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Refactor assessment module and update dependencies; remove unused extractor and graph loader files

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Santiago Martinez-Avial
2025-12-20 17:37:57 +01:00
parent 97b7a15977
commit 1180b2a64e
17 changed files with 525 additions and 411 deletions
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2
.gitignore vendored
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@@ -6,6 +6,8 @@ dist/
wheels/
*.egg-info
.env
# Virtual environments
.venv

116
README.md
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@@ -1,20 +1,27 @@
# Helia
Agentic Interview Framework for ingesting, analyzing, and querying transcript data.
**A Modular Agent Framework for Therapeutic Interview Analysis**
This project is the core implementation for the Bachelor Thesis: *"Comparing Local, Self-Hosted, and Cloud LLM Deployments for Therapeutic Interview Analysis"*.
## Project Context
Helia aims to bridge the gap between AI and mental healthcare by automating the analysis of diagnostic interviews. Specifically, it tests whether **local, privacy-first LLMs** can match the performance of cloud-based models when mapping therapy transcripts to standardized **PHQ-8** (Patient Health Questionnaire) scores.
## Project Structure
```
src/helia/
├── agent/
│ └── workflow.py # LangGraph agent workflow
│ └── workflow.py # LangGraph agent & router
├── analysis/
│ └── extractor.py # LLM metadata extraction
├── graph/
│ ├── loader.py # Neo4j data loading
│ └── schema.py # Pydantic graph models
│ └── extractor.py # Metadata extraction (LLM-agnostic)
├── assessment/
│ ├── core.py # Clinical assessment logic (PHQ-8)
│ └── schema.py # Data models (AssessmentResult, PHQ8Item)
├── ingestion/
│ └── parser.py # Transcript parsing logic
│ └── parser.py # Transcript parsing (DAIC-WOZ support)
├── db.py # MongoDB persistence layer
└── main.py # CLI entry point
```
@@ -23,90 +30,59 @@ src/helia/
```mermaid
graph TD
A[Transcript File<br/>TSV/TXT] -->|TranscriptParser| B(Utterance Objects)
B -->|MetadataExtractor<br/>+ OpenAI LLM| C(Enriched UtteranceNodes)
C -->|GraphLoader| D[(Neo4j Database)]
E[User Question] -->|LangGraph Agent| F{Router}
F -->|Graph Tool| D
F -->|Vector Tool| G[(Vector Store)]
D --> H[Context]
G --> H
H -->|Synthesizer| I[Answer]
B -->|MetadataExtractor<br/>+ LLM| C(Enriched Utterances)
C -->|Assessment Engine<br/>+ Clinical Logic| D(PHQ-8 Scores & Evidence)
D -->|Persistence Layer| E[(MongoDB / Beanie)]
U[User Query] -->|LangGraph Agent| R{Router}
R -->|Assessment Tool| D
R -->|Search Tool| E
```
1. **Ingestion**: `TranscriptParser` reads TSV/txt files into `Utterance` objects.
2. **Analysis**: `MetadataExtractor` enriches utterances with sentiment and tone using LLMs.
3. **Graph**: `GraphLoader` pushes nodes and relationships to Neo4j database.
