tazarov

version: '3.9'

networks:
  net:
    driver: bridge
services:
  chromadb:
    image: chromadb/chroma:latest
    volumes:
      - ./chromadb:/chroma/chroma

tazarov

from llama_index.embeddings import OpenAIEmbedding
from llama_index.embeddings.base import BaseEmbedding
import chromadb
from chromadb.api.types import EmbeddingFunction

class LlamaIndexEmbeddingAdapter(EmbeddingFunction):
  def __init__(self,ef:BaseEmbedding):
    self.ef = ef

  def __call__(self, input: Documents) -> Embeddings:

tazarov

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tazarov

import chromadb
from chromadb.config import Settings

client = chromadb.PersistentClient(path="test", settings=Settings(allow_reset=True))

client.reset()
col = client.get_or_create_collection("test")

col.add(ids=["1", "2", "3"], documents=["He is a technology freak and he loves AI topics", "AI technology are advancing at a fast pace", "Innovation in LLMs is a hot topic"])
col.query(query_texts=["technology"], where_document={"$or":[{"$contains":"technology"}, {"$contains":"freak"}]})

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tazarov

import uuid

from chromadb.utils import embedding_functions
import chromadb
ef = embedding_functions.DefaultEmbeddingFunction()

docs = ["Article by john", "Article by Jack", "Article by Jill"]
client = chromadb.Client()
embeddings = ef(docs)
collection = client.get_or_create_collection("test-where-list")

tazarov

import uuid

from chromadb.utils import embedding_functions
import chromadb
ef = embedding_functions.DefaultEmbeddingFunction()

docs = ["Article by john", "Article by Jack", "Article by Jill"]
client = chromadb.Client()
embeddings = ef(docs)
collection = client.get_or_create_collection("test-where-list")

tazarov

import uuid

from chromadb.utils import embedding_functions
from langchain.schema import Document
from langchain.text_splitter import RecursiveCharacterTextSplitter

long_text = """
The Downsides of LLMs (Logical Language Models)
In the age of artificial intelligence, Logical Language Models (LLMs) represent a significant leap forward in the field of natural language processing. These models are capable of comprehending, generating, and reasoning about human languages in a way that mimics human-like understanding. While the benefits of LLMs are numerous, it's essential to also recognize the downsides that accompany these advancements. This essay will explore the negative aspects of LLMs in terms of ethics, job displacement, security, and potential biases.