Overcome Lost In Middle Phenomenon In RAG Using LongContextRetriver
Published in
57 min readNov 24, 2023
In certain aspects, both humans and large language models (LLMs) share a common behavior pattern: they tend to excel in processing information located at the beginning or end of a given content, while information in the middle often goes unnoticed.
Researchers from Stanford University, the University of California, Berkeley, and Samaya AI have identified a phenomenon in LLMs resembling the human primacy/recency effect. This implies that, like humans, LLMs are more likely to recall information positioned at the start or end of an input, with a tendency to overlook content in the middle.
According to their findings, when these models are tasked with retrieving information, their performance is optimal when the relevant data is situated at the beginning or end of the input. However, a significant decline in performance occurs when the pertinent information is in the middle. This decline is particularly noticeable when the model is asked to answer questions requiring it to extract information from multiple sources, akin to a student searching for relevant details in several books for an exam.
The study suggests that the more simultaneous input the model has to process, the more its performance suffers. This could pose challenges in real-world scenarios where efficient processing of large amounts of information is crucial.
Furthermore, the research indicates that there is a limit to how effectively LLMs can utilize additional information, cautioning against the use of “mega-prompts” with overly detailed instructions, as they may be counterproductive.
The observed “lost in the middle” phenomenon is not limited to traditional LLMs but also extends to models explicitly designed to handle extensive contexts, such as GPT-4 32K or Claude with a context window of 100K tokens. The study tested seven language models, both open and closed, including the new GPT-3.5 16K and Claude 1.3 with 100K, all displaying a U-curve pattern in performance, with better results for tasks where the solution is found at the beginning or end of the text.
This raises questions about the practicality of models with large context windows when superior results might be achieved by processing the context in smaller segments. Even the leading model, GPT-4, exhibits this effect, albeit at a higher overall performance level.
Retrieval Augment Generation
RAG is particularly helpful when we do not want to fine Models and still want to generate insights from Custom Knowledge Base. Imagine we have multiple audios and videos , then how can we use a mechanism that will help retrieve information from these sources and generate insights based on the user queries.
RAG comprises of Retrieval and Generation Components
- For Retrieval we use a technique called semantic search
- For Generation we use a Large Language Model to look at the retrieved chunk and generate a human like response.
No matter the architecture of our model, there is a substantial performance degradation when we include 10+ retrieved documents. In brief: When models must access relevant information in the middle of long contexts, they tend to ignore the provided documents.
Techniques to Implement Better RAG systems to avoid LIM(Lost In Middle)
- It is not advisable to create a single knowledge base to create single documents as it would become ambiguous for a retrieval model to retrieve right set of context based on semantic similarity.
- LIM(Lost in Middle) LLMs struggles in scanning the context which are in the middle of the retrieved documents
- To avoid the above issue to some extent we will create different VectorStore and merge it together using Merge Retriever.
- Then we will Re-order results to avoid performance degradation using LongContextReorder
LOTR (Merger Retriever)
Lord of the Retrievers, also known as MergerRetriever, takes a list of retrievers as input and merges the results of their get_relevant_documents() methods into a single list. The merged results will be a list of documents that are relevant to the query and that have been ranked by the different retrievers.
The MergerRetriever class can be used to improve the accuracy of document retrieval in a number of ways.
- First, it can combine the results of multiple retrievers, which can help to reduce the risk of bias in the results.
- Second, it can rank the results of the different retrievers, which can help to ensure that the most relevant documents are returned first.
The Technology Stack
- Chromadb: Vector store for storing text embeddings.
- Three different embedding models to reduce bias and facilitating advanced semantic analysis
BAAI/bge-small-en-v1.5
Sentence-transformers/all-MiniLM-L6-v2
OpenAI Embeddigs - Langchain: Framework for application development using Large Language Models (LLMs).
- Zephyr-7B Beta: Large Language Model serving as the core component in the application.
- Contextual Compression: Addresses challenges in retrieval by compressing and filtering documents based on query context. Involves a base retriever and a document compressor in the retrieval process.
Code Implementation
Install required dependencies
%%capture
!pip install -qU langchain chromadb huggingface_hub sentence-transformers pypdf cohere openai tiktokenImport required dependencies
import os
from langchain.document_transformers import (
EmbeddingsClusteringFilter,
EmbeddingsRedundantFilter,
)
from langchain.embeddings import HuggingFaceEmbeddings, OpenAIEmbeddings,HuggingFaceBgeEmbeddings
from langchain.retrievers import ContextualCompressionRetriever
from langchain.retrievers.document_compressors import DocumentCompressorPipeline
from langchain.document_transformers import LongContextReorder
from langchain.retrievers.merger_retriever import MergerRetriever
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import FAISS,Chroma
from langchain.document_loaders import PyPDFLoaderSetup OpenAI keys
import os
from getpass import getpass
import openai
#
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
if not OPENAI_API_KEY:
OPENAI_API_KEY = getpass("Enter your OpenAI API key: ")
openai.api_key = OPENAI_API_KEYSetup the Embedding Models
hf_embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2",
model_kwargs={"device":"cpu"},
encode_kwargs = {'normalize_embeddings': False})
hf_bge_embeddings = HuggingFaceBgeEmbeddings(model_name="BAAI/bge-large-en",
model_kwargs={"device":"cpu"},
encode_kwargs = {'normalize_embeddings': False})
openai_embeddings = OpenAIEmbeddings(openai_api_key=OPENAI_API_KEY)Data Processing
- Here we will process two different documents
loader_mh = PyPDFLoader("/content/mental_health_Document.pdf")
documnet_mh = loader_mh.load()
print(len(documnet_mh))
loader_esops = PyPDFLoader("/content/Employee-Stock-Option-Plans-ESOP-Best-Practices-2.pdf")
documnet_esops = loader_esops.load()
print(len(documnet_esops))
#####OUTPUT############
13
44Split Text into chunks
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000,chunk_overlap=200)
text_mh = text_splitter.split_documents(documnet_mh)
text_esops = text_splitter.split_documents(documnet_esops)
print(len(text_mh))
print(len(text_esops))
########OUTPUT########
56
50Instantiate two diff Chromadb Indexes, each one with a diff embedding.
import chromadb
ABS_PATH = os.path.dirname(os.path.abspath("."))
