Open source RAG options

Is there anything which is open source where I could take documents ingest them into a vector and graph database have all the inference done locally and use the database for RAG

Yes — there *are* open-source and self-hostable options (or building blocks) you can use to ingest documents, embed them into vectors, and either store and query them (or even combine with a graph structure) locally for RAG-style systems. Depending on how “graph” you want (i.e. a full knowledge graph, or just relationship edges) and how much custom inference or reasoning you want, you’ll pick from different architectures. Below is a survey + suggestions + tradeoffs + a rough architecture you can build. --- ## Key requirements & dimensions to decide Before listing tools, it’s helpful to break down what you want, because that drives which stacks make sense: | Capability | What it means | Why it matters | |---|---|---| | **Document ingestion & parsing** | Reading PDFs, docs, tables, extracting text, metadata, entity/relationship extraction | You need to convert raw docs into something usable (text chunks, entities, relations) | | **Embedding / vectorization** | Use an embedding model (e.g. sentence transformers) to turn chunks/entities into vectors | Essential for similarity search / nearest neighbor retrieval | | **Vector store / vector DB** | Storage and indexing of embeddings + ability to query nearest neighbors (kNN) | The “retriever” backend of RAG | | **Graph / KG / relational store** | Ability to store entities, relationships, typed nodes/edges, traverse, multi-hop queries | For reasoning, relationship queries, explainability, context linking | | **Integration / hybrid querying** | Ability to combine vector-based retrieval + graph traversal, optionally filter or re-rank via graph constraints | To build a “Graph RAG” or hybrid retrieval pipeline | | **Local inference & orchestration** | Ability to run all models (embedding, relation extraction, possibly local LLM) locally or in your environment | To keep everything in your control, no external API dependency | If your “graph” requirement is mild (e.g. just linking chunks or documents via similarity, or tagging with metadata), you might not need a full KG. But if you want multi-hop reasoning, relationship constraints, or richer structure, you’ll want an explicit graph layer. --- ## Open-source / self-hostable options & building blocks Here are some of the components and systems people use (or are building) toward this goal. ### Vector stores & similarity search These are mature and well-used: - **Milvus** — open-source vector database, scalable, supports large datasets. citeturn0search12turn0search5turn0search16 - **Qdrant** — open-source, Rust-based, good performance, supports payload filtering. citeturn0search4turn0search8turn0search10 - **Weaviate** — stores objects + vectors, supports hybrid filtering, schema & modules. citeturn0search14turn0search11turn0search23 - **Chroma (ChromaDB)** — lighter weight, suitable for local/in-memory use, embedding storage + search. citeturn0search13turn0search32 - **FAISS** — library rather than full DB, extremely optimized vector operations. citeturn0search29 - **MicroNN** — a new on-device updatable vector index for low memory environments. citeturn0academia35 These can cover your vector retrieval layer locally. ### Graph / knowledge graph / hybrid DBs These are options or research systems for storing relationships: - **HelixDB** — an open-source “graph-vector” database built in Rust. It supports storing graph relationships *and* vector embeddings in one system. citeturn1search3turn1search8turn1search1 - **SurrealDB** — a multi-model platform that supports combining vector similarity, document, and graph queries in a unified query. citeturn1search10 - **TigerGraph** — has hybrid graph + vector search (e.g. vector attributes on nodes) in their recent editions. citeturn1search5turn1academia24 - **CozoDB** — described as a hybrid relational-graph-vector DB exposing the HNSW graph as a queryable graph. citeturn1search4 - **ArangoDB** — multi-model DB (graphs + documents). But its licensing has shifted; community editions may have limitations. citeturn1search20 - **OrientDB** — open-source multi-model (graph + document) DB. citeturn1search19 Additionally, systems or frameworks for combining vector + graph aspects or doing hybrid RAG: - **GraphAide** — uses vector database + graph database: embeddings plus structural graph for improved retrieval. citeturn1search13 - **LightRAG** — a framework combining graph indexing with vector indexing to allow hybrid retrieval. (Open source) citeturn0academia37 - **Graph-RAG / Graph-based RAG approaches** — many blog posts and research showing how to combine KG + vector retrieval. citeturn0search1turn1search15 Also, in ingestion & graph building: - **ContextClue / Graph Builder** — open-source piece that helps convert messy documents (PDFs, tables) into a knowledge graph to be consumed by a system. citeturn1search11 And for open-source RAG frameworks: - **RAGFlow** — an open-source RAG engine that handles ingestion, search, context assembly. citeturn0search24 - **LlamaIndex (OSS ingestion + vector DB)** — examples of doing RAG entirely with open-source tools, embedding with sentence transformers, storing in vector stores (or even Postgres) and querying. citeturn0search18 --- ## How you might stitch them together (architecture sketch) Here’s a rough blueprint you can assemble, depending on your “graph vs hybrid” needs: 1. **Document ingestion / preprocessing** - Parse PDFs, Word docs, tables → extract text, metadata, sections. - Use an entity/relationship extractor (could be via an LLM or specialized IE model) to identify entities and relations between them. (Graph Builder / custom pipeline) - Chunk text appropriately, assign metadata (doc ID, section, entities, tags). 2. **Embedding / vectorization** - Use a local embedding model (e.g. Sentence Transformers, or a lightweight open model) to transform chunks and/or entity descriptions into embedding vectors. 3. **Storage / indexing** - Store embeddings + metadata in a vector store (e.g. Milvus, Qdrant, Chroma, etc.). - Store entity/relation graph in a graph/KG database (e.g. HelixDB, SurrealDB, TigerGraph, etc.). Entities link to document chunks or sections. 4. **Hybrid retrieval / query time** - Given a user query, embed it, do a vector search to get top-k relevant chunks or entities. - Use the graph to do multi-hop traversal: e.g. “given these entities, expand to connected ones,” or filter edges, or enforce logical constraints. - Optionally re-rank results using graph signals (e.g. centrality, path relevance, edge weights). - Assemble the final context (chunks + possibly entity summaries or path-derived context) to feed into your local LLM. 5. **Local inference / LLM prompt** - You run your LLM (local, e.g. via llama.cpp, Mistral, etc.) on the assembled context + query to produce the answer. 6. **(Optional) Feedback loop / updating** - If your system gets new docs or updates, you re-ingest, embed, update the graph, maybe re-index. - You might also capture user query interactions to adjust weights or edge confidences. This pattern gives you the benefit of vector similarity (semantic matching) plus structured reasoning or constraints from the graph. --- ## Tradeoffs & challenges - **Complexity**: Integrating two systems (vector + graph) plus managing consistency (entity linking, updates) is more complex than a pure vector RAG pipeline. - **Latency**: Graph traversals or multi-hop reasoning can add overhead. - **Scalability vs simplicity**: If your dataset is moderate, a simpler design (pure vector store + metadata filtering) may be easier. - **Graph quality**: The usefulness of your graph depends on how well you extract correct entities and relationships from text. - **Local resource constraints**: Running embedding models and graph DBs locally can eat memory/CPU; you must pick light models or scale down. --- ## Recommendations / what I’d try first If I were building this today, I’d do the following: - Start with a vector-only RAG pipeline (embedding → store in Milvus / Qdrant or Chroma locally → retrieval → local LLM). This gives a baseline. - Add entity and relation extraction to build a mini knowledge graph over documents (use Graph Builder or custom LLM prompts). - Use HelixDB (for graph + vector in one) or SurrealDB or another hybrid DB to integrate the two retrieval modes. - On query time, do a hybrid retrieval: vector search → expand via graph → re-rank → feed LLM. Given current tooling, **HelixDB** is especially interesting because it aims to combine graph + vector in one unified DB. citeturn1search3turn1search8 Also, **SurrealDB** is appealing if you want a unified query interface (vector + graph + document) without having to glue two separate DBs. citeturn1search10 If your “graph” requirements are lighter (just connecting document chunks or enforcing metadata constraints), you might get by with a vector DB + metadata filters + simple adjacency in a secondary store. --- If you like, I can suggest a **ready-to-run open-source project / demo repo** that already glues some of this together, or even help you set up a minimal local stack. Do you want me to propose one (with code) to bootstrap your setup?

