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