{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-mayooear--ai-pdf-chatbot-langchain","slug":"mayooear--ai-pdf-chatbot-langchain","name":"ai-pdf-chatbot-langchain","type":"framework","url":"https://www.youtube.com/watch?v=OF6SolDiEwU","page_url":"https://unfragile.ai/mayooear--ai-pdf-chatbot-langchain","categories":["automation"],"tags":["agents","ai","chatbot","langchain","langgraph","nextjs","openai","pdf","typescript"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"pending_review","verified":false},"capabilities":[{"id":"github-mayooear--ai-pdf-chatbot-langchain__cap_0","uri":"capability://data.processing.analysis.pdf.document.ingestion.with.vector.embedding.pipeline","name":"pdf document ingestion with vector embedding pipeline","description":"Processes uploaded PDF files through a LangGraph-orchestrated ingestion graph that extracts text, chunks documents, generates vector embeddings via OpenAI's embedding API, and persists them to Supabase's pgvector-enabled PostgreSQL database. Uses LangChain's document loaders and text splitters to handle variable PDF structures and sizes, with configurable chunking strategies to balance retrieval granularity and context window efficiency.","intents":["Upload multiple PDF documents and have them automatically indexed for semantic search","Configure document chunking strategy (chunk size, overlap) to optimize retrieval quality","Store embeddings in a persistent vector database that scales with document volume","Support batch processing of PDFs without blocking the UI"],"best_for":["Teams building document Q&A systems who need a production-ready ingestion pipeline","Developers extending LangChain/LangGraph patterns for RAG applications","Organizations migrating from simple keyword search to semantic document retrieval"],"limitations":["PDF parsing relies on LangChain's PDF loader — complex layouts (tables, multi-column) may lose structural information","Embedding generation is synchronous per document — large batch uploads (100+ PDFs) may timeout without async job queuing","No built-in deduplication — duplicate PDFs will create redundant embeddings, increasing storage and retrieval noise","Chunking strategy is static per deployment — no dynamic adjustment based on document type or query patterns"],"requires":["Node.js 18+","OpenAI API key with embedding model access (text-embedding-3-small or equivalent)","Supabase project with pgvector extension enabled","TypeScript 5.0+"],"input_types":["PDF files (via multipart/form-data upload)","Configuration objects specifying chunk size, overlap, embedding model"],"output_types":["Vector embeddings (1536-dimensional for text-embedding-3-small)","Metadata JSON (document name, chunk index, page numbers)","Stored records in Supabase vector table"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mayooear--ai-pdf-chatbot-langchain__cap_1","uri":"capability://search.retrieval.semantic.document.retrieval.with.query.routing","name":"semantic document retrieval with query routing","description":"Implements a LangGraph-based retrieval graph that accepts natural language queries, routes them through a decision node (using an LLM to determine if document context is needed), performs vector similarity search against embedded PDFs when relevant, and returns ranked results with source attribution. Uses cosine similarity on pgvector embeddings and implements a configurable similarity threshold to filter low-confidence matches, reducing hallucination by grounding responses in actual document content.","intents":["Ask questions about uploaded PDFs and receive answers grounded in document content","Retrieve specific document sections with page numbers and chunk references","Route queries that don't require document context (e.g., 'what is 2+2?') directly to the LLM without retrieval overhead","Control retrieval quality via similarity thresholds and result ranking"],"best_for":["Developers building RAG systems who need intelligent query routing to reduce latency and cost","Teams implementing document Q&A where source attribution is critical for compliance or trust","Organizations with heterogeneous query patterns (some document-specific, some general knowledge)"],"limitations":["Query routing decision is made by a single LLM call — edge cases (ambiguous queries) may route incorrectly, requiring manual tuning of routing prompts","Similarity threshold is global — no per-document or per-query-type tuning without code changes","Retrieval is limited to top-k results (typically 3-5 chunks) — complex questions requiring synthesis across many document sections may miss relevant context","No re-ranking or relevance feedback loop — initial retrieval order is final, no iterative refinement"],"requires":["Populated vector database with embedded documents","OpenAI API key for both routing LLM and response generation","Supabase connection with pgvector-enabled table","LangGraph 0.1.0+"],"input_types":["Natural language query string","Optional conversation history for multi-turn context","Configuration object with similarity threshold, top-k, routing prompt"],"output_types":["Retrieved document chunks with metadata (source, page, similarity score)","LLM-generated response text","Source attribution array with document