chore: adding llm example (#4023)

* chore: adding llm example

* update configs

* wip

* removing pycache files

* update gitignore

* adding tests

* update apps to receive tracebased test

* update apps

* update tests

* update example to have running trace based test

* update tests

* update example

examples/quick-start-llm-python/.gitignore

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+_venv
+.env
+
+node_modules
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/latest/usage/project/#working-with-version-control
+.pdm.toml
+.pdm-python
+.pdm-build/
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
+node_modules/
+/test-results/
+/playwright-report/
+/blob-report/
+/playwright/.cache/

examples/quick-start-llm-python/.python-version

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+3.12

examples/quick-start-llm-python/Makefile

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+help: Makefile ## show list of commands
+	@echo "Choose a command to run:"
+	@echo ""
+	@awk 'BEGIN {FS = ":.*?## "} /[a-zA-Z_-]+:.*?## / {sub("\\\\n",sprintf("\n%22c"," "), $$2);printf "\033[36m%-40s\033[0m %s\n", $$1, $$2}' $(MAKEFILE_LIST) | sort
+
+# Added to skip list to avoid warnings
+DISABLED_INSTRUMENTATIONS=aleph_alpha_client,chromadb,cohere,groq,haystack-ai,lancedb,llama-index,marqo,milvus,mistralai,pinecone_client,qdrant_client,replicate,together,google_cloud_aiplatform,ibm-watson-machine-learning,weaviate_client
+
+build/docker: ## build images used by docker compose file
+	@docker compose build
+
+start/on-docker: build/docker ## build and run app using docker compose
+	@docker compose up
+
+start/on-docker/only-observability: ## run observability stack using docker compose in debug mode
+	@docker compose up -d otel-collector jaeger
+
+start/local-ui: start/on-docker/only-observability ## run UI app using docker compose
+	@OTEL_SERVICE_NAME=quick-start-llm \
+	 OTEL_TRACES_EXPORTER=otlp \
+	 OTEL_EXPORTER_OTLP_TRACES_ENDPOINT=http://localhost:4317 \
+	 OTEL_PYTHON_DISABLED_INSTRUMENTATIONS=$(DISABLED_INSTRUMENTATIONS) \
+	 opentelemetry-instrument streamlit run ./app/streamlit_app.py
+
+start/local-api: start/on-docker/only-observability ## run UI app using docker compose
+	@OTEL_SERVICE_NAME=quick-start-llm \
+	 OTEL_TRACES_EXPORTER=otlp \
+	 OTEL_METRICS_EXPORTER=none \
+	 OTEL_EXPORTER_OTLP_TRACES_ENDPOINT=http://localhost:4317 \
+	 OTEL_PYTHON_DISABLED_INSTRUMENTATIONS=$(DISABLED_INSTRUMENTATIONS) \
+	 opentelemetry-instrument python ./app/flask_app.py
+
+stop: ## stop all running containers
+	@docker compose down
+
+test: ## run e2e tests
+	@tracetest run -f ./tests/run-gemini.yaml

examples/quick-start-llm-python/README.md

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+## Quick Start LLM app
+
+This is an example of a simple LLM app that uses the `langchain` library to summarize the content of a URL, based on [this example](https://github.com/alphasecio/langchain-examples/tree/main/url-summary)
+
+### Running example with docker
+
+```bash
+make start/on-docker
+```
+
+### Running example with locally
+
+#### Setting up the environment
+
+```bash
+
+# create venv
+python -m venv ./_venv
+
+# activate env
+source _venv/bin/activate
+
+# install requirements
+pip install -r app/requirements.txt
+
+# install OTel auto-instrumentation
+opentelemetry-bootstrap -a install
+
+# add openai api key
+echo "OPENAI_API_KEY={your-open-ai-api-key}" >> .env
+# add google gemini api key
+echo "GOOGLE_API_KEY={your-google-gemini-api-key}" >> .env
+
+# add tracetest agent keys
+echo "TRACETEST_API_KEY={your-tracetest-api-key}" >> .env
+echo "TRACETEST_ENVIRONMENT_ID={your-tracetest-env-id}" >> .env
+
+# add tracetest token for playwright tests with TS lib
+echo "TRACETEST_API_TOKEN={your-tracetest-token-for-ts-libs}" >> ./tests/.env
+```
+
+#### Running the apps
+
+```bash
+
+# start the app running the UI locally
+make start/local-ui
+
+# start the app running the API locally
+make start/local-api
+
+# start the app everything on docker
+make start/on-docker
+
+```

examples/quick-start-llm-python/app/Dockerfile

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+FROM python:3.12.4-slim
+
+WORKDIR /opt/app
+COPY requirements.txt ./
+
+RUN pip install --no-cache-dir -r requirements.txt
+RUN opentelemetry-bootstrap -a install
+
+COPY . .

