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docs/fix links (#6498)

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Davis Chase
2023-06-20 14:06:50 -07:00
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2
docs/snippets/modules/chains/how_to/memory.mdx
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@@ -21,5 +21,5 @@ conversation.run("And the next 4?")
</CodeOutputBlock>
Essentially, `BaseMemory` defines an interface of how `langchain` stores memory. It allows reading of stored data through `load_memory_variables` method and storing new data through `save_context` method. You can learn more about it in [Memory](../memory.html) section.
Essentially, `BaseMemory` defines an interface of how `langchain` stores memory. It allows reading of stored data through `load_memory_variables` method and storing new data through `save_context` method. You can learn more about it in the [Memory](/docs/modules/memory/) section.

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docs/snippets/modules/chains/popular/sqlite.mdx
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Under the hood, LangChain uses SQLAlchemy to connect to SQL databases. The `SQLDatabaseChain` can therefore be used with any SQL dialect supported by SQLAlchemy, such as MS SQL, MySQL, MariaDB, PostgreSQL, Oracle SQL, [Databricks](../../../ecosystem/integrations/databricks.html) and SQLite. Please refer to the SQLAlchemy documentation for more information about requirements for connecting to your database. For example, a connection to MySQL requires an appropriate connector such as PyMySQL. A URI for a MySQL connection might look like: `mysql+pymysql://user:pass@some_mysql_db_address/db_name`.
Under the hood, LangChain uses SQLAlchemy to connect to SQL databases. The `SQLDatabaseChain` can therefore be used with any SQL dialect supported by SQLAlchemy, such as MS SQL, MySQL, MariaDB, PostgreSQL, Oracle SQL, [Databricks](/docs/ecosystem/integrations/databricks.html) and SQLite. Please refer to the SQLAlchemy documentation for more information about requirements for connecting to your database. For example, a connection to MySQL requires an appropriate connector such as PyMySQL. A URI for a MySQL connection might look like: `mysql+pymysql://user:pass@some_mysql_db_address/db_name`.
This demonstration uses SQLite and the example Chinook database.
To set it up, follow the instructions on https://database.guide/2-sample-databases-sqlite/, placing the `.db` file in a notebooks folder at the root of this repository.

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docs/snippets/modules/chains/popular/vector_db_qa.mdx
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@@ -35,7 +35,7 @@ qa.run(query)
</CodeOutputBlock>
## Chain Type
You can easily specify different chain types to load and use in the RetrievalQA chain. For a more detailed walkthrough of these types, please see [this notebook](question_answering.html).
You can easily specify different chain types to load and use in the RetrievalQA chain. For a more detailed walkthrough of these types, please see [this notebook](/docs/modules/chains/additional/question_answering.html).
There are two ways to load different chain types. First, you can specify the chain type argument in the `from_chain_type` method. This allows you to pass in the name of the chain type you want to use. For example, in the below we change the chain type to `map_reduce`.
@@ -58,7 +58,7 @@ qa.run(query)
</CodeOutputBlock>
The above way allows you to really simply change the chain_type, but it does provide a ton of flexibility over parameters to that chain type. If you want to control those parameters, you can load the chain directly (as you did in [this notebook](question_answering.html)) and then pass that directly to the the RetrievalQA chain with the `combine_documents_chain` parameter. For example:
The above way allows you to really simply change the chain_type, but it does provide a ton of flexibility over parameters to that chain type. If you want to control those parameters, you can load the chain directly (as you did in [this notebook](/docs/modules/chains/additional/question_answering.html)) and then pass that directly to the the RetrievalQA chain with the `combine_documents_chain` parameter. For example:
```python
@@ -82,7 +82,7 @@ qa.run(query)
</CodeOutputBlock>
## Custom Prompts
You can pass in custom prompts to do question answering. These prompts are the same prompts as you can pass into the [base question answering chain](./question_answering.html)
You can pass in custom prompts to do question answering. These prompts are the same prompts as you can pass into the [base question answering chain](/docs/modules/chains/additional/question_answering.html)
```python