LangCore

Overview

LangCore is a Python library for LLM-powered structured information extraction from unstructured text. It is built on top of Google's open-source LangCore library (Apache 2.0), extending it with additional capabilities for production document processing workflows.

Attribution: The core extraction engine is derived from LangCore by Google LLC. See the NOTICE file for full attribution details.

Overview
Feature Overview
Core Capabilities
Quick Start
Schema-First Extraction with Pydantic
Multi-Model Consensus Extraction
Confidence Scoring
Extraction Reliability Score
Extraction Hooks & Events
Quality Metrics & Evaluation
Ecosystem Plugins
Installation
API Key Setup for Cloud Models
Adding Custom Model Providers
Using OpenAI Models
Using Local LLMs with Ollama
More Examples
Contributing
Testing
License

Feature Overview

LangCore is a batteries-included extraction framework. Core features ship with the langcore package; additional capabilities are available through first-party plugin packages that you install as needed.

Core Features (`langcore`)

Feature	Description
Few-Shot Extraction	Define extraction tasks with a prompt and examples — no fine-tuning required
Pydantic Schema Extraction	Define targets as Pydantic models; prompts, JSON schema constraints, and seed examples are auto-generated
Schema Validation Retry	Instructor-style validate → re-ask loop: validates extractions against the Pydantic schema and retries invalid ones with error feedback
Multi-Model Consensus	Run extraction across multiple LLM providers and merge results — extractions confirmed by multiple models receive higher confidence
Confidence Scoring	Per-extraction confidence (0.0–1.0) combining alignment quality + token overlap, with configurable weights
Extraction Reliability Score	Composite quality metric (0.0–1.0) combining confidence, schema validity, field completeness, and source grounding
Source Grounding & Alignment	Maps every extraction to its exact character position in the source text for traceability and verification
Long Document Chunking	Optimized chunking + parallel processing overcomes the "needle-in-a-haystack" problem in large documents
Multi-Pass Extraction	Configurable `extraction_passes` for higher recall with cross-pass confidence boosting
Schema Utilities	`to_pydantic()`, `schema_from_pydantic()`, `schema_from_example()`, `schema_from_examples()` — convert between Pydantic models, dicts, and LangCore's internal format
Extraction Hooks & Events	6 lifecycle events (`start`, `chunk`, `llm_call`, `alignment`, `complete`, `error`) with fault-tolerant callbacks
Global Configuration	`lx.configure(hooks=...)` sets app-wide hooks; per-call hooks compose via `+` operator
Prompt Alignment Validation	Automatic warnings when example `extraction_text` doesn't appear verbatim in the example `text`
Quality Metrics & Evaluation	Built-in P/R/F1/accuracy with per-field and per-document breakdown, fuzzy matching, and averaging modes
Response Caching	Built-in LLM response cache with automatic cache-busting for multi-pass extraction
Interactive Visualization	Generates self-contained HTML to review extractions in their original context
Flexible Model Support	Built-in providers for Gemini, OpenAI, and Ollama; extensible via plugins
Custom Provider System	`BaseLanguageModel` ABC + entry-point plugin registry with priority-based resolution
Async & Parallel	`async_extract()` with `max_workers` for concurrent chunk processing
URL/File Input	Accepts URLs, file paths, and raw text directly
Batch API	Vertex AI Batch API support for large-scale jobs
Controlled Generation	JSON schema constraints via supported models (Gemini)

Plugin Features

Feature	Package	Description
100+ LLM Providers	`langcore-litellm`	OpenAI, Anthropic, Azure, Mistral, Groq, Cohere, HuggingFace, Ollama, vLLM, and more via LiteLLM
Output Validation & Retry	`langcore-guardrails`	7 built-in validators with corrective retry loop and 4 on-fail actions
Grounding Validator	`langcore-guardrails`	Rejects hallucinated extractions using alignment quality and character coverage checks
Confidence Threshold	`langcore-guardrails`	Filters extractions below a confidence score cutoff
Schema / JSON Validation	`langcore-guardrails`	Pydantic and JSON Schema validators with strict/lenient modes
Consistency Rules	`langcore-guardrails`	Cross-checks extracted values using user-supplied business rules
Regex Validation	`langcore-guardrails`	Match extracted output against regex patterns
Field Completeness	`langcore-guardrails`	Ensure required schema fields are present and non-empty
Validator Chaining	`langcore-guardrails`	Compose multiple validators with per-validator failure actions
Validator Registry	`langcore-guardrails`	`@register_validator` decorator for plugging in custom validators
Audit Logging	`langcore-audit`	Structured audit records for every LLM call — pluggable sinks (logging, JSONL, OpenTelemetry)
Hybrid Rules + LLM	`langcore-hybrid`	Deterministic regex/function rules with LLM fallback — save 50–80% on LLM costs
Prompt Optimization	`langcore-dspy`	Automatic prompt and few-shot example optimization using DSPy (MIPROv2, GEPA)
Evaluation (P/R/F1)	`langcore-dspy`	Built-in evaluation with per-document precision, recall, and F1 for optimized configs
RAG Query Parsing	`langcore-rag`	Decompose natural-language queries into semantic terms + structured metadata filters
Query Caching	`langcore-rag`	LRU cache for parsed queries with Pydantic schema introspection
PDF Support	`langcore-docling`	Native PDF extraction via Docling — drop-in replacement for `lx.extract()`
HTTP API	`langcore-api`	Production-ready REST service with task queuing, caching, webhooks, and Prometheus metrics

