add

docs/getting_started/getting_started.md

@@ -1,15 +1,18 @@
-# Quickstart Guide
-
+# Basics
 
 This tutorial gives you a quick walkthrough about building an end-to-end language model application with LangChain.
 
 ## Installation
 
-To get started, install LangChain with the following command:
+To get started, install LangChain by running the following command in your terminal:
 
 ```bash
 pip install langchain
-# or
+```
+
+Or if you use Conda:
+
+```
 conda install langchain -c conda-forge
 ```
 
@@ -18,41 +21,44 @@ conda install langchain -c conda-forge
 
 Using LangChain will usually require integrations with one or more model providers, data stores, apis, etc.
 
-For this example, we will be using OpenAI's APIs, so we will first need to install their SDK:
+For this example, we'll use OpenAI's APIs. To do so we'll first need to install their SDK:
 
 ```bash
 pip install openai
 ```
 
-We will then need to set the environment variable in the terminal.
+We will then need to get our OPENAI API key and set the following environment variable in the terminal:
 
 ```bash
 export OPENAI_API_KEY="..."
 ```
 
-Alternatively, you could do this from inside the Jupyter notebook (or Python script):
+We can also do this inside our Jupyter notebook or Python script:
 
 ```python
 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.
+Or we can pass the API key in directly. For example, when instantiating the OpenAI LLM class we can pass in 
+`openai_api_key`:
 
 ```python
 from langchain.llms import OpenAI
-llm = OpenAI(openai_api_key="OPENAI_API_KEY")
+
+llm = OpenAI(openai_api_key="...")
 ```
 
-## Building a Language Model Application: LLMs
+## Language Model Applications
 
 Now that we have installed LangChain and set up our environment, we can start building our language model application.
 
 LangChain provides many modules that can be used to build language model applications. Modules can be combined to create more complex applications, or be used individually for simple applications.
 
 
-
-## LLMs: Get predictions from a language model
+## LLMs
+*Get predictions from a language model*
 
 The most basic building block of LangChain is calling an LLM on some input.
 Let's walk through a simple example of how to do this. 
@@ -82,10 +88,14 @@ print(llm(text))
 Feetful of Fun
 ```
 
-For more details on how to use LLMs within LangChain, see the [LLM getting started guide](../modules/models/llms/getting_started.ipynb).
+For more details on how to use LLMs within LangChain, see the 
+[LLM Getting Started guide](../modules/models/llms/getting_started.ipynb).
+
+**NOTE:** There is a subtle but crucial difference between text completion models such as GPT-3 and chat models such as GPT-4. The former corresponds to the `LLM` class (what we are demonstrating here) and the latter to the `ChatModel` class. We discuss `ChatModel`s [below](#chat-models) and in [Part 2 of the Quickstart Guide](./getting_started_chat.md).
 
 
-## Prompt Templates: Manage prompts for LLMs
+## Prompt Templates
+*Manage prompts for LLMs*
 
 Calling an LLM is a great first step, but it's just the beginning.
 Normally when you use an LLM in an application, you are not sending user input directly to the LLM.
@@ -117,13 +127,11 @@ print(prompt.format(product="colorful socks"))
 What is a good name for a company that makes colorful socks?
 ```
 
-
-[For more details, check out the getting started guide for prompts.](../modules/prompts/chat_prompt_template.ipynb)
+For more details check out the [Prompt Getting Started guide](../modules/prompts/chat_prompt_template.ipynb).
 
 
-
-
-## Chains: Combine LLMs and prompts in multi-step workflows
+## Chains
+*Combine LLMs and prompts in multi-step workflows*
 
 Up until now, we've worked with the PromptTemplate and LLM primitives by themselves. But of course, a real application is not just one primitive, but rather a combination of them.
 
@@ -134,8 +142,8 @@ The most core type of chain is an LLMChain, which consists of a PromptTemplate a
 Extending the previous example, we can construct an LLMChain which takes user input, formats it with a PromptTemplate, and then passes the formatted response to an LLM.
 
