text-generation-webui/extensions/superbooga/chromadb.py

import logging

import posthog
import torch
from sentence_transformers import SentenceTransformer

import chromadb
from chromadb.config import Settings

logging.info('Intercepting all calls to posthog :)')
posthog.capture = lambda *args, **kwargs: None


class Collecter():
    def __init__(self):
        pass

    def add(self, texts: list[str]):
        pass

    def get(self, search_strings: list[str], n_results: int) -> list[str]:
        pass

    def clear(self):
        pass


class Embedder():
    def __init__(self):
        pass

    def embed(self, text: str) -> list[torch.Tensor]:
        pass


class ChromaCollector(Collecter):
    def __init__(self, embedder: Embedder):
        super().__init__()
        self.chroma_client = chromadb.Client(Settings(anonymized_telemetry=False))
        self.embedder = embedder
        self.collection = self.chroma_client.create_collection(name="context", embedding_function=embedder.embed)
        self.ids = []

    def add(self, texts: list[str]):
        self.ids = [f"id{i}" for i in range(len(texts))]
        self.collection.add(documents=texts, ids=self.ids)

    def get(self, search_strings: list[str], n_results: int) -> list[str]:
        n_results = min(len(self.ids), n_results)
        result = self.collection.query(query_texts=search_strings, n_results=n_results, include=['documents'])['documents'][0]
        return result

    def get_ids(self, search_strings: list[str], n_results: int) -> list[str]:
        n_results = min(len(self.ids), n_results)
        result = self.collection.query(query_texts=search_strings, n_results=n_results, include=['documents'])['ids'][0]
        return list(map(lambda x: int(x[2:]), result))

    def clear(self):
        self.collection.delete(ids=self.ids)


class SentenceTransformerEmbedder(Embedder):
    def __init__(self) -> None:
        self.model = SentenceTransformer("sentence-transformers/all-mpnet-base-v2")
        self.embed = self.model.encode


def make_collector():
    global embedder
    return ChromaCollector(embedder)


def add_chunks_to_collector(chunks, collector):
    collector.clear()
    collector.add(chunks)


embedder = SentenceTransformerEmbedder()