feat: Use Taskprocessing TextToText provider as LLM

Signed-off-by: Marcel Klehr <[email protected]>

Author: marcelklehr

config.cpu.yaml

@@ -40,6 +40,8 @@ embedding:
       device: cpu
 
 llm:
+  nc_texttotext:
+
   llama:
     model_path: dolphin-2.2.1-mistral-7b.Q5_K_M.gguf
     n_batch: 512

config.gpu.yaml

@@ -40,6 +40,8 @@ embedding:
       device: cuda
 
 llm:
+  nc_texttotext:
+
   llama:
     model_path: dolphin-2.2.1-mistral-7b.Q5_K_M.gguf
     n_batch: 512
@@ -69,4 +71,4 @@ llm:
     pipeline_kwargs:
       config:
         max_length: 200
-    template: ""
+    template: ""

context_chat_backend/chain/query_proc.py

@@ -25,7 +25,7 @@ def get_pruned_query(llm: LLM, config: TConfig, query: str, template: str, text_
 		or llm_config.get('config', {}).get('max_new_tokens') \
 		or max(
 			llm_config.get('pipeline_kwargs', {}).get('config', {}).get('max_new_tokens', 0),
-			llm_config.get('pipeline_kwargs', {}).get('config', {}).get('max_length')
+			llm_config.get('pipeline_kwargs', {}).get('config', {}).get('max_length', 0)
 		) \
 		or 4096
 

context_chat_backend/download.py

@@ -202,7 +202,8 @@ def background_init(app: FastAPI):
 	for model_type in ('embedding', 'llm'):
 		model_name = _get_model_name_or_path(config, model_type)
 		if model_name is None:
-			raise Exception(f'Error: Model name/path not found for {model_type}')
+			update_progress(app, progress := progress + 50)
+			continue
 
 		if not _download_model(model_name):
 			raise Exception(f'Error: Model download failed for {model_name}')

context_chat_backend/models/__init__.py

@@ -5,7 +5,7 @@
 from langchain.schema.embeddings import Embeddings
 
 _embedding_models = ['llama', 'hugging_face', 'instructor']
-_llm_models = ['llama', 'hugging_face', 'ctransformer']
+_llm_models = ['nc_texttotext', 'llama', 'hugging_face', 'ctransformer']
 
 models = {
 	'embedding': _embedding_models,

context_chat_backend/models/nc_texttotext.py

@@ -0,0 +1,83 @@
+import json
+import time
+from typing import Any, Dict, List, Optional
+
+from nc_py_api import Nextcloud
+from langchain_core.callbacks.manager import CallbackManagerForLLMRun
+from langchain_core.language_models.llms import LLM
+
+def get_model_for(model_type: str, model_config: dict):
+	if model_config is None:
+		return None
+
+	if model_type == 'llm':
+		return CustomLLM()
+
+	return None
+
+class CustomLLM(LLM):
+	"""A custom chat model that queries Nextcloud's TextToText provider
+    """
+
+	def _call(
+			self,
+			prompt: str,
+			stop: Optional[List[str]] = None,
+			run_manager: Optional[CallbackManagerForLLMRun] = None,
+			**kwargs: Any,
+	) -> str:
+		"""Run the LLM on the given input.
+
+        Override this method to implement the LLM logic.
+
+        Args:
+            prompt: The prompt to generate from.
+            stop: Stop words to use when generating. Model output is cut off at the
+                first occurrence of any of the stop substrings.
+                If stop tokens are not supported consider raising NotImplementedError.
+            run_manager: Callback manager for the run.
+            **kwargs: Arbitrary additional keyword arguments. These are usually passed
+                to the model provider API call.
+
+        Returns:
+            The model output as a string. Actual completions SHOULD NOT include the prompt.
+        """
+		nc = Nextcloud()
+
+		print(json.dumps(prompt))
+
+		response = nc.ocs("POST", "/ocs/v1.php/taskprocessing/schedule", json={
+			"type": "core:text2text",
+			"appId": "context_chat_backend",
+			"input": {
+				"input": prompt
+			}
+		})
+
+		task_id = response["task"]["id"]
+
+		while response['task']['status'] != 'STATUS_SUCCESSFUL' and response['task']['status'] != 'STATUS_FAILED':
+			time.sleep(5)
+			response = nc.ocs("GET", f"/ocs/v1.php/taskprocessing/task/{task_id}")
+			print(json.dumps(response))
+
+		if response['task']['status'] == 'STATUS_FAILED':
+			raise RuntimeError('Nextcloud TaskProcessing Task failed')
+
+		return response['task']['output']['output']
+
+	@property
+	def _identifying_params(self) -> Dict[str, Any]:
+		"""Return a dictionary of identifying parameters."""
+		return {
+			# The model name allows users to specify custom token counting
+			# rules in LLM monitoring applications (e.g., in LangSmith users
+			# can provide per token pricing for their model and monitor
+			# costs for the given LLM.)
+			"model_name": "NextcloudTextToTextProvider",
+		}
+
+	@property
+	def _llm_type(self) -> str:
+		"""Get the type of language model used by this chat model. Used for logging purposes only."""
+		return "nc_texttotetx"

requirements.in.txt

@@ -29,3 +29,4 @@ unstructured @ git+https://github.com/kyteinsky/unstructured@d3a404cfb541dae8e16
 unstructured-client
 weaviate-client
 xlrd
+nc_py_api

requirements.txt

@@ -82,6 +82,7 @@ mpmath==1.3.0
 msg-parser==1.2.0
 multidict==6.0.5
 mypy-extensions==1.0.0
+nc-py-api==0.14.0
 nest-asyncio==1.6.0
 networkx==3.3
 nltk==3.8.1
@@ -189,5 +190,6 @@ websockets==12.0
 wrapt==1.16.0
 xlrd==2.0.1
 XlsxWriter==3.2.0
+xmltodict==0.13.0
 yarl==1.9.4
 zipp==3.19.2