4. **Agent**: ReAct workflow queries graph/vector data to answer user questions.
1. **Ingestion**: `TranscriptParser` reads clinical interview files (e.g., DAIC-WOZ).
2. **Analysis**: `MetadataExtractor` enriches data with sentiment/tone using interchangeable LLMs.
3. **Assessment**: The core engine maps dialogue to clinical criteria (PHQ-8), generating scores and citing evidence.
4. **Persistence**: Results are stored as structured `AssessmentResult` documents in MongoDB for analysis and benchmarking.
## Implemented Features
- Parse DAIC-WOZ transcripts and simple text formats.
- Extract metadata (sentiment, tone, speech acts) via OpenAI.
- Load `Utterance` and `Speaker` nodes into Neo4j.
- Run basic LangGraph agent with planner and router.
- **Modular LLM Backend**: designed to switch between Cloud (OpenAI) and Local models (Tier 1-3 comparative benchmark).
- **Clinical Parsing**: Native support for DAIC-WOZ transcript formats.
- **Structured Assessment**: Maps unstructured conversation to validatable PHQ-8 scores.
- **Document Persistence**: Stores full experimental context (config + evidence + scores) in MongoDB using Beanie.
## Roadmap
- Add robust error handling for LLM API failures.
- Implement real `graph_tool` and `vector_tool` logic.
- Enhance agent planning capabilities.
- Add comprehensive test suite.
- [ ] **Comparative Benchmark**: Run full evaluation across Local vs. Cloud tiers.
- [ ] **Vector Search**: Implement semantic search over transcript evidence.
- [ ] **Test Suite**: Add comprehensive tests for the assessment logic.
## Installation
Install the package using `uv`.
```sh
uv pip install helia
uv sync
```
## Quick Start
Run the agent directly from the command line.
1. **Environment Setup**:
```sh
export OPENAI_API_KEY=sk-...
# Ensure MongoDB is running (e.g., via Docker)
```
```sh
export OPENAI_API_KEY=sk-...
export NEO4J_URI=bolt://localhost:7687
export NEO4J_PASSWORD=password
2. **Run an Assessment**:
```sh
python -m helia.main "assess --input data/transcript.tsv"
```
python -m helia.main "How many interruptions occurred?"
```
## Development
## Usage
Parse a transcript file programmatically.
```python
from helia.ingestion.parser import TranscriptParser
from pathlib import Path
parser = TranscriptParser()
utterances = parser.parse(Path("transcript.tsv"))
```
Extract metadata from utterances.
```python
from helia.analysis.extractor import MetadataExtractor
extractor = MetadataExtractor()
nodes = extractor.extract(utterances)
```
Load data into Neo4j.
```python
from helia.graph.loader import GraphLoader
loader = GraphLoader()
loader.connect()
loader.load_utterances(nodes)
loader.close()
```
## Contributing
Fork the project and submit a pull request.
- **Linting**: `uv run ruff check .`
- **Formatting**: `uv run ruff format .`
- **Type Checking**: `uv run pyrefly`
## License

