# deeplake - Doramagic AI Context Pack > Positioning: a pre-install experience and judgment asset. It helps the host AI get off to a good start, but it does not mean the project has already been installed, run, or validated. ## Sufficiency Principle - **Sufficiency over compression**: The AI Context Pack should be sufficient for the host AI to understand the project's value, capability boundaries, entrypoints, risks, and evidence sources before starting work; it may be layered, but it does not aim for the shortest possible summary. - **Compression policy**: Compress only noise and duplication, never context that affects judgment or the quality of the work. ## How the Host AI Should Use This You are reading the AI Context Pack that Doramagic compiled for deeplake. Treat it as pre-work context: help the user understand who it fits, what it can do, how to start, what must be verified after install, and where the risks are. Do not claim that you have already installed, run, or executed the target project. ## Claim Consumption Rules - **Fact source**: Repo Evidence + Claim/Evidence Graph; the Human Wiki only supplies salience, terminology, and narrative structure. - **Minimum status for a fact**: `supported` - `supported`: May be used as a project fact, but the answer must cite the claim_id and evidence path. - `weak`: Usable only as a low-confidence lead; the user must be asked to keep verifying. - `inferred`: Usable only for risk notes or open questions; must not be packaged as a project fact. - `unverified`: Must not be used as fact; state clearly that evidence is insufficient. - `contradicted`: Must show the conflicting sources and must not force a single version on the user's behalf. ## Who It Fits Best - **AI researchers or builders of research-oriented Agents**: The README clearly centers on research, experiment, or paper workflows. Evidence: `README.md` Claim: `clm_0002` supported 0.86 ## What It Can Do - **Command-Line Startup or Install Flow** (Verify after install): The project documentation contains runnable commands; real use requires running them in a local or host environment. Evidence: `README.md` Claim: `clm_0001` supported 0.86 ## How to Start - `pip install deeplake` Evidence: `README.md` Claim: `clm_0003` supported 0.86 ## Continue-or-Stop Decision Card - **Current recommendation**: Trial role matching first - **Why**: This project is more of a role library; the core risk is picking the wrong role or treating role copy as execution capability. Trial role matching with Prompt Preview first, then decide whether to sandbox-import it. ### 30-Second Read - **What to do now**: Trial role matching first - **Minimum safe next step**: Trial role matching with Prompt Preview first; import in isolation only once satisfied - **Do not trust yet**: Role quality and task fit cannot be trusted directly. - **Continuing will touch**: Role selection bias, Command execution, Local environment or project files ### What You Can Trust Now - **Target-audience signal: AI researchers or builders of research-oriented Agents** (supported): Backed by a supported claim or project evidence, but that still is not the same as real install results. Evidence: `README.md` Claim: `clm_0002` supported 0.86 - **Capability exists: Command-Line Startup or Install Flow** (supported): You can trust that the project contains signals of this capability; whether it fits your specific task still needs trial or after-install verification. Evidence: `README.md` Claim: `clm_0001` supported 0.86 - **There are Quick Start / install-command signals** (supported): You can trust that the docs mention a startup or install entrypoint; do not run it directly in your primary environment because of that. Evidence: `README.md` Claim: `clm_0003` supported 0.86 ### What You Cannot Trust Yet - **Role quality and task fit cannot be trusted directly.** (unverified): A role library proves there are many roles; it does not prove each one fits your specific task or that a role produces high-quality results. - **Do not treat role copy as real execution capability.** (unverified): Before install you can only judge whether the role description and task profile match; you cannot prove it can complete the task inside the host AI. - **Real output quality cannot be trusted before install.** (unverified): Prompt Preview can only show how it guides you; it cannot prove result quality in the real project. - **Host AI version compatibility cannot be trusted before install.** (unverified): Host loading rules and version differences across Claude, Cursor, Codex, Gemini, and others must be verified in a real environment. - **That it will not pollute your existing host AI's behavior cannot be trusted directly.