LangChain and RAG Integration

rewrite this in my voice: I thought the same. It is like I am talking to the past version of myself. But they have a free version you can use since the code is open and you just hook it up to ollama. But the problem is that in the integration with ollama it only has llama3.2 available and any other model is not listed. I am sure it is just some failure on my part but you can easily use the framework for free if you tinker with it. I say you are a past version because someone else brought up n8n and I looked it up and saw it cost money and instantly exited the page and then replied back. It was only later when I downloaded the repo and tinkered with it that I found it useful. But it is really useful in terms of giving you a UI to use and that helps with planning in a lot of ways. Kind of why I like ComfyUI as well. They are comfy. No but instead I do small projects and then integrate each of the small projects into a larger version. And for that I have found that LangChain is more useful than n8n. The next project I want to do is to take the reasoning from reasoing models that are open like Deepseekr1 and use summarizers to generate metadata stored in a ChromaDB vector database so you can strip with RE the <think> tagged content and remove it so you can use the prompt's output correctly and parse it if you are requesting only JSON for instance which a lot of applications I make do that. So instead I want to keep the reasoning and generate meta data from it so that you can include it in the context for future calls using RAG and LangChain with LangGraph will indeed take you much further. That is why I stopped using n8n after briefly testing it out. So I can easily use LangChain recursive chain construction to generate meta data and store the reasoning that has was done to generate the entire output including every call to it. This way you would remove as much of the black box in terms of being able to observe further and further granular detail. Just imagine writing a book and then generating a Persona from it. That is what I did. But I did it using Grok a while back and now I know more and ran out of free credits at XAi. I later rewrote it for Ollama instead, like I did all the other programs I wrote which I am eventually going to combine into a singular program. So I am resurrecting my friend. I just have not created the dataset for it yet. I have to fund it. So I am going to launch a small data annotation platform and hire people to annotate data for me. Except I am going to develop the entire site myself and integrate whatever backend I want by building a basic server and just run it live at all times so it can interact with the internet and publish more papers like it did with the one I used the recursive calling functions to generate using the increased context method I thought of, which other people have done better I know, but I thought of how to do it and wrote about all of these things on my website: danielkliewer.com but that is a non-monetized site and is just a basic Jekyll blog that is as basic as can be hosted on netlify for free so I just pay for the domain. The backend for the site is just on my computer so I just push any updates I want with git. All you have to do is make a blog like this and use automate pushes to netlify which triggers content fetching from Reddit to generate the next post and so on and on forever and ever. But I created a PersonaGen app which allows you to take documents and analyze it with the LLM to generate JSON which is then saved in the database in Django where I tie it to a Vite Frontend. But now I know so much more and I am tying it all together. So I take the text I generate from my data annotation platform. I use it to train the Chrisbot. The Chrisbot is then hosted as a blog which is merely a static site. I like Streamlit or FastAPI a lot now though for quick mock ups but the Vite Django stack I prefer just because I am more comfortable with it. Comfy. But now I could use something like SQLite to store the structured JSON calls and the ChromaDB to be the vector database for it which stores the reasoning context metadata that would be generated by summary calls that summarize and generate meta data for the reasoning that accompanies each call to a reasoning model like DeepSeekR1. So now I can give it infinite context. All you have to do is publish in Reddit your ideas and then the blog generates a new guide you can follow along to code the backend and frontend for the web app which is just the fine tuning you hired the human annotators to label data for you as to allow you to make better models iteratively like Meta does. So what I am thinking of is a web app that you run locally as a Django backend and then you feed to a Jekyll blog frontend .md files generated by the application which distills the LLM analysis of Reddit content in order to generate responses in the form of markdown files published to danielkliewer.com That is what I built. It automatically generates markdown for the site which I just save in a folder and push with git. At this point it does not run on its own but instead the trigger event is simply the run of the program rather than being live. But it would be simple to integrate a trigger to the program. Anyway. At this point I just use LLM calls with structured JSON output to generate metadata which is used by iterative LLM calls to analyze and distill the content into a final markdown file which is then uploaded with git. So my program takes what I write on reddit as the initial prompt to the LLM. Well it is a value in an FString I use for the prompt but that is how I then structure future calls by passing a new FString with the key and value pairs generated by previous LLM calls. Except I include a Vite frontend which allows the local user to alter and adapt the program and generated content from the UI. Something comfy. Maybe n8n. Just kidding. I am just going to build it from scratch. Not really I have already assembled each of the small pieces of the program. For each concept I have built a sample guide and repo for it and then I am just going to combine what I have learned into something more. But my next plan is to adapt the PersonaGen to use DeepSeekR1 and use the <think> tagged content in recursive LLM prompts which generate structured JSON metadata to be stored in the Django SQLite database. Then you can access that with the Vite frontend. You could just have a button that you push which publishes the edited version from the Vite frontend and use that to commit the final markdown to be pushed to the Jekyll static blog. Except that is what I did. Except now I am going to adapt it to open source reasoning models like DeepSeekR1. I am hoping that other LLM developers also include the reasoning in such an easy to parse way as DeepSeekR1 does by enclosing it in easy to RE and alter <think> tags. Or they could include a library which generates this metadata from all of your LLM calls so that you can have a memory for the LLM of all of the reasoning it has generated so that you can then use that to generate the Vite Frontend values for states. Then you pass those states through Axios or whatever to the Typescript frontend. You can use the /src/components/ format to easily put together and organize the structure like I like to. I work in inventory management for my day job. Lots of numbers. But I like making things work. Kind of how I like tinkering. LLMs allow you to tinker with reality it is fascinating. Anyway this was all generated by a human. But will soon become a robot generated blog post, just you wait.

