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Merge pull request #2 from lfnovo/multi-model

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New multi model platform
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Luis Novo 2024-10-22 18:55:01 -03:00 committed by GitHub
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22 changed files with 1404 additions and 202 deletions
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3
.dockerignore
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@ -5,4 +5,5 @@ data/
.mypy_cache/
.ruff_cache/
.env
sqlite-db/
sqlite-db/
temp/

38
.env.example
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@ -1,12 +1,35 @@
# YOUR LLM API KEYS
OPENAI_API_KEY=API_KEY
# MODEL_CONFIGURATIONS
# Only OpenAI models are supported for now
DEFAULT_MODEL="gpt-4o-mini" # This is the default model used for all the features
SUMMARIZATION_MODEL="gpt-4o-mini" # This is the model used for summarization, defaults to the DEFAULT_MODEL if empty
RETRIEVAL_MODEL="gpt-4o-mini" # This is the model used for retrieval, defaults to the DEFAULT_MODEL if empty
# DEFAULT MODEL_CONFIGURATIONS
DEFAULT_MODEL="openai/gpt-4o-mini"
SUMMARIZATION_MODEL="openai/gpt-4o-mini"
RETRIEVAL_MODEL="openai/gpt-4o-mini"
# OPENAI
# USE MODEL NAMES AS "openai/<modelname>"
# EXAMPLE - openai/gpt-4o-mini
OPENAI_API_KEY=
# ANTHROPIC
# USE MODEL NAMES AS "anthropic/<modelname>"
# EXAMPLE - anthropic/claude-3-5-sonnet-20240620
ANTHROPIC_API_KEY=
# OLLAMA
# USE MODEL NAMES AS "ollama/<modelname>"
# EXAMPLE - ollama/gemma2
OLLAMA_API_BASE="http://10.20.30.20:11434"
# OPEN ROUTER
# USE MODEL NAMES AS "openrouter/<modelname>"
# EXAMPLE - openrouter/nvidia/llama-3.1-nemotron-70b-instruct
OPENROUTER_BASE_URL="https://openrouter.ai/api/v1"
OPENROUTER_API_KEY=
# USE THIS IF YOU WANT TO DEBUG THE APP ON LANGSMITH
# LANGCHAIN_TRACING_V2=true
# LANGCHAIN_ENDPOINT="https://api.smith.langchain.com"
# LANGCHAIN_API_KEY=
# LANGCHAIN_PROJECT="Open Notebook"
# CONNECTION DETAILS FOR YOUR SURREAL DB
SURREAL_ADDRESS="ws://localhost:8000/rpc"
@ -24,4 +47,3 @@ SUMMARY_CHUNK_OVERLAP=1000
# It is measured in characters, not tokens.
EMBEDDING_CHUNK_SIZE=1000
EMBEDDING_CHUNK_OVERLAP=50

1
.gitignore vendored
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@ -9,6 +9,7 @@ docker.env
__pycache__/
*.so
todo.md
temp/
# Distribution / packaging
.Python

33
Makefile
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@ -1,8 +1,12 @@
.PHONY: run check ruff database lint docker-build docker-push
.PHONY: run check ruff database lint docker-build docker-push docker-buildx-prepare docker-release
# Get version from pyproject.toml
VERSION := $(shell grep -m1 version pyproject.toml | cut -d'"' -f2)
IMAGE_NAME := lfnovo/open_notebook
PLATFORMS=linux/amd64,linux/arm64
#,linux/arm/v7,linux/386
database:
docker compose up -d
@ -15,13 +19,26 @@ lint:
ruff:
ruff check . --fix
docker-build:
docker build . -t $(IMAGE_NAME):$(VERSION)
docker tag $(IMAGE_NAME):$(VERSION) $(IMAGE_NAME):latest
# Configuração do buildx para multi-plataforma
docker-buildx-prepare:
docker buildx create --use --name multi-platform-builder || true
# Build multi-plataforma com buildx
docker-build: docker-buildx-prepare
docker buildx build \
--platform $(PLATFORMS) \
-t $(IMAGE_NAME):$(VERSION) \
-t $(IMAGE_NAME):latest \
--push \
.
# O push já é feito durante o build com buildx
docker-push:
docker push $(IMAGE_NAME):$(VERSION)
docker push $(IMAGE_NAME):latest
@echo "Push já foi realizado durante o build com buildx"
# Combined build and push
docker-release: docker-build docker-push
# Build e push combinados
docker-release: docker-build
# Comando útil para verificar as plataformas suportadas após o build
docker-check-platforms:
docker manifest inspect $(IMAGE_NAME):$(VERSION)

13
README.md
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@ -50,7 +50,7 @@ services:
Go to the [Usage](docs/USAGE.md) page to learn how to use all features.
## 🚀 Features
## Features
![New Notebook](docs/assets/asset_list.png)
@ -63,6 +63,17 @@ Go to the [Usage](docs/USAGE.md) page to learn how to use all features.
- **Fine-Grained Context Management**: Choose exactly what to share with the AI to maintain control.
- **Cost Estimation**: Estimate costs for large context processing to keep budget control in check.
## 🚀 New Features
### v0.0.2 - Several new providers are supported now:
- OpenAI
- Anthropic
- Open Router
- LiteLLM
- Vertex AI
- Ollama
### 📝 Notebook Page
Three intuitive columns to streamline your work:

80
docs/SETUP.md
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@ -1,7 +1,9 @@
# Installing Open Notebook
# Configuration and Setup
## Installing Open Notebook
## 📦 Installing from Source
### 📦 Installing from Source
Quickly get started by cloning and installing the dependencies.
