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NirantandAtita Arora
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* * feat(gemini.md): add documentation for integrating Gemini embeddings with Qdrant * * refactor(integrations): move cohere.md to embeddings folder * refactor(integrations): move openai.md to embeddings folder * refactor(integrations): move autogen.md to frameworks folder * refactor(integrations): move langchain.md to frameworks folder * blacken * * feat(embedding, frameworks): reorganise integrations into embedding and frameworks, add _index.md to both * * chore(gemini.md): remove old Gemini integration documentation * * chore(embedding/_index.md): update weight from 24 to 23 and set is_empty to false * chore(frameworks/_index.md): update weight from 24 to 23 and set is_empty to false * Split integrations into embedding and frameworks * Update heading level for embedding a document * Update Gemini embedding documentation * Update titles for embedding and frameworks sections * Try again with nesting * Add documentation for integrated frameworks and embedding options * Delete integrations documentation file * Add Delimiter; unknown weights * Change all weights to 3x * Delimiter reorg * Update qdrant-landing/content/documentation/embedding/gemini.md Co-authored-by: Atita Arora <atarora@users.noreply.github.com> * Update qdrant-landing/content/documentation/embedding/gemini.md Co-authored-by: Atita Arora <atarora@users.noreply.github.com> * Update qdrant-landing/content/documentation/embedding/gemini.md Co-authored-by: Atita Arora <atarora@users.noreply.github.com> * Update qdrant-landing/content/documentation/embedding/gemini.md Co-authored-by: Atita Arora <atarora@users.noreply.github.com> * Update qdrant-landing/content/documentation/embedding/gemini.md Co-authored-by: Atita Arora <atarora@users.noreply.github.com> * Update qdrant-landing/content/documentation/embedding/gemini.md Co-authored-by: Atita Arora <atarora@users.noreply.github.com> * Update qdrant-landing/content/documentation/embedding/gemini.md Co-authored-by: Atita Arora <atarora@users.noreply.github.com> * Update qdrant-landing/content/documentation/embedding/gemini.md Co-authored-by: Atita Arora <atarora@users.noreply.github.com> * Update qdrant-landing/content/documentation/embedding/gemini.md Co-authored-by: Atita Arora <atarora@users.noreply.github.com> * * docs(embedding/gemini.md): update Gemini Embedding Model API documentation * * - Add information about the new Gemini Embedding Model and its compatibility with Qdrant * - Clarify the usage of the `task_type` parameter in the API call * - Provide a list of supported task types and * * docs(embedding): update list of embedding integrations * * refactor(fifty-one.md): Rename file from embedding/fifty-one.md to frameworks/fifty-one.md * refactor(txtai.md): Rename file from embedding/txtai.md to frameworks/txtai.md * * chore(embedding): update is_empty value to true in _index.md * chore(embedding): remove Fifty One from embedding/_index.md * embedding -> embeddings --------- Co-authored-by: Atita Arora <atarora@users.noreply.github.com>
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title, weight
| title | weight |
|---|---|
| OpenAI | 800 |
OpenAI
Qdrant can also easily work with OpenAI embeddings.
There is an official OpenAI Python package that simplifies obtaining them, and it might be installed with pip:
pip install openai
Once installed, the package exposes the method allowing to retrieve the embedding for given text. OpenAI requires an API key that has to be provided either as an environmental variable OPENAI_API_KEY or set in the source code directly, as presented below:
import openai
import qdrant_client
from qdrant_client.http.models import Batch
# Provide OpenAI API key and choose one of the available models:
# https://beta.openai.com/docs/models/overview
openai.api_key = "<< your_api_key >>"
embedding_model = "text-embedding-ada-002"
response = openai.Embedding.create(
input="The best vector database",
model=embedding_model,
)
qdrant_client = qdrant_client.QdrantClient()
qdrant_client.upsert(
collection_name="MyCollection",
points=Batch(
ids=[1],
vectors=[response["data"][0]["embedding"]],
),
)