awesome-repositories.com
Blog
awesome-repositories.com

Descoperă cele mai bune repository-uri open source cu căutare AI.

ExploreazăCăutări recomandateAlternative open-sourceSoftware self-hostedBlogHartă site
ProiectDespreCum realizăm clasamentulPresăServer MCP
LegalConfidențialitateTermeni
© 2026 Bringes Technology SRL·VAT RO45896025·hello@awesome-repositories.com
·

2 repository-uri

Awesome GitHub RepositoriesFact Extraction Pipelines

Automated processes that isolate and save confirmed facts from unstructured interactions into structured memory entries.

Distinct from Document and Unstructured Extraction: Specifically targets the extraction of discrete facts for long-term memory, not general document parsing.

Explore 2 awesome GitHub repositories matching data & databases · Fact Extraction Pipelines. Refine with filters or upvote what's useful.

Awesome Fact Extraction Pipelines GitHub Repositories

Găsește cele mai bune repo-uri cu AI.Vom căuta cele mai potrivite repository-uri folosind AI.
  • embedchain/embedchainAvatar embedchain

    embedchain/embedchain

    58,769Vezi pe GitHub↗

    Embedchain is an LLM memory management framework and RAG orchestration engine designed to provide AI agents with a persistent storage layer. It functions as a long-term memory pipeline that extracts facts from unstructured interactions and stores them as permanent knowledge base entries to retain user preferences and interaction history across sessions. The system employs a hybrid vector database interface that combines semantic embeddings with traditional keyword search. It utilizes an entity-linking knowledge graph to connect related information points and applies temporal ranking to distin

    Provides a pipeline to process unstructured interactions and isolate confirmed facts as permanent long-term memory entries.

    Python
    Vezi pe GitHub↗58,769
  • caviraoss/openmemoryAvatar CaviraOSS

    CaviraOSS/OpenMemory

    3,350Vezi pe GitHub↗

    OpenMemory is an embeddable memory engine for LLM agents that stores, retrieves, and manages conversational context and agent state using semantic indexing and temporal facts. It functions as a semantic memory store backed by vector indexing, where memories are organized by meaning rather than by exact key, and includes a tiered decay engine that gradually reduces the salience of unused memories while compressing cold vectors and fingerprinting dormant entries to conserve storage. The system also maintains a temporal fact database that records factual statements with subject-predicate-object s

    Records factual statements with subject-predicate-object structure and explicit validity windows for time-aware knowledge queries.

    TypeScriptaiai-agentsai-infrastructure
    Vezi pe GitHub↗3,350
  1. Home
  2. Data & Databases
  3. Data Processing Pipelines
  4. Data Processing
  5. Document and Unstructured Extraction
  6. Fact Extraction Pipelines

Explorează sub-etichetele

  • Temporal Fact StoresFact stores that record subject-predicate-object statements with validity periods and support timeline queries and state transitions. **Distinct from Fact Extraction Pipelines:** Distinct from Fact Extraction Pipelines: focuses on storing and querying facts with temporal validity windows, not on extracting facts from unstructured data.