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_ollama_multi_query | ||
| tests | ||
| LICENSE | ||
| poetry.lock | ||
| pyproject.toml | ||
| rag_ollama_multi_query.ipynb | ||
| README.md | ||
rag-ollama-multi-query
This template performs RAG using Ollama and OpenAI with a multi-query retriever.
The multi-query retriever is an example of query transformation, generating multiple queries from different perspectives based on the user's input query.
For each query, it retrieves a set of relevant documents and takes the unique union across all queries for answer synthesis.
We use a private, local LLM for the narrow task of query generation to avoid excessive calls to a larger LLM API.
See an example trace for Ollama LLM performing the query expansion here.
But we use OpenAI for the more challenging task of answer syntesis (full trace example here).
Environment Setup
To set up the environment, you need to download Ollama.
Follow the instructions here.
You can choose the desired LLM with Ollama.
This template uses zephyr, which can be accessed using ollama pull zephyr.
There are many other options available here.
Set the OPENAI_API_KEY environment variable to access the OpenAI models.
Usage
To use this package, you should first install the LangChain CLI:
pip install -U langchain-cli
To create a new LangChain project and install this package, do:
langchain app new my-app --package rag-ollama-multi-query
To add this package to an existing project, run:
langchain app add rag-ollama-multi-query
And add the following code to your server.py file:
from rag_ollama_multi_query import chain as rag_ollama_multi_query_chain
add_routes(app, rag_ollama_multi_query_chain, path="/rag-ollama-multi-query")
(Optional) Now, let's 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 running locally at http://localhost:8000
You can see all templates at http://127.0.0.1:8000/docs You can access the playground at http://127.0.0.1:8000/rag-ollama-multi-query/playground
To access the template from code, use:
from langserve.client import RemoteRunnable
runnable = RemoteRunnable("http://localhost:8000/rag-ollama-multi-query")