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Allow index name customization via env var in rag-conversation (#12315)

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Jacob Lee 2023-10-25 22:11:13 -07:00 committed by GitHub
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2 changed files with 16 additions and 7 deletions
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4
templates/rag-conversation/README.md
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@ -1,4 +1,4 @@
# Conversational RAG # Conversational RAG
This template performs [conversational](https://python.langchain.com/docs/expression_language/cookbook/retrieval#conversational-retrieval-chain) [retrieval](https://python.langchain.com/docs/use_cases/question_answering/), which is one of the most popular LLM use-cases. This template performs [conversational](https://python.langchain.com/docs/expression_language/cookbook/retrieval#conversational-retrieval-chain) [retrieval](https://python.langchain.com/docs/use_cases/question_answering/), which is one of the most popular LLM use-cases.
@ -10,4 +10,4 @@ Be sure that `OPENAI_API_KEY` is set in order to use the OpenAI models.
## Pinecone ## Pinecone
Be sure that `PINECONE_API_KEY` is set in order to use Pinecone. This template uses Pinecone as a vectorstore and requires that `PINECONE_API_KEY`, `PINECONE_ENVIRONMENT`, and `PINECONE_INDEX` are set.

19
templates/rag-conversation/rag_conversation/chain.py
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@ -1,3 +1,4 @@
import os
from typing import Tuple, List from typing import Tuple, List
from pydantic import BaseModel from pydantic import BaseModel
from operator import itemgetter from operator import itemgetter
@ -10,6 +11,14 @@ from langchain.prompts.prompt import PromptTemplate
from langchain.schema.output_parser import StrOutputParser from langchain.schema.output_parser import StrOutputParser
from langchain.schema.runnable import RunnablePassthrough, RunnableBranch, RunnableLambda, RunnableMap from langchain.schema.runnable import RunnablePassthrough, RunnableBranch, RunnableLambda, RunnableMap
if os.environ.get("PINECONE_API_KEY", None) is None:
raise Exception("Missing `PINECONE_API_KEY` environment variable.")
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 ### Ingest code - you may need to run this the first time
# Load # Load
# from langchain.document_loaders import WebBaseLoader # from langchain.document_loaders import WebBaseLoader
@ -20,14 +29,14 @@ from langchain.schema.runnable import RunnablePassthrough, RunnableBranch, Runna
# from langchain.text_splitter import RecursiveCharacterTextSplitter # from langchain.text_splitter import RecursiveCharacterTextSplitter
# text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0) # text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)
# all_splits = text_splitter.split_documents(data) # all_splits = text_splitter.split_documents(data)
#
# # Add to vectorDB # # Add to vectorDB
# vectorstore = Pinecone.from_documents( # vectorstore = Pinecone.from_documents(
# documents=all_splits, embedding=OpenAIEmbeddings(), index_name='langchain-test' # documents=all_splits, embedding=OpenAIEmbeddings(), index_name=PINECONE_INDEX_NAME
# ) # )
# retriever = vectorstore.as_retriever() # retriever = vectorstore.as_retriever()
vectorstore = Pinecone.from_existing_index("langchain-test", OpenAIEmbeddings()) vectorstore = Pinecone.from_existing_index(PINECONE_INDEX_NAME, OpenAIEmbeddings())
retriever = vectorstore.as_retriever() retriever = vectorstore.as_retriever()
# Condense a chat history and follow-up question into a standalone question # Condense a chat history and follow-up question into a standalone question
@ -62,9 +71,9 @@ def _format_chat_history(chat_history: List[Tuple[str, str]]) -> List:
buffer.append(AIMessage(content=ai)) buffer.append(AIMessage(content=ai))
return buffer return buffer
# User input # User input
class ChatHistory(BaseModel): class ChatHistory(BaseModel):
chat_history: List[Tuple[str, str]] chat_history: List[Tuple[str, str]]
question: str question: str