quivr
RepositoryFreeDump all your files and chat with it using your generative AI second brain using LLMs & embeddings.
Capabilities13 decomposed
multi-format document ingestion and chunking
Medium confidenceAccepts diverse file formats (PDF, DOCX, TXT, CSV, JSON, Markdown, code files) and automatically chunks them into semantically meaningful segments using configurable chunk sizes and overlap strategies. The system normalizes different file types into a unified text representation before applying recursive character-based or token-based splitting, enabling consistent downstream embedding generation regardless of source format.
Supports simultaneous ingestion of code files, structured data, and unstructured documents with format-specific parsing pipelines, rather than treating all inputs as plain text
Handles code-specific chunking (preserving function boundaries) better than generic RAG frameworks like LangChain's default splitters, reducing semantic fragmentation
vector embedding generation and storage
Medium confidenceConverts chunked documents into dense vector embeddings using pluggable embedding models (OpenAI, Cohere, HuggingFace, local models) and persists them in a vector database (Pinecone, Weaviate, Supabase pgvector, or local Qdrant). The system maintains a mapping between embeddings and source documents, enabling efficient semantic similarity search without requiring full document re-embedding on queries.
Abstracts vector database and embedding model selection through a provider-agnostic interface, allowing runtime switching between OpenAI, Cohere, HuggingFace, and local models without code changes
More flexible than Pinecone-only solutions or LangChain's default embedding chains because it decouples embedding generation from storage, enabling cost optimization and infrastructure control
api endpoint exposure for programmatic access
Medium confidenceExposes REST API endpoints for document ingestion, search, and chat functionality, enabling external applications to integrate with Quivr without using the web UI. The API supports authentication via API keys, request/response validation, and standard HTTP methods (POST for uploads, GET for search, etc.), allowing developers to build custom applications on top of Quivr.
Exposes full Quivr functionality through REST API endpoints with API key authentication, enabling external applications to integrate without using the web UI
More flexible than web UI-only solutions because it enables programmatic integration, though requires more development effort than using the web interface
web ui for document management and chat
Medium confidenceProvides a web-based interface for uploading documents, managing knowledge bases, and conducting conversations with the AI assistant. The UI includes drag-and-drop file uploads, document browser, search interface, and chat window, enabling non-technical users to interact with Quivr without API knowledge. The interface is built with modern web frameworks (React, Vue, or similar) and communicates with the backend via REST API.
Provides an integrated web UI for document management and chat, rather than requiring users to use separate tools or APIs, enabling non-technical users to interact with Quivr
More user-friendly than command-line or API-only tools because it provides visual feedback and drag-and-drop uploads, though less customizable than building a custom UI on the API
configurable embedding and llm model selection
Medium confidenceAllows users to select embedding models (OpenAI, Cohere, HuggingFace, local models) and LLM providers (OpenAI, Anthropic, Ollama, etc.) through configuration files or environment variables, without code changes. The system validates model availability, handles authentication, and provides fallback options if the primary model is unavailable.
Allows runtime configuration of embedding and LLM models through environment variables or config files, enabling users to switch models without code changes or redeployment
More flexible than hardcoded model selection because it enables cost optimization and experimentation, though requires more configuration management than single-model systems
semantic similarity search with metadata filtering
Medium confidenceExecutes vector similarity queries against stored embeddings using cosine distance or other metrics, returning ranked results with configurable filtering by document source, date, or custom metadata. The search pipeline converts user queries into embeddings using the same model as the document corpus, then performs approximate nearest neighbor (ANN) search in the vector database, optionally re-ranking results by relevance or metadata constraints.
Integrates metadata filtering at the vector database level rather than post-processing, reducing latency for filtered queries and supporting complex filter expressions across multiple document attributes
Faster than keyword-based search (Elasticsearch, full-text SQL) for semantic queries, and more flexible than single-provider vector search because it supports multiple database backends
llm-powered conversational chat with document context
Medium confidenceChains semantic search results with LLM inference to generate contextual responses to user queries. The system retrieves relevant document chunks via vector search, constructs a prompt that includes the retrieved context, and sends it to a configurable LLM (OpenAI, Anthropic, Ollama, HuggingFace) with conversation history. The LLM generates responses grounded in the document context, with optional citation tracking to identify which source documents informed the answer.
