From 67e8cc98b9ec436d9ba0b9b905f3d1cd2c19d293 Mon Sep 17 00:00:00 2001 From: chasefortier Date: Thu, 19 Mar 2026 18:14:48 -0400 Subject: [PATCH] Remove opik examples Removing Opik examples, which have all been migrated to the opik-examples repo --- opik/README.md | 2 + opik/otel_with_offline_eval_example/README.md | 86 +- .../evaluate.py | 65 - .../otel_with_offline_eval_example/llm_app.py | 79 - opik/streamlit/call-summarizer/.env.example | 11 - opik/streamlit/call-summarizer/.gitignore | 51 - .../call-summarizer/.pre-commit-config.yaml | 27 - opik/streamlit/call-summarizer/README.md | 267 +- opik/streamlit/call-summarizer/app.py | 289 - opik/streamlit/call-summarizer/poetry.lock | 6555 ----------------- opik/streamlit/call-summarizer/pyproject.toml | 76 - .../src/call_summarizer/__init__.py | 1 - .../src/call_summarizer/config.py | 40 - .../src/call_summarizer/models/models.py | 46 - .../services/category_manager.py | 136 - .../services/summarization_workflow.py | 208 - .../call_summarizer/services/vector_store.py | 195 - .../src/call_summarizer/utils/file_utils.py | 63 - .../call-summarizer/tests/__init__.py | 0 19 files changed, 4 insertions(+), 8193 deletions(-) create mode 100644 opik/README.md delete mode 100644 opik/otel_with_offline_eval_example/evaluate.py delete mode 100644 opik/otel_with_offline_eval_example/llm_app.py delete mode 100644 opik/streamlit/call-summarizer/.env.example delete mode 100644 opik/streamlit/call-summarizer/.gitignore delete mode 100644 opik/streamlit/call-summarizer/.pre-commit-config.yaml delete mode 100644 opik/streamlit/call-summarizer/app.py delete mode 100644 opik/streamlit/call-summarizer/poetry.lock delete mode 100644 opik/streamlit/call-summarizer/pyproject.toml delete mode 100644 opik/streamlit/call-summarizer/src/call_summarizer/__init__.py delete mode 100644 opik/streamlit/call-summarizer/src/call_summarizer/config.py delete mode 100644 opik/streamlit/call-summarizer/src/call_summarizer/models/models.py delete mode 100644 opik/streamlit/call-summarizer/src/call_summarizer/services/category_manager.py delete mode 100644 opik/streamlit/call-summarizer/src/call_summarizer/services/summarization_workflow.py delete mode 100644 opik/streamlit/call-summarizer/src/call_summarizer/services/vector_store.py delete mode 100644 opik/streamlit/call-summarizer/src/call_summarizer/utils/file_utils.py delete mode 100644 opik/streamlit/call-summarizer/tests/__init__.py diff --git a/opik/README.md b/opik/README.md new file mode 100644 index 00000000..4f75e539 --- /dev/null +++ b/opik/README.md @@ -0,0 +1,2 @@ +# Opik Examples +This folder has been deprecated and is no longer maintained. Please instead use [opik-examples](https://github.com/comet-ml/opik-examples/tree/main). \ No newline at end of file diff --git a/opik/otel_with_offline_eval_example/README.md b/opik/otel_with_offline_eval_example/README.md index 94c5525d..aa7fe867 100644 --- a/opik/otel_with_offline_eval_example/README.md +++ b/opik/otel_with_offline_eval_example/README.md @@ -1,87 +1,3 @@ # OTel Tracing + Opik Offline Evaluation -Demonstrates how to use OpenTelemetry for application tracing **alongside** -Opik's offline evaluation workflow. These are complementary tools, not alternatives. - -## Architecture - -``` -┌─────────────────────────────────────┐ ┌───────────────────────────────────────┐ -│ llm_app.py │ │ evaluate.py │ -│ (your application code) │ │ (offline evaluation script) │ -│ │ │ │ -│ - OTel for tracing │ │ - Opik Python SDK │ -│ - Runs in production/dev │ │ - Runs separately (CI, scheduled) │ -│ - Sends spans → Opik OTLP endpoint │ │ - Imports answer_question() from app │ -│ │ │ - Runs task against a dataset │ -│ │ │ - Creates an Experiment in Opik │ -└─────────────────────────────────────┘ └───────────────────────────────────────┘ - │ │ - └──────────────────────┬───────────────────────┘ - ▼ - ┌─────────────────────┐ - │ Opik │ - │ │ - │ Traces (from OTel) │ - │ Experiments (from │ - │ evaluate()) │ - └─────────────────────┘ -``` - -## Key Insight - -| | OTel tracing | `opik.evaluate()` | -|---|---|---| -| Purpose | Observe your app in real-time | Test quality offline against a dataset | -| When it runs | During application execution | In a separate evaluation script | -| What you get in Opik | Traces / Spans | Experiments with scored results | -| SDK | OTel SDK | Opik Python SDK (`pip install opik`) | - -**There is no OTel-native way to run offline experiments.** The offline evaluation -workflow requires the Opik Python SDK because it needs to: -1. Read items from an Opik Dataset -2. Run your LLM task against each item -3. Score outputs with metrics -4. Record results as an Experiment in Opik - -## Setup - -```bash -pip install opentelemetry-sdk opentelemetry-exporter-otlp-proto-http opik -``` - -Environment variables for `llm_app.py`: -```bash -export OPIK_OTLP_ENDPOINT="https://your-opik-host/opik/api/v1/private/otel/v1/traces" -export OPIK_API_KEY="your-api-key" -export OPIK_WORKSPACE="your-workspace" -``` - -Environment variables for `evaluate.py`: -```bash -export OPIK_API_KEY="your-api-key" -export OPIK_WORKSPACE="your-workspace" -export OPIK_URL_OVERRIDE="https://your-opik-host/opik/api" -``` - -## Running - -```bash -# Run your application normally (OTel tracing active) -python llm_app.py - -# Run offline evaluation separately -python evaluate.py -``` - -## Notes for self-hosted Opik - -The OTel OTLP endpoint for self-hosted Opik is: -``` -https:///opik/api/v1/private/otel/v1/traces -``` - -The Opik SDK base URL override for self-hosted is: -``` -https:///opik/api -``` +This example has been moved to [opik-examples](https://github.com/comet-ml/opik-examples/tree/main/examples/otel_with_offline_eval_example). diff --git a/opik/otel_with_offline_eval_example/evaluate.py b/opik/otel_with_offline_eval_example/evaluate.py deleted file mode 100644 index ab414a0b..00000000 --- a/opik/otel_with_offline_eval_example/evaluate.py +++ /dev/null @@ -1,65 +0,0 @@ -""" -FILE 2: evaluate.py -Separate offline evaluation script — uses the Opik Python SDK. -Import your application function directly and wrap it as an Opik task. - -This does NOT replace OTel tracing in your app. It runs offline experiments -against a dataset and records scored results as an Experiment in Opik. - -Setup: - pip install opik - -Environment variables: - OPIK_API_KEY - your Opik API key - OPIK_WORKSPACE - your Opik workspace name - OPIK_URL_OVERRIDE - your self-hosted Opik URL - (e.g. https://your-opik-host/opik/api) -""" - -import opik -from opik.evaluation.metrics import Equals, Hallucination -# Note: Hallucination is an LLM-judge metric — it requires an LLM provider to be -# configured (e.g. set OPENAI_API_KEY). Replace or remove it if you don't have one. - -# Import the function from your application code. -# opik.evaluate() will call this function for each dataset item. -from llm_app import answer_question - - -# --- Step 1: Create (or get) a dataset --- - -client = opik.Opik() - -dataset = client.get_or_create_dataset("capital-cities-qa") -dataset.insert([ - {"question": "What is the capital of France?", "expected": "Paris"}, - {"question": "What is the capital of Japan?", "expected": "Tokyo"}, - {"question": "What is the capital of Germany?", "expected": "Berlin"}, -]) - - -# --- Step 2: Define the evaluation task --- -# This is a thin wrapper that maps dataset item fields to your app