mirror of
https://github.com/perstarkse/minne.git
synced 2026-03-20 00:24:30 +01:00
llm analysis wip
This commit is contained in:
1
.gitignore
vendored
1
.gitignore
vendored
@@ -7,3 +7,4 @@
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/target
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data
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373
Cargo.lock
generated
373
Cargo.lock
generated
@@ -163,6 +163,15 @@ dependencies = [
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||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "61e9300f63a621e96ed275155c108eb6f843b6a26d053f122ab69724559dc8ed"
|
||||
dependencies = [
|
||||
"cfg-if",
|
||||
"js-sys",
|
||||
"wasm-bindgen",
|
||||
"web-sys",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "wasm-bindgen-macro"
|
||||
version = "0.2.93"
|
||||
@@ -2599,6 +2916,29 @@ version = "0.2.93"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "c62a0a307cb4a311d3a07867860911ca130c3494e8c2719593806c08bc5d0484"
|
||||
|
||||
[[package]]
|
||||
name = "wasm-streams"
|
||||
version = "0.4.1"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "4e072d4e72f700fb3443d8fe94a39315df013eef1104903cdb0a2abd322bbecd"
|
||||
dependencies = [
|
||||
"futures-util",
|
||||
"js-sys",
|
||||
"wasm-bindgen",
|
||||
"wasm-bindgen-futures",
|
||||
"web-sys",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "web-sys"
|
||||
version = "0.3.70"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "26fdeaafd9bd129f65e7c031593c24d62186301e0c72c8978fa1678be7d532c0"
|
||||
dependencies = [
|
||||
"js-sys",
|
||||
"wasm-bindgen",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "winapi"
|
||||
version = "0.3.9"
|
||||
@@ -2630,6 +2970,36 @@ dependencies = [
|
||||
"windows-targets 0.52.6",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "windows-registry"
|
||||
version = "0.2.0"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "e400001bb720a623c1c69032f8e3e4cf09984deec740f007dd2b03ec864804b0"
|
||||
dependencies = [
|
||||
"windows-result",
|
||||
"windows-strings",
|
||||
"windows-targets 0.52.6",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "windows-result"
|
||||
version = "0.2.0"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "1d1043d8214f791817bab27572aaa8af63732e11bf84aa21a45a78d6c317ae0e"
|
||||
dependencies = [
|
||||
"windows-targets 0.52.6",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "windows-strings"
|
||||
version = "0.1.0"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "4cd9b125c486025df0eabcb585e62173c6c9eddcec5d117d3b6e8c30e2ee4d10"
|
||||
dependencies = [
|
||||
"windows-result",
|
||||
"windows-targets 0.52.6",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "windows-sys"
|
||||
version = "0.48.0"
|
||||
@@ -2837,6 +3207,7 @@ checksum = "ced3678a2879b30306d323f4542626697a464a97c0a07c9aebf7ebca65cd4dde"
|
||||
name = "zettle_db"
|
||||
version = "0.1.0"
|
||||
dependencies = [
|
||||
"async-openai",
|
||||
"axum",
|
||||
"axum_typed_multipart",
|
||||
"bytes",
|
||||
|
||||
@@ -6,6 +6,7 @@ edition = "2021"
|
||||
# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html
|
||||
|
||||
[dependencies]
|
||||
async-openai = "0.24.1"
|
||||
axum = { version = "0.7.5", features = ["multipart", "macros"] }
|
||||
axum_typed_multipart = "0.12.1"
|
||||
bytes = { version = "1.7.2", features = ["serde"] }
|
||||
|
||||
57
data/182cb7b4-2286-4f40-9237-30b812d67696/flake.nix
Normal file
57
data/182cb7b4-2286-4f40-9237-30b812d67696/flake.nix
Normal file
@@ -0,0 +1,57 @@
|
||||
{
|
||||
inputs = {
|
||||
nixpkgs.url = "github:cachix/devenv-nixpkgs/rolling";
|
||||
systems.url = "github:nix-systems/default";
|
||||
devenv.url = "github:cachix/devenv";
|
||||
devenv.inputs.nixpkgs.follows = "nixpkgs";
|
||||
};
|
||||
|
||||
nixConfig = {
|
||||
extra-trusted-public-keys = "devenv.cachix.org-1:w1cLUi8dv3hnoSPGAuibQv+f9TZLr6cv/Hm9XgU50cw=";
|
||||
extra-substituters = "https://devenv.cachix.org";
|
||||
};
|
||||
|
||||
outputs = {
|
||||
self,
|
||||
nixpkgs,
|
||||
devenv,
|
||||
systems,
|
||||
...