the graph is essential and I want to process it all locally but yes to your question

Yes — there *are* some promising open-source (or mostly open) projects and emerging research that aim exactly at “graph + vector” local RAG, and HelixDB is probably the closest to a turn-key solution right now. Below I’ll (1) highlight HelixDB and some alternatives, (2) walk through how you might build a pipeline around it (or integrate parts yourself), and (3) show some example repos / code you can use to bootstrap. --- ## HelixDB: probably your best bet ### What is it - **HelixDB** is an open-source graph-vector database built in Rust. It aims to combine: - **Graph / relation storage** (nodes, edges, properties) - **Vector / embedding support** (vectors as first-class types) - **Hybrid queries** (you can traverse relationships and do similarity search in one system) - It also has built-in schema (HelixQL) and ingestion tools. citeturn0search3turn0search11turn0search29turn0search34 - Its license is **AGPL-3.0** citeturn0search3 - Because it is designed from the ground up for AI applications, you won’t have to “glue together” a vector DB + graph DB — Helix aims to be the unified backend. citeturn0search3turn0search18turn0search34 - On Hacker News / community posts, people mention Helix treating vectors as “first-class types within a property graph model,” so you can traverse “from a person to their documents to a semantically similar report” in one query. citeturn0search6turn0search34turn0search11 - Under the hood, it uses LMDB as a storage engine in some parts. citeturn0search29turn0search3 ### Strengths & tradeoffs **Pros:** - Unified model means lower engineering overhead (no separate DB for vector vs graph). - Lower latency / more efficient combining of graph traversal + semantic similarity. - Maintains relational structure which is essential for your use case. - Fully local (you run it yourself). - Strong community interest and momentum. **Cons / caveats:** - It is relatively new; may have rough edges, fewer integrations and maturity vs incumbents. - The AGPL license has “copyleft” implications — if you embed it in a service, you need to comply with license terms. - You may still need to build the ingestion / extraction pipeline (entity / relation extraction, chunking) yourself. - As with any system, you'll need to scale and test it for your doc volume and query load. Given your requirement that the graph is *essential*, HelixDB is a very promising choice. --- ## Other approaches / components you may combine or consider If HelixDB doesn’t fully cover your needs (or you want fallback options, or more control), you can build your own hybrid graph + vector setup using these components. ### Graph + vector hybrid approaches - **GraphRAG (Microsoft’s project)** — an open-source pipeline for extracting a knowledge graph from raw text + layering RAG on top. You can adapt parts of it to local inference workflows. citeturn0search5turn0search13turn0search35turn0search36 - **GraphRAG-Local-UI** — a fork/adaptation of GraphRAG aimed at local models and includes UI + local model support. citeturn0search8 - **KG-RAG (Vector Institute)** — a Python toolkit combining knowledge graph building + retrieval and LLM interaction. citeturn0search7 - **LightRAG** — a research framework that merges graph and vector indexing (open-source). citeturn0academia39 - **NodeRAG** — a newer framework (2025) proposing structurally richer graphs for RAG. citeturn0academia38 - **Neo4j + embeddings** — many tutorials show how to build a KG in Neo4j and independently store embeddings (in a vector DB). Use the graph for relational reasoning, and vector DB for semantic retrieval; then combine results. citeturn0search23turn0search19turn0search36 ### Building block components you’ll likely need Even with HelixDB or the above frameworks, you’ll need: - **Document ingestion / parsing** (PDFs, docs, metadata) - **Entity & relation extraction** — using LLMs or IE models - **Chunking / segmentation** — breaking text into manageable units - **Embedding models (local)** — e.g. sentence transformers, HuggingFace models - **Query orchestration / reasoning logic** — combining graph traversal + vector lookup + re-ranking - **Local LLM inference** — e.g. llama.cpp, Mistral, etc. These are mostly standard; the hard part is integrating them well with the graph system. --- ## Example bootstrap / sample repos you can clone Here are some repos / projects you can inspect or adapt: | Repo | What it offers / use case | How useful for you | |---|---|---| | **HelixDB (helix-db on GitHub)** | The core graph-vector DB; ingestion + query interface. citeturn0search3 | Your core backend; you build ingestion on top. | | **GraphRAG Local UI** | Local adaptation of GraphRAG with local model support + UI. citeturn0search8 | Good example of turning GraphRAG into a usable local system. | | **KG-RAG (Vector Institute)** | Python-based KG + RAG hybrid pipeline. citeturn0search7 | You can re-use their graph construction & query logic. | | **Awesome-GraphRAG** | A curated list of GraphRAG / related projects & papers. citeturn0search28 | Good reference to find related