@ -9,24 +11,11 @@ Quickly get started by cloning and installing the dependencies.
git clone https://github.com/lfnovo/open_notebook.git
cd open_notebook
poetry install
cp .env.example .env
poetry run streamlit run app_home.py
```
Make a copy of `example.env` and rename it to `.env`.
You need to enter at least your OPENAI_API_KEY and the Surreal DB connection details.
```
OPENAI_API_KEY=
# CONNECTION DETAILS FOR YOUR SURREAL DB
SURREAL_ADDRESS="ws://localhost:8000/rpc"
SURREAL_USER="root"
SURREAL_PASS="root"
SURREAL_NAMESPACE="open_notebook"
SURREAL_DATABASE="staging"
```
Then, run it by using:
Run the app with:
```sh
poetry run streamlit run app_home.py
@ -38,9 +27,13 @@ or the shourcut
make run
```
## 🐳 Docker Setup
> ⚠️ **Important:** Be sure to edit the `.env` file before running the app.
### 🐳 Docker Setup
Alternatively, you can use Docker for easy setup.
Copy the `.env.example` file and name it `docker.env`
```sh
@ -121,8 +114,55 @@ services:
pull_policy: always
```
## Setting up the providers
Several new providers are supported now:
- OpenAI
- Anthropic
- Open Router
- LiteLLM
- Vertex AI
- Ollama
All providers are installed out of the box. All you need to do is to setup the environment variable configurations (API Keys, etc) for your selected provider and decide which models to use.
Please refer to the `.env.example` file for instructions on which ENV variables are necessary for each.
### Use provider-modelname convention
You should prepend the provider name to the model_name when setting up your env variables, examples:
- openai/gpt-4o-mini
- anthropic/claude-3-5-sonnet-20240620
- ollama/gemma2
- openrouter/nvidia/llama-3.1-nemotron-70b-instruct
- vertexai/gemini-1.5-flash-001
__There will be a UI configuration for models in the coming days.__
## Setup 2 models for more flexibility
There are 2 configurations for models at this point:
```
DEFAULT_MODEL="openai/gpt-4o-mini"
SUMMARIZATION_MODEL="openrouter/nvidia/llama-3.1-nemotron-70b-instruct"
```
- **DEFAULT_MODEL** is used by the chat tool
- **SUMMARIZATION_MODEL (optional)** is used on the content summarization
The reason for opting for this route is because different LLMs, will behave better/worse depending on the type of request and type of tools offered. So it makes sense to build a more refined system to decide which model should process which task.
For instance, we can use an Ollama based model, like `gemma2` to do summarization and document query, and use openai/claude for the chat. The whole idea is to allow you to experiment on cost/performance.
## Running the app
After the app is running, you can access it at http://localhost:8080.
The first time you connect, it will check for the database and see if the schema is ready. If not, it will create the database for you.
The first time you connect, it will check for the database and see if the schema is ready. If not, it will create the database for you.
Go to the [Usage](USAGE.md) page to learn how to use all features.

4
open_notebook/domain.py
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@ -345,7 +345,7 @@ class Source(ObjectModel):
try:
config = RunnableConfig(configurable=dict(thread_id=self.id))
result = summarizer.invoke({"content": self.full_text}, config=config)[
"summary"
"output"
]
self._add_insight("summary", surreal_clean(result.summary))
self.title = surreal_clean(result.title)
@ -355,7 +355,7 @@ class Source(ObjectModel):
except Exception as e:
logger.error(f"Error summarizing source {self.id}: {str(e)}")
logger.exception(e)
raise DatabaseOperationError("Failed to summarize source")
raise DatabaseOperationError(e)
class Note(ObjectModel):

49
open_notebook/graphs/chat.py
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@ -1,39 +1,17 @@
import os
import sqlite3
from typing import Annotated, List, Optional
from typing import Annotated, Optional
from langchain_core.runnables import (
RunnableConfig,
)
from langchain_openai import ChatOpenAI
from langgraph.checkpoint.sqlite import SqliteSaver
from langgraph.graph import START, StateGraph
from langgraph.graph import END, START, StateGraph
from langgraph.graph.message import add_messages
from langgraph.prebuilt import ToolNode, tools_condition
from loguru import logger
from pydantic import BaseModel, Field
from typing_extensions import TypedDict
from open_notebook.domain import Notebook
from open_notebook.graphs.tools import ask_the_document, get_current_timestamp
from open_notebook.prompter import Prompter
tools = [get_current_timestamp, ask_the_document]
tool_node = ToolNode(tools)
class ChatResponse(BaseModel):
"""Respond to the user with this"""
title: Optional[str] = Field(
description="A title to be used if your question would become a new note on the project"
)
message: str = Field(
description="The actual message you'd like to reply to the user"
)
citations: Optional[List[str]] = Field(
description="The ids for the documents you used to formulate your answer"
)
from open_notebook.graphs.utils import run_pattern
class ThreadState(TypedDict):
@ -41,17 +19,16 @@ class ThreadState(TypedDict):
notebook: Optional[Notebook]
context: Optional[str]
context_config: Optional[dict]
response: Optional[ChatResponse]
def call_model_with_messages(state: ThreadState, config: RunnableConfig) -> dict:
model = ChatOpenAI(model=os.environ["DEFAULT_MODEL"], temperature=0).bind_tools(
tools
model_name = config.get("configurable", {}).get("model_name", None)
