Model parameters option to pass in model tuning, arbitrary parameters

docs/source/users/index.md

@@ -855,30 +855,138 @@ The `--response-path` option is a [JSONPath](https://goessner.net/articles/JsonP
 
 ## Configuration
 
-You can specify an allowlist, to only allow only a certain list of providers, or a blocklist, to block some providers.
+You can specify an allowlist, to only allow only a certain list of providers, or
+a blocklist, to block some providers.
 
 ### Blocklisting providers
-This configuration allows for blocking specific providers in the settings panel. This list takes precedence over the allowlist in the next section.
+
+This configuration allows for blocking specific providers in the settings panel.
+This list takes precedence over the allowlist in the next section.
 
 ```
 jupyter lab --AiExtension.blocked_providers=openai
 ```
 
-To block more than one provider in the block-list, repeat the runtime configuration.
+To block more than one provider in the block-list, repeat the runtime
+configuration.
 
 ```
 jupyter lab --AiExtension.blocked_providers=openai --AiExtension.blocked_providers=ai21
 ```
 
 ### Allowlisting providers
-This configuration allows for filtering the list of providers in the settings panel to only an allowlisted set of providers.
+
+This configuration allows for filtering the list of providers in the settings
+panel to only an allowlisted set of providers.
 
 ```
 jupyter lab --AiExtension.allowed_providers=openai
 ```
 
-To allow more than one provider in the allowlist, repeat the runtime configuration.
+To allow more than one provider in the allowlist, repeat the runtime
+configuration.
 
 ```
 jupyter lab --AiExtension.allowed_providers=openai --AiExtension.allowed_providers=ai21
 ```
+
+### Model parameters
+
+This configuration allows specifying arbitrary parameters that are unpacked and
+passed to the provider class. This is useful for passing parameters such as
+model tuning that affect the response generation by the model. This is also an
+appropriate place to pass in custom attributes required by certain
+providers/models.
+
+The accepted value is a dictionary, with top level keys as the model id
+(provider:model_id), and value should be any arbitrary dictionary which is
+unpacked and passed as-is to the provider class.
+
+#### Configuring as a startup option
+
+In this sample, the `bedrock` provider will be created with the value for
+`model_kwargs` when `ai21.j2-mid-v1` model is selected.
+
+```bash
+jupyter lab --AiExtension.model_parameters bedrock:ai21.j2-mid-v1='{"model_kwargs":{"maxTokens":200}}'
+```
+
+Note the usage of single quotes surrounding the dictionary to escape the double
+quotes. This is required in some shells. The above will result in the following
+LLM class to be generated.
+
+```python
+BedrockProvider(model_kwargs={"maxTokens":200}, ...)
+```
+
+Here is another example, where `anthropic` provider will be created with the
+values for `max_tokens` and `temperature`, when `claude-2` model is selected.
+
+
+```bash
+jupyter lab --AiExtension.model_parameters anthropic:claude-2='{"max_tokens":1024,"temperature":0.9}'
+```
+
+The above will result in the following LLM class to be generated.
+
+```python
+AnthropicProvider(max_tokens=1024, temperature=0.9, ...)
+```
+
+To pass multiple sets of model parameters for multiple models in the
+command-line, you can append them as additional arguments to
+`--AiExtension.model_parameters`, as shown below.
+
+```bash
+jupyter lab \
+--AiExtension.model_parameters bedrock:ai21.j2-mid-v1='{"model_kwargs":{"maxTokens":200}}' \
+--AiExtension.model_parameters anthropic:claude-2='{"max_tokens":1024,"temperature":0.9}'
+```
+
+However, for more complex configuration, we highly recommend that you specify
+this in a dedicated configuration file. We will describe how to do so in the
+following section.
+
+#### Configuring as a config file
+
+This configuration can also be specified in a config file in json format. The
+file should be named `jupyter_jupyter_ai_config.json` and saved in a path that
+JupyterLab can pick from. You can find this path by running `jupyter --paths`
+command, and picking one of the paths from the `config` section.
+
+Here is an example of running the `jupyter --paths` command.
+
+```bash
+(jupyter-ai-lab4) ➜ jupyter --paths
+config:
+    /opt/anaconda3/envs/jupyter-ai-lab4/etc/jupyter
+    /Users/3coins/.jupyter
+    /Users/3coins/.local/etc/jupyter
+    /usr/3coins/etc/jupyter
+    /etc/jupyter
+data:
+    /opt/anaconda3/envs/jupyter-ai-lab4/share/jupyter
+    /Users/3coins/Library/Jupyter
+    /Users/3coins/.local/share/jupyter
+    /usr/local/share/jupyter
+    /usr/share/jupyter
+runtime:
+    /Users/3coins/Library/Jupyter/runtime
+```
+
+Here is an example for configuring the `bedrock` provider for `ai21.j2-mid-v1`
+model.
+
+```json
+{
+    "AiExtension": {
+        "model_parameters": {
+            "bedrock:ai21.j2-mid-v1": {
+                "model_kwargs": {
+                    "maxTokens": 200
+                }
+            }
+        }
+    }
+}
+```

