code: def embed_function(texts: Documents) -> Embeddings:
      # Embed the documents using any supported method
      if str(MODEL.name) == "models/embedding-001":
          # Use the new `embed_content()` method if it's the new Gemini embedding model.
          return [
              palm.embed_content(model=MODEL, content=text)["embedding"]
              for text in texts
          ]
      else:
          return [
              palm.generate_embeddings(model=MODEL, text=text)[
                  "embedding"
              ]
              for text in texts
          ]
  
file_path: generative-ai-docs/demos/palm/python/docs-agent/scripts/populate_vector_database.py
imports: import os
  import sys
  import re
  import json
  import chromadb
  import flatdict
  import uuid
  from chromadb.config import Settings
  from chromadb.utils import embedding_functions
  from chromadb.api.types import Documents, Embeddings
  import google.generativeai
  from ratelimit import limits, sleep_and_retry
  import read_config
label_desc: Use image node_id 1 for calling a global function with example usage: embed_function(texts) and returns: unknown, unknown