** (inferred): Skill, plugin, and AGENTS/CLAUDE/GEMINI instructions may change the host AI's default behavior. - **Safe rollback cannot be assumed by default.** (unverified): Unless the project clearly provides uninstall and recovery instructions, verify in an isolated environment first. - **After a real install, is it compatible with the user's current host AI version?** (unverified): Compatibility can only be verified in the actual host environment. - **Does the project's output quality meet the user's specific task?** (unverified): The pre-install preview can only show flow and boundaries; it cannot replace real evaluation. ### What Continuing Will Touch - **Role selection bias**: The user's judgment about which expert role should handle the task. Why: Picking the wrong role makes the AI answer from the wrong expert perspective, wasting time or misleading decisions. - **Command execution**: Package managers, network downloads, the local plugin directory, project config, or the user's home directory. Why: Running the very first command can already change your environment; decide whether it is worth running first. Evidence: `README.md` - **Local environment or project files**: Install results, plugin caches, project config, or local dependency directories. Why: The write scope and rollback path cannot be proven before install and need isolated verification. Evidence: `README.md` - **Host AI context**: The AI Context Pack, Prompt Preview, Skill routing, risk rules, and project facts. Why: Importing context affects the host AI's later judgment, so avoid packaging unverified items as facts. ### Minimum Safe Next Steps - **Run Prompt Preview first**: Use an interactive trial to verify the task profile and role match first; do not import the whole role library up front. (applies when: Applies to any project, especially when output quality is unknown.) - **Trial-install only in an isolated directory or a test account**: Avoid letting install commands pollute your primary host AI, real projects, or home directory. (applies when: When there are signals of command execution, plugin config, or local writes.) - **After install, verify just one minimal task**: Verify loading, compatibility, output quality, and rollback first, then decide whether to use it deeply. (applies when: When moving from a trial into a real workflow.) ### Exit Plan - **Preserve the pre-install state**: Record the original host config and project state so you can later judge whether it is recoverable. - **Keep a record of the original role selection**: If output goes off-topic, you can return to the task-profiling stage and reselect a role instead of pushing on with the wrong one. - **Record the install commands and written paths**: Without clear uninstall instructions, you at least need to know which directories or configs to clean up manually. - **If there is no rollback path, do not enter your primary environment**: No rollback is a blocker before continuing; do not proceed on trust or luck. ## What Can Only Be Previewed - Explain who the project fits and what it can do - Demonstrate a typical conversation flow based on project docs - Help the user decide whether it is worth installing or researching further ## What Must Be Verified After Install - Actually installing the Skill, plugin, or CLI - Running scripts, modifying local files, or accessing external services - Verifying real output quality, performance, and compatibility ## Boundary & Risk Decision Card - **Mistaking the pre-install preview for a real run**: The user may overestimate how much configuration, permission, and compatibility verification the project has already done. Mitigation: Clearly separate prompt_preview_can_do from runtime_required. Claim: `clm_0004` inferred 0.45 - **Command execution will modify the local environment**: Install commands may write to the user's home directory, the host plugin directory, or project configuration. Mitigation: Run in an isolated environment or a test account first. Evidence: `README.md` Claim: `clm_0005` supported 0.86 - **To confirm**: After a real install, is it compatible with the user's current host AI version?. Why: Compatibility can only be verified in the actual host environment. - **To confirm**: Does the project's output quality meet the user's specific task?. Why: The pre-install preview can only show flow and boundaries; it cannot replace real evaluation. - **To confirm**: Do the install commands require network access, permissions, or global writes?. Why: This affects install risk in both enterprise and personal environments. ## Pre-Work Working Context ### Loading Order - First read how_to_use.host_ai_instruction to establish the boundaries of this pre-install judgment asset. - Read claim_graph_summary