mirror of
https://github.com/hwchase17/langchain
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105 lines
3.2 KiB
Markdown
105 lines
3.2 KiB
Markdown
# sql-pgvector
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This template enables user to use `pgvector` for combining postgreSQL with semantic search / RAG.
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It uses [PGVector](https://github.com/pgvector/pgvector) extension as shown in the [RAG empowered SQL cookbook](cookbook/retrieval_in_sql.ipynb)
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## Environment Setup
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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`
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And you can configure configure the following environment variables
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for use by the template (defaults are in parentheses)
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- `POSTGRES_USER` (postgres)
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- `POSTGRES_PASSWORD` (test)
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- `POSTGRES_DB` (vectordb)
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- `POSTGRES_HOST` (localhost)
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- `POSTGRES_PORT` (5432)
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If you don't have a postgres instance, you can run one locally in docker:
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```bash
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docker run \
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--name some-postgres \
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-e POSTGRES_PASSWORD=test \
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-e POSTGRES_USER=postgres \
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-e POSTGRES_DB=vectordb \
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-p 5432:5432 \
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postgres:16
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```
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And to start again later, use the `--name` defined above:
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```bash
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docker start some-postgres
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```
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### PostgreSQL Database setup
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Apart from having `pgvector` extension enabled, you will need to do some setup before being able to run semantic search within your SQL queries.
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In order to run RAG over your postgreSQL database you will need to generate the embeddings for the specific columns you want.
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This process is covered in the [RAG empowered SQL cookbook](cookbook/retrieval_in_sql.ipynb), but the overall approach consist of:
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1. Querying for unique values in the column
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2. Generating embeddings for those values
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3. Store the embeddings in a separate column or in an auxiliary table.
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## Usage
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To use this package, you should first have the LangChain CLI installed:
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```shell
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pip install -U langchain-cli
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```
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To create a new LangChain project and install this as the only package, you can do:
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```shell
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langchain app new my-app --package sql-pgvector
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```
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If you want to add this to an existing project, you can just run:
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```shell
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langchain app add sql-pgvector
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```
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And add the following code to your `server.py` file:
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```python
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from sql_pgvector import chain as sql_pgvector_chain
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add_routes(app, sql_pgvector_chain, path="/sql-pgvector")
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```
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(Optional) Let's now configure LangSmith.
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LangSmith will help us trace, monitor and debug LangChain applications.
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LangSmith is currently in private beta, you can sign up [here](https://smith.langchain.com/).
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If you don't have access, you can skip this section
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```shell
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export LANGCHAIN_TRACING_V2=true
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export LANGCHAIN_API_KEY=<your-api-key>
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export LANGCHAIN_PROJECT=<your-project> # if not specified, defaults to "default"
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```
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If you are inside this directory, then you can spin up a LangServe instance directly by:
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```shell
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langchain serve
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```
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This will start the FastAPI app with a server is running locally at
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[http://localhost:8000](http://localhost:8000)
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We can see all templates at [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs)
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We can access the playground at [http://127.0.0.1:8000/sql-pgvector/playground](http://127.0.0.1:8000/sql-pgvector/playground)
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We can access the template from code with:
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```python
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from langserve.client import RemoteRunnable
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runnable = RemoteRunnable("http://localhost:8000/sql-pgvector")
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``` |