Core Capabilities

Precise Source Grounding: Maps every extraction to its exact location in the source text, enabling visual highlighting for easy traceability and verification.
Reliable Structured Outputs: Enforces a consistent output schema based on your few-shot examples, leveraging controlled generation in supported models like Gemini to guarantee robust, structured results.
Optimized for Long Documents: Overcomes the "needle-in-a-haystack" challenge of large document extraction by using an optimized strategy of text chunking, parallel processing, and multiple passes for higher recall.
Interactive Visualization: Instantly generates a self-contained, interactive HTML file to visualize and review thousands of extracted entities in their original context.
Flexible LLM Support: Supports your preferred models, from cloud-based LLMs like the Google Gemini family to local open-source models via the built-in Ollama interface.
Adaptable to Any Domain: Define extraction tasks for any domain using just a few examples — no model fine-tuning required.
Leverages LLM World Knowledge: Utilize precise prompt wording and few-shot examples to influence how the extraction task may utilize LLM knowledge.

Quick Start

Note: Using cloud-hosted models like Gemini requires an API key. See the API Key Setup section for instructions on how to get and configure your key.

Extract structured information with just a few lines of code.

1. Define Your Extraction Task

First, create a prompt that clearly describes what you want to extract. Then, provide a high-quality example to guide the model.

import langcore as lx
import textwrap

# 1. Define the prompt and extraction rules
prompt = textwrap.dedent("""\
    Extract characters, emotions, and relationships in order of appearance.
    Use exact text for extractions. Do not paraphrase or overlap entities.
    Provide meaningful attributes for each entity to add context.""")

# 2. Provide a high-quality example to guide the model
examples = [
    lx.data.ExampleData(
        text="ROMEO. But soft! What light through yonder window breaks? It is the east, and Juliet is the sun.",
        extractions=[
            lx.data.Extraction(
                extraction_class="character",
                extraction_text="ROMEO",
                attributes={"emotional_state": "wonder"}
            ),
            lx.data.Extraction(
                extraction_class="emotion",
                extraction_text="But soft!",
                attributes={"feeling": "gentle awe"}
            ),
            lx.data.Extraction(
                extraction_class="relationship",
                extraction_text="Juliet is the sun",
                attributes={"type": "metaphor"}
            ),
        ]
    )
]

Note: Examples drive model behavior. Each extraction_text should ideally be verbatim from the example's text (no paraphrasing), listed in order of appearance. LangCore raises Prompt alignment warnings by default if examples don't follow this pattern—resolve these for best results.

2. Run the Extraction

Provide your input text and the prompt materials to the lx.extract function.

# The input text to be processed
input_text = "Lady Juliet gazed longingly at the stars, her heart aching for Romeo"

# Run the extraction
result = lx.extract(
    text_or_documents=input_text,
    prompt_description=prompt,
    examples=examples,
    model_id="gemini-2.5-flash",
)

Model Selection: gemini-2.5-flash is the recommended default, offering an excellent balance of speed, cost, and quality. For highly complex tasks requiring deeper reasoning, gemini-2.5-pro may provide superior results. For large-scale or production use, a Tier 2 Gemini quota is suggested to increase throughput and avoid rate limits. See the rate-limit documentation for details.

Model Lifecycle: Note that Gemini models have a lifecycle with defined retirement dates. Users should consult the official model version documentation to stay informed about the latest stable and legacy versions.

3. Visualize the Results

The extractions can be saved to a .jsonl file, a popular format for working with language model data. LangCore can then generate an interactive HTML visualization from this file to review the entities in context.

# Save the results to a JSONL file
lx.io.save_annotated_documents([result], output_name="extraction_results.jsonl", output_dir=".")

# Generate the visualization from the file
html_content = lx.visualize("extraction_results.jsonl")
with open("visualization.html", "w") as f:
    if hasattr(html_content, 'data'):
        f.write(html_content.data)  # For Jupyter/Colab
    else:
        f.write(html_content)

This creates an animated and interactive HTML file:

Note on LLM Knowledge Utilization: This example demonstrates extractions that stay close to the text evidence - extracting "longing" for Lady Juliet's emotional state and identifying "yearning" from "gazed longingly at the stars." The task could be modified to generate attributes that draw more heavily from the LLM's world knowledge (e.g., adding "identity": "Capulet family daughter" or "literary_context": "tragic heroine"). The balance between text-evidence and knowledge-inference is controlled by your prompt instructions and example attributes.