function. -# The dict keys returned here are used by scoring metrics below. - -def evaluation_task(dataset_item: dict) -> dict: - answer = answer_question(dataset_item["question"]) - return { - "output": answer, - "reference": dataset_item["expected"], # passed to Equals metric - "input": dataset_item["question"], # passed to Hallucination metric - } - - -# --- Step 3: Run the experiment --- - -opik.evaluate( - dataset=dataset, - task=evaluation_task, - scoring_metrics=[ - Equals(name="exact_match"), - Hallucination(name="hallucination"), - ], - experiment_name="capital-cities-baseline", - project_name="mediatek-llm-eval", -) diff --git a/opik/otel_with_offline_eval_example/llm_app.py b/opik/otel_with_offline_eval_example/llm_app.py deleted file mode 100644 index d9a3af1c..00000000 --- a/opik/otel_with_offline_eval_example/llm_app.py +++ /dev/null @@ -1,79 +0,0 @@ -""" -FILE 1: llm_app.py -Your application code — uses OpenTelemetry for tracing. -No Opik SDK required here. - -This is the code that runs in production or dev environments. -OTel spans are exported to Opik via the OTLP endpoint. - -Setup: - pip install opentelemetry-sdk opentelemetry-exporter-otlp-proto-http - -Environment variables: - OPIK_OTLP_ENDPOINT - e.g. https://your-opik-host/opik/api/v1/private/otel/v1/traces - OPIK_API_KEY - your Opik API key - OPIK_WORKSPACE - your Opik workspace name -""" - -import os -from opentelemetry import trace -from opentelemetry.sdk.trace import TracerProvider -from opentelemetry.sdk.trace.export import BatchSpanProcessor -from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter - - -def setup_otel(): - """Configure OTel to export traces to Opik.""" - otlp_endpoint = os.environ["OPIK_OTLP_ENDPOINT"] - api_key = os.environ["OPIK_API_KEY"] - workspace = os.environ["OPIK_WORKSPACE"] - - exporter = OTLPSpanExporter( - endpoint=otlp_endpoint, - headers={ - "Authorization": f"Bearer {api_key}", - "Comet-Workspace": workspace, - }, - ) - - provider = TracerProvider() - provider.add_span_processor(BatchSpanProcessor(exporter)) - trace.set_tracer_provider(provider) - - -tracer = trace.get_tracer(__name__) - - -def call_llm(prompt: str) -> str: - """ - Your LLM call — wrapped in an OTel span for tracing. - Replace this with your actual model call (OpenAI, vLLM, etc.) - """ - with tracer.start_as_current_span("llm_call") as span: - span.set_attribute("input.prompt", prompt) - - # --- replace with your actual LLM call --- - response = f"[LLM response to: {prompt}]" - # ----------------------------------------- - - span.set_attribute("output.response", response) - return response - - -def answer_question(user_question: str) -> str: - """ - Your application logic — also traced via OTel. - This is the function you'll reuse in evaluate.py. - """ - with tracer.start_as_current_span("answer_question") as span: - span.set_attribute("input.question", user_question) - answer = call_llm(user_question) - span.set_attribute("output.answer", answer) - return answer - - -if __name__ == "__main__": - # Set up OTel when running as the application entry point - setup_otel() - result = answer_question("What is the capital of France?") - print(result) diff --git a/opik/streamlit/call-summarizer/.env.example b/opik/streamlit/call-summarizer/.env.example deleted file mode 100644 index 00635874..00000000 --- a/opik/streamlit/call-summarizer/.env.example +++ /dev/null @@ -1,11 +0,0 @@ -# OpenAI API Key -OPENAI_API_KEY=your_openai_api_key_here - -# Application Settings -VECTOR_STORE_PATH=./data/vector_store -DATA_DIR=./data - -# Opik Settings (if needed) -OPIK_API_KEY=your_opik_api_key_here -OPIK_WORKSPACE="default" -OPIK_PROJECT_NAME="demo-langgraph-callsummarizer" diff --git a/opik/streamlit/call-summarizer/.gitignore b/opik/streamlit/call-summarizer/.gitignore deleted file mode 100644 index a9752a4d..00000000 --- a/opik/streamlit/call-summarizer/.gitignore +++ /dev/null @@ -1,51 +0,0 @@ -# Python -__pycache__/ -*.py[cod] -*$py.class -*.so -.Python -build/ -develop-eggs/ -dist/ -downloads/ -eggs/ -.eggs/ -lib/ -lib64/ -parts/ -sdist/ -var/ -wheels/ -*.egg-info/ -.installed.cfg -*.egg - -# Environment variables -.env - -# Virtual Environment -venv/ -env/ -ENV/ - -# IDE -.idea/ -.vscode/ -*.swp -*.swo - -# Local data -/data/ -/vector_store/ - -# Logs -*.log - -# OS generated files -.DS_Store -.DS_Store? -._* -.Spotlight-V100 -.Trashes -ehthumbs.db -Thumbs.db diff --git a/opik/streamlit/call-summarizer/.pre-commit-config.yaml b/opik/streamlit/call-summarizer/.pre-commit-config.yaml deleted file mode 100644 index f1a73da3..00000000 --- a/opik/streamlit/call-summarizer/.pre-commit-config.yaml +++ /dev/null @@ -1,27 +0,0 @@ -repos: -- repo: https://github.com/pre-commit/pre-commit-hooks - rev: v4.6.0 - hooks: - - id: trailing-whitespace - - id: end-of-file-fixer - - id: check-yaml - - id: check-toml - - id: check-added-large-files - args: ["--maxkb=10000"] - - id: check-merge-conflict - - id: detect-private-key - - id: check-case-conflict - - id: mixed-line-ending - -- repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.4.4 - hooks: - - id: ruff-format - - id: ruff - args: [--fix, --exit-non-zero-on-fix] - -- repo: https://github.com/commitizen-tools/commitizen - rev: v3.27.0 - hooks: - - id: commitizen - stages: [commit-msg] diff --git a/opik/streamlit/call-summarizer/README.md b/opik/streamlit/call-summarizer/README.md index d6b36f27..228a37d7 100644 --- a/opik/streamlit/call-summarizer/README.md +++ b/opik/streamlit/call-summarizer/README.md @@ -1,268 +1,3 @@ # 📞 Call Summarizer -A Streamlit application for summarizing and categorizing call transcripts using LangGraph and LangChain. This application helps you quickly extract key information, action items, and insights from call recordings or transcripts. - -## Features - -- **Automatic Call Summarization**: Generate concise summaries of call transcripts -- **Smart Categorization**: Automatically categorize calls into predefined categories -- **Action Item Extraction**: Identify and extract action items from calls -- **Vector Search**: Semantic search through past call summaries -- **Call History**: View, search, and manage your call history -- **Customizable Categories**: Define custom call categories with specific summarization prompts -- **Export Options**: Export summaries in various formats (TXT, JSON, CSV) -- **Responsive Design**: Works on both desktop and mobile devices -- **Opik Integration**: Comprehensive tracing of all LLM interactions for monitoring and debugging -- **Natural Language Query**: Chat with your call data using natural language -- **Persistent Storage**: All data is stored locally for privacy -- **Call History Management**: View and manage your call history with the ability to delete entries - -## 🚀 Getting Started - -### Prerequisites - -- Python 3.12 or higher (recommended) -- [Poetry](https://python-poetry.org/) for dependency management -- [OpenAI API key](https://platform.openai.com/api-keys) -- [Opik API key](https://www.comet.com/signup?utm_source=call-summarizer&utm_medium=referral&utm_campaign=github) (optional but recommended for tracing) -- (Optional) [pyenv](https://github.com/pyenv/pyenv) for Python version management -- (Recommended) [direnv](https://direnv.net/) for environment variable management - -### Installation - -1. Clone the repository: - - ```bash - git clone https://github.com/yourusername/call-summarizer.git - cd call-summarizer - ``` - -2. Install Python dependencies with Poetry: - - ```bash - poetry install - ``` - -3. Copy the example environment file and update with your API keys: - - ```bash - cp .env.example .env - # Edit .env with your API keys - ``` - -4. Activate the virtual environment: - - ```bash - poetry shell - ``` - -## Configuration - -The application can be configured using environment variables. Create a `.env` file in the project root with the following variables: - -```ini -# Required -OPENAI_API_KEY=your_openai_api_key_here - -# Optional - Opik for LLM tracing -OPIK_API_KEY=your_opik_api_key_here - -# Storage paths -VECTOR_STORE_PATH=./data/vector_store -DATA_DIR=./data -``` - -## Opik Integration - -The application includes built-in support for Opik tracing, which provides: - -- Detailed tracing of all LLM calls -- Performance metrics and token usage -- Debugging information for prompt engineering -- Conversation history and chain visualization - -To enable Opik tracing: - -1. Sign up for a free account at [Opik](https://www.comet.com/signup) -2. Get your API key from the dashboard -3. Add it to your `.env` file -4. Restart the application - -All LLM interactions will now be traced and visible in your Opik dashboard. - -### Running the Application - -1. Start the Streamlit application: - - ```bash - streamlit run app.py - ``` - -2. Open your browser to the URL shown in the terminal (usually http://localhost:8501) - -### Development - -This project uses [Poetry](https://python-poetry.org/) for dependency management. For code quality and consistency, we use: - -- **[Ruff](https://github.com/astral-sh/ruff)**: For extremely fast Python linting and formatting (replaces Black, isort, Flake8). -- **[pre-commit](https://pre-commit.com/)**: For managing and maintaining multi-language pre-commit hooks. -- **[Commitizen](https://commitizen-tools.github.io/commitizen/)**: For standardized commit messages (following Conventional Commits) and automated version bumping. - -**1. Install Development Dependencies:** - This includes tools like Ruff, pre-commit, Commitizen, and pytest. - - ```bash - poetry install --with dev - ``` - -**2. Set Up Pre-commit Hooks:** -After installing dependencies, activate the pre-commit hooks in your local repository. These hooks will run automatically before each commit. - -```bash -poetry run pre-commit install --hook-type commit-msg --hook-type pre-commit -``` - -The configured hooks will: - -- Format your code using Ruff. -- Lint your code using Ruff. -- Check your commit message format using Commitizen. -- Perform other general checks (e.g., for trailing whitespace, valid YAML/TOML). - -**3. Running Linters and Formatters Manually:** -While pre-commit hooks handle this automatically, you can also run these tools manually: - -- **Format code with Ruff:** - -```bash -poetry run ruff format . -``` - -- **Check for linting issues with Ruff (and autofix where possible):** - -```bash -poetry run ruff check . --fix -``` - -**4. Running Tests:** - -```bash -poetry run pytest -``` - -**5. Commit Guidelines (Conventional Commits):** -This project adheres to the [Conventional Commits](https://www.conventionalcommits.org/) specification. This practice helps in creating an explicit commit history, which is useful for automated version bumping and changelog generation. - -To make a commit: - -1. Stage your changes (`git add ...`). -2. Run `git commit`. - - The pre-commit hooks will execute first. If any hook fails (e.g., Ruff finds formatting issues it can't fix automatically, or your commit message doesn't conform), the commit will be aborted. Address the issues and try committing again. - - If all pre-commit hooks pass, Commitizen will prompt you interactively to build a conventional commit message. - Alternatively, you can directly use Commitizen's interactive prompt for committing: - -```bash -poetry run cz c -``` - -This command also respects pre-commit hooks if they are installed. - -**6. Release Process (Versioning):** -When you're ready to release a new version of the project (e.g., after merging significant features or fixes): - -1. Ensure your local main branch (or your primary development branch) is up-to-date with the remote repository and that your working directory is clean (no uncommitted changes). -2. Run the Commitizen bump command: - - ```bash - poetry run cz bump --changelog - ``` - - This command performs several actions: - - - Analyzes your commit history since the last version tag. - - Determines the appropriate new version (patch, minor, or major) based on your conventional commits. - - Updates the version string in `pyproject.toml` (under `tool.poetry.version`) and `src/call_summarizer/__init__.py` (the `__version__` attribute). - - Creates a new commit with these version changes. - - Tags the new commit with the new version number (e.g., `v0.2.0`). - - The `--changelog` flag will also attempt to generate or update a changelog file. (Note: For more advanced changelog generation, you might need to configure `commitizen` further, for example, by setting `update_changelog_on_bump = true` and specifying a changelog file in `pyproject.toml` if you want it automatically updated). - -3. Push the new commit and the new tag to the remote repository: - -```bash -git push --follow-tags -``` - -This process ensures that versioning is consistent and automated. - -## 🛠️ Usage - -### Summarize a Call - -1. Click on "Summarize Call" in the sidebar -2. Upload a text file with the call transcript or paste it directly -3. Select a category for the call -4. Click "Summarize Call" to generate the summary and action items - -### View Call History - -1. Click on "View History" in the sidebar -2. Browse through previous call summaries -3. Filter by category if needed - -### Manage Categories - -1. Click on "Manage Categories" in the sidebar -2. View existing categories and their prompt templates -3. Add new categories with custom prompts -4. Delete categories you no longer need - -### Chat with Call Data - -1. Click on "Chat with Data" in the sidebar -2. Ask questions about your call history in natural language -3. The AI will search through your call summaries to provide relevant answers - -## 🏗️ Project Structure - -```text -call-summarizer/ -├── .env.example # Example environment variables -├── app.py # Main Streamlit application -├── init_app.py # Application initialization script -├── pyproject.toml # Project dependencies and configuration (Poetry) -├── README.md # This file -└── src/ - └── call_summarizer/ - ├── __init__.py - ├── config.py # Application configuration - ├── models/ # Data models - │ └── models.py - ├── services/ # Business logic - │ ├── category_manager.py - │ ├── summarization_workflow.py - │ └── vector_store.py - └── utils/ # Utility functions - └── file_utils.py -``` - -## 🤖 Technologies Used - -- **Streamlit**: Web application framework -- **LangGraph**: For creating the summarization workflow -- **LangChain**: For LLM integration and prompt engineering -- **OpenAI**: For generating summaries and action items -- **ChromaDB**: Vector database for storing and searching call summaries -- **LlamaIndex**: For semantic search and retrieval -- **Opik**: For LLM tracing and debugging -- **Poetry**: Dependency management -- **Pydantic**: Data validation and settings management - -## 🙏 Acknowledgments - -- Built with ❤️ using amazing open-source libraries -- Inspired by the need for better call analysis tools -- Special thanks to the LangChain and ChromaDB communities for their great tools - ---- - -Happy summarizing! 