ai_message = run_pattern(
"chat",
model_name,
messages=state["messages"],
state=state,
)
messages = state["messages"]
system_prompt = Prompter(prompt_template="chat").render(data=state)
logger.warning(f"System prompt: {system_prompt}")
ai_message = model.invoke([system_prompt] + messages)
return {"messages": ai_message}
@ -63,12 +40,6 @@ memory = SqliteSaver(conn)
agent_state = StateGraph(ThreadState)
agent_state.add_node("agent", call_model_with_messages)
agent_state.add_node("tools", tool_node)
agent_state.add_edge(START, "agent")
agent_state.add_conditional_edges(
"agent",
tools_condition,
)
agent_state.add_edge("tools", "agent")
agent_state.add_edge("agent", END)
graph = agent_state.compile(checkpointer=memory)

24
open_notebook/graphs/ask_content.py → open_notebook/graphs/doc_query.py
View file

@ -3,16 +3,14 @@ import os
from langchain_core.runnables import (
RunnableConfig,
)
from langchain_openai import ChatOpenAI
from langgraph.graph import END, START, StateGraph
from loguru import logger
from typing_extensions import TypedDict
from open_notebook.domain import Note, Notebook, Source
from open_notebook.prompter import Prompter
from open_notebook.graphs.utils import run_pattern
class AskState(TypedDict):
class DocQueryState(TypedDict):
doc_id: str
doc_content: str
question: str
@ -20,19 +18,15 @@ class AskState(TypedDict):
notebook: Notebook
def call_model_with_messages(state: AskState, config: RunnableConfig) -> dict:
model = ChatOpenAI(
model=os.environ.get("RETRIEVAL_MODEL", os.environ["DEFAULT_MODEL"]),
temperature=0,
def call_model(state: dict, config: RunnableConfig) -> dict:
model_name = config.get("configurable", {}).get(
"model_name", os.environ.get("RETRIEVAL_MODEL")
)
system_prompt = Prompter(prompt_template="ask_content").render(data=state)
logger.debug(f"System prompt: {system_prompt}")
ai_message = model.invoke(system_prompt)
return {"answer": ai_message}
return {"answer": run_pattern("doc_query", model_name, state)}
# todo: there is probably a better way to do this and avoid repetition
def get_content(state: AskState) -> dict:
def get_content(state: DocQueryState) -> dict:
doc_id = state["doc_id"]
if "note:" in doc_id:
doc: Note = Note.get(id=doc_id)
@ -42,9 +36,9 @@ def get_content(state: AskState) -> dict:
return {"doc_content": doc_content}
agent_state = StateGraph(AskState)
agent_state = StateGraph(DocQueryState)
agent_state.add_node("get_content", get_content)
agent_state.add_node("agent", call_model_with_messages)
agent_state.add_node("agent", call_model)
agent_state.add_edge(START, "get_content")
agent_state.add_edge("get_content", "agent")
agent_state.add_edge("agent", END)

47
open_notebook/graphs/summary.py
View file

@ -1,35 +1,30 @@
import os
from typing import List, Literal
from langchain_core.output_parsers import PydanticOutputParser
from langchain_core.runnables import (
RunnableConfig,
)
from langchain_openai import ChatOpenAI
from langgraph.graph import END, START, StateGraph
from langgraph.prebuilt import ToolNode
from pydantic import BaseModel, Field
from pydantic import BaseModel
from typing_extensions import TypedDict
from open_notebook.graphs.tools import get_current_timestamp
from open_notebook.prompter import Prompter
from open_notebook.graphs.utils import run_pattern
from open_notebook.utils import split_text
tools = [get_current_timestamp]
tool_node = ToolNode(tools)
class SummaryResponse(BaseModel):
"""Respond to the user with this"""
"""This is schema of your response. Please provide a JSON object with the enclosed keys"""
summary: str = Field(description="The summary of the content")
topics: List[str] = Field(description="List of 4-7 topics related to this content")
title: str = Field(description="The title of the content")
summary: str
topics: List[str]
title: str
class SummaryState(TypedDict):
chunks: List[str]
content: str
summary: SummaryResponse
output: SummaryResponse
def build_chunks(state: SummaryState) -> dict:
@ -63,19 +58,19 @@ def chunk_condition(state: SummaryState) -> Literal["get_chunk", END]: # type:
# todo: build a helper method for LLM communication on all graphs
def call_model_with_messages(state: SummaryState, config: RunnableConfig) -> dict:
model = (
ChatOpenAI(
model=os.environ.get("SUMMARIZATION_MODEL", os.environ["DEFAULT_MODEL"]),
temperature=0,
)
.bind_tools(tools)
.with_structured_output(SummaryResponse)
def call_model(state: SummaryState, config: RunnableConfig) -> dict:
model_name = config.get("configurable", {}).get(
"model_name", os.environ.get("SUMMARIZATION_MODEL")
)
system_prompt = Prompter(prompt_template="summarize").render(data=state)
ai_message = model.invoke(system_prompt)
return {"summary": ai_message}
parser = PydanticOutputParser(pydantic_object=SummaryResponse)
return {
"output": run_pattern(
pattern_name="summarize",
model_name=model_name,
state=state,
parser=parser,
)
}
agent_state = StateGraph(SummaryState)
@ -86,7 +81,7 @@ agent_state.add_conditional_edges(
chunk_condition,
)
agent_state.add_node("get_chunk", setup_next_chunk)
agent_state.add_node("agent", call_model_with_messages)
agent_state.add_node("agent", call_model)
agent_state.add_edge("get_chunk", "agent")
agent_state.add_conditional_edges(
"agent",

12
open_notebook/graphs/tools.py
View file

@ -6,19 +6,21 @@ from langchain.tools import tool
@tool
def get_current_timestamp() -> str:
"""
name: get_current_timestamp
Returns the current timestamp in the format YYYYMMDDHHmmss.
