Alow failure on evidence gathering without crashing (#129)

* Alow failure on evidence gathering without crashing

* Fixed errors on key filters

paperqa/docs.py

@@ -27,10 +27,11 @@
     search_prompt,
     select_paper_prompt,
     summary_prompt,
+    get_score,
 )
 from .readers import read_doc
 from .types import Answer, Context
-from .utils import maybe_is_text, md5sum, gather_with_concurrency
+from .utils import maybe_is_text, md5sum, gather_with_concurrency, guess_is_4xx
 
 os.makedirs(os.path.dirname(CACHE_PATH), exist_ok=True)
 langchain.llm_cache = SQLiteCache(CACHE_PATH)
@@ -373,31 +374,46 @@ async def aget_evidence(
             docs = self._faiss_index.similarity_search(
                 answer.question, k=_k, fetch_k=5 * _k
             )
+        # ok now filter
+        if key_filter is not None:
+            docs = [doc for doc in docs if doc.metadata["dockey"] in key_filter][:k]
 
         async def process(doc):
             if doc.metadata["dockey"] in self._deleted_keys:
                 return None, None
-            if key_filter is not None and doc.metadata["dockey"] not in key_filter:
-                return None, None
             # check if it is already in answer (possible in agent setting)
             if doc.metadata["key"] in [c.key for c in answer.contexts]:
                 return None, None
             callbacks = [OpenAICallbackHandler()] + get_callbacks(
                 "evidence:" + doc.metadata["key"]
             )
             summary_chain = make_chain(summary_prompt, self.summary_llm)
-            c = Context(
-                key=doc.metadata["key"],
-                citation=doc.metadata["citation"],
-                context=await summary_chain.arun(
+            # This is dangerous because it
+            # could mask errors that are important
+            # I also cannot know what the exception
+            # type is because any model could be used
+            # my best idea is see if there is a 4XX
+            # http code in the exception
+            try:
+                context = await summary_chain.arun(
                     question=answer.question,
                     context_str=doc.page_content,
                     citation=doc.metadata["citation"],
                     callbacks=callbacks,
-                ),
+                )
+            except Exception as e:
+                if guess_is_4xx(e):
+                    return None, None
+                raise e
+            c = Context(
+                key=doc.metadata["key"],
+                citation=doc.metadata["citation"],
+                context=context,
                 text=doc.page_content,
+                score=get_score(context),
             )
             if "not applicable" not in c.context.casefold():
+                print(c.score)
                 return c, callbacks[0]
             return None, None
 
@@ -411,7 +427,7 @@ async def process(doc):
         contexts = [c for c, _ in results if c is not None]
         if len(contexts) == 0:
             return answer
-        contexts = sorted(contexts, key=lambda x: len(x.context), reverse=True)
+        contexts = sorted(contexts, key=lambda x: x.score, reverse=True)
         contexts = contexts[:max_sources]
         # add to answer (if not already there)
         keys = [c.key for c in answer.contexts]
@@ -499,11 +515,12 @@ async def aquery(
         if answer is None:
             answer = Answer(query)
         if len(answer.contexts) == 0:
-            if key_filter or (key_filter is None and len(self.docs) > 5):
+            if key_filter or (key_filter is None and len(self.docs) > max_sources):
                 callbacks = [OpenAICallbackHandler()] + get_callbacks("filter")
                 keys = await self.adoc_match(answer.question, callbacks=callbacks)
                 answer.tokens += callbacks[0].total_tokens
                 answer.cost += callbacks[0].total_cost
+                key_filter = True if len(keys) > 0 else False
             answer = await self.aget_evidence(
                 answer,
                 k=k,
@@ -532,8 +549,8 @@ async def aquery(
             answer.tokens += cb.total_tokens
             answer.cost += cb.total_cost
         # it still happens lol
-        if "(Foo2012)" in answer_text:
-            answer_text = answer_text.replace("(Foo2012)", "")
+        if "(Example2012)" in answer_text:
+            answer_text = answer_text.replace("(Example2012)", "")
         for c in contexts:
             key = c.key
             text = c.context

paperqa/qaprompts.py

@@ -1,6 +1,7 @@
 import copy
 from datetime import datetime
 from typing import Any, Dict, List, Optional
+import re
 
 import langchain.prompts as prompts
 from langchain.callbacks.manager import AsyncCallbackManagerForChainRun
@@ -11,27 +12,26 @@
 
