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Example of combining undatasio PDF parsing with Langchain and Qdrant.

By stay, Tech Enthusiast @Undatasio

Installing the Undatasio Python API library

# install undatasio
!pip install -U -q undatasio

Install the **python-dotenv module and load environment variables using the load_dotenv() function.**

If you are unsure which environment variables are required, you can check the file named dev.env for explanations of the environment variables.

!conda install -c conda-forge python-dotenv -y -q

Collecting package metadata (current_repodata.json): ...working... done
Solving environment: ...working... done

# All requested packages already installed.

import os
from dotenv import load_dotenv

load_dotenv('.env')

True

Use the Undatasio python SDK

To import an UnDataIO object, you need a token and an optional task name from the Undatasio platform.

from undatasio.undatasio import UnDatasIO

undatasio_obj = UnDatasIO(os.getenv("UNDATASIO_API_KEY"))

The get_result_to_langchain_document function of the Undatasio object returns a Langchain Document object. Parameters for this function can be gleaned from the data returned by the show_version function.

lc_document = undatasio_obj.get_result_to_langchain_document(
    type_info=['text'],
    file_name='1d8c9bc374114b6e901da.pdf',
    version='v26'
)
lc_document

Document(metadata={'source': '_v26_1d8c9bc374114b6e901da.pdf_[text]'}, page_content='2. Profit-takingxpullback has developed into a minor correction. While the10\\%\ncorrection Since mid-May is broadly in-line with the historical norms of most technical\n bull runs, the six-week market weakness has prompted increasing investor questions\nabout the strengthof thepolicy put, and concerns regarding a redux of the powerful but\nshort-livedRe0peningrallyinlate2022/early2023.Empirically,inthe23episodesinthe\npast 20 years where MSCl China rallied more than20\\%, the market almost in all cases\n(22 out of 23) experienced at least a5\\% pullback after entering a technical bull phase.\nThese corrections averaged12\\%by magnitude, and 32 days in duration, although their\nsubsequent return trajectory varied significantly post the initial retreat, with earnings\naccrual/upgrades typically taking over from valuation to drive further gains when these\nupswings transitioned into a fundamental-led cycle. See point 6 for detail.\nSource:FactSet,MSCl,GoldmanSachsGlobalInvestmentResearch\n3.All eyes are on the July policy meetings. July will be a hectic month for China\npolicy watchers: The Third Plenum of the Chinese Communist Party is scheduled for July\n15 to 18, followed by the economically-focused Politburo meeting in July, usually\nconvened end of the month. As detailed in their Third Plenum preview note, our\neconomists expect the reform focus to be on both containing left-tail risks and growing\nright-tail potential for China in the "post-property" era, with fiscal/tax and land reforms\npossibly taking the center stage. However, "big bazooka" policy surprises that some\ninvestors have hoped for since the onset of the housing market downturn remain\nunlikely. Indeed, both onshore and offshore investors we have met in the past month\nare somewhat disappointed with the policy intensity, efficacy, and momentum since the\nApril Politburo meeting, and their expectations on additional stimulus and major\nreform breakthroughs from the July policy meetings are conservative across the\nbroad. A few clients even expressed concerns about downside policy risks relatingjtol :$\nconsumption tax hikes and other contractionary fiscal policy (e.g. tax investigation for\ncorporates).\n5. A tepid recovery in sentiment and flows. "What could turn things around?" was\nheavilyfeaturedinmanyofourclientconversations,reflectingcontinuedreluctance\nfrom fundamental investors to underwrite Chinese growth beyond the short term, in our\nview.However,the+30\\%rally(\\mathsf{U S S1.5t n}increaseinmarketcap)inthesecondlargest\nequitymarketgloballyhasmarginallyhelpedreviveinvestorinterestinChinese\nequity,evidencedbytherisingturnoverinandthestrongSouthboundflowsintothe\nHong Kong market, and the reopening of the capital market for Chinese corporates in\n2Q, led by CB issuances. But, fund positioning and portfolio flows haven\'t fully\ncaughtupwiththeimprovedsentiment:mutualfund andhedgefundexposuresto\nChina equity are still at the low-end of the historical ranges, onshore mutual funds\' cash\nratios are atrecent-year highs, and local retail flows(proxied by mutual fund subscription\nand margin financing balance in A shares) have been stagnated for several months.\nBesides Southbound buying, which has amounted to US$48bn ytd compared to\nonshore ETF trading and inflows in the past week, suggesting to us that the\ncommitment and readiness for government intervention is still strong when SHCOMP is\nnear or below the 3,0o0 psychological threshold.