Scaling to Longer Documents

For larger texts, you can process entire documents directly from URLs with parallel processing and enhanced sensitivity:

# Process Romeo & Juliet directly from Project Gutenberg
result = lx.extract(
    text_or_documents="https://www.gutenberg.org/files/1513/1513-0.txt",
    prompt_description=prompt,
    examples=examples,
    model_id="gemini-2.5-flash",
    extraction_passes=3,    # Improves recall through multiple passes
    max_workers=20,         # Parallel processing for speed
    max_char_buffer=1000    # Smaller contexts for better accuracy
)

Multi-pass & caching: When extraction_passes > 1, the first pass uses normal caching behaviour while subsequent passes include a pass_num keyword argument that providers can use to bypass response caches. The langcore-litellm provider does this automatically — passes ≥ 2 always hit the live LLM API.

This approach can extract hundreds of entities from full novels while maintaining high accuracy. The interactive visualization seamlessly handles large result sets, making it easy to explore hundreds of entities from the output JSONL file. See the full Romeo and Juliet extraction example → for detailed results and performance insights.

Vertex AI Batch Processing

Save costs on large-scale tasks by enabling Vertex AI Batch API: language_model_params={"vertexai": True, "batch": {"enabled": True}}.

See an example of the Vertex AI Batch API usage in this example.

Schema-First Extraction with Pydantic

Instead of manually constructing ExampleData objects, you can define your extraction schema as a Pydantic model. LangCore will auto-generate the prompt and schema constraints for you.

from pydantic import BaseModel, Field
import langcore as lx

class Invoice(BaseModel):
    invoice_number: str = Field(description="Invoice ID like INV-001")
    amount: float = Field(description="Total amount in dollars")
    due_date: str = Field(description="Due date in YYYY-MM-DD format")

result = lx.extract(
    text="Invoice INV-2024-789 for $3,450 is due April 20th, 2024",
    schema=Invoice,
    model_id="gemini-2.5-flash",
)

# Convert extractions back to typed Pydantic instances
invoices = result.to_pydantic(Invoice)
for inv in invoices:
    print(f"{inv.invoice_number}: ${inv.amount} due {inv.due_date}")

You can also combine schema with explicit examples for the best of both worlds — the Pydantic model defines the structure, and examples provide few-shot guidance:

result = lx.extract(
    text="...",
    schema=Invoice,
    examples=[
        lx.data.ExampleData(
            text="Invoice INV-001 for $100 due Jan 1, 2024",
            extractions=[
                lx.data.Extraction(
                    extraction_class="Invoice",
                    extraction_text="INV-001",
                    attributes={"amount": "100.0", "due_date": "2024-01-01"},
                )
            ],
        )
    ],
    model_id="gemini-2.5-flash",
)

Tip: Use lx.schema_from_pydantic(Invoice) to inspect the auto-generated prompt and JSON schema before running extraction. Use lx.schema_from_example({"name": "John", "age": 30}) to auto-generate a Pydantic model from a plain dict, or lx.schema_from_examples([{"name": "John"}, {"name": "Jane", "age": 30}]) to merge multiple examples (fields default to optional when absent from some examples). When a field has mixed types across examples (e.g. int in one and str in another), the generated model uses a Union type (int | str) so Pydantic accepts any of the observed types.

Under the hood: The PydanticSchemaAdapter converts your Pydantic model into LangCore's internal SchemaConfig — auto-generating the prompt description, JSON schema, and seed examples. You can use it directly for advanced scenarios: from langcore.pydantic_schema import PydanticSchemaAdapter.

Schema Validation Retries

When you pass a schema, validation retries are auto-enabled (1 retry by default). After extraction, each result is validated against the Pydantic model; extractions that fail trigger a re-extraction with the validation error feedback, following the Instructor-style "validate → re-ask" pattern.

# Auto-enabled — retries once automatically when schema is provided
result = lx.extract(
    text="Invoice INV-2024-789 for $3,450 is due April 20th, 2024",
    schema=Invoice,
    model_id="gemini-2.5-flash",
)

You can increase or decrease the retry count, or disable retries entirely:

# Allow up to 3 retry attempts
result = lx.extract(text="...", schema=Invoice, schema_validation_retries=3)

# Explicitly disable retries (schema extraction only, no validation)
result = lx.extract(text="...", schema=Invoice, schema_validation_retries=0)

How it works:

Valid extractions from the first pass are always preserved.
Invalid extractions are collected with their Pydantic validation errors.
A correction prompt containing the specific error messages is appended.
Chunk-level retry: Only the text regions surrounding failing extractions are re-sent to the LLM (using char_interval to identify positions), not the entire document. Overlapping regions are merged automatically.
Newly valid results are offset back to document coordinates and merged in.
Steps 2–5 repeat up to schema_validation_retries times.
Token usage from all retry regions is accumulated in the final usage.

