| Data Ingestion & Knowledge Sources |
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Takes a code-first approach: plug in document-loader modules for just about any file type—from PDFs with PyPDF to CSV, JSON, or HTML via Unstructured.
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Lets developers craft custom ingestion and indexing pipelines, so niche or proprietary data sources are no problem.
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- Drop content in via REST: upload PDFs, Markdown, or TXT
[Upload File]
or send raw text
[Upload Text].
- No one-click Google Drive or Notion connectors—you’ll script the fetch and hit the API yourself.
- Because it’s open source, you can build connectors to anything—Postgres, Mongo, S3, you name it.
- Runs on Supabase and scales sideways, chunking millions of docs for fast retrieval.
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- Lets you ingest more than 1,400 file formats—PDF, DOCX, TXT, Markdown, HTML, and many more—via simple drag-and-drop or API.
- Crawls entire sites through sitemaps and URLs, automatically indexing public help-desk articles, FAQs, and docs.
- Turns multimedia into text on the fly: YouTube videos, podcasts, and other media are auto-transcribed with built-in OCR and speech-to-text.
View Transcription Guide
- Connects to Google Drive, SharePoint, Notion, Confluence, HubSpot, and more through API connectors or Zapier.
See Zapier Connectors
- Supports both manual uploads and auto-sync retraining, so your knowledge base always stays up to date.
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| Integrations & Channels |
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Ships without a built-in web UI, so you’ll build your own front-end or pair it with something like Streamlit or React.
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Includes libraries and examples for Slack (and other platforms), but you’ll handle the coding and config yourself.
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- Pure REST for retrieval and generation—no built-in widget or Slack bot.
- You code the chat UI or Slack bridge, calling Supavec for answers.
- No Zapier—webhooks and automations are DIY inside your app.
- If it speaks HTTP, it can talk to Supavec—you just handle the front-end.
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- Embeds easily—a lightweight script or iframe drops the chat widget into any website or mobile app.
- Offers ready-made hooks for Slack, Microsoft Teams, WhatsApp, Telegram, and Facebook Messenger.
Explore API Integrations
- Connects with 5,000+ apps via Zapier and webhooks to automate your workflows.
- Supports secure deployments with domain allowlisting and a ChatGPT Plugin for private use cases.
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| Core Chatbot Features |
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Provides retrieval-augmented QA chains that blend LLM answers with data fetched from vector stores.
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Supports multi-turn dialogue through configurable memory modules; you’ll add source citations manually if you need them.
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Lets you build agents that call external APIs or tools for more advanced reasoning.
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- Just the essentials: retrieve chunks + LLM answer. Calls are stateless, no baked-in chat history.
- No lead capture or human handoff—add those in your own layer.
- Pulls the right text fast, then lets your LLM craft the reply.
- Perfect if you only need raw RAG and will build the conversation bits yourself.
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- Powers retrieval-augmented Q&A with GPT-4 and GPT-3.5 Turbo, keeping answers anchored to your own content.
- Reduces hallucinations by grounding replies in your data and adding source citations for transparency.
Benchmark Details
- Handles multi-turn, context-aware chats with persistent history and solid conversation management.
- Speaks 90+ languages, making global rollouts straightforward.
- Includes extras like lead capture (email collection) and smooth handoff to a human when needed.
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| Customization & Branding |
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Gives you the framework to design any UI you want, but offers no out-of-the-box white-label or branding features.
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Total freedom to match corporate branding—just expect extra lift to build or integrate your own interface.
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- No pre-made UI, no theming—branding lives in whatever front-end you create.
- Open source means zero “Supavec” label to hide—your app, your look.
- Add domain checks or auth however you like in your code.
- It’s “white-label” by default because Supavec is API-only.
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- Fully white-labels the widget—colors, logos, icons, CSS, everything can match your brand.
White-label Options
- Provides a no-code dashboard to set welcome messages, bot names, and visual themes.
- Lets you shape the AI’s persona and tone using pre-prompts and system instructions.
- Uses domain allowlisting to ensure the chatbot appears only on approved sites.
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| LLM Model Options |
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Is completely model-agnostic—swap between OpenAI, Anthropic, Cohere, Hugging Face, and more through the same interface.
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Easily adjust parameters and pick your embeddings or vector DB (FAISS, Pinecone, Weaviate) in just a few lines of code.
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- Model-agnostic: defaults to GPT-3.5, but switch to GPT-4 or any self-hosted model if you’d like.
