(wip) docs: revamp conceptual docs #1663
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…anggraph into vb/revamp-conceptual-docs
…ain-ai/langgraph into vb/revamp-conceptual-docs
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isahers1
reviewed
Sep 13, 2024
| Since LangGraph nodes can be arbitrary Python functions, you can do this however you want. If you want to use LangChain, [this how-to guide](https://python.langchain.com/v0.2/docs/how_to/structured_output/) is a starting point. | ||
| - An LLM can route between two potential paths | ||
| - An LLM can decide which of many tools to call | ||
| - An LLM can decide whether the generated answer is sufficient or more work is need |
isahers1
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Sep 13, 2024
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| LangGraph is perfectly suited to give you full control over the memory of your application. With user defined [`State`](./low_level.md#state) you can specify the exact schema of the memory you want to retain. With [checkpointers](./persistence.md) you can store checkpoints of previous interactions and resume from there in follow up interactions. | ||
| Structured outputs with LLMs work by providing a specific format or schema that the LLM should follow in its response. This is similar to tool calling, but more general. While tool calling typically involves selecting and using predefined functions, structured outputs can be used for any type of formatted response.Common methods to achieve structured outputs include: |
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missing space before "Common methods ..."
isahers1
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Sep 13, 2024
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| This "reflection" step often uses an LLM, but doesn't have to. A good example of where using an LLM may not be necessary is in coding, when you can try to compile the generated code and use any errors as the feedback. | ||
| - [`State`](./low_level.md#state): User-defined schema specifying the exact structure of memory to retain. | ||
| - [Checkpointers](./persistence.md): Mechanism to store state at every step across different interactions. |
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very minor nit, but would prefer if both state/checkpointers were in back quotes or neither of them were
isahers1
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Sep 13, 2024
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| ### ReAct implementation | ||
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| There are several differences between this paper and the pre-built [`create_react_agent`](../reference/prebuilt.md#create_react_agent) implementation: | ||
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| - First, we use [tool-calling](#tool-calling) to have LLMs call tools, whereas the paper used prompting + parsing of raw output. This is because tool calling did not exist when the paper was written, but is generally better and more reliable. | ||
| - Second, we use messages to prompt the LLM, whereas the paper used string formatting. This is because at the time of writing, LLMs didn't even expose a message-based interface, whereas now that's the only interface they expose. | ||
| - Third, the paper required all inputs to the tools to be a single string. This was largely due to LLMs not being super capable at the time, and only really being able to generate a single input. Our implementation allows for using tools that require multiple inputs. | ||
| - Forth, the paper only looks at calling a single tool at the time, largely due to limitations in LLMs performance at the time. Our implementation allows for calling multiple tools at a time. |
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