Retries also work for Document list inputs — each document is validated and retried independently.

When schema is not provided, schema_validation_retries defaults to 0 (no-op).

Multi-Model Consensus Extraction

Run the same extraction across multiple LLM providers and automatically merge results. Extractions confirmed by multiple models receive higher confidence scores, while unique findings from individual models are still preserved.

import langcore as lx

result = lx.extract(
    text="Patient Jane Doe received Lisinopril 10mg for hypertension.",
    examples=[...],
    consensus_models=["gemini-2.5-flash", "gpt-4o", "litellm/anthropic/claude-sonnet-4"],
)

# Extractions agreed upon by 2+ models get higher confidence
for ext in result.extractions:
    model = ext.attributes.get("_consensus_model_id", "unknown")
    print(f"{ext.extraction_text} (confidence={ext.confidence_score}, from={model})")

Accepted model IDs:

Each string in consensus_models is resolved through the same provider router used by model_id. Any model identifier that works with extract() works here too:

Provider	Format	Examples
Gemini (built-in)	`gemini-*`	`gemini-2.5-flash`, `gemini-2.5-pro`, `gemini-2.0-flash`
OpenAI (built-in)	`gpt-*`	`gpt-4o`, `gpt-4o-mini`, `gpt-4-turbo`
Ollama (built-in)	Model family name	`llama3.2:1b`, `mistral:7b`, `qwen2.5:72b`, `deepseek-coder-v2`
LiteLLM (plugin)	`litellm/<provider>/<model>`	`litellm/anthropic/claude-sonnet-4`, `litellm/gpt-4o`, `litellm/ollama/llama3`, `litellm/bedrock/anthropic.claude-3`
Custom plugins	Any registered pattern	Any model ID matched by a `langcore.providers` entry-point plugin

You can freely mix providers in a single list — e.g. ["gemini-2.5-flash", "gpt-4o", "litellm/anthropic/claude-sonnet-4"].

LiteLLM support: Install the langcore-litellm plugin (pip install langcore-litellm) to access 100+ models via a unified interface. Prefix model IDs with litellm/ so the provider router dispatches correctly. See the langcore-litellm README for full details.

How it works:

Each model in consensus_models runs extraction independently on the same text.
Results are merged using overlap-aware deduplication (same logic as multi-pass extraction).
Confidence is computed as agreement_ratio × alignment_confidence, where agreement_ratio = models_that_found_it / total_models.
Each extraction is tagged with _consensus_model_id in its attributes.
Token usage from all models is summed in the result.

Consensus works with all other features — schema validation retries, reliability scoring, and hooks all apply to the merged result. Both the sync and async versions run all models concurrently — async via asyncio.gather, sync via ThreadPoolExecutor — for maximum throughput.

When only one model is in the list, it falls back to standard single-model extraction.

Confidence Scoring

Every extraction is automatically assigned a confidence_score between 0.0 and 1.0 after alignment. The score is computed by compute_alignment_confidence() in the resolver and combines two signals:

Alignment quality (w_alignment, default 70%) — how well the extraction text matched the source: exact match = 1.0, lesser = 0.8, greater = 0.7, fuzzy = 0.5, unaligned = 0.2.
Token overlap ratio (w_overlap, default 30%) — how many tokens in the extraction text vs. the matched source span.

All weights are configurable — pass w_alignment and w_overlap keyword arguments to compute_alignment_confidence() to tune the balance for your use case. The unaligned default (0.2) can also be overridden via unaligned_confidence when you want a different baseline for extractions that couldn't be located in the source text.

result = lx.extract(
    text="Patient Jane Doe received Lisinopril for hypertension.",
    examples=[...],
    model_id="gemini-2.5-flash",
)

for extraction in result.extractions:
    print(f"{extraction.extraction_class}: {extraction.extraction_text} "
          f"(confidence: {extraction.confidence_score})")

# Document-level average confidence
print(f"Average confidence: {result.average_confidence}")

For multi-pass extraction, confidence is further augmented by cross-pass appearance frequency — extractions confirmed across multiple passes receive higher scores (cross_pass_ratio × alignment_confidence).

Extraction Reliability Score

While confidence_score measures alignment quality, the reliability score is a composite metric that combines multiple quality signals into a single reliability_score (0.0–1.0) per extraction:

Signal	Default Weight	What it measures
Confidence	40%	Alignment-based `confidence_score`
Schema validity	20%	Does the extraction pass Pydantic validation?
Field completeness	20%	Are all required schema fields non-empty?
Source grounding	20%	Does the extraction have a valid `char_interval`?

