【AI大模型应用开发】【LangChain系列】实战案例3：深入LangChain源码，你不知道的WebResearchRetriever与RAG联合之力

GOOGLE_API_KEY = "YOUR GOOGLE API KEY"

GOOGLE_CSE_ID = "xxxxxxx"

pip install google-api-core==2.11.1
pip install -i https://pypi.tuna.tsinghua.edu.cn/simple google-api-python-client==2.95.0

streamlit==1.25.0
langchain==0.0.244
chromadb==0.4.3
openai==0.27.8
html2text==2020.1.16
google-api-core==2.11.1
google-api-python-client==2.95.0
google-auth==2.22.0
google-auth-httplib2==0.1.0
googleapis-common-protos==1.59.1
tiktoken==0.4.0
faiss-cpu==1.7.4

from langchain.retrievers.web_research import WebResearchRetriever
from langchain_community.utilities import GoogleSearchAPIWrapper
from langchain_community.vectorstores import Chroma
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
# Vectorstore
vectorstore = Chroma(
    embedding_function=OpenAIEmbeddings(), persist_directory="./chroma_db_oai"
)
# LLM
llm = ChatOpenAI(temperature=0)
# Search
search = GoogleSearchAPIWrapper()
# Initialize
web_research_retriever = WebResearchRetriever.from_llm(
    vectorstore=vectorstore, llm=llm, search=search
)
# Run
import logging
logging.basicConfig()
logging.getLogger("langchain.retrievers.web_research").setLevel(logging.INFO)
from langchain.chains import RetrievalQAWithSourcesChain
user_input = "How do LLM Powered Autonomous Agents work?"
qa_chain = RetrievalQAWithSourcesChain.from_chain_type(
    llm, retriever=web_research_retriever
)
result = qa_chain({"question": user_input})
print(result)

# Initialize
web_research_retriever = WebResearchRetriever.from_llm(
    vectorstore=vectorstore, llm=llm, search=search
)

class WebResearchRetriever(BaseRetriever):
    """`Google Search API` retriever."""
  search: GoogleSearchAPIWrapper = Field(..., description="Google Search API Wrapper")

def from_llm(
        cls,
        vectorstore: VectorStore,
        llm: BaseLLM,
        search: GoogleSearchAPIWrapper,
        prompt: Optional[BasePromptTemplate] = None,
        num_search_results: int = 1,
        text_splitter: RecursiveCharacterTextSplitter = RecursiveCharacterTextSplitter(
            chunk_size=1500, chunk_overlap=150
        ),
    ) -> "WebResearchRetriever":
        """Initialize from llm using default template.
        Args:
            vectorstore: Vector store for storing web pages
            llm: llm for search question generation
            search: GoogleSearchAPIWrapper
            prompt: prompt to generating search questions
            num_search_results: Number of pages per Google search
            text_splitter: Text splitter for splitting web pages into chunks
        Returns:
            WebResearchRetriever
        """
        if not prompt:
            QUESTION_PROMPT_SELECTOR = ConditionalPromptSelector(
                default_prompt=DEFAULT_SEARCH_PROMPT,
                conditionals=[
                    (lambda llm: isinstance(llm, LlamaCpp), DEFAULT_LLAMA_SEARCH_PROMPT)
                ],
            )
            prompt = QUESTION_PROMPT_SELECTOR.get_prompt(llm)
        # Use chat model prompt
        llm_chain = LLMChain(
            llm=llm,
            prompt=prompt,
            output_parser=QuestionListOutputParser(),
        )
        return cls(
            vectorstore=vectorstore,
            llm_chain=llm_chain,
            search=search,
            num_search_results=num_search_results,
            text_splitter=text_splitter,
        )

DEFAULT_SEARCH_PROMPT = PromptTemplate(
    input_variables=["question"],
    template="""You are an assistant tasked with improving Google search \
results. Generate THREE Google search queries that are similar to \
this question. The output should be a numbered list of questions and each \
should have a question mark at the end: {question}""",
)

def _get_relevant_documents(
        self,
        query: str,
        *,
        run_manager: CallbackManagerForRetrieverRun,
    ) -> List[Document]:
        """Search Google for documents related to the query input.
        Args:
            query: user query
        Returns:
            Relevant documents from all various urls.
        """
        # Get search questions
        logger.info("Generating questions for Google Search ...")
        result = self.llm_chain({"question": query})
        logger.info(f"Questions for Google Search (raw): {result}")
        questions = result["text"]
        logger.info(f"Questions for Google Search: {questions}")
        # Get urls
        logger.info("Searching for relevant urls...")
        urls_to_look = []
        for query in questions:
            # Google search
            search_results = self.search_tool(query, self.num_search_results)
            logger.info("Searching for relevant urls...")
            logger.info(f"Search results: {search_results}")
            for res in search_results:
                if res.get("link", None):
                    urls_to_look.append(res["link"])
        # Relevant urls
        urls = set(urls_to_look)
        # Check for any new urls that we have not processed
        new_urls = list(urls.difference(self.url_database))
        logger.info(f"New URLs to load: {new_urls}")
        # Load, split, and add new urls to vectorstore
        if new_urls:
            loader = AsyncHtmlLoader(new_urls, ignore_load_errors=True)
            html2text = Html2TextTransformer()
            logger.info("Indexing new urls...")
            docs = loader.load()
            docs = list(html2text.transform_documents(docs))
            docs = self.text_splitter.split_documents(docs)
            self.vectorstore.add_documents(docs)
            self.url_database.extend(new_urls)
        # Search for relevant splits
        # TODO: make this async
        logger.info("Grabbing most relevant splits from urls...")
        docs = []
        for query in questions:
            docs.extend(self.vectorstore.similarity_search(query))
        # Get unique docs
        unique_documents_dict = {
            (doc.page_content, tuple(sorted(doc.metadata.items()))): doc for doc in docs
        }
        unique_documents = list(unique_documents_dict.values())
        return unique_documents

class GoogleSearchAPIWrapper(BaseModel):
    """Wrapper for Google Search API."""

qa_chain = RetrievalQAWithSourcesChain.from_chain_type(
    llm, retriever=web_research_retriever
)

class RetrievalQAWithSourcesChain(BaseQAWithSourcesChain):
    """Question-answering with sources over an index."""
    retriever: BaseRetriever = Field(exclude=True)
    """Index to connect to."""
    reduce_k_below_max_tokens: bool = False
    """Reduce the number of results to return from store based on tokens limit"""
    max_tokens_limit: int = 3375
    """Restrict the docs to return from store based on tokens,
    enforced only for StuffDocumentChain and if reduce_k_below_max_tokens is to true"""