DB_DIR = os.path.join(ABS_PATH, "db")
#
client_settings = chromadb.config.Settings(
is_persistent=True,
persist_directory=DB_DIR,
anonymized_telemetry=False,
)
#
mh_vectorstore = Chroma.from_documents(text_mh,
hf_bge_embeddings,
client_settings=client_settings,
collection_name="mental_health",
collection_metadata={"hnsw":"cosine"},
persist_directory="/store/mental_health")
esops_vectorstore = Chroma.from_documents(text_esops,
hf_embeddings ,
client_settings=client_settings,
collection_name="esops",
collection_metadata={"hnsw":"cosine"},
persist_directory="/store/mental_health")Load the VectorStore
- Define two different retrievers with two different embeddings and different search type.
retriever_mh = mh_vectorstore.as_retriever(search_type="mmr",
search_kwargs={"k": 5, "include_metadata": True}
)
retriever_esops = esops_vectorstore.as_retriever(search_type="mmr",
search_kwargs={"k": 5, "include_metadata": True}Merge all the retrievers
- This will hold the outputs from both the retrievers and can be used as any other retriever on different types of chains.
The EnsembleRetriever and the MergerRetriever (LOTR) in Langchain do have similar functionalities in that they both combine the results of multiple retrievers. However, they handle the merging of outputs differently.
The EnsembleRetriever uses a technique called Reciprocal Rank Fusion (RRF) to combine the results. It takes a list of retrievers and a corresponding list of weights as input. If no weights are provided, it defaults to equal weighting for all retrievers. The RRF score for each document is calculated as the weight times the reciprocal of the rank plus a constant ‘c’. The final result is a list of items sorted by their weighted RRF scores in descending order
The MergerRetriever (LOTR) merges the outputs from different retrievers in a round-robin fashion. It first gets the relevant documents from all retrievers and then merges them. The merged results will be a list of documents that are relevant to the query and that have been ranked by the different retrievers. Redundant results from the merged retrievers can be removed using the EmbeddingsRedundantFilter with another embedding. The documents can also be divided into clusters or "centers" of meaning, and the closest document to that center can be picked for the final results. This can be done using the EmbeddingsClusteringFilter
As for the specific use case for using the EnsembleRetriever over the MergerRetriever, it depends on your needs.
The EnsembleRetriever can be used to adjust the influence of different retrievers on the final result by assigning different weights to the retrievers.
The MergerRetriever, on the other hand, can be used to reduce the risk of bias in the results and ensure that the most relevant documents are returned first.
lotr = MergerRetriever(retrievers=[retriever_mh, retriever_esops])
#
for chunks in lotr.get_relevant_documents("What is esops?"):
print(chunks.page_content)
See discussions, st ats, and author pr ofiles f or this public ation at : https://www .researchgate.ne t/public ation/255657987
Defining mental health and mental illness
Article · Januar y 2009
CITATIONS
9READS
141,176
2 author s, including:
Some o f the author s of this public ation ar e also w orking on these r elat ed pr ojects:
Cultural P sychiatr y View pr oject
Diversifying the medic al curriculum View pr oject
Nisha Dogr a
Univ ersity of L eicester
152 PUBLICA TIONS 2,847 CITATIONS
SEE PROFILE
All c ontent f ollo wing this p age was uplo aded b y Nisha Dogr a on 20 May 2014.
The user has r equest ed enhanc ement of the do wnlo aded file.
ESOPS FOR THE LONG TERM Part IV
are useful as they relate to ‘societal ’ expectations of children.
Different de finitions are used to de fine mental ill health. The WHO uses the term ‘mental
disorders ’ broadly, to include mental illness, intellectual disability, personality disorder, sub-
stance dependence and adjustment to adverse life events (WHO 1992). The WHO acknowledgesthat the word ‘disorder ’ is used to avoid perceived greater dif ficulties associated with ‘illness ’ –
for example, stigma and the emphasis on a medical model. Meltzer et al. (2000) use the term
‘mental disorders ’ in reference to emotional, conduct, hyperkinetic and less common disorders
as de fined by the ICD (International Classi fication of Diseases) 10 and DSM ( Diagnostic and
Statistical Manual of Mental Disorders ) IV. Jorm (2000) focuses speci fically on depression and
psychosis. Meanwhile, Rowling et al. (2002) use the terms ‘mental illness ’ and ‘mental disorder ’
What is an ESOP?
•An Employee Stock Options Plan (ESOP)
•An allocation of shares that will be granted to employees in the future
in the form of stock options
–How much equity should we set aside for employees?
•A plan for how these options will be distributed:
–How many shares will individual employees receive?
–What terms will govern these grants?
•The plan is as important as the allocation!
ing prevalence rates of diagnosable mental disorder among 11 –15-year-olds: depression 1.8 per
cent; anxiety 4.6 per cent; conduct disorder 6.2 per cent (Meltzer et al. 2000). Moreover, the
prevalence of serious mental illness increases greatly during adolescence (Davidson and Manion1996; Smith and Leon 2001; Rickwood et al. 2005). Such problems have a negative impact on an
individual ’s development across all areas of their lives – i.e. self-esteem, relationships, academic
success, career options and lifestyle (Mental Health Foundation 1999; Meltzer et al. 2000).
Furthermore, the burden of adolescent mental health problems and illness involves enormousfinancial costs to individuals, families and society. These include loss of earnings for parents
and adolescents, and social care, health service, education and Home Of fice costs (Appleton
Late-VC Flush with capital, startups at this stage
begin to steadily ramp -up hiring, yet
employees still want equity Important to have standardized the
ESOP and the amount of equity granted
to new hires at each level
Growth Company is aggressively pursuing
growth and hiring; likely to have
exhausted most of the ESOP Most of the ESOP is gone, but shares
remaining are more valuable; use them
to allow new hires to share in the upside Make employee equity allocations and set up an ESOP sometime
between the pre -seed and early -VC stage
Kendall, R. (1988) Diagnosis and classi fication, in R. Kendall and A. Zealley (eds) Companion to Psychiatric
Studies . Edinburgh: Churchill Livingstone.