22
docker-compose.yml
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@@ -1,27 +1,23 @@
services:
neo4j:
image: neo4j:5
container_name: helia-neo4j
mongo:
image: mongo:latest
container_name: helia-mongo
ports:
- "7474:7474" # Neo4j Browser / HTTP
- "7687:7687" # Bolt
- "27017:27017"
environment:
# Matches defaults in `src/helia/graph/loader.py`
- NEO4J_AUTH=neo4j/password
MONGO_INITDB_ROOT_USERNAME: root
MONGO_INITDB_ROOT_PASSWORD: password
volumes:
- neo4j_data:/data
- neo4j_logs:/logs
- mongo_data:/data/db
qdrant:
image: qdrant/qdrant:latest
container_name: helia-qdrant
ports:
- "6333:6333" # HTTP
- "6334:6334" # gRPC
- "6333:6333"
- "6334:6334"
volumes:
- qdrant_storage:/qdrant/storage
volumes:
neo4j_data:
neo4j_logs:
qdrant_storage:

45
pyproject.toml
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@@ -11,51 +11,34 @@ requires-python = ">=3.13"
dependencies = [
"langchain>=0.1.0",
"langchain-openai>=0.1.0",
"langgraph",
"neo4j",
"qdrant-client",
"pydantic",
"openai",
"pydantic-settings>=2.12.0",
"PyYAML>=6.0.1",
"langgraph>=1.0.5",
"openai>=2.14.0",
"pydantic>=2.12.5",
"beanie>=2.0.1",
"motor>=3.7.1",
"neo4j>=5.19.0",
]
[tool.hatch.build.targets.wheel]
packages = ["src/helia"]
[dependency-groups]
dev = [
"ruff>=0.14.7",
"pyrefly>=0.43.1",
]
dev = ["ruff>=0.14.10", "pyrefly>=0.46.0"]
[tool.ruff]
line-length = 100
target-version = "py314"
[tool.ruff.lint]
extend-select = [
"F", # Pyflakes rules
"W", # PyCodeStyle warnings
"E", # PyCodeStyle errors
"I", # Sort imports properly
"UP", # Warn if certain things can changed due to newer Python versions
"C4", # Catch incorrect use of comprehensions, dict, list, etc
"FA", # Enforce from __future__ import annotations
"ISC", # Good use of string concatenation
"ICN", # Use common import conventions
"RET", # Good return practices
"SIM", # Common simplification rules
"TID", # Some good import practices
"TC", # Enforce importing certain types in a TYPE_CHECKING block
"PTH", # Use pathlib instead of os.path
"TD", # Be diligent with TODO comments
"NPY", # Numpy-specific rules
"COM", # enforce trailing comma rules
"DTZ", # require strict timezone manipulation with datetime
"FBT", # detect boolean traps
"N", # enforce naming conventions, e.g. ClassName vs function_name
]
ignore = ["E501", "COM812", "TD003"]
select = ["ALL"]
ignore = ["D", "BLE", "EM101", "EM102", "E501", "COM812", "TD003", "TRY003"]
[tool.ruff.lint.pydocstyle]
# https://github.com/google/styleguide/blob/gh-pages/pyguide.md#38-comments-and-docstrings
convention = "google"
[tool.pyrefly]
search-path = ["src"]

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src/helia/agent/__init__.py Normal file
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src/helia/agent/workflow.py
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@@ -17,7 +17,7 @@ class AgentState(TypedDict):
critique: str | None
def planner_node(state: AgentState) -> dict[str, Any]:
def planner_node(_state: AgentState) -> dict[str, Any]:
plan: list[str] = ["Understand question", "Retrieve info", "Synthesize answer"]
return {"plan": plan}
@@ -72,7 +72,7 @@ def synthesizer_node(state: AgentState) -> dict[str, Any]:
return {"answer": answer}
def reflector_node(state: AgentState) -> dict[str, Any]:
def reflector_node(_state: AgentState) -> dict[str, Any]:
return {"critique": "Answer appears sufficient."}

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src/helia/analysis/extractor.py
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@@ -1,92 +0,0 @@
from __future__ import annotations
import json
from typing import TYPE_CHECKING, Any
from helia.graph.schema import UtteranceNode
from helia.llm.client import get_openai_client
from helia.llm.settings import settings
if TYPE_CHECKING:
from helia.ingestion.parser import Utterance
class MetadataExtractor:
def __init__(self):
self.llm = get_openai_client()
def extract(self, utterances: list[Utterance]) -> list[UtteranceNode]:
nodes: list[UtteranceNode] = []
window_size = 3
for i, utt in enumerate(utterances):
if i > 0:
prev_utt = utterances[i - 1]
if (
utt.start_time is not None
and prev_utt.end_time is not None
and utt.start_time < prev_utt.end_time
):
utt.metadata["is_interrupted"] = True
prev_utt.metadata["was_interrupted_by"] = utt.id
start_idx = max(0, i - window_size + 1)
context_window = utterances[start_idx : i + 1]
metadata = self._analyze_with_llm(utt, context_window)
utt.metadata.update(metadata)
node = UtteranceNode(
id=utt.id,
speaker_id=utt.speaker,
text=utt.text,
start_time=utt.start_time if utt.start_time is not None else 0.0,
end_time=utt.end_time if utt.end_time is not None else 0.0,
sentiment=metadata.get("sentiment"),
tone=metadata.get("tone"),
speech_act=metadata.get("speech_act"),
)
nodes.append(node)
return nodes
def _analyze_with_llm(self, target_utt: Utterance, context: list[Utterance]) -> dict[str, Any]:
"""
Constructs the prompt and calls the LLM.
"""
context_text = "\n".join([f"{u.speaker}: {u.text}" for u in context])
prompt = f"""
Analyze the last utterance in this conversation context:
CONTEXT:
{context_text}
Analyze the LAST utterance (by {target_utt.speaker}) for:
1. Sentiment (Positive, Negative, Neutral)
2. Tone (e.g., Confident, Hesitant, Aggressive, Polite, etc.)
3. Speech Act (e.g., Question, Statement, Agreement, Disagreement, etc.)
Return ONLY valid JSON with keys: "sentiment", "tone", "speech_act".
"""
try:
response = self.llm.chat.completions.create(
model=settings.model,
messages=[
{
"role": "system",
"content": "You are an expert linguistic analyst. Output JSON only.",
},
{"role": "user", "content": prompt},
],
response_format={"type": "json_object"},
)
content = response.choices[0].message.content
if content:
return json.loads(content)
except Exception as e:
print(f"LLM analysis failed: {e}")
# Fallback if LLM fails
return {"sentiment": "Neutral", "tone": "Confident", "speech_act": "Statement"}