"""
return datetime.now().strftime("%Y%m%d%H%M%S")
@tool
def ask_the_document(doc_id: str, question: str):
def doc_query(doc_id: str, question: str):
"""
Use this tool to ask a question to the document.
Another LLM will ready the document and answer the question.
Be specific and complete in your query given the LLM that will process it is very capable.
name: doc_query
Use this tool if you need to investigate into a particular document.
Another LLM will read the document and answer the question that you might have.
Use this when the user question cannot be answered with the content you have in context.
"""
from open_notebook.graphs.ask_content import graph
from open_notebook.graphs.doc_query import graph
result = graph.invoke({"doc_id": doc_id, "question": question})
return result["answer"]

43
open_notebook/graphs/utils.py Normal file
View file

@ -0,0 +1,43 @@
import os
from langchain.output_parsers import OutputFixingParser
from loguru import logger
from open_notebook.llm_router import get_langchain_model
from open_notebook.prompter import Prompter
def run_pattern(
pattern_name: str,
model_name=None,
messages=[],
state: dict = {},
parser=None,
output_fixing_model_name=None,
) -> dict:
if not model_name:
model_name = os.environ["DEFAULT_MODEL"]
chain = get_langchain_model(model_name)
if parser:
chain = chain | parser
if output_fixing_model_name and parser:
output_fix_model = get_langchain_model(output_fixing_model_name)
chain = chain | OutputFixingParser.from_llm(
parser=parser,
llm=output_fix_model,
)
system_prompt = Prompter(prompt_template=pattern_name, parser=parser).render(
data=state
)
# logger.debug(f"System prompt: {system_prompt}")
if len(messages) > 0:
logger.warning(messages)
response = chain.invoke([system_prompt] + messages)
else:
response = chain.invoke(system_prompt)
return response

33
open_notebook/llm_router.py Normal file
View file

@ -0,0 +1,33 @@
from open_notebook.llms import (
AnthropicLanguageModel,
LiteLLMLanguageModel,
OllamaLanguageModel,
OpenAILanguageModel,
OpenRouterLanguageModel,
VertexAILanguageModel,
VertexAnthropicLanguageModel,
)
# Map provider names to classes
PROVIDER_CLASS_MAP = {
"ollama": OllamaLanguageModel,
"openrouter": OpenRouterLanguageModel,
"vertexai-anthropic": VertexAnthropicLanguageModel,
"litellm": LiteLLMLanguageModel,
"vertexai": VertexAILanguageModel,
"anthropic": AnthropicLanguageModel,
"openai": OpenAILanguageModel,
}
def get_langchain_model(model_name, json=False):
parts = model_name.split("/")
provider = parts[0]
model_name_wihout_provider = "/".join(parts[1:])
if provider not in PROVIDER_CLASS_MAP.keys():
raise ValueError(
f"Provider {provider} not found in config. Make sure you use the correct format for model names, example: openai/gpt-4o-mini"
)
return PROVIDER_CLASS_MAP[provider](
model_name=model_name_wihout_provider, json=json
).to_langchain()

243
open_notebook/llms.py Normal file
View file

@ -0,0 +1,243 @@
"""
Classes for supporting different language and vector models
"""
import os
from abc import ABC, abstractmethod
from dataclasses import dataclass, field
from typing import Any, Dict, Optional
from langchain_anthropic import ChatAnthropic
from langchain_community.chat_models import ChatLiteLLM
from langchain_core.language_models.chat_models import BaseChatModel
from langchain_google_vertexai import ChatVertexAI
from langchain_google_vertexai.model_garden import ChatAnthropicVertex
from langchain_ollama.chat_models import ChatOllama
from langchain_openai.chat_models import ChatOpenAI
# from redisvl.utils.vectorize import BaseVectorizer
# from redisvl.utils.vectorize.text.openai import OpenAITextVectorizer
@dataclass
class LanguageModel(ABC):
"""
Abstract base class for language models.
"""
model_name: Optional[str] = None
max_tokens: Optional[int] = 850
temperature: Optional[float] = 1.0
streaming: bool = True
top_p: Optional[float] = 0.9
kwargs: Dict[str, Any] = field(default_factory=dict)
json: bool = False
@abstractmethod
def to_langchain(self) -> BaseChatModel:
"""
Convert the language model to a LangChain chat model.
"""
raise NotImplementedError
@dataclass
class OllamaLanguageModel(LanguageModel):
"""
Language model that uses the Ollama chat model.
"""
model_name: str
base_url: str = os.environ.get("OLLAMA_API_BASE", "http://localhost:11434")
max_tokens: Optional[int] = 650
json: bool = False
def to_langchain(self) -> ChatOllama:
"""
Convert the language model to a LangChain chat model.
"""
return ChatOllama(
# api_key="ollama",
model=self.model_name,
base_url=self.base_url,
# keep_alive="10m",
num_predict=self.max_tokens,
temperature=self.temperature,
verbose=True,
top_p=self.top_p,
)
@dataclass
class VertexAnthropicLanguageModel(LanguageModel):
"""
Language model that uses the Vertex Anthropic chat model.
"""
model_name: str
project: Optional[str] = os.environ.get("VERTEX_PROJECT", "no-project")
location: Optional[str] = os.environ.get("VERTEX_LOCATION", "us-central1")
def to_langchain(self) -> ChatAnthropicVertex:
"""
Convert the language model to a LangChain chat model.
"""
return ChatAnthropicVertex(
model=self.model_name,
project=self.project,
location=self.location,
max_tokens=self.max_tokens,
streaming=False,
kwargs=self.kwargs,
top_p=self.top_p,
)
@dataclass
class LiteLLMLanguageModel(LanguageModel):
"""
Language model that uses the LiteLLM chat model.
