trustgraph/docs/tech-specs/jsonl-prompt-output.md

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default	JSONL Prompt Output Technical Specification	Tech Specs

JSONL Prompt Output Technical Specification

Overview

This specification describes the implementation of JSONL (JSON Lines) output format for prompt responses in TrustGraph. JSONL enables truncation-resilient extraction of structured data from LLM responses, addressing critical issues with JSON array outputs being corrupted when LLM responses hit output token limits.

This implementation supports the following use cases:

1. Truncation-Resilient Extraction: Extract valid partial results even when LLM output is truncated mid-response
2. Large-Scale Extraction: Handle extraction of many items without risk of complete failure due to token limits
3. Mixed-Type Extraction: Support extraction of multiple entity types (definitions, relationships, entities, attributes) in a single prompt
4. Streaming-Compatible Output: Enable future streaming/incremental processing of extraction results

Goals

• Backward Compatibility: Existing prompts using response-type: "text" and response-type: "json" continue to work without modification
• Truncation Resilience: Partial LLM outputs yield partial valid results rather than complete failure
• Schema Validation: Support JSON Schema validation for individual objects
• Discriminated Unions: Support mixed-type outputs using a type field discriminator
• Minimal API Changes: Extend existing prompt configuration with new response type and schema key

Background

Current Architecture

The prompt service supports two response types:

1. response-type: "text" - Raw text response returned as-is
2. response-type: "json" - JSON parsed from response, validated against optional schema

Current implementation in trustgraph-flow/trustgraph/template/prompt_manager.py:

class Prompt:
    def __init__(self, template, response_type = "text", terms=None, schema=None):
        self.template = template
        self.response_type = response_type
        self.terms = terms
        self.schema = schema

Current Limitations

When extraction prompts request output as JSON arrays ([{...}, {...}, ...]):

• Truncation corruption: If the LLM hits output token limits mid-array, the entire response becomes invalid JSON and cannot be parsed
• All-or-nothing parsing: Must receive complete output before parsing
• No partial results: A truncated response yields zero usable data
• Unreliable for large extractions: More extracted items = higher failure risk

This specification addresses these limitations by introducing JSONL format for extraction prompts, where each extracted item is a complete JSON object on its own line.

Technical Design

Response Type Extension

Add a new response type "jsonl" alongside existing "text" and "json" types.

Configuration Changes

New response type value:

"response-type": "jsonl"

Schema interpretation:

The existing "schema" key is used for both "json" and "jsonl" response types. The interpretation depends on the response type:

• "json": Schema describes the entire response (typically an array or object)
• "jsonl": Schema describes each individual line/object

{
  "response-type": "jsonl",
  "schema": {
    "type": "object",
    "properties": {
      "entity": { "type": "string" },
      "definition": { "type": "string" }
    },
    "required": ["entity", "definition"]
  }
}

This avoids changes to prompt configuration tooling and editors.

JSONL Format Specification

Simple Extraction

For prompts extracting a single type of object (definitions, relationships, topics, rows), the output is one JSON object per line with no wrapper:

Prompt output format:

{"entity": "photosynthesis", "definition": "Process by which plants convert sunlight"}
{"entity": "chlorophyll", "definition": "Green pigment in plants"}
{"entity": "mitochondria", "definition": "Powerhouse of the cell"}

Contrast with previous JSON array format:

[
  {"entity": "photosynthesis", "definition": "Process by which plants convert sunlight"},
  {"entity": "chlorophyll", "definition": "Green pigment in plants"},
  {"entity": "mitochondria", "definition": "Powerhouse of the cell"}
]

If the LLM truncates after line 2, the JSON array format yields invalid JSON, while JSONL yields two valid objects.