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src/helia/assessment/__init__.py Normal file
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src/helia/assessment/core.py Normal file
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@@ -0,0 +1,152 @@
from __future__ import annotations
import json
from typing import TYPE_CHECKING
from helia.assessment.schema import AssessmentResult, Evidence, PHQ8Item, RunConfig
from helia.ingestion.parser import TranscriptParser
from helia.llm.client import get_openai_client
if TYPE_CHECKING:
from pathlib import Path
# PHQ-8 Scoring Constants
DIAGNOSIS_THRESHOLD = 10
SYMPTOM_SEVERITY_THRESHOLD = 2
MAJOR_DEPRESSION_MIN_SEVERE = 5
OTHER_DEPRESSION_MIN_SEVERE = 2
DEFAULT_PROMPT = """
You are an expert clinical psychologist analyzing a patient interview transcript.
Your task is to assess the patient according to the PHQ-8 (Patient Health Questionnaire-8) criteria.
The transcript is provided below. You must analyze the ENTIRE transcript to find evidence for each of the 8 criteria.
CRITERIA TO ASSESS:
1. Little interest or pleasure in doing things (Anhedonia)
2. Feeling down, depressed, or hopeless
3. Trouble falling or staying asleep, or sleeping too much
4. Feeling tired or having little energy
5. Poor appetite or overeating
6. Feeling bad about yourself - or that you are a failure or have let yourself or your family down
7. Trouble concentrating on things, such as reading the newspaper or watching television
8. Moving or speaking so slowly that other people could have noticed? Or the opposite - being so fidgety or restless that you have been moving around a lot more than usual (Psychomotor agitation/retardation)
SCORING SCALE:
0 = Not at all
1 = Several days
2 = More than half the days
3 = Nearly every day
INSTRUCTIONS:
- For EACH of the 8 items, you must provide:
- A score (0-3).
- A list of DIRECT QUOTES from the transcript that support this score.
- A reasoning explanation.
- If there is NO evidence for a symptom, score it as 0.
- Be conservative: do not hallucinate symptoms. Only score if the patient explicitly mentions it or strong context implies it.
TRANSCRIPT:
{transcript_text}
OUTPUT FORMAT:
Return a JSON object with a key "items" which is a list of 8 objects.
Each object must have:
- "question_id": (int) 1-8
- "question_text": (str) The text of the criterion
- "score": (int) 0-3
- "evidence": (list) List of objects with "quote" and "reasoning".
"""
# PHQ-8 Scoring Constants
DIAGNOSIS_THRESHOLD = 10
SYMPTOM_SEVERITY_THRESHOLD = 2
MAJOR_DEPRESSION_MIN_SEVERE = 5
OTHER_DEPRESSION_MIN_SEVERE = 2
class PHQ8Evaluator:
def __init__(self, config: RunConfig) -> None:
self.config = config
self.client = get_openai_client() # Client config is global, but model is per-request
self.parser = TranscriptParser()
def _load_prompt(self, prompt_id: str) -> str:
if prompt_id == "default":
return DEFAULT_PROMPT
raise ValueError(f"Unknown prompt_id: {prompt_id}")
def evaluate(self, file_path: Path) -> AssessmentResult:
# 1. Parse Transcript
utterances = self.parser.parse(file_path)
transcript_text = "\n".join([f"{u.speaker}: {u.text}" for u in utterances])
# 2. Prepare Prompt
base_prompt = self._load_prompt(self.config.prompt_id)
final_prompt = base_prompt.format(transcript_text=transcript_text)
# 3. Call LLM
response = self.client.chat.completions.create(
model=self.config.model_name,
messages=[
{
"role": "system",
"content": "You are a clinical assessment system. Output valid JSON.",
},
{"role": "user", "content": final_prompt},
],
temperature=self.config.temperature,
response_format={"type": "json_object"},
)
content = response.choices[0].message.content
if not content:
raise ValueError("LLM returned empty response")
data = json.loads(content)
# 4. Parse Response into Schema
items = []
for item_data in data.get("items", []):
evidence_list = [
Evidence(quote=ev.get("quote", ""), reasoning=ev.get("reasoning", ""))
for ev in item_data.get("evidence", [])
]
items.append(
PHQ8Item(
question_id=item_data["question_id"],
question_text=item_data["question_text"],
score=item_data["score"],
evidence=evidence_list,
)
)
# 5. Calculate Diagnostics
total_score = sum(item.score for item in items)
diagnosis_cutpoint = total_score >= DIAGNOSIS_THRESHOLD
# Simple algorithm check (Simplified from paper for Phase 1)
# Major Dep: 5+ items >= 2 (must include Q1 or Q2)
# Other Dep: 2-4 items >= 2 (must include Q1 or Q2)
count_severe = sum(1 for i in items if i.score >= SYMPTOM_SEVERITY_THRESHOLD)
has_core = (items[0].score >= SYMPTOM_SEVERITY_THRESHOLD) or (
items[1].score >= SYMPTOM_SEVERITY_THRESHOLD
) # Q1 or Q2
diagnosis_algorithm = "None"
if has_core:
if count_severe >= MAJOR_DEPRESSION_MIN_SEVERE:
diagnosis_algorithm = "Major Depression"
elif count_severe >= OTHER_DEPRESSION_MIN_SEVERE:
diagnosis_algorithm = "Other Depression"
return AssessmentResult(
transcript_id=file_path.stem,
config=self.config,
items=items,
total_score=total_score,
diagnosis_algorithm=diagnosis_algorithm,
diagnosis_cutpoint=diagnosis_cutpoint,
)