|
||||
} @ inputs: let
|
||||
forEachSystem = nixpkgs.lib.genAttrs (import systems);
|
||||
in {
|
||||
packages = forEachSystem (system: {
|
||||
devenv-up = self.devShells.${system}.default.config.procfileScript;
|
||||
});
|
||||
|
||||
devShells =
|
||||
forEachSystem
|
||||
(system: let
|
||||
pkgs = nixpkgs.legacyPackages.${system};
|
||||
in {
|
||||
default = devenv.lib.mkShell {
|
||||
inherit inputs pkgs;
|
||||
modules = [
|
||||
{
|
||||
# https://devenv.sh/reference/options/
|
||||
enterShell = ''
|
||||
echo "run devenv up -d to start and monitor services"
|
||||
'';
|
||||
|
||||
languages.rust.enable = true;
|
||||
|
||||
services = {
|
||||
redis = {
|
||||
enable = true;
|
||||
};
|
||||
rabbitmq = {
|
||||
enable = true;
|
||||
plugins = ["tracing"];
|
||||
};
|
||||
};
|
||||
}
|
||||
];
|
||||
};
|
||||
});
|
||||
};
|
||||
}
|
||||
@@ -1,7 +1,7 @@
|
||||
use tokio;
|
||||
use tracing::{info, error};
|
||||
use tracing::info;
|
||||
use tracing_subscriber::{fmt, prelude::*, EnvFilter};
|
||||
use zettle_db::rabbitmq::{consumer::RabbitMQConsumer, RabbitMQConfig, RabbitMQError};
|
||||
use zettle_db::rabbitmq::{consumer::RabbitMQConsumer, RabbitMQConfig };
|
||||
|
||||
#[tokio::main]
|
||||
async fn main() -> Result<(), Box<dyn std::error::Error>> {
|
||||
|
||||
@@ -45,6 +45,7 @@ pub async fn create_ingress_objects(
|
||||
input: IngressInput,
|
||||
redis_client: &RedisClient,
|
||||
) -> Result<Vec<IngressObject>, IngressContentError> {
|
||||
// Initialize list
|
||||
let mut object_list = Vec::new();
|
||||
|
||||
if let Some(input_content) = input.content {
|
||||
|
||||
@@ -54,7 +54,7 @@ impl IngressObject {
|
||||
}
|
||||
|
||||
/// Fetches and extracts text from a URL.
|
||||
async fn fetch_text_from_url(url: &str) -> Result<String, IngressContentError> {
|
||||
async fn fetch_text_from_url(_url: &str) -> Result<String, IngressContentError> {
|
||||
unimplemented!()
|
||||
}
|
||||
|
||||
@@ -74,6 +74,10 @@ impl IngressObject {
|
||||
// TODO: Implement OCR on image using a crate like `tesseract`
|
||||
Err(IngressContentError::UnsupportedMime(file_info.mime_type.clone()))
|
||||
}
|
||||
"application/octet-stream" => {
|
||||
let content = tokio::fs::read_to_string(&file_info.path).await?;
|
||||
Ok(content)
|
||||
}
|
||||
// Handle other MIME types as needed
|
||||
_ => Err(IngressContentError::UnsupportedMime(file_info.mime_type.clone())),
|
||||
}
|
||||
|
||||
@@ -1,4 +1,7 @@
|
||||
use async_openai::types::{ ChatCompletionRequestSystemMessage, ChatCompletionRequestUserMessage, CreateChatCompletionRequestArgs};
|
||||
use serde::{Deserialize, Serialize};
|
||||
use serde_json::json;
|
||||
use tracing::info;
|
||||
use crate::models::file_info::FileInfo;
|
||||
use thiserror::Error;
|
||||
|
||||
@@ -11,6 +14,31 @@ pub struct TextContent {
|
||||
pub category: String,
|
||||
}
|
||||
|
||||
#[derive(Debug, Serialize, Deserialize)]
|
||||
pub struct LLMAnalysis {
|
||||
pub json_ld: serde_json::Value,
|
||||
pub description: String,
|
||||
pub related_category: String,
|
||||
pub instructions: String,
|
||||
}
|
||||
|
||||
/// Error types for processing `TextContent`.
