[AUR-395, AUR-415] Adopt Example1 Injury pipeline; add .flow() for enabling bottom-up pipeline execution (#32)

* add example1/injury pipeline example
* add dotenv
* update various api

knowledgehub/llms/base.py

@@ -1,25 +1,13 @@
-from dataclasses import dataclass, field
 from typing import List
 
-from ..base import BaseComponent
+from pydantic import Field
+
+from kotaemon.documents.base import Document
 
 
-@dataclass
-class LLMInterface:
-    text: List[str]
+class LLMInterface(Document):
+    candidates: List[str]
     completion_tokens: int = -1
     total_tokens: int = -1
     prompt_tokens: int = -1
-    logits: List[List[float]] = field(default_factory=list)
-
-
-class PromptTemplate(BaseComponent):
-    pass
-
-
-class Extract(BaseComponent):
-    pass
-
-
-class PromptNode(BaseComponent):
-    pass
+    logits: List[List[float]] = Field(default_factory=list)

knowledgehub/llms/chats/base.py

@@ -11,7 +11,17 @@ Message = TypeVar("Message", bound=BaseMessage)
 
 
 class ChatLLM(BaseComponent):
-    ...
+    def flow(self):
+        if self.inflow is None:
+            raise ValueError("No inflow provided.")
+
+        if not isinstance(self.inflow, BaseComponent):
+            raise ValueError(
+                f"inflow must be a BaseComponent, found {type(self.inflow)}"
+            )
+
+        text = self.inflow.flow().text
+        return self.__call__(text)
 
 
 class LangchainChatLLM(ChatLLM):
@@ -44,8 +54,10 @@ class LangchainChatLLM(ChatLLM):
 
     def run_document(self, text: List[Message], **kwargs) -> LLMInterface:
         pred = self.agent.generate([text], **kwargs)  # type: ignore
+        all_text = [each.text for each in pred.generations[0]]
         return LLMInterface(
-            text=[each.text for each in pred.generations[0]],
+            text=all_text[0] if len(all_text) > 0 else "",
+            candidates=all_text,
             completion_tokens=pred.llm_output["token_usage"]["completion_tokens"],
             total_tokens=pred.llm_output["token_usage"]["total_tokens"],
             prompt_tokens=pred.llm_output["token_usage"]["prompt_tokens"],

knowledgehub/llms/completions/base.py

@@ -33,8 +33,10 @@ class LangchainLLM(LLM):
 
     def run_raw(self, text: str) -> LLMInterface:
         pred = self.agent.generate([text])
+        all_text = [each.text for each in pred.generations[0]]
         return LLMInterface(
-            text=[each.text for each in pred.generations[0]],
+            text=all_text[0] if len(all_text) > 0 else "",
+            candidates=all_text,
             completion_tokens=pred.llm_output["token_usage"]["completion_tokens"],
             total_tokens=pred.llm_output["token_usage"]["total_tokens"],
             prompt_tokens=pred.llm_output["token_usage"]["prompt_tokens"],