Reliability scoring is automatic — every call to extract() computes it. When a schema is provided, the schema validity and field completeness signals are evaluated against it; otherwise they default to 1.0 (neutral).

import langcore as lx
from pydantic import BaseModel

class Invoice(BaseModel):
    text: str
    amount: float
    currency: str = "USD"

result = lx.extract(text, schema=Invoice, model_id="gemini-2.5-flash")

for ext in result.extractions:
    print(f"{ext.extraction_text}: reliability={ext.reliability_score:.2f}")

# Document-level average reliability
print(f"Average reliability: {result.average_reliability}")

Custom weights — pass a ReliabilityConfig to tune the balance:

from langcore import ReliabilityConfig

# Emphasise confidence and grounding, ignore schema signals
config = ReliabilityConfig(
    w_confidence=0.6,
    w_schema_valid=0.0,
    w_completeness=0.0,
    w_grounding=0.4,
)
result = lx.extract(text, schema=Invoice, reliability_config=config)

To disable reliability scoring entirely, pass reliability_config=False.

Extraction Hooks & Events

The langcore.hooks module provides a lightweight event system inspired by Instructor hooks to inject custom logic at every stage of the extraction pipeline — without modifying core code.

Lifecycle events are defined by the HookName enum (you can also use plain strings):

Event	`HookName`	Fires when	Payload keys
`extraction:start`	`HookName.START`	Pipeline begins (after components are built)	`text`, `examples`, `model_id`
`extraction:chunk`	`HookName.CHUNK`	A document chunk has been processed	`chunk_index`, `num_chunks`, `chunk_text`, `extractions`
`extraction:llm_call`	`HookName.LLM_CALL`	An LLM inference call completes	`prompt`, `response`
`extraction:alignment`	`HookName.ALIGNMENT`	Extraction alignment is performed	`extractions`
`extraction:complete`	`HookName.COMPLETE`	Pipeline finishes successfully	`result`
`extraction:error`	`HookName.ERROR`	An exception is raised	`error`

Quick example:

from langcore.hooks import Hooks, HookName

hooks = Hooks()
hooks.on(HookName.START, lambda payload: print("Starting extraction…"))
hooks.on(HookName.LLM_CALL, lambda payload: print(f"LLM responded"))
hooks.on(HookName.ERROR, lambda payload: alert_team(payload["error"]))

result = lx.extract(
    text="Patient received Lisinopril 10mg daily.",
    examples=[...],
    model_id="gemini-2.5-flash",
    hooks=hooks,
)

Programmatic emission — use emit() (sync) or async_emit() (async) to fire events from your own code or custom providers:

hooks.emit("extraction:start", {"text": "hello", "model_id": "gpt-4o"})

# In async contexts, async_emit() awaits coroutine handlers
await hooks.async_emit("extraction:complete", {"result": result})

Composing hooks — merge two Hooks instances with +:

logging_hooks = Hooks().on("extraction:llm_call", log_llm_call)
metrics_hooks = Hooks().on("extraction:complete", record_metrics)
combined = logging_hooks + metrics_hooks

Removing handlers — use off() to remove a specific handler, or clear() to remove all:

hooks.off(HookName.LLM_CALL, log_llm_call)
hooks.clear()

Callbacks are fault-tolerant: if a handler raises an exception it is logged and swallowed so it never breaks the extraction pipeline.

Global hooks via lx.configure() — set hooks once and they apply to every extract() / async_extract() call without passing hooks= each time:

import langcore as lx
from langcore.hooks import Hooks, HookName

# Set up global observability hooks once at startup
global_hooks = Hooks()
global_hooks.on(HookName.EXTRACTION_START, lambda cfg: print("Starting:", cfg["model_id"]))
global_hooks.on(HookName.EXTRACTION_ERROR, lambda err: alert_team(err))
lx.configure(hooks=global_hooks)

# Every extract() call now emits to global hooks automatically
result = lx.extract(text="...", examples=[...])

# Per-call hooks still work and fire AFTER global hooks
per_call = Hooks().on(HookName.EXTRACTION_COMPLETE, lambda r: log_result(r))
result = lx.extract(text="...", examples=[...], hooks=per_call)

# Inspect or reset global config
lx.get_config()   # {"hooks": <Hooks instance>}
lx.reset()         # Clears all global configuration

Quality Metrics & Evaluation

The langcore.evaluation module provides built-in quality metrics for measuring extraction accuracy against ground truth. Compute precision, recall, F1, and accuracy at both the extraction level and per-field level.

from langcore.evaluation import ExtractionMetrics

# Quick static helpers
print(ExtractionMetrics.f1(predictions=results, ground_truth=expected))
print(ExtractionMetrics.precision(predictions=results, ground_truth=expected))

