RAG Text Chunker — Metni örtüşen chunk'lara bölün ve RAG için Ollama /api/embed isteği oluşturun

Ollama'ya erişebilen bir makinede çalıştırın. Yanıt, her chunk için bu sırada bir vektör içerir.

curl http://localhost:11434/api/embed -d '{
  "model": "embeddinggemma",
  "input": [
    "Embeddings turn text into numeric vectors. You can store them in a vector database and search them with cosine similarity, or use them in a RAG pipeline.\n\nThe length of each vector depends on the embedding model. Use the same model when you index your documents and when you embed a question.\n\n",
    "Ollama'\''s /api/embed endpoint takes a single string or an array of strings. By default, input that is longer than the model'\''s context window is truncated. Set truncate to false to get an error instead.\n\n",
    "Split long documents into smaller chunks before you embed them, so that each chunk fits in the context window and a search can return the part that answers the question."
  ],
  "truncate": false
}'

Chunk'lar

3 chunk. En uzunu 294 karakter.

1. #0 · 294 karakter · 0 konumunda başlar

   Embeddings turn text into numeric vectors. You can store them in a vector database and search them with cosine similarity, or use them in a RAG pipeline. The length of each vector depends on the embedding model. Use the same model when you index your documents and when you embed a question.

2. #1 · 202 karakter · 294 konumunda başlar

   Ollama's /api/embed endpoint takes a single string or an array of strings. By default, input that is longer than the model's context window is truncated. Set truncate to false to get an error instead.

3. #2 · 169 karakter · 496 konumunda başlar

   Split long documents into smaller chunks before you embed them, so that each chunk fits in the context window and a search can return the part that answers the question.

{
  "model": "embeddinggemma",
  "input": [
    "Embeddings turn text into numeric vectors. You can store them in a vector database and search them with cosine similarity, or use them in a RAG pipeline.\n\nThe length of each vector depends on the embedding model. Use the same model when you index your documents and when you embed a question.\n\n",
    "Ollama's /api/embed endpoint takes a single string or an array of strings. By default, input that is longer than the model's context window is truncated. Set truncate to false to get an error instead.\n\n",
    "Split long documents into smaller chunks before you embed them, so that each chunk fits in the context window and a search can return the part that answers the question."
  ],
  "truncate": false
}