mirror of
https://github.com/hwchase17/langchain
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1335f2b9f8
Updated `MLflow` examples with links to the examples from MLflow @baskaryan
215 lines
5.9 KiB
Plaintext
215 lines
5.9 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "5d184f91",
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"metadata": {},
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"source": [
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"# MLflow\n",
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"\n",
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">[MLflow](https://www.mlflow.org/docs/latest/what-is-mlflow.html) is a versatile, expandable, open-source platform for managing workflows and artifacts across the machine learning lifecycle. It has built-in integrations with many popular ML libraries, but can be used with any library, algorithm, or deployment tool. It is designed to be extensible, so you can write plugins to support new workflows, libraries, and tools.\n",
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"\n",
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"This notebook goes over how to track your LangChain experiments into your `MLflow Server`"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ea73efae-7182-4a89-a492-c865b1fcf981",
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"metadata": {},
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"source": [
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"## External examples"
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]
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},
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{
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"cell_type": "markdown",
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"id": "97361a84-4e8f-45ba-b291-814cf73cd8f2",
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"metadata": {},
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"source": [
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"`MLflow` provides [several examples](https://github.com/mlflow/mlflow/tree/master/examples/langchain) for the `LangChain` integration:\n",
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"- [simple_chain](https://github.com/mlflow/mlflow/blob/master/examples/langchain/simple_chain.py)\n",
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"- [simple_agent](https://github.com/mlflow/mlflow/blob/master/examples/langchain/simple_agent.py)\n",
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"- [retriever_chain](https://github.com/mlflow/mlflow/blob/master/examples/langchain/retriever_chain.py)\n",
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"- [retrieval_qa_chain](https://github.com/mlflow/mlflow/blob/master/examples/langchain/retrieval_qa_chain.py)\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e0cbd74b-1542-45a4-a72b-b2eedeffd2e0",
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"metadata": {},
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"source": [
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"## Example"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "ca7bd72f",
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"metadata": {},
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"outputs": [],
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"source": [
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"!pip install azureml-mlflow\n",
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"!pip install pandas\n",
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"!pip install textstat\n",
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"!pip install spacy\n",
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"!pip install openai\n",
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"!pip install google-search-results\n",
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"!python -m spacy download en_core_web_sm"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "bf8e1f5c",
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"\n",
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"os.environ[\"MLFLOW_TRACKING_URI\"] = \"\"\n",
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"os.environ[\"OPENAI_API_KEY\"] = \"\"\n",
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"os.environ[\"SERPAPI_API_KEY\"] = \"\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "fd49fd45",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.callbacks import MlflowCallbackHandler\n",
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"from langchain.llms import OpenAI"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "578cac8c",
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"metadata": {},
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"outputs": [],
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"source": [
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"\"\"\"Main function.\n",
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"\n",
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"This function is used to try the callback handler.\n",
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"Scenarios:\n",
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"1. OpenAI LLM\n",
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"2. Chain with multiple SubChains on multiple generations\n",
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"3. Agent with Tools\n",
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"\"\"\"\n",
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"mlflow_callback = MlflowCallbackHandler()\n",
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"llm = OpenAI(\n",
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" model_name=\"gpt-3.5-turbo\", temperature=0, callbacks=[mlflow_callback], verbose=True\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "9b20acae",
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"metadata": {},
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"outputs": [],
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"source": [
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"# SCENARIO 1 - LLM\n",
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"llm_result = llm.generate([\"Tell me a joke\"])\n",
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"\n",
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"mlflow_callback.flush_tracker(llm)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "8b872046",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.prompts import PromptTemplate\n",
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"from langchain.chains import LLMChain"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "1b2627ef",
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"metadata": {},
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"outputs": [],
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"source": [
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"# SCENARIO 2 - Chain\n",
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"template = \"\"\"You are a playwright. Given the title of play, it is your job to write a synopsis for that title.\n",
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"Title: {title}\n",
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"Playwright: This is a synopsis for the above play:\"\"\"\n",
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"prompt_template = PromptTemplate(input_variables=[\"title\"], template=template)\n",
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"synopsis_chain = LLMChain(llm=llm, prompt=prompt_template, callbacks=[mlflow_callback])\n",
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"\n",
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"test_prompts = [\n",
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" {\n",
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" \"title\": \"documentary about good video games that push the boundary of game design\"\n",
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" },\n",
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"]\n",
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"synopsis_chain.apply(test_prompts)\n",
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"mlflow_callback.flush_tracker(synopsis_chain)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "e002823a",
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"metadata": {
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"id": "_jN73xcPVEpI"
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},
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"outputs": [],
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"source": [
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"from langchain.agents import initialize_agent, load_tools\n",
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"from langchain.agents import AgentType"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "655bd47e",
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"metadata": {
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"id": "Gpq4rk6VT9cu"
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},
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"outputs": [],
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"source": [
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"# SCENARIO 3 - Agent with Tools\n",
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"tools = load_tools([\"serpapi\", \"llm-math\"], llm=llm, callbacks=[mlflow_callback])\n",
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"agent = initialize_agent(\n",
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" tools,\n",
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" llm,\n",
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" agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,\n",
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" callbacks=[mlflow_callback],\n",
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" verbose=True,\n",
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")\n",
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"agent.run(\n",
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" \"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?\"\n",
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")\n",
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"mlflow_callback.flush_tracker(agent, finish=True)"
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]
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}
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],
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"metadata": {
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"colab": {
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"provenance": []
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},
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.12"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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