🎉 +This has been migrated to [opik-examples](https://github.com/comet-ml/opik-examples/tree/main/examples/streamlit-call-summarizer) diff --git a/opik/streamlit/call-summarizer/app.py b/opik/streamlit/call-summarizer/app.py deleted file mode 100644 index 984ca3e2..00000000 --- a/opik/streamlit/call-summarizer/app.py +++ /dev/null @@ -1,289 +0,0 @@ -"""Streamlit app for call summarization.""" - -import sys -from pathlib import Path - -# Add the src directory to the Python path BEFORE attempting to import from src -# This assumes app.py is in the project root, and 'src' is a direct subdirectory. -sys.path.append(str(Path(__file__).resolve().parent)) - -from dotenv import load_dotenv -import opik # For opik.configure -import streamlit as st -from opik import track # For the decorator - -# Now import local modules from 'src' -from src.call_summarizer.config import settings -from src.call_summarizer.models.models import CallSummary -from src.call_summarizer.services.category_manager import CategoryManager -from src.call_summarizer.services.summarization_workflow import CallSummarizer -from src.call_summarizer.services.vector_store import VectorStoreConfig, VectorStoreService - -# Load environment variables after all imports are declared -load_dotenv() - -opik = opik.configure( - api_key=settings.opik_api_key, - workspace=settings.opik_workspace, - use_local=False, -) - -# Page configuration -st.set_page_config( - page_title="Call Summarizer", - page_icon="📞", - layout="wide", - initial_sidebar_state="expanded", -) - - -# Initialize services -@st.cache_resource -def get_category_manager(): - return CategoryManager() - - -@st.cache_resource -def get_vector_store(): - return VectorStoreService(VectorStoreConfig(persist_dir=settings.vector_store_path, collection_name="call_summaries")) - - -@st.cache_resource -def get_summarizer(): - return CallSummarizer(category_manager=get_category_manager()) - - -category_manager = get_category_manager() -vector_store = get_vector_store() -summarizer = get_summarizer() - -# Ensure default categories exist -category_manager.ensure_default_categories_exist() - - -def main(): - """Main application function.""" - st.sidebar.title("Navigation") - page = st.sidebar.radio( - "Go to", - ["Summarize Call", "View History", "Manage Categories", "Chat with Data"], - ) - - if page == "Summarize Call": - show_summarize_call() - elif page == "View History": - show_history() - elif page == "Manage Categories": - show_manage_categories() - elif page == "Chat with Data": - show_chat() - - -@track(flush=True) -def process_transcript( - transcript: str, category_name: str, summarizer: CallSummarizer = summarizer, vector_store: VectorStoreService = vector_store -) -> CallSummary: - """Process the transcript and generate a summary.""" - # Generate the summary - call_summary = summarizer.summarize_transcript(transcript=transcript, category_name=category_name) - - # Save to vector store - vector_store.add_call_summary(call_summary) - - return call_summary - - -def show_summarize_call(): - """Show the call summarization interface.""" - st.title("📞 Call Summarizer") - - # File upload - uploaded_file = st.file_uploader( - "Upload a call transcript", type=["txt", "md"], help="Upload a text or markdown file containing the call transcript" - ) - - # Or paste text - transcript = st.text_area("Or paste the call transcript here", height=200, placeholder="Paste the call transcript here...") - - # Category selection - categories = category_manager.list_categories() - category_names = [cat.name for cat in categories] - selected_category = st.selectbox( - "Select a category", category_names, index=category_names.index("other") if "other" in [c.lower() for c in category_names] else 0 - ) - - # Summarize button - if st.button("Summarize Call", type="primary"): - if not (uploaded_file or transcript.strip()): - st.error("Please upload a file or paste a transcript.") - return - - with st.spinner("Generating summary..."): - try: - # Read the transcript - if uploaded_file: - transcript = uploaded_file.read().decode("utf-8") - - # Process the transcript and generate summary - call_summary = process_transcript(transcript, selected_category, summarizer, vector_store) - - # Display the results - display_call_summary(call_summary) - - except Exception as e: - st.error(f"Error generating summary: {str(e)}") - - -def display_call_summary(call_summary: CallSummary): - """Display a call summary.""" - st.subheader("Call Summary") - st.write(call_summary.summary) - - st.subheader("Action Items") - for item in call_summary.action_items: - st.write(f"- {item}") - - st.subheader("Details") - col1, col2 = st.columns(2) - with col1: - st.metric("Category", call_summary.category) - with col2: - st.metric("Created", call_summary.created_at.strftime("%Y-%m-%d %H:%M")) - - -def show_history(): - """Show the call history with delete functionality.""" - st.title("📜 Call History") - - # Get all summaries - summaries = vector_store.get_all_summaries() - - if not summaries: - st.info("No call summaries found.") - return - - # Filter by category - categories = list({s["category"] for s in summaries}) - selected_category = st.selectbox("Filter by category", ["All"] + sorted(categories)) - - if selected_category != "All": - summaries = [s for s in summaries if s["category"] == selected_category] - - # Display summaries with delete buttons - for summary in summaries: - col1, col2 = st.columns([0.9, 0.1]) - - with col1: - with st.expander(f"{summary['summary'][:100]}..."): - st.write(summary["summary"]) - st.caption(f"Category: {summary['category']} | Created: {summary['created_at']}") - - with col2: - # Create a unique key for the delete button - delete_key = f"delete_{summary['id']}" - if st.button("🗑️", key=delete_key, help="Delete this summary"): - # Store the ID to delete in the session state - st.session_state["delete_summary_id"] = summary["id"] - - # Confirmation dialog - if "delete_summary_id" in st.session_state: - st.warning("Are you sure you want to delete this summary? This action cannot be undone.") - col1, col2 = st.columns(2) - - with col1: - if st.button("✅ Yes, delete it"): - summary_id = st.session_state["delete_summary_id"] - if vector_store.delete_summary(summary_id): - st.success("Summary deleted successfully!") - # Clear the delete state - del st.session_state["delete_summary_id"] - # Rerun to refresh the list - st.rerun() - else: - st.error("Failed to delete summary.") - - with col2: - if st.button("❌ No, keep it"): - # Clear the delete state - del st.session_state["delete_summary_id"] - st.rerun() - - -def show_manage_categories(): - """Show the category management interface.""" - st.title("🗂️ Manage Categories") - - # List existing categories - st.subheader("Existing Categories") - categories = category_manager.list_categories() - - if not categories: - st.info("No categories found. Create one below.") - else: - for category in categories: - with st.expander(f"{category.name}"): - st.write(category.description) - st.code(category.prompt_template, language="text") - - if st.button(f"Delete {category.name}", key=f"del_{category.name}"): - if category_manager.delete_category(category.name): - st.success(f"Category '{category.name}' deleted.") - st.rerun() - - # Add new category - st.subheader("Add New Category") - with st.form("add_category"): - name = st.text_input("Name") - description = st.text_area("Description") - prompt_template = st.text_area("Prompt Template", help="Use {transcript} as a placeholder for the call transcript.") - - if st.form_submit_button("Add Category"): - if not all([name, description, prompt_template]): - st.error("All fields are required.") - else: - try: - category_manager.create_category(name=name, description=description, prompt_template=prompt_template) - st.success(f"Category '{name}' created successfully!") - st.rerun() - except 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-[tool.poetry.dependencies] -python = "^3.12" -streamlit = "^1.32.0" -opik = "^1.7.26" -langchain = "^0.3.25" -langgraph = "^0.4.5" -langchain-openai = "^0.3.18" -langchain-chroma = "^0.1.1" -chromadb = "^0.5.0" -pydantic = "^2.7.0" -python-dotenv = "^1.0.1" -python-multipart = "^0.0.9" -pyyaml = "^6.0.1" -pydantic-settings = "^2.9.1" -beautifulsoup4 = "^4.12.3" -watchdog = "^6.0.0" -mcp-server-fetch = "^2025.4.7" - -[tool.poetry.group.dev.dependencies] -pytest = "^8.3.5" -ruff = "^0.4.0" # Or latest version -pre-commit = "^3.7.0" -commitizen = "^3.13.0" - -[build-system] -requires = ["poetry-core>=2.0.0"] -build-backend = "poetry.core.masonry.api" - -[tool.ruff] -# Same as Black. -line-length = 140 - -# Assume Python 3.12. -target-version = "py312" - -# Specify the source directory for import sorting and other path-dependent checks. -src = ["src"] - -[tool.ruff.lint] -# Enable Pyflakes (F) and pycodestyle (E, W) and McCabe (C90) complexity. -select = ["F", "E", "W", "C90"] -ignore = [] - -# Allow unused variables when underscore-prefixed. -# dummy-variable-rgx = "^(_+|(_+[a-zA-Z0-9_]*[a-zA-Z0-9]+?))$" - -[tool.ruff.format] -# Like Black, use double quotes for strings. -quote-style = "double" - -# Like Black, indent with spaces, rather than tabs. -indent-style = "space" - -# Like Black, respect magic trailing commas. -skip-magic-trailing-comma = false - -# Like Black, automatically detect the appropriate line ending. -line-ending = "auto" - -[tool.commitizen] -name = "cz_conventional_commits" -tag_format = "$version" -version_provider = "poetry" -version_files = [ - "pyproject.toml:version", - "src/call_summarizer/__init__.py:__version__" # Assuming you might have version here -] -# You can add more commitizen settings if needed, like update_changelog_on_bump diff --git a/opik/streamlit/call-summarizer/src/call_summarizer/__init__.py b/opik/streamlit/call-summarizer/src/call_summarizer/__init__.py deleted file mode 100644 index 3dc1f76b..00000000 --- a/opik/streamlit/call-summarizer/src/call_summarizer/__init__.py +++ /dev/null @@ -1 +0,0 @@ -__version__ = "0.1.0" diff --git a/opik/streamlit/call-summarizer/src/call_summarizer/config.py b/opik/streamlit/call-summarizer/src/call_summarizer/config.py deleted file mode 100644 index 8e2ec55c..00000000 --- a/opik/streamlit/call-summarizer/src/call_summarizer/config.py +++ /dev/null @@ -1,40 +0,0 @@ -"""Application configuration and settings.""" - -import os -from typing import Optional - -from dotenv import load_dotenv -from pydantic import Field -from pydantic_settings import BaseSettings, SettingsConfigDict - -# Load environment variables from .env file -load_dotenv() - - -class Settings(BaseSettings): - """Application settings.""" - - # OpenAI - openai_api_key: str = Field(..., alias="OPENAI_API_KEY") - - # Application - vector_store_path: str = Field("./data/vector_store", alias="VECTOR_STORE_PATH") - data_dir: str = Field("./data", alias="DATA_DIR") - - # Opik - opik_api_key: Optional[str] = Field(default=None, alias="OPIK_API_KEY") - opik_workspace: Optional[str] = Field(default=None, alias="OPIK_WORKSPACE") - opik_project_name: Optional[str] = Field(default=None, alias="OPIK_PROJECT_NAME") - - # Pydantic v2 config - model_config = SettingsConfigDict(env_file=".env", env_file_encoding="utf-8", extra="ignore") - - def ensure_dirs_exist(self) -> None: - """Ensure that all required directories exist.""" - os.makedirs(self.vector_store_path, exist_ok=True) - os.makedirs(self.data_dir, exist_ok=True) - - -# Initialize settings -settings = Settings() -settings.ensure_dirs_exist() diff --git a/opik/streamlit/call-summarizer/src/call_summarizer/models/models.py b/opik/streamlit/call-summarizer/src/call_summarizer/models/models.py deleted file mode 100644 index 552c5e6c..00000000 --- a/opik/streamlit/call-summarizer/src/call_summarizer/models/models.py +++ /dev/null @@ -1,46 +0,0 @@ -"""Data models for the call summarizer application.""" - -from datetime import datetime -from enum import Enum -from typing import Dict, List - -from pydantic import BaseModel, Field - - -class CallCategory(str, Enum): - """Categories for call summaries.""" - - SALES = "sales" - SUPPORT = "support" - INTERVIEW = "interview" - MEETING = "meeting" - OTHER = "other" - - -class CallSummary(BaseModel): - """Model for call summary data.""" - - id: str = Field(..., description="Unique identifier for the call summary") - transcript: str = Field(..., description="The full text of the call transcript") - summary: str = Field(..., description="Generated summary of the call") - action_items: List[str] = Field(default_factory=list, description="List of action items from the call") - category: CallCategory = Field(default=CallCategory.OTHER, description="Category of the call") - created_at: datetime = Field(default_factory=datetime.utcnow, description="When the summary was created") - metadata: Dict = Field(default_factory=dict, description="Additional metadata about the call") - - -class CallCategoryConfig(BaseModel): - """Configuration for a call category including its prompt template.""" - - name: str = Field(..., description="Name of the category") - description: str = Field(..., description="Description of when to use this category") - prompt_template: str = Field(..., description="Template for generating summaries") - created_at: datetime = Field(default_factory=datetime.utcnow, description="When the category was created") - updated_at: datetime = Field(default_factory=datetime.utcnow, description="When the category was last updated") - - -class VectorStoreConfig(BaseModel): - """Configuration for the vector store.""" - - persist_dir: str = Field(..., description="Directory to persist the vector store") - collection_name: str = Field("call_summaries", description="Name of the collection in the vector store") diff --git a/opik/streamlit/call-summarizer/src/call_summarizer/services/category_manager.py b/opik/streamlit/call-summarizer/src/call_summarizer/services/category_manager.py deleted file mode 100644 index 5d9f0a8c..00000000 --- a/opik/streamlit/call-summarizer/src/call_summarizer/services/category_manager.py +++ /dev/null @@ -1,136 +0,0 @@ -"""Manages call categories and their configurations.""" - -from datetime import datetime -from pathlib import Path -from typing import Dict, List, Optional - -from ..config import settings -from ..models.models import CallCategory, CallCategoryConfig -from ..utils.file_utils import load_list_of_configs, save_list_of_configs - - -class CategoryManager: - """Manages call categories and their configurations.""" - - def __init__(self, config_file: Optional[str] = None): - """Initialize the