references"],"categories":["search-retrieval","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mayooear--ai-pdf-chatbot-langchain__cap_10","uri":"capability://data.processing.analysis.document.metadata.extraction.and.indexing","name":"document metadata extraction and indexing","description":"Extracts and indexes document metadata (filename, upload timestamp, page count, chunk count) alongside embeddings, enabling filtering and sorting of search results by document properties. Stores metadata as JSON in the pgvector table, allowing SQL queries to filter by document attributes before or after similarity search. Implements automatic metadata generation during ingestion, with optional user-provided metadata (tags, categories) for custom filtering.","intents":["Filter search results by document name, upload date, or custom tags","Track document provenance and version history","Enable users to search within specific documents or date ranges","Organize documents by category or custom metadata"],"best_for":["Applications with large document collections requiring organization","Teams needing audit trails and document versioning","Systems where users need to filter results by document properties"],"limitations":["Metadata filtering requires additional SQL queries — combining vector similarity with metadata filters adds latency","No automatic metadata extraction from document content — metadata must be provided by user or hardcoded","Metadata schema is flexible but unvalidated — inconsistent metadata across documents may cause filtering issues","No metadata versioning — updating document metadata overwrites previous values"],"requires":["PostgreSQL JSON support (built-in)","Metadata schema definition (optional, for validation)","User input or automatic metadata generation during ingestion"],"input_types":["Document file with automatic metadata (filename, size, upload time)","Optional user-provided metadata (tags, category, description)"],"output_types":["Metadata JSON object stored with embeddings","Filtered search results based on metadata criteria"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mayooear--ai-pdf-chatbot-langchain__cap_11","uri":"capability://safety.moderation.error.handling.and.recovery.with.graceful.degradation","name":"error handling and recovery with graceful degradation","description":"Implements error boundaries at multiple layers (API routes, React components, LangGraph nodes) to catch and handle failures gracefully. API routes return meaningful HTTP status codes and error messages; React components display error UI without crashing; LangGraph nodes implement retry logic and fallback paths. Uses try-catch blocks and error callbacks to transform backend exceptions into user-friendly messages, preventing technical errors from reaching end users.","intents":["Handle API failures (network errors, timeouts, rate limits) gracefully","Display user-friendly error messages instead of technical stack traces","Implement retry logic for transient failures","Prevent UI crashes from unhandled errors"],"best_for":["Production applications requiring high reliability","Teams building user-facing applications where errors impact UX","Systems with external dependencies (APIs) that may fail"],"limitations":["Error handling is manual — developers must implement try-catch at each layer","Retry logic is not automatic — requires explicit configuration per operation","Error messages are hardcoded — no centralized error catalog or i18n support","No error monitoring or alerting — errors are logged locally but not sent to external services"],"requires":["Error handling patterns in TypeScript (try-catch, error types)","React error boundaries for component-level error handling","LangGraph error handling in node implementations"],"input_types":["Exceptions from API calls, database queries, or LLM requests","HTTP error responses"],"output_types":["User-friendly error messages","HTTP status codes","Error logging for debugging"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mayooear--ai-pdf-chatbot-langchain__cap_12","uri":"capability://automation.workflow.monorepo.structure.with.turborepo.build.orchestration","name":"monorepo structure with turborepo build orchestration","description":"Organizes the application as a monorepo with separate frontend (Next.js) and backend (Node.js/LangGraph) workspaces, coordinated by Turborepo for efficient builds and dependency management. Turborepo caches build artifacts and skips rebuilds for unchanged packages, reducing build time. Shared types and utilities are extracted to a common package, enabling type-safe communication between frontend and backend without duplication.","intents":["Manage frontend and backend code in a single repository","Share types and utilities between frontend and backend","Optimize build times with Turborepo caching","Simplify deployment by building both services together"],"best_for":["Full-stack teams building tightly-coupled frontend and backend services","Projects where frontend and backend share types or utilities","Organizations wanting to simplify deployment and versioning"],"limitations":["Monorepo adds complexity to CI/CD pipelines — requires careful configuration to avoid rebuilding unchanged packages","Shared dependencies can create version conflicts — different packages may require incompatible versions of the same library","Monorepo