\n6.Earningshavebeendelivered,veryselectively.MSCIChinareceiveditsfirst\nmonthly consensus earnings upgrade in May in the past 12 months, offering investors a\nglimmer of hope that the protracted earnings downcycle is near the tail end. We hold a\nneutral view on earnings and reiterate our\\scriptstyle{\\mathfrak{s}}\\%EPS growth assumption for MScl\nChinain2024,considering: 1)sell-side consensus forecast of12\\%and14\\%EPS\ngrowth for 2024 and 2025 still looks overly optimistic, even taking technical factors\n(dilution,indexrebalancing)andsectorrepresentationissuesintoaccount; 2)the\nupgrades have only manifested in the TMT and service-oriented sectors but the top-line \nand profitability headwinds are still in place for the property and manufacturingcohorts;\n3) high-frequency profit indicators for 2Q suggest that the earnings momentum for the\nbroader market may have stabilized, but seems prematurejtocall for an inflection point.\nEncouragingly, from our extensive discussions with China-focused analysts and PMs,\nbuy-side earnings growth expectations seem to be around mid-single-digit territory,\nimplying that the potential earnings misses to sell-side expectations may not be as\ninfluential to equityprices as the headline numbers would suggest.\nSource: FactSet, MSCl, CSI, IBES, Goldman Sachs Global Investment Research\n7. Left tail risks have been curtailed in equity valuations. The latest correction has\nlowered MSCl China fP/E from10.6\\timesat recent peaks to9.2\\timesat present, and from12.2\\times\nto11.4\\timesfor A shares, but still meaningfully above the lows of7.9\\timesand9.6\\timesin late\nJanuary respectively. The policy put—spanning conventional macro policy, the housing\nmarket,andthecapitalmarkets—hashelpedcontainedsystemicrisksstemmingfrom\nLGFVs and property-related assets, thereby catalyzing the tail-risk-compression rally in\nequities from April to May, and providing a floor to valuation. Our macro-derived\n10.5\\times\nmacro outlooks per GS forecasts, suggesting moderate potential re-rating:gains from\nhere. On our central premise that the strike price of the policy putisunlikely to be raised\nand further policy delivery on housing will be made, we see the risk to valuation\n更多\nfavorablyskewedtotheupside\n70\nSource:GoldmanSachs Global Investment Research\n8. Tariff risks may keep some investors at bay before the US election. The\nforthcoming US presidential election and its implications on China are top-of-mind. The\n10. The "Returns" of "Shareholders" in China. Despite its outperformance in past few\nyears, we believe the theme of shareholder returns will continue to produce strong and\nstable returns for equity investors for five reasons: a) strong policy push and delivery to\nimprove shareholder returns; b) Additional fiscal revenue for financially-strained\ngovernments: every 1pp rise in payout ratios could raise fiscal revenue by Rmb23bn; c)\nChina\'s ability to return cash to shareholders looks promising——low payout ratios (30\\%+)\nbut with record-high cash balances (Rmb18tn) and free cashflows (FY23: Rmb2.6tn); d)\nstable dividend incomes are attractively priced in China where rates are falling; and, e)\nlikelystructural demand forstableincomesfrom domesticinstitutionalinvestors.We\nconstruct a China Shareholder Returns Portfolio, which consists of 3 broad equity\ncohorts-StableCashCows,Dividend/buybacksurprisecandidates,andSelect\nexpected togenerate3\\%shareholder return yields and5\\%FCF yields in 2024 on16\\%\ncash/marketcapratioatmedianstock.\nWe, Kinger Lau, CFA, Timothy Moe, CFA, Si Fu, Ph.D. and Kevin Wang, CFA, hereby certify that all of the views expressed in this report accurately\nreflect our personal views, which have not been influenced by considerations of the firm\'s business or client relationships.\nUnless otherwise stated, the individuals listed on the cover page of this report are analysts in Goldman Sachs\' Global Investment Research division.\nSee company-specific regulatory disclosures above for any of the following disclosures required as to companies referred to in this report: manager or\nco-manager inapendingtransaction;\\uparrow\\%or other ownership; compensation for certain services; types of client relationships; managed/co-managed\npublic offeringsinpriorperiods; directorships;for equitysecurities, market making and/orspecialistrole.GoldmanSachs trades or maytrade as a\nprincipal indebtsecurities(or inrelatedderivatives)ofissuersdiscussed in thisreport.\nThe following are additional required disclosures: Ownership and material conflicts of interest: Goldman Sachs policy prohibits its analysts,\nprofessionals reporting to analysts and members of their households from owning securities of any company in the analyst\'s area of coverage.\nAnalyst compensation: Analysts are paid in part based on the profitability of Goldman Sachs, which includes investment banking revenues. 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Install the required third-party libraries.