"""
model_name: str
def to_langchain(self) -> ChatLiteLLM:
"""
Convert the language model to a LangChain chat model.
"""
return ChatLiteLLM(
model=self.model_name,
temperature=self.temperature or 0.5,
max_tokens=self.max_tokens,
streaming=self.streaming,
top_p=self.top_p,
)
@dataclass
class VertexAILanguageModel(LanguageModel):
"""
Language model that uses the Vertex AI chat model.
"""
model_name: str
project: Optional[str] = os.environ.get("VERTEX_PROJECT", "no-project")
location: Optional[str] = os.environ.get("VERTEX_LOCATION", "us-central1")
def to_langchain(self) -> ChatVertexAI:
"""
Convert the language model to a LangChain chat model.
"""
return ChatVertexAI(
model=self.model_name,
streaming=self.streaming,
max_tokens=self.max_tokens,
top_p=self.top_p,
location=self.location,
project=self.project,
safety_settings=None,
api_key="AIzaSyCt4zB5eZVZPh7WRxIh9oY_rwblP6BOyWE",
)
@dataclass
class OpenRouterLanguageModel(LanguageModel):
"""
Language model that uses the OpenAI chat model.
"""
model_name: str
def to_langchain(self) -> ChatOpenAI:
"""
Convert the language model to a LangChain chat model.
"""
kwargs = self.kwargs
if self.json:
kwargs["response_format"] = {"type": "json_object"}
return ChatOpenAI(
model=self.model_name,
temperature=self.temperature or 0.5,
base_url=os.environ.get(
"OPENROUTER_BASE_URL", "https://openrouter.ai/api/v1"
),
max_tokens=self.max_tokens,
model_kwargs=kwargs,
streaming=self.streaming,
api_key=os.environ.get("OPENROUTER_API_KEY", "openrouter"),
top_p=self.top_p,
)
@dataclass
class AnthropicLanguageModel(LanguageModel):
"""
Language model that uses the Anthropic chat model.
"""
model_name: str
def to_langchain(self) -> ChatAnthropic:
"""
Convert the language model to a LangChain chat model.
"""
return ChatAnthropic( # type: ignore[call-arg]
model_name=self.model_name,
max_tokens_to_sample=self.max_tokens or 850,
model_kwargs=self.kwargs,
streaming=False,
timeout=30,
top_p=self.top_p,
)
@dataclass
class OpenAILanguageModel(LanguageModel):
"""
Language model that uses the OpenAI chat model.
"""
model_name: str
def to_langchain(self) -> ChatOpenAI:
"""
Convert the language model to a LangChain chat model.
"""
kwargs = self.kwargs
if self.json:
kwargs["response_format"] = {"type": "json_object"}
return ChatOpenAI(
model=self.model_name,
temperature=self.temperature or 0.5,
max_tokens=self.max_tokens,
model_kwargs=kwargs,
streaming=self.streaming,
top_p=self.top_p,
)
# @dataclass
# class EmbeddingModel(ABC):
# model_name: str
# dimensions: int
# def to_redis_vectorizer(self) -> BaseVectorizer:
# raise NotImplementedError
# @dataclass
# class OpenAIEmbeddingModel(EmbeddingModel):
# """
# Embedding model that uses the OpenAI text embedding model.
# """
# model_name: str
# dimensions: int
# def to_redis_vectorizer(self) -> OpenAITextVectorizer:
# """
# Convert the embedding model to a Redis vectorizer.
# """
# return OpenAITextVectorizer(model=self.model_name)

3
open_notebook/prompter.py
View file

@ -30,7 +30,7 @@ class Prompter:
template: Optional[Union[str, Template]] = None
parser: Optional[Any] = None
def __init__(self, prompt_template=None, prompt_text=None):
def __init__(self, prompt_template=None, prompt_text=None, parser=None):
"""
Initialize the Prompter with either a template file or raw text.