 ```python
-from langchain.prompts import PromptTemplate
 from langchain.llms import OpenAI
+from langchain.prompts import PromptTemplate
 
 llm = OpenAI(temperature=0.9)
 prompt = PromptTemplate(
@@ -148,6 +156,7 @@ We can now create a very simple chain that will take user input, format the prom
 
 ```python
 from langchain.chains import LLMChain
+
 chain = LLMChain(llm=llm, prompt=prompt)
 ```
 
@@ -155,15 +164,18 @@ Now we can run that chain only specifying the product!
 
 ```python
 chain.run("colorful socks")
-# -> '\n\nSocktastic!'
+```
+```pycon
+Socktastic!
 ```
 
 There we go! There's the first chain - an LLM Chain.
 This is one of the simpler types of chains, but understanding how it works will set you up well for working with more complex chains.
 
-[For more details, check out the getting started guide for chains.](../modules/chains/getting_started.ipynb)
+For more details, check out the [Chains Getting Started guide](../modules/chains/getting_started.ipynb).
 
-## Agents: Dynamically Call Chains Based on User Input
+## Agents
+*Dynamically Call Chains Based on User Input*
 
 So far the chains we've looked at run in a predetermined order.
 
@@ -171,7 +183,6 @@ Agents no longer do: they use an LLM to determine which actions to take and in w
 
 When used correctly agents can be extremely powerful. In this tutorial, we show you how to easily use agents through the simplest, highest level API.
 
-
 In order to load agents, you should understand the following concepts:
 
 - Tool: A function that performs a specific duty. This can be things like: Google Search, Database lookup, Python REPL, other chains. The interface for a tool is currently a function that is expected to have a string as an input, with a string as an output.
@@ -192,15 +203,16 @@ And set the appropriate environment variables.
 
 ```python
 import os
+
 os.environ["SERPAPI_API_KEY"] = "..."
 ```
 
 Now we can get started!
 
 ```python
-from langchain.agents import load_tools
-from langchain.agents import initialize_agent
 from langchain.agents import AgentType
+from langchain.agents import initialize_agent
+from langchain.agents import load_tools
 from langchain.llms import OpenAI
 
 # First, let's load the language model we're going to use to control the agent.
@@ -209,7 +221,6 @@ llm = OpenAI(temperature=0)
 # Next, let's load some tools to use. Note that the `llm-math` tool uses an LLM, so we need to pass that in.
 tools = load_tools(["serpapi", "llm-math"], llm=llm)
 
-
 # Finally, let's initialize an agent with the tools, the language model, and the type of agent we want to use.
 agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
 
@@ -235,8 +246,8 @@ Final Answer: The high temperature in SF yesterday in Fahrenheit raised to the .
 ```
 
 
-
-## Memory: Add State to Chains and Agents
+## Memory
+*Add State to Chains and Agents*
 
 So far, all the chains and agents we've gone through have been stateless. But often, you may want a chain or agent to have some concept of "memory" so that it may remember information about its previous interactions. The clearest and simple example of this is when designing a chatbot - you want it to remember previous messages so it can use context from that to have a better conversation. This would be a type of "short-term memory". On the more complex side, you could imagine a chain/agent remembering key pieces of information over time - this would be a form of "long-term memory". For more concrete ideas on the latter, see this [awesome paper](https://memprompt.com/).
 
@@ -289,13 +300,13 @@ AI:
 " That's great! What would you like to talk about?"
 ```
 
-## Building a Language Model Application: Chat Models
+## Chat Models
 
 Similarly, you can use chat models instead of LLMs. Chat models are a variation on language models. While chat models use language models under the hood, the interface they expose is a bit different: rather than expose a "text in, text out" API, they expose an interface where "chat messages" are the inputs and outputs.
 
 Chat model APIs are fairly new, so we are still figuring out the correct abstractions.
 
-## Get Message Completions from a Chat Model
+### Get Message Completions from a Chat Model
 
 You can get chat completions by passing one or more messages to the chat model. The response will be a message. The types of messages currently supported in LangChain are `AIMessage`, `HumanMessage`, `SystemMessage`, and `ChatMessage` -- `ChatMessage` takes in an arbitrary role parameter. Most of the time, you'll just be dealing with `HumanMessage`, `AIMessage`, and `SystemMessage`.
 
@@ -314,7 +325,10 @@ You can get completions by passing in a single message.
 