Here's the rewrite in your voice: --- Yeah, I thought the same. Feels like I’m talking to a past version of myself. They actually have a free version since the code is open—you just hook it up to Ollama. The issue is that in the Ollama integration, it only has Llama 3.2 available, and other models aren’t listed. Probably just something I overlooked, but you can tinker with it and get it working for free. I say "past version" because someone mentioned n8n, and I looked it up, saw it cost money, and immediately closed the page. Later, I actually downloaded the repo, messed with it, and found it useful. It gives you a UI for automation, which helps a lot with planning—kind of why I like ComfyUI. They’re... well, comfy. But instead of relying on n8n, I tend to build small projects and integrate them into a larger system over time. And for that, LangChain has been way more useful. Next, I want to take the reasoning from open models like DeepSeekR1, use summarizers to generate metadata stored in a ChromaDB vector database, and strip out the `<think>`-tagged reasoning content when needed—so if you request JSON output, you actually get clean JSON. A lot of my apps do that. But instead of discarding reasoning, I want to keep it as metadata so I can use it in future calls via RAG. LangChain + LangGraph can take that a lot further, which is why I dropped n8n after testing it briefly. LangChain’s recursive chain construction lets me store metadata and track the reasoning behind every output, removing a lot of the "black box" effect. Imagine writing a book and then generating a Persona from it—that’s what I did. Originally used Grok for it, but ran out of free XAi credits, so I rewrote it for Ollama, just like all my other programs. Eventually, they’re all getting merged into one. Resurrecting my friend—I just haven’t built the dataset yet. Need funding. So I’m launching a small data annotation platform and hiring people to label data for me. But I’m building the whole site myself, running a backend server 24/7 to interact with the internet and publish research—like the paper I generated using recursive calls and extended context (others have done better, but I figured it out myself). Wrote about all of it on [danielkliewer.com](https://danielkliewer.com), but that’s just a free Jekyll blog on Netlify, nothing fancy. Backend’s local, and I push updates with Git. All you need is a setup like mine—automated Git pushes to Netlify, scraping Reddit for content, generating new posts forever. That’s how I built PersonaGen: it analyzes documents with an LLM, generates JSON, and saves it in a Django database, tied to a Vite frontend. Now I’m refining it, bringing it all together. Text from my data annotation platform trains the Chrisbot. Chrisbot runs as a static blog. Simple. I like Streamlit and FastAPI for quick mockups, but Vite + Django is what I’m most comfortable with. Comfy. Next, I’m using SQLite for structured JSON calls, ChromaDB for reasoning metadata, and recursive summarization to create context-aware vector storage for models like DeepSeekR1. This gives it infinite context. Just publish ideas on Reddit, let the blog generate guides, code the backend/frontend, and fine-tune models with human annotations—same process Meta uses. The web app I’m building runs a Django backend locally, feeding markdown files to a Jekyll frontend. It distills LLM analysis of Reddit content into markdown, automatically published to my site. Right now, I manually trigger it, but automating that would be easy. At the core, my program takes what I write on Reddit as an initial LLM prompt. Each call structures the next, passing key-value pairs iteratively. I include a Vite frontend so users can tweak things directly. Something comfy. Maybe n8n? Just kidding. Building it from scratch. Not really—I already built each piece separately. Now I’m just assembling everything. Next step: adapting PersonaGen to use DeepSeekR1, parsing `<think>`-tagged content, and recursively generating structured JSON stored in Django/SQLite. The Vite frontend lets you edit and publish straight to Jekyll. That’s what I did. Now, I’m adapting it to open-source reasoning models like DeepSeekR1. Hoping more LLMs use `<think>` tags or provide built-in reasoning metadata. If they don’t, I’ll just write a library for it. With that, every reasoning step can be stored, giving LLMs memory. That metadata feeds into a Vite frontend as state values, passed through Axios (or whatever), structured in `/src/components/`. I do inventory management by day—lots of numbers. But I like making things work. Tinkering. LLMs let you tinker with reality. It’s fascinating. Anyway, all of this was written by a human. But soon, it'll be a robot-generated blog post. Just wait.

