mirror of
https://github.com/hwchase17/langchain
synced 2024-11-02 09:40:22 +00:00
62 lines
1.9 KiB
Python
62 lines
1.9 KiB
Python
import os
|
|
|
|
from langchain_community.chat_models import ChatOpenAI
|
|
from langchain_community.document_loaders import WebBaseLoader
|
|
from langchain_community.embeddings import OpenAIEmbeddings
|
|
from langchain_community.vectorstores import Weaviate
|
|
from langchain_core.output_parsers import StrOutputParser
|
|
from langchain_core.prompts import ChatPromptTemplate
|
|
from langchain_core.pydantic_v1 import BaseModel
|
|
from langchain_core.runnables import RunnableParallel, RunnablePassthrough
|
|
from langchain_text_splitters import RecursiveCharacterTextSplitter
|
|
|
|
if os.environ.get("WEAVIATE_API_KEY", None) is None:
|
|
raise Exception("Missing `WEAVIATE_API_KEY` environment variable.")
|
|
|
|
if os.environ.get("WEAVIATE_ENVIRONMENT", None) is None:
|
|
raise Exception("Missing `WEAVIATE_ENVIRONMENT` environment variable.")
|
|
|
|
WEAVIATE_INDEX_NAME = os.environ.get("WEAVIATE_INDEX", "langchain-test")
|
|
|
|
### Ingest code - you may need to run this the first time
|
|
# Load
|
|
loader = WebBaseLoader("https://lilianweng.github.io/posts/2023-06-23-agent/")
|
|
data = loader.load()
|
|
|
|
# # Split
|
|
text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)
|
|
all_splits = text_splitter.split_documents(data)
|
|
|
|
# # Add to vectorDB
|
|
# vectorstore = Weaviate.from_documents(
|
|
# documents=all_splits, embedding=OpenAIEmbeddings(), index_name=WEAVIATE_INDEX_NAME
|
|
# )
|
|
# retriever = vectorstore.as_retriever()
|
|
|
|
vectorstore = Weaviate.from_existing_index(WEAVIATE_INDEX_NAME, OpenAIEmbeddings())
|
|
retriever = vectorstore.as_retriever()
|
|
|
|
# RAG prompt
|
|
template = """Answer the question based only on the following context:
|
|
{context}
|
|
Question: {question}
|
|
"""
|
|
prompt = ChatPromptTemplate.from_template(template)
|
|
|
|
# RAG
|
|
model = ChatOpenAI()
|
|
chain = (
|
|
RunnableParallel({"context": retriever, "question": RunnablePassthrough()})
|
|
| prompt
|
|
| model
|
|
| StrOutputParser()
|
|
)
|
|
|
|
|
|
# Add typing for input
|
|
class Question(BaseModel):
|
|
__root__: str
|
|
|
|
|
|
chain = chain.with_types(input_type=Question)
|