tooling (Turborepo) adds build overhead — simple projects may not benefit from the complexity","Scaling to many packages (10+) requires careful workspace organization to avoid circular dependencies"],"requires":["Turborepo 1.0+","Node.js 18+","npm or yarn for package management","TypeScript for type sharing"],"input_types":["Source code in separate workspace directories","Workspace configuration in package.json"],"output_types":["Built artifacts for each workspace","Shared type definitions","Build cache for incremental builds"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mayooear--ai-pdf-chatbot-langchain__cap_2","uri":"capability://text.generation.language.streaming.response.generation.with.source.attribution","name":"streaming response generation with source attribution","description":"Generates LLM responses in real-time using OpenAI's streaming API, with each token streamed to the frontend via Server-Sent Events (SSE). Maintains a parallel metadata stream that tracks which source documents contributed to each response section, enabling inline source attribution in the UI. Uses LangChain's streaming callbacks to intercept token events and map them back to retrieved document chunks, providing transparent provenance for every answer.","intents":["Display AI responses in real-time as they're generated, improving perceived responsiveness","Show which PDF sections and pages support each part of the answer","Maintain conversation history with full source attribution for audit trails","Enable users to click through to source documents for verification"],"best_for":["Web applications requiring real-time user feedback and low perceived latency","Compliance-heavy domains (legal, finance, healthcare) where source attribution is mandatory","Teams building transparent AI systems where users need to verify answer provenance"],"limitations":["SSE streaming requires persistent HTTP connections — incompatible with some proxies or load balancers without configuration","Source attribution metadata is generated post-hoc from retrieval context — if the LLM synthesizes information across multiple chunks, attribution may be ambiguous or incomplete","Streaming adds complexity to error handling — partial responses may be sent before an error occurs, requiring client-side recovery logic","Token-level source mapping is approximate — the LLM may reference a source without explicit mention, making attribution incomplete"],"requires":["Next.js API route with streaming response support","OpenAI API key with streaming-enabled models (gpt-4, gpt-3.5-turbo)","Frontend with EventSource or fetch with ReadableStream support (modern browsers)","LangChain 0.1.0+ with streaming callback support"],"input_types":["User query string","Retrieved document chunks with metadata","Conversation history (optional, for multi-turn context)"],"output_types":["Server-Sent Events stream with JSON objects containing token text and source metadata","Final response object with complete text and source attribution array"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mayooear--ai-pdf-chatbot-langchain__cap_3","uri":"capability://memory.knowledge.multi.turn.conversation.state.management.with.context.window.optimization","name":"multi-turn conversation state management with context window optimization","description":"Maintains conversation history in frontend state (React hooks) and backend session storage, with automatic context window management that truncates or summarizes older messages to fit within the LLM's token limit. Uses a sliding window strategy where recent messages are always included, and older messages are progressively dropped or compressed based on token count. Implements conversation reset and context clearing to allow users to start fresh without losing document embeddings.","intents":["Ask follow-up questions that reference previous answers without re-stating context","Maintain coherent multi-turn conversations over dozens of exchanges","Automatically manage token budgets to prevent context overflow errors","Clear conversation history while keeping uploaded documents indexed"],"best_for":["Applications with extended user sessions where conversation history is critical","Teams building chatbots where token efficiency directly impacts cost and latency","Systems where users need to reference earlier parts of the conversation"],"limitations":["Context window optimization is lossy — older messages are dropped or summarized, potentially losing nuanced context from early conversation turns","No persistent conversation storage by default — refreshing the page loses conversation history (requires external DB integration)","Token counting is approximate — uses LangChain's token counter which may differ from actual OpenAI tokenization, causing occasional overflow","No conversation branching — users cannot explore alternative response paths from earlier points in the conversation"],"requires":["React 18+ for state management hooks","LangChain's token counter utility","OpenAI API key to determine model context window size","Optional: backend session store (Redis, Supabase) for persistence"],"input_types":["User message string","Previous conversation messages array","Model name (to determine context window)"],"output_types":["Optimized messages array with old messages