feat: Draft ollama test (#566)
<!-- .github/pull_request_template.md --> ## Description <!-- Provide a clear description of the changes in this PR --> ## DCO Affirmation I affirm that all code in every commit of this pull request conforms to the terms of the Topoteretes Developer Certificate of Origin <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - **Tests** - Introduced new automated testing workflows for Ollama and Gemini, triggered by pull requests and manual dispatch. - The Ollama workflow sets up the service and executes a simple example test to enhance continuous integration. - Enhanced dependency update workflow with new triggers for push and pull request events, and added an optional debug logging parameter. - Added new capabilities for audio and image transcription within the Ollama API adapter. <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Co-authored-by: Daniel Molnar <soobrosa@gmail.com>
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Vasilije
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4 changed files with 224 additions and 1 deletions
33
.github/workflows/test_gemini.yml
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.github/workflows/test_gemini.yml
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@ -0,0 +1,33 @@
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name: test | gemini
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on:
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workflow_dispatch:
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pull_request:
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types: [labeled, synchronize]
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concurrency:
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group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
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cancel-in-progress: true
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jobs:
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run_simple_example_test:
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uses: ./.github/workflows/reusable_python_example.yml
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with:
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example-location: ./examples/python/simple_example.py
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secrets:
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OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
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GRAPHISTRY_USERNAME: ${{ secrets.GRAPHISTRY_USERNAME }}
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GRAPHISTRY_PASSWORD: ${{ secrets.GRAPHISTRY_PASSWORD }}
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EMBEDDING_PROVIDER: "gemini"
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EMBEDDING_API_KEY: ${{ secrets.GEMINI_API_KEY }}
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EMBEDDING_MODEL: "gemini/text-embedding-004"
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EMBEDDING_ENDPOINT: "https://generativelanguage.googleapis.com/v1beta/models/text-embedding-004"
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EMBEDDING_API_VERSION: "v1beta"
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EMBEDDING_DIMENSIONS: 768
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EMBEDDING_MAX_TOKENS: 8076
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LLM_PROVIDER: "gemini"
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LLM_API_KEY: ${{ secrets.GEMINI_API_KEY }}
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LLM_MODEL: "gemini/gemini-1.5-flash"
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LLM_ENDPOINT: "https://generativelanguage.googleapis.com/"
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LLM_API_VERSION: "v1beta"
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116
.github/workflows/test_ollama.yml
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.github/workflows/test_ollama.yml
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@ -0,0 +1,116 @@
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name: test | ollama
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on:
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workflow_dispatch:
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pull_request:
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types: [ labeled, synchronize ]
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jobs:
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run_simple_example_test:
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# needs 16 Gb RAM for phi4
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runs-on: buildjet-4vcpu-ubuntu-2204
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# services:
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# ollama:
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# image: ollama/ollama
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# ports:
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# - 11434:11434
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steps:
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- name: Checkout repository
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uses: actions/checkout@v4
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- name: Setup Python
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uses: actions/setup-python@v5
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with:
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python-version: '3.12.x'
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- name: Install Poetry
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uses: snok/install-poetry@v1.4.1
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with:
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virtualenvs-create: true
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virtualenvs-in-project: true
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installer-parallel: true
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- name: Install dependencies
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run: |
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poetry install --no-interaction --all-extras
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poetry add torch
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# - name: Install ollama
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# run: curl -fsSL https://ollama.com/install.sh | sh
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# - name: Run ollama
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# run: |
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# ollama serve --openai &
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# ollama pull llama3.2 &
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# ollama pull avr/sfr-embedding-mistral:latest
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- name: Start Ollama container
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run: |
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docker run -d --name ollama -p 11434:11434 ollama/ollama
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sleep 5
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docker exec -d ollama bash -c "ollama serve --openai"
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- name: Check Ollama logs
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run: docker logs ollama
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- name: Wait for Ollama to be ready
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run: |
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for i in {1..30}; do
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if curl -s http://localhost:11434/v1/models > /dev/null; then
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echo "Ollama is ready"
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exit 0
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fi
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echo "Waiting for Ollama... attempt $i"
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sleep 2
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done
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echo "Ollama failed to start"
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exit 1
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- name: Pull required Ollama models
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run: |
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curl -X POST http://localhost:11434/api/pull -d '{"name": "phi4"}'
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curl -X POST http://localhost:11434/api/pull -d '{"name": "avr/sfr-embedding-mistral:latest"}'
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- name: Call ollama API
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run: |
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curl -X POST http://localhost:11434/v1/chat/completions \
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-H "Content-Type: application/json" \
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-d '{
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"model": "phi4",
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"stream": false,
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"messages": [
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{ "role": "system", "content": "You are a helpful assistant." },
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{ "role": "user", "content": "Whatever I say, answer with Yes." }
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]
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}'
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curl -X POST http://127.0.0.1:11434/v1/embeddings \
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-H "Content-Type: application/json" \
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-d '{
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"model": "avr/sfr-embedding-mistral:latest",
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"input": "This is a test sentence to generate an embedding."