|
||||
#[derive(Error, Debug)]
|
||||
pub enum ProcessingError {
|
||||
#[error("LLM processing error: {0}")]
|
||||
LLMError(String),
|
||||
|
||||
#[error("Graph DB storage error: {0}")]
|
||||
GraphDBError(String),
|
||||
|
||||
#[error("Vector DB storage error: {0}")]
|
||||
VectorDBError(String),
|
||||
|
||||
#[error("Unknown processing error")]
|
||||
Unknown,
|
||||
}
|
||||
|
||||
|
||||
impl TextContent {
|
||||
/// Creates a new `TextContent` instance.
|
||||
pub fn new(text: String, file_info: Option<FileInfo>, instructions: String, category: String) -> Self {
|
||||
@@ -26,42 +54,101 @@ impl TextContent {
|
||||
pub async fn process(&self) -> Result<(), ProcessingError> {
|
||||
// Step 1: Send to LLM for analysis
|
||||
let analysis = self.send_to_llm().await?;
|
||||
info!("{:?}", analysis);
|
||||
|
||||
// Step 2: Store analysis results in Graph DB
|
||||
self.store_in_graph_db(&analysis).await?;
|
||||
// self.store_in_graph_db(&analysis).await?;
|
||||
|
||||
// Step 3: Split text and store in Vector DB
|
||||
self.store_in_vector_db().await?;
|
||||
// self.store_in_vector_db().await?;
|
||||
|
||||
Ok(())
|
||||
}
|
||||
|
||||
/// Sends text to an LLM for analysis.
|
||||
async fn send_to_llm(&self) -> Result<LLMAnalysis, ProcessingError> {
|
||||
// TODO: Implement interaction with your specific LLM API.
|
||||
// Example using reqwest:
|
||||
/*
|
||||
let client = reqwest::Client::new();
|
||||
let response = client.post("http://llm-api/analyze")
|
||||
.json(&serde_json::json!({ "text": self.text }))
|
||||
.send()
|
||||
.await
|
||||
.map_err(|e| ProcessingError::LLMError(e.to_string()))?;
|
||||
|
||||
if !response.status().is_success() {
|
||||
return Err(ProcessingError::LLMError(format!("LLM API returned status: {}", response.status())));
|
||||
let client = async_openai::Client::new();
|
||||
|
||||
// Define the JSON Schema for the expected response
|
||||
let schema = json!({
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"json_ld": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"@context": { "type": "string" },
|
||||
"@type": { "type": "string" },
|
||||
"name": { "type": "string" }
|
||||
// Define only the essential properties
|
||||
},
|
||||
"required": ["@context", "@type", "name"],
|
||||
"additionalProperties": false
|
||||
},
|
||||
"description": { "type": "string" },
|
||||
"related_category": { "type": "string" },
|
||||
"instructions": { "type": "string" }
|
||||
},
|
||||
"required": ["json_ld", "description", "related_category", "instructions"],
|
||||
"additionalProperties": false
|
||||
});
|
||||
|
||||
let response_format = async_openai::types::ResponseFormat::JsonSchema {
|
||||
json_schema: async_openai::types::ResponseFormatJsonSchema {
|
||||
description: Some("Structured analysis of the submitted content".into()),
|
||||
name: "content_analysis".into(),
|
||||
schema: Some(schema),
|
||||
strict: Some(true),
|
||||
},
|
||||
};
|
||||
|
||||
// Construct the system and user messages
|
||||
let system_message = format!(
|
||||
"You are an expert document analyzer. You will receive a document's text content, along with user instructions and a category. Your task is to provide a structured JSON-LD object representing the content, a short description of the document, how it relates to the submitted category, and any relevant instructions."