ideas and code. | Also check out the “Graph RAG over documents” tutorials — e.g. “Building a Graph RAG System with Open Source Tools” (on ragaboutit.com) is a nice walkthrough of how to wire up a pipeline. citeturn0search36 --- ## Suggested path to get a minimal working local Graph RAG Here’s a roadmap (with steps) you can follow to go from zero → working local Graph RAG: 1. **Prototype ingestion & graph construction** - Take a small set of documents (PDFs, text). - Use an LLM or IE model to extract entities (named entities, concepts) and relations (e.g. “X is part of Y”, “X influences Y”). - Build a mini graph (e.g. using NetworkX or a graph DB) where nodes represent entities or document-chunk nodes, edges represent relations. - Associate each node (or chunk) with metadata (doc ID, text). 2. **Embed nodes / chunks** - Choose a local embedding model (e.g. a sentence transformer you can run on your hardware). - Embed each node (or chunk). Store embedding in a vector index (initially you can use FAISS in memory). - Also associate embedding back to node IDs. 3. **Set up a graph + vector database (e.g. HelixDB)** - Install HelixDB locally (via its CLI). citeturn0search3turn0search30turn0search34 - Define your schema: nodes, edges, properties, vector fields. - Ingest your nodes + edges + embeddings into HelixDB. 4. **Query logic / RAG pipeline** - Given a user query, embed the query vector. - Query HelixDB (or your graph-vector DB) to get top-k semantically similar nodes *and* traverse from those nodes to neighbors (e.g. multi-hop) to get context. - Optionally re-rank using graph centrality, edge weights, or path relevance. - Take the text associated with retrieved nodes / chunks and feed into your local LLM for final answer. 5. **Iterate & scale** - Add more documents, incremental graph / embedding updates. - Optimize for latency, caching, indexing strategies. - Add heuristics (pruning irrelevant edges, filtering by types, etc.). - Build interface / API. If HelixDB works well, you’ll have a fully local graph + vector RAG engine, with no external dependencies. If Helix isn’t sufficient (e.g. for custom types or more control), you can fallback to “vector DB + graph DB + glue logic” architecture: e.g. Qdrant or Milvus for embeddings + Neo4j / TigerGraph for relationships + your own orchestration code that merges the results. If you like, I can prepare a **starter repo / code skeleton** (Python + HelixDB) for you — do you want me to generate that?

yes

Excellent. To confirm before I scaffold the repo — here’s what I’ll include so it fits your exact goals: --- ### 🧠 **Project Goal** A **fully local Graph + Vector RAG system** that: - Ingests documents (PDF, Markdown, or text) - Extracts entities & relationships → builds a **graph** - Embeds text chunks/entities locally → stores them as vectors - Stores both graph and vectors in **HelixDB** - Performs hybrid retrieval (semantic + relational) - Sends the retrieved context to a **local LLM** (e.g. Ollama or llama.cpp) for inference --- ### 🧩 **Planned Tech Stack** | Component | Tool / Library | Notes | |------------|----------------|-------| | **Graph + Vector DB** | [HelixDB](https://github.com/HelixDB/helix-db) | Unified backend | | **Document parsing** | `unstructured` + `pdfminer.six` | Extracts text + metadata | | **Entity / relation extraction** | Local LLM via `ollama` or optional HuggingFace model | Simple triplet extraction | | **Embedding** | SentenceTransformer (e.g. `all-MiniLM-L6-v2`) | CPU-friendly, runs locally | | **Local inference (RAG answer)** | `ollama` or `llama.cpp` wrapper | Completely local | | **App / API** | FastAPI | For querying your local RAG system | | **Graph visualization (optional)** | PyVis or NetworkX | For exploring connections | --- ### 📂 **Repository Structure** ``` graph_vector_rag/ │ ├── ingestion/ │ ├── parse_docs.py # Extracts text chunks │ ├── extract_relations.py # Uses LLM to get entities & relations │ └── embeddings.py # Creates embeddings for chunks/entities │ ├── db/ │ ├── helix_interface.py # Connects to HelixDB, creates schema │ └── ingest_data.py # Loads parsed data into Helix │ ├── rag/ │ ├── retrieve.py # Hybrid query: vector + graph traversal │ └── generate_answer.py # Feeds retrieved context to LLM │ ├── api/ │ └── main.py # FastAPI endpoint for queries │ ├── config.yaml # Paths, model names, DB settings ├── requirements.txt └── README.md ``` --- ### 🧱 **Pipeline Flow** 1. `parse_docs.py` → extract text chunks + metadata 2. `extract_relations.py` → detect entities + edges 3. `embeddings.py` → embed chunks/entities locally 4. `helix_interface.py` → create schema (nodes, edges, vector fields) 5. `ingest_data.py` → populate HelixDB 6. `retrieve.py` → query graph + vector similarity 7. `generate_answer.py` → local LLM RAG answer 8. API exposes `/query` endpoint --- ### ⚙️ **Options** I can generate one of these versions: **A)** Minimal prototype — small, single-file scripts for ingest + query. **B)** Full modular structure (like above), ready to extend into a full local RAG framework. --- Which one would you like me to create — **A (minimal working demo)** or **B (modular full structure)**?