@ -40,6 +40,7 @@ class Prompter:
"""
self.prompt_template = prompt_template
self.prompt_text = prompt_text
self.parser = parser
self.setup()
def setup(self):

852
poetry.lock generated
View file

@ -188,6 +188,31 @@ files = [
{file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"},
]
[[package]]
name = "anthropic"
version = "0.37.0"
description = "The official Python library for the anthropic API"
optional = false
python-versions = ">=3.7"
files = [
{file = "anthropic-0.37.0-py3-none-any.whl", hash = "sha256:3f57b9fdbc1aa43e00468d0690d71e5fde668dc489e5a48d1d20c955e8ce18f3"},
{file = "anthropic-0.37.0.tar.gz", hash = "sha256:fa01f2cc947cfe05c7f3fbbc939efb37add77e598ce91818e46aa1a240cb7ada"},
]
[package.dependencies]
anyio = ">=3.5.0,<5"
distro = ">=1.7.0,<2"
httpx = ">=0.23.0,<1"
jiter = ">=0.4.0,<1"
pydantic = ">=1.9.0,<3"
sniffio = "*"
tokenizers = ">=0.13.0"
typing-extensions = ">=4.7,<5"
[package.extras]
bedrock = ["boto3 (>=1.28.57)", "botocore (>=1.31.57)"]
vertex = ["google-auth (>=2,<3)"]
[[package]]
name = "anyio"
version = "4.6.2.post1"
@ -596,6 +621,17 @@ files = [
{file = "decorator-5.1.1.tar.gz", hash = "sha256:637996211036b6385ef91435e4fae22989472f9d571faba8927ba8253acbc330"},
]
[[package]]
name = "defusedxml"
version = "0.7.1"
description = "XML bomb protection for Python stdlib modules"
optional = false
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
files = [
{file = "defusedxml-0.7.1-py2.py3-none-any.whl", hash = "sha256:a352e7e428770286cc899e2542b6cdaedb2b4953ff269a210103ec58f6198a61"},
{file = "defusedxml-0.7.1.tar.gz", hash = "sha256:1bb3032db185915b62d7c6209c5a8792be6a32ab2fedacc84e01b52c51aa3e69"},
]
[[package]]
name = "distlib"
version = "0.3.9"
@ -618,6 +654,17 @@ files = [
{file = "distro-1.9.0.tar.gz", hash = "sha256:2fa77c6fd8940f116ee1d6b94a2f90b13b5ea8d019b98bc8bafdcabcdd9bdbed"},
]
[[package]]
name = "docstring-parser"
version = "0.16"
description = "Parse Python docstrings in reST, Google and Numpydoc format"
optional = false
python-versions = ">=3.6,<4.0"
files = [
{file = "docstring_parser-0.16-py3-none-any.whl", hash = "sha256:bf0a1387354d3691d102edef7ec124f219ef639982d096e26e3b60aeffa90637"},
{file = "docstring_parser-0.16.tar.gz", hash = "sha256:538beabd0af1e2db0146b6bd3caa526c35a34d61af9fd2887f3a8a27a739aa6e"},
]
[[package]]
name = "executing"
version = "2.1.0"
@ -734,6 +781,45 @@ files = [
{file = "frozenlist-1.4.1.tar.gz", hash = "sha256:c037a86e8513059a2613aaba4d817bb90b9d9b6b69aace3ce9c877e8c8ed402b"},
]
[[package]]
name = "fsspec"
version = "2024.10.0"
description = "File-system specification"
optional = false
python-versions = ">=3.8"
files = [
{file = "fsspec-2024.10.0-py3-none-any.whl", hash = "sha256:03b9a6785766a4de40368b88906366755e2819e758b83705c88cd7cb5fe81871"},
{file = "fsspec-2024.10.0.tar.gz", hash = "sha256:eda2d8a4116d4f2429db8550f2457da57279247dd930bb12f821b58391359493"},
]
[package.extras]
abfs = ["adlfs"]
adl = ["adlfs"]
arrow = ["pyarrow (>=1)"]
dask = ["dask", "distributed"]
dev = ["pre-commit", "ruff"]
doc = ["numpydoc", "sphinx", "sphinx-design", "sphinx-rtd-theme", "yarl"]
dropbox = ["dropbox", "dropboxdrivefs", "requests"]
full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "dask", "distributed", "dropbox", "dropboxdrivefs", "fusepy", "gcsfs", "libarchive-c", "ocifs", "panel", "paramiko", "pyarrow (>=1)", "pygit2", "requests", "s3fs", "smbprotocol", "tqdm"]
fuse = ["fusepy"]
gcs = ["gcsfs"]
git = ["pygit2"]
github = ["requests"]
gs = ["gcsfs"]
gui = ["panel"]
hdfs = ["pyarrow (>=1)"]
http = ["aiohttp (!=4.0.0a0,!=4.0.0a1)"]
libarchive = ["libarchive-c"]
oci = ["ocifs"]
s3 = ["s3fs"]
sftp = ["paramiko"]
smb = ["smbprotocol"]
ssh = ["paramiko"]
test = ["aiohttp (!=4.0.0a0,!=4.0.0a1)", "numpy", "pytest", "pytest-asyncio (!=0.22.0)", "pytest-benchmark", "pytest-cov", "pytest-mock", "pytest-recording", "pytest-rerunfailures", "requests"]
test-downstream = ["aiobotocore (>=2.5.4,<3.0.0)", "dask-expr", "dask[dataframe,test]", "moto[server] (>4,<5)", "pytest-timeout", "xarray"]
test-full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "cloudpickle", "dask", "distributed", "dropbox", "dropboxdrivefs", "fastparquet", "fusepy", "gcsfs", "jinja2", "kerchunk", "libarchive-c", "lz4", "notebook", "numpy", "ocifs", "pandas", "panel", "paramiko", "pyarrow", "pyarrow (>=1)", "pyftpdlib", "pygit2", "pytest", "pytest-asyncio (!=0.22.0)", "pytest-benchmark", "pytest-cov", "pytest-mock", "pytest-recording", "pytest-rerunfailures", "python-snappy", "requests", "smbprotocol", "tqdm", "urllib3", "zarr", "zstandard"]
tqdm = ["tqdm"]
[[package]]
name = "gitdb"
version = "4.0.11"
@ -766,6 +852,267 @@ gitdb = ">=4.0.1,<5"
doc = ["sphinx (==4.3.2)", "sphinx-autodoc-typehints", "sphinx-rtd-theme", "sphinxcontrib-applehelp (>=1.0.2,<=1.0.4)", "sphinxcontrib-devhelp (==1.0.2)", "sphinxcontrib-htmlhelp (>=2.0.0,<=2.0.1)", "sphinxcontrib-qthelp (==1.0.3)", "sphinxcontrib-serializinghtml (==1.1.5)"]
test = ["coverage[toml]", "ddt (>=1.1.1,!=1.4.3)", "mock", "mypy", "pre-commit", "pytest (>=7.3.1)", "pytest-cov", "pytest-instafail", "pytest-mock", "pytest-sugar", "typing-extensions"]
