Merge branch 'dev'

Author: thiswillbeyourgithub

DocToolsLLM/DocToolsLLM.py

@@ -78,7 +78,7 @@
 class DocToolsLLM_class:
     "This docstring is dynamically replaced by the content of DocToolsLLM/docs/USAGE.md"
 
-    VERSION: str = "0.37"
+    VERSION: str = "0.38"
 
     #@optional_typecheck
     @typechecked
@@ -1064,6 +1064,10 @@ def evaluate_doc_chain(
             ) -> List[str]:
             if "n" in self.eval_llm_params or self.query_eval_check_number == 1:
                 out = self.eval_llm._generate_with_cache(PR_EVALUATE_DOC.format_messages(**inputs))
+                reasons = [gen.generation_info["finish_reason"] for gen in out.generations]
+                # don't crash if finish_reason is not stop, because it can sometimes still be parsed.
+                if not all(r == "stop" for r in reasons):
+                    red(f"Unexpected generation finish_reason: '{reasons}'")
                 outputs = [gen.text for gen in out.generations]
                 assert outputs, "No generations found by query eval llm"
                 outputs = [parse_eval_output(o) for o in outputs]
@@ -1085,12 +1089,15 @@ async def eval(inputs):
                 ]
                 try:
                     loop = asyncio.get_event_loop()
-                except:
+                except RuntimeError:
                     loop = asyncio.new_event_loop()
                     asyncio.set_event_loop(loop)
                 outs = loop.run_until_complete(asyncio.gather(*outs))
                 for out in outs:
                     assert len(out.generations) == 1, f"Query eval llm produced more than 1 evaluations: '{out.generations}'"
+                    finish_reason = out.generations[0].generation_info["finish_reason"]
+                    if not finish_reason == "stop":
+                        red(f"Unexpected finish_reason: '{finish_reason}'")
                     outputs.append(out.generations[0].text)
                     if out.llm_output:
                         new_p += out.llm_output["token_usage"]["prompt_tokens"]
@@ -1219,67 +1226,6 @@ def retrieve_documents(inputs):
                         | StrOutputParser()
                 }
 
-            # the eval doc chain needs its own caching
-            if self.no_llm_cache:
-                def eval_cache_wrapper(func): return func
-            else:
-                eval_cache_wrapper = doc_eval_cache.cache
-
-            @chain
-            @optional_typecheck
-            @eval_cache_wrapper
-            def evaluate_doc_chain(
-                    inputs: dict,
-                    query_nb: int = self.query_eval_check_number,
-                    eval_model_name: str = self.query_eval_modelname,
-                ) -> List[str]:
-                if "n" in self.eval_llm_params or self.query_eval_check_number == 1:
-                    out = self.eval_llm._generate_with_cache(PR_EVALUATE_DOC.format_messages(**inputs))
-                    reasons = [gen.generation_info["finish_reason"] for gen in out.generations]
-                    assert all(r == "stop" for r in reasons), f"Unexpected generation finish_reason: '{reasons}'"
-                    outputs = [gen.text for gen in out.generations]
-                    assert outputs, "No generations found by query eval llm"
-                    outputs = [parse_eval_output(o) for o in outputs]
-                    if out.llm_output:
-                        new_p = out.llm_output["token_usage"]["prompt_tokens"]
-                        new_c = out.llm_output["token_usage"]["completion_tokens"]
-                    else:
-                        new_p = 0
-                        new_c = 0
-                else:
-                    outputs = []
-                    new_p = 0
-                    new_c = 0
-                    async def eval(inputs):
-                        return await self.eval_llm._agenerate_with_cache(PR_EVALUATE_DOC.format_messages(**inputs))
-                    outs = [
-                        eval(inputs)
-                        for i in range(self.query_eval_check_number)
-                    ]
-                    try:
-                        loop = asyncio.get_event_loop()
-                    except RuntimeError:
-                        loop = asyncio.new_event_loop()
-                        asyncio.set_event_loop(loop)
-                    outs = loop.run_until_complete(asyncio.gather(*outs))
-                    for out in outs:
-                        assert len(out.generations) == 1, f"Query eval llm produced more than 1 evaluations: '{out.generations}'"
-                        outputs.append(out.generations[0].text)
-                        finish_reason = out.generations[0].generation_info["finish_reason"]
-                        assert finish_reason == "stop", f"unexpected finish_reason: '{finish_reason}'"
-                        if out.llm_output:
-                            new_p += out.llm_output["token_usage"]["prompt_tokens"]
-                            new_c += out.llm_output["token_usage"]["completion_tokens"]
-                    assert outputs, "No generations found by query eval llm"
-                    outputs = [parse_eval_output(o) for o in outputs]
-
-                assert len(outputs) == self.query_eval_check_number, f"query eval model failed to produce {self.query_eval_check_number} outputs"
-
-                self.eval_llm.callbacks[0].prompt_tokens += new_p
-                self.eval_llm.callbacks[0].completion_tokens += new_c
-                self.eval_llm.callbacks[0].total_tokens += new_p + new_c
-                return outputs
-
             # for some reason I needed to have at least one chain object otherwise rag_chain is a dict
             @chain
             def retrieve_documents(inputs):

DocToolsLLM/docs/USAGE.md

@@ -248,11 +248,12 @@
     * List of fields to keep
 * `--anki_mode`: str
     * any of `window`, `concatenate`, `single_note`: (or _ separated
-    value like `concatenate_window`). By default `window_single_note`
+    value like `concatenate_window`). By default `single_note`
     is used.
     * Modes:
         * `single_note`: 1 document is 1 anki note.
-        * `window`: 1 documents is 5 anki note, overlapping
+        * `window`: 1 documents is 5 anki note, overlapping (so
+        10 anki notes will result in 5 documents)
         * `concatenate`: 1 document is all anki notes concatenated as a
         single wall of text then split like any long document.
 

DocToolsLLM/utils/loaders.py

@@ -503,7 +503,7 @@ def load_online_pdf(debug: bool, task: str, path: str, **kwargs) -> List[Documen
 @optional_typecheck
 def load_anki(
     anki_profile: str,
-    anki_mode: str = "window_single_note",
+    anki_mode: str = "single_note",
     anki_deck: Optional[str] = None,
     anki_fields: Optional[List[str]] = None,
     anki_notetype: Optional[str] = None,

bumpver.toml

@@ -1,5 +1,5 @@
 [bumpver]
-current_version = "0.37"
+current_version = "0.38"
 version_pattern = "MAJOR.MINOR"
 commit_message = "bump version {old_version} -> {new_version}"
 tag_message = "{new_version}"

setup.py

@@ -28,7 +28,7 @@ def run(self):
 
 setup(
     name="DocToolsLLM",
-    version="0.37",
+    version="0.38",
     description="A perfect RAG and AI summary setup for my needs. Supports all LLM, virt. any filetypes (epub, youtube_playlist, pdf, mp3, etc)",
     long_description=long_description,
     long_description_content_type="text/markdown",