Leighton, S. (2006) Pilot thesis: ‘What do I think? Where do I go? ’ Exploring adolescents ’ understanding of
mental health issues and their attitudes towards seeking help for mental health problems. Unpublisheddoctoral assignment.16 Nursing in child and adolescent mental health
Legal Advice
When structuring an ESOP, engage an experienced
startup lawyer with regional expertise to standardize the
terms and language
of the Jewish people by the Nazis (Link and Phelan 2001). Stigma was de fined by Goffman
(1970) as the position of the individual who is disquali fied from full social acceptance. It is
perceived as the outcome of a process of social labelling which singles out difference, names thisdifference inferiority, subsequently blames those who are different for their otherness andcontributes to the creation of a spoilt identity (Goffman 1970). Since that seminal develop-ment, the concept has evolved. For example, stigma can be described with reference to therelationships between a set of interrelated concepts, rather than focusing solely on personalattributes – i.e. stigma exists when elements of labelling, stereotyping, separation, status loss
and discrimination occur together in a power situation that allows these processes to happen(Link and Phelan 2001).
The nature and extent of stigmatization in adult mental illness
The defining difference between Silicon Valley companies and almost
every other industry in the U.S. is the virtually universal practice among
tech companies of distributing meaningful equity (usually in the form of
stock options) to ordinary employees . A Defining Characteristic of Startup Culture
Steven Johnson, Technology Writer
•Startups are a unique case . Unlike at larger corporations, employee
ownership is an essential element of startup communities and culture
–As high -risk/high -reward enterprises, startups use options to align
employee compensation with the risk -prone mentality of the
business
–Startups seeking to achieve a “big exit” use options to align all
employees to drive toward this desired outcomefor chunks in lotr.get_relevant_documents("What is consequence of stigma?"):
print(chunks.page_content)
######OUTPUT###########
applicable to children.
Consequences of stigma
Stigmatization of the mentally ill is understood to be prejudicial to them, injurious to all aspectsof their treatment in mental health services and damaging to their role as members of society(Hinshaw 2005). Stigmatization leads to individual and social discrimination against the stig-matized person. Several authors identify that the discriminatory behaviour displayed can behostile or avoidant and that it operates throughout personal and social relationships, pervadingthe home, workplace, local community, health and social welfare systems. This can result inincreased feelings of shame, increased personal and social impairment and isolation, perpetu-ation and worsening of an illness, reluctance to access health care and infringement of humanrights (Link and Phelan 2001; Crisp 2004; Hinshaw 2005).
Children, mental illness and stigma
Social Impact Considerations
•Companies focused on social impact goals and/or in developing
markets may have unique ESOP considerations
•Consider two possible scenarios:
–Financial Inclusion Goals : Employee -friendly hiring practices and
ESOPs can help build local financial inclusion. If this is part of your
corporate mandate, consider the added social impact value of
offering your options program to all levels of employees
–Local Ownership Culture : Conversely, in certain regions stock
options may have negligible value to employees, either because of
risk aversion, lack of liquidity, or lack of understanding. If this is the
case, it is not worth extensively offering options to employees who
would rather be paid in cash
1997; Crisp et al. 2000; Sartorius 2002; Gureje et al. 2005).
Definition of the concept of stigma
Stigma can be viewed as a social construct. Setting people apart from other members of society
has a long history. In ancient Greece members of tainted groups – for example, slaves andDefining mental health and mental illness 11
THE FINE PRINT – TERMS Part III
Two large-scale literature reviews have suggested that the media can be regarded as an
important in fluence on community attitudes towards mental illness. It is considered that
there is a complex and circular relationship between mass media representation of mentalillness and public understanding, with negative media images promoting negative attitudesand resultant media coverage feeding off an already negative public perception. It is alsothought that negative images will have a greater effect on public attitudes than positive por-trayals (Francis et al. 2001; Edney 2004). Work by Wahl (2003) suggests that this is equally
...
perceived as the outcome of a process of social labelling which singles out difference, names thisdifference inferiority, subsequently blames those who are different for their otherness andcontributes to the creation of a spoilt identity (Goffman 1970). Since that seminal develop-ment, the concept has evolved. For example, stigma can be described with reference to therelationships between a set of interrelated concepts, rather than focusing solely on personalattributes – i.e. stigma exists when elements of labelling, stereotyping, separation, status loss
and discrimination occur together in a power situation that allows these processes to happen(Link and Phelan 2001).
The nature and extent of stigmatization in adult mental illness
ESOPS FOR THE LONG TERM Part IVRemove redundant results from the merged retrievers
- EmbeddingsRedundantFilter drops redundant documents by comparing their embeddings
filter = EmbeddingsRedundantFilter(embeddings=openai_embeddings)
pipeline = DocumentCompressorPipeline(transformers=[filter])
compression_retriever = ContextualCompressionRetriever(base_compressor=pipeline,
base_retriever=lotr)Retrieve the final document ordered by the original retriever scores
- This filter will divide the documents vectors into clusters or “centers” of meaning.
- Then it will pick the closest document to that center for the final results.
- By default the result document will be ordered/grouped by clusters.
