mirror of
https://github.com/hwchase17/langchain
synced 2024-11-16 06:13:16 +00:00
51 lines
1.3 KiB
Python
51 lines
1.3 KiB
Python
import os
|
|
|
|
from langchain_community.chat_models import ChatOpenAI
|
|
from langchain_community.embeddings import OpenAIEmbeddings
|
|
from langchain_community.vectorstores.supabase import SupabaseVectorStore
|
|
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 supabase.client import create_client
|
|
|
|
supabase_url = os.environ.get("SUPABASE_URL")
|
|
supabase_key = os.environ.get("SUPABASE_SERVICE_KEY")
|
|
supabase = create_client(supabase_url, supabase_key)
|
|
|
|
embeddings = OpenAIEmbeddings()
|
|
|
|
vectorstore = SupabaseVectorStore(
|
|
client=supabase,
|
|
embedding=embeddings,
|
|
table_name="documents",
|
|
query_name="match_documents",
|
|
)
|
|
|
|
retriever = vectorstore.as_retriever()
|
|
|
|
template = """Answer the question based only on the following context:
|
|
{context}
|
|
|
|
Question: {question}
|
|
"""
|
|
|
|
prompt = ChatPromptTemplate.from_template(template)
|
|
|
|
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)
|