mirror of
https://github.com/hwchase17/langchain
synced 2024-11-08 07:10:35 +00:00
84 lines
2.2 KiB
Python
84 lines
2.2 KiB
Python
import os
|
|
|
|
from langchain_community.chat_models import ChatOpenAI
|
|
from langchain_community.document_loaders import PyPDFLoader
|
|
from langchain_community.embeddings import OpenAIEmbeddings
|
|
from langchain_community.vectorstores import MongoDBAtlasVectorSearch
|
|
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 (
|
|
RunnableLambda,
|
|
RunnableParallel,
|
|
RunnablePassthrough,
|
|
)
|
|
from langchain_text_splitters import RecursiveCharacterTextSplitter
|
|
from pymongo import MongoClient
|
|
|
|
# Set DB
|
|
if os.environ.get("MONGO_URI", None) is None:
|
|
raise Exception("Missing `MONGO_URI` environment variable.")
|
|
MONGO_URI = os.environ["MONGO_URI"]
|
|
|
|
DB_NAME = "langchain-test-2"
|
|
COLLECTION_NAME = "test"
|
|
ATLAS_VECTOR_SEARCH_INDEX_NAME = "default"
|
|
|
|
client = MongoClient(MONGO_URI)
|
|
db = client[DB_NAME]
|
|
MONGODB_COLLECTION = db[COLLECTION_NAME]
|
|
|
|
# Read from MongoDB Atlas Vector Search
|
|
vectorstore = MongoDBAtlasVectorSearch.from_connection_string(
|
|
MONGO_URI,
|
|
DB_NAME + "." + COLLECTION_NAME,
|
|
OpenAIEmbeddings(disallowed_special=()),
|
|
index_name=ATLAS_VECTOR_SEARCH_INDEX_NAME,
|
|
)
|
|
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)
|
|
|
|
|
|
def _ingest(url: str) -> dict:
|
|
loader = PyPDFLoader(url)
|
|
data = loader.load()
|
|
|
|
# Split docs
|
|
text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)
|
|
docs = text_splitter.split_documents(data)
|
|
|
|
# Insert the documents in MongoDB Atlas Vector Search
|
|
_ = MongoDBAtlasVectorSearch.from_documents(
|
|
documents=docs,
|
|
embedding=OpenAIEmbeddings(disallowed_special=()),
|
|
collection=MONGODB_COLLECTION,
|
|
index_name=ATLAS_VECTOR_SEARCH_INDEX_NAME,
|
|
)
|
|
return {}
|
|
|
|
|
|
ingest = RunnableLambda(_ingest)
|