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108 lines
5.3 KiB
Markdown
108 lines
5.3 KiB
Markdown
# RAG with Timescale Vector using hybrid search
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This template shows how to use timescale-vector with the self-query retriver to perform hybrid search on similarity and time.
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This is useful any time your data has a strong time-based component. Some examples of such data are:
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- News articles (politics, business, etc)
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- Blog posts, documentation or other published material (public or private).
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- Social media posts
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- Changelogs of any kind
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- Messages
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Such items are often searched by both similarity and time. For example: Show me all news about Toyota trucks from 2022.
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[Timescale Vector](https://www.timescale.com/ai?utm_campaign=vectorlaunch&utm_source=langchain&utm_medium=referral) provides superior performance when searching for embeddings within a particular timeframe by leveraging automatic table partitioning to isolate data for particular time-ranges.
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Langchain's self-query retriever allows deducing time-ranges (as well as other search criteria) from the text of user queries.
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## What is Timescale Vector?
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**[Timescale Vector](https://www.timescale.com/ai?utm_campaign=vectorlaunch&utm_source=langchain&utm_medium=referral) is PostgreSQL++ for AI applications.**
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Timescale Vector enables you to efficiently store and query billions of vector embeddings in `PostgreSQL`.
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- Enhances `pgvector` with faster and more accurate similarity search on 1B+ vectors via DiskANN inspired indexing algorithm.
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- Enables fast time-based vector search via automatic time-based partitioning and indexing.
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- Provides a familiar SQL interface for querying vector embeddings and relational data.
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Timescale Vector is cloud PostgreSQL for AI that scales with you from POC to production:
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- Simplifies operations by enabling you to store relational metadata, vector embeddings, and time-series data in a single database.
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- Benefits from rock-solid PostgreSQL foundation with enterprise-grade feature liked streaming backups and replication, high-availability and row-level security.
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- Enables a worry-free experience with enterprise-grade security and compliance.
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### How to access Timescale Vector
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Timescale Vector is available on [Timescale](https://www.timescale.com/products?utm_campaign=vectorlaunch&utm_source=langchain&utm_medium=referral), the cloud PostgreSQL platform. (There is no self-hosted version at this time.)
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- LangChain users get a 90-day free trial for Timescale Vector.
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- To get started, [signup](https://console.cloud.timescale.com/signup?utm_campaign=vectorlaunch&utm_source=langchain&utm_medium=referral) to Timescale, create a new database and follow this notebook!
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- See the [installation instructions](https://github.com/timescale/python-vector) for more details on using Timescale Vector in python.
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## Environment Setup
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This template uses Timescale Vector as a vectorstore and requires that `TIMESCALES_SERVICE_URL`. Signup for a 90-day trial [here](https://console.cloud.timescale.com/signup?utm_campaign=vectorlaunch&utm_source=langchain&utm_medium=referral) if you don't yet have an account.
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To load the sample dataset, set `LOAD_SAMPLE_DATA=1`. To load your own dataset see the section below.
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Set the `OPENAI_API_KEY` environment variable to access the OpenAI models.
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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 rag-timescale-hybrid-search-time
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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 rag-timescale-hybrid-search-time
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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 rag_timescale_hybrid_search.chain import chain as rag_timescale_hybrid_search_chain
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add_routes(app, rag_timescale_hybrid_search_chain, path="/rag-timescale-hybrid-search")
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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/rag-timescale-hybrid-search/playground](http://127.0.0.1:8000/rag-timescale-hybrid-search/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/rag-timescale-hybrid-search")
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```
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## Loading your own dataset
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To load your own dataset you will have to modify the code in the `DATASET SPECIFIC CODE` section of `chain.py`.
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This code defines the name of the collection, how to load the data, and the human-language description of both the
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contents of the collection and all of the metadata. The human-language descriptions are used by the self-query retriever
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to help the LLM convert the question into filters on the metadata when searching the data in Timescale-vector. |