examples/quick-start-llm-python/app/example.txt

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+Born in London, Turing was raised in southern England. He graduated from King's College, Cambridge, and in 1938, earned a doctorate degree from Princeton University. During World War II, Turing worked for the Government Code and Cypher School at Bletchley Park, Britain's codebreaking centre that produced Ultra intelligence. He led Hut 8, the section responsible for German naval cryptanalysis. Turing devised techniques for speeding the breaking of German ciphers, including improvements to the pre-war Polish bomba method, an electromechanical machine that could find settings for the Enigma machine. He played a crucial role in cracking intercepted messages that enabled the Allies to defeat the Axis powers in many crucial engagements, including the Battle of the Atlantic.
+
+After the war, Turing worked at the National Physical Laboratory, where he designed the Automatic Computing Engine, one of the first designs for a stored-program computer. In 1948, Turing joined Max Newman's Computing Machine Laboratory at the Victoria University of Manchester, where he helped develop the Manchester computers[12] and became interested in mathematical biology. Turing wrote on the chemical basis of morphogenesis and predicted oscillating chemical reactions such as the Belousov–Zhabotinsky reaction, first observed in the 1960s. Despite these accomplishments, he was never fully recognised during his lifetime because much of his work was covered by the Official Secrets Act.

examples/quick-start-llm-python/app/flask_app.py

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+from dotenv import load_dotenv
+
+# Load environment variables from .env file
+load_dotenv()
+
+# Initialize telemetry
+from telemetry import init as telemetry_init
+tracer = telemetry_init() # run telemetry.init() before loading any other modules to capture any module-level telemetry
+
+from opentelemetry import trace
+from opentelemetry.instrumentation.flask import FlaskInstrumentor
+
+# from telemetry import heartbeat as telemetry_heartbeat
+# telemetry_heartbeat(tracer)
+
+from llm.providers import get_provider, get_providers
+from flask import Flask, request, jsonify, make_response
+
+instrumentor = FlaskInstrumentor()
+
+app = Flask(__name__)
+instrumentor.instrument_app(app)
+
+api_port = '8800'
+
+@app.route('/summarizeText', methods=['POST'])
+def summarize_text():
+  data = request.json
+
+  provider_type = data['provider']
+
+  providers = get_providers()
+  has_provider = provider_type in providers
+
+  if not has_provider:
+    return make_response(jsonify({ "error": "Invalid provider" }), 400)
+
+  source_text = data['text']
+
+  provider = get_provider(provider_type)
+  summarize_text =  provider.summarize(source_text)
+
+  # Get trace ID from current span
+  span = trace.get_current_span()
+  trace_id = span.get_span_context().trace_id
+
+  # Convert trace_id to a hex string
+  trace_id_hex = format(trace_id, '032x')
+
+  return jsonify({"summary": summarize_text, "trace_id": trace_id_hex})
+
+if __name__ == '__main__':
+  print('Running on port: ' + api_port)
+  app.run(host='0.0.0.0', port=api_port)

examples/quick-start-llm-python/app/llm/provider_google_gemini.py

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+from langchain.chains.summarize import load_summarize_chain
+from langchain_community.docstore.document import Document
+from langchain_text_splitters import CharacterTextSplitter
+
+from langchain_google_genai import ChatGoogleGenerativeAI
+
+class GoogleGeminiProvider:
+  def provider_name(self):
+    return "Google (Gemini)"
+
+  def summarize(self, text):
+    chat = ChatGoogleGenerativeAI(model="gemini-pro")
+
+    # Split the source text
+    text_splitter = CharacterTextSplitter()
+    texts = text_splitter.split_text(text)
+
+    # Create Document objects for the texts (max 3 pages)
+    docs = [Document(page_content=t) for t in texts[:3]]
+
+    # Load and run the summarize chain
+    chain = load_summarize_chain(chat, chain_type="map_reduce")
+    return chain.run(docs)