fill out readme (run-llama#2)

README.md

@@ -1 +1,69 @@
-# rags
+# RAGs
+
+RAGs is a Streamlit app that lets you create a RAG pipeline from a data source using natural language.
+
+You get to do the following:
+1. Describe your task (e.g. "load this web page") and the parameters you want from your RAG systems (e.g. "i want to retrieve X number of docs")
+2. Go into the config view and view/alter generated parameters (top-k, summarization, etc.) as needed.
+3. Query the RAG agent over data with your questions.
+
+This project is inspired by [GPTs](https://openai.com/blog/introducing-gpts), launched by OpenAI.
+
+## Installation and Setup 
+
+Clone this project, go into the `rags` project folder.
+
+```
+pip install -r requirements.txt
+```
+
+Then run the app from the "home page" file.
+
+```
+
+streamlit run 1_🏠_Home.py
+
+```
+
+## Detailed Overview
+
+The app contains the following sections, corresponding to the steps listed above.
+
+### 1. 🏠 Home Page
+This is the section where you build a RAG pipeline by instructing the "builder agent". Typically to setup a RAG pipeline you need the following components:
+1. Describe the dataset. Currently we support either **a single local file** or a **web page**. We're open to suggestions here! 
+2. Describe the task. Concretely this description will be used to initialize the "system prompt" of the LLM powering the RAG pipeline.
+3. Define the typical parameters for a RAG setup. See the below section for the list of parameters.
+
+### 2. ⚙️ RAG Config
+
+This section contains the RAG parameters, generated by the "builder agent" in the previous section. In this section, you have a UI showcasing the generated parameters and have full freedom to manually edit/change them as necessary.
+
+Currently the set of parameters is as follows:
+- System Prompt
+- Include Summarization: whether to also add a summarization tool (instead of only doing top-k retrieval.)
+- Top-K
+- Chunk Size
+- Embed Model
+- LLM 
+
+If you manually change parameters, you can press the "Update Agent" button in order to update the agent.
+
+```{tip}
+If you don't see the `Update Agent` button, that's because you haven't created the agent yet. Please go to the previous "Home" page and complete the setup process.
+```
+
+We can always add more parameters to make this more "advanced" 🛠️, but thought this would be a good place to start.
+
+### 3. Generated RAG Agent
+
+Once your RAG agent is created, you have access to this page.
+
+This is a standard chatbot interface where you can query the RAG agent and it will answer questions over your data.
+
+It will be able to pick the right RAG tools (either top-k vector search or optionally summarization) in order to fulfill the query.
+
+
+## Issues / Contributions
+
+Running into issues? Please file a Github issue or join our [Discord](https://discord.gg/dGcwcsnxhU).

agent_utils.py

@@ -175,7 +175,7 @@ def get_rag_params(self) -> Dict:
         
         """
         rag_params = self._cache.rag_params
-        return rag_params.model_dump()
+        return rag_params.dict()
 
 
     def set_rag_params(self, **rag_params: Dict):
@@ -188,7 +188,7 @@ def set_rag_params(self, **rag_params: Dict):
             **rag_params (Dict): dictionary of RAG parameters. 
         
         """
-        new_dict = self._cache.rag_params.model_dump()
+        new_dict = self._cache.rag_params.dict()
         new_dict.update(rag_params)
         rag_params_obj = RAGParams(**new_dict)
         self._cache.rag_params = rag_params_obj

requirements.txt

@@ -1,5 +1,5 @@
 streamlit
 streamlit_pills
-llama-index==0.9.0.post1
+llama-index==0.9.4
 llama-hub==0.0.44
 langchain==0.0.305