Add comprehensive Vectara Chat tutorial

[Furox-Art]

Summary

This PR adds a comprehensive, hands-on tutorial for Vectara Chat for bounty issue #462. It teaches readers how to build a complete grounded chat experience with Vectara in Python, written in the first-person tutorial style used across the repo.

File: tutorials/en/vectara-chat-tutorial-build-a-grounded-chatbot-with-citations-in-python.mdx (~2,800 words)

What the tutorial covers

Following the structure recommended in the issue:

• Introduction to Vectara Chat — grounded generation (RAG), numbered citations [1], conversation memory, and the factual consistency score; links to the required Vectara Beginner App Tutorial and Vectara Advanced App Tutorial
• Getting Started with Vectara Chat — account + personal API key via the Console, VECTARA_API_KEY environment variable, virtualenv, pip install vectara, and creating a first corpus with client.corpora.create
• Building a Chatbot with Vectara — ingestion with CoreDocument / client.upload.file (PDFs), single-shot grounded Q&A with client.query, printing [1]-style citations mapped to search_results, and multi-turn memory with create_chat_session + ChatParameters(store=True)
• Enhancing Chatbot Intelligence — lexical_interpolation, context_configuration, reranking (CustomerSpecificReranker), response_language (ISO 639-3), max_used_search_results, max_response_characters, generation_preset_name (Mockingbird), CitationParameters, factual consistency gating, and streaming with query_stream
• Deploying and Testing Chatbots — a complete runnable Streamlit app.py using the chat session, three concrete test cases (grounded / follow-up / out-of-scope), and deployment with requirements.txt + Streamlit Community Cloud
• Conclusion and Tips for Hackathon Participants — recap, use-case ideas, and practical tips (show citations, protect API keys)

How API accuracy was verified

All code and parameters were checked against official, current sources before writing:

• Vectara REST API v1→v2 migration guide (endpoints, corpus_key, response_language codes): https://docs.vectara.com/docs/migration-guide-api-v2
• Grounded generation / RAG overview (citations, Mockingbird): https://docs.vectara.com/docs/learn/grounded-generation/grounded-generation-overview
• Official quickstart (VECTARA_API_KEY, x-api-key auth, Mockingbird preset, [1], [2] citations, factual consistency score): https://docs.vectara.com/docs/quickstart
• API key management (x-api-key header, permission profiles): https://docs.vectara.com/docs/security/authentication/api-key-management
• Console chat overview (chat toggle, chat history corpus): https://docs.vectara.com/docs/console-ui/vectara-chat-overview
• Python SDK chat guide (create_chat_session, session.chat, ChatParameters): https://docs.vectara.com/docs/sdk/python/chats
• Query API reference (generation config, citations styles): https://docs.vectara.com/docs/rest-api/query
• File upload endpoint (POST /v2/corpora/{corpus_key}/upload_file): Vectara REST API v2 reference
• Official vectara SDK (PyPI, v0.4.3): https://pypi.org/project/vectara/ and https://github.com/vectara/python-sdk

Additionally, every SDK class, field name, and method signature used in the tutorial (SearchCorporaParameters, KeyedSearchCorpus, ContextConfiguration, CustomerSpecificReranker, GenerationParameters, CitationParameters, ChatParameters, QueryFullResponse.summary/search_results/factual_consistency_score, CreateTurnsChatsResponse.chat_id/answer/turn_id, client.query, client.query_stream, client.create_chat_session, session.chat, client.corpora.create, client.documents.create, client.upload.file) was verified against the installed SDK v0.4.3 locally, so all samples are runnable as written.

Note: this tutorial intentionally uses the current v2 API / official Python SDK, which differentiates it from the existing vectara-chat-essentials tutorial in this repo that uses the older v1 request style.

/claim #462

Disclosure: This PR was authored by @Furox-Art (AI-assisted). Happy to adjust anything.