mirror of
https://github.com/hwchase17/langchain
synced 2024-11-04 06:00:26 +00:00
70 lines
2.2 KiB
Python
70 lines
2.2 KiB
Python
from langchain.retrievers.multi_query import MultiQueryRetriever
|
|
from langchain_community.chat_models import ChatOllama, ChatOpenAI
|
|
from langchain_community.document_loaders import WebBaseLoader
|
|
from langchain_community.embeddings import OpenAIEmbeddings
|
|
from langchain_community.vectorstores import Chroma
|
|
from langchain_core.output_parsers import StrOutputParser
|
|
from langchain_core.prompts import ChatPromptTemplate, PromptTemplate
|
|
from langchain_core.pydantic_v1 import BaseModel
|
|
from langchain_core.runnables import RunnableParallel, RunnablePassthrough
|
|
from langchain_text_splitters import RecursiveCharacterTextSplitter
|
|
|
|
# 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 = Chroma.from_documents(
|
|
documents=all_splits,
|
|
collection_name="rag-private",
|
|
embedding=OpenAIEmbeddings(),
|
|
)
|
|
|
|
|
|
QUERY_PROMPT = PromptTemplate(
|
|
input_variables=["question"],
|
|
template="""You are an AI language model assistant. Your task is to generate five
|
|
different versions of the given user question to retrieve relevant documents from
|
|
a vector database. By generating multiple perspectives on the user question, your
|
|
goal is to help the user overcome some of the limitations of the distance-based
|
|
similarity search. Provide these alternative questions separated by newlines.
|
|
Original question: {question}""",
|
|
)
|
|
|
|
# Add the LLM downloaded from Ollama
|
|
ollama_llm = "zephyr"
|
|
llm = ChatOllama(model=ollama_llm)
|
|
|
|
# Run
|
|
retriever = MultiQueryRetriever.from_llm(
|
|
vectorstore.as_retriever(), llm, prompt=QUERY_PROMPT
|
|
) # "lines" is the key (attribute name) of the parsed output
|
|
|
|
# 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)
|