category manager.""" - self.config_file = config_file or str(Path(settings.data_dir) / "categories.json") - self._categories: Dict[str, CallCategoryConfig] = self._load_categories() - - def _load_categories(self) -> Dict[str, CallCategoryConfig]: - """Load categories from the configuration file.""" - categories = load_list_of_configs(self.config_file, CallCategoryConfig) - return {category.name.lower(): category for category in categories} - - def _save_categories(self) -> None: - """Save categories to the configuration file.""" - save_list_of_configs(list(self._categories.values()), self.config_file) - - def create_category(self, name: str, description: str, prompt_template: str) -> CallCategoryConfig: - """Create a new call category.""" - if name.lower() in self._categories: - raise ValueError(f"Category '{name}' already exists") - - category = CallCategoryConfig(name=name, description=description, prompt_template=prompt_template) - - self._categories[name.lower()] = category - self._save_categories() - return category - - def update_category(self, name: str, **updates) -> Optional[CallCategoryConfig]: - """Update an existing call category.""" - if name.lower() not in self._categories: - return None - - category = self._categories[name.lower()] - - # Update fields that were provided - for key, value in updates.items(): - if hasattr(category, key): - setattr(category, key, value) - - # Always update the updated_at timestamp - category.updated_at = datetime.utcnow() - - self._save_categories() - return category - - def delete_category(self, name: str) -> bool: - """Delete a call category.""" - if name.lower() not in self._categories: - return False - - del self._categories[name.lower()] - self._save_categories() - return True - - def get_category(self, name: str) -> Optional[CallCategoryConfig]: - """Get a call category by name.""" - return self._categories.get(name.lower()) - - def list_categories(self) -> List[CallCategoryConfig]: - """List all call categories.""" - return list(self._categories.values()) - - def get_category_prompt(self, name: str) -> Optional[str]: - """Get the prompt template for a category.""" - category = self.get_category(name) - return category.prompt_template if category else None - - def get_default_category(self) -> Optional[CallCategoryConfig]: - """Get the default category.""" - return self._categories.get(CallCategory.OTHER.value) - - def ensure_default_categories_exist(self) -> None: - """Ensure that default categories exist.""" - default_categories = [ - { - "name": CallCategory.SALES.value, - "description": "Sales calls with potential or existing customers", - "prompt_template": ( - "Summarize the following sales call transcript. " - "Include key discussion points, customer needs, and next steps.\n\n" - "Transcript:\n{transcript}" - ), - }, - { - "name": CallCategory.SUPPORT.value, - "description": "Customer support or service calls", - "prompt_template": ( - "Summarize the following support call transcript. " - "Include the customer's issue, troubleshooting steps, and resolution.\n\n" - "Transcript:\n{transcript}" - ), - }, - { - "name": CallCategory.INTERVIEW.value, - "description": "Job interviews or candidate screenings", - "prompt_template": ( - "Summarize the following interview transcript. " - "Include key qualifications discussed, strengths, and areas for follow-up.\n\n" - "Transcript:\n{transcript}" - ), - }, - { - "name": CallCategory.MEETING.value, - "description": "General business meetings", - "prompt_template": ( - "Summarize the following meeting transcript. " - "Include key decisions, action items, and next steps.\n\n" - "Transcript:\n{transcript}" - ), - }, - { - "name": CallCategory.OTHER.value, - "description": "Other types of calls", - "prompt_template": ( - "Summarize the following call transcript. " "Include key points and action items.\n\n" "Transcript:\n{transcript}" - ), - }, - ] - - for category_data in default_categories: - if category_data["name"].lower() not in self._categories: - self.create_category( - name=category_data["name"], description=category_data["description"], prompt_template=category_data["prompt_template"] - ) diff --git a/opik/streamlit/call-summarizer/src/call_summarizer/services/summarization_workflow.py b/opik/streamlit/call-summarizer/src/call_summarizer/services/summarization_workflow.py deleted file mode 100644 index d88cb4f6..00000000 --- a/opik/streamlit/call-summarizer/src/call_summarizer/services/summarization_workflow.py +++ /dev/null @@ -1,208 +0,0 @@ -"""LangGraph workflow for call summarization with Opik tracing.""" - -import uuid -from datetime import datetime -from typing import Dict, List, Optional, TypedDict - -from langchain_core.messages import HumanMessage, SystemMessage -from langchain_openai import ChatOpenAI -from langgraph.graph import Graph, StateGraph -from opik.integrations.langchain import OpikTracer - -from ..config import settings -from ..models.models import CallCategoryConfig, CallSummary - - -class WorkflowState(TypedDict): - """State for the summarization workflow.""" - - transcript: str - category: str - category_config: CallCategoryConfig - summary: str - action_items: List[str] - metadata: Dict - - -def create_summarization_workflow() -> Graph: - """Create the LangGraph workflow for call summarization with Opik tracing.""" - # Initialize the LLM - llm = ChatOpenAI( - model="gpt-4o-mini", - temperature=0.3, - ) - - # Define the nodes - def route_to_category(state: WorkflowState) -> WorkflowState: - """Route the call to the appropriate category handler.""" - # If no category is provided, use the default category - if not state.get("category"): - state["category"] = "other" - - # Get the category config - category_config = state.get("category_config") - if not category_config: - raise ValueError(f"No configuration found for category: {state['category']}") - - # This node doesn't modify the state further, just ensures category is set - return state - - def summarize_call(state: WorkflowState) -> WorkflowState: - """Summarize the call transcript based on the category.""" - transcript = state["transcript"] - category_config = state["category_config"] - - prompt = f"""{category_config.prompt_template}\n\n" - f"Call Transcript:\n{transcript}\n\n" - f"Please provide a concise summary of the call based on the category '{category_config.name}'.\n" - f"IMPORTANT: Your main task here is to generate the narrative summary. " - f"Do NOT include a list or section detailing specific action items in THIS summary. " - f"Action items will be extracted and listed entirely separately. " - f"Focus only on the overall discussion, key points, decisions, and outcomes as guided by the category template.