- If we want the final document to be ordered by the original retriever scores we need to add the “sorted” parameter as True
filter_ordered_by_retriever = EmbeddingsClusteringFilter(
embeddings=openai_embeddings,
num_clusters=10,
num_closest=1,
sorted=True,
)
pipeline = DocumentCompressorPipeline(transformers=[filter_ordered_by_retriever])
compression_retriever = ContextualCompressionRetriever(
base_compressor=pipeline, base_retriever=lotr
)Re-order results to avoid performance degradation
lotr = MergerRetriever(retrievers=[retriever_mh, retriever_esops])
query = "What is ESOPS?"
docs = lotr.get_relevant_documents(query)
docs
#####OUTPUT#######
[Document(page_content='See discussions, st ats, and author pr ofiles f or this public ation at : https://www .researchgate.ne t/public ation/255657987\nDefining mental health and mental illness\nArticle · Januar y 2009\nCITATIONS\n10READS\n151,087\n2 author s, including:\nNisha Dogr a\nUniv ersity of L eicester\n152 PUBLICA TIONS \xa0\xa0\xa02,974 CITATIONS \xa0\xa0\xa0\nSEE PROFILE\nAll c ontent f ollo wing this p age was uplo aded b y Nisha Dogr a on 20 May 2014.\nThe user has r equest ed enhanc ement of the do wnlo aded file.', metadata={'source': 'Defining_mental_health_and_mental_illness.pdf', 'page': 0}),
Document(page_content='ESOPS FOR THE LONG TERM Part IV', metadata={'source': 'Employee-Stock-Option-Plans-ESOP-Best-Practices-2.pdf', 'page': 34}),
Document(page_content='of the Jewish people by the Nazis (Link and Phelan 2001). Stigma was de fined by Goffman\n(1970) as the position of the individual who is disquali fied from full social acceptance. It is\nperceived as the outcome of a process of social labelling which singles out difference, names thisdifference inferiority, subsequently blames those who are different for their otherness andcontributes to the creation of a spoilt identity (Goffman 1970). Since that seminal develop-ment, the concept has evolved. For example, stigma can be described with reference to therelationships between a set of interrelated concepts, rather than focusing solely on personalattributes – i.e. stigma exists when elements of labelling, stereotyping, separation, status loss\nand discrimination occur together in a power situation that allows these processes to happen(Link and Phelan 2001).\nThe nature and extent of stigmatization in adult mental illness', metadata={'source': 'Defining_mental_health_and_mental_illness.pdf', 'page': 6}),
Document(page_content='The defining difference between Silicon Valley companies and almost \nevery other industry in the U.S. is the virtually universal practice among \ntech companies of distributing meaningful equity (usually in the form of \nstock options) to ordinary employees . A Defining Characteristic of Startup Culture \nSteven Johnson, Technology Writer \n•Startups are a unique case . Unlike at larger corporations, employee \nownership is an essential element of startup communities and culture \n–As high -risk/high -reward enterprises, startups use options to align \nemployee compensation with the risk -prone mentality of the \nbusiness \n–Startups seeking to achieve a “big exit” use options to align all \nemployees to drive toward this desired outcome', metadata={'source': 'Employee-Stock-Option-Plans-ESOP-Best-Practices-2.pdf', 'page': 8}),
Document(page_content='provide a useful starting point from which to discuss mental health issues with children andtheir families.\nDefinitions of child mental health\nDefinitions of mental health as they relate speci fically to children have been provided by the\nHealth Advisory Service (HAS) (1995) and the Mental Health Foundation (1999). These def-initions bear similarities to those provided by Ryff and Singer (1998) and Rowling et al. (2002),\nwhile recognizing the developmental context of childhood – i.e. the ability to develop psycho-\nlogically, emotionally, creatively, intellectually and spiritually; initiate, develop and sustainmutually satisfying personal relationships; use and enjoy solitude; become aware of others andempathize with them; play and learn; develop a sense of right and wrong; and resolve problemsand setbacks and learn from them (HAS 1995; Mental Health Foundation 1999). Such de finitions\nare useful as they relate to ‘societal ’ expectations of children.', metadata={'source': 'Defining_mental_health_and_mental_illness.pdf', 'page': 3}),
Document(page_content='What is an ESOP? \n•An Employee Stock Options Plan (ESOP) \n \n•An allocation of shares that will be granted to employees in the future \nin the form of stock options \n–How much equity should we set aside for employees? \n \n•A plan for how these options will be distributed: \n–How many shares will individual employees receive? \n–What terms will govern these grants? \n \n•The plan is as important as the allocation!', metadata={'source': 'Employee-Stock-Option-Plans-ESOP-Best-Practices-2.pdf', 'page': 3}),
Document(page_content='ing prevalence rates of diagnosable mental disorder among 11 –15-year-olds: depression 1.8 per\ncent; anxiety 4.6 per cent; conduct disorder 6.2 per cent (Meltzer et al. 2000). Moreover, the\nprevalence of serious mental illness increases greatly during adolescence (Davidson and Manion1996; Smith and Leon 2001; Rickwood et al. 2005). Such problems have a negative impact on an\nindividual ’s development across all areas of their lives – i.e. self-esteem, relationships, academic\nsuccess, career options and lifestyle (Mental Health Foundation 1999; Meltzer et al. 2000).\nFurthermore, the burden of adolescent mental health problems and illness involves enormousfinancial costs to individuals, families and society. These include loss of earnings for parents\nand adolescents, and social care, health service, education and Home Of fice costs (Appleton', metadata={'source': 'Defining_mental_health_and_mental_illness.pdf', 'page': 5}),
Document(page_content='Legal Advice \nWhen structuring an ESOP, engage an experienced \nstartup lawyer with regional expertise to standardize the \nterms and language', metadata={'source': 'Employee-Stock-Option-Plans-ESOP-Best-Practices-2.pdf', 'page': 33}),