Full evaluation with per-field breakdown — pass a Pydantic schema for field-level metrics:

from pydantic import BaseModel, Field
from langcore.evaluation import ExtractionMetrics

class Invoice(BaseModel):
    invoice_number: str = Field(description="Invoice ID")
    amount: str = Field(description="Total amount")
    due_date: str = Field(description="Due date YYYY-MM-DD")

metrics = ExtractionMetrics(schema=Invoice)
report = metrics.evaluate(predictions=results, ground_truth=expected)
print(report.f1)          # 0.92
print(report.per_field)   # {"invoice_number": FieldReport(...), "amount": ...}

Convenience function — lx.evaluate() wraps ExtractionMetrics for quick one-liners:

import langcore as lx

report = lx.evaluate(predictions=results, ground_truth=expected, schema=Invoice)

Averaging modes — control how multi-document metrics are aggregated:

from langcore.evaluation import ExtractionMetrics

# Macro (default) — pool all extractions, compute P/R/F1 once
metrics = ExtractionMetrics(schema=Invoice, averaging="macro")

# Micro — compute P/R/F1 per document, then take unweighted mean
metrics = ExtractionMetrics(schema=Invoice, averaging="micro")

# Weighted — per-document P/R/F1 weighted by ground-truth count
metrics = ExtractionMetrics(schema=Invoice, averaging="weighted")

Fuzzy matching — allow near-matches instead of exact string equality:

# Match extractions with ≥80% string similarity (difflib.SequenceMatcher)
metrics = ExtractionMetrics(fuzzy_threshold=0.8)
report = metrics.evaluate(predictions=results, ground_truth=expected)

# Also available via lx.evaluate()
import langcore as lx
report = lx.evaluate(predictions=results, ground_truth=expected, fuzzy_threshold=0.8)

The EvaluationReport includes:

Aggregate precision, recall, f1, accuracy
averaging — the strategy used ("macro", "micro", or "weighted")
per_document — list of per-document metric dicts
per_field — dict of FieldReport objects with field-level P/R/F1 and support counts
strict_attributes=True mode for matching on attribute values (not just class + text)

Installation

From Source

LangCore uses modern Python packaging with pyproject.toml for dependency management:

git clone https://github.com/IgnatG/langcore.git
cd langcore

# For basic installation:
pip install -e .

# For development (includes linting tools):
pip install -e ".[dev]"

# For testing (includes pytest):
pip install -e ".[test]"

Docker

docker build -t langcore .
docker run --rm -e LANGCORE_API_KEY="your-api-key" langcore python your_script.py

API Key Setup for Cloud Models

When using LangCore with cloud-hosted models (like Gemini or OpenAI), you'll need to set up an API key. On-device models don't require an API key. For developers using local LLMs, LangCore offers built-in support for Ollama and can be extended to other third-party APIs by updating the inference endpoints.

API Key Sources

Get API keys from:

AI Studio for Gemini models
Vertex AI for enterprise use
OpenAI Platform for OpenAI models

Setting up API key in your environment

Option 1: Environment Variable

export LANGCORE_API_KEY="your-api-key-here"

Option 2: .env File (Recommended)

Add your API key to a .env file:

# Add API key to .env file
cat >> .env << 'EOF'
LANGCORE_API_KEY=your-api-key-here
EOF

# Keep your API key secure
echo '.env' >> .gitignore

In your Python code:

import langcore as lx

result = lx.extract(
    text_or_documents=input_text,
    prompt_description="Extract information...",
    examples=[...],
    model_id="gemini-2.5-flash"
)

Option 3: Direct API Key (Not Recommended for Production)

You can also provide the API key directly in your code, though this is not recommended for production use:

result = lx.extract(
    text_or_documents=input_text,
    prompt_description="Extract information...",
    examples=[...],
    model_id="gemini-2.5-flash",
    api_key="your-api-key-here"  # Only use this for testing/development
)

Option 4: Vertex AI (Service Accounts)

Use Vertex AI for authentication with service accounts:

result = lx.extract(
    text_or_documents=input_text,
    prompt_description="Extract information...",
    examples=[...],
    model_id="gemini-2.5-flash",
    language_model_params={
        "vertexai": True,
        "project": "your-project-id",
        "location": "global"  # or regional endpoint
    }
)

Adding Custom Model Providers

LangCore supports custom LLM providers via a lightweight plugin system. You can add support for new models without changing core code.

Add new model support independently of the core library
Distribute your provider as a separate Python package
Keep custom dependencies isolated
Override or extend built-in providers via priority-based resolution

See the detailed guide in Provider System Documentation to learn how to:

Register a provider with @registry.register(...)
Publish an entry point for discovery
Optionally provide a schema with get_schema_class() for structured output
Integrate with the factory via create_model(...)

