diff --git a/.dockerignore b/.dockerignore
index a975cf5..294de3c 100644
--- a/.dockerignore
+++ b/.dockerignore
@@ -5,4 +5,5 @@ data/
 .mypy_cache/
 .ruff_cache/
 .env
-sqlite-db/
\ No newline at end of file
+sqlite-db/
+temp/
\ No newline at end of file
diff --git a/.env.example b/.env.example
index 8557f70..3363878 100644
--- a/.env.example
+++ b/.env.example
@@ -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
-
diff --git a/.gitignore b/.gitignore
index d500152..b821613 100644
--- a/.gitignore
+++ b/.gitignore
@@ -9,6 +9,7 @@ docker.env
 __pycache__/
 *.so
 todo.md
+temp/
 
 # Distribution / packaging
 .Python
diff --git a/Makefile b/Makefile
index da0320b..8650bb2 100644
--- a/Makefile
+++ b/Makefile
@@ -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
\ No newline at end of file
+# 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)
\ No newline at end of file
diff --git a/README.md b/README.md
index 7767678..3fbce0f 100644
--- a/README.md
+++ b/README.md
@@ -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:
diff --git a/docs/SETUP.md b/docs/SETUP.md
index 3150c06..a179385 100644
--- a/docs/SETUP.md
+++ b/docs/SETUP.md
@@ -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. 
\ No newline at end of file
+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.
diff --git a/open_notebook/domain.py b/open_notebook/domain.py
index d94c620..5141a65 100644
--- a/open_notebook/domain.py
+++ b/open_notebook/domain.py
@@ -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):
diff --git a/open_notebook/graphs/chat.py b/open_notebook/graphs/chat.py
index 8852c0d..3be8f31 100644
--- a/open_notebook/graphs/chat.py
+++ b/open_notebook/graphs/chat.py
@@ -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)
diff --git a/open_notebook/graphs/ask_content.py b/open_notebook/graphs/doc_query.py
similarity index 55%
rename from open_notebook/graphs/ask_content.py
rename to open_notebook/graphs/doc_query.py
index 41d4a40..2c673db 100644
--- a/open_notebook/graphs/ask_content.py
+++ b/open_notebook/graphs/doc_query.py
@@ -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)
diff --git a/open_notebook/graphs/summary.py b/open_notebook/graphs/summary.py
index 1c771b7..27d6396 100644
--- a/open_notebook/graphs/summary.py
+++ b/open_notebook/graphs/summary.py
@@ -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",
diff --git a/open_notebook/graphs/tools.py b/open_notebook/graphs/tools.py
index 2c35c6f..636e25b 100644
--- a/open_notebook/graphs/tools.py
+++ b/open_notebook/graphs/tools.py
@@ -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"]
diff --git a/open_notebook/graphs/utils.py b/open_notebook/graphs/utils.py
new file mode 100644
index 0000000..8c51458
--- /dev/null
+++ b/open_notebook/graphs/utils.py
@@ -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
diff --git a/open_notebook/llm_router.py b/open_notebook/llm_router.py
new file mode 100644
index 0000000..9fdb85d
--- /dev/null
+++ b/open_notebook/llm_router.py
@@ -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()
diff --git a/open_notebook/llms.py b/open_notebook/llms.py
new file mode 100644
index 0000000..1f82ac7
--- /dev/null
+++ b/open_notebook/llms.py
@@ -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)
diff --git a/open_notebook/prompter.py b/open_notebook/prompter.py
index 6782ec3..fc10679 100644
--- a/open_notebook/prompter.py
+++ b/open_notebook/prompter.py
@@ -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):
diff --git a/poetry.lock b/poetry.lock
index ea38ca9..28940ac 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -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"},
+]
+
+[package.dependencies]
+cachetools = ">=2.0.0,<6.0"
+pyasn1-modules = ">=0.2.1"
+rsa = ">=3.1.4,<5"
+
+[package.extras]