packages/jupyter-ai-magics/jupyter_ai_magics/embedding_providers.py

@@ -1,4 +1,4 @@
-from typing import ClassVar, List, Type
+from typing import ClassVar, List
 
 from jupyter_ai_magics.providers import (
     AuthStrategy,
@@ -12,7 +12,6 @@
     HuggingFaceHubEmbeddings,
     OpenAIEmbeddings,
 )
-from langchain.embeddings.base import Embeddings
 from pydantic import BaseModel, Extra
 
 

packages/jupyter-ai-magics/jupyter_ai_magics/magics.py

@@ -392,9 +392,11 @@ def handle_error(self, args: ErrorArgs):
 
         prompt = f"Explain the following error:\n\n{last_error}"
         # Set CellArgs based on ErrorArgs
-        cell_args = CellArgs(
-            type="root", model_id=args.model_id, format=args.format, reset=False
-        )
+        values = args.dict()
+        values["type"] = "root"
+        values["reset"] = False
+        cell_args = CellArgs(**values)
+
         return self.run_ai_cell(cell_args, prompt)
 
     def _append_exchange_openai(self, prompt: str, output: str):
@@ -518,16 +520,9 @@ def run_ai_cell(self, args: CellArgs, prompt: str):
             provider_params["request_schema"] = args.request_schema
             provider_params["response_path"] = args.response_path
 
-            # Validate that the request schema is well-formed JSON
-            try:
-                json.loads(args.request_schema)
-            except json.JSONDecodeError as e:
-                raise ValueError(
-                    "request-schema must be valid JSON. "
-                    f"Error at line {e.lineno}, column {e.colno}: {e.msg}"
-                ) from None
+        model_parameters = json.loads(args.model_parameters)
 
-        provider = Provider(**provider_params)
+        provider = Provider(**provider_params, **model_parameters)
 
         # Apply a prompt template.
         prompt = provider.get_prompt_template(args.format).format(prompt=prompt)

packages/jupyter-ai-magics/jupyter_ai_magics/parsers.py

@@ -1,3 +1,4 @@
+import json
 from typing import Literal, Optional, get_args
 
 import click
@@ -32,12 +33,21 @@
     + "does nothing with other providers."
 )
 
+MODEL_PARAMETERS_SHORT_OPTION = "-m"
+MODEL_PARAMETERS_LONG_OPTION = "--model-parameters"
+MODEL_PARAMETERS_HELP = (
+    "A JSON value that specifies extra values that will be passed "
+    "to the model. The accepted value parsed to a dict, unpacked "
+    "and passed as-is to the provider class."
+)
+
 
 class CellArgs(BaseModel):
     type: Literal["root"] = "root"
     model_id: str
     format: FORMAT_CHOICES_TYPE
     reset: bool
+    model_parameters: Optional[str]
     # The following parameters are required only for SageMaker models
     region_name: Optional[str]
     request_schema: Optional[str]
@@ -49,6 +59,7 @@ class ErrorArgs(BaseModel):
     type: Literal["error"] = "error"
     model_id: str
     format: FORMAT_CHOICES_TYPE
+    model_parameters: Optional[str]
     # The following parameters are required only for SageMaker models
     region_name: Optional[str]
     request_schema: Optional[str]
@@ -93,6 +104,19 @@ def get_help(self, ctx):
             click.echo(super().get_help(ctx))
 
 
+def verify_json_value(ctx, param, value):
+    if not value:
+        return value
+    try:
+        json.loads(value)
+    except json.JSONDecodeError as e:
+        raise ValueError(
+            f"{param.get_error_hint(ctx)} must be valid JSON. "
+            f"Error at line {e.lineno}, column {e.colno}: {e.msg}"
+        )
+    return value
+
+
 @click.command()
 @click.argument("model_id")
 @click.option(
@@ -120,13 +144,22 @@ def get_help(self, ctx):
     REQUEST_SCHEMA_LONG_OPTION,
     required=False,
     help=REQUEST_SCHEMA_HELP,
+    callback=verify_json_value,
 )
 @click.option(
     RESPONSE_PATH_SHORT_OPTION,
     RESPONSE_PATH_LONG_OPTION,
     required=False,
     help=RESPONSE_PATH_HELP,
 )
+@click.option(
+    MODEL_PARAMETERS_SHORT_OPTION,
+    MODEL_PARAMETERS_LONG_OPTION,
+    required=False,
+    help=MODEL_PARAMETERS_HELP,
+    callback=verify_json_value,
+    default="{}",
+)
 def cell_magic_parser(**kwargs):
     """
     Invokes a language model identified by MODEL_ID, with the prompt being
@@ -166,13 +199,22 @@ def line_magic_parser():
     REQUEST_SCHEMA_LONG_OPTION,
     required=False,
     help=REQUEST_SCHEMA_HELP,
+    callback=verify_json_value,
 )
 @click.option(
     RESPONSE_PATH_SHORT_OPTION,
     RESPONSE_PATH_LONG_OPTION,
     required=False,
     help=RESPONSE_PATH_HELP,
 )
+@click.option(
+    MODEL_PARAMETERS_SHORT_OPTION,
+    MODEL_PARAMETERS_LONG_OPTION,
+    required=False,
+    help=MODEL_PARAMETERS_HELP,
+    callback=verify_json_value,
+    default="{}",
+)
 def error_subparser(**kwargs):
     """
     Explains the most recent error. Takes the same options (except -r) as

