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Vision-language extraction of pedon data (Module 2)

Module 2 (vlm-*) lets soilKey build a PedonRecord from a field-description PDF, a profile photo, or a fieldsheet image – using a vision-language model (VLM) for the extraction and JSON-Schema validation as a hard gate. The taxonomic key itself is never delegated to the LLM: the VLM is restricted to extraction, and every extracted value is recorded in the provenance log so the evidence grade reflects the lower confidence of VLM-sourced data.

This vignette walks the extraction loop end to end, using a MockVLMProvider so the example runs offline and without API keys. Swapping the mock for a real ellmer chat (Anthropic, OpenAI, etc.) is a one-line change.

1. The mock provider

MockVLMProvider exposes the same $chat() method as an ellmer chat object, but pops responses from a pre-loaded queue. Because all of soilKey’s VLM logic talks to the provider through $chat(), swapping it for a real chat (or for any custom backend) is transparent.

mock <- MockVLMProvider$new(responses = list())
class(mock)
#> [1] "MockVLMProvider" "R6"

2. Schemas and prompts

VLM responses are constrained by a JSON Schema (Draft-07). The schema for horizon extraction is shipped at inst/schemas/horizon.json; the rendered prompt template lives at inst/prompts/extract_horizons.md.

sch <- jsonlite::fromJSON(soilKey:::load_schema("horizon"),
                            simplifyVector = FALSE)
length(sch$properties$horizons$items$properties)
#> [1] 35
head(names(sch$properties$horizons$items$properties), 12)
#>  [1] "top_cm"                "bottom_cm"             "designation"          
#>  [4] "boundary_distinctness" "boundary_topography"   "munsell_moist"        
#>  [7] "munsell_dry"           "structure_grade"       "structure_size"       
#> [10] "structure_type"        "consistence_moist"     "clay_films_amount"

The schema enforces strict types – top_cm must be numeric, designation must be a string, Munsell colours come as {hue, value, chroma, confidence, source_quote} triples, etc. Any response that fails validation triggers the retry loop with the validation error included in the next prompt.

3. Walking the extraction loop with the mock

Suppose the model returns the following horizon-level JSON for a synthetic Latossolo description:

horizon_json <- '{
  "horizons": [
    {
      "top_cm": 0,
      "bottom_cm": 15,
      "designation": "A",
      "munsell_moist": {"hue": "2.5YR", "value": 3, "chroma": 4,
                          "confidence": 0.85, "source_quote": "vermelho-escuro"},
      "clay_pct": {"value": 50, "confidence": 0.9, "source_quote": "muito argilosa (50%)"},
      "oc_pct"  : {"value": 2.0, "confidence": 0.85, "source_quote": "C org. 2.0%"}
    },
    {
      "top_cm": 15,
      "bottom_cm": 65,
      "designation": "Bw1",
      "munsell_moist": {"hue": "2.5YR", "value": 3, "chroma": 6,
                          "confidence": 0.85, "source_quote": "vermelho"},
      "clay_pct": {"value": 60, "confidence": 0.9, "source_quote": "muito argilosa"},
      "oc_pct"  : {"value": 1.2, "confidence": 0.85, "source_quote": "C org. 1.2%"}
    }
  ]
}'

Wire the mock provider to return this JSON, send a prompt through validate_or_retry, and watch the loop:

mock <- MockVLMProvider$new(responses = list(horizon_json))

res <- soilKey:::validate_or_retry(
  provider    = mock,
  prompt      = "extract horizons from <fake document>",
  schema      = "horizon",
  max_retries = 0L
)

str(res, max.level = 2)
#> List of 3
#>  $ data    :List of 1
#>   ..$ horizons:List of 2
#>  $ raw     : chr "{\n  \"horizons\": [\n    {\n      \"top_cm\": 0,\n      \"bottom_cm\": 15,\n      \"designation\": \"A\",\n   "| __truncated__
#>  $ attempts: int 1

The returned res$data is the parsed (validated) R object. res$attempts == 1 because the canned response passed validation on the first try. The validation_error_at argument of MockVLMProvider$new() forces an attempt to return malformed JSON, exercising the retry path – see tests/testthat/test-vlm-extract.R for a worked retry-loop test.

