@githubprojects writes about Quivr-core, a Python package extracted from Quivr.com's production retrieval-augmented generation pipeline that lets developers get a working system in five lines of code. It centers on a `Brain` class that ingests files and answers questions, with a YAML-configurable workflow (filter history → rewrite → retrieve → generate) that keeps the pipeline inspectable rather than a black box. It supports OpenAI, Anthropic, Mistral, and local Ollama models, and handles PDFs, Markdown, and TXT files out of the box.
- Integrates with Megaparse for more sophisticated document ingestion without switching frameworks.
- The opinionated philosophy is the core differentiator: sensible defaults over an infinite configuration surface.
- Integrates with Megaparse for more sophisticated document ingestion without switching frameworks.
- The opinionated philosophy is the core differentiator: sensible defaults over an infinite configuration surface.
The article clarifies that RAG and fine-tuning are complementary rather than competing techniques for LLM development. RAG works by retrieving external information at inference time, which enables models to access new data and provide citable answers without changing the model weights. In contrast, fine-tuning adjusts a model's internal weights to improve its behavior, such as tone or adherence to specific output formats like JSON.
- RAG provides dynamic knowledge retrieval for accuracy and traceability.
- Fine-tuning improves task performance, style, and formatting consistency.
- Combining both methods allows developers to manage both what a model knows and how it communicates.
- RAG provides dynamic knowledge retrieval for accuracy and traceability.
- Fine-tuning improves task performance, style, and formatting consistency.
- Combining both methods allows developers to manage both what a model knows and how it communicates.
Memori is an agent-native memory infrastructure that acts as an LLM-agnostic layer to transform AI agent execution and conversations into structured, persistent state for production systems. It integrates seamlessly into existing architectures, allowing agents to automatically capture and recall information from past interactions without requiring changes to core code or prompts.
Key features and points:
* Provides advanced augmentation of memories including attributes, facts, preferences, relationships, and skills at the entity, process, and session levels.
* Achieves high accuracy and token efficiency in long-conversation memory as demonstrated by LoCoMo benchmark results.
* Offers dedicated SDKs for both Python and TypeScript.
* Supports Model Context Protocol (MCP) for easy connection to developer tools like Claude Code and Cursor.
* Compatible with a wide range of LLMs including OpenAI, Anthropic, Gemini, DeepSeek, and Grok, as well as frameworks like LangChain and Pydantic AI.
Key features and points:
* Provides advanced augmentation of memories including attributes, facts, preferences, relationships, and skills at the entity, process, and session levels.
* Achieves high accuracy and token efficiency in long-conversation memory as demonstrated by LoCoMo benchmark results.
* Offers dedicated SDKs for both Python and TypeScript.
* Supports Model Context Protocol (MCP) for easy connection to developer tools like Claude Code and Cursor.
* Compatible with a wide range of LLMs including OpenAI, Anthropic, Gemini, DeepSeek, and Grok, as well as frameworks like LangChain and Pydantic AI.
A post-retrieval temporal layer designed to improve RAG systems by addressing time-blindness in vector searches. This library implements validity filtering, document kind classification, and exponential decay scoring to ensure retrieved information is fresh and accurate. It functions downstream of existing vector search systems without requiring re-indexing or new infrastructure.
This article details building end-to-end observability for LLM applications using FastAPI and OpenTelemetry. It emphasizes a code-first approach, manually designing traces, spans, and semantic attributes to capture the full lifecycle of LLM-powered requests. The guide advocates for a structured approach to tracing RAG workflows, focusing on clear span boundaries, safe metadata capture (hashing prompts/responses), token usage tracking, and integration with observability backends like Jaeger, Grafana Tempo, or specialized LLM platforms. It highlights the importance of understanding LLM behavior beyond traditional infrastructure metrics.
Zvec is an open-source, in-process vector database — lightweight, lightning-fast, and designed to embed directly into applications. Built on Proxima (Alibaba's battle-tested vector search engine), it delivers production-grade, low-latency, scalable similarity search with minimal setup.
Here’s the simplest version — key sentence extraction:
<pre>
```
def extract_relevant_sentences(document, query, top_k=5):
sentences = document.split('.')
query_embedding = embed(query)
scored = »
for sentence in sentences:
similarity = cosine_sim(query_embedding, embed(sentence))
scored.append((sentence, similarity))
scored.sort(key=lambda x: x 1 » , reverse=True)
return '. '.join( s[0 » for s in scored :top_k » ])
```
</pre>
For each sentence, compute similarity to the query. Keep the top 5. Discard the rest
<pre>
```
def extract_relevant_sentences(document, query, top_k=5):
sentences = document.split('.')
query_embedding = embed(query)
scored = »
for sentence in sentences:
similarity = cosine_sim(query_embedding, embed(sentence))
scored.append((sentence, similarity))
scored.sort(key=lambda x: x 1 » , reverse=True)
return '. '.join( s[0 » for s in scored :top_k » ])
```
</pre>
For each sentence, compute similarity to the query. Keep the top 5. Discard the rest
An AI-powered document search agent that explores files like a human would — scanning, reasoning, and following cross-references. Unlike traditional RAG systems that rely on pre-computed embeddings, this agent dynamically navigates documents to find answers.
A polyglot document intelligence framework with a Rust core that extracts text, metadata, and structured information from PDFs, Office documents, images, and 50+ formats. Available for Rust, Python, Ruby, Java, Go, PHP, Elixir, C#, TypeScript (Node/Bun/Wasm/Deno) or use via CLI, REST API, or MCP server.
FailSafe is an open-source, modular framework designed to automate the verification of textual claims. It employs a multi-stage pipeline that integrates Large Language Models (LLMs) with retrieval-augmented generation (RAG) techniques.
