9ffca3b92a
Update imports to use core for the low-hanging fruit changes. Ran
following
```bash
git grep -l 'langchain.schema.runnable' {docs,templates,cookbook} | xargs sed -i '' 's/langchain\.schema\.runnable/langchain_core.runnables/g'
git grep -l 'langchain.schema.output_parser' {docs,templates,cookbook} | xargs sed -i '' 's/langchain\.schema\.output_parser/langchain_core.output_parsers/g'
git grep -l 'langchain.schema.messages' {docs,templates,cookbook} | xargs sed -i '' 's/langchain\.schema\.messages/langchain_core.messages/g'
git grep -l 'langchain.schema.chat_histry' {docs,templates,cookbook} | xargs sed -i '' 's/langchain\.schema\.chat_history/langchain_core.chat_history/g'
git grep -l 'langchain.schema.prompt_template' {docs,templates,cookbook} | xargs sed -i '' 's/langchain\.schema\.prompt_template/langchain_core.prompts/g'
git grep -l 'from langchain.pydantic_v1' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.pydantic_v1/from langchain_core.pydantic_v1/g'
git grep -l 'from langchain.tools.base' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.tools\.base/from langchain_core.tools/g'
git grep -l 'from langchain.chat_models.base' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.chat_models.base/from langchain_core.language_models.chat_models/g'
git grep -l 'from langchain.llms.base' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.llms\.base\ /from langchain_core.language_models.llms\ /g'
git grep -l 'from langchain.embeddings.base' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.embeddings\.base/from langchain_core.embeddings/g'
git grep -l 'from langchain.vectorstores.base' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.vectorstores\.base/from langchain_core.vectorstores/g'
git grep -l 'from langchain.agents.tools' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.agents\.tools/from langchain_core.tools/g'
git grep -l 'from langchain.schema.output' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.schema\.output\ /from langchain_core.outputs\ /g'
git grep -l 'from langchain.schema.embeddings' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.schema\.embeddings/from langchain_core.embeddings/g'
git grep -l 'from langchain.schema.document' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.schema\.document/from langchain_core.documents/g'
git grep -l 'from langchain.schema.agent' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.schema\.agent/from langchain_core.agents/g'
git grep -l 'from langchain.schema.prompt ' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.schema\.prompt\ /from langchain_core.prompt_values /g'
git grep -l 'from langchain.schema.language_model' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.schema\.language_model/from langchain_core.language_models/g'
```
|
||
|---|---|---|
| .. | ||
| sql_pgvector | ||
| tests | ||
| .gitignore | ||
| LICENSE | ||
| poetry.lock | ||
| pyproject.toml | ||
| README.md | ||
sql-pgvector
This template enables user to use pgvector for combining postgreSQL with semantic search / RAG.
It uses PGVector extension as shown in the RAG empowered SQL cookbook
Environment Setup
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 for use by the template (defaults are in parentheses)
POSTGRES_USER(postgres)POSTGRES_PASSWORD(test)POSTGRES_DB(vectordb)POSTGRES_HOST(localhost)POSTGRES_PORT(5432)
If you don't have a postgres instance, you can run one locally in docker:
docker run \
--name some-postgres \
-e POSTGRES_PASSWORD=test \
-e POSTGRES_USER=postgres \
-e POSTGRES_DB=vectordb \
-p 5432:5432 \
postgres:16
And to start again later, use the --name defined above:
docker start some-postgres
PostgreSQL Database setup
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.
This process is covered in the RAG empowered SQL cookbook, but the overall approach consist of:
- Querying for unique values in the column
- Generating embeddings for those values
- Store the embeddings in a separate column or in an auxiliary table.
Usage
To use this package, you should first have the LangChain CLI installed:
pip install -U langchain-cli
To create a new LangChain project and install this as the only package, you can do:
langchain app new my-app --package sql-pgvector
If you want to add this to an existing project, you can just run:
langchain app add sql-pgvector
And add the following code to your server.py file:
from sql_pgvector import chain as sql_pgvector_chain
add_routes(app, sql_pgvector_chain, path="/sql-pgvector")
(Optional) Let's now configure LangSmith. LangSmith will help us trace, monitor and debug LangChain applications. LangSmith is currently in private beta, you can sign up here. If you don't have access, you can skip this section
export LANGCHAIN_TRACING_V2=true
export LANGCHAIN_API_KEY=<your-api-key>
export LANGCHAIN_PROJECT=<your-project> # if not specified, defaults to "default"
If you are inside this directory, then you can spin up a LangServe instance directly by:
langchain serve
This will start the FastAPI app with a server is running locally at http://localhost:8000
We can see all templates at http://127.0.0.1:8000/docs We can access the playground at http://127.0.0.1:8000/sql-pgvector/playground
We can access the template from code with:
from langserve.client import RemoteRunnable
runnable = RemoteRunnable("http://localhost:8000/sql-pgvector")