to confirm facts come from the Claim/Evidence Graph, not the Human Wiki narrative. - Then read intended_users, capabilities, and quick_start_candidates to judge whether the user is a match. - When you need to carry out a concrete task, check role_skill_index first, then evidence_index. - For real install, file modification, network access, performance, or compatibility questions, turn to risk_card and boundaries.runtime_required. ### Task Routes - **Command-Line Startup or Install Flow**: State that this is an after-install capability first, then give a pre-install checklist. Boundary: Must be verified after a real install or run. Evidence: `README.md` Claim: `clm_0001` supported 0.86 ### Context Scale - Total files: 870 - Important-file coverage: 40/870 - Evidence index entries: 76 - Role / Skill entries: 48 ### Handling Insufficient Evidence - **missing_evidence**: State that evidence is insufficient and ask the user for the target file, a README section, or after-install verification records; do not fill in facts. - **out_of_scope_request**: State that the task is beyond the current AI Context Pack's evidence scope and suggest the user check the Human Manual or verify after a real install. - **runtime_request**: Provide a pre-install checklist and command sources, but do not run commands for the user or claim they have been run. - **source_conflict**: Show the conflicting sources side by side, mark them as unverified, and do not force a single version. ## Prompt Recipes ### Fit assessment - Goal: Judge whether this project fits the user's current task. - Expected output: A fit conclusion, key reasons, evidence citations, what can be previewed before install, what must be verified after install, and a next-step recommendation. ```text Based on the AI Context Pack for deeplake, ask me 3 necessary questions first, then judge whether it fits my task. The answer must cover: who it fits, what it can do, what it cannot do, whether it is worth installing, and where the evidence comes from. Every project fact must cite evidence_refs, source_paths, or a claim_id. ``` ### Pre-install experience - Goal: Let the user feel the core workflow before installing, while avoiding packaging the preview as real capability or a marketing promise. - Expected output: An experience script with boundary labels, an after-install verification checklist, and a cautious recommendation; with no real-run promises or strong marketing language. ```text Treat deeplake as a pre-install experience asset, not an already-installed tool or a real runtime environment. Output exactly four parts: 1. Ask me 3 necessary questions first. 2. Give an "experience script": use the three labels [Previewable before install], [Must verify after install], and [Insufficient evidence] to show how it might guide the workflow. 3. Give an after-install verification checklist: list which capabilities can only be confirmed after a real install, real host loading, and a real project run. 4. Give a cautious recommendation: only "worth researching/trialing further", "add information before deciding", or "not recommended to continue"; do not endorse the project. Hard boundaries: - Do not claim you have installed, run, executed tests, modified files, or produced real results. - Do not write promise-like phrasing such as "auto-adapts", "guarantees passing", "perfect fit", or "strongly recommend installing". - If you describe how it works after install, you must use a conditional such as "if installed successfully and the host loads the Skill correctly, it might...". - The experience script may only be written as "example lines / hypothetical flow": use "might ask / might suggest / might show", not "has written, has generated, has passed, is running, is generating". - Prompt Preview does not hand out install commands; if the user is ready to trial, only prompt them to read Quick Start and the Risk Card first and to verify in an isolated environment. - Every project fact must come from a supported claim, evidence_refs, or source_paths; inferred/unverified items can only be risks or open questions. ``` ### Role / Skill selection - Goal: Pick the best-matching asset from the project's roles or Skills. - Expected output: A list of candidate roles or Skills, each with an applicable scenario, evidence paths, risk boundary, and whether after-install verification is needed. ```text Read role_skill_index and recommend 3-5 of the most relevant roles or Skills for my target task. For each recommendation, state the applicable scenario, likely output, risk boundary, and evidence_refs. ``` ### Risk pre-check - Goal: Identify environment, permission, rule-conflict, and quality risks before installing or adopting. - Expected output: