Fixes to the docs for timescale vector template (langchain-ai#12756)

templates/docs/INDEX.md

@@ -21,7 +21,7 @@ These templates cover advanced retrieval techniques.
 - [Anthropic Iterative Search](../anthropic-iterative-search): This retrieval technique uses iterative prompting to determine what to retrieve and whether the retriever documents are good enough.
 - [Neo4j Parent Document Retrieval](../neo4j-parent): This retrieval technique stores embeddings for smaller chunks, but then returns larger chunks to pass to the model for generation.
 - [Semi-Structured RAG](../rag-semi-structured): The template shows how to do retrieval over semi-structured data (e.g. data that involves both text and tables).
-- [Temporal RAG](../rag-timescale-hybrid-search-time): The template shows how to do retrieval over data that has a strong time-based component.
+- [Temporal RAG](../rag-timescale-hybrid-search-time): The template shows how to do hybrid search over data with a time-based component using [Timescale Vector](https://www.timescale.com/ai?utm_campaign=vectorlaunch&utm_source=langchain&utm_medium=referral).
 
 ## 🔍Advanced Retrieval - Query Transformation
 

templates/rag-timescale-hybrid-search-time/README.md

@@ -10,8 +10,7 @@ This is useful any time your data has a strong time-based component. Some exampl
 
 Such items are often searched by both similarity and time. For example: Show me all news about Toyota trucks from 2022.
 
-[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.
+[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.
 
 Langchain's self-query retriever allows deducing time-ranges (as well as other search criteria) from the text of user queries.
 
@@ -35,29 +34,75 @@ Timescale Vector is available on [Timescale](https://www.timescale.com/products?
 - 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!
 - See the [installation instructions](https://github.com/timescale/python-vector) for more details on using Timescale Vector in python.
 
-### Using Timescale Vector with this template
+## Environment Setup
 
-This template uses TimescaleVector as a vectorstore and requires that `TIMESCALES_SERVICE_URL` is set.
+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.
 
-##  LLM
+To load the sample dataset, set `LOAD_SAMPLE_DATA=1`. To load your own dataset see the section below.
 
-Be sure that `OPENAI_API_KEY` is set in order to the OpenAI models.
+Set the `OPENAI_API_KEY` environment variable to access the OpenAI models.
 
-## Loading sample data
+## Usage
 
-We have provided a sample dataset you can use for demoing this template. It consists of the git history of the timescale project.
+To use this package, you should first have the LangChain CLI installed:
 
-To load this dataset, set the `LOAD_SAMPLE_DATA` environmental variable.
+```shell
+pip install -U "langchain-cli[serve]"
+```
 
-## Loading your own dataset.
+To create a new LangChain project and install this as the only package, you can do:
 
-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.
+```shell
+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:
+
+```shell
+langchain app add rag-timescale-hybrid-search-time
+```
+
+And add the following code to your `server.py` file:
+```python
+from rag_timescale_hybrid_search 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](https://smith.langchain.com/).
+If you don't have access, you can skip this section
+
+```shell
+export LANGCHAIN_TRACING_V2=true
+export LANGCHAIN_API_KEY=<your-api-key>
+export LANGCHAIN_PROJECT=<your-project>  # if not specified, defaults to "default"
+```
 
-## Using in your own applications
+If you are inside this directory, then you can spin up a LangServe instance directly by:
 
-This is a standard LangServe template. Instructions on how to use it with your LangServe applications are [here](https://github.com/langchain-ai/langchain/blob/master/templates/README.md).
+```shell
+langchain serve
+```
 
+This will start the FastAPI app with a server is running locally at
+[http://localhost:8000](http://localhost:8000)
 
+We can see all templates at [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs)
+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)
+
+We can access the template from code with:
+
+```python
+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.