+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 = [
+    {file = "google-cloud-aiplatform-1.70.0.tar.gz", hash = "sha256:e8edef6dbc7911380d0ea55c47544e799f62b891cb1a83b504ca1c09fff9884b"},
+    {file = "google_cloud_aiplatform-1.70.0-py2.py3-none-any.whl", hash = "sha256:690e6041f03d3aa85102ac3f316c958d6f43a99aefb7fb3f8938dee56d08abd9"},
+]
+
+[package.dependencies]
+docstring-parser = "<1"
+google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.8.dev0,<3.0.0dev", extras = ["grpc"]}
+google-auth = ">=2.14.1,<3.0.0dev"
+google-cloud-bigquery = ">=1.15.0,<3.20.0 || >3.20.0,<4.0.0dev"
+google-cloud-resource-manager = ">=1.3.3,<3.0.0dev"
+google-cloud-storage = ">=1.32.0,<3.0.0dev"
+packaging = ">=14.3"
+proto-plus = ">=1.22.3,<2.0.0dev"
+protobuf = ">=3.20.2,<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,<6.0.0dev"
+pydantic = "<3"
+shapely = "<3.0.0dev"
+
+[package.extras]
+autologging = ["mlflow (>=1.27.0,<=2.16.0)"]
+cloud-profiler = ["tensorboard-plugin-profile (>=2.4.0,<3.0.0dev)", "tensorflow (>=2.4.0,<3.0.0dev)", "werkzeug (>=2.0.0,<2.1.0dev)"]
+datasets = ["pyarrow (>=10.0.1)", "pyarrow (>=14.0.0)", "pyarrow (>=3.0.0,<8.0dev)"]
+endpoint = ["requests (>=2.28.1)"]
+evaluation = ["pandas (>=1.0.0,<2.2.0)", "tqdm (>=4.23.0)"]
+full = ["docker (>=5.0.3)", "explainable-ai-sdk (>=1.0.0)", "fastapi (>=0.71.0,<=0.114.0)", "google-cloud-bigquery", "google-cloud-bigquery-storage", "google-vizier (>=0.1.6)", "httpx (>=0.23.0,<0.25.0)", "immutabledict", "lit-nlp (==0.4.0)", "mlflow (>=1.27.0,<=2.16.0)", "numpy (>=1.15.0)", "pandas (>=1.0.0)", "pandas (>=1.0.0,<2.2.0)", "pyarrow (>=10.0.1)", "pyarrow (>=14.0.0)", "pyarrow (>=3.0.0,<8.0dev)", "pyarrow (>=6.0.1)", "pyyaml (>=5.3.1,<7)", "ray[default] (>=2.4,<2.5.dev0 || >2.9.0,!=2.9.1,!=2.9.2,<2.10.dev0 || >=2.33.dev0,<=2.33.0)", "ray[default] (>=2.5,<=2.33.0)", "requests (>=2.28.1)", "setuptools (<70.0.0)", "starlette (>=0.17.1)", "tensorboard-plugin-profile (>=2.4.0,<3.0.0dev)", "tensorflow (>=2.3.0,<3.0.0dev)", "tensorflow (>=2.3.0,<3.0.0dev)", "tensorflow (>=2.4.0,<3.0.0dev)", "tqdm (>=4.23.0)", "urllib3 (>=1.21.1,<1.27)", "uvicorn[standard] (>=0.16.0)", "werkzeug (>=2.0.0,<2.1.0dev)"]
+langchain = ["langchain (>=0.1.16,<0.3)", "langchain-core (<0.3)", "langchain-google-vertexai (<2)", "openinference-instrumentation-langchain (>=0.1.19,<0.2)", "orjson (<=3.10.6)", "tenacity (<=8.3)"]
+langchain-testing = ["absl-py", "cloudpickle (>=3.0,<4.0)", "google-cloud-trace (<2)", "langchain (>=0.1.16,<0.3)", "langchain-core (<0.3)", "langchain-google-vertexai (<2)", "openinference-instrumentation-langchain (>=0.1.19,<0.2)", "opentelemetry-exporter-gcp-trace (<2)", "opentelemetry-sdk (<2)", "orjson (<=3.10.6)", "pydantic (>=2.6.3,<3)", "pytest-xdist", "tenacity (<=8.3)"]
+lit = ["explainable-ai-sdk (>=1.0.0)", "lit-nlp (==0.4.0)", "pandas (>=1.0.0)", "tensorflow (>=2.3.0,<3.0.0dev)"]
+metadata = ["numpy (>=1.15.0)", "pandas (>=1.0.0)"]
+pipelines = ["pyyaml (>=5.3.1,<7)"]
+prediction = ["docker (>=5.0.3)", "fastapi (>=0.71.0,<=0.114.0)", "httpx (>=0.23.0,<0.25.0)", "starlette (>=0.17.1)", "uvicorn[standard] (>=0.16.0)"]
+private-endpoints = ["requests (>=2.28.1)", "urllib3 (>=1.21.1,<1.27)"]
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+optional = false
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+
 [[package]]
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 version = "0.14.0"
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+optional = false
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+
 [[package]]
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 version = "4.11.0"
@@ -979,6 +1459,29 @@ files = [
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 all = ["flake8 (>=7.1.1)", "mypy (>=1.11.2)", "pytest (>=8.3.2)", "ruff (>=0.6.2)"]
 