- No fancy toggle—just change a config or prompt path in code.
- No extra prompt magic or anti-hallucination layer—plain RAG.
- Quality rests on the LLM you choose and how you prompt it.
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- Taps into top models—OpenAI’s GPT-4, GPT-3.5 Turbo, and even Anthropic’s Claude for enterprise needs.
- Automatically balances cost and performance by picking the right model for each request.
Model Selection Details
- Uses proprietary prompt engineering and retrieval tweaks to return high-quality, citation-backed answers.
- Handles all model management behind the scenes—no extra API keys or fine-tuning steps for you.
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| Developer Experience (API & SDKs) |
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Comes as a Python or JavaScript library you import directly—there’s no hosted REST API by default.
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Extensive docs, tutorials, and a huge community smooth the learning curve—but you do need programming skills.
Reference
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- Straightforward REST endpoints for file uploads, text uploads, and search.
[Examples]
- No official SDKs—use fetch/axios or roll your own wrapper.
- Docs are concise with JS snippets; Postman collection included.
- Full source is on GitHub, welcoming community tweaks.
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- Ships a well-documented REST API for creating agents, managing projects, ingesting data, and querying chat.
API Documentation
- Offers open-source SDKs—like the Python
customgpt-client—plus Postman collections to speed integration.
Open-Source SDK
- Backs you up with cookbooks, code samples, and step-by-step guides for every skill level.
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| Integration & Workflow |
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Chain together LLM calls, retrievers, and prompt templates directly in code to create custom workflows.
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Fits into CI/CD and event-driven architectures, though you’ll script the automation yourself.
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- Think of it as a Lego brick: upload content, query matches, feed results to your LLM.
- No built-in triggers—external actions are on you.
- Scale horizontally on Supabase when self-hosted, or use the hosted plan (with API-call limits).
- Big orgs can chat about higher tiers or dedicated infra for heavy traffic.
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- Gets you live fast with a low-code dashboard: create a project, add sources, and auto-index content in minutes.
- Fits existing systems via API calls, webhooks, and Zapier—handy for automating CRM updates, email triggers, and more.
Auto-sync Feature
- Slides into CI/CD pipelines so your knowledge base updates continuously without manual effort.
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| Performance & Accuracy |
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Accuracy hinges on your chosen LLM and prompt engineering—tune them well for top performance.
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Response speed depends on the model and infra you choose; any extra optimization is up to your deployment.
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- Accuracy = GPT quality + standard RAG lift—no extra guardrails.
- Postgres vector search keeps retrieval snappy, even with millions of chunks.
- No public head-to-head benchmarks yet; expect “typical GPT-3.5/4 RAG” results.
- If you want citations or extra checks, you’ll prompt-engineer them yourself.
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- Delivers sub-second replies with an optimized pipeline—efficient vector search, smart chunking, and caching.
- Independent tests rate median answer accuracy at 5/5—outpacing many alternatives.
Benchmark Results
- Always cites sources so users can verify facts on the spot.
- Maintains speed and accuracy even for massive knowledge bases with tens of millions of words.
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| Customization & Flexibility (Behavior & Knowledge) |
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Gives you full control over prompts, retrieval settings, and integration logic—mix and match data sources on the fly.
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Makes it possible to add custom behavioral rules and decision logic for highly tailored agents.
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- Upload or overwrite docs any time—re-embeds almost instantly.
- Behavior lives in your prompts; there’s no GUI for personas.
- Multi-lingual works fine—just tell the LLM in your prompt.
- Add metadata, tweak chunking—then build logic around it as needed.
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- Lets you add, remove, or tweak content on the fly—automatic re-indexing keeps everything current.
- Shapes agent behavior through system prompts and sample Q&A, ensuring a consistent voice and focus.
Learn How to Update Sources
- Supports multiple agents per account, so different teams can have their own bots.
- Balances hands-on control with smart defaults—no deep ML expertise required to get tailored behavior.
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| Pricing & Scalability |
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LangChain itself is open-source and free; costs come from the LLM APIs and infrastructure you run underneath.
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Scaling is DIY: you manage hosting, vector-DB growth, and cost optimization—potentially very efficient once tuned.
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- MIT-licensed open source: self-host for free (pay your own infra).
- Hosted plans: Free (100 calls/mo), Basic $190/yr (750 calls/mo), Enterprise $1,490/yr (5 k calls/mo).
[Pricing]
- Need more calls? Negotiate or self-host to ditch caps.