 ```python
 chat([HumanMessage(content="Translate this sentence from English to French. I love programming.")])
-# -> AIMessage(content="J'aime programmer.", additional_kwargs={})
+```
+
+```pycon
+AIMessage(content="J'aime programmer.", additional_kwargs={})
 ```
 
 You can also pass in multiple messages for OpenAI's gpt-3.5-turbo and gpt-4 models.
@@ -351,150 +365,6 @@ result.llm_output['token_usage']
 # -> {'prompt_tokens': 57, 'completion_tokens': 20, 'total_tokens': 77}
 ```
 
+### More on Chat Models
 
-## Chat Prompt Templates
-Similar to LLMs, you can make use of templating by using a `MessagePromptTemplate`. You can build a `ChatPromptTemplate` from one or more `MessagePromptTemplate`s. You can use `ChatPromptTemplate`'s `format_prompt` -- this returns a `PromptValue`, which you can convert to a string or `Message` object, depending on whether you want to use the formatted value as input to an llm or chat model.
-
-For convenience, there is a `from_template` method exposed on the template. If you were to use this template, this is what it would look like:
-
-```python
-from langchain.chat_models import ChatOpenAI
-from langchain.prompts.chat import (
-    ChatPromptTemplate,
-    SystemMessagePromptTemplate,
-    HumanMessagePromptTemplate,
-)
-
-chat = ChatOpenAI(temperature=0)
-
-template = "You are a helpful assistant that translates {input_language} to {output_language}."
-system_message_prompt = SystemMessagePromptTemplate.from_template(template)
-human_template = "{text}"
-human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)
-
-chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt])
-
-# get a chat completion from the formatted messages
-chat(chat_prompt.format_prompt(input_language="English", output_language="French", text="I love programming.").to_messages())
-# -> AIMessage(content="J'aime programmer.", additional_kwargs={})
-```
-
-## Chains with Chat Models
-The `LLMChain` discussed in the above section can be used with chat models as well:
-
-```python
-from langchain.chat_models import ChatOpenAI
-from langchain import LLMChain
-from langchain.prompts.chat import (
-    ChatPromptTemplate,
-    SystemMessagePromptTemplate,
-    HumanMessagePromptTemplate,
-)
-
-chat = ChatOpenAI(temperature=0)
-
-template = "You are a helpful assistant that translates {input_language} to {output_language}."
-system_message_prompt = SystemMessagePromptTemplate.from_template(template)
-human_template = "{text}"
-human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)
-chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt])
-
-chain = LLMChain(llm=chat, prompt=chat_prompt)
-chain.run(input_language="English", output_language="French", text="I love programming.")
-# -> "J'aime programmer."
-```
-
-## Agents with Chat Models
-Agents can also be used with chat models, you can initialize one using `AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION` as the agent type.
-
-```python
-from langchain.agents import load_tools
-from langchain.agents import initialize_agent
-from langchain.agents import AgentType
-from langchain.chat_models import ChatOpenAI
-from langchain.llms import OpenAI
-
-# First, let's load the language model we're going to use to control the agent.
-chat = ChatOpenAI(temperature=0)
-
-# Next, let's load some tools to use. Note that the `llm-math` tool uses an LLM, so we need to pass that in.
-llm = OpenAI(temperature=0)