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}'
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- name: Dump Docker logs
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run: |
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docker ps
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docker logs $(docker ps --filter "ancestor=ollama/ollama" --format "{{.ID}}")
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- name: Run example test
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env:
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OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
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GRAPHISTRY_USERNAME: ${{ secrets.GRAPHISTRY_USERNAME }}
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GRAPHISTRY_PASSWORD: ${{ secrets.GRAPHISTRY_PASSWORD }}
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PYTHONFAULTHANDLER: 1
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LLM_PROVIDER: "ollama"
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LLM_API_KEY: "ollama"
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LLM_ENDPOINT: "http://localhost:11434/v1/"
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LLM_MODEL: "phi4"
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EMBEDDING_PROVIDER: "ollama"
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EMBEDDING_MODEL: "avr/sfr-embedding-mistral:latest"
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EMBEDDING_ENDPOINT: "http://localhost:11434/v1/"
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EMBEDDING_DIMENSIONS: "4096"
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HUGGINGFACE_TOKENIZER: "Salesforce/SFR-Embedding-Mistral"
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run: poetry run python ./examples/python/simple_example.py
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23
.github/workflows/upgrade_deps.yml
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.github/workflows/upgrade_deps.yml
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@ -2,8 +2,29 @@ name: Update Poetry Dependencies
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on:
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schedule:
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- cron: '0 3 * * 0'
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- cron: '0 3 * * 0' # Runs at 3 AM every Sunday
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push:
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paths:
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- 'poetry.lock'
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- 'pyproject.toml'
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branches:
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- main
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- dev
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pull_request:
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paths:
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- 'poetry.lock'
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- 'pyproject.toml'
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types: [opened, synchronize, reopened]
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branches:
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- main
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- dev
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workflow_dispatch:
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inputs:
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debug_enabled:
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type: boolean
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description: 'Run the update with debug logging'
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required: false
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default: false
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jobs:
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update-dependencies:
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@ -4,6 +4,8 @@ import instructor
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from cognee.infrastructure.llm.llm_interface import LLMInterface
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from cognee.infrastructure.llm.config import get_llm_config
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from openai import OpenAI
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import base64
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import os
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class OllamaAPIAdapter(LLMInterface):
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@ -42,3 +44,54 @@ class OllamaAPIAdapter(LLMInterface):
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)
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return response
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def create_transcript(self, input_file: str) -> str:
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"""Generate an audio transcript from a user query."""
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if not os.path.isfile(input_file):
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raise FileNotFoundError(f"The file {input_file} does not exist.")
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with open(input_file, "rb") as audio_file:
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transcription = self.aclient.audio.transcriptions.create(
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model="whisper-1", # Ensure the correct model for transcription
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file=audio_file,
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language="en",
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)
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# Ensure the response contains a valid transcript
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if not hasattr(transcription, "text"):
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raise ValueError("Transcription failed. No text returned.")
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return transcription.text
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def transcribe_image(self, input_file: str) -> str:
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"""Transcribe content from an image using base64 encoding."""
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if not os.path.isfile(input_file):
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raise FileNotFoundError(f"The file {input_file} does not exist.")
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with open(input_file, "rb") as image_file:
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encoded_image = base64.b64encode(image_file.read()).decode("utf-8")
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response = self.aclient.chat.completions.create(
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model=self.model,
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messages=[
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{
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"role": "user",
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"content": [
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{"type": "text", "text": "What’s in this image?"},
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{
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"type": "image_url",
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"image_url": {"url": f"data:image/jpeg;base64,{encoded_image}"},
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},
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],
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}
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],
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max_tokens=300,
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)
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# Ensure response is valid before accessing .choices[0].message.content
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if not hasattr(response, "choices") or not response.choices:
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raise ValueError("Image transcription failed. No response received.")
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return response.choices[0].message.content
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