Document(page_content='Mental health literacy\nFinally, in this section, it is also worth considering how the mental health ‘literacy ’ of adults\nand children in the general population varies from that of professionals. In all phases of a recentresearch project, conceptual confusion was identi fied in the literature review and among ado-\nlescent participants (Leighton 2006, 2008). Focus group participants did not find the single\ncontinuum model suggested by the WHO (2000) helpful (Leighton 2006). Furthermore, in thefocus group feedback session, participants suggested that labelling serious mental illnesses suchas schizophrenia and major depression, as ‘mental health problems ’, diminishes the seriousness\nof mental illness, with implications for attitudes towards, and treatment of, those with mentalillness (Leighton 2006). It is also evident that there is considerable confusion for young peoplebetween the terms ‘mental health ’, ‘mental illness ’ and ‘learning disability ’ (Dogra et al. 2007;\nRose et al. 2007).', metadata={'source': 'Defining_mental_health_and_mental_illness.pdf', 'page': 5}),
Document(page_content='Late-VC Flush with capital, startups at this stage \nbegin to steadily ramp -up hiring, yet \nemployees still want equity Important to have standardized the \nESOP and the amount of equity granted \nto new hires at each level \nGrowth Company is aggressively pursuing \ngrowth and hiring; likely to have \nexhausted most of the ESOP Most of the ESOP is gone, but shares \nremaining are more valuable; use them \nto allow new hires to share in the upside Make employee equity allocations and set up an ESOP sometime \nbetween the pre -seed and early -VC stage', metadata={'source': 'Employee-Stock-Option-Plans-ESOP-Best-Practices-2.pdf', 'page': 10})]Reordered Docs (Tackling Lost in the Middle)
reordering = LongContextReorder()
reordered_docs = reordering.transform_documents(docs)
reordered_docs
#####OUTPUT###
[Document(page_content='ESOPS FOR THE LONG TERM Part IV', metadata={'source': 'Employee-Stock-Option-Plans-ESOP-Best-Practices-2.pdf', 'page': 34}),
Document(page_content='The defining difference between Silicon Valley companies and almost \nevery other industry in the U.S. is the virtually universal practice among \ntech companies of distributing meaningful equity (usually in the form of \nstock options) to ordinary employees . A Defining Characteristic of Startup Culture \nSteven Johnson, Technology Writer \n•Startups are a unique case . Unlike at larger corporations, employee \nownership is an essential element of startup communities and culture \n–As high -risk/high -reward enterprises, startups use options to align \nemployee compensation with the risk -prone mentality of the \nbusiness \n–Startups seeking to achieve a “big exit” use options to align all \nemployees to drive toward this desired outcome', metadata={'source': 'Employee-Stock-Option-Plans-ESOP-Best-Practices-2.pdf', 'page': 8}),
Document(page_content='What is an ESOP? \n•An Employee Stock Options Plan (ESOP) \n \n•An allocation of shares that will be granted to employees in the future \nin the form of stock options \n–How much equity should we set aside for employees? \n \n•A plan for how these options will be distributed: \n–How many shares will individual employees receive? \n–What terms will govern these grants? \n \n•The plan is as important as the allocation!', metadata={'source': 'Employee-Stock-Option-Plans-ESOP-Best-Practices-2.pdf', 'page': 3}),
Document(page_content='Legal Advice \nWhen structuring an ESOP, engage an experienced \nstartup lawyer with regional expertise to standardize the \nterms and language', metadata={'source': 'Employee-Stock-Option-Plans-ESOP-Best-Practices-2.pdf', 'page': 33}),
Document(page_content='Late-VC Flush with capital, startups at this stage \nbegin to steadily ramp -up hiring, yet \nemployees still want equity Important to have standardized the \nESOP and the amount of equity granted \nto new hires at each level \nGrowth Company is aggressively pursuing \ngrowth and hiring; likely to have \nexhausted most of the ESOP Most of the ESOP is gone, but shares \nremaining are more valuable; use them \nto allow new hires to share in the upside Make employee equity allocations and set up an ESOP sometime \nbetween the pre -seed and early -VC stage', metadata={'source': 'Employee-Stock-Option-Plans-ESOP-Best-Practices-2.pdf', 'page': 10}),
Document(page_content='Mental health literacy\nFinally, in this section, it is also worth considering how the mental health ‘literacy ’ of adults\nand children in the general population varies from that of professionals. In all phases of a recentresearch project, conceptual confusion was identi fied in the literature review and among ado-\nlescent participants (Leighton 2006, 2008). Focus group participants did not find the single\ncontinuum model suggested by the WHO (2000) helpful (Leighton 2006). Furthermore, in thefocus group feedback session, participants suggested that labelling serious mental illnesses suchas schizophrenia and major depression, as ‘mental health problems ’, diminishes the seriousness\nof mental illness, with implications for attitudes towards, and treatment of, those with mentalillness (Leighton 2006). It is also evident that there is considerable confusion for young peoplebetween the terms ‘mental health ’, ‘mental illness ’ and ‘learning disability ’ (Dogra et al. 2007;\nRose et al. 2007).', metadata={'source': 'Defining_mental_health_and_mental_illness.pdf', 'page': 5}),
