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'
```
|
||
|---|---|---|
| .. | ||
| data | ||
| rag_redis | ||
| tests | ||
| ingest.py | ||
| LICENSE | ||
| poetry.lock | ||
| pyproject.toml | ||
| rag_redis.ipynb | ||
| README.md | ||
rag-redis
This template performs RAG using Redis (vector database) and OpenAI (LLM) on financial 10k filings docs for Nike.
It relies on the sentence transformer all-MiniLM-L6-v2 for embedding chunks of the pdf and user questions.
Environment Setup
Set the OPENAI_API_KEY environment variable to access the OpenAI models:
export OPENAI_API_KEY= <YOUR OPENAI API KEY>
Set the following Redis environment variables:
export REDIS_HOST = <YOUR REDIS HOST>
export REDIS_PORT = <YOUR REDIS PORT>
export REDIS_USER = <YOUR REDIS USER NAME>
export REDIS_PASSWORD = <YOUR REDIS PASSWORD>
Supported Settings
We use a variety of environment variables to configure this application
| Environment Variable | Description | Default Value |
|---|---|---|
DEBUG |
Enable or disable Langchain debugging logs | True |
REDIS_HOST |
Hostname for the Redis server | "localhost" |
REDIS_PORT |
Port for the Redis server | 6379 |
REDIS_USER |
User for the Redis server | "" |
REDIS_PASSWORD |
Password for the Redis server | "" |
REDIS_URL |
Full URL for connecting to Redis | None, Constructed from user, password, host, and port if not provided |
INDEX_NAME |
Name of the vector index | "rag-redis" |
Usage
To use this package, you should first have the LangChain CLI and Pydantic installed in a Python virtual environment:
pip install -U langchain-cli pydantic==1.10.13
To create a new LangChain project and install this as the only package, you can do:
langchain app new my-app --package rag-redis
If you want to add this to an existing project, you can just run:
langchain app add rag-redis
And add the following code snippet to your app/server.py file:
from rag_redis.chain import chain as rag_redis_chain
add_routes(app, rag_redis_chain, path="/rag-redis")
(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-redis/playground
We can access the template from code with:
from langserve.client import RemoteRunnable
runnable = RemoteRunnable("http://localhost:8000/rag-redis")