packages/jupyter-ai-magics/jupyter_ai_magics/providers.py

@@ -5,7 +5,17 @@
 import io
 import json
 from concurrent.futures import ThreadPoolExecutor
-from typing import Any, ClassVar, Coroutine, Dict, List, Literal, Optional, Union
+from typing import (
+    Any,
+    ClassVar,
+    Coroutine,
+    Dict,
+    List,
+    Literal,
+    Mapping,
+    Optional,
+    Union,
+)
 
 from jsonpath_ng import parse
 from langchain.chat_models import (
@@ -621,6 +631,7 @@ def __init__(self, *args, **kwargs):
         content_handler = JsonContentHandler(
             request_schema=request_schema, response_path=response_path
         )
+
         super().__init__(*args, **kwargs, content_handler=content_handler)
 
     async def _acall(self, *args, **kwargs) -> Coroutine[Any, Any, str]:

packages/jupyter-ai/jupyter_ai/chat_handlers/ask.py

@@ -36,7 +36,8 @@ def __init__(self, retriever, *args, **kwargs):
     def create_llm_chain(
         self, provider: Type[BaseProvider], provider_params: Dict[str, str]
     ):
-        self.llm = provider(**provider_params)
+        model_parameters = self.get_model_parameters(provider, provider_params)
+        self.llm = provider(**provider_params, **model_parameters)
         memory = ConversationBufferWindowMemory(
             memory_key="chat_history", return_messages=True, k=2
         )

packages/jupyter-ai/jupyter_ai/chat_handlers/base.py

@@ -3,7 +3,7 @@
 import traceback
 
 # necessary to prevent circular import
-from typing import TYPE_CHECKING, Dict, Optional, Type
+from typing import TYPE_CHECKING, Any, Dict, Optional, Type
 from uuid import uuid4
 
 from jupyter_ai.config_manager import ConfigManager, Logger
@@ -23,10 +23,12 @@ def __init__(
         log: Logger,
         config_manager: ConfigManager,
         root_chat_handlers: Dict[str, "RootChatHandler"],
+        model_parameters: Dict[str, Dict],
     ):
         self.log = log
         self.config_manager = config_manager
         self._root_chat_handlers = root_chat_handlers
+        self.model_parameters = model_parameters
         self.parser = argparse.ArgumentParser()
         self.llm = None
         self.llm_params = None
@@ -122,6 +124,13 @@ def get_llm_chain(self):
         self.llm_params = lm_provider_params
         return self.llm_chain
 
+    def get_model_parameters(
+        self, provider: Type[BaseProvider], provider_params: Dict[str, str]
+    ):
+        return self.model_parameters.get(
+            f"{provider.id}:{provider_params['model_id']}", {}
+        )
+
     def create_llm_chain(
         self, provider: Type[BaseProvider], provider_params: Dict[str, str]
     ):

packages/jupyter-ai/jupyter_ai/chat_handlers/default.py

@@ -40,7 +40,8 @@ def __init__(self, chat_history: List[ChatMessage], *args, **kwargs):
     def create_llm_chain(
         self, provider: Type[BaseProvider], provider_params: Dict[str, str]
     ):
-        llm = provider(**provider_params)
+        model_parameters = self.get_model_parameters(provider, provider_params)
+        llm = provider(**provider_params, **model_parameters)
 
         if llm.is_chat_provider:
             prompt_template = ChatPromptTemplate.from_messages(

packages/jupyter-ai/jupyter_ai/chat_handlers/generate.py

@@ -226,7 +226,9 @@ def __init__(self, root_dir: str, *args, **kwargs):
     def create_llm_chain(
         self, provider: Type[BaseProvider], provider_params: Dict[str, str]
     ):
-        llm = provider(**provider_params)
+        model_parameters = self.get_model_parameters(provider, provider_params)
+        llm = provider(**provider_params, **model_parameters)
+
         self.llm = llm
         return llm