+[[package]]
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+description = "Read metadata from Python packages"
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+python-versions = ">=3.8"
+files = [
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+type = ["pytest-mypy"]
+
 [[package]]
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@@ -1295,6 +1798,23 @@ requests = ">=2,<3"
 SQLAlchemy = ">=1.4,<3"
 tenacity = ">=8.1.0,<8.4.0 || >8.4.0,<10"
 
+[[package]]
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+optional = false
+python-versions = "<4.0,>=3.9"
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+pydantic = ">=2.7.4,<3.0.0"
+
 [[package]]
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@@ -1345,6 +1865,44 @@ PyYAML = ">=5.3"
 tenacity = ">=8.1.0,<8.4.0 || >8.4.0,<10.0.0"
 typing-extensions = ">=4.7"
 
+[[package]]
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+optional = false
+python-versions = "<4.0,>=3.9"
+files = [
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+[package.dependencies]
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+langchain-core = ">=0.3.0,<0.4"
+pydantic = ">=2,<3"
+
+[package.extras]
+anthropic = ["anthropic[vertexai] (>=0.35.0,<1)"]
+mistral = ["langchain-mistralai (>=0.2.0,<1)"]
+
+[[package]]
+name = "langchain-ollama"
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+optional = false
+python-versions = "<4.0,>=3.9"
+files = [
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+ollama = ">=0.3.0,<1"
+
 [[package]]
 name = "langchain-openai"
 version = "0.2.3"
@@ -1472,6 +2030,34 @@ pydantic = [
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 requests-toolbelt = ">=1.0.0,<2.0.0"
 
+[[package]]
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+tiktoken = ">=0.7.0"
+tokenizers = "*"
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+
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+optional = false
+python-versions = "<4.0,>=3.8"
+files = [
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@@ -2543,6 +3160,31 @@ numpy = ">=1.16.6"
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 test = ["cffi", "hypothesis", "pandas", "pytest", "pytz"]
 
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+test = ["pytest", "pytest-cov"]
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 [[package]]
 name = "six"
 version = "1.16.0"
@@ -3620,6 +4334,123 @@ requests = ">=2.26.0"
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 blobfile = ["blobfile (>=2)"]
 
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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"
diff --git a/prompts/chat.jinja b/prompts/chat.jinja
index 5593915..0ba3471 100644
--- a/prompts/chat.jinja
+++ b/prompts/chat.jinja
@@ -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 %}
\ No newline at end of file
diff --git a/prompts/ask_content.jinja b/prompts/doc_query.jinja
similarity index 100%
rename from prompts/ask_content.jinja
rename to prompts/doc_query.jinja
diff --git a/prompts/spr.jinja b/prompts/spr.jinja
deleted file mode 100644
index 441e0e1..0000000
--- a/prompts/spr.jinja
+++ /dev/null
@@ -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  #}
\ No newline at end of file
diff --git a/prompts/summarize.jinja b/prompts/summarize.jinja
index c106dea..f8b65ab 100644
--- a/prompts/summarize.jinja
+++ b/prompts/summarize.jinja
@@ -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
\ No newline at end of file
+{% endif %}
diff --git a/pyproject.toml b/pyproject.toml
index 7407f8d..d219602 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -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"
diff --git a/stream_app/source.py b/stream_app/source.py
index f0d78f2..2ad6803 100644
--- a/stream_app/source.py
+++ b/stream_app/source.py
@@ -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