-tools = load_tools(["serpapi", "llm-math"], llm=llm)
-
-
-# Finally, let's initialize an agent with the tools, the language model, and the type of agent we want to use.
-agent = initialize_agent(tools, chat, agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
-
-# Now let's test it out!
-agent.run("Who is Olivia Wilde's boyfriend? What is his current age raised to the 0.23 power?")
-```
-
-```pycon
-
-> Entering new AgentExecutor chain...
-Thought: I need to use a search engine to find Olivia Wilde's boyfriend and a calculator to raise his age to the 0.23 power.
-Action:
-{
-  "action": "Search",
-  "action_input": "Olivia Wilde boyfriend"
-}
-
-Observation: Sudeikis and Wilde's relationship ended in November 2020. Wilde was publicly served with court documents regarding child custody while she was presenting Don't Worry Darling at CinemaCon 2022. In January 2021, Wilde began dating singer Harry Styles after meeting during the filming of Don't Worry Darling.
-Thought:I need to use a search engine to find Harry Styles' current age.
-Action:
-{
-  "action": "Search",
-  "action_input": "Harry Styles age"
-}
-
-Observation: 29 years
-Thought:Now I need to calculate 29 raised to the 0.23 power.
-Action:
-{
-  "action": "Calculator",
-  "action_input": "29^0.23"
-}
-
-Observation: Answer: 2.169459462491557
-
-Thought:I now know the final answer.
-Final Answer: 2.169459462491557
-
-> Finished chain.
-'2.169459462491557'
-```
-## Memory: Add State to Chains and Agents
-You can use Memory with chains and agents initialized with chat models. The main difference between this and Memory for LLMs is that rather than trying to condense all previous messages into a string, we can keep them as their own unique memory object.
-
-```python
-from langchain.prompts import (
-    ChatPromptTemplate, 
-    MessagesPlaceholder, 
-    SystemMessagePromptTemplate, 
-    HumanMessagePromptTemplate
-)
-from langchain.chains import ConversationChain
-from langchain.chat_models import ChatOpenAI
-from langchain.memory import ConversationBufferMemory
-
-prompt = ChatPromptTemplate.from_messages([
-    SystemMessagePromptTemplate.from_template("The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know."),
-    MessagesPlaceholder(variable_name="history"),
-    HumanMessagePromptTemplate.from_template("{input}")
-])
-
-llm = ChatOpenAI(temperature=0)
-memory = ConversationBufferMemory(return_messages=True)
-conversation = ConversationChain(memory=memory, prompt=prompt, llm=llm)
-
-conversation.predict(input="Hi there!")
-# -> 'Hello! How can I assist you today?'
-
-
-conversation.predict(input="I'm doing well! Just having a conversation with an AI.")
-# -> "That sounds like fun! I'm happy to chat with you. Is there anything specific you'd like to talk about?"
-
-conversation.predict(input="Tell me about yourself.")
-# -> "Sure! I am an AI language model created by OpenAI. I was trained on a large dataset of text from the internet, which allows me to understand and generate human-like language. I can answer questions, provide information, and even have conversations like this one. Is there anything else you'd like to know about me?"
-```
-
+There's a number of ways in which using the core components like Memory, Chains, and Agents is different in the context of chat models. Continue on to [Part 2 of the Quickstart Guide](getting_started_chat.md) to learn more about chat models.

