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docs: templates updated titles (#25646)

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Updated titles into a consistent format. 
Fixed links to the diagrams.
Fixed typos.
Note: The Templates menu in the navbar is now sorted by the file names.
I'll try sorting the navbar menus by the page titles, not the page file
names.
This commit is contained in:
Leonid Ganeline
2024-08-23 01:19:38 -07:00
committed by GitHub
parent 1b2ae40d45
commit 163ef35dd1
106 changed files with 366 additions and 344 deletions
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templates/sql-pgvector/README.md
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@@ -1,6 +1,6 @@
# sql-pgvector
# SQL - Postgres + pgvector
This template enables user to use `pgvector` for combining postgreSQL with semantic search / RAG.
This template enables user to use `pgvector` for combining `PostgreSQL` with semantic search / RAG.
It uses [PGVector](https://github.com/pgvector/pgvector) extension as shown in the [RAG empowered SQL cookbook](https://github.com/langchain-ai/langchain/blob/master/cookbook/retrieval_in_sql.ipynb)
@@ -8,7 +8,7 @@ It uses [PGVector](https://github.com/pgvector/pgvector) extension as shown in t
If you are using `ChatOpenAI` as your LLM, make sure the `OPENAI_API_KEY` is set in your environment. You can change both the LLM and embeddings model inside `chain.py`
And you can configure configure the following environment variables
And you can configure the following environment variables
for use by the template (defaults are in parentheses)
- `POSTGRES_USER` (postgres)
@@ -38,7 +38,7 @@ docker start some-postgres
Apart from having `pgvector` extension enabled, you will need to do some setup before being able to run semantic search within your SQL queries.
In order to run RAG over your postgreSQL database you will need to generate the embeddings for the specific columns you want.
In order to run RAG over your PostgreSQL database you will need to generate the embeddings for the specific columns you want.
This process is covered in the [RAG empowered SQL cookbook](https://github.com/langchain-ai/langchain/blob/master/cookbook/retrieval_in_sql.ipynb), but the overall approach consist of:
1. Querying for unique values in the column