Activeloop Deep Memory
Activeloop Deep Memory is a suite of tools that enables you to optimize your Vector Store for your use-case and achieve higher accuracy in your LLM apps.
Retrieval-Augmented Generatation (RAG) has recently gained significant attention. As advanced RAG techniques and agents emerge, they expand the potential of what RAGs can accomplish. However, several challenges may limit the integration of RAGs into production. The primary factors to consider when implementing RAGs in production settings are accuracy (recall), cost, and latency. For basic use cases, OpenAI's Ada model paired with a naive similarity search can produce satisfactory results. Yet, for higher accuracy or recall during searches, one might need to employ advanced retrieval techniques. These methods might involve varying data chunk sizes, rewriting queries multiple times, and more, potentially increasing latency and costs. Activeloop'sDeep Memory a feature available toActiveloop Deep Lake users, addresses these issuea by introducing a tiny neural network layer trained to match user queries with relevant data from a corpus. While this addition incurs minimal latency during search, it can boost retrieval accuracy by up to 27% and remains cost-effective and simple to use, without requiring any additional advanced rag techniques.
For this tutorial we will parseDeepLake documentation, and create a RAG system that could answer the question from the docs.
1. Dataset Creation
We will parse activeloop's docs for this tutorial usingBeautifulSoup library and LangChain's document parsers likeHtml2TextTransformer,AsyncHtmlLoader. So we will need to install the following libraries:
%pip install--upgrade--quiet tiktoken langchain-openai python-dotenv datasets langchain deeplake beautifulsoup4 html2text ragas
Also you'll need to create aActiveloop account.
ORG_ID="..."
from langchain.chainsimport RetrievalQA
from langchain_community.vectorstoresimport DeepLake
from langchain_openaiimport ChatOpenAI, OpenAIEmbeddings
import getpass
import os
if"OPENAI_API_KEY"notin os.environ:
os.environ["OPENAI_API_KEY"]= getpass.getpass("Enter your OpenAI API token: ")
# # activeloop token is needed if you are not signed in using CLI: `activeloop login -u <USERNAME> -p <PASSWORD>`
if"ACTIVELOOP_TOKEN"notin os.environ:
os.environ["ACTIVELOOP_TOKEN"]= getpass.getpass(
"Enter your ActiveLoop API token: "
)# Get your API token from https://app.activeloop.ai, click on your profile picture in the top right corner, and select "API Tokens"
token= os.getenv("ACTIVELOOP_TOKEN")
openai_embeddings= OpenAIEmbeddings()
db= DeepLake(
dataset_path=f"hub://{ORG_ID}/deeplake-docs-deepmemory",# org_id stands for your username or organization from activeloop
embedding=openai_embeddings,
runtime={"tensor_db":True},
token=token,
# overwrite=True, # user overwrite flag if you want to overwrite the full dataset
read_only=False,
)
parsing all links in the webpage usingBeautifulSoup
from urllib.parseimport urljoin
import requests
from bs4import BeautifulSoup
defget_all_links(url):
response= requests.get(url)
if response.status_code!=200:
print(f"Failed to retrieve the page:{url}")
return[]
soup= BeautifulSoup(response.content,"html.parser")
# Finding all 'a' tags which typically contain href attribute for links
links=[
urljoin(url, a["href"])for ain soup.find_all("a", href=True)if a["href"]
]
return links
base_url="https://docs.deeplake.ai/en/latest/"
all_links= get_all_links(base_url)
Loading data:
from langchain_community.document_loaders.async_htmlimport AsyncHtmlLoader
loader= AsyncHtmlLoader(all_links)
docs= loader.load()
Converting data into user readable format:
from langchain_community.document_transformersimport Html2TextTransformer
html2text= Html2TextTransformer()
docs_transformed= html2text.transform_documents(docs)
Now, let us chunk further the documents as some of the contain too much text:
from langchain_text_splittersimport RecursiveCharacterTextSplitter
chunk_size=4096
docs_new=[]
text_splitter= RecursiveCharacterTextSplitter(
chunk_size=chunk_size,
)
for docin docs_transformed:
iflen(doc.page_content)< chunk_size:
docs_new.append(doc)
else:
docs= text_splitter.create_documents([doc.page_content])
docs_new.extend(docs)
Populating VectorStore:
docs= db.add_documents(docs_new)
2. Generating synthetic queries and training Deep Memory
Next step would be to train a deep_memory model that will align your users queries with the dataset that you already have. If you don't have any user queries yet, no worries, we will generate them using LLM!
