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Minor grammar fixes for memory docs to improve readability (#303)

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Nothing of substance was changed. I simply corrected a few minor errors
that could slow down the reader.

Co-authored-by: Hunter Gerlach <hunter@huntergerlach.com>
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Hunter Gerlach 2022-12-10 19:18:01 -05:00 committed by GitHub
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docs/getting_started/memory.ipynb
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@ -6,9 +6,9 @@
"metadata": {},
"source": [
"# Memory\n",
"So far, all the chains and agents we've gone through have been stateless. But often, you may want a chain or agent to have some concept of \"memory\" so that it may remember information about its previous interactions. The most clear and simple example of this is when designing a chatbot - you want it to remember previous messages so it can use context from that to have a better conversation. This would be a type of \"short-term memory\". On the more complex side, you could imagine a chain/agent remembering key pieces of information over time - this would be a form of \"long-term memory\". For more concrete ideas on the later, see this [awesome paper](https://memprompt.com/).\n",
"So far, all the chains and agents we've gone through have been stateless. But often, you may want a chain or agent to have some concept of \"memory\" so that it may remember information about its previous interactions. The clearest and simple example of this is when designing a chatbot - you want it to remember previous messages so it can use context from that to have a better conversation. This would be a type of \"short-term memory\". On the more complex side, you could imagine a chain/agent remembering key pieces of information over time - this would be a form of \"long-term memory\". For more concrete ideas on the latter, see this [awesome paper](https://memprompt.com/).\n",
"\n",
"LangChain provides several specially created chains just for this purpose. This notebook walk throughs using one of those chains (the `ConversationChain`) with two different types of memory."
"LangChain provides several specially created chains just for this purpose. This notebook walks through using one of those chains (the `ConversationChain`) with two different types of memory."
]
},
{
@ -17,7 +17,7 @@
"metadata": {},
"source": [
"### ConversationChain with default memory\n",
"By default, the `ConversationChain` has a simple type of memory which remebers all previes inputs/outputs and adds them to the context that is passed. Let's take a look at using this chain (setting `verbose=True` so we can see the prompt)."
"By default, the `ConversationChain` has a simple type of memory that remembers all previous inputs/outputs and adds them to the context that is passed. Let's take a look at using this chain (setting `verbose=True` so we can see the prompt)."
]
},
{
@ -154,7 +154,7 @@
"metadata": {},
"source": [
"### ConversationChain with ConversationSummaryMemory\n",
"Now lets take a look at using a slightly more complex type of memory - `ConversationSummaryMemory`. This type of memory creates a summary of the conversation over time. This can be useful for condensing information from the conversation over time.\n",
"Now let's take a look at using a slightly more complex type of memory - `ConversationSummaryMemory`. This type of memory creates a summary of the conversation over time. This can be useful for condensing information from the conversation over time.\n",
"\n",
"Let's walk through an example, again setting `verbose=True` so we can see the prompt."
]
@ -297,7 +297,7 @@
"source": [
"### More Resources on Memory\n",
"\n",
"This just scratches the surface of what you can do with memory. For more examples on things like how to implement custom memory classes, how to add memory to a custom LLM chain and how to use memory with and agent, please see the [How-To: Memory](../../examples/memory) section. For even more advanced ideas on memory (which will hopefully be included in LangChain soon!) see the [MemPrompt](https://memprompt.com/) paper."
"This just scratches the surface of what you can do with memory. For more examples on things like how to implement custom memory classes, how to add memory to a custom LLM chain and how to use memory with an agent, please see the [How-To: Memory](../../examples/memory) section. For even more advanced ideas on memory (which will hopefully be included in LangChain soon!) see the [MemPrompt](https://memprompt.com/) paper."
]
},
{