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Merge pull request #202 from dandv/patch-1

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Weaviate: update links, minor corrections
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colin-openai 2023-03-10 01:04:30 -08:00 committed by GitHub
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examples/vector_databases/Using_vector_databases_for_embeddings_search.ipynb
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@ -597,7 +597,7 @@
"* for any CRUD operations\n",
"* for semantic search\n",
"\n",
"Check out the [Getting Started with Weaviate and OpenAI module cookbook](./weaviate/getting-started-with-text2vec-openai.ipynb) to learn step by step how to import and vectorize data in one step."
"Check out the [Getting Started with Weaviate and OpenAI module cookbook](./weaviate/getting-started-with-weaviate-and-openai.ipynb) to learn step by step how to import and vectorize data in one step."
]
},
{
@ -608,7 +608,7 @@
"source": [
"### Setup\n",
"\n",
"To run Weaviate locally, you'll need [Docker](https://www.docker.com/). Following the instructions contained in the Weaviate documentation [here](https://weaviate.io/developers/weaviate/current/installation/docker-compose.html), we created an example docker-compose.yml file in this repo saved at [./weaviate/docker-compose.yml](./weaviate/docker-compose.yml).\n",
"To run Weaviate locally, you'll need [Docker](https://www.docker.com/). Following the instructions contained in the Weaviate documentation [here](https://weaviate.io/developers/weaviate/installation/docker-compose), we created an example docker-compose.yml file in this repo saved at [./weaviate/docker-compose.yml](./weaviate/docker-compose.yml).\n",
"\n",
"After starting Docker, you can start Weaviate locally by navigating to the `examples/vector_databases/weaviate/` directory and running `docker-compose up -d`.\n",
"\n",
@ -620,7 +620,7 @@
" * Weaviate Version: Use default (latest)\n",
" * OIDC Authentication: `Disabled`\n",
"3. your instance should be ready in a minute or two\n",
"4. make a note of the `Cluster Id`. The link will take you to the full path of your cluster (you will need it later to connect to it). It should be something like: `https://your-project-name.weaviate.network` "
"4. make a note of the `Cluster Id`. The link will take you to the full path of your cluster (you will need it later to connect to it). It should be something like: `https://your-project-name-suffix.weaviate.network` "
]
},
{
@ -816,7 +816,7 @@
"id": "46050ca9",
"metadata": {},
"source": [
"### Search Data\n",
"### Search data\n",
"\n",
"As above, we'll fire some queries at our new Index and get back results based on the closeness to our existing vectors"
]
@ -841,7 +841,7 @@
" # Queries input schema with vectorised user query\n",
" query_result = (\n",
" client.query\n",
" .get(collection_name,[\"title\",\"content\", \"_additional {certainty distance}\"])\n",
" .get(collection_name, [\"title\", \"content\", \"_additional {certainty distance}\"])\n",
" .with_near_vector(near_vector)\n",
" .with_limit(top_k)\n",
" .do()\n",
@ -857,7 +857,7 @@
"metadata": {},
"outputs": [],
"source": [
"query_result = query_weaviate(\"modern art in Europe\",\"Article\")\n",
"query_result = query_weaviate(\"modern art in Europe\", \"Article\")\n",
"counter = 0\n",
"for article in query_result[\"data\"][\"Get\"][\"Article\"]:\n",
" counter += 1\n",
@ -871,7 +871,7 @@
"metadata": {},
"outputs": [],
"source": [
"query_result = query_weaviate(\"Famous battles in Scottish history\",\"Article\")\n",
"query_result = query_weaviate(\"Famous battles in Scottish history\", \"Article\")\n",
"counter = 0\n",
"for article in query_result[\"data\"][\"Get\"][\"Article\"]:\n",
" counter += 1\n",