Document(page_content='ing prevalence rates of diagnosable mental disorder among 11 –15-year-olds: depression 1.8 per\ncent; anxiety 4.6 per cent; conduct disorder 6.2 per cent (Meltzer et al. 2000). Moreover, the\nprevalence of serious mental illness increases greatly during adolescence (Davidson and Manion1996; Smith and Leon 2001; Rickwood et al. 2005). Such problems have a negative impact on an\nindividual ’s development across all areas of their lives – i.e. self-esteem, relationships, academic\nsuccess, career options and lifestyle (Mental Health Foundation 1999; Meltzer et al. 2000).\nFurthermore, the burden of adolescent mental health problems and illness involves enormousfinancial costs to individuals, families and society. These include loss of earnings for parents\nand adolescents, and social care, health service, education and Home Of fice costs (Appleton', metadata={'source': 'Defining_mental_health_and_mental_illness.pdf', 'page': 5}),
Document(page_content='provide a useful starting point from which to discuss mental health issues with children andtheir families.\nDefinitions of child mental health\nDefinitions of mental health as they relate speci fically to children have been provided by the\nHealth Advisory Service (HAS) (1995) and the Mental Health Foundation (1999). These def-initions bear similarities to those provided by Ryff and Singer (1998) and Rowling et al. (2002),\nwhile recognizing the developmental context of childhood – i.e. the ability to develop psycho-\nlogically, emotionally, creatively, intellectually and spiritually; initiate, develop and sustainmutually satisfying personal relationships; use and enjoy solitude; become aware of others andempathize with them; play and learn; develop a sense of right and wrong; and resolve problemsand setbacks and learn from them (HAS 1995; Mental Health Foundation 1999). Such de finitions\nare useful as they relate to ‘societal ’ expectations of children.', metadata={'source': 'Defining_mental_health_and_mental_illness.pdf', 'page': 3}),
Document(page_content='of the Jewish people by the Nazis (Link and Phelan 2001). Stigma was de fined by Goffman\n(1970) as the position of the individual who is disquali fied from full social acceptance. It is\nperceived as the outcome of a process of social labelling which singles out difference, names thisdifference inferiority, subsequently blames those who are different for their otherness andcontributes to the creation of a spoilt identity (Goffman 1970). Since that seminal develop-ment, the concept has evolved. For example, stigma can be described with reference to therelationships between a set of interrelated concepts, rather than focusing solely on personalattributes – i.e. stigma exists when elements of labelling, stereotyping, separation, status loss\nand discrimination occur together in a power situation that allows these processes to happen(Link and Phelan 2001).\nThe nature and extent of stigmatization in adult mental illness', metadata={'source': 'Defining_mental_health_and_mental_illness.pdf', 'page': 6}),
Document(page_content='See discussions, st ats, and author pr ofiles f or this public ation at : https://www .researchgate.ne t/public ation/255657987\nDefining mental health and mental illness\nArticle · Januar y 2009\nCITATIONS\n10READS\n151,087\n2 author s, including:\nNisha Dogr a\nUniv ersity of L eicester\n152 PUBLICA TIONS \xa0\xa0\xa02,974 CITATIONS \xa0\xa0\xa0\nSEE PROFILE\nAll c ontent f ollo wing this p age was uplo aded b y Nisha Dogr a on 20 May 2014.\nThe user has r equest ed enhanc ement of the do wnlo aded file.', metadata={'source': 'Defining_mental_health_and_mental_illness.pdf', 'page': 0})]Note : You can see a difference in the order of the context retrieved after applying LongContextReorder which reorders the retrieved context base on the relevance of the context to the query asked.
Setup Filtering and LongContext Retriever Pipeline
from re import search
filter = EmbeddingsRedundantFilter(embeddings=hf_bge_embeddings_small)
reordering = LongContextReorder()
pipeline = DocumentCompressorPipeline(transformers=[filter, reordering])
compression_retriever_reordered = ContextualCompressionRetriever(
base_compressor=pipeline, base_retriever=lotr,search_kwargs={"k": 5, "include_metadata": True}
)
#
docs = compression_retriever_reordered.get_relevant_documents("What is esops?")
print(len(docs))
#
print(docs[0].page_content)
#
print(compression_retriever_reordered.get_relevant_documents("What is the stigma associated with mental health?")[0].page_content)
####OUTPUT######
10
ESOPS FOR THE LONG TERM Part IV
Social Impact Considerations
•Companies focused on social impact goals and/or in developing
markets may have unique ESOP considerations
•Consider two possible scenarios:
–Financial Inclusion Goals : Employee -friendly hiring practices and
ESOPs can help build local financial inclusion. If this is part of your
corporate mandate, consider the added social impact value of
offering your options program to all levels of employees
–Local Ownership Culture : Conversely, in certain regions stock
options may have negligible value to employees, either because of
risk aversion, lack of liquidity, or lack of understanding. If this is the
case, it is not worth extensively offering options to employees who
would rather be paid in cashImplementing Generation Pipeline Using Langchain
Download the Model Parameter File
https://huggingface.co/TheBloke/zephyr-7B-beta-GGUF/blob/main/zephyr-7b-beta.Q4_K_M.gguf
Download the model file from the above link and save it to the current working directory.
Setup the LLM
from langchain.llms import LlamaCpp
llms = LlamaCpp(streaming=True,
model_path="zephyr-7b-beta.Q4_K_M.gguf",
max_tokens = 1500,
temperature=0.75,
top_p=1,
gpu_layers=0,
stream=True,
verbose=True,n_threads = int(os.cpu_count()/2),
n_ctx=4096)Setup the RetrievalQA chain
from langchain.chains import RetrievalQA
#
qa = RetrievalQA.from_chain_type(
llm=llms,
chain_type="stuff",
retriever = compression_retriever_reordered,
return_source_documents = True
)Querying
query ="What is esop?"
results = qa(query)
print(results['result']
#
print(results["source_documents"])
######OUTPUT############
' An Employee Stock Options Plan (ESOP) is a plan for allocating shares that will be granted to employees in the future, typically in the form of stock options. It covers the allocation, distribution, and terms of these grants. The ESOP is as important as the allocation!'