Mixed-Type Extraction (Discriminated Unions)

For prompts extracting multiple types of objects (e.g., both definitions and relationships, or entities, relationships, and attributes), use a "type" field as discriminator:

Prompt output format:

{"type": "definition", "entity": "DNA", "definition": "Molecule carrying genetic instructions"}
{"type": "relationship", "subject": "DNA", "predicate": "located_in", "object": "cell nucleus", "object-entity": true}
{"type": "definition", "entity": "RNA", "definition": "Molecule that carries genetic information"}
{"type": "relationship", "subject": "RNA", "predicate": "transcribed_from", "object": "DNA", "object-entity": true}

Schema for discriminated unions uses oneOf:

{
  "response-type": "jsonl",
  "schema": {
    "oneOf": [
      {
        "type": "object",
        "properties": {
          "type": { "const": "definition" },
          "entity": { "type": "string" },
          "definition": { "type": "string" }
        },
        "required": ["type", "entity", "definition"]
      },
      {
        "type": "object",
        "properties": {
          "type": { "const": "relationship" },
          "subject": { "type": "string" },
          "predicate": { "type": "string" },
          "object": { "type": "string" },
          "object-entity": { "type": "boolean" }
        },
        "required": ["type", "subject", "predicate", "object", "object-entity"]
      }
    ]
  }
}

Ontology Extraction

For ontology-based extraction with entities, relationships, and attributes:

Prompt output format:

{"type": "entity", "entity": "Cornish pasty", "entity_type": "fo/Recipe"}
{"type": "entity", "entity": "beef", "entity_type": "fo/Food"}
{"type": "relationship", "subject": "Cornish pasty", "subject_type": "fo/Recipe", "relation": "fo/has_ingredient", "object": "beef", "object_type": "fo/Food"}
{"type": "attribute", "entity": "Cornish pasty", "entity_type": "fo/Recipe", "attribute": "fo/serves", "value": "4 people"}

Implementation Details

Prompt Class

The existing Prompt class requires no changes. The schema field is reused for JSONL, with its interpretation determined by response_type:

class Prompt:
    def __init__(self, template, response_type="text", terms=None, schema=None):
        self.template = template
        self.response_type = response_type
        self.terms = terms
        self.schema = schema  # Interpretation depends on response_type

PromptManager.load_config

No changes required - existing configuration loading already handles the schema key.

JSONL Parsing

Add a new parsing method for JSONL responses:

def parse_jsonl(self, text):
    """
    Parse JSONL response, returning list of valid objects.

    Invalid lines (malformed JSON, empty lines) are skipped with warnings.
    This provides truncation resilience - partial output yields partial results.
    """
    results = []

    for line_num, line in enumerate(text.strip().split('\n'), 1):
        line = line.strip()

        # Skip empty lines
        if not line:
            continue

        # Skip markdown code fence markers if present
        if line.startswith('```'):
            continue

        try:
            obj = json.loads(line)
            results.append(obj)
        except json.JSONDecodeError as e:
            # Log warning but continue - this provides truncation resilience
            logger.warning(f"JSONL parse error on line {line_num}: {e}")

    return results

PromptManager.invoke Changes

Extend the invoke method to handle the new response type:

async def invoke(self, id, input, llm):
    logger.debug("Invoking prompt template...")

    terms = self.terms | self.prompts[id].terms | input
    resp_type = self.prompts[id].response_type

    prompt = {
        "system": self.system_template.render(terms),
        "prompt": self.render(id, input)
    }

    resp = await llm(**prompt)

    if resp_type == "text":
        return resp

    if resp_type == "json":
        try:
            obj = self.parse_json(resp)
        except:
            logger.error(f"JSON parse failed: {resp}")
            raise RuntimeError("JSON parse fail")

        if self.prompts[id].schema:
            try:
                validate(instance=obj, schema=self.prompts[id].schema)
                logger.debug("Schema validation successful")
            except Exception as e:
                raise RuntimeError(f"Schema validation fail: {e}")

        return obj

    if resp_type == "jsonl":
        objects = self.parse_jsonl(resp)

        if not objects:
            logger.warning("JSONL parse returned no valid objects")
            return []

        # Validate each object against schema if provided
        if self.prompts[id].schema:
            validated = []
            for i, obj in enumerate(objects):
                try:
                    validate(instance=obj, schema=self.prompts[id].schema)
                    validated.append(obj)
                except Exception as e:
                    logger.warning(f"Object {i} failed schema validation: {e}")
            return validated

        return objects

    raise RuntimeError(f"Response type {resp_type} not known")

Affected Prompts

The following prompts should be migrated to JSONL format:

Prompt ID	Description	Type Field
extract-definitions	Entity/definition extraction	No (single type)
extract-relationships	Relationship extraction	No (single type)
extract-topics	Topic/definition extraction	No (single type)
extract-rows	Structured row extraction	No (single type)
agent-kg-extract	Combined definition + relationship extraction	Yes: "definition", "relationship"
extract-with-ontologies / ontology-extract	Ontology-based extraction	Yes: "entity", "relationship", "attribute"

API Changes

Client Perspective

JSONL parsing is transparent to prompt service API callers. The parsing occurs server-side in the prompt service, and the response is returned via the standard PromptResponse.object field as a serialized JSON array.