Using OpenAI Models

LangCore supports OpenAI models (requires optional dependency: pip install langcore[openai]):

import langcore as lx

result = lx.extract(
    text_or_documents=input_text,
    prompt_description=prompt,
    examples=examples,
    model_id="gpt-4o",  # Automatically selects OpenAI provider
    api_key=os.environ.get('OPENAI_API_KEY'),
    fence_output=True,
    use_schema_constraints=False
)

Note: OpenAI models require fence_output=True and use_schema_constraints=False because LangCore doesn't implement schema constraints for OpenAI yet.

Using Local LLMs with Ollama

LangCore supports local inference using Ollama, allowing you to run models without API keys:

import langcore as lx

result = lx.extract(
    text_or_documents=input_text,
    prompt_description=prompt,
    examples=examples,
    model_id="gemma2:2b",  # Automatically selects Ollama provider
    model_url="http://localhost:11434",
    fence_output=False,
    use_schema_constraints=False
)

Quick setup: Install Ollama from ollama.com, run ollama pull gemma2:2b, then ollama serve.

For detailed installation, Docker setup, and examples, see examples/ollama/.

More Examples

Additional examples of LangCore in action:

Romeo and Juliet Full Text Extraction

LangCore can process complete documents directly from URLs. This example demonstrates extraction from the full text of Romeo and Juliet from Project Gutenberg (147,843 characters), showing parallel processing, sequential extraction passes, and performance optimization for long document processing.

View Romeo and Juliet Full Text Example →

Medication Extraction

Disclaimer: This demonstration is for illustrative purposes of LangCore's baseline capability only. It does not represent a finished or approved product, is not intended to diagnose or suggest treatment of any disease or condition, and should not be used for medical advice.

LangCore excels at extracting structured medical information from clinical text. These examples demonstrate both basic entity recognition (medication names, dosages, routes) and relationship extraction (connecting medications to their attributes), showing LangCore's effectiveness for healthcare applications.

View Medication Examples →

Radiology Report Structuring: RadExtract

Explore RadExtract, a live interactive demo on HuggingFace Spaces that shows how LangCore can automatically structure radiology reports. Try it directly in your browser with no setup required.

View RadExtract Demo →

Ecosystem Plugins

LangCore has a growing ecosystem of first-party plugins. Each one is an independent package you install as needed — no bloated dependencies.

langcore-litellm — Universal Model Access

Access 100+ language models through a single unified interface via LiteLLM. OpenAI, Anthropic, Azure, Google, Mistral, Groq, Cohere, HuggingFace, Ollama, vLLM, and more.

Native async with asyncio.Semaphore concurrency control
Multi-pass cache bypass (fresh responses on repeat passes)
Token usage tracking (UsageStats)
Full parameter passthrough (temperature, top_p, timeout, etc.)

result = lx.extract(text, examples=examples, model_id="litellm/anthropic/claude-sonnet-4")

langcore-guardrails — Output Validation & Retry

Wraps any LangCore model with output validation and automatic corrective retry. 7 built-in validators:

Validator	Purpose
`JsonSchemaValidator`	Validate against JSON Schema with auto-repair
`RegexValidator`	Match output against regex patterns
`SchemaValidator`	Validate against Pydantic models (strict/lenient)
`ConfidenceThresholdValidator`	Reject extractions below a confidence cutoff
`FieldCompletenessValidator`	Ensure required fields are present and non-empty
`ConsistencyValidator`	Cross-check values using custom business rules
`GroundingValidator`	Reject hallucinated extractions via alignment quality + character coverage

4 on-fail actions: EXCEPTION, REASK, FILTER, NOOP
Validator chaining with ValidatorChain
Validator registry with @register_validator
Error-only correction mode to save tokens
Batch-independent retries

from langcore_guardrails import GuardrailLanguageModel, SchemaValidator, GroundingValidator, OnFailAction

guarded = GuardrailLanguageModel(
    inner=base_model, validators=[SchemaValidator(Invoice, on_fail=OnFailAction.REASK)]
)

# Post-alignment grounding check
validator = GroundingValidator(min_alignment_quality="MATCH_FUZZY", min_coverage=0.5)
passed, filtered = validator.validate_extractions(result.extractions, source_text=result.text)

langcore-audit — Audit Logging

Structured audit records for every LLM call — latency, token usage, prompt/response hashes, success/failure status.

Pluggable sinks: Python logging, JSONL files, OpenTelemetry spans
Thread-safe, fault-tolerant (errors never affect inference)
Opt-in prompt/response sampling

from langcore_audit import AuditLanguageModel, LoggingSink

audited = AuditLanguageModel(inner=base_model, sinks=[LoggingSink()])

langcore-hybrid — Rules + LLM Fallback

Evaluate deterministic rules (regex, Python functions) before falling back to an LLM — save 50–80% on API costs for documents with predictable patterns.