!pip install -U -q langchain-qdrant

Import the necessary classes and functions for the example.

from qdrant_client.http.models import Distance, VectorParams
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_qdrant import QdrantVectorStore
from langchain_openai import OpenAIEmbeddings
from qdrant_client import QdrantClient

Create a model object for BAAI/bge-m3 from Hugging Face.

model_name = "BAAI/bge-m3"
model_kwargs = {'device': 'cpu'}
encode_kwargs = {'normalize_embeddings': False}
embedding = HuggingFaceEmbeddings(
    model_name=model_name,
    model_kwargs=model_kwargs,
    encode_kwargs=encode_kwargs
)

Steps for using Qdrant:

• Create an in-memory Qdrant client object.

• Create a Qdrant client collection.

• Generate a vector store object based on the Qdrant client.

client = QdrantClient(":memory:")

client.create_collection(
    collection_name="collection",
    vectors_config=VectorParams(size=1024, distance=Distance.COSINE)
)

vector_store = QdrantVectorStore(
    client=client,
    collection_name="collection",
    embedding=embedding,
)

Splitting a LangChain Document object using the RecursiveCharacterTextSplitter class.

from langchain.text_splitter import RecursiveCharacterTextSplitter

text_splitter = RecursiveCharacterTextSplitter(chunk_size=200, chunk_overlap=20)
docs = text_splitter.split_documents([lc_document])

Add the split text chunks to the vector store.

from uuid import uuid4

ids = [str(uuid4()) for _ in range(len(docs))]
vector_store.add_documents(documents=docs, ids=ids)

