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docs/langchain-retrieval-agent.ipynb

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"\n",
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"- `name` can be anything we like. The name is used as an identifier for the index when performing other operations such as `describe_index`, `delete_index`, and so on. \n",
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"- `metric` specifies the similarity metric that will be used later when you make queries to the index.\n",
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"- `dimension` should correspond to the dimension of the dense vectors produced by your embedding model. In this quick start, we are using made-up data so a small value is simplest.\n",
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"- `dimension` should correspond to the dimension of the dense vectors produced by your embedding model.\n",
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"- `spec` holds a specification which tells Pinecone how you would like to deploy our index. You can find a list of all [available providers and regions here](https://docs.pinecone.io/troubleshooting/available-cloud-regions).\n",
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"\n",
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"There are more configurations available, but this minimal set will get us started."

docs/langchain-retrieval-augmentation.ipynb

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"\n",
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"- `name` can be anything we like. The name is used as an identifier for the index when performing other operations such as `describe_index`, `delete_index`, and so on. \n",
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"- `metric` specifies the similarity metric that will be used later when you make queries to the index.\n",
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"- `dimension` should correspond to the dimension of the dense vectors produced by your embedding model. In this quick start, we are using made-up data so a small value is simplest.\n",
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"- `dimension` should correspond to the dimension of the dense vectors produced by your embedding model.\n",
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"- `spec` holds a specification which tells Pinecone how you would like to deploy our index. You can find a list of all [available providers and regions here](https://docs.pinecone.io/docs/projects).\n",
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"\n",
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"There are more configurations available, but this minimal set will get us started."

docs/semantic-search.ipynb

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"\n",
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"- `name` can be anything we like. The name is used as an identifier for the index when performing other operations such as `describe_index`, `delete_index`, and so on. \n",
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"- `metric` specifies the similarity metric that will be used later when you make queries to the index.\n",
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"- `dimension` should correspond to the dimension of the dense vectors produced by your embedding model. In this quick start, we are using made-up data so a small value is simplest.\n",
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"- `dimension` should correspond to the dimension of the dense vectors produced by your embedding model.\n",
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"- `spec` holds a specification which tells Pinecone how you would like to deploy our index. You can find a list of all [available providers and regions here](https://docs.pinecone.io/docs/projects).\n",
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"\n",
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"There are more configurations available, but this minimal set will get us started."

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