import logging
import re
from typing import Any, List, Optional
from langchain.chains import LLMChain
from langchain.chains.prompt_selector import ConditionalPromptSelector
from langchain_core.callbacks import (
AsyncCallbackManagerForRetrieverRun,
CallbackManagerForRetrieverRun,
)
from langchain_core.documents import Document
from langchain_core.language_models import BaseLLM
from langchain_core.output_parsers import BaseOutputParser
from langchain_core.prompts import BasePromptTemplate, PromptTemplate
from langchain_core.pydantic_v1 import BaseModel, Field
from langchain_core.retrievers import BaseRetriever
from langchain_core.vectorstores import VectorStore
from langchain_text_splitters import RecursiveCharacterTextSplitter, TextSplitter
from langchain_community.document_loaders import AsyncHtmlLoader
from langchain_community.document_transformers import Html2TextTransformer
from langchain_community.llms import LlamaCpp
from langchain_community.utilities import GoogleSearchAPIWrapper
logger = logging.getLogger(__name__)
[docs]class SearchQueries(BaseModel):
"""Search queries to research for the user's goal."""
queries: List[str] = Field(
..., description="List of search queries to look up on Google"
)
DEFAULT_LLAMA_SEARCH_PROMPT = PromptTemplate(
input_variables=["question"],
template="""<<SYS>> \n You are an assistant tasked with improving Google search \
results. \n <</SYS>> \n\n [INST] 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: \n\n {question} [/INST]""",
)
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}""",
)
[docs]class QuestionListOutputParser(BaseOutputParser[List[str]]):
"""Output parser for a list of numbered questions."""
[docs] def parse(self, text: str) -> List[str]:
lines = re.findall(r"\d+\..*?(?:\n|$)", text)
return lines
[docs]class WebResearchRetriever(BaseRetriever):
"""`Google Search API` retriever."""
# Inputs
vectorstore: VectorStore = Field(
..., description="Vector store for storing web pages"
)
llm_chain: LLMChain
search: GoogleSearchAPIWrapper = Field(..., description="Google Search API Wrapper")
num_search_results: int = Field(1, description="Number of pages per Google search")
text_splitter: TextSplitter = Field(
RecursiveCharacterTextSplitter(chunk_size=1500, chunk_overlap=50),
description="Text splitter for splitting web pages into chunks",
)
url_database: List[str] = Field(
default_factory=list, description="List of processed URLs"
)
trust_env: bool = Field(
False,
description="Whether to use the http_proxy/https_proxy env variables or "
"check .netrc for proxy configuration",
)
allow_dangerous_requests: bool = False
"""A flag to force users to acknowledge the risks of SSRF attacks when using
this retriever.
Users should set this flag to `True` if they have taken the necessary precautions
to prevent SSRF attacks when using this retriever.
For example, users can run the requests through a properly configured
proxy and prevent the crawler from accidentally crawling internal resources.
"""
def __init__(self, **kwargs: Any) -> None:
"""Initialize the retriever."""
allow_dangerous_requests = kwargs.get("allow_dangerous_requests", False)
if not allow_dangerous_requests:
raise ValueError(
"WebResearchRetriever crawls URLs surfaced through "
"the provided search engine. It is possible that some of those URLs "
"will end up pointing to machines residing on an internal network, "
"leading"
"to an SSRF (Server-Side Request Forgery) attack. "
"To protect yourself against that risk, you can run the requests "
"through a proxy and prevent the crawler from accidentally crawling "
"internal resources."
"If've taken the necessary precautions, you can set "
"`allow_dangerous_requests` to `True`."
)
super().__init__(**kwargs)
[docs] @classmethod
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
),
trust_env: bool = False,
allow_dangerous_requests: bool = False,
) -> "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
trust_env: Whether to use the http_proxy/https_proxy env variables
or check .netrc for proxy configuration
allow_dangerous_requests: A flag to force users to acknowledge
the risks of SSRF attacks when using this retriever
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,
trust_env=trust_env,
allow_dangerous_requests=allow_dangerous_requests,
)
[docs] def clean_search_query(self, query: str) -> str:
# Some search tools (e.g., Google) will
# fail to return results if query has a
# leading digit: 1. "LangCh..."
# Check if the first character is a digit
if query[0].isdigit():
# Find the position of the first quote
first_quote_pos = query.find('"')
if first_quote_pos != -1:
# Extract the part of the string after the quote
query = query[first_quote_pos + 1 :]
# Remove the trailing quote if present
if query.endswith('"'):
query = query[:-1]
return query.strip()
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, trust_env=self.trust_env
)
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
async def _aget_relevant_documents(
self,
query: str,
*,
run_manager: AsyncCallbackManagerForRetrieverRun,
) -> List[Document]:
raise NotImplementedError
