112 lines
3.4 KiB
Markdown
112 lines
3.4 KiB
Markdown
---
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layout: default
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title: "Embedding"
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parent: "Utility Function"
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nav_order: 6
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---
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# Embedding
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Below you will find an overview table of various text embedding APIs, along with example Python code.
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> Embedding is more a micro optimization, compared to the Flow Design.
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>
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> It's recommended to start with the most convenient one and optimize later.
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{: .best-practice }
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| **API** | **Free Tier** | **Pricing Model** | **Docs** |
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| --- | --- | --- | --- |
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| **OpenAI** | ~$5 credit | ~$0.0001/1K tokens | [OpenAI Embeddings](https://platform.openai.com/docs/api-reference/embeddings) |
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| **Azure OpenAI** | $200 credit | Same as OpenAI (~$0.0001/1K tokens) | [Azure OpenAI Embeddings](https://learn.microsoft.com/azure/cognitive-services/openai/how-to/create-resource?tabs=portal) |
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| **Google Vertex AI** | $300 credit | ~$0.025 / million chars | [Vertex AI Embeddings](https://cloud.google.com/vertex-ai/docs/generative-ai/embeddings/get-text-embeddings) |
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| **AWS Bedrock** | No free tier, but AWS credits may apply | ~$0.00002/1K tokens (Titan V2) | [Amazon Bedrock](https://docs.aws.amazon.com/bedrock/) |
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| **Cohere** | Limited free tier | ~$0.0001/1K tokens | [Cohere Embeddings](https://docs.cohere.com/docs/cohere-embed) |
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| **Hugging Face** | ~$0.10 free compute monthly | Pay per second of compute | [HF Inference API](https://huggingface.co/docs/api-inference) |
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| **Jina** | 1M tokens free | Pay per token after | [Jina Embeddings](https://jina.ai/embeddings/) |
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## Example Python Code
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### 1. OpenAI
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```python
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import openai
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openai.api_key = "YOUR_API_KEY"
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resp = openai.Embedding.create(model="text-embedding-ada-002", input="Hello world")
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vec = resp["data"][0]["embedding"]
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print(vec)
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```
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### 2. Azure OpenAI
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```python
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import openai
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openai.api_type = "azure"
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openai.api_base = "https://YOUR_RESOURCE_NAME.openai.azure.com"
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openai.api_version = "2023-03-15-preview"
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openai.api_key = "YOUR_AZURE_API_KEY"
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resp = openai.Embedding.create(engine="ada-embedding", input="Hello world")
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vec = resp["data"][0]["embedding"]
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print(vec)
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```
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### 3. Google Vertex AI
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```python
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from vertexai.preview.language_models import TextEmbeddingModel
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import vertexai
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vertexai.init(project="YOUR_GCP_PROJECT_ID", location="us-central1")
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model = TextEmbeddingModel.from_pretrained("textembedding-gecko@001")
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emb = model.get_embeddings(["Hello world"])
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print(emb[0])
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```
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### 4. AWS Bedrock
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```python
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import boto3, json
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client = boto3.client("bedrock-runtime", region_name="us-east-1")
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body = {"inputText": "Hello world"}
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resp = client.invoke_model(modelId="amazon.titan-embed-text-v2:0", contentType="application/json", body=json.dumps(body))
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resp_body = json.loads(resp["body"].read())
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vec = resp_body["embedding"]
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print(vec)
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```
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### 5. Cohere
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```python
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import cohere
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co = cohere.Client("YOUR_API_KEY")
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resp = co.embed(texts=["Hello world"])
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vec = resp.embeddings[0]
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print(vec)
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```
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### 6. Hugging Face
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```python
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import requests
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API_URL = "https://api-inference.huggingface.co/models/sentence-transformers/all-MiniLM-L6-v2"
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HEADERS = {"Authorization": "Bearer YOUR_HF_TOKEN"}
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res = requests.post(API_URL, headers=HEADERS, json={"inputs": "Hello world"})
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vec = res.json()[0]
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print(vec)
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```
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### 7. Jina
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```python
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import requests
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url = "https://api.jina.ai/v2/embed"
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headers = {"Authorization": "Bearer YOUR_JINA_TOKEN"}
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payload = {"data": ["Hello world"], "model": "jina-embeddings-v3"}
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res = requests.post(url, headers=headers, json=payload)
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vec = res.json()["data"][0]["embedding"]
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print(vec)
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```
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