dropped/summarized","Token count metadata","Conversation state object for UI rendering"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mayooear--ai-pdf-chatbot-langchain__cap_4","uri":"capability://planning.reasoning.langgraph.state.machine.orchestration.for.multi.step.workflows","name":"langgraph state machine orchestration for multi-step workflows","description":"Orchestrates complex document processing and query workflows using LangGraph's directed acyclic graph (DAG) execution model, where each node represents a discrete step (PDF load, chunk, embed, retrieve, generate) and edges define control flow. Implements conditional routing nodes that branch execution based on query type or document availability, with built-in error handling and state persistence. Uses LangGraph's compiled graph execution to optimize performance and enable step-by-step debugging.","intents":["Define complex multi-step workflows (ingestion, retrieval, generation) as explicit, debuggable graphs","Route queries conditionally based on content or metadata without nested if-else logic","Persist workflow state at each step for observability and error recovery","Reuse workflow components across different applications"],"best_for":["Teams building production RAG systems where workflow transparency and debuggability are critical","Developers extending LangChain with custom orchestration logic","Organizations implementing complex document processing pipelines with multiple decision points"],"limitations":["LangGraph introduces abstraction overhead — simple workflows may be slower than direct function calls due to state serialization and node invocation overhead","Debugging distributed graphs is harder than linear code — requires understanding DAG execution order and state transitions","State serialization can be expensive for large documents or embeddings — passing multi-MB objects between nodes may cause latency spikes","Limited built-in error recovery — node failures require custom error handlers; no automatic retry or fallback logic"],"requires":["LangGraph 0.1.0+","LangChain 0.1.0+","TypeScript 5.0+","Understanding of DAG execution models and state machines"],"input_types":["Graph definition (nodes, edges, state schema)","Input state object matching the graph's state schema","Configuration for node execution (timeouts, retries)"],"output_types":["Final state object after graph execution","Execution trace with intermediate states at each node","Error objects if any node fails"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mayooear--ai-pdf-chatbot-langchain__cap_5","uri":"capability://automation.workflow.pdf.file.upload.with.client.side.validation.and.progress.tracking","name":"pdf file upload with client-side validation and progress tracking","description":"Implements a Next.js API route that accepts multipart/form-data file uploads, validates file type and size on both client and server, and streams upload progress back to the UI via chunked responses. Uses React hooks to manage upload state (in-progress, success, error) and displays real-time progress bars. Integrates with the ingestion graph to trigger document processing immediately after upload completes, with error boundaries to handle processing failures gracefully.","intents":["Upload one or multiple PDF files through a web interface with visual feedback","Validate file types and sizes before processing to prevent invalid data","Track upload progress for large files to provide user feedback","Handle upload errors gracefully with clear error messages"],"best_for":["Web applications where users need intuitive file upload UX","Teams building document management systems with large file support","Applications requiring immediate feedback on upload status"],"limitations":["Client-side validation is bypassable — server must re-validate all constraints","Progress tracking is approximate — actual bytes uploaded may differ from reported progress due to compression or network buffering","No resume capability — failed uploads must restart from the beginning","File size limits are enforced by Next.js middleware — very large files (>100MB) may require chunked upload strategy"],"requires":["Next.js 13+ with App Router","React 18+ for state management","Multipart form data parser (built into Next.js)","File size limit configuration in next.config.js"],"input_types":["PDF files via HTML file input or drag-and-drop","Optional metadata (document name, category, tags)"],"output_types":["Upload status updates (in-progress, complete, error)","Progress percentage","Document ID or reference for subsequent queries"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mayooear--ai-pdf-chatbot-langchain__cap_6","uri":"capability://tool.use.integration.configurable.embedding.model.selection.with.provider.abstraction","name":"configurable embedding model selection with provider abstraction","description":"Abstracts embedding model selection through a configuration layer that supports multiple providers (OpenAI, Hugging Face, local models) without changing application code. Uses LangChain's embedding interface to swap implementations at runtime based on environment variables or configuration files. Enables cost optimization (using cheaper models for non-critical embeddings) and privacy compliance (using local models instead of cloud