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Update quickstart.mdx (#17659)

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https://github.com/langchain-ai/langchain/issues/17657

Thank you for contributing to LangChain!

Checklist:

- [ ] PR title: Please title your PR "package: description", where
"package" is whichever of langchain, community, core, experimental, etc.
is being modified. Use "docs: ..." for purely docs changes, "templates:
..." for template changes, "infra: ..." for CI changes.
  - Example: "community: add foobar LLM"
- [ ] PR message: **Delete this entire template message** and replace it
with the following bulleted list
    - **Description:** a description of the change
    - **Issue:** the issue # it fixes, if applicable
    - **Dependencies:** any dependencies required for this change
- **Twitter handle:** if your PR gets announced, and you'd like a
mention, we'll gladly shout you out!
- [ ] Pass lint and test: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified to check that you're
passing lint and testing. See contribution guidelines for more
information on how to write/run tests, lint, etc:
https://python.langchain.com/docs/contributing/
- [ ] Add tests and docs: If you're adding a new integration, please
include
1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use. It lives in
`docs/docs/integrations` directory.

Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.

If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, hwchase17.
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@ -58,7 +58,7 @@ LangChain enables building application that connect external sources of data and
In this quickstart, we will walk through a few different ways of doing that. In this quickstart, we will walk through a few different ways of doing that.
We will start with a simple LLM chain, which just relies on information in the prompt template to respond. We will start with a simple LLM chain, which just relies on information in the prompt template to respond.
Next, we will build a retrieval chain, which fetches data from a separate database and passes that into the prompt template. Next, we will build a retrieval chain, which fetches data from a separate database and passes that into the prompt template.
We will then add in chat history, to create a conversation retrieval chain. This allows you interact in a chat manner with this LLM, so it remembers previous questions. We will then add in chat history, to create a conversation retrieval chain. This allows you to interact in a chat manner with this LLM, so it remembers previous questions.
Finally, we will build an agent - which utilizes an LLM to determine whether or not it needs to fetch data to answer questions. Finally, we will build an agent - which utilizes an LLM to determine whether or not it needs to fetch data to answer questions.
We will cover these at a high level, but there are lot of details to all of these! We will cover these at a high level, but there are lot of details to all of these!
We will link to relevant docs. We will link to relevant docs.