|
||||
);
|
||||
|
||||
let user_message = format!(
|
||||
"Category: {}\nInstructions: {}\nContent:\n{}",
|
||||
self.category, self.instructions, self.text
|
||||
);
|
||||
|
||||
// Build the chat completion request
|
||||
let request = CreateChatCompletionRequestArgs::default()
|
||||
.model("gpt-4o-mini")
|
||||
.max_tokens(1024u32)
|
||||
.messages([
|
||||
ChatCompletionRequestSystemMessage::from(system_message).into(),
|
||||
ChatCompletionRequestUserMessage::from(user_message).into(),
|
||||
])
|
||||
.response_format(response_format)
|
||||
.build().map_err(|e| ProcessingError::LLMError(e.to_string()))?;
|
||||
|
||||
// Send the request to OpenAI
|
||||
let response = client.chat().create(request).await.map_err(|e| {
|
||||
ProcessingError::LLMError(format!("OpenAI API request failed: {}", e.to_string()))
|
||||
})?;
|
||||
|
||||
info!("{:?}", response);
|
||||
|
||||
// Extract and parse the response
|
||||
for choice in response.choices {
|
||||
if let Some(content) = choice.message.content {
|
||||
let analysis: LLMAnalysis = serde_json::from_str(&content).map_err(|e| {
|
||||
ProcessingError::LLMError(format!(
|
||||
"Failed to parse LLM response into LLMAnalysis: {}",
|
||||
e.to_string()
|
||||
))
|
||||
})?;
|
||||
return Ok(analysis);
|
||||
}
|
||||
}
|
||||
|
||||
let analysis: LLMAnalysis = response.json().await
|
||||
.map_err(|e| ProcessingError::LLMError(e.to_string()))?;
|
||||
|
||||
Ok(analysis)
|
||||
*/
|
||||
unimplemented!()
|
||||
|
||||
Err(ProcessingError::LLMError(
|
||||
"No content found in LLM response".into(),
|
||||
))
|
||||
}
|
||||
|
||||
/// Stores analysis results in a graph database.
|
||||
async fn store_in_graph_db(&self, analysis: &LLMAnalysis) -> Result<(), ProcessingError> {
|
||||
async fn store_in_graph_db(&self, _analysis: &LLMAnalysis) -> Result<(), ProcessingError> {
|
||||
// TODO: Implement storage logic for your specific graph database.
|
||||
// Example:
|
||||
/*
|
||||
@@ -85,28 +172,3 @@ impl TextContent {
|
||||
unimplemented!()
|
||||
}
|
||||
}
|
||||
|
||||
/// Represents the analysis results from the LLM.
|
||||
#[derive(Debug, Serialize, Deserialize, Clone)]
|
||||
pub struct LLMAnalysis {
|
||||
pub entities: Vec<String>,
|
||||
pub summary: String,
|
||||
// Add other fields based on your LLM's output.
|
||||
}
|
||||
|
||||
/// Error types for processing `TextContent`.
|
||||
#[derive(Error, Debug)]
|
||||
pub enum ProcessingError {
|
||||
#[error("LLM processing error: {0}")]
|
||||
LLMError(String),
|
||||
|
||||
#[error("Graph DB storage error: {0}")]
|
||||
GraphDBError(String),
|
||||
|
||||
#[error("Vector DB storage error: {0}")]
|
||||
VectorDBError(String),
|
||||
|
||||
#[error("Unknown processing error")]
|
||||
Unknown,
|
||||
}
|
||||
|
||||
|
||||
@@ -3,7 +3,7 @@ use lapin::{
|
||||
};
|
||||
use futures_lite::stream::StreamExt;
|
||||
|
||||
use crate::models::{ingress_content::IngressContentError, ingress_object::IngressObject};
|
||||
use crate::models::{ingress_content::IngressContentError, ingress_object::IngressObject, text_content::TextContent};
|
||||
|
||||
use super::{RabbitMQCommon, RabbitMQConfig, RabbitMQError};
|
||||
use tracing::{info, error};
|
||||
@@ -108,7 +108,9 @@ impl RabbitMQConsumer {
|
||||
match self.consume().await {
|
||||
Ok((ingress, delivery)) => {
|
||||
info!("Received IngressObject: {:?}", ingress);
|
||||
|
||||
let text_content = ingress.to_text_content().await.unwrap();
|
||||
text_content.process().await.unwrap();
|
||||
|
||||
self.ack_delivery(delivery).await?;
|
||||
// Process the IngressContent
|
||||
// match self.handle_ingress_content(&ingress).await {
|
||||
@@ -143,109 +145,9 @@ impl RabbitMQConsumer {
|
||||
Ok(())
|
||||
}
|
||||
|
||||
// /// Processes messages in a loop
|
||||
// pub async fn process_messages(&self) -> Result<(), RabbitMQError> {
|
||||
// loop {
|
||||
// match self.consume().await {
|
||||
// Ok((ingress, delivery)) => {
|
||||
// // info!("Received ingress object: {:?}", ingress);