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src/helia/assessment/schema.py Normal file
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@@ -0,0 +1,34 @@
from beanie import Document
from pydantic import BaseModel, Field
class RunConfig(BaseModel):
model_name: str
prompt_id: str
temperature: float
timestamp: str
class Evidence(BaseModel):
quote: str
line_number: int | None = None
reasoning: str
class PHQ8Item(BaseModel):
question_id: int
question_text: str
score: int = Field(..., ge=0, le=3, description="Score between 0 and 3")
evidence: list[Evidence]
class AssessmentResult(Document):
transcript_id: str
config: RunConfig
items: list[PHQ8Item]
total_score: int
diagnosis_algorithm: str
diagnosis_cutpoint: bool
class Settings:
name = "assessment_results"

37
src/helia/configuration.py Normal file
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@@ -0,0 +1,37 @@
from __future__ import annotations
from pathlib import Path
from typing import Annotated, Literal
import yaml
from pydantic import BaseModel, Field, TypeAdapter
class MongoConfig(BaseModel):
uri: str = "mongodb://localhost:27017"
database_name: str = "helia"
class AssessConfig(BaseModel):
command: Literal["assess"] = "assess"
input_file: str
model: str
prompt_id: str = "default"
temperature: float = 1.0
database: MongoConfig = Field(default_factory=MongoConfig)
class AgentConfig(BaseModel):
command: Literal["agent"] = "agent"
question: str = "How many times did the interviewer interrupt?"
ConfigType = Annotated[AssessConfig | AgentConfig, Field(discriminator="command")]
def load_config(path: str | Path) -> ConfigType:
with Path(path).open() as f:
data = yaml.safe_load(f)
adapter = TypeAdapter(ConfigType)
return adapter.validate_python(data)

19
src/helia/db.py Normal file
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@@ -0,0 +1,19 @@
from __future__ import annotations
from typing import TYPE_CHECKING
from beanie import init_beanie
from motor.motor_asyncio import AsyncIOMotorClient
from helia.assessment.schema import AssessmentResult
if TYPE_CHECKING:
from helia.configuration import MongoConfig
async def init_db(config: MongoConfig) -> None:
client = AsyncIOMotorClient(config.uri)
await init_beanie(
database=client[config.database_name], # type: ignore[arg-type]
document_models=[AssessmentResult],
)

95
src/helia/graph/loader.py
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@@ -1,95 +0,0 @@
import os
from typing import TYPE_CHECKING
from neo4j import Driver, GraphDatabase
if TYPE_CHECKING:
from helia.graph.schema import UtteranceNode
class GraphLoader:
def __init__(
self, uri: str | None = None, user: str | None = None, password: str | None = None
):
self.uri = uri or os.environ.get("NEO4J_URI", "bolt://localhost:7687")
self.user = user or os.environ.get("NEO4J_USER", "neo4j")
self.password = password or os.environ.get("NEO4J_PASSWORD", "password")
self.driver: Driver | None = None
def connect(self):
driver = GraphDatabase.driver(self.uri, auth=(self.user, self.password))
driver.verify_connectivity()
self.driver = driver
print(f"Connected to Neo4j at {self.uri}")
def close(self):
if self.driver:
self.driver.close()
def clear_database(self):
"""Clears all nodes and relationships. Use with caution!"""
if not self.driver:
return
with self.driver.session() as session:
session.run("MATCH (n) DETACH DELETE n")
def load_utterances(self, nodes: list[UtteranceNode]):
"""
Loads a list of enriched UtteranceNodes into Neo4j.
Creates Speaker nodes, Utterance nodes, and the NEXT chain.
"""
if not self.driver:
raise RuntimeError("Driver not connected.")
with self.driver.session() as session:
for i, node in enumerate(nodes):
session.run(
"""
MERGE (u:Utterance {id: $id})
SET u.text = $text,
u.start_time = $start_time,
u.end_time = $end_time,
u.sentiment = $sentiment,
u.tone = $tone,
u.speech_act = $speech_act
""",
node.model_dump(),
)
if i > 0:
prev_node = nodes[i - 1]
session.run(
"""
MATCH (prev:Utterance {id: $prev_id})
MATCH (curr:Utterance {id: $curr_id})
MERGE (prev)-[:NEXT]->(curr)
""",
prev_id=prev_node.id,
curr_id=node.id,
)
session.run(
"""
MERGE (s:Speaker {id: $speaker_id})
WITH s
MATCH (u:Utterance {id: $utterance_id})
MERGE (s)-[:SPOKE]->(u)
""",
speaker_id=node.speaker_id,
utterance_id=node.id,
)
def create_interruption(self, interrupter_id: str, interrupted_id: str):
if not self.driver:
return
with self.driver.session() as session:
session.run(
"""
MATCH (a:Utterance {id: $interrupter_id})
MATCH (b:Utterance {id: $interrupted_id})
MERGE (a)-[:INTERRUPTED]->(b)
""",
interrupter_id=interrupter_id,
interrupted_id=interrupted_id,
)