rewrite this: You're right about the global OSS momentum (Mistral/Qwen/DeepSeek are 🔥), but don't sleep on US models - **Llama3-70B** still leads in reasoning/RAG benchmarks, and **OLMo**'s full-stack openness (weights+data+training code) sets a new standard. Here's the multi-model FastAPI/Streamlit stack I'm using: ```python # Core async orchestration models = { "US": ["meta/llama3-70b", "allenai/olmo-65b"], "Non-US": [ "tiiuae/falcon-180b", "deepseek-ai/deepseek-r1", "yandex/yalm-100b" ] } async def generate_all(prompt: str): # Async Ollama calls with geo load balancing tasks = [ ollama.generate(model=model, prompt=prompt, options={ "num_ctx": 16384, "temperature": 0.7 }) for model in models["US"] + models["Non-US"] ] return await asyncio.gather(*tasks) # RAG validation layer def rank_outputs(prompt: str, outputs: list): embeddings = HuggingFaceEmbeddings() chroma_db.add_documents([ {"content": o["response"], "metadata": { "model": o["model"], "reasoning": extract_think_tags(o["response"]) }} for o in outputs ]) return chroma_db.similarity_search(prompt, k=3) ``` **Key architecture**: 1. **Model Diversity**: Mixes US (Llama3/OLMo) with UAE/CN/RU models via unified Ollama API 2. **Compliance**: Local execution avoids cloud API NDAs - GGUF quantized models only 3. **Validation**: Type-safe Pydantic schemas + ChromaDB similarity checks filter hallucinations **Surprise insight**: Falcon-180B (UAE) often outperforms smaller US models in creative tasks, but Llama3-70B dominates structured outputs. The real magic happens in the RAG distillation layer - it creates consensus between geopolitical "AI perspectives". Full code's messy but functional - prioritizing model interoperability over corporate alignment. What's your stack look like?

You're spot on about the global OSS wave—Mistral, Qwen, and DeepSeek are killing it—but US models are still leading in key areas. **Llama3-70B** dominates reasoning and RAG benchmarks, while **OLMo** is setting a new standard with full-stack openness (weights, data, and training code). Here's my multi-model FastAPI/Streamlit stack: ```python # Async multi-model inference models = { "US": ["meta/llama3-70b", "allenai/olmo-65b"], "Global": [ "tiiuae/falcon-180b", "deepseek-ai/deepseek-r1", "yandex/yalm-100b" ] } async def generate_all(prompt: str): # Parallel Ollama calls with geo-aware load balancing tasks = [ ollama.generate(model=m, prompt=prompt, options={ "num_ctx": 16384, "temperature": 0.7 }) for m in models["US"] + models["Global"] ] return await asyncio.gather(*tasks) # RAG validation + ranking def rank_outputs(prompt: str, outputs: list): embeddings = HuggingFaceEmbeddings() chroma_db.add_documents([ {"content": o["response"], "metadata": { "model": o["model"], "reasoning": extract_think_tags(o["response"]) }} for o in outputs ]) return chroma_db.similarity_search(prompt, k=3) ``` ### **Core Architecture:** 1. **Model Diversity** – Blends US (Llama3/OLMo) with UAE/CN/RU models in a unified Ollama API 2. **Privacy & Compliance** – Runs locally, sidestepping API NDAs (only GGUF quantized models) 3. **Hallucination Filtering** – Uses Pydantic type safety + ChromaDB semantic validation ### **Interesting Insight:** Falcon-180B (UAE) beats smaller US models in creative tasks, but Llama3-70B is still king for structured outputs. The real magic happens in the RAG distillation—where competing "AI perspectives" are reconciled into a single consensus layer. Code’s a mess but functional—my priority is model interoperability over corporate ecosystems. What’s your stack looking like?