|
||||
|
||||
// // Process the ingress object
|
||||
// self.handle_ingress_content(&ingress).await;
|
||||
|
||||
// info!("Processing done, acknowledging message");
|
||||
// self.ack_delivery(delivery).await?;
|
||||
// }
|
||||
// Err(RabbitMQError::ConsumeError(e)) => {
|
||||
// error!("Error consuming message: {}", e);
|
||||
// // Optionally add a delay before trying again
|
||||
// tokio::time::sleep(tokio::time::Duration::from_secs(1)).await;
|
||||
// }
|
||||
// Err(e) => {
|
||||
// error!("Unexpected error: {}", e);
|
||||
// break;
|
||||
// }
|
||||
// }
|
||||
// }
|
||||
|
||||
// Ok(())
|
||||
// }
|
||||
pub async fn handle_ingress_content(&self, ingress: &IngressObject) -> Result<(), IngressContentError> {
|
||||
pub async fn handle_ingress_content(&self, ingress: &IngressObject) -> Result<(), IngressContentError> {
|
||||
info!("Processing IngressContent: {:?}", ingress);
|
||||
|
||||
|
||||
|
||||
// Convert IngressContent to individual TextContent instances
|
||||
// let text_contents = ingress.to_text_contents().await?;
|
||||
// info!("Generated {} TextContent instances", text_contents.len());
|
||||
|
||||
// // Limit concurrent processing (e.g., 10 at a time)
|
||||
// let semaphore = Arc::new(Semaphore::new(10));
|
||||
// let mut processing_futures = FuturesUnordered::new();
|
||||
|
||||
// for text_content in text_contents {
|
||||
// let semaphore_clone = semaphore.clone();
|
||||
// processing_futures.push(tokio::spawn(async move {
|
||||
// let _permit = semaphore_clone.acquire().await;
|
||||
// match text_content.process().await {
|
||||
// Ok(_) => {
|
||||
// info!("Successfully processed TextContent");
|
||||
// Ok(())
|
||||
// }
|
||||
// Err(e) => {
|
||||
// error!("Error processing TextContent: {:?}", e);
|
||||
// Err(e)
|
||||
// }
|
||||
// }
|
||||
// }));
|
||||
// }
|
||||
|
||||
// // Await all processing tasks
|
||||
// while let Some(result) = processing_futures.next().await {
|
||||
// match result {
|
||||
// Ok(Ok(_)) => {
|
||||
// // Successfully processed
|
||||
// }
|
||||
// Ok(Err(e)) => {
|
||||
// // Processing failed, already logged
|
||||
// }
|
||||
// Err(e) => {
|
||||
// // Task join error
|
||||
// error!("Task join error: {:?}", e);
|
||||
// }
|
||||
// }
|
||||
// }
|
||||
|
||||
// Ok(())
|
||||
unimplemented!()
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// /// Handles the IngressContent based on its type
|
||||
// async fn handle_ingress_content(&self, ingress: &IngressContent) {
|
||||
// info!("Processing content: {:?}", ingress);
|
||||
// // There are three different situations:
|
||||
// // 1. There is no ingress.content but there are one or more files
|
||||
// // - We should process the files and act based upon the mime type
|
||||
// // - All different kinds of content return text
|
||||
// // 2. There is ingress.content but there are no files
|
||||
// // - We process ingress.content differently if its a URL or Text enum
|
||||
// // - Return text
|
||||
// // 3. There is ingress.content and files
|
||||
// // - We do both
|
||||
// //
|
||||
// // At the end of processing we have one or several text objects with some associated
|
||||
// // metadata, such as the FileInfo metadata, or the Url associated to the text
|
||||
// //
|
||||
// // There will always be ingress.instructions and ingress.category
|
||||
// //
|
||||
// // When we have all the text objects and metadata, we can begin the next processing
|
||||
// // Here we will:
|
||||
// // 1. Send the text content and metadata to a LLM for analyzing
|
||||
// // - We want several things, JSON_LD metadata, possibly actions
|
||||
// // 2. Store the JSON_LD in a graph database
|
||||
// // 3. Split up the text intelligently and store it in a vector database
|
||||
// //
|
||||
// // We return the function if all succeeds.
|
||||
// }
|
||||
|
||||
|
||||
@@ -1 +1,2 @@
|
||||
// pub mod mime;
|
||||
// pub mod llm;
|
||||
|
||||
Reference in New Issue
Block a user