When clients call the prompt service (via PromptClient.prompt() or similar):

• response-type: "json" with array schema → client receives Python list
• response-type: "jsonl" → client receives Python list

From the client's perspective, both return identical data structures. The difference is entirely in how the LLM output is parsed server-side:

• JSON array format: Single json.loads() call; fails completely if truncated
• JSONL format: Line-by-line parsing; yields partial results if truncated

This means existing client code expecting a list from extraction prompts requires no changes when migrating prompts from JSON to JSONL format.

Server Return Value

For response-type: "jsonl", the PromptManager.invoke() method returns a list[dict] containing all successfully parsed and validated objects. This list is then serialized to JSON for the PromptResponse.object field.

Error Handling

• Empty results: Returns empty list [] with warning log
• Partial parse failure: Returns list of successfully parsed objects with warning logs for failures
• Complete parse failure: Returns empty list [] with warning logs

This differs from response-type: "json" which raises RuntimeError on parse failure. The lenient behavior for JSONL is intentional to provide truncation resilience.

Configuration Example

Complete prompt configuration example:

{
  "prompt": "Extract all entities and their definitions from the following text. Output one JSON object per line.\n\nText:\n{{text}}\n\nOutput format per line:\n{\"entity\": \"<name>\", \"definition\": \"<definition>\"}",
  "response-type": "jsonl",
  "schema": {
    "type": "object",
    "properties": {
      "entity": {
        "type": "string",
        "description": "The entity name"
      },
      "definition": {
        "type": "string",
        "description": "A clear definition of the entity"
      }
    },
    "required": ["entity", "definition"]
  }
}

Security Considerations

• Input Validation: JSON parsing uses standard json.loads() which is safe against injection attacks
• Schema Validation: Uses jsonschema.validate() for schema enforcement
• No New Attack Surface: JSONL parsing is strictly safer than JSON array parsing due to line-by-line processing

Performance Considerations

• Memory: Line-by-line parsing uses less peak memory than loading full JSON arrays
• Latency: Parsing performance is comparable to JSON array parsing
• Validation: Schema validation runs per-object, which adds overhead but enables partial results on validation failure

Testing Strategy

Unit Tests

• JSONL parsing with valid input
• JSONL parsing with empty lines
• JSONL parsing with markdown code fences
• JSONL parsing with truncated final line
• JSONL parsing with invalid JSON lines interspersed
• Schema validation with oneOf discriminated unions
• Backward compatibility: existing "text" and "json" prompts unchanged

Integration Tests

• End-to-end extraction with JSONL prompts
• Extraction with simulated truncation (artificially limited response)
• Mixed-type extraction with type discriminator
• Ontology extraction with all three types

Extraction Quality Tests

• Compare extraction results: JSONL vs JSON array format
• Verify truncation resilience: JSONL yields partial results where JSON fails

Migration Plan

Phase 1: Implementation

1. Implement parse_jsonl() method in PromptManager
2. Extend invoke() to handle response-type: "jsonl"
3. Add unit tests

Phase 2: Prompt Migration

1. Update extract-definitions prompt and configuration
2. Update extract-relationships prompt and configuration
3. Update extract-topics prompt and configuration
4. Update extract-rows prompt and configuration
5. Update agent-kg-extract prompt and configuration
6. Update extract-with-ontologies prompt and configuration

Phase 3: Downstream Updates

1. Update any code consuming extraction results to handle list return type
2. Update code that categorizes mixed-type extractions by type field
3. Update tests that assert on extraction output format

Open Questions

None at this time.

References

• Current implementation: trustgraph-flow/trustgraph/template/prompt_manager.py
• JSON Lines specification: https://jsonlines.org/
• JSON Schema oneOf: https://json-schema.org/understanding-json-schema/reference/combining.html#oneof
• Related specification: Streaming LLM Responses (docs/tech-specs/streaming-llm-responses.md)