\n" - f"Summary:\n" - f""" - - messages = [ - SystemMessage(content=f"You are an expert call summarizer for the category: {category_config.name}."), - HumanMessage(content=prompt), - ] - - response = llm.invoke(messages) - state["summary"] = response.content.strip() - return state - - def extract_action_items(state: WorkflowState) -> WorkflowState: - """Extract action items from the call transcript.""" - transcript = state["transcript"] - summary = state["summary"] # Use the generated summary as context - - prompt = f""" - Given the following call transcript and its summary, please extract key action items. - If no specific action items are mentioned, state 'No action items identified'. - Format the action items as a list. - - Call Transcript: - {transcript} - - Summary: - {summary} - - Action Items: - - [Action Item 1] - - [Action Item 2] - ... - """ - - messages = [ - SystemMessage(content="You are an expert in identifying action items from call transcripts."), - HumanMessage(content=prompt), - ] - - response = llm.invoke(messages) - action_items_text = response.content.strip() - - if "No action items identified" in action_items_text: - state["action_items"] = [] - else: - # Basic parsing, assuming action items are listed with '-' - parsed_items = [item.strip() for item in action_items_text.split("-") if item.strip()] - # Remove duplicates, preserving order - state["action_items"] = list(dict.fromkeys(parsed_items)) - return state - - def add_metadata(state: WorkflowState) -> WorkflowState: - """Add metadata to the call summary.""" - state["metadata"] = { - "summarization_date": datetime.utcnow().isoformat(), - "llm_model_used": "gpt-4o-mini", # Or dynamically get from llm object if possible - "workflow_version": "1.0", - } - return state - - # Define the graph - graph = StateGraph(WorkflowState) - - graph.add_node("route_to_category", route_to_category) - graph.add_node("summarize_call", summarize_call) - graph.add_node("extract_action_items", extract_action_items) - graph.add_node("add_metadata", add_metadata) - - # Define the edges - graph.set_entry_point("route_to_category") - graph.add_edge("route_to_category", "summarize_call") - graph.add_edge("summarize_call", "extract_action_items") - graph.add_edge("extract_action_items", "add_metadata") - graph.set_finish_point("add_metadata") - - # Compile the graph - compiled_workflow = graph.compile() - - return compiled_workflow - - -class CallSummarizer: - """Service for summarizing call transcripts using LangGraph.""" - - def __init__(self, category_manager): - """Initialize the call summarizer.""" - self.workflow = create_summarization_workflow() # This is the compiled graph - self.category_manager = category_manager - self.opik_tracer = None # Initialize opik_tracer attribute - - if settings.opik_api_key: - try: - # Ensure self.workflow has get_graph method and xray is a valid param for it in this context - # The Opik documentation implies app.get_graph(xray=True) is standard. - graph_for_tracer = self.workflow.get_graph(xray=True) - self.opik_tracer = OpikTracer( - graph=graph_for_tracer, tags=["langchain", "call-summarizer", "langgraph"], metadata={"use-case": "call-summarizer"} - ) - except Exception as e: - # Fallback if get_graph(xray=True) or OpikTracer init with graph fails - print(f"Warning: Could not initialize OpikTracer with graph details: {e}. Falling back to basic OpikTracer.") - self.opik_tracer = OpikTracer( - tags=["langchain", "call-summarizer", "langgraph"], metadata={"use-case": "call-summarizer"} - ) # Basic tracer as a fallback - - def summarize_transcript( - self, - transcript: str, - category_name: Optional[str] = None, - ) -> CallSummary: - """Summarize a call transcript.""" - # Get the category config - if not category_name: - category = self.category_manager.get_default_category() - else: - category = self.category_manager.get_category(category_name) - - if not category: - raise ValueError(f"No category found: {category_name}") - - # Prepare the initial state - initial_state = { - "transcript": transcript, - "category": category.name.lower(), - "category_config": category, - "summary": "", - "action_items": [], - "metadata": {}, - } - - # Prepare the run configuration for Langchain callbacks - run_config = {"recursion_limit": 25} - if self.opik_tracer: # Use the instance's opik_tracer - run_config["callbacks"] = [self.opik_tracer] # Use the instance's opik_tracer - - # Run the workflow synchronously, passing the config - result = self.workflow.invoke(initial_state, config=run_config) - - # Create and return the call summary - return CallSummary( - id=str(uuid.uuid4()), - transcript=transcript, - summary=result["summary"], - action_items=result["action_items"], - category=result["category"], - metadata=result["metadata"], - ) diff --git a/opik/streamlit/call-summarizer/src/call_summarizer/services/vector_store.py b/opik/streamlit/call-summarizer/src/call_summarizer/services/vector_store.py deleted file mode 100644 index 7a8949df..00000000 --- a/opik/streamlit/call-summarizer/src/call_summarizer/services/vector_store.py +++ /dev/null @@ -1,195 +0,0 @@ -"""Vector store service for storing and querying call summaries using ChromaDB with Opik tracing.""" - -from datetime import datetime -from typing import Any, Dict, List, Optional -from uuid import uuid4 - -import chromadb - -# RetrievalQA is deprecated, will be replaced by create_retrieval_chain -# from langchain.chains import RetrievalQA -from langchain.chains import create_retrieval_chain -from langchain.chains.combine_documents import create_stuff_documents_chain - -# from langchain.chains.question_answering import load_qa_chain # No longer needed -from langchain.prompts import PromptTemplate -from langchain_chroma import Chroma -from langchain_core.documents import Document as LangchainDocument -from langchain_openai import ChatOpenAI, OpenAIEmbeddings -from opik import track -from opik.integrations.langchain import OpikTracer - -from ..config import settings -from ..models.models import CallSummary, VectorStoreConfig - -# Define the custom prompt for the QA chain -PROMPT_TEMPLATE_STR = ( - "You are a helpful assistant for querying call summaries.\n" - "Use only the following pieces of context (call transcripts and their metadata) to answer the question.\n" - "The metadata for each call includes 'id', 'summary', 'action_items', 'category', and 'created_at'.\n" - "If the provided context does not contain the answer to the question, " - "state that the information was not found in the call history for that query.\n" - "Do not make up information or answer questions outside of the provided context.\n" - "Always answer in the same language as the question.\n" - "Always answer in markdown format.\n\n" - "Context:\n{context}\n\n" - "Question: {input} \n" - "Helpful Answer:" -) # Note: Changed {question} to {input} -CUSTOM_PROMPT = PromptTemplate(template=PROMPT_TEMPLATE_STR, input_variables=["context", "input"]) # Note: Changed 'question' to 'input' - - -class VectorStoreService: - """Service for managing call summaries in a ChromaDB vector store.""" - - def __init__(self, config: Optional[VectorStoreConfig] = None): - """Initialize the vector store service with Opik tracing.""" - self.config = config or VectorStoreConfig(persist_dir=settings.vector_store_path, collection_name="call_summaries") - - # Initialize the embedding model with Opik tracing - self.opik_tracer = OpikTracer() if settings.opik_api_key else None - self.embedding_model = OpenAIEmbeddings() - - # Initialize ChromaDB client and collection - self.chroma_client = chromadb.PersistentClient(path=str(self.config.persist_dir)) - - # Create or get the collection - self.collection = self.chroma_client.get_or_create_collection( - name=self.config.collection_name, - metadata={"hnsw:space": "cosine"}, # Use cosine similarity - ) - - # Initialize the LangChain Chroma vector store with Opik tracing - self.vector_store = Chroma( - client=self.chroma_client, - collection_name=self.config.collection_name, - embedding_function=self.embedding_model, - persist_directory=str(self.config.persist_dir), - ) - - # Initialize