Add Gemini Embedding Model 001 (#457)

* * 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>
2026-09-29 16:08:32 +02:00 · 2023-12-11 17:50:42 +05:30
parent 6073405006
commit d90efb359d
28 changed files with 148 additions and 29 deletions
@@ -0,0 +1,43 @@
+---
+title: OpenAI
+weight: 800
+---
+
+# OpenAI
+
+Qdrant can also easily work with [OpenAI embeddings](https://beta.openai.com/docs/guides/embeddings/embeddings). 
+
+There is an official OpenAI Python package that simplifies obtaining them, and it might be installed with pip:
+
+```bash
+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:
+
+```python
+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"]],
+    ),
+)
+```
+