`,
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### Observations
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`CmdOutputObservation`,
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`AgentMessageObservation`,
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### Methods
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| Method | Description |
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| --------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `__init__` | Initializes an agent with `llm` and a list of messages `List[Mapping[str, str]]` |
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| `step` | First, gets messages from state and then compiles them into a list for context. Next, pass the context list with the prompt to get the next command to execute. Finally, Execute command if valid, else return `AgentEchoAction(INVALID_INPUT_MESSAGE)` |
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| `search_memory` | Not yet implemented |
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