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Refactor superbooga
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oobabooga
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826c74c201
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78
extensions/superbooga/chromadb.py
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78
extensions/superbooga/chromadb.py
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@ -0,0 +1,78 @@
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import logging
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import posthog
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import torch
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from sentence_transformers import SentenceTransformer
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import chromadb
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from chromadb.config import Settings
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logging.info('Intercepting all calls to posthog :)')
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posthog.capture = lambda *args, **kwargs: None
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class Collecter():
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def __init__(self):
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pass
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def add(self, texts: list[str]):
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pass
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def get(self, search_strings: list[str], n_results: int) -> list[str]:
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pass
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def clear(self):
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pass
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class Embedder():
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def __init__(self):
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pass
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def embed(self, text: str) -> list[torch.Tensor]:
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pass
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class ChromaCollector(Collecter):
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def __init__(self, embedder: Embedder):
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super().__init__()
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self.chroma_client = chromadb.Client(Settings(anonymized_telemetry=False))
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self.embedder = embedder
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self.collection = self.chroma_client.create_collection(name="context", embedding_function=embedder.embed)
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self.ids = []
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def add(self, texts: list[str]):
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self.ids = [f"id{i}" for i in range(len(texts))]
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self.collection.add(documents=texts, ids=self.ids)
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def get(self, search_strings: list[str], n_results: int) -> list[str]:
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n_results = min(len(self.ids), n_results)
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result = self.collection.query(query_texts=search_strings, n_results=n_results, include=['documents'])['documents'][0]
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return result
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def get_ids(self, search_strings: list[str], n_results: int) -> list[str]:
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n_results = min(len(self.ids), n_results)
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result = self.collection.query(query_texts=search_strings, n_results=n_results, include=['documents'])['ids'][0]
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return list(map(lambda x: int(x[2:]), result))
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def clear(self):
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self.collection.delete(ids=self.ids)
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class SentenceTransformerEmbedder(Embedder):
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def __init__(self) -> None:
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self.model = SentenceTransformer("sentence-transformers/all-mpnet-base-v2")
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self.embed = self.model.encode
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def make_collector():
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global embedder
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return ChromaCollector(embedder)
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def add_chunks_to_collector(chunks, collector):
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collector.clear()
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collector.add(chunks)
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embedder = SentenceTransformerEmbedder()
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@ -2,22 +2,14 @@ import logging
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import re
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import textwrap
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import chromadb
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import gradio as gr
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import posthog
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import torch
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from bs4 import BeautifulSoup
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from chromadb.config import Settings
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from sentence_transformers import SentenceTransformer
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from modules import chat, shared
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from .chromadb import add_chunks_to_collector, make_collector
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from .download_urls import download_urls
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logging.info('Intercepting all calls to posthog :)')
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posthog.capture = lambda *args, **kwargs: None
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# These parameters are customizable through settings.json
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params = {
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'chunk_count': 5,
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'chunk_length': 700,
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@ -25,72 +17,11 @@ params = {
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'threads': 4,
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}
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class Collecter():
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def __init__(self):
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pass
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def add(self, texts: list[str]):
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pass
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def get(self, search_strings: list[str], n_results: int) -> list[str]:
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pass
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def clear(self):
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pass
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class Embedder():
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def __init__(self):
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pass
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def embed(self, text: str) -> list[torch.Tensor]:
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pass
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class ChromaCollector(Collecter):
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def __init__(self, embedder: Embedder):
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super().__init__()
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self.chroma_client = chromadb.Client(Settings(anonymized_telemetry=False))
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self.embedder = embedder
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self.collection = self.chroma_client.create_collection(name="context", embedding_function=embedder.embed)
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self.ids = []
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def add(self, texts: list[str]):
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self.ids = [f"id{i}" for i in range(len(texts))]
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self.collection.add(documents=texts, ids=self.ids)
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def get(self, search_strings: list[str], n_results: int) -> list[str]:
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n_results = min(len(self.ids), n_results)
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result = self.collection.query(query_texts=search_strings, n_results=n_results, include=['documents'])['documents'][0]
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return result
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def get_ids(self, search_strings: list[str], n_results: int) -> list[str]:
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n_results = min(len(self.ids), n_results)
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result = self.collection.query(query_texts=search_strings, n_results=n_results, include=['documents'])['ids'][0]
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return list(map(lambda x: int(x[2:]), result))
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def clear(self):
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self.collection.delete(ids=self.ids)
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class SentenceTransformerEmbedder(Embedder):
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def __init__(self) -> None:
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self.model = SentenceTransformer("sentence-transformers/all-mpnet-base-v2")
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self.embed = self.model.encode
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embedder = SentenceTransformerEmbedder()
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collector = ChromaCollector(embedder)
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chat_collector = ChromaCollector(embedder)
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collector = make_collector()
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chat_collector = make_collector()
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chunk_count = 5
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def add_chunks_to_collector(chunks, collector):
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collector.clear()
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collector.add(chunks)
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def feed_data_into_collector(corpus, chunk_len):
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global collector
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@ -150,6 +81,7 @@ def apply_settings(_chunk_count):
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settings_to_display = {
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'chunk_count': chunk_count,
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}
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yield f"The following settings are now active: {str(settings_to_display)}"
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@ -193,10 +125,8 @@ def custom_generate_chat_prompt(user_input, state, **kwargs):
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def remove_special_tokens(string):
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for k in ['<|begin-user-input|>', '<|end-user-input|>', '<|injection-point|>']:
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string = string.replace(k, '')
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return string.strip()
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pattern = r'(<\|begin-user-input\|>|<\|end-user-input\|>|<\|injection-point\|>)'
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return re.sub(pattern, '', string)
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def input_modifier(string):
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@ -208,17 +138,14 @@ def input_modifier(string):
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match = re.search(pattern, string)
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if match:
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user_input = match.group(1).strip()
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else:
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return remove_special_tokens(string)
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# Get the most similar chunks
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results = collector.get(user_input, n_results=chunk_count)
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# Get the most similar chunks
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results = collector.get(user_input, n_results=chunk_count)
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# Make the replacements
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string = string.replace('<|begin-user-input|>', '').replace('<|end-user-input|>', '')
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string = string.replace('<|injection-point|>', '\n'.join(results))
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# Make the injection
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string = string.replace('<|injection-point|>', '\n'.join(results))
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return string
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return remove_special_tokens(string)
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def ui():
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@ -250,7 +177,7 @@ def ui():
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...
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```
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The injection doesn't make it into the chat history. It is only used in the current generation.
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The injection doesn't make it into the chat history. It is only used in the current generation.
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#### Regular chat
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