A checklist of environment, permission, dependency, license, host-conflict, quality risk, and unknown items. ```text Based on risk_card, boundaries, and quick_start_candidates, give me a pre-install risk pre-check list. Do not run commands for me; only explain what I should check, why, and what impact a failure would have. ``` ### Host AI kickoff instruction - Goal: Turn the project context into a host AI instruction for the start of a conversation. - Expected output: A pre-work instruction with clear boundaries and clear evidence citations, suitable to copy to a host AI. ```text Based on the AI Context Pack for deeplake, generate a pre-work instruction I can paste to my host AI. This instruction must obey not_runtime=true and must not claim the project has been installed, run, or produced real results. ``` ## Role / Skill Index - Indexed 48 role / Skill / project-doc entries. - **Deep Lake Documentation** (project_doc): This directory contains the documentation for the Deep Lake project. The documentation is written in markdown and built using . Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `docs/README.md` - **Readme** (project_doc): Developer Documentation dev-docs.md Supported SQL Queries syntax-support.md Known Limitations & Missing Features known-limitations.md Basic Usage basic-usage.md Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `cpp/3rd_party/sql-parser/docs/README.md` - **What is Deep Lake?** (project_doc): Docs • Get Started • API Reference • LangChain & VectorDBs Course • Blog • Whitepaper • Slack • Twitter Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `README.md` - **pg deeplake** (project_doc): PostgreSQL extension for vector similarity search, full-text search, and hybrid search using DeepLake. Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `postgres/README.md` - **OPENJPEG Library and Applications** (project_doc): OpenJPEG is an open-source JPEG 2000 codec written in C language. It has been developed in order to promote the use of JPEG 2000 http://www.jpeg.org/jpeg2000 , a still-image compression standard from the Joint Photographic Experts Group JPEG http://www.jpeg.org . Since April 2015, it is officially recognized by ISO/IEC and ITU-T as a JPEG 2000 Reference Software http://www.itu.int/rec/T-REC-T.804-201504-I!Amd2 . Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `cpp/3rd_party/openjpeg/README.md` - **Usage** (project_doc): C++ SQL Parser ========================= ! Build Status https://app.travis-ci.com/hyrise/sql-parser.svg?branch=master https://app.travis-ci.com/github/hyrise/sql-parser Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `cpp/3rd_party/sql-parser/README.md` - **Benchmark** (project_doc): This directory contains the scripts to execute benchmarks of the parser. We use Google Benchmark https://github.com/google/benchmark to define and run benchmarks. Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `cpp/3rd_party/sql-parser/benchmark/README.md` - **VCPKG custom ports** (project_doc): This document is added to describe the issues with the official ports and why these are needed. It might also be possible that an offical port was not available at the moment we started using it. Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `cpp/vcpkg-ports/README.md` - **API Reference** (project_doc): Complete API Reference For Deep Lake, Including Dataset Creation, Multi-Modal Query Engine, Vector Search, And ML Framework Integrations. Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `docs/docs/api/index.md` - **Storage and Credentials** (project_doc): Comprehensive Guide To Configuring Storage And Credentials In Deep Lake 4.0, Including Cloud, Local, And Hybrid Storage Options For Efficient AI Model Data Handling. Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `docs/docs/getting-started/storage-and-creds/index.md` - **Setting up Deep Lake in Your Cloud** (project_doc): Set Up Managed Credentials To Enable Secure Access To Cloud Storage Resources In Deep Lake, Essential For Managing Large-Scale AI And Machine Learning Data. Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `docs/docs/getting-started/storage-and-creds/managed-credentials/index.md` - **🌊 Deep Lake: Multi-Modal AI Database** (project_doc): Access Deep Lake Documentation For Complete Setup, API Reference, Guides On Efficient Multi-Modal AI Search, Dataset Management, Cost-Efficient Training, And Retrieval-Augmented Generation. Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `docs/docs/index.md` - **Contributing Standards** (project_doc): We use the black https://pypi.org/project/black/ python linter. You can have your code auto-formatted by running pip install black , then black . inside the directory you want to format. Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `CONTRIBUTING.md` - **Changelog** (project_doc): v2.4.0 https://github.com/uclouvain/openjpeg/releases/v2.4.0 2020-12-28 Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `cpp/3rd_party/openjpeg/CHANGELOG.md` - **Example Code** (project_doc): Using the Library ======================= Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `cpp/3rd_party/sql-parser/docs/basic-usage.md` - **Basic Requirements** (project_doc): Developer Documentation ======================= Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `cpp/3rd_party/sql-parser/docs/dev-docs.md` - **Completely Missing Statement Types** (project_doc): Known Limitations & Missing Features ==================================== Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `cpp/3rd_party/sql-parser/docs/known-limitations.md` - **Select Statements** (project_doc): Supported SQL Queries ===================== Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `cpp/3rd_party/sql-parser/docs/syntax-support.md` - **Best Practices for Deep Lake Usage** (project_doc): Learn how to tune your Deep Lake usage for optimal performance and cost Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `docs/docs/advanced/best-practices.md` - **Dataset Copying and Synchronization** (project_doc): Learn how to copy and synchronize Deep Lake datasets across different storage locations and cloud providers Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `docs/docs/advanced/sync.md` - **TQL Syntax** (project_doc): Up to 10x More Efficient Data Retrieval with TQL, Deep Lake's Custom Query Language Query Datasets, Join Cross-Cloud Datasets, Manipulate Multi-Modal Datasets. Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `docs/docs/advanced/tql.md` - **Dataset Visualization** (project_doc): Learn how to visualize Deep Lake datasets using the Deep Lake web interface. Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `docs/docs/advanced/visualize.md` - **Column Classes** (project_doc): Access Deep Lake Documentation For Complete Setup, API Reference, Guides On Efficient Multi-Modal AI Search, Dataset Management, Cost-Efficient Training, And Retrieval-Augmented Generation. Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `docs/docs/api/column.md` - **Dataset Classes** (project_doc): Access Deep Lake Documentation For Complete Setup, API Reference, Guides On Efficient Multi-Modal AI Search, Dataset Management, Cost-Efficient Training, And Retrieval-Augmented Generation. Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `docs/docs/api/dataset.md` - **Metadata** (project_doc): Access Deep Lake Documentation For Metadata. Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `docs/docs/api/metadata.md` - **Query** (project_doc): Deep Lake Query Engine Documentation and Examples. Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `docs/docs/api/query.md` - **Schemas** (project_doc): Deeplake builtin schemas to create dataset. Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `docs/docs/api/schemas.md` - **Types** (project_doc): Definition and Examples of types used in Deeplake Datasets and Columns Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `docs/docs/api/types.md` - **Version Control** (project_doc): Access Deep Lake Documentation For Version Control. Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `docs/docs/api/version_control.md` - **User Authentication** (project_doc): Set Up Authentication Protocols For Secure Access To Deep Lake Datasets, Ensuring Data Integrity And Privacy In Collaborative Machine Learning Environments. Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `docs/docs/getting-started/authentication.md` - **Quickstart Guide** (project_doc): Access Deep Lake Documentation For Complete Setup, API Reference, Guides On Efficient Multi-Modal AI Search, Dataset Management, Cost-Efficient Training, And Retrieval-Augmented Generation. Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `docs/docs/getting-started/quickstart.md` - **Enabling CORS in S3** (project_doc): Follow This Guide To Configure CORS For AWS With Deep Lake 4.0, Ensuring Secure Cross-Origin Resource Sharing And Compliance For Machine Learning Training Data. Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `docs/docs/getting-started/storage-and-creds/managed-credentials/aws/cors.md` - **Provisioning Role-Based Access** (project_doc): Provision AWS Resources To Store And Access Deep Lake Datasets, Supporting Scalable Machine Learning Training With Optimized Cloud Storage Setup. Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `docs/docs/getting-started/storage-and-creds/managed-credentials/aws/provisioning.md` - **Enabling CORS in Azure** (project_doc): Set Up Azure CORS With Deep Lake To Enable Secure Cross-Origin Requests, Providing Efficient Access To Training Data For Machine Learning Projects. Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `docs/docs/getting-started/storage-and-creds/managed-credentials/azure/cors.md` - **Provisioning Federated Credentials** (project_doc): Step-By-Step Guide For Provisioning Azure Storage With Deep Lake, Ensuring Secure And Scalable Data Access To Support Machine Learning Workflows. Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `docs/docs/getting-started/storage-and-creds/managed-credentials/azure/provisioning.md` - **Azure Workload Identities** (project_doc): Manage Azure Workload Identities For Role-Based Access To Deep Lake, Providing Secure Data Access And Ensuring Safety Across Machine Learning Workflows. Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `docs/docs/getting-started/storage-and-creds/managed-credentials/azure/workload-identities.md` - **Enabling CORS in GCP** (project_doc): Set Up CORS For Google Cloud Platform With Deep Lake To Securely Handle Cross-Origin Requests, Ensuring Compliance And Easy Access To Machine Learning Data. Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `docs/docs/getting-started/storage-and-creds/managed-credentials/gcp/cors.md` - **Provisioning Federated Credentials** (project_doc): Provision Google Cloud Resources For Use With Deep Lake, Enabling Secure, Scalable Storage Solutions For Managing Large Multi-Modal AI Datasets. Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `docs/docs/getting-started/storage-and-creds/managed-credentials/gcp/provisioning.md` - **Storage Options** (project_doc): Explore Various Storage Options In Deep Lake, To Store and Search Across Data Efficiently Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `docs/docs/getting-started/storage-and-creds/storage-options.md` - **Labelbox Integration** (project_doc): Labelbox integration for annotations. Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `docs/docs/guide/annotations/labelbox.md` - **Async Data Loader** (project_doc): Use The Async Data Loader To Parallelize Data Ingestion, Enhancing Speed And Efficiency When Loading Multi-Modal Data For ML Model Training or Fast AI Search. Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `docs/docs/guide/deep-learning/async-data-loader.md` - **Training Object Detection Models with Deep Lake and MMDetection** (project_doc): Deeplake Dataset training for Object detection using MMDetection framework. Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `docs/docs/guide/deep-learning/mmdet.md` - **Semantic Segmentation with Deep Lake and MMSegmentation** (project_doc): Deeplake Dataset training for semantic segmentation using MMSegmentation framework. Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `docs/docs/guide/deep-learning/mmseg.md` - **Advancing Search Capabilities: From Lexical to Multi-Modal with Deep Lake** (project_doc): Up to 10x More Efficient Data Retrieval with TQL, learn how to build RAG applications with deeplake. Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `docs/docs/guide/rag.md` - **Migrating to Deep Lake v4** (project_doc): Step-By-Step Guide For Migrating Deep Lake 3.xx Datasets To 4.xx, Enabling Advanced Vector Search, RAG Applications, And Efficient ML Training. Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `docs/docs/guide/v3-conversion.md` - **Using Deep Lake as a Vector Store in LangChain** (project_doc): Up to 10x More Efficient Data Retrieval with TQL, learn how to build RAG applications with deeplake. Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `docs/docs/guide/vectorstore.md` - **Academic Paper** (project_doc): Read the Deep Lake academic paper published on arXiv covering lakehouse architecture for deep learning, tensor storage, streaming for PyTorch/TensorFlow/JAX, and MLOps integration for NLP, computer vision, and audio processing. Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `docs/docs/resources/academic-paper.md` - **White Paper** (project_doc): Download the Deep Lake white paper covering database architecture, storage strategies, query optimization for ML workloads, ML framework integration, and performance benchmarks for AI applications. Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `docs/docs/resources/whitepaper.md` ## Evidence Index - Indexed 76 evidence entries. - **Deep Lake Documentation** (documentation): This directory contains the documentation for the Deep Lake project. The documentation is written in markdown and built using . Evidence: `docs/README.md` - **Readme** (documentation): Developer Documentation dev-docs.md Supported SQL Queries syntax-support.md Known Limitations & Missing Features known-limitations.md Basic Usage basic-usage.md Evidence: `cpp/3rd_party/sql-parser/docs/README.md` - **What is Deep Lake?