APIs) through simple configuration changes.","intents":["Switch between embedding providers (OpenAI, open-source) without code changes","Use cheaper embedding models for cost optimization","Deploy with local embeddings for privacy compliance","Experiment with different embedding models to optimize retrieval quality"],"best_for":["Teams optimizing embedding costs across large document collections","Organizations with data privacy requirements preventing cloud API usage","Developers experimenting with different embedding models for quality tuning"],"limitations":["Different embedding models produce incompatible vector spaces — switching models requires re-embedding all documents","Local embedding models require GPU resources — CPU-only deployment may be too slow for production","No automatic model selection based on document type — all documents use the same embedding model","Embedding quality varies significantly across models — cheaper models may produce lower-quality retrievals"],"requires":["LangChain 0.1.0+ with embedding provider support","API keys for cloud providers (OpenAI, Hugging Face) or local model setup","Environment variables or configuration file for model selection","TypeScript for type-safe configuration"],"input_types":["Configuration object specifying embedding provider and model name","Text documents to embed"],"output_types":["Vector embeddings (dimension varies by model)","Embedding metadata (model name, provider, timestamp)"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mayooear--ai-pdf-chatbot-langchain__cap_7","uri":"capability://memory.knowledge.supabase.pgvector.integration.for.persistent.vector.storage","name":"supabase pgvector integration for persistent vector storage","description":"Integrates with Supabase's PostgreSQL database with pgvector extension to store document embeddings, metadata, and retrieval indices. Uses SQL queries with pgvector's similarity operators (<->, <#>) to perform vector similarity search directly in the database, avoiding separate vector DB infrastructure. Implements automatic index creation for performance optimization and handles vector dimension validation to ensure consistency across embeddings.","intents":["Store document embeddings persistently without managing separate vector DB infrastructure","Perform fast similarity searches using pgvector's optimized operators","Query embeddings alongside document metadata using standard SQL","Scale vector storage with PostgreSQL's proven reliability and backup mechanisms"],"best_for":["Teams wanting to avoid separate vector DB infrastructure (Pinecone, Weaviate)","Organizations already using Supabase for other application data","Projects with moderate embedding volume (millions of vectors) where PostgreSQL performance is sufficient"],"limitations":["pgvector performance degrades with very large collections (100M+ vectors) compared to specialized vector DBs","No built-in sharding or distributed execution — single PostgreSQL instance is a bottleneck at scale","Vector index creation requires manual SQL commands — no automatic index management","Similarity search is slower than specialized vector DBs due to PostgreSQL's general-purpose query optimizer"],"requires":["Supabase project with pgvector extension enabled","PostgreSQL 12+ (pgvector requires PostgreSQL 11+)","Supabase client library (supabase-js) or direct PostgreSQL driver","Vector dimension matching the embedding model (1536 for text-embedding-3-small)"],"input_types":["Vector embeddings (float arrays)","Document metadata (JSON objects with document name, page, chunk index)","Similarity threshold for filtering results"],"output_types":["Retrieved document chunks with similarity scores","Metadata objects with source attribution","Query execution time and result count"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mayooear--ai-pdf-chatbot-langchain__cap_8","uri":"capability://tool.use.integration.next.js.api.route.abstraction.for.backend.service.calls","name":"next.js api route abstraction for backend service calls","description":"Implements Next.js API routes that act as a thin HTTP layer between the frontend and backend LangGraph services. Routes handle request parsing, error transformation, and response formatting, abstracting away backend complexity from the frontend. Uses Next.js middleware for authentication, rate limiting, and request logging. Supports both request-response and streaming patterns, with automatic error handling that converts backend exceptions into HTTP status codes.","intents":["Provide a clean HTTP API for frontend components to call backend services","Implement authentication and rate limiting at the API layer","Handle errors gracefully and return meaningful HTTP status codes","Support streaming responses for real-time feedback"],"best_for":["Full-stack teams building Next.js applications with separate backend services","Developers wanting to abstract backend complexity from frontend code","Applications requiring API authentication and rate limiting"],"limitations":["API routes add latency compared to direct backend calls — each request traverses HTTP layer","Error handling is manual — developers must map backend exceptions to HTTP status codes","No built-in API versioning — breaking changes require careful migration planning","Streaming responses