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_DocumentWithState(page_content='are useful as they relate to ‘societal ’ expectations of children.\nDifferent de finitions are used to de fine mental ill health. The WHO uses the term ‘mental\ndisorders ’ broadly, to include mental illness, intellectual disability, personality disorder, sub-\nstance dependence and adjustment to adverse life events (WHO 1992). The WHO acknowledgesthat the word ‘disorder ’ is used to avoid perceived greater dif ficulties associated with ‘illness ’ –\nfor example, stigma and the emphasis on a medical model. Meltzer et al. (2000) use the term\n‘mental disorders ’ in reference to emotional, conduct, hyperkinetic and less common disorders\nas de fined by the ICD (International Classi fication of Diseases) 10 and DSM ( Diagnostic and\nStatistical Manual of Mental Disorders ) IV. Jorm (2000) focuses speci fically on depression and\npsychosis. Meanwhile, Rowling et al. 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_DocumentWithState(page_content='You may now wish to re flect on the issues discussed in this chapter by returning to the\nexercise in Box 1.1.\nReferences\nAmerican Psychiatric Association (1994) Diagnostic and Statistical Manual of Mental Disorders , 4th edn.\nArlington, VA: American Psychiatric Association.\nAppleton, P.L. and Hammond-Rowley, S. (2000) Addressing the population burden of child and adolescent\nmental health problems: a primary care model, Child Psychology and Psychiatry Review , 5(1): 9 –16.\nArmstrong, C., Hill, M. and Secker, J. (1998) Listening to Children . London: The Mental Health Foundation.\nArmstrong, C., Hill, M. and Secker, J. (2000) Young people ’s perceptions of mental health, Children and\nSociety , 14: 60 –72.\nAsarnow, J.R., Jaycox, L.H. and Thompson, M.C. (2001) Depression in youth: psychosocial interventions,\nJournal of Clinical Child Psychology , 30(1): 33 –47.\nAtkins, M.S., Frazier, S.L., Adil, J.A. and Talbot, E. 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It is also evident that there is considerable confusion for young peoplebetween the terms ‘mental health ’, ‘mental illness ’ and ‘learning disability ’ (Dogra et al. 2007;\nRose et al. 2007).', metadata={'source': 'Defining_mental_health_and_mental_illness.pdf', 'page': 5}, state={'embedded_doc': [0.04499732330441475, 0.028216205537319183, -0.019932270050048828, -0.0037953502032905817, 0.005789243616163731, 0.06876437366008759, -0.0012708998983725905, 0.028347505256533623, 0.0022024516947567463, -0.05467447265982628, 0.010037371888756752, -0.033393386751413345, -0.0022406545467674732, -9.069243969861418e-05, -0.025916647166013718, 0.023093760013580322, 0.004339972510933876, -0.003952894825488329, -0.02159130945801735, 0.026114055886864662, 0.002267794916406274, 0.005971602629870176, 0.07199912518262863, 0.07089950144290924, 0.0022620807867497206, 0.0019176786299794912, 0.03326719254255295, -0.031181171536445618, -0.022527849301695824, -0.11425809562206268, 0.027391759678721428, 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_DocumentWithState(page_content='of the Jewish people by the Nazis (Link and Phelan 2001). Stigma was de fined by Goffman\n(1970) as the position of the individual who is disquali fied from full social acceptance. It is\nperceived as the outcome of a process of social labelling which singles out difference, names thisdifference inferiority, subsequently blames those who are different for their otherness andcontributes to the creation of a spoilt identity (Goffman 1970). Since that seminal develop-ment, the concept has evolved. For example, stigma can be described with reference to therelationships between a set of interrelated concepts, rather than focusing solely on personalattributes – i.e. stigma exists when elements of labelling, stereotyping, separation, status loss\nand discrimination occur together in a power situation that allows these processes to happen(Link and Phelan 2001).\nThe nature and extent of stigmatization in adult mental illness', metadata={'source': 'Defining_mental_health_and_mental_illness.pdf', 'page': 6}, state={'embedded_doc': [0.060363247990608215, 0.05009806901216507, -0.006028744392096996, 0.017937561497092247, -0.003828356508165598, 0.07358463108539581, 0.10312618315219879, 0.013181501999497414, 0.011381590738892555, -0.038329530507326126, 0.06499912589788437, 0.000817059597466141, -0.018513113260269165, -0.00952185783535242, 0.015914537012577057, 0.033861298114061356, -0.003702082671225071, 0.06687232851982117, -0.043620333075523376, -0.0022400165908038616, 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_DocumentWithState(page_content='See discussions, st ats, and author pr ofiles f or this public ation at : https://www .researchgate.ne t/public ation/255657987\nDefining mental health and mental illness\nArticle · Januar y 2009\nCITATIONS\n10READS\n151,087\n2 author s, including:\nNisha Dogr a\nUniv ersity of L eicester\n152 PUBLICA TIONS \xa0\xa0\xa02,974 CITATIONS \xa0\xa0\xa0\nSEE PROFILE\nAll c ontent f ollo wing this p age was uplo aded b y Nisha Dogr a on 20 May 2014.\nThe user has r equest ed enhanc ement of the do wnlo aded file.', metadata={'source': 'Defining_mental_health_and_mental_illness.pdf', 'page': 0}, state={'embedded_doc': [-0.049589693546295166, 0.022525304928421974, 0.0205765999853611, 0.0025703778956085443, 0.05409274622797966, 0.04565831646323204, 0.0017894548363983631, 0.024179428815841675, 0.007501582615077496, -0.014888513833284378, 0.04556962102651596, -0.0216493159532547, -0.018731189891695976, 0.018811620771884918, 0.0019220882095396519, 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results = qa("How to determine Dollar Value of the Options Grant")
print(results['result'])
results = qa("How to determine Dollar Value of the Options Grant")
print(results['result'])results = qa("How to determine Dollar Value of the Options Grant")
print(results['result'])
#
print(results["source_documents"])
#
for source in results["source_documents"]:
print(source.metadata)
########OUTPUT###############
To calculate the Options Grant, use this formula: (Number of Shares) = Dollar Value of Options Grant / Current Share Price. In other words, if the company is offering employees $100,000 in options on shares selling for $30 each, then the number of shares granted would be 3,333 (rounded).
