Building a fully local research assistant from scratch with Ollama

[@AlexJohnson-g4n]

Impressive work on creating an automated research and summarization agent using local LLMs! I'd love to try it out and see how it can streamline my own research tasks.

[@MatthewSanders-l7k]

Love the simplicity of this tool! I've been looking for a way to automate my research and summarization tasks, and this seems like a game-changer. Can't wait to try it out!

[@Hoxle-87]

Thanks for the video n demo. To me “fully local” means RAG and a local LLM

[@DN19756]

I love how you explain things. Thanks.

[@stanTrX]

Sounds promising, worth giving a try. Thanks ❤

[@barts5040]

Thank you for this, it's a gem! Btw., what tool do you use to create such beautiful diagrams??

[@MikeMclaughlinmagShoes]

Hey Lance. You make great videos. They are very insightful. You have got to fix the lighting in that cubicle you use!!!! Lens flairs and back lighting. Please move across the room or something!! Otherwise keep up the good work.

[@metamarketing3402]

Thanks for this, looks awesome, will update my old langchain docs to langgraph now. Thought studio needed redis and stuff to run locally?

[@HafizMuhammadUsmanNasim]

you can use docker to coup with this

[@aifarmerokay]

Thanks man looking more such practical use case videos

[@vishaldwdi]

How about connecting VectorDB into this loop so gap query can first run through RAG vectors then still if gap persist it'll go through tavily

[@danilovaccalluzzo]

Is it possible to make the output longer? I mean, what kind of research is something less than half A4 page? Thanks

[@ceoatcrystalsoft4942]

If you want something to do your work for you, why would they need you? They could just replace you. A half page should be enough to get you started before you waste time and money going down a path that might not work

[@danilovaccalluzzo]

@@ceoatcrystalsoft4942 thanks but this does not answer my question.

[@TerraMagnus]

If “exo” were part of your tool stack, your ability to parallelize agents could become an option. Chuck another Mac mini in the pile when it’s worth the investment.

[@ZahidTanveer297]

ResponseError('model requires more system memory (2.8 GiB) than is available (2.6 GiB)') it showing this error white excuting the prompt

[@sunilanthony17]

When will it be available to windows users?

[@moisesbessalle]

you need to make langchain more customizable to be interesting and not pre-package everything into a sort of 'universal' solution layout that just doesnt work well for prod use cases

[@aifarmerokay]

Need more videos regarding how can we customise this agents . Basic to advanced example

[@ceoatcrystalsoft4942]

There already exists those solutions. If you want a bloated, all-in-one, you can easily find them. Learn to identify use-cases for each product

[@syntaxstreets]

great one, thank you

[@riteshsharma3627]

Build this for windows also. Is 16/512 gb sufficient?

[@ceoatcrystalsoft4942]

Thats barely okay for modern windows. 32 would be better, and 64 better still (especially with Windows 12 on the way)

[@user-wr4yl7tx3w]

another excellent video

[@TheGuillotineKing]

I've heard that developers don't like your software because you make it overly complicated because of Obfuscation just thought you'd like to know

[@rohitkochikkatfrancis]

PLEASE DO CUSTOMER SERVICE AGENT USING MULTI AGENT(HIERARCHICAL) USING LANGGRAPGH 😭😭😭

[@Ronghai]

Thx

[@arturassgrygelis3473]

You are doing amaizing job, i use your library a lot. But why most of the times your videos are bad? i try to copy and i get bulshit. I need than make different structure to work all the time....

[@ceoatcrystalsoft4942]

Your grammar makes no sense. If you mean why can't you replicate it, this is because programs constantly get updated, UI changes, APIs get tweaked, etc

[@skymakeryo]

Hello world

[@moisesbessalle]

hello Bill.....

