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small fixes to tutorials (#168)

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Co-authored-by: Jason Liu <jxnl@users.noreply.github.com>
This commit is contained in:
Francisco Ingham
2023-11-11 19:00:31 -08:00
committed by GitHub
parent df20cb4d16
commit 1ec9114d61
8 changed files with 109 additions and 43 deletions
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tutorials/1.introduction.ipynb
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@@ -38,7 +38,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"We have a `name` field, which is a string, and an `age` field, which is an integer. However, if we were to load this into a dictionary, we would have no way of knowing if the data is valid. For example, we could have a string for the age, or we could have a float for the age. We could also have a string for the name, or we could have a list for the name. We have no way of knowing if the data is valid, and we have no way of knowing if the data is valid."
"We have a `name` field, which is a string, and an `age` field, which is an integer. However, if we were to load this into a dictionary, we would have no way of knowing if the data is valid. For example, we could have a string for the age, or we could have a float for the age. We could also have a string for the name, or we could have a list for the name."
]
},
{
@@ -486,7 +486,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"Now you can see that when we set response_model create call will now return a pydantic model, and we can use that to validate the data. and work with it as if it was a python object."
"Now you can see that when we set `response_model` create call will now return a pydantic model, and we can use that to validate the data. and work with it as if it was a python object."
]
}
],
tutorials/2.applications-rag.ipynb
+3 -8
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@@ -107,7 +107,7 @@
"source": [
"### Example 1) Improving Extractions\n",
"\n",
"One of the big limitations is that often times the query we embed and the text \n",
"One of the big limitations is that often times the query we embed and the text that we want to retrieve are not sufficiently close in the semantic space.\n",
"A common method of using structured output is to extract information from a document and use it to answer a question. Directly, we can be creative in how we extract, summarize and generate potential questions in order for our embeddings to do better. \n",
"\n",
"For example, instead of using just a text chunk we could try to:\n",
@@ -511,9 +511,9 @@
"source": [
"### Example 4) Decomposing questions \n",
"\n",
"Lastly, a lightly more complex example of a problem that can be solved with structured output is decomposing questions. Where you ultimately want to decompose a question into a series of sub-questions that can be answered by a search backend. For example \n",
"Lastly, a lightly more complex example of a problem that can be solved with structured output is decomposing questions. Where you ultimately want to decompose a question into a series of sub-questions that can be answered by a search backend. For example:\n",
"\n",
"\"Whats the difference in populations of jason's home country and canadata?\"\n",
"\"Whats the difference in populations of jason's home country and canada?\"\n",
"\n",
"You'd ultimately need to know a few things\n",
"\n",
@@ -525,11 +525,6 @@
"This would not be done correctly as a single query, nor would it be done in parallel, however there are some opportunities try to be parallel since not all of the sub-questions are dependent on each other."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": []
},
{
"cell_type": "code",
"execution_count": 35,