Add template for Pinecone + Multi-Query (#12353)

templates/rag-pinecone/pinecone_rag.ipynb

@@ -1,84 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "681a5d1e",
-   "metadata": {},
-   "source": [
-    "## Connect to template\n",
-    "\n",
-    "`Context`\n",
-    " \n",
-    "* LangServe apps gives you access to templates.\n",
-    "* Templates LLM pipeline (runnables or chains) end-points accessible via FastAPI.\n",
-    "* The environment for these templates is managed by Poetry.\n",
-    "\n",
-    "`Create app`\n",
-    "\n",
-    "* Install LangServe and create an app.\n",
-    "* This will create a new Poetry environment /\n",
-    "```\n",
-    "pip install < to add > \n",
-    "langchain serve new my-app\n",
-    "cd my-app\n",
-    "```\n",
-    "\n",
-    "`Add templates`\n",
-    "\n",
-    "* When we add a template, we update the Poetry config file with the necessary dependencies.\n",
-    "* It also automatically installed these template dependencies in your Poetry environment\n",
-    "```\n",
-    "langchain serve add rag-pinecone\n",
-    "```\n",
-    "\n",
-    "`Start FastAPI server`\n",
-    "\n",
-    "```\n",
-    "langchain start\n",
-    "```\n",
-    "\n",
-    "Note, we can now look at the endpoints:\n",
-    "\n",
-    "http://127.0.0.1:8000/docs#\n",
-    "\n",
-    "And look specifically at our loaded template:\n",
-    "\n",
-    "http://127.0.0.1:8000/docs#/default/invoke_rag_pinecone_invoke_post\n",
-    " \n",
-    "We can also use remote runnable to call it:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d774be2a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langserve.client import RemoteRunnable\n",
-    "rag_app_pinecone = RemoteRunnable('http://localhost:8000/rag-pinecone')"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.16"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

templates/rag-pinecone/rag_pinecone.ipynb

@@ -0,0 +1,45 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "681a5d1e",
+   "metadata": {},
+   "source": [
+    "## Connect to template"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d774be2a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langserve.client import RemoteRunnable\n",
+    "rag_app_pinecone = RemoteRunnable('http://localhost:8000/rag-pinecone')\n",
+    "rag_app_pinecone.invoke(\"How does agent memory work?\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

templates/rag-pinecone/rag_pinecone/chain.py

@@ -8,25 +8,32 @@ from langchain.embeddings import OpenAIEmbeddings
 from langchain.schema.output_parser import StrOutputParser
 from langchain.schema.runnable import RunnablePassthrough, RunnableParallel
 
-# Pinecone init
-# Find API key in console at app.pinecone.io
-YOUR_API_KEY = os.getenv('PINECONE_API_KEY') or 'PINECONE_API_KEY'
-# Find ENV (cloud region) next to API key in console
-YOUR_ENV = os.getenv('PINECONE_ENVIRONMENT') or 'PINECONE_ENV'
-# Init
-pinecone.init(
-    api_key=YOUR_API_KEY,
-    environment=YOUR_ENV
-)
+if os.environ.get("PINECONE_API_KEY", None) is None:
+    raise Exception("Missing `PINECONE_API_KEY` environment variable.")
 
-# Get vectorstore
-text_field = "text"
-index_name = "langchain-multi-query-demo"
-index = pinecone.Index(index_name)
-vectorstore = Pinecone(index, 
-                       OpenAIEmbeddings(), 
-                       text_field)
+if os.environ.get("PINECONE_ENVIRONMENT", None) is None:
+    raise Exception("Missing `PINECONE_ENVIRONMENT` environment variable.")
 
+PINECONE_INDEX_NAME = os.environ.get("PINECONE_INDEX", "langchain-test")
+
+### Ingest code - you may need to run this the first time
+# Load
+# from langchain.document_loaders import WebBaseLoader
+# loader = WebBaseLoader("https://lilianweng.github.io/posts/2023-06-23-agent/")
+# data = loader.load()
+
+# # Split
+# from langchain.text_splitter import RecursiveCharacterTextSplitter
+# text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)
+# all_splits = text_splitter.split_documents(data)
+
+# # Add to vectorDB
+# vectorstore = Pinecone.from_documents(
+#     documents=all_splits, embedding=OpenAIEmbeddings(), index_name=PINECONE_INDEX_NAME
+# )
+# retriever = vectorstore.as_retriever()
+
+vectorstore = Pinecone.from_existing_index(PINECONE_INDEX_NAME, OpenAIEmbeddings())
 retriever = vectorstore.as_retriever()
 
 # RAG prompt