Regex rules with named capture groups
Callable rules for arbitrary logic
Confidence thresholds for rule → LLM fallback
Observability counters (rule_hits vs llm_fallbacks)

from langcore_hybrid import HybridLanguageModel, RegexRule, RuleConfig

hybrid = HybridLanguageModel(
    inner=base_model,
    rule_config=RuleConfig(rules=[RegexRule(r"Date:\s*(?P<date>\d{4}-\d{2}-\d{2})")])
)

langcore-dspy — Prompt Optimization

Automatically optimize extraction prompts and few-shot examples using DSPy. Given training data, searches for the best prompt to maximize precision and recall.

MIPROv2 optimizer (fast, general-purpose)
GEPA optimizer (reflective, feedback-driven)
Persist/load optimized configs to disk
Built-in evaluation (P/R/F1)
optimized_config parameter in lx.extract()

from langcore_dspy import DSPyOptimizer

optimizer = DSPyOptimizer(model_id="openai/gpt-4o-mini")
config = optimizer.optimize(prompt_description="...", examples=examples, train_texts=texts, expected_results=expected)
result = lx.extract(text, optimized_config=config)

langcore-rag — RAG Query Parsing

Parse natural-language queries into semantic search terms and structured metadata filters for hybrid RAG pipelines.

Pydantic schema introspection (auto-discovers filterable fields)
MongoDB-style operators ($eq, $gte, $lte, $in, etc.)
Confidence scoring and human-readable explanation
Query caching (LRU) and Jupyter-safe sync bridge

from langcore_rag import QueryParser

parser = QueryParser(schema=Invoice, model_id="gemini/gemini-2.5-flash")
parsed = parser.parse("invoices over $5000 due in March 2024")
print(parsed.structured_filters)  # {"amount": {"$gte": 5000}, ...}

langcore-api — Production HTTP Service

Production-ready REST API wrapping the full LangCore ecosystem. Submit documents via HTTP and get structured entities back.

FastAPI + Celery + Redis task queue
Single and batch extraction endpoints
Multi-tier caching (LLM response + extraction result)
Webhook delivery with HMAC signing
Prometheus metrics, structured logging, SSRF protection
Full plugin integration (all of the above)
Docker-ready with web, worker, and Flower profiles

curl -X POST http://localhost:8000/api/v1/extract \
  -H "Content-Type: application/json" \
  -d '{"text": "Invoice INV-001 for $500", "model_id": "litellm/gpt-4o"}'

For detailed instructions on creating your own provider plugin, see the Custom Provider Plugin Example.

Contributing

Contributions are welcome! See CONTRIBUTING.md to get started with development, testing, and pull requests.

Testing

# Install with test dependencies
pip install -e ".[test]"

# Run all tests
pytest tests

Or reproduce the full CI matrix locally with tox:

tox

Ollama Integration Testing

If you have Ollama installed locally, you can run integration tests:

# Test Ollama integration (requires Ollama running with gemma2:2b model)
tox -e ollama-integration

Development

Code Formatting

# Auto-format all code
./autoformat.sh

# Or run formatters separately
isort langcore tests --profile google --line-length 80
pyink langcore tests --config pyproject.toml

Pre-commit Hooks

pre-commit install  # One-time setup
pre-commit run --all-files  # Manual run

Linting

pylint --rcfile=.pylintrc langcore tests

See CONTRIBUTING.md for full development guidelines.

License

Licensed under the Apache License, Version 2.0. See LICENSE for full terms.

This project includes code originally developed by Google LLC as LangCore. See NOTICE for attribution details.

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CHANGELOG.md		CHANGELOG.md
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LangCore

Overview

Table of Contents

Feature Overview

Core Features (langcore)

Plugin Features

Core Capabilities

Quick Start

1. Define Your Extraction Task

2. Run the Extraction

3. Visualize the Results

Scaling to Longer Documents

Vertex AI Batch Processing

Schema-First Extraction with Pydantic

Schema Validation Retries

Multi-Model Consensus Extraction

Confidence Scoring

Extraction Reliability Score

Extraction Hooks & Events

Quality Metrics & Evaluation

Installation

From Source

Docker

API Key Setup for Cloud Models

API Key Sources

Setting up API key in your environment

Adding Custom Model Providers

Using OpenAI Models

Using Local LLMs with Ollama

More Examples

Romeo and Juliet Full Text Extraction

Medication Extraction

Radiology Report Structuring: RadExtract

Ecosystem Plugins

langcore-litellm — Universal Model Access

langcore-guardrails — Output Validation & Retry

langcore-audit — Audit Logging

langcore-hybrid — Rules + LLM Fallback

langcore-dspy — Prompt Optimization

langcore-rag — RAG Query Parsing

langcore-api — Production HTTP Service

Contributing

Testing

Ollama Integration Testing

Development

Code Formatting

Pre-commit Hooks

Linting

License

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Core Features (`langcore`)

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