- Storage isn’t metered—only query volume counts toward the plan.
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- Runs on straightforward subscriptions: Standard (~$99/mo), Premium (~$449/mo), and customizable Enterprise plans.
- Gives generous limits—Standard covers up to 60 million words per bot, Premium up to 300 million—all at flat monthly rates.
View Pricing
- Handles scaling for you: the managed cloud infra auto-scales with demand, keeping things fast and available.
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| Security & Privacy |
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Security is fully in your hands—deploy on-prem or in your own cloud to meet whatever compliance rules you have.
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No built-in security stack; you’ll add encryption, authentication, and compliance tooling yourself.
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- Self-hosting keeps everything on your servers—great for tight compliance.
[Privacy note]
- Hosted Supavec runs on Supabase with row-level security—each team’s data is fenced off.
- No training on your docs—data stays yours.
- Enterprises can go dedicated or on-prem for HIPAA/GDPR peace of mind.
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- Protects data in transit with SSL/TLS and at rest with 256-bit AES encryption.
- Holds SOC 2 Type II certification and complies with GDPR, so your data stays isolated and private.
Security Certifications
- Offers fine-grained access controls—RBAC, two-factor auth, and SSO integration—so only the right people get in.
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| Observability & Monitoring |
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You’ll wire up observability in your app—LangChain doesn’t include a native analytics dashboard.
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Tools like LangSmith give deep debugging and monitoring for tracing agent steps and LLM outputs.
Reference
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- No dashboard baked in—log requests yourself or use Supabase metrics when self-hosting.
- Hosted plan shows basic call counts; no transcript analytics out of the box.
- Need deep insights? Wire up your own monitoring layer.
- Designed to play nicely with external logging tools, not ship its own.
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- Comes with a real-time analytics dashboard tracking query volumes, token usage, and indexing status.
- Lets you export logs and metrics via API to plug into third-party monitoring or BI tools.
Analytics API
- Provides detailed insights for troubleshooting and ongoing optimization.
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| Support & Ecosystem |
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Backed by an active open-source community—docs, GitHub discussions, Discord, and Stack Overflow are all busy.
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A wealth of community projects, plugins, and tutorials helps you find solutions fast.
Reference
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- Community help via GitHub/Discord; paid plans unlock email or priority support.
[Docs]
- Open-source means forks, PRs, and home-grown connectors are welcome.
- Docs are lean—mostly endpoint references rather than big tutorials.
- Code samples pop up in the community, but it’s not a huge library yet.
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- Supplies rich docs, tutorials, cookbooks, and FAQs to get you started fast.
Developer Docs
- Offers quick email and in-app chat support—Premium and Enterprise plans add dedicated managers and faster SLAs.
Enterprise Solutions
- Benefits from an active user community plus integrations through Zapier and GitHub resources.
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| Additional Considerations |
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Total freedom to pick and swap models, embeddings, and vector stores—great for fast-evolving solutions.
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Can power innovative, multi-step, tool-using agents, but reaching enterprise-grade polish takes serious engineering time.
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- No vendor lock-in: transparent code, offline option, host wherever you like.
- Focuses on core RAG—no SSO, dashboards, or fancy UI included.
- Great for devs who want full control or must keep data in-house.
- Conversation flow, advanced prompts, fancy UI—all yours to build.
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- Slashes engineering overhead with an all-in-one RAG platform—no in-house ML team required.
- Gets you to value quickly: launch a functional AI assistant in minutes.
- Stays current with ongoing GPT and retrieval improvements, so you’re always on the latest tech.
- Balances top-tier accuracy with ease of use, perfect for customer-facing or internal knowledge projects.
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| No-Code Interface & Usability |
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Offers no native no-code interface—the framework is aimed squarely at developers.
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Low-code wrappers (Streamlit, Gradio) exist in the community, but a full end-to-end UX still means custom development.
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- No drag-and-drop dashboard—everything’s via API or CLI.
- Meant for code-first teams who’ll bolt it into their own chat or workflow.
- Self-hosters can craft custom GUIs on top, but Supavec keeps the slate blank.
- If you want a business-user UI like CustomGPT, you’ll layer that yourself.
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- Offers a wizard-style web dashboard so non-devs can upload content, brand the widget, and monitor performance.
- Supports drag-and-drop uploads, visual theme editing, and in-browser chatbot testing.
User Experience Review
- Uses role-based access so business users and devs can collaborate smoothly.
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Langchain
Supavec
CustomGPT