are limited by HTTP/1.1 connection pooling — many concurrent streams may exhaust connections"],"requires":["Next.js 13+ with App Router","TypeScript for type-safe request/response handling","Backend service (LangGraph) running and accessible","Optional: authentication middleware (NextAuth, JWT)"],"input_types":["HTTP requests (POST, GET) with JSON body or query parameters","Optional: authentication tokens in headers"],"output_types":["JSON responses with data or error messages","HTTP status codes (200, 400, 500, etc.)","Server-Sent Events stream for streaming responses"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mayooear--ai-pdf-chatbot-langchain__cap_9","uri":"capability://text.generation.language.react.component.state.management.for.chat.ui.with.message.history","name":"react component state management for chat ui with message history","description":"Manages chat UI state using React hooks (useState, useCallback) to track messages, loading states, and error conditions. Implements a message array that stores both user and assistant messages with metadata (timestamp, source attribution, error status). Uses useCallback to memoize event handlers and prevent unnecessary re-renders. Integrates with the streaming API to append tokens to the current message in real-time, creating a responsive chat experience without full-page re-renders.","intents":["Display a chat interface with user and assistant messages","Show real-time token streaming as responses are generated","Manage loading and error states for user feedback","Maintain message history for conversation context"],"best_for":["React developers building chat interfaces","Teams wanting a pre-built chat UI component","Applications requiring real-time message streaming"],"limitations":["State management is local to the component — no persistence across page refreshes without external storage","Large message histories (1000+ messages) may cause performance degradation due to array operations","No built-in message editing or deletion — users cannot modify sent messages","Streaming state management is complex — requires careful handling of partial messages and error states"],"requires":["React 18+","TypeScript for type-safe state management","Tailwind CSS or custom CSS for styling"],"input_types":["User message text","Streaming token events from API","Error objects from failed requests"],"output_types":["Rendered chat UI with messages","Message array for external persistence","Loading and error state indicators"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":48,"verified":false,"data_access_risk":"high","permissions":["Node.js 18+","OpenAI API key with embedding model access (text-embedding-3-small or equivalent)","Supabase project with pgvector extension enabled","TypeScript 5.0+","Populated vector database with embedded documents","OpenAI API key for both routing LLM and response generation","Supabase connection with pgvector-enabled table","LangGraph 0.1.0+","PostgreSQL JSON support (built-in)","Metadata schema definition (optional, for validation)"],"failure_modes":["PDF parsing relies on LangChain's PDF loader — complex layouts (tables, multi-column) may lose structural information","Embedding generation is synchronous per document — large batch uploads (100+ PDFs) may timeout without async job queuing","No built-in deduplication — duplicate PDFs will create redundant embeddings, increasing storage and retrieval noise","Chunking strategy is static per deployment — no dynamic adjustment based on document type or query patterns","Query routing decision is made by a single LLM call — edge cases (ambiguous queries) may route incorrectly, requiring manual tuning of routing prompts","Similarity threshold is global — no per-document or per-query-type tuning without code changes","Retrieval is limited to top-k results (typically 3-5 chunks) — complex questions requiring synthesis across many document sections may miss relevant context","No re-ranking or relevance feedback loop — initial retrieval order is final, no iterative refinement","Metadata filtering requires additional SQL queries — combining vector similarity with metadata filters adds latency","No automatic metadata extraction from document content — metadata must be provided by user or hardcoded","builder identity is not verified yet","artifact is still pending review"],"rank_breakdown":{"adoption":0.7343324850230646,"quality":0.25,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.5,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"pending_review","updated_at":"2026-05-24T12:16:22.062Z","last_scraped_at":"2026-05-03T13:58:24.502Z","last_commit":"2026-03-27T16:04:05Z"},"community":{"stars":16487,"forks":3227,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=mayooear--ai-pdf-chatbot-langchain","compare_url":"https://unfragile.ai/compare?artifact=mayooear--ai-pdf-chatbot-langchain"}},"signature":"V8yMzv8+yc1tY0NHHBAlxnUp1TRo+e2y5Er6p2N0SWp8x0BzIfAGCNRfklnr+xBrIPqS7JMEOAGJREvccjPgCQ==","signedAt":"2026-06-20T09:04:13.326Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mayooear--ai-pdf-chatbot-langchain","artifact":"https://unfragile.ai/mayooear--ai-pdf-chatbot-langchain","verify":"https://unfragile.ai/api/v1/verify?slug=mayooear--ai-pdf-chatbot-langchain","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}