Add documentation about adding reasoning and indexing pipelines to the application (#138)

docs/app.md

@@ -0,0 +1,237 @@
+# Add new indexing and reasoning pipeline to the application
+
+@trducng
+
+At high level, to add new indexing and reasoning pipeline:
+
+1. You define your indexing or reasoning pipeline as a class from
+   `BaseComponent`.
+2. You declare that class in the setting files `flowsettings.py`.
+
+Then when `python launch.py`, the application will dynamically load those
+pipelines.
+
+The below sections talk in more detail about how the pipelines should be
+constructed.
+
+## Define a pipeline as a class
+
+In essence, a pipeline will subclass from `kotaemon.base.BaseComponent`.
+Each pipeline has 2 main parts:
+
+- All declared arguments and sub-pipelines.
+- The logic inside the pipeline.
+
+An example pipeline:
+
+```python
+from kotaemon.base import BaseComponent
+
+
+class SoSimple(BaseComponent):
+    arg1: int
+    arg2: str
+
+    def run(self, arg3: str):
+        return self.arg1 * self.arg2 + arg3
+```
+
+This pipeline is named `SoSimple`. It takes `arg1` and `arg2` as init argument.
+It takes `arg3` as run argument.
+
+```python
+>> pipeline = SoSimple(arg1=2, arg2="ha")
+>> pipeline("x")
+hahax
+```
+
+This pipeline is simple for demonstration purpose, but we can imagine pipelines
+with much more arguments, that can take other pipelines as arguments, and have
+more complicated logic in the `run` method.
+
+**_An indexing or reasoning pipeline is just a class subclass from
+`BaseComponent` like above._**
+
+## Run signatures
+
+**Note**: this section is tentative at the moment. We will finalize `def run`
+function signature by latest early April.
+
+The indexing pipeline:
+
+```python
+    def run(
+        self,
+        file_paths: str | Path | list[str | Path],
+        reindex: bool = False,
+        **kwargs,
+    ):
+        """Index files to intermediate representation (e.g. vector, database...)
+
+        Args:
+            file_paths: the list of paths to files
+            reindex: if True, files in `file_paths` that already exists in database
+                should be reindex.
+        """
+```
+
+The reasoning pipeline:
+
+```python
+    def run(self, question: str, history: list, **kwargs) -> Document:
+        """Answer the question
+
+        Args:
+            question: the user input
+            history: the chat history [(user_msg1, bot_msg1), (user_msg2, bot_msg2)...]
+
+        Returns:
+            kotaemon.base.Document: the final answer
+        """
+```
+
+## Register your pipeline to ktem
+
+To register your pipelines to ktem, you declare it in the `flowsettings.py`
+file. This file locates at the current working directory where you start the
+ktem. In most use cases, it is this
+[one](https://github.com/Cinnamon/kotaemon/blob/main/libs/ktem/flowsettings.py).
+
+```
+KH_REASONING = ["<python.module.path.to.the.reasoning.class>"]
+
+KH_INDEX = "<python.module.path.to.the.indexing.class>"
+```
+
+You can register multiple reasoning pipelines to ktem by populating the
+`KH_REASONING` list. The user can select which reasoning pipeline to use
+in their Settings page.
+
+For now, there's only one supported index option for `KH_INDEX`.
+
+Make sure that your class is discoverable by Python.
+
+## Allow users to customize your pipeline in the app settings
+
+To allow the users to configure your pipeline, you need to declare what you
+allow the users to configure as a dictionary. `ktem` will include them into the
+application settings.
+
+In your pipeline class, add a classmethod `get_user_settings` that returns a
+setting dictionary, add a classmethod `get_info` that returns an info
+dictionary. Example:
+
+```
+class SoSimple(BaseComponent):
+
+    ... # as above
+
+    @classmethod
+    def get_user_settings(cls) -> dict:
+        """The settings to the user"""
+        return {
+            "setting_1": {
+                "name": "Human-friendly name",
+                "value": "Default value",
+                "choices": [("Human-friendly Choice 1", "choice1-id"), ("HFC 2", "choice2-id")], # optional
+                "component": "Which Gradio UI component to render, can be: text, number, checkbox, dropdown, radio, checkboxgroup"
+            },
+            "setting_2": {
+                # follow the same rule as above
+            }
+        }
+
+    @classmethod
+    def get_info(cls) -> dict:
+        """Pipeline information for bookkeeping purpose"""
+        return {
+            "id": "a unique id to differentiate this pipeline from other pipeline",
+            "name": "Human-friendly name of the pipeline",
+            "description": "Can be a short description of this pipeline"
+        }
+```
+
+Once adding these methods to your pipeline class, `ktem` will automatically
+extract and add them to the settings.
+
+## Construct to pipeline object
+
+Once `ktem` runs your pipeline, it will call your classmethod `get_pipeline`
+with the full user settings and expect to obtain the pipeline object. Within
+this `get_pipeline` method, you implement all the necessary logics to initiate
+the pipeline object. Example:
+
+```python
+class SoSimple(BaseComponent):
+    ... # as above
+
+    @classmethod
+    def get_pipeline(self, setting):
+        obj = cls(arg1=setting["reasoning.id.setting1"])
+        return obj
+```
+
+## Reasoning: Stream output to UI
+
+For fast user experience, you can stream the output directly to UI. This way,
+user can start observing the output as soon as the LLM model generates the 1st
+token, rather than having to wait the pipeline finishes to read the whole message.
+
+To stream the output, you need to;
+
+1. Turn the `run` function to async.
+2. Pass in the output to a special queue with `self.report_output`.
+
+```python
+
+    async def run(self, question: str, history: list, **kwargs) -> Document:
+        for char in "This is a long messages":
+            self.report_output({"output": text.text})
+```
+
+The argument to `self.report_output` is a dictionary, that contains either or
+all of these 2 keys: "output", "evidence". The "output" string will be streamed
+to the chat message, and the "evidence" string will be streamed to the
+information panel.
+
+## Access application LLMs, Embeddings
+
+You can access users' collections of LLMs and embedding models with:
+
+```python
+from ktem.components import llms, embeddings
+
+
+llm = llms.get_default()
+embedding_model = embeddings.get_default()
+```
+
+You can also allow the users to specifically select which llms or embedding
+models they want to use through the settings.
+
+```python
+    @classmethod
+    def get_user_settings(cls) -> dict:
+        from ktem.components import llms
+
+        return {
+            "citation_llm": {
+                "name": "LLM for citation",
+                "value": llms.get_lowest_cost_name(),
+                "component: "dropdown",
+                "choices": list(llms.options().keys()),
+            },
+            ...
+        }
+```
+
+## Optional: Access application data
+
+You can access the user's application database, vector store as follow:
+
+```python
+# get the database that contains the source files
+from ktem.db.models import Source, Index, Conversation, User
+
+# get the vector store
+```