LLM for QA with Opik tracing - self.llm = ChatOpenAI(model_name="gpt-4-turbo-preview", callbacks=[self.opik_tracer] if self.opik_tracer else None) - - @track(name="add_call_summary_to_vector_store", flush=True) - def add_call_summary(self, call_summary: CallSummary) -> str: - """Add a call summary to the vector store with Opik tracing.""" - # Create a document from the call summary - doc_id = str(uuid4()) - document = LangchainDocument( - page_content=call_summary.transcript, - metadata={ - "id": doc_id, - "summary": call_summary.summary, - "action_items": "\n".join(call_summary.action_items), - "category": call_summary.category.value, - "created_at": call_summary.created_at.isoformat(), - **call_summary.metadata, - }, - ) - - # Add the document to the vector store with tracing - self.vector_store.add_documents([document], ids=[doc_id], callbacks=[self.opik_tracer] if self.opik_tracer else None) - - return doc_id - - @track(name="search_call_summaries", flush=True) - def search(self, query: str, top_k: int = 5) -> List[Dict[str, Any]]: - """Search for call summaries similar to the query with Opik tracing.""" - # Search for similar documents with tracing - docs = self.vector_store.similarity_search_with_score(query, k=top_k, callbacks=[self.opik_tracer] if self.opik_tracer else None) - - # Convert to result format - results = [] - for doc, score in docs: - results.append({"id": doc.metadata.get("id", ""), "text": doc.page_content, "score": float(score), "metadata": doc.metadata}) - - return results - - @track(name="chat_with_call_history_query", flush=True) - def query(self, query: str, response_mode: str = "compact") -> str: - """Query the vector store with a natural language question.""" - callbacks = [self.opik_tracer] if self.opik_tracer else [] - config = {} - if callbacks: - config["callbacks"] = callbacks - - # Use the original query for the LLM part of the chain - llm_question = query - - if "today" in query.lower(): - today_date_str = datetime.now().strftime("%Y-%m-%d") - - # For 'today' queries, we retrieve more documents and filter them by date. - # The query for retrieval might be the original query, or a modified one if needed. - retrieval_query = query - try: - all_potentially_relevant_docs = self.vector_store.similarity_search( - retrieval_query, - k=20, # Retrieve more documents to filter from - ) - - filtered_docs = [ - doc for doc in all_potentially_relevant_docs if doc.metadata.get("created_at", "").startswith(today_date_str) - ] - except Exception as e: - # Consider logging this error properly - print(f"Error during similarity search for 'today' query: {e}") - filtered_docs = [] - - if not filtered_docs: - return "I did not find any call summaries recorded for today." - - # Use create_stuff_documents_chain with the filtered documents and custom prompt - # This chain directly processes documents and a question (as 'input') - document_chain = create_stuff_documents_chain(self.llm, CUSTOM_PROMPT) - - # The input to this chain is a dictionary with 'context' (the documents) - # and 'input' (the question/query based on CUSTOM_PROMPT) - result_str = document_chain.invoke({"context": filtered_docs, "input": llm_question}, config=config) - return result_str if result_str else "I could not generate a response based on today's calls." - - else: - # Modern approach for non-"today" queries using create_retrieval_chain - retriever = self.vector_store.as_retriever(search_kwargs={"k": 5}) # Standard k for general queries - - # Create the document chain that will process the retrieved documents - document_chain = create_stuff_documents_chain(self.llm, CUSTOM_PROMPT) - - # Create the retrieval chain - retrieval_chain = create_retrieval_chain(retriever, document_chain) - - response = retrieval_chain.invoke({"input": llm_question}, config=config) # New chains expect "input" - # The answer is in response['answer'] - # Retrieved documents are in response['context'] - return response.get("answer", "I could not generate a response based on the available call history.") - - def get_all_summaries(self) -> List[Dict[str, Any]]: - """Get all call summaries from the vector store.""" - # Get all documents from the collection - results = self.chroma_client.get_collection(self.config.collection_name).get() - - # Convert to summary format - summaries = [] - for i, doc_id in enumerate(results["ids"]): - metadata = results["metadatas"][i] - document = results["documents"][i] - - summary = { - "id": doc_id, - "text": (document[:500] + "...") if len(document) > 500 else document, - "summary": metadata.get("summary", ""), - "category": metadata.get("category", "other"), - "created_at": metadata.get("created_at", ""), - } - summaries.append(summary) - - return summaries - - def delete_summary(self, summary_id: str) -> bool: - """Delete a call summary from the vector store.""" - try: - self.vector_store._collection.delete(ids=[summary_id]) - return True - except Exception as e: - print(f"Error deleting summary {summary_id}: {e}") - return False diff --git a/opik/streamlit/call-summarizer/src/call_summarizer/utils/file_utils.py b/opik/streamlit/call-summarizer/src/call_summarizer/utils/file_utils.py deleted file mode 100644 index aa9ca47a..00000000 --- a/opik/streamlit/call-summarizer/src/call_summarizer/utils/file_utils.py +++ /dev/null @@ -1,63 +0,0 @@ -"""Utility functions for file operations.""" - -import json -import os -from typing import List, Optional, Type, TypeVar - -from pydantic import BaseModel - -T = TypeVar("T", bound=BaseModel) - - -def ensure_dir_exists(file_path: str) -> None: - """Ensure the directory of the given file path exists.""" - os.makedirs(os.path.dirname(file_path), exist_ok=True) - - -def save_config(data: BaseModel, file_path: str) -> None: - """Save a Pydantic model to a JSON file.""" - ensure_dir_exists(file_path) - with open(file_path, "w", encoding="utf-8") as f: - json.dump(data.dict(), f, indent=2, default=str) - - -def load_config(file_path: str, model_class: Type[T]) -> Optional[T]: - """Load a Pydantic model from a JSON file.""" - if not os.path.exists(file_path): - return None - - with open(file_path, "r", encoding="utf-8") as f: - data = json.load(f) - - return model_class(**data) - - -def save_list_of_configs(data_list: List[BaseModel], file_path: str) -> None: - """Save a list of Pydantic models to a JSON file.""" - ensure_dir_exists(file_path) - with open(file_path, "w", encoding="utf-8") as f: - json.dump([item.dict() for item in data_list], f, indent=2, default=str) - - -def load_list_of_configs(file_path: str, model_class: Type[T]) -> List[T]: - """Load a list of Pydantic models from a JSON file.""" - if not os.path.exists(file_path): - return [] - - with open(file_path, "r", encoding="utf-8") as f: - data_list = json.load(f) - - return [model_class(**item) for item in data_list] - - -def read_text_file(file_path: str) -> str: - """Read text content from a file.""" - with open(file_path, "r", encoding="utf-8") as f: - return f.read() - - -def write_text_file(file_path: str, content: str) -> None: - """Write text content to a file.""" - ensure_dir_exists(file_path) - with open(file_path, "w", encoding="utf-8") as f: - f.write(content) diff --git a/opik/streamlit/call-summarizer/tests/__init__.py b/opik/streamlit/call-summarizer/tests/__init__.py deleted file mode 100644 index e69de29b..00000000