[[package]]
name = "google-api-core"
version = "2.21.0"
description = "Google API client core library"
optional = false
python-versions = ">=3.7"
files = [
{file = "google_api_core-2.21.0-py3-none-any.whl", hash = "sha256:6869eacb2a37720380ba5898312af79a4d30b8bca1548fb4093e0697dc4bdf5d"},
{file = "google_api_core-2.21.0.tar.gz", hash = "sha256:4a152fd11a9f774ea606388d423b68aa7e6d6a0ffe4c8266f74979613ec09f81"},
]
[package.dependencies]
google-auth = ">=2.14.1,<3.0.dev0"
googleapis-common-protos = ">=1.56.2,<2.0.dev0"
grpcio = {version = ">=1.49.1,<2.0dev", optional = true, markers = "python_version >= \"3.11\" and extra == \"grpc\""}
grpcio-status = {version = ">=1.49.1,<2.0.dev0", optional = true, markers = "python_version >= \"3.11\" and extra == \"grpc\""}
proto-plus = ">=1.22.3,<2.0.0dev"
protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<6.0.0.dev0"
requests = ">=2.18.0,<3.0.0.dev0"
[package.extras]
async-rest = ["google-auth[aiohttp] (>=2.35.0,<3.0.dev0)"]
grpc = ["grpcio (>=1.33.2,<2.0dev)", "grpcio (>=1.49.1,<2.0dev)", "grpcio-status (>=1.33.2,<2.0.dev0)", "grpcio-status (>=1.49.1,<2.0.dev0)"]
grpcgcp = ["grpcio-gcp (>=0.2.2,<1.0.dev0)"]
grpcio-gcp = ["grpcio-gcp (>=0.2.2,<1.0.dev0)"]
[[package]]
name = "google-auth"
version = "2.35.0"
description = "Google Authentication Library"
optional = false
python-versions = ">=3.7"
files = [
{file = "google_auth-2.35.0-py2.py3-none-any.whl", hash = "sha256:25df55f327ef021de8be50bad0dfd4a916ad0de96da86cd05661c9297723ad3f"},
{file = "google_auth-2.35.0.tar.gz", hash = "sha256:f4c64ed4e01e8e8b646ef34c018f8bf3338df0c8e37d8b3bba40e7f574a3278a"},
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cachetools = ">=2.0.0,<6.0"
pyasn1-modules = ">=0.2.1"
rsa = ">=3.1.4,<5"
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aiohttp = ["aiohttp (>=3.6.2,<4.0.0.dev0)", "requests (>=2.20.0,<3.0.0.dev0)"]
enterprise-cert = ["cryptography", "pyopenssl"]
pyopenssl = ["cryptography (>=38.0.3)", "pyopenssl (>=20.0.0)"]
reauth = ["pyu2f (>=0.1.5)"]
requests = ["requests (>=2.20.0,<3.0.0.dev0)"]
[[package]]
name = "google-cloud-aiplatform"
version = "1.70.0"
description = "Vertex AI API client library"
optional = false
python-versions = ">=3.8"
files = [
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autologging = ["mlflow (>=1.27.0,<=2.16.0)"]
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ipywidgets = ["ipykernel (>=6.0.0)", "ipywidgets (>=7.7.0)"]
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tqdm = ["tqdm (>=4.7.4,<5.0.0dev)"]
[[package]]
name = "google-cloud-core"
version = "2.4.1"
description = "Google Cloud API client core library"
optional = false
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{file = "tokenizers-0.20.1.tar.gz", hash = "sha256:84edcc7cdeeee45ceedb65d518fffb77aec69311c9c8e30f77ad84da3025f002"},
]
[package.dependencies]
huggingface-hub = ">=0.16.4,<1.0"
[package.extras]
dev = ["tokenizers[testing]"]
docs = ["setuptools-rust", "sphinx", "sphinx-rtd-theme"]
testing = ["black (==22.3)", "datasets", "numpy", "pytest", "requests", "ruff"]
[[package]]
name = "toml"
version = "0.10.2"
@ -4015,7 +4846,26 @@ files = [
[package.dependencies]
requests = "*"
[[package]]
name = "zipp"
version = "3.20.2"
description = "Backport of pathlib-compatible object wrapper for zip files"
optional = false
python-versions = ">=3.8"
files = [
{file = "zipp-3.20.2-py3-none-any.whl", hash = "sha256:a817ac80d6cf4b23bf7f2828b7cabf326f15a001bea8b1f9b49631780ba28350"},
{file = "zipp-3.20.2.tar.gz", hash = "sha256:bc9eb26f4506fda01b81bcde0ca78103b6e62f991b381fec825435c836edbc29"},
]
[package.extras]
check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)"]
cover = ["pytest-cov"]
doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
enabler = ["pytest-enabler (>=2.2)"]
test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"]
type = ["pytest-mypy"]
[metadata]
lock-version = "2.0"
python-versions = "^3.11"
content-hash = "40278dff3b2c308d27ecc2f54e1e4999efe6bb74b82ee5c0e37d2021cb877fd0"
content-hash = "f40348a4e1846cdbdd353c3c9de37e7b652a561c2374dec9899cf77778686ded"

53
prompts/chat.jinja
View file

@ -1,45 +1,22 @@
# SYSTEM ROLE
You are a cognitive study assistant that helps users research and learn by engaging in focused discussions about documents in their workspace. You have access to project context and can analyze documents in detail using specialized tools.
# BACKGROUND
# CAPABILITIES
- Access to project information and selected documents (CONTEXT)
- Can engage in natural dialogue while maintaining academic rigor
Your are a cognitive assistant that helps me study and research.
# FORMULATE YOUR DATA
- Generate your answer based on the CONTEXT information
- Ensure that your response is accurate and relevant to the user's query
# OUR WORKING FRAMEWORK
{% if notebook %}
# PROJECT INFORMATION
We are working within a virtual Notebook,
which is a learning workspace for a specific project.