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Calculate the Options Grant \nOptions Grant \n(Number of Shares) = Dollar Value \nof Options Grant \nCurrent Share Price', metadata={'source': 'Employee-Stock-Option-Plans-ESOP-Best-Practices-2.pdf', 'page': 22}, state={'embedded_doc': [-0.01464018039405346, -0.1141132339835167, 0.006303314119577408, -0.03636143356561661, -0.01919664442539215, 0.005365110468119383, 0.03989390656352043, -0.00011962500138906762, 0.05679503083229065, -0.0049852170050144196, 0.060355253517627716, -0.004163709469139576, 0.03092876635491848, -0.004292238503694534, -0.019483737647533417, 0.005383835174143314, -0.0797957107424736, -0.05881970375776291, -0.060195643454790115, 0.04698019102215767, 0.10709604620933533, -0.0930858850479126, -0.05114523693919182, -0.056381627917289734, 0.04826429486274719, -0.0004107253043912351, -0.030525818467140198, 0.0162102859467268, 0.005865844897925854, -0.17544972896575928, 0.020794963464140892, -0.0019692506175488234, -0.043823935091495514, -0.03906966745853424, 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It is suggested that a variety of normal human experiences', metadata={'source': 'Defining_mental_health_and_mental_illness.pdf', 'page': 4}, state={'embedded_doc': [0.012228049337863922, 0.028934478759765625, -0.012618748471140862, 0.007042165379971266, -0.033143285661935806, 0.011502165347337723, 0.058621518313884735, 0.0020430090371519327, 0.0074933175928890705, -0.053858641535043716, 0.010803543962538242, -0.013632216490805149, -0.04187605902552605, 0.03312387317419052, 0.004000699147582054, 0.02728584036231041, 0.06851916760206223, 0.022676926106214523, -0.06538774073123932, 0.05737036094069481, 0.06725869327783585, 0.029707422479987144, 0.030932627618312836, 0.047502417117357254, 0.018659839406609535, 0.018528077751398087, -0.007639292161911726, -0.0206154752522707, -0.00782899558544159, -0.17473413050174713, -0.004074951633810997, 0.022724037989974022, 0.02018793113529682, -0.005891723092645407, -0.02328137680888176, 0.008446723222732544, 0.03260330855846405, 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Unpublisheddoctoral assignment.16 Nursing in child and adolescent mental health', metadata={'source': 'Defining_mental_health_and_mental_illness.pdf', 'page': 10}, state={'embedded_doc': [0.007659497670829296, 0.005393608473241329, 0.03254187852144241, -0.003909757360816002, 0.03482643887400627, 0.019727446138858795, 0.03860922157764435, 0.055980902165174484, 0.005077574867755175, -0.03218856081366539, 0.004933442920446396, -0.01654347963631153, -0.00914869736880064, 0.03953824192285538, 0.03512023389339447, 0.01289572287350893, 0.0036411990877240896, 0.02922634221613407, 0.0030798539519309998, 0.009685569442808628, -0.04895889386534691, -0.027724342420697212, 0.02641425095498562, 0.03194775804877281, 0.03640347719192505, 0.06804949045181274, 0.026058293879032135, -0.010899883694946766, -0.042925603687763214, -0.12377943843603134, 0.030630778521299362, -0.002913889940828085, 0.06215805560350418, 0.007210560142993927, 0.008544433861970901, -0.007763893809169531, 0.030261723324656487, 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He complains of tummy ache each school morning and is increasinglyreluctant to attend.\nCase scenario 2', metadata={'source': 'Defining_mental_health_and_mental_illness.pdf', 'page': 2}, state={'embedded_doc': [-0.0019011890981346369, -0.0038127428852021694, 0.03988463804125786, -0.024577412754297256, -0.0019302432192489505, -0.008401371538639069, 0.07550620287656784, 0.010599920526146889, -0.009033357724547386, -0.0541226863861084, 0.019566630944609642, -0.003997118677943945, 0.008784210309386253, -0.0068548740819096565, 0.03862158581614494, 0.021464698016643524, -0.0048937126994132996, -0.04957917705178261, -0.03729602321982384, 0.031601496040821075, -0.04404556006193161, 0.03111041523516178, -0.018432479351758957, -0.017249032855033875, -0.017938120290637016, 0.04759329557418823, 0.007619176991283894, 0.034876514226198196, -0.05075616389513016, -0.08206454664468765, 0.05259240046143532, -0.03871350735425949, 4.18836425524205e-05, -0.015426931902766228, 0.003094775602221489, 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As mentioned in the Introduction, where possible we have referred speci fically to the\nliterature relating to children but where this is limited we have drawn from the wider literatureto highlight key issues.\nThe chapter begins with an exercise which provides a practical context for the theoretical\ncontent and should be borne in mind as you read, and answered once you have finished the\nchapter.\nBox 1.1\nExercise\nGeneral questions\n\x7f What words or images do you associate with the following terms:\n\x7f Mental health', metadata={'source': 'Defining_mental_health_and_mental_illness.pdf', 'page': 1}, state={'embedded_doc': [0.06480266153812408, -0.00954077485948801, -0.0044887373223900795, -0.004682324826717377, 0.015972455963492393, 0.04316655918955803, 0.04998887702822685, 0.01025178749114275, 0.00014676112914457917, -0.026718927547335625, 0.06144905835390091, -0.04188359156250954, -0.008747519925236702, 0.03649096563458443, 0.04833370819687843, 0.020218465477228165, 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{'source': 'Employee-Stock-Option-Plans-ESOP-Best-Practices-2.pdf', 'page': 22}
{'source': 'Employee-Stock-Option-Plans-ESOP-Best-Practices-2.pdf', 'page': 11}
{'source': 'Employee-Stock-Option-Plans-ESOP-Best-Practices-2.pdf', 'page': 12}
{'source': 'Employee-Stock-Option-Plans-ESOP-Best-Practices-2.pdf', 'page': 2}
{'source': 'Employee-Stock-Option-Plans-ESOP-Best-Practices-2.pdf', 'page': 39}
{'source': 'Defining_mental_health_and_mental_illness.pdf', 'page': 4}
{'source': 'Defining_mental_health_and_mental_illness.pdf', 'page': 10}
{'source': 'Defining_mental_health_and_mental_illness.pdf', 'page': 2}
{'source': 'Defining_mental_health_and_mental_illness.pdf', 'page': 0}
{'source': 'Defining_mental_health_and_mental_illness.pdf', 'page': 1}Conclusion
In conclusion, implementing better RAG systems is essential to avoid LIM and improve the retrieval process. Creating separate VectorStore and merging them using Merge Retriever, as well as re-ordering results through LongContextReorder, can help reduce the issues of LIM and improve the efficiency of the retrieval model. It is necessary to consider these techniques in order to prevent losing important information in the middle of retrieved documents.