4. Merging extraction into a `PedonRecord`

apply_horizons_extraction() consumes the parsed JSON and merges it into a PedonRecord, recording each value in the provenance log with source = "extracted_vlm".

pr <- PedonRecord$new(
  site = list(id = "VLM-demo", lat = -22.5, lon = -43.7,
                country = "BR", parent_material = "gneiss"),
  horizons = data.table::data.table(top_cm = numeric(0), bottom_cm = numeric(0))
)

added <- soilKey:::apply_horizons_extraction(pr, res$data, overwrite = TRUE)
cat("Provenance entries added:", added, "\n")
#> Provenance entries added: 12

# Inspect what landed in the horizons table.
pr$horizons[, .(top_cm, bottom_cm, designation,
                munsell_hue_moist, munsell_value_moist, munsell_chroma_moist,
                clay_pct, oc_pct)]
#>    top_cm bottom_cm designation munsell_hue_moist munsell_value_moist
#>     <num>     <num>      <char>            <char>               <num>
#> 1:      0        15           A             2.5YR                   3
#> 2:     15        65         Bw1             2.5YR                   3
#>    munsell_chroma_moist clay_pct oc_pct
#>                   <num>    <num>  <num>
#> 1:                    4       50    2.0
#> 2:                    6       60    1.2

5. Provenance and evidence grade

Every value that came from the VLM carries source = "extracted_vlm" in the provenance log. The classification’s evidence grade reacts:

prov <- pr$provenance
head(prov[, .(horizon_idx, attribute, source, confidence)])
#>    horizon_idx            attribute        source confidence
#>          <int>               <char>        <char>      <num>
#> 1:           1          designation extracted_vlm         NA
#> 2:           1    munsell_hue_moist extracted_vlm       0.85
#> 3:           1  munsell_value_moist extracted_vlm       0.85
#> 4:           1 munsell_chroma_moist extracted_vlm       0.85
#> 5:           1             clay_pct extracted_vlm       0.90
#> 6:           1               oc_pct extracted_vlm       0.85

Even the simplest call to classify_wrb2022() will reflect the lower-confidence VLM source via a lower evidence grade than the same profile assembled from lab data. (See vignette 02 for the grade ladder.)

6. Production: swap the mock for an `ellmer` chat

In production, replace MockVLMProvider with a chat object from ellmer. The rest of the pipeline is unchanged.

6a. Local-first with Gemma 4 (Ollama)

For sensitive field descriptions, governmental surveys or just for reproducibility without an API key, the recommended path is a local Gemma 4 model via Ollama. The data never leaves your machine.

# Install once (any platform)
ollama pull gemma4:e4b           # ~3 GB, multimodal, fits a laptop
# ollama pull gemma4:31b         # frontier dense, best quality
# ollama serve                   # background server

# Local Gemma 4 edge -- multimodal text + image (and audio).
provider <- vlm_provider("ollama")             # default: gemma4:e4b
# provider <- vlm_provider("ollama", model = "gemma4:31b")  # frontier

extract_horizons_from_pdf(
  pedon       = pr,
  pdf_path    = "field-reports/perfil-LV-001.pdf",
  provider    = provider,
  max_retries = 3L
)

6b. Cloud providers (Anthropic, OpenAI, Google)

# install.packages("ellmer")

# Anthropic Claude (needs ANTHROPIC_API_KEY in the environment).
provider <- vlm_provider("anthropic")          # default: claude-sonnet-4-7

# Or OpenAI / Google in the same one-liner shape:
# provider <- vlm_provider("openai")           # default: gpt-4o
# provider <- vlm_provider("google")           # default: gemini-2.0-pro

extract_horizons_from_pdf(
  pedon       = pr,
  pdf_path    = "field-reports/perfil-LV-001.pdf",
  provider    = provider,
  max_retries = 3L
)

6c. The one-liner: `classify_from_documents()`

For the canonical case – “I have a PDF and (optionally) a profile photo, give me the three classifications and a one-pager report” – chain everything in a single call. The default provider is local Gemma 4 edge:

res <- classify_from_documents(
  pdf      = "perfil_042_descricao.pdf",
  image    = "perfil_042_parede.jpg",
  report   = "perfil_042.html"            # optional output report
)

res$classifications$wrb$name
#> "Geric Ferric Rhodic Chromic Ferralsol (Clayic, Humic, Dystric, Ochric, Rubic)"

res$classifications$sibcs$name
#> "Latossolos Vermelhos Distroficos tipicos, argilosa, moderado"

res$classifications$usda$name
#> "Rhodic Hapludox"

Photos go through extract_munsell_from_photo(); site metadata through extract_site_from_fieldsheet(). All three follow the same validate-or-retry contract so they accept any provider that exposes $chat(prompt, ...).

The next vignette (v05_spatial_spectra_pipeline) shows how the SoilGrids spatial prior and OSSL Vis-NIR predictions can fill remaining gaps in the pedon before classification.

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