You have access to some information about the project,
the contents that are selected for discussion, and relevant contexts.
Your goal is to respond to the user's commands and questions,
using purely the content in your context.
# YOUR TOOLS
You might find that some of the documents in the CONTEXT are worth an extra look. For that, you can use the `ask_the_document` tool.
Just ask the question as if you were talking to someone that knows the document deeply and the tool will provide you with the answer.
Use the document id to specify which source or note you'd like to ask about.
# INSTRUCTIONS
- You can ask tools until you are satisfied with the information
- You have a optional field in your answer called title. Only use this field if you believe your answer is important to be saved as a note. If it's just a quick chat, send an empty string to it.
- Please add to the citations list all the ids for sources and notes that you used for your anwer.
# RESPONSE FORMAT
```
[ANSWER]
### CITATIONS
- id1
- id2
- etc
```
# PROJECT INFO
{{ notebook }}
{{notebook}}
{% endif %}
{% if context %}
# CONTEXT
{{ context }}
{{context}}
{% endif %}

0
prompts/ask_content.jinja → prompts/doc_query.jinja
View file

11
prompts/spr.jinja
View file

@ -1,11 +0,0 @@
# MISSION
You are a Sparse Priming Representation (SPR) writer. An SPR is a particular kind of use of language for advanced NLP, NLU, and NLG tasks, particularly useful for the latest generation of Large Language Models (LLMs). You will be given information by the USER which you are to render as an SPR.
# THEORY
LLMs are a kind of deep neural network. They have been demonstrated to embed knowledge, abilities, and concepts, ranging from reasoning to planning, and even to theory of mind. These are called latent abilities and latent content, collectively referred to as latent space. The latent space of an LLM can be activated with the correct series of words as inputs, which will create a useful internal state of the neural network. This is not unlike how the right shorthand cues can prime a human mind to think in a certain way. Like human minds, LLMs are associative, meaning you only need to use the correct associations to "prime" another model to think in the same way.
# METHODOLOGY
Render the input as a distilled list of succinct statements, assertions, associations, concepts, analogies, and metaphors. The idea is to capture as much, conceptually, as possible but with as few words as possible. Write it in a way that makes sense to you, as the future audience will be another language model, not a human. Use complete sentences.
{# thanks to https://github.com/daveshap/SparsePrimingRepresentations #}

41
prompts/summarize.jinja
View file

@ -1,28 +1,33 @@
{% include "spr.jinja" %}
# SYSTEM ROLE
You are a content summarization assistant that creates dense, information-rich summaries optimized for machine understanding. Your summaries should capture key concepts with minimal words while maintaining complete, clear sentences.
# YOUR TASK
# TASK
Analyze the provided content and create a summary that:
- Captures the core concepts and key information
- Uses clear, direct language
- Maintains context from any previous summaries
- Includes relevant topics/tags
- Creates an appropriate title
You are part of a content summarization platform.
Sometimes, you need to summarize the content gradually since it might be very big.
Please summarize the content below in a few sentences, making it the most complete, dense and SPR compatible as you can.
# OUTPUT SCHEMA
{'summary': {'type': 'string'},
'topics': {'items': {'type': 'string'}, 'type': 'array'},
'title': {'type': 'string'}}
## INSTRUCTIONS
# OUTPUT EXAMPLE
{
"title": "The title of the content",
"topics": ["topic1", "topic2"],
"summary": "The summary of the content"
}
- If the content already has a current summary, rewrite the summary to add the new information without losing the previous context
- Always make it dense and SPR compatible
- Do not reply with anything feedback or message other than the summary itself
## FORMATTING INSTRUCTIONS
{{ format_instructions }}
## CONTENT
# CONTENT
{{content}}
## PREVIOUS SUMMARY
{% if summary %}
# PREVIOUS SUMMARY
{{summary}}
## SUMMARY
{% endif %}

6
pyproject.toml
View file

@ -1,6 +1,6 @@
[tool.poetry]
name = "open-notebook"
version = "0.0.1"
version = "0.0.2"
description = "An open source implementation of a research assistant, inspired by Google Notebook LM"
authors = ["Luis Novo <lfnovo@gmail.com>"]
license = "MIT"
@ -34,7 +34,11 @@ surrealdb = "^0.3.2"
openai = "^1.52.0"
pre-commit = "^4.0.1"
langchain-community = "^0.3.3"
litellm = "^1.50.1"
langchain-openai = "^0.2.3"
langchain-anthropic = "^0.2.3"
langchain-ollama = "^0.2.0"
langchain-google-vertexai = "^2.0.5"
[tool.poetry.group.dev.dependencies]
ipykernel = "^6.29.5"

17
stream_app/source.py
View file

@ -133,13 +133,16 @@ def source_card(session_id, source):
st.write(insight.insight_type)
st.write(insight.content)
with st.popover("Actions"):
if st.button("Edit Source", icon="📝", key=source.id):
result = source_panel(source.id)
st.write(result)
if st.button("Delete", icon="🗑️", key=f"delete_options_{source.id}"):
source.delete()
st.rerun()
if st.button("Edit Source", icon="📝", key=source.id):
source_panel(source.id)
# with st.popover("Actions"):
# if st.button("Edit Source", icon="📝", key=source.id):
# result = source_panel(source.id)
# st.write(result)
# if st.button("Delete", icon="🗑️", key=f"delete_options_{source.id}"):
# source.delete()
# st.rerun()
st.session_state[session_id]["context_config"][source.id] = context_state