 summary_prompt = prompts.PromptTemplate(
     input_variables=["question", "context_str", "citation"],
-    template="Summarize and provide direct quotes from the text below to help answer a question. "
-    "Do not directly answer the question, instead summarize and "
-    "quote to give evidence to help answer the question. "
-    "Do not use outside sources. "
-    'Reply with only "Not applicable" if the text is unrelated to the question. '
-    "Use 100 or less words."
+    template="Summarize the text below to help answer a question. "
+    "Do not directly answer the question, instead summarize "
+    "to give evidence to help answer the question. Include direct quotes. "
+    'Reply "Not applicable" if text is irrelevant. '
+    "Use around 100 words. At the end of your response, provide a score from 1-10 on a newline "
+    "indicating relevance to question. Do not explain your score. "
     "\n\n"
     "{context_str}\n"
     "Extracted from {citation}\n"
     "Question: {question}\n"
     "Relevant Information Summary:",
 )
 
-
 qa_prompt = prompts.PromptTemplate(
     input_variables=["question", "context_str", "length"],
     template="Write an answer ({length}) "
     "for the question below based on the provided context. "
     "If the context provides insufficient information, "
     'reply "I cannot answer". '
-    "For each sentence in your answer, indicate which sources most support it "
+    "For each part of your answer, indicate which sources most support it "
     "via valid citation markers at the end of sentences, like (Example2012). "
     "Answer in an unbiased, comprehensive, and scholarly tone. "
     "If the question is subjective, provide an opinionated answer in the concluding 1-2 sentences. "
@@ -98,12 +98,21 @@ async def agenerate(
 def make_chain(prompt, llm):
     if type(llm) == ChatOpenAI:
         system_message_prompt = SystemMessage(
-            content="You are a scholarly researcher that answers in an unbiased, concise, scholarly tone. "
-            "You sometimes refuse to answer if there is insufficient information. "
-            "If there are potentially ambiguous terms or acronyms, first define them. ",
+            content="Answer in an unbiased, concise, scholarly tone. "
+            "You may refuse to answer if there is insufficient information. "
+            "If there are ambiguous terms or acronyms, first define them. ",
         )
         human_message_prompt = HumanMessagePromptTemplate(prompt=prompt)
         prompt = ChatPromptTemplate.from_messages(
             [system_message_prompt, human_message_prompt]
         )
     return FallbackLLMChain(prompt=prompt, llm=llm)
+
+
+def get_score(text):
+    score = re.search(r"[sS]core[:is\s]+([0-9]+)", text)
+    if score:
+        return int(score.group(1))
+    if len(text) < 100:
+        return 1
+    return 5

paperqa/types.py

@@ -5,14 +5,29 @@
 StrPath = Union[str, Path]
 
 
+@dataclass
+class Context:
+    """A class to hold the context of a question."""
+
+    key: str
+    citation: str
+    context: str
+    text: str
+    score: int = 5
+
+    def __str__(self) -> str:
+        """Return the context as a string."""
+        return self.context
+
+
 @dataclass
 class Answer:
     """A class to hold the answer to a question."""
 
     question: str
     answer: str = ""
     context: str = ""
-    contexts: List[Any] = None
+    contexts: List[Context] = None
     references: str = ""
     formatted_answer: str = ""
     passages: Dict[str, str] = None
@@ -29,17 +44,3 @@ def __post_init__(self):
     def __str__(self) -> str:
         """Return the answer as a string."""
         return self.formatted_answer
-
-
-@dataclass
-class Context:
-    """A class to hold the context of a question."""
-
-    key: str
-    citation: str
-    context: str
-    text: str
-
-    def __str__(self) -> str:
-        """Return the context as a string."""
-        return self.context

paperqa/utils.py

@@ -1,5 +1,6 @@
 import math
 import string
+import re
 import asyncio
 
 import pypdf
@@ -80,3 +81,9 @@ async def sem_coro(coro):
             return await coro
 
     return await asyncio.gather(*(sem_coro(c) for c in coros))
+
+
+def guess_is_4xx(msg: str) -> bool:
+    if re.search(r"4\d\d", msg):
+        return True
+    return False

paperqa/version.py

@@ -1 +1 @@
-__version__ = "1.12.0"
+__version__ = "1.13.0"