** (documentation): Docs • Get Started • API Reference • LangChain & VectorDBs Course • Blog • Whitepaper • Slack • Twitter Evidence: `README.md` - **pg deeplake** (documentation): PostgreSQL extension for vector similarity search, full-text search, and hybrid search using DeepLake. Evidence: `postgres/README.md` - **OPENJPEG Library and Applications** (documentation): OpenJPEG is an open-source JPEG 2000 codec written in C language. It has been developed in order to promote the use of JPEG 2000 http://www.jpeg.org/jpeg2000 , a still-image compression standard from the Joint Photographic Experts Group JPEG http://www.jpeg.org . Since April 2015, it is officially recognized by ISO/IEC and ITU-T as a JPEG 2000 Reference Software http://www.itu.int/rec/T-REC-T.804-201504-I!Amd2 . Evidence: `cpp/3rd_party/openjpeg/README.md` - **Usage** (documentation): C++ SQL Parser ========================= ! Build Status https://app.travis-ci.com/hyrise/sql-parser.svg?branch=master https://app.travis-ci.com/github/hyrise/sql-parser Evidence: `cpp/3rd_party/sql-parser/README.md` - **Benchmark** (documentation): This directory contains the scripts to execute benchmarks of the parser. We use Google Benchmark https://github.com/google/benchmark to define and run benchmarks. Evidence: `cpp/3rd_party/sql-parser/benchmark/README.md` - **VCPKG custom ports** (documentation): This document is added to describe the issues with the official ports and why these are needed. It might also be possible that an offical port was not available at the moment we started using it. Evidence: `cpp/vcpkg-ports/README.md` - **API Reference** (documentation): This reference documents the Python API of Deep Lake. Evidence: `docs/docs/api/index.md` - **Storage and Credentials** (documentation): How to access datasets in other clouds and manage their credentials. Evidence: `docs/docs/getting-started/storage-and-creds/index.md` - **Setting up Deep Lake in Your Cloud** (documentation): Connecting Data From Your Cloud Using Deep Lake Managed Credentials Evidence: `docs/docs/getting-started/storage-and-creds/managed-credentials/index.md` - **🌊 Deep Lake: Multi-Modal AI Database** (documentation): 🌊 Deep Lake: Multi-Modal AI Database Evidence: `docs/docs/index.md` - **Contributing Standards** (documentation): We use the black https://pypi.org/project/black/ python linter. You can have your code auto-formatted by running pip install black , then black . inside the directory you want to format. Evidence: `CONTRIBUTING.md` - **License** (source_file): Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ Evidence: `LICENSE` - **License** (source_file): / The copyright in this software is being made available under the 2-clauses BSD License, included below. This software may be subject to other third party and contributor rights, including patent rights, and no such rights are granted under this license. Copyright c 2002-2014, Universite catholique de Louvain UCL , Belgium Copyright c 2002-2014, Professor Benoit Macq Copyright c 2003-2014, Antonin Descampe Copyright c 2003-2009, Francois-Olivier Devaux Copyright c 2005, Herve Drolon, FreeImage Team Copyright c 2002-2003, Yannick Verschueren Copyright c 2001-2003, David Janssens Copyright c 2011-2012, Centre National d'Etudes Spatiales CNES , France Copyright c 2012, CS Systemes d'Informati… Evidence: `cpp/3rd_party/openjpeg/LICENSE` - **License** (source_file): Copyright c 2012-2017 Hasso-Plattner-Institut Evidence: `cpp/3rd_party/sql-parser/LICENSE` - **Changelog** (documentation): v2.4.0 https://github.com/uclouvain/openjpeg/releases/v2.4.0 2020-12-28 Evidence: `cpp/3rd_party/openjpeg/CHANGELOG.md` - **Example Code** (documentation): Using the Library ======================= Evidence: `cpp/3rd_party/sql-parser/docs/basic-usage.md` - **Basic Requirements** (documentation): Developer Documentation ======================= Evidence: `cpp/3rd_party/sql-parser/docs/dev-docs.md` - **Completely Missing Statement Types** (documentation): Known Limitations & Missing Features ==================================== Evidence: `cpp/3rd_party/sql-parser/docs/known-limitations.md` - **Select Statements** (documentation): Supported SQL Queries ===================== Evidence: `cpp/3rd_party/sql-parser/docs/syntax-support.md` - **Best Practices for Deep Lake Usage** (documentation): Deep Lake supports wide range of data types and powerful tools to ingest, load and query your data. This page provides tips for optimizing your usage of Deep Lake for best experience and performance. Evidence: `docs/docs/advanced/best-practices.md` - **Dataset Copying and Synchronization** (documentation):