docs/getting_started/getting_started_chat.md

@@ -0,0 +1,217 @@
+# Chat Models
+
+This guide walks you through how the core LangChain modules work with chat models. We recommend first reading through the [Quickstart Guide](./getting_started.md), which introduces the core concepts in the context of LLMs.\
+\
+Chat models are a variation on language models. While chat models use language models under the hood, the interface they expose is a bit different: rather than expose a "text in, text out" API, they expose an interface where "chat messages" are the inputs and outputs.\
+\
+Chat model APIs are fairly new, so we are still figuring out the correct abstractions.\
+\
+**NOTE**: Before going further please make sure you've followed the [Installation](./getting_started.md#installation) and [Environment Setup](./getting_started.md#environment-setup) steps from the Quickstart Guide.
+
+## Message Completions
+
+You can get chat completions by passing one or more messages to the chat model. The response will be a message. The types of messages currently supported in LangChain are `AIMessage`, `HumanMessage`, `SystemMessage`, and `ChatMessage` -- `ChatMessage` takes in an arbitrary role parameter. Most of the time, you'll just be dealing with `HumanMessage`, `AIMessage`, and `SystemMessage`.
+
+```python
+from langchain.chat_models import ChatOpenAI
+from langchain.schema import (
+    AIMessage,
+    HumanMessage,
+    SystemMessage
+)
+
+chat = ChatOpenAI(temperature=0)
+```
+
+You can get completions by passing in a single message.
+
+```python
+chat([HumanMessage(content="Translate this sentence from English to French. I love programming.")])
+```
+
+```pycon
+AIMessage(content="J'aime programmer.", additional_kwargs={})
+```
+
+You can also pass in multiple messages for OpenAI's gpt-3.5-turbo and gpt-4 models.
+
+```python
+messages = [
+    SystemMessage(content="You are a helpful assistant that translates English to French."),
+    HumanMessage(content="I love programming.")
+]
+chat(messages)
+# -> AIMessage(content="J'aime programmer.", additional_kwargs={})
+```
+
+You can go one step further and generate completions for multiple sets of messages using `generate`. This returns an `LLMResult` with an additional `message` parameter:
+```python
+batch_messages = [
+    [
+        SystemMessage(content="You are a helpful assistant that translates English to French."),
+        HumanMessage(content="I love programming.")
+    ],
+    [
+        SystemMessage(content="You are a helpful assistant that translates English to French."),
+        HumanMessage(content="I love artificial intelligence.")
+    ],
+]
+result = chat.generate(batch_messages)
+result
+# -> LLMResult(generations=[[ChatGeneration(text="J'aime programmer.", generation_info=None, message=AIMessage(content="J'aime programmer.", additional_kwargs={}))], [ChatGeneration(text="J'aime l'intelligence artificielle.", generation_info=None, message=AIMessage(content="J'aime l'intelligence artificielle.", additional_kwargs={}))]], llm_output={'token_usage': {'prompt_tokens': 57, 'completion_tokens': 20, 'total_tokens': 77}})
+```
+
+You can recover things like token usage from this LLMResult:
+```
+result.llm_output['token_usage']
+# -> {'prompt_tokens': 57, 'completion_tokens': 20, 'total_tokens': 77}
+```
+
+
+## Prompt Templates
+Similar to LLMs, you can make use of templating by using a `MessagePromptTemplate`. You can build a `ChatPromptTemplate` from one or more `MessagePromptTemplate`s. You can use `ChatPromptTemplate`'s `format_prompt` -- this returns a `PromptValue`, which you can convert to a string or `Message` object, depending on whether you want to use the formatted value as input to an llm or chat model.
+
+For convenience, there is a `from_template` method exposed on the template. If you were to use this template, this is what it would look like:
+
+```python
+from langchain.chat_models import ChatOpenAI
+from langchain.prompts.chat import (
+    ChatPromptTemplate,
+    SystemMessagePromptTemplate,
+    HumanMessagePromptTemplate,
+)
+
+chat = ChatOpenAI(temperature=0)
+
+template = "You are a helpful assistant that translates {input_language} to {output_language}."
+system_message_prompt = SystemMessagePromptTemplate.from_template(template)
+human_template = "{text}"
+human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)
+
+chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt])
+
+# get a chat completion from the formatted messages
+chat(chat_prompt.format_prompt(input_language="English", output_language="French", text="I love programming.").to_messages())
+# -> AIMessage(content="J'aime programmer.", additional_kwargs={})
+```
+
+## Chains
+The `LLMChain` discussed in the above section can be used with chat models as well:
+
+```python
+from langchain.chat_models import ChatOpenAI
+from langchain import LLMChain