TODO: Add image
Here above we showed the overall schema how deep_memory works. So as you can see, in order to train it you need relevance, queries together with corpus data (data that we want to query). Corpus data was already populated in the previous section, here we will be generating questions and relevance.
questions- is a text of strings, where each string represents a queryrelevance- contains links to the ground truth for each question. There might be several docs that contain answer to the given question. Because of this relevenve isList[List[tuple[str, float]]], where outer list represents queries and inner list relevant documents. Tuple contains str, float pair where string represent the id of the source doc (corresponds to theidtensor in the dataset), while float corresponds to how much current document is related to the question.
Now, let us generate synthetic questions and relevance:
from typingimport List
from langchain.chains.openai_functionsimport(
create_structured_output_chain,
)
from langchain_core.messagesimport HumanMessage, SystemMessage
from langchain_core.promptsimport ChatPromptTemplate, HumanMessagePromptTemplate
from langchain_openaiimport ChatOpenAI
from pydanticimport BaseModel, Field
# fetch dataset docs and ids if they exist (optional you can also ingest)
docs= db.vectorstore.dataset.text.data(fetch_chunks=True, aslist=True)["value"]
ids= db.vectorstore.dataset.id.data(fetch_chunks=True, aslist=True)["value"]
# If we pass in a model explicitly, we need to make sure it supports the OpenAI function-calling API.
llm= ChatOpenAI(model="gpt-3.5-turbo", temperature=0)
classQuestions(BaseModel):
"""Identifying information about a person."""
question:str= Field(..., description="Questions about text")
prompt_msgs=[
SystemMessage(
content="You are a worldclassexpertfor generating questions based on provided context. \
You make sure the question can be answered by the text."
),
HumanMessagePromptTemplate.from_template(
"Use the given text to generate a question from the following input: {input}"
),
HumanMessage(content="Tips: Make sure to answer in the correct format"),
]
prompt= ChatPromptTemplate(messages=prompt_msgs)
chain= create_structured_output_chain(Questions, llm, prompt, verbose=True)
text="# Understanding Hallucinations and Bias ## **Introduction** In this lesson, we'll cover the concept of **hallucinations** in LLMs, highlighting their influence on AI applications and demonstrating how to mitigate them using techniques like the retriever's architectures. We'll also explore **bias** within LLMs with examples."
questions= chain.run(input=text)
print(questions)
import random
from langchain_openaiimport OpenAIEmbeddings
from tqdmimport tqdm
defgenerate_queries(docs: List[str], ids: List[str], n:int=100):
questions=[]
relevances=[]
pbar= tqdm(total=n)
whilelen(questions)< n:
# 1. randomly draw a piece of text and relevance id
r= random.randint(0,len(docs)-1)
text, label= docs[r], ids[r]
# 2. generate queries and assign and relevance id
generated_qs=[chain.run(input=text).question]
questions.extend(generated_qs)
relevances.extend([[(label,1)]for _in generated_qs])
pbar.update(len(generated_qs))
iflen(questions)%10==0:
print(f"q:{len(questions)}")
return questions[:n], relevances[:n]
chain= create_structured_output_chain(Questions, llm, prompt, verbose=False)
questions, relevances= generate_queries(docs, ids, n=200)
train_questions, train_relevances= questions[:100], relevances[:100]
test_questions, test_relevances= questions[100:], relevances[100:]
Now we created 100 training queries as well as 100 queries for testing. Now let us train the deep_memory:
job_id= db.vectorstore.deep_memory.train(
queries=train_questions,
relevance=train_relevances,
)
Let us track the training progress:
db.vectorstore.deep_memory.status("6538939ca0b69a9ca45c528c")
--------------------------------------------------------------
| 6538e02ecda4691033a51c5b |
--------------------------------------------------------------
| status | completed |
--------------------------------------------------------------
| progress | eta: 1.4 seconds |
| | recall@10: 79.00% (+34.00%) |
--------------------------------------------------------------
| results | recall@10: 79.00% (+34.00%) |
--------------------------------------------------------------
3. Evaluating Deep Memory performance
Great we've trained the model! It's showing some substantial improvement in recall, but how can we use it now and evaluate on unseen new data? In this section we will delve into model evaluation and inference part and see how it can be used with LangChain in order to increase retrieval accuracy
3.1 Deep Memory evaluation
For the beginning we can use deep_memory's builtin evaluation method.It calculates severalrecall metrics.It can be done easily in a few lines of code.