55
src/helia/graph/schema.py
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@@ -1,55 +0,0 @@
from pydantic import BaseModel, Field
class SpeakerNode(BaseModel):
id: str = Field(..., description="Unique identifier for the speaker (e.g., 'speaker_01')")
name: str | None = Field(None, description="Real name if known")
role: str | None = Field(
None, description="Role in the conversation (e.g., 'Interviewer', 'Candidate')"
)
class UtteranceNode(BaseModel):
id: str = Field(..., description="Unique ID for the utterance")
speaker_id: str = Field(..., description="ID of the speaker who said this")
text: str = Field(..., description="The content of the speech")
start_time: float
end_time: float
# Metadata extracted by the agent
sentiment: str | None = Field(None, description="Sentiment: Positive, Negative, Neutral")
tone: str | None = Field(None, description="Tone: Aggressive, Hesitant, Confident")
speech_act: str | None = Field(None, description="Type: Question, Statement, Agreement")
class TopicNode(BaseModel):
name: str = Field(..., description="Topic name (e.g., 'Salary', 'Project X')")
description: str | None = None
class SpokeRel(BaseModel):
"""(Speaker)-[:SPOKE]->(Utterance)"""
speaker_id: str
utterance_id: str
class NextRel(BaseModel):
"""(Utterance A)-[:NEXT]->(Utterance B)"""
from_id: str
to_id: str
time_gap: float = 0.0
class InterruptedRel(BaseModel):
"""(Utterance A)-[:INTERRUPTED]->(Utterance B)"""
interrupter_utterance_id: str
interrupted_utterance_id: str
class MentionsRel(BaseModel):
"""(Utterance)-[:MENTIONS]->(Topic)"""
utterance_id: str
topic_name: str

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src/helia/ingestion/__init__.py Normal file
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src/helia/main.py
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@@ -1,21 +1,70 @@
import sys
import argparse
import asyncio
import logging
from datetime import UTC, datetime
from pathlib import Path
from helia.agent.workflow import run_agent
from helia.assessment.core import PHQ8Evaluator
from helia.assessment.schema import RunConfig
from helia.configuration import load_config
from helia.db import init_db
logger = logging.getLogger(__name__)
def main():
from helia.agent.workflow import run_agent
async def main() -> None:
logging.basicConfig(level=logging.INFO, format="%(message)s")
print("Initializing Agentic Interview Framework...")
parser = argparse.ArgumentParser(description="Helia Agentic Interview Framework")
parser.add_argument(
"config",
nargs="?",
default="config.yaml",
help="Path to YAML configuration file (default: config.yaml)",
)
if len(sys.argv) > 1:
question = " ".join(sys.argv[1:])
else:
question = "How many times did the interviewer interrupt?"
args = parser.parse_args()
config_path = Path(args.config)
print(f"\nRunning Re-Agent with question: '{question}'\n")
if not config_path.exists():
logger.error("Configuration file not found: %s", config_path)
logger.info("Please provide a valid configuration file.")
return
result = run_agent(question)
print(result["answer"])
try:
config = load_config(config_path)
except Exception:
logger.exception("Error loading configuration")
return
if config.command == "assess":
await init_db(config.database)
logger.info("Running assessment on %s...", config.input_file)
run_config = RunConfig(
model_name=config.model,
prompt_id=config.prompt_id,
temperature=config.temperature,
timestamp=datetime.now(tz=UTC).isoformat(),
)
evaluator = PHQ8Evaluator(run_config)
result = evaluator.evaluate(Path(config.input_file))
await result.insert()
logger.info("Assessment complete. Saved to MongoDB with ID: %s", result.id)
logger.info("Total Score: %s", result.total_score)
logger.info("Diagnosis (Alg): %s", result.diagnosis_algorithm)
elif config.command == "agent":
question = config.question
logger.info("\nRunning Re-Agent with question: '%s'\n", question)
result = run_agent(question)
logger.info(result["answer"])
if __name__ == "__main__":
main()
asyncio.run(main())

190
uv.lock generated
View File

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