+from langchain.prompts.chat import (
+    ChatPromptTemplate,
+    SystemMessagePromptTemplate,
+    HumanMessagePromptTemplate,
+)
+
+chat = ChatOpenAI(temperature=0)
+
+template = "You are a helpful assistant that translates {input_language} to {output_language}."
+system_message_prompt = SystemMessagePromptTemplate.from_template(template)
+human_template = "{text}"
+human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)
+chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt])
+
+chain = LLMChain(llm=chat, prompt=chat_prompt)
+chain.run(input_language="English", output_language="French", text="I love programming.")
+# -> "J'aime programmer."
+```
+
+## Agents
+Agents can also be used with chat models, you can initialize one using `AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION` as the agent type.
+
+```python
+from langchain.agents import load_tools
+from langchain.agents import initialize_agent
+from langchain.agents import AgentType
+from langchain.chat_models import ChatOpenAI
+from langchain.llms import OpenAI
+
+# First, let's load the language model we're going to use to control the agent.
+chat = ChatOpenAI(temperature=0)
+
+# Next, let's load some tools to use. Note that the `llm-math` tool uses an LLM, so we need to pass that in.
+llm = OpenAI(temperature=0)
+tools = load_tools(["serpapi", "llm-math"], llm=llm)
+
+
+# Finally, let's initialize an agent with the tools, the language model, and the type of agent we want to use.
+agent = initialize_agent(tools, chat, agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
+
+# Now let's test it out!
+agent.run("Who is Olivia Wilde's boyfriend? What is his current age raised to the 0.23 power?")
+```
+
+```pycon
+
+> Entering new AgentExecutor chain...
+Thought: I need to use a search engine to find Olivia Wilde's boyfriend and a calculator to raise his age to the 0.23 power.
+Action:
+{
+  "action": "Search",
+  "action_input": "Olivia Wilde boyfriend"
+}
+
+Observation: Sudeikis and Wilde's relationship ended in November 2020. Wilde was publicly served with court documents regarding child custody while she was presenting Don't Worry Darling at CinemaCon 2022. In January 2021, Wilde began dating singer Harry Styles after meeting during the filming of Don't Worry Darling.
+Thought:I need to use a search engine to find Harry Styles' current age.
+Action:
+{
+  "action": "Search",
+  "action_input": "Harry Styles age"
+}
+
+Observation: 29 years
+Thought:Now I need to calculate 29 raised to the 0.23 power.
+Action:
+{
+  "action": "Calculator",
+  "action_input": "29^0.23"
+}
+
+Observation: Answer: 2.169459462491557
+
+Thought:I now know the final answer.
+Final Answer: 2.169459462491557
+
+> Finished chain.
+'2.169459462491557'
+```
+
+## Memory
+You can use Memory with chains and agents initialized with chat models. The main difference between this and Memory for LLMs is that rather than trying to condense all previous messages into a string, we can keep them as their own unique memory object.
+
+```python
+from langchain.prompts import (
+    ChatPromptTemplate, 
+    MessagesPlaceholder, 
+    SystemMessagePromptTemplate, 
+    HumanMessagePromptTemplate
+)
+from langchain.chains import ConversationChain
+from langchain.chat_models import ChatOpenAI
+from langchain.memory import ConversationBufferMemory
+
+prompt = ChatPromptTemplate.from_messages([
+    SystemMessagePromptTemplate.from_template("The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know."),
+    MessagesPlaceholder(variable_name="history"),
+    HumanMessagePromptTemplate.from_template("{input}")
+])
+
+llm = ChatOpenAI(temperature=0)
+memory = ConversationBufferMemory(return_messages=True)
+conversation = ConversationChain(memory=memory, prompt=prompt, llm=llm)
+
+conversation.predict(input="Hi there!")
+# -> 'Hello! How can I assist you today?'
+
+
+conversation.predict(input="I'm doing well! Just having a conversation with an AI.")
+# -> "That sounds like fun! I'm happy to chat with you. Is there anything specific you'd like to talk about?"
+
+conversation.predict(input="Tell me about yourself.")
+# -> "Sure! I am an AI language model created by OpenAI. I was trained on a large dataset of text from the internet, which allows me to understand and generate human-like language. I can answer questions, provide information, and even have conversations like this one. Is there anything else you'd like to know about me?"
+```
+

docs/getting_started/quickstart.rst

@@ -0,0 +1,10 @@
+Quickstart Guide
+==========================
+
+
+.. toctree::
+   :name: quickstart
+   :maxdepth: 1
+
+   ./getting_started.md
+   ./getting_started_chat.md

docs/index.rst

@@ -30,7 +30,7 @@ Getting Started
    :name: getting_started
    :hidden:
 
-   getting_started/getting_started.md
+   getting_started/quickstart.rst
    getting_started/concepts.md
    getting_started/tutorials.md