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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>
63 lines
1.4 KiB
Markdown
63 lines
1.4 KiB
Markdown
---
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sidebar_label: agent
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title: agenthub.monologue_agent.agent
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---
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## MonologueAgent Objects
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```python
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class MonologueAgent(Agent)
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```
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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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#### \_\_init\_\_
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```python
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def __init__(llm: LLM)
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```
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Initializes the Monologue Agent with an llm, monologue, and memory.
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**Arguments**:
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- llm (LLM): The llm to be used by this agent
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#### step
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```python
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def step(state: State) -> Action
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```
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Modifies the current state by adding the most recent actions and observations, then prompts the model to think about it's next action to take using monologue, memory, and hint.
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**Arguments**:
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- state (State): The current state based on previous steps taken
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**Returns**:
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- Action: The next action to take based on LLM response
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#### search\_memory
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```python
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def search_memory(query: str) -> List[str]
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```
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Uses VectorIndexRetriever to find related memories within the long term memory.
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Uses search to produce top 10 results.
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**Arguments**:
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- query (str): The query that we want to find related memories for
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**Returns**:
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- List[str]: A list of top 10 text results that matched the query
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