Local Language Model for a Research Institute: Search a Scientific Archive with Source References
Contents
Research institutes have vast archives: decades of research reports, unpublished results, laboratory notebooks, patent materials, and grant documentation. Finding what you need can take hours, and some knowledge leaves with departing employees. These materials cannot be uploaded to public AI services: they are the institute’s intellectual property and often contain restricted-access data.
A local language model with archive search addresses both challenges: it gives researchers a powerful tool while keeping data inside the institute.
How it works
The technology is called RAG, or retrieval-augmented generation:
- Upload. The institute’s documents are added to the system: PDFs, Word files, spreadsheets, and scans of old reports.
- Recognition. Text is recognized in scans, including tables and formulas as far as scan quality allows.
- Indexing. Documents are split into meaningful passages with metadata: subject, year, laboratory, authors, and access classification.
- Question. The researcher asks a question in plain language.
- Search. The system finds relevant passages based on access permissions.
- Answer. The language model generates an answer based on the retrieved material.
- References. The answer identifies specific documents, pages, and quotes.
The model is not trained on the institute’s documents. Knowledge is stored in a managed index that can be updated and from which materials can be removed.
Use cases for research institutes
| Scenario | Example question | Benefit |
|---|---|---|
| Searching internal reports | which experiments used this method, and what were the results | avoid repeating work already done |
| Literature review | a summary of the topic from the archive and publications, with sources | faster article preparation |
| Comparing methods | how conditions and results differed across studies | analysis without manually compiling information |
| Finding contradictions | where the results of different studies disagree | directions for new research |
| Preparing applications | the team’s previous work and results on the topic | faster grant applications |
| Knowledge transfer | the history of a research area for a new employee | faster onboarding |
Why deploy locally
- Intellectual property. Unpublished results and patent applications stay within the institute.
- Restricted-access data. It stays in a secure environment.
- Personal data. The law requires it to be stored in Kazakhstan.
- Control. The institute can see who is making requests and what they are requesting, and manage access.
- Independence. Operations do not depend on the availability or terms of a foreign service.
A local model can run on the institute’s servers, in a Kazakhstan provider’s private cloud, or on a fully isolated network.
Architecture
| Component | Purpose |
|---|---|
| Ingestion pipeline | validation, recognition, splitting, metadata, versions |
| Search index | semantic search across passages with access-based filters |
| Language model | generating an answer based on retrieved material |
| Recognition model | scans, tables, images |
| Access control | employee login, permissions by laboratory and subject |
| Journal | who requested what and when |
| Quality assessment | benchmark questions, accuracy and citation metrics |
Characteristics of scientific documents
- Formulas and tables. They require careful recognition and verification.
- Terminology. Specialized terms in Kazakh, Russian, and English.
- Versions. Drafts and final reports on the same subject.
- Access classification. Different levels of access within the institute.
- A large volume of old scans of varying quality.
Each of these characteristics is accounted for in the processing pipeline and checked during the pilot.
How to get started
- Audit. Identify the archives, their formats and owners, access restrictions, and infrastructure.
- Select an archive for the pilot. For example, 10 years of reports from one department.
- A benchmark set of questions from researchers.
- A pilot using 2-3 models, with access permissions and speed measurements.
- Evaluation using benchmark questions and user feedback.
- Expansion to other archives and departments.
Example: a pilot at an industry research institute
A hypothetical institute has an archive of research reports spanning 15 years: about ten thousand documents, some of them scans. Researchers spend hours finding out whether similar experiments have been conducted before.
| Step | Work performed |
|---|---|
| Archive selection | 10 years of reports from one laboratory |
| Preparation | scan recognition, tagging by subject and year, access permissions |
| Benchmark set | 70 questions from researchers with known answers |
| Model comparison | 3 models, with accuracy, references, and speed measured |
| Pilot | 10 researchers over several weeks |
| Decision | model selection, infrastructure sizing, expansion plan |
The pilot’s results are measured by numbers, not impressions: the share of correct answers, reference accuracy, and time saved on a typical search.
How to evaluate quality
- Accuracy: the answer matches what an expert considers correct.
- Grounding: every claim is supported by a retrieved passage.
- Completeness: the system finds all relevant documents, not just the first one.
- Security: restricted materials are not available to users without access.
- Speed: response time is acceptable for day-to-day work.
Frequently asked questions
Does the system support Kazakh?
Quality depends on the model. Kazakhstani and international models are compared using the institute’s documents before one is selected.
What about old scans?
They are added using text recognition. Quality depends on the condition of the scans, and poor-quality pages are flagged for manual review.
Can AI make mistakes?
It can. That is why every answer includes links to sources, which the researcher can check in seconds.
Do we need to buy servers?
Not necessarily at the start. The pilot can run on rented computing resources in a Kazakhstan data center, and equipment can be selected based on measurements.
Can external publication databases be connected?
Yes, if their licenses allow downloading and indexing. This is checked separately for each source.
Who owns the results and the index?
The institute does. The index, settings, and logs are stored within its own environment or with a provider in Kazakhstan under contract.
Can experimental data be connected, not just text?
Yes, tables and structured data are connected separately, with field descriptions so the system can use them.
