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Merge pull request #305 from liuliuOD/fix/zilliz

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[Fix] typo, grammar and link
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Logan Kilpatrick 2023-03-31 07:23:44 -05:00 committed by GitHub
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6
examples/vector_databases/zilliz/Filtered_search_with_Zilliz_and_OpenAI.ipynb
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@ -31,7 +31,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"To get Zilliz up and running take a look [here](https://zilliz.com/doc/get_started_overview). With your account and database set up, proceed to set the following values:\n",
"To get Zilliz up and running take a look [here](https://zilliz.com/doc/quick_start). With your account and database set up, proceed to set the following values:\n",
"- URI: The URI your database is running on\n",
"- USER: Your database username\n",
"- PASSWORD: Your database password\n",
@ -134,7 +134,7 @@
"metadata": {},
"source": [
"## Dataset\n",
"With Zilliz up and running we can begin grabbing our data. Hugging Face Datasets is a hub that holds many different user datasets, and for this example we are using HuggingLearners's netflix-shows dataset. This dataset contains movies and their metadata pairs for over 8 thousand movies. We are going to embed each description and store it within Zilliz along with its title, type, release_year and rating."
"With Zilliz up and running we can begin grabbing our data. `Hugging Face Datasets` is a hub that holds many different user datasets, and for this example we are using HuggingLearners's netflix-shows dataset. This dataset contains movies and their metadata pairs for over 8 thousand movies. We are going to embed each description and store it within Zilliz along with its title, type, release_year and rating."
]
},
{
@ -240,7 +240,7 @@
"metadata": {},
"source": [
"## Query the Database\n",
"With our data safely inserted in Zilliz, we can now perform a query. The query takes in a tuple of the movie description you are searching for an the filter to use. More info about the filter can be found [here](https://milvus.io/docs/boolean.md). The search first prints out your description and filter expression. After that for each result we print the score, title, type, release year, rating, and description of the result movies. "
"With our data safely inserted into Zilliz, we can now perform a query. The query takes in a tuple of the movie description you are searching for and the filter to use. More info about the filter can be found [here](https://milvus.io/docs/boolean.md). The search first prints out your description and filter expression. After that for each result we print the score, title, type, release year, rating and description of the result movies. "
]
},
{

12
examples/vector_databases/zilliz/Getting_started_with_Zilliz_and_OpenAI.ipynb
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@ -77,7 +77,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"To get Zilliz up and running take a look [here](https://zilliz.com/doc/get_started_overview). With your account and database set up, proceed to set the following values:\n",
"To get Zilliz up and running take a look [here](https://zilliz.com/doc/quick_start). With your account and database set up, proceed to set the following values:\n",
"- URI: The URI your database is running on\n",
"- USER: Your database username\n",
"- PASSWORD: Your database password\n",
@ -126,7 +126,7 @@
"metadata": {},
"source": [
"## Zilliz\n",
"This segment deals with Zilliz and setting up the database for this use case. Within Zilliz we need to setup a collection and index the collection. "
"This segment deals with Zilliz and setting up the database for this use case. Within Zilliz we need to setup a collection and index it."
]
},
{
@ -186,7 +186,7 @@
"metadata": {},
"source": [
"## Dataset\n",
"With Zilliz up and running we can begin grabbing our data. Hugging Face Datasets is a hub that holds many different user datasets, and for this example we are using Skelebor's book dataset. This dataset contains title-description pairs for over 1 million books. We are going to embed each description and store it within Zilliz along with its title. "
"With Zilliz up and running we can begin grabbing our data. `Hugging Face Datasets` is a hub that holds many different user datasets, and for this example we are using Skelebor's book dataset. This dataset contains title-description pairs for over 1 million books. We are going to embed each description and store it within Zilliz along with its title."
]
},
{
@ -217,7 +217,7 @@
"metadata": {},
"source": [
"## Insert the Data\n",
"Now that we have our data on our machine we can begin embedding it and inserting it into Zilliz. The embedding function takes in text and returns the embeddings in a list format. "
"Now that we have our data on our machine we can begin embedding it and inserting it into Zilliz. The embedding function takes in text and returns the embeddings in a list format."
]
},
{
@ -240,7 +240,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"This next step does the actual inserting. Due to having so many datapoints, if you want to immidiately test it out you can stop the inserting cell block early and move along. Doing this will probably decrease the accuracy of the results due to less datapoints, but it should still be good enough. "
"This next step does the actual inserting. Due to having so many datapoints, if you want to immediately test it out you can stop the inserting cell block early and move along. Doing this will probably decrease the accuracy of the results due to less datapoints, but it should still be good enough."
]
},
{
@ -306,7 +306,7 @@
"metadata": {},
"source": [
"## Query the Database\n",
"With our data safely inserted in Zilliz, we can now perform a query. The query takes in a string or a list of strings and searches them. The resuts print out your provided description and the results that include the result score, the result title, and the result book description. "
"With our data safely inserted in Zilliz, we can now perform a query. The query takes in a string or a list of strings and searches them. The results print out your provided description and the results that include the result score, the result title, and the result book description."
]
},
{