# Inference API plugin for python SDK
## Installation
The plugin is distributed separately from the core python sdk.
```
# Install the base python SDK, version 4.1.1 or higher
pip install pinecone-client
# And also the plugin functionality
pip install pinecone-plugin-inference
```
## Usage
Interact with Pinecone's Inference APIs, e.g. create embeddings (currently in preview).
Models currently supported:
- [multilingual-e5-large](https://arxiv.org/pdf/2402.05672)
## Generate embeddings
The following example highlights how to use an embedding model to generate embeddings for a list of documents and a
user query, with the ultimate goal of retrieving similar documents from a Pinecone index.
```python
from pinecone import Pinecone
pc = Pinecone(api_key="<<PINECONE_API_KEY>>")
model = "multilingual-e5-large"
# Embed documents
text = [
"Turkey is a classic meat to eat at American Thanksgiving.",
"Many people enjoy the beautiful mosques in Turkey.",
]
text_embeddings = pc.inference.embed(
model=model,
inputs=text,
parameters={"input_type": "passage", "truncate": "END"},
)
# <<Upsert documents into Pinecone index>>
# Embed query
query = ["How should I prepare my turkey?"]
query_embeddings = pc.inference.embed(
model=model,
inputs=query,
parameters={"input_type": "query", "truncate": "END"},
)
# <<Send query to Pinecone index to retrieve similar documents>>
```
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"description": "# Inference API plugin for python SDK\n\n## Installation\n\nThe plugin is distributed separately from the core python sdk.\n\n```\n# Install the base python SDK, version 4.1.1 or higher\npip install pinecone-client\n\n# And also the plugin functionality\npip install pinecone-plugin-inference\n```\n\n## Usage\n\nInteract with Pinecone's Inference APIs, e.g. create embeddings (currently in preview).\n\nModels currently supported:\n- [multilingual-e5-large](https://arxiv.org/pdf/2402.05672)\n\n## Generate embeddings\nThe following example highlights how to use an embedding model to generate embeddings for a list of documents and a \nuser query, with the ultimate goal of retrieving similar documents from a Pinecone index.\n\n```python\nfrom pinecone import Pinecone\n\npc = Pinecone(api_key=\"<<PINECONE_API_KEY>>\")\nmodel = \"multilingual-e5-large\"\n\n# Embed documents\ntext = [\n \"Turkey is a classic meat to eat at American Thanksgiving.\",\n \"Many people enjoy the beautiful mosques in Turkey.\",\n]\ntext_embeddings = pc.inference.embed(\n model=model,\n inputs=text,\n parameters={\"input_type\": \"passage\", \"truncate\": \"END\"},\n)\n\n# <<Upsert documents into Pinecone index>>\n\n# Embed query\nquery = [\"How should I prepare my turkey?\"]\nquery_embeddings = pc.inference.embed(\n model=model,\n inputs=query,\n parameters={\"input_type\": \"query\", \"truncate\": \"END\"},\n)\n\n# <<Send query to Pinecone index to retrieve similar documents>>\n```\n",
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