Malayalam FilmGraph — GraphRAG over Malayalam cinema

Live demo: malayalam-filmgraph.vercel.app — ask in plain English, then open How this was answered to see the entities, intent scores and the exact SQL behind every answer.

A proof-of-concept GraphRAG system: a knowledge graph of Malayalam cinema (films, people, songs, places, remakes, adaptations, awards) that you can question in plain English. It shows answers as chat cards with Wikipedia / IMDb / YouTube / where-to-watch links, an interactive graph that highlights the result, and a step-by-step trace of how each answer was produced.

It is built deterministic-first, local-model-second. An intent router plus entity recognition fills parameterised graph queries; this is the same pattern as a Grakn + Dialogflow assistant. Small local models (Ollama) are used only for relation extraction, plot search, era summaries and NLU fallback. No paid AI API is used.

Graph	6,791 films · 23,729 people · 132k credit triples · 13k songs / 39k song credits · ~4.9k link-anchored relation triples (remakes, adaptations, shooting locations, awards, debuts…) · 17.7k text chunks · 74k Neo4j nodes / 240k relationships
Sources	Wikipedia film lists and pages (CC BY-SA) · Wikidata (CC0) · IMDb non-commercial datasets (local only, never deployed)
Stores	Neo4j 5.26 (Docker or AuraDB Free) or a SQLite mirror with the same query templates
Models	gemma4-e4b (chat/JSON) + embeddinggemma (embeddings) via Ollama, both local; optional WebLLM in the browser
Quality	see evals/report.md: intent accuracy, entity-linking F1, count exactness vs ground-truth SQL, retrieval ablation, Neo4j/SQLite parity

More detail: docs/graphrag.md (architecture, diagram, design decisions, interview notes) · docs/product-and-cost-plan.md (product, quotas, costs).

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0. Try it

Answers below are from the live demo (hosted mode: no LLM, every number comes from a graph query).

Question	Answer (abridged)
How many films did Mohanlal and Mammootty do together?	53 films with both on screen: 46 in full roles, 7 where one is a cameo or plays himself. Narration-only credits (Barroz, Pathonpatham Noottandu) are listed separately
Mohanlal actor and Priyadarshan director	30 films (1984–2021), e.g. Marakkar (2021), Oppam (2016)
Which director has Mammootty worked with most?	Joshiy and I. V. Sasi, 33 films each
Who directed Chemmeen?	Ramu Kariat (1966)
Songs sung by K. J. Yesudas composed by Raveendran	174 songs (1979–2006)
How is Fahadh Faasil connected to Prem Nazir?	2 films apart, via M. B. Sreenivasan
Which films are remakes of Manichitrathazhu?	Chandramukhi and 3 more, each with its evidence sentence
Films shot in Munnar	10 films (2011–2025)
Films like Kireedam	Chenkol first: shared creatives fused with plot similarity
Mohanlal movies in the 90s	65 films (1990–1999)

Follow-ups keep context: Mohanlal and Mammootty films → what about with Priyadarshan? → only in the 90s.

Architecture: local full version vs hosted demo

flowchart LR
  subgraph Local["Local (laptop): full GraphRAG"]
    direction TB
    SRC["Wikipedia · Wikidata · IMDb (local only)"] --> BUILD["Build pipeline<br/>collect → resolve → extract → embed → communities"]
    BUILD --> DB[("films.sqlite3<br/>raw claims + kg_* graph")]
    DB --> NEO[("Neo4j<br/>(optional)")]
    DB & NEO --> SRV["filmgraph serve"]
    OLL["Ollama: Gemma 4 + embeddinggemma"] --> SRV
  end
  DB -- "filmgraph snapshot<br/>IMDb stripped, graph rebuilt" --> SNAP[("deploy.sqlite3.gz<br/>read-only")]
  subgraph Hosted["Vercel (hosted demo): no LLM, $0"]
    direction TB
    EDGE["Vercel edge<br/>DDoS · WAF rate limit · bot rules"] --> FN["Python function<br/>same server code"]
    FN --> RO[("SQLite in /tmp<br/>PRAGMA query_only")]
  end
  SNAP --> RO
  USER(("Visitor")) --> EDGE

Build process (offline)

flowchart LR
  A["Wikipedia film lists<br/>1928 → today"] --> B["Film pages<br/>infobox · cast · songs · plot"]
  W["Wikidata claims"] --> C
  B --> C[("Raw claims<br/>every row keeps its source URL")]
  C --> D["Entity resolution<br/>shared IDs → name blocking"]
  D --> E[("Canonical graph kg_*<br/>films · people · credits")]
  B --> F["Triples from prose<br/>hyperlinks → types → cue rules → LLM relabel"]
  F --> E
  E --> AP["Appearance types<br/>role · cameo · self · voice · archive"]
  B --> G["Chunks → BM25 + embeddings"] --> E
  E --> H["Louvain communities + reports"]
  E --> I["Snapshot for hosting<br/>drop IMDb, rebuild graph, gzip"]

How one question is answered

sequenceDiagram
  autonumber
  actor U as Visitor
  participant API as /api/chat
  participant NLU as NLU
  participant R as Intent router
  participant G as Graph query
  participant C as Answer composer
  U->>API: "Mohanlal actor and Priyadarshan director"
  API->>API: rate limits · size and origin checks
  API->>NLU: question
  NLU->>NLU: gazetteer finds 2 people, binds roles actor / director
  NLU->>R: "PERSON actor and PERSON director"
  R->>R: rules → classifier (TF-IDF + LR) → intent = collaboration
  R->>G: films_with(Mohanlal as actor, Priyadarshan as director)
  G-->>C: films with provenance per credit
  C-->>U: count + film cards + answer subgraph + trace (SQL, scores, timings)

This is the beginning of the README. Read the rest at the original. Read the full README ↗