Maintains conversation history across multiple turns while dynamically retrieving relevant context for each query, rather than treating each query independently, enabling coherent multi-turn dialogue grounded in documents
More context-aware than vanilla LLM chat because it retrieves relevant documents per query, and more scalable than fine-tuning because it doesn't require model retraining when documents change
multi-provider llm abstraction with unified interface
Medium confidenceProvides a unified API for interacting with multiple LLM providers (OpenAI, Anthropic, Cohere, HuggingFace, Ollama, Azure OpenAI) without provider-specific code. The system abstracts provider differences (API formats, authentication, parameter names) behind a common interface, allowing developers to switch providers by changing configuration rather than refactoring code. Supports streaming responses, token counting, and provider-specific features through optional parameters.
Abstracts LLM provider differences through a unified interface that supports streaming, token counting, and provider-specific features, enabling runtime provider switching without code changes
More flexible than LangChain's LLM base class because it includes built-in support for local models (Ollama) and cost estimation, and simpler than managing provider SDKs directly
user authentication and access control
Medium confidenceImplements user authentication (email/password, OAuth, API keys) and role-based access control (RBAC) to restrict document access and chat functionality. The system maintains user sessions, validates API keys for programmatic access, and enforces permissions at the document and conversation level, preventing unauthorized users from accessing other users' knowledge bases or chat histories.
Implements multi-tenant access control at the document and conversation level, rather than just user-level authentication, enabling fine-grained sharing within organizations
More comprehensive than basic API key authentication because it includes session management and role-based access, though less sophisticated than enterprise IAM systems like Okta or Auth0
conversation history persistence and retrieval
Medium confidenceStores user conversations (queries and LLM responses) in a persistent database with timestamps and metadata, enabling users to retrieve past conversations, resume interrupted chats, and analyze conversation patterns. The system indexes conversations by user, date, and topic, supporting full-text search and filtering to help users find relevant past discussions without manual scrolling.
Persists full conversation history with metadata indexing, enabling search and retrieval of past conversations, rather than treating conversations as ephemeral
More comprehensive than stateless chat APIs because it maintains conversation context across sessions, though requires more storage and database infrastructure
document source tracking and citation generation
Medium confidenceMaintains metadata about document sources (filename, upload date, document type, chunk position) and automatically generates citations when the LLM references retrieved chunks. The system tracks which source documents contributed to each LLM response, enabling transparency about information provenance and allowing users to verify answers by reviewing original documents.
Automatically tracks and generates citations from retrieved documents, providing transparency about information sources rather than treating LLM responses as black boxes
More transparent than vanilla RAG systems because it explicitly shows source documents, though citation accuracy depends on chunk metadata quality and LLM response parsing
batch document processing and async ingestion
Medium confidenceProcesses multiple documents asynchronously in the background, avoiding blocking the API during long-running embedding and storage operations. The system queues documents, processes them in batches, tracks ingestion progress, and notifies users when documents are ready for querying. This enables users to upload large document collections without waiting for completion.
Implements asynchronous batch document processing with progress tracking and retry logic, rather than synchronous single-document uploads, enabling scalable ingestion of large collections
More scalable than synchronous uploads because it doesn't block the API, and more reliable than simple async calls because it includes progress tracking and error handling
knowledge base organization and tagging
Medium confidenceAllows users to organize documents into collections or projects, apply tags and categories to documents, and filter search results by these organizational attributes. The system maintains a hierarchical structure (projects → documents → chunks) and enables users to manage document metadata (title, description, tags) for better discoverability and organization.
Implements hierarchical document organization with tagging and filtering, enabling users to structure knowledge bases by project or domain rather than treating all documents as a flat collection
More organized than flat document lists because it supports projects and tags, though less sophisticated than enterprise knowledge management systems like Confluence or Notion
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
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Best For
- ✓teams building knowledge bases from heterogeneous document sources
- ✓developers creating RAG systems that need to handle mixed content types
- ✓non-technical users who want to upload files without format conversion
- ✓teams building production RAG systems with cost-sensitive embedding operations
- ✓developers who need multi-provider embedding flexibility for cost optimization
- ✓organizations with data residency requirements preventing cloud embedding services
- ✓developers building applications that need RAG capabilities
- ✓teams integrating Quivr with existing workflows (document management, CRM)
Known Limitations
- ⚠chunking strategy is fixed per file type — no dynamic adjustment based on content semantics
- ⚠large binary files (>100MB PDFs) may timeout during processing
- ⚠OCR for scanned PDFs not included — requires external preprocessing
- ⚠chunk overlap can create redundant embeddings, increasing storage costs
- ⚠embedding generation is synchronous — large document batches (>10k chunks) may block the API
- ⚠no automatic re-embedding when documents are updated — requires manual refresh
Requirements
Input / Output
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Dump all your files and chat with it using your generative AI second brain using LLMs & embeddings.
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