['ca1cf787-e046-497d-a33c-96b8422ba58f',
 'f4648dac-22ce-484c-9782-3a167d3a782d',
 '96e6ba35-001e-4078-9775-fc0e0f307c81',
 'e4b75bd3-3536-4d90-a4e7-f4eb36573cbb',
 'c30f0413-995f-4edd-bb0b-0f00dcc844f7',
 'eecb42a2-4df7-41a3-a7d8-549556e36d97',
 'f9d08e04-1d1c-4d0f-b1be-218b223572d2',
 '6d1c9815-f988-4bb7-95a6-f75052e2ddc3',
 'feebd3c1-cb7b-48f1-add4-8b3b8a1cadda',
 '5c77338f-4e24-4c0a-b8dd-f77101babc42',
 '061d5ced-d155-4b23-ad23-d67675f04cba',
 '8feb625e-431f-4789-9579-141cd081951c',
 '1d92f5f4-6ae6-4933-b771-8d7dcee7b07c',
 '0eededae-ffb9-4bef-98ec-ff79aceb8ad7',
 '809731f0-7b68-4825-b76f-087b3ccdbf69',
 '48edd14e-6d89-49c4-9679-52f0c138dba7',
 '1456a054-ec8a-4650-b5fe-1bd038bdef1c',
 '9facc180-a201-41ac-84f0-e115926188c0',
 '22e9f280-9bd7-40dc-bcf7-038852c7a2a4',
 '0b42bd36-60e0-4b0b-97e8-9e946652034a',
 '1f31c928-7dcb-4bb1-9c3c-625d2842f127',
 'a94947f0-81e5-430e-be89-d7ea5a224fcc',
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 '1222798d-99bf-4083-8eb6-bb7ce03e8f66',
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 'cad6be5f-3c92-4bba-9d3c-8cd23660a7d9',
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 'ca3cf114-535d-432a-bdbc-231c73df31bb',
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 'f9fb8631-6026-4f57-a02f-b09ace925886',
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 '5dafb85f-cce0-4f8b-9393-cefccaa32b64',
 'da9596f3-13ea-42d9-a23c-3fe23c0be197',
 '69f619b1-6470-4180-a31d-421b49664cbb',
 'ebc1a74e-56e2-4e78-aac7-b32e2efc4674',
 '137edac0-c086-4a52-9d44-87b4a59a37e0',
 '9e9a4a3b-c968-4235-a6d9-92cd50c12de3',
 '253e59cf-e79f-49c7-814c-c0fd507d583a',
 'd40d527f-609f-449f-9ea2-240de40d4559',
 '2a3fc09d-9d3f-4533-9903-85c3d8a5284b',
 '0ab4de4c-fbcd-4849-a431-a29214873be6',
 '1bfbf6e9-6598-416f-ad3a-e285a73d6d59',
 'd37620f4-b357-47f0-97d0-8710708800db',
 '586f1377-9aeb-4bcb-af72-41b2eb0df93f',
 'c45a0ba3-4e39-42fa-b043-0888647ef15c',
 'f4834458-0e59-4d66-8349-297e410631e4',
 '6d7cc195-77d9-4b07-a5fb-3bd194fe5302',
 '00e37182-a15d-479a-aec3-5529e94b98d8',
 '5a4708db-1867-4fab-8090-f83643298567',
 '9d58ac7a-cef9-4c7b-b43d-6ba1f026339b',
 '863aa524-e8d5-4d4e-a21f-fda7ce82383d',
 '1eecd29c-09bc-40c2-9b09-908b44e3d0c3',
 '41368122-7684-461f-a16b-86a8694db6fc',
 'cd4ccb19-aa5f-46a8-9bf7-61311bb0b077',
 '75c02f44-c598-455c-98c7-aaf40e400ec6',
 '6944410c-767d-46af-83c6-77f69fcd52fd',
 'b50412e3-81ac-43f2-ae30-883ecd04b69f',
 'f683f2f2-f9eb-44f3-958c-fedbe190f7c7',
 'ca02ee03-de1a-479c-a355-120b4ce97875',
 'f2a2a285-8e42-4917-8253-18de70e758b6',
 'b5085910-b3f6-498d-ab66-282b7753d1b6',
 '0bc936c9-d383-4863-a4ef-1138469fe634',
 '38c9a344-20ef-4c6d-b764-92c3263afaaf',
 '81175e68-32a1-4ae7-9f24-4ddcb64250ec',
 '66f4199e-6819-4c9b-8147-78e254827315']

Query the vector store.

query="All eyes are on the July policy meetings."

results = vector_store.similarity_search(query=query, k=1)
for doc in results:
    print(f"* {doc.page_content} [{doc.metadata}]")

* 3.All eyes are on the July policy meetings. July will be a hectic month for China
policy watchers: The Third Plenum of the Chinese Communist Party is scheduled for July [{'source': '_v26_1d8c9bc374114b6e901da.pdf_[text]', '_id': 'f9d08e04-1d1c-4d0f-b1be-218b223572d2', '_collection_name': 'collection'}]