diff --git a/docs/getting_started/getting_started.md b/docs/getting_started/getting_started.md index 4faae9b7a2f79..9abe5edd7da20 100644 --- a/docs/getting_started/getting_started.md +++ b/docs/getting_started/getting_started.md @@ -37,6 +37,12 @@ import os os.environ["OPENAI_API_KEY"] = "..." ``` +If you want to set the API key dynamically, you can use the openai_api_key parameter when initiating OpenAI class—for instance, each user's API key. + +```python +from langchain.llms import OpenAI +llm = OpenAI(openai_api_key="OPENAI_API_KEY") +``` ## Building a Language Model Application: LLMs diff --git a/docs/modules/chains/getting_started.ipynb b/docs/modules/chains/getting_started.ipynb index 570697a78e115..53d6ccc3ea016 100644 --- a/docs/modules/chains/getting_started.ipynb +++ b/docs/modules/chains/getting_started.ipynb @@ -68,7 +68,7 @@ "text": [ "\n", "\n", - "SockSplash!\n" + "Colorful Toes Co.\n" ] } ], @@ -81,15 +81,50 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "You can use a chat model in an `LLMChain` as well:" + "If there are multiple variables, you can input them all at once using a dictionary." ] }, { "cell_type": "code", "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Socktopia Colourful Creations.\n" + ] + } + ], + "source": [ + "prompt = PromptTemplate(\n", + " input_variables=[\"company\", \"product\"],\n", + " template=\"What is a good name for {company} that makes {product}?\",\n", + ")\n", + "chain = LLMChain(llm=llm, prompt=prompt)\n", + "print(chain.run({\n", + " 'company': \"ABC Startup\",\n", + " 'product': \"colorful socks\"\n", + " }))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can use a chat model in an `LLMChain` as well:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, "metadata": { "tags": [] }, @@ -98,7 +133,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Rainbow Sox Co.\n" + "Rainbow Socks Co.\n" ] } ], @@ -131,7 +166,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -141,7 +176,7 @@ " 'text': 'Why did the tomato turn red? Because it saw the salad dressing!'}" ] }, - "execution_count": 4, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -166,7 +201,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -175,7 +210,7 @@ "{'text': 'Why did the tomato turn red? Because it saw the salad dressing!'}" ] }, - "execution_count": 5, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -193,7 +228,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -202,7 +237,7 @@ "['text']" ] }, - "execution_count": 6, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -214,7 +249,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -223,7 +258,7 @@ "'Why did the tomato turn red? Because it saw the salad dressing!'" ] }, - "execution_count": 7, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -241,7 +276,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -251,7 +286,7 @@ " 'text': 'Why did the tomato turn red? Because it saw the salad dressing!'}" ] }, - "execution_count": 8, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -284,7 +319,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -293,7 +328,7 @@ "'The next four colors of a rainbow are green, blue, indigo, and violet.'" ] }, - "execution_count": 9, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -331,7 +366,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -358,7 +393,7 @@ "'ChatGPT is an AI language model developed by OpenAI. It is based on the GPT-3 architecture and is capable of generating human-like responses to text prompts. ChatGPT has been trained on a massive amount of text data and can understand and respond to a wide range of topics. It is often used for chatbots, virtual assistants, and other conversational AI applications.'" ] }, - "execution_count": 10, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -387,7 +422,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -407,7 +442,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -420,12 +455,12 @@ "\u001b[36;1m\u001b[1;3mRainbow Socks Co.\u001b[0m\n", "\u001b[33;1m\u001b[1;3m\n", "\n", - "\"Step into Color with Rainbow Socks!\"\u001b[0m\n", + "\"Put a little rainbow in your step!\"\u001b[0m\n", "\n", "\u001b[1m> Finished chain.\u001b[0m\n", "\n", "\n", - "\"Step into Color with Rainbow Socks!\"\n" + "\"Put a little rainbow in your step!\"\n" ] } ], @@ -456,7 +491,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -496,7 +531,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -506,9 +541,9 @@ "Concatenated output:\n", "\n", "\n", - "Socktastic Colors.\n", + "Funky Footwear Company\n", "\n", - "\"Put Some Color in Your Step!\"\n" + "\"Brighten Up Your Day with Our Colorful Socks!\"\n" ] } ], @@ -554,7 +589,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.6" + "version": "3.9.16" }, "vscode": { "interpreter": {