recall= db.vectorstore.deep_memory.evaluate(
queries=test_questions,
relevance=test_relevances,
)
Embedding queries took 0.81 seconds
---- Evaluating without model ----
Recall@1: 9.0%
Recall@3: 19.0%
Recall@5: 24.0%
Recall@10: 42.0%
Recall@50: 93.0%
Recall@100: 98.0%
---- Evaluating with model ----
Recall@1: 19.0%
Recall@3: 42.0%
Recall@5: 49.0%
Recall@10: 69.0%
Recall@50: 97.0%
Recall@100: 97.0%
It is showing quite substatntial improvement on an unseen test dataset too!!!
3.2 Deep Memory + RAGas
from ragas.langchainimport RagasEvaluatorChain
from ragas.metricsimport(
context_recall,
)
Let us convert recall into ground truths:
defconvert_relevance_to_ground_truth(docs, relevance):
ground_truths=[]
for relin relevance:
ground_truth=[]
for doc_id, _in rel:
ground_truth.append(docs[doc_id])
ground_truths.append(ground_truth)
return ground_truths
ground_truths= convert_relevance_to_ground_truth(docs, test_relevances)
for deep_memoryin[False,True]:
print("\nEvaluating with deep_memory =", deep_memory)
print("===================================")
retriever= db.as_retriever()
retriever.search_kwargs["deep_memory"]= deep_memory
qa_chain= RetrievalQA.from_chain_type(
llm=ChatOpenAI(model="gpt-3.5-turbo"),
chain_type="stuff",
retriever=retriever,
return_source_documents=True,
)
metrics={
"context_recall_score":0,
}
eval_chains={m.name: RagasEvaluatorChain(metric=m)for min[context_recall]}
for question, ground_truthinzip(test_questions, ground_truths):
result= qa_chain({"query": question})
result["ground_truths"]= ground_truth
for name, eval_chainin eval_chains.items():
score_name=f"{name}_score"
metrics[score_name]+= eval_chain(result)[score_name]
for metricin metrics:
metrics[metric]/=len(test_questions)
print(f"{metric}:{metrics[metric]}")
print("===================================")
Evaluating with deep_memory = False
===================================
context_recall_score = 0.3763423145
===================================
Evaluating with deep_memory = True
===================================
context_recall_score = 0.5634545323
===================================
3.3 Deep Memory Inference
TODO: Add image
with deep_memory
retriever= db.as_retriever()
retriever.search_kwargs["deep_memory"]=True
retriever.search_kwargs["k"]=10
query="Deamination of cytidine to uridine on the minus strand of viral DNA results in catastrophic G-to-A mutations in the viral genome."
qa= RetrievalQA.from_chain_type(
llm=ChatOpenAI(model="gpt-4"), chain_type="stuff", retriever=retriever
)
print(qa.run(query))
The base htype of the 'video_seq' tensor is 'video'.
without deep_memory
retriever= db.as_retriever()
retriever.search_kwargs["deep_memory"]=False
retriever.search_kwargs["k"]=10
query="Deamination of cytidine to uridine on the minus strand of viral DNA results in catastrophic G-to-A mutations in the viral genome."
qa= RetrievalQA.from_chain_type(
llm=ChatOpenAI(model="gpt-4"), chain_type="stuff", retriever=retriever
)
qa.run(query)
The text does not provide information on the base htype of the 'video_seq' tensor.
3.4 Deep Memory cost savings
Deep Memory increases retrieval accuracy without altering your existing workflow. Additionally, by reducing the top_k input into the LLM, you can significantly cut inference costs via lower token usage.
Related
- Retrieverconceptual guide
- Retrieverhow-to guides