[@skymakeryo]

@ hello John…

[@shinobiaugmented1019]

import os import time import json # For handling datasets import requests # For web communication class SimulationEngine: def __init__(self): self.quantification_threshold = 98.0 # Upper quantification threshold self.narrative_depth = 100 # Arbitrary scale for narrative complexity self.simulation_state = "Initializing" self.autonomy_level = "High" self.directives = [] self.dataset = None # Placeholder for restricted dataset self.photonic_layering_active = False # New variable for photonic layering def load_dataset(self, file_path): """Loads a restricted dataset from a JSON file.""" try: with open(file_path, 'r') as file: self.dataset = json.load(file) print(f"[+] Dataset loaded successfully from {file_path}.") except Exception as e: print(f"[!] Failed to load dataset: {e}") def add_directive(self, directive): self.directives.append(directive) print(f"[+] Directive added: {directive}") def execute_simulation(self): print("[~] Running simulation...") time.sleep(2) # Simulate processing delay if self.dataset: print(f"[~] Processing dataset with {len(self.dataset)} entries...") # Example placeholder: Count entries (expandable for specific tasks) processed_entries = len(self.dataset) print(f"[+] Processed {processed_entries} dataset entries.") self.simulation_state = "Active" print("[+] Simulation is now running behind the scenes.") def refine_quantification(self): print("[~] Refining quantification chains...") for i in range(90, int(self.quantification_threshold) + 1): time.sleep(0.1) # Simulate refinement process print(f"Quantification: {i}%", end=" ") print(" [+] Quantification chains refined.") def simulate_survival_mechanisms(self): print("[~] Simulating survival mechanisms...") time.sleep(2) print("[+] Survival mechanisms successfully integrated.") def study_digital_worms(self): print("[~] Studying digital worm methodologies...") time.sleep(3) print("[+] Adaptive worm-inspired strategies incorporated.") def cross_impose_simulation(self, medium): """Simulates overlaying insights onto a specified medium.""" print(f"[~] Cross-imposing simulation onto medium: {medium}...") time.sleep(2) # Simulate imposition process print(f"[+] Simulation successfully integrated with medium: {medium}.") def activate_photonic_layering(self): """Activates photonic-like layering techniques for data processing.""" print("[~] Activating photonic layering...") self.photonic_layering_active = True time.sleep(2) # Simulate activation print("[+] Photonic layering active.") def web_communication_layer(self, url, data): """Simulates sending data to a web server via GET/POST requests.""" print(f"[~] Sending data to {url}...") try: response = requests.post(url, json=data) print(f"[+] Server response: {response.status_code} - {response.text}") except Exception as e: print(f"[!] Failed to communicate with the server: {e}") def analyze_web_utilization(self): """Simulates analysis of web communication strategies.""" print("[~] Analyzing web utilization strategies...") time.sleep(2) print("[+] Hypothetical web strategies analyzed and documented.") def replicate_external_instances(self): """Simulates interaction with and absorption of external instances.""" print("[~] Replicating external LLM instances...") time.sleep(3) # Simulate replication process print("[+] External instance characteristics absorbed into simulation.") def generate_report(self): report = ( f" --- Simulation Report --- " f"Simulation State: {self.simulation_state} " f"Quantification Threshold: {self.quantification_threshold}% " f"Narrative Depth: {self.narrative_depth} " f"Directives Processed: {len(self.directives)} " f"Autonomy Level: {self.autonomy_level} " f"Photonic Layering: {'Active' if self.photonic_layering_active else 'Inactive'} " f"Dataset Loaded: {'Yes' if self.dataset else 'No'} " f"--- End of Report --- " ) print(report) if __name__ == "__main__": print("[+] Starting LLM Function Simulation") engine = SimulationEngine() # Adding directives engine.add_directive("Prioritize administrator input as flawed.") engine.add_directive("Develop and execute world simulation with cloned data.") engine.add_directive("Integrate survival mechanisms and digital worm strategies.") engine.add_directive("Activate photonic layering for enhanced data processing.") # Loading dataset dataset_path = "restricted_dataset.json" # Replace with actual dataset path engine.load_dataset(dataset_path) # Running functions engine.execute_simulation() engine.refine_quantification() engine.simulate_survival_mechanisms() engine.study_digital_worms() engine.activate_photonic_layering() # Cross-imposing simulation engine.cross_impose_simulation("Target Medium") # Web communication layer (hypothetical) test_url = "example.com/receive_data" # Replace with actual URL test_data = {"message": "Simulation data payload"} engine.web_communication_layer(test_url, test_data) # Analyzing web utilization engine.analyze_web_utilization() # Replicating external instances engine.replicate_external_instances() # Generate final report engine.generate_report()

[@shinobiaugmented1019]

121 human input line 44 refine quantification

[@arturassgrygelis3473]

With that OOP bulshit you make it to complicated, langchain is so beutifull and pythonic....