docs/contributing.md

@@ -19,6 +19,7 @@
 - Install all
 
   ```shell
+  cd libs/kotaemon
   pip install -e ".[dev]"
   ```
 

libs/ktem/flowsettings.py

@@ -68,9 +68,7 @@ KH_EMBEDDINGS = {
         "default": True,
     },
 }
-KH_REASONINGS = {
+KH_REASONINGS = ["ktem.reasoning.simple.FullQAPipeline"]
-    "simple": "ktem.reasoning.simple.FullQAPipeline",
-}
 
 
 SETTINGS_APP = {

libs/ktem/ktem/app.py

@@ -73,11 +73,12 @@ class BaseApp:
         if getattr(settings, "KH_REASONINGS", None) is None:
             return
 
-        for name, value in settings.KH_REASONINGS.items():
+        for value in settings.KH_REASONINGS:
             reasoning_cls = import_dotted_string(value, safe=False)
-            reasonings[name] = reasoning_cls
+            rid = reasoning_cls.get_info()["id"]
+            reasonings[rid] = reasoning_cls
             options = reasoning_cls().get_user_settings()
-            self.default_settings.reasoning.options[name] = BaseSettingGroup(
+            self.default_settings.reasoning.options[rid] = BaseSettingGroup(
                 settings=options
             )
 

libs/ktem/ktem/pages/chat/events.py

@@ -128,7 +128,7 @@ async def chat_fn(chat_history, files, settings):
     pipeline = create_pipeline(settings, files)
     pipeline.set_output_queue(queue)
 
-    asyncio.create_task(pipeline(chat_input))
+    asyncio.create_task(pipeline(chat_input, chat_history))
     text, refs = "", ""
 
     while True:

libs/ktem/ktem/reasoning/simple.py

@@ -3,7 +3,7 @@ import logging
 import warnings
 from collections import defaultdict
 from functools import partial
-from typing import Iterator, Optional
+from typing import Optional
 
 import tiktoken
 from ktem.components import embeddings, get_docstore, get_vectorstore, llms
@@ -278,7 +278,7 @@ class AnswerWithContextPipeline(BaseComponent):
 
     async def run(  # type: ignore
         self, question: str, evidence: str, evidence_mode: int = 0
-    ) -> Document | Iterator[Document]:
+    ) -> Document:
         """Answer the question based on the evidence
 
         In addition to the question and the evidence, this method also take into
@@ -342,7 +342,9 @@ class FullQAPipeline(BaseComponent):
     evidence_pipeline: PrepareEvidencePipeline = PrepareEvidencePipeline.withx()
     answering_pipeline: AnswerWithContextPipeline = AnswerWithContextPipeline.withx()
 
-    async def run(self, question: str, **kwargs) -> Document:  # type: ignore
+    async def run(  # type: ignore
+        self, question: str, history: list, **kwargs  # type: ignore
+    ) -> Document:  # type: ignore
         docs = self.retrieval_pipeline(text=question)
         evidence_mode, evidence = self.evidence_pipeline(docs).content
         answer = await self.answering_pipeline(
@@ -455,3 +457,11 @@ class FullQAPipeline(BaseComponent):
                 "choices": main_llm_choices,
             },
         }
+
+    @classmethod
+    def get_info(cls) -> dict:
+        return {
+            "id": "simple",
+            "name": "Simple QA",
+            "description": "Simple QA pipeline",
+        }