langchain/templates/rag-ollama-multi-query/rag_ollama_multi_query/chain.py

from typing import List

from langchain.chains import LLMChain
from langchain.output_parsers import PydanticOutputParser
from langchain.prompts import ChatPromptTemplate, PromptTemplate
from langchain.retrievers.multi_query import MultiQueryRetriever
from langchain.text_splitter import RecursiveCharacterTextSplitter
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.pydantic_v1 import BaseModel, Field
from langchain_core.runnables import RunnableParallel, RunnablePassthrough

# 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(),
)


# Output parser will split the LLM result into a list of queries
class LineList(BaseModel):
    # "lines" is the key (attribute name) of the parsed output
    lines: List[str] = Field(description="Lines of text")


class LineListOutputParser(PydanticOutputParser):
    def __init__(self) -> None:
        super().__init__(pydantic_object=LineList)

    def parse(self, text: str) -> LineList:
        lines = text.strip().split("\n")
        return LineList(lines=lines)


output_parser = LineListOutputParser()

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)

# Chain
llm_chain = LLMChain(llm=llm, prompt=QUERY_PROMPT, output_parser=output_parser)

# Run
retriever = MultiQueryRetriever(
    retriever=vectorstore.as_retriever(), llm_chain=llm_chain, parser_key="lines"
)  # "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)