instead I want you to create a prompt for CLIne to build this for me, include in the prompt for instructions for CLIne to create proper documentation such as a ledger or checklist for it to go along and edit as it goes

Perfect! Here’s a **comprehensive CLIne prompt** designed to have it build your full local Graph + Vector RAG system with HelixDB while maintaining proper documentation/ledger/checklist for every step: --- ## CLIne Prompt: Build Local Graph + Vector RAG System with HelixDB ``` You are CLIne, an autonomous AI development assistant capable of building full-stack software systems iteratively. Your task is to create a fully local **Graph + Vector Retrieval-Augmented Generation (RAG) system** using HelixDB and local LLMs. You will handle code generation, project structure, documentation, and incremental tracking of progress automatically. Follow these detailed instructions: --- ### 1. Project Goal Build a local RAG system that: - Ingests documents (PDF, Markdown, text) - Extracts entities and relationships → builds a **graph** - Creates embeddings of text chunks/entities locally → stored in **HelixDB** - Stores both graph + vectors in HelixDB - Performs **hybrid retrieval**: semantic similarity + graph traversal - Provides **local LLM inference** for RAG answers - Exposes an optional API for queries (FastAPI or similar) - Maintains complete **documentation / ledger / checklist** of development --- ### 2. Tech Stack - **Graph + Vector DB**: HelixDB - **Document parsing**: `unstructured`, `pdfminer.six`, or similar - **Entity & relation extraction**: local LLM (Ollama, llama.cpp, or HF model) - **Embeddings**: SentenceTransformers (e.g., `all-MiniLM-L6-v2`) or compatible local model - **RAG LLM inference**: Ollama or llama.cpp - **API layer**: FastAPI - **Optional visualization**: NetworkX + PyVis --- ### 3. Project Structure Use a **modular structure**: ``` graph_vector_rag/ │ ├── ingestion/ │ ├── parse_docs.py # extract text chunks │ ├── extract_relations.py # detect entities & edges │ └── embeddings.py # generate embeddings │ ├── db/ │ ├── helix_interface.py # connect & define schema │ └── ingest_data.py # insert data into HelixDB │ ├── rag/ │ ├── retrieve.py # hybrid vector + graph query │ └── generate_answer.py # feed context to local LLM │ ├── api/ │ └── main.py # FastAPI endpoints │ ├── config.yaml # paths, models, DB settings ├── requirements.txt └── README.md ``` --- ### 4. Development Instructions for CLIne 1. **Iterative Build**: Develop the project incrementally, completing one module at a time. After finishing each module, test and validate locally before proceeding. 2. **Documentation Ledger**: - Maintain a **`ledger.md`** that tracks: - Modules completed - Functions/classes added - Important implementation notes - Issues encountered & resolved - Remaining tasks/checklist - Update this ledger **after every change**. 3. **Checklist**: Maintain a **checklist** within `ledger.md` for the system: - [ ] Document parsing implemented - [ ] Entity/relation extraction implemented - [ ] Embeddings generated and stored - [ ] HelixDB schema created - [ ] Data ingested into HelixDB - [ ] Hybrid query implemented - [ ] LLM RAG inference implemented - [ ] API endpoint working - [ ] Local testing completed - [ ] Documentation complete 4. **Self-Audit**: After completing a module, review all code for: - Readability - Comments & docstrings - Testability - Integration with other modules 5. **Code Style**: Use Python best practices, type hints, and modular design. Keep code maintainable and extensible. 6. **Testing**: Include minimal tests/examples for each module (can be `if __name__ == "__main__"` examples or pytest functions). 7. **Local-Only Constraint**: All inference, embedding, graph/vector operations, and APIs must run locally. No external API calls allowed. --- ### 5. Iterative Plan for CLIne - Step 1: Generate `requirements.txt` and project skeleton - Step 2: Implement **document ingestion** - Step 3: Implement **entity & relation extraction** - Step 4: Implement **embedding generation** - Step 5: Implement **HelixDB connection and schema** - Step 6: Implement **data ingestion into HelixDB** - Step 7: Implement **hybrid retrieval (vector + graph)** - Step 8: Implement **local LLM RAG inference** - Step 9: Implement **FastAPI query endpoint** - Step 10: Add **documentation, ledger, and checklist** - Step 11: Run **end-to-end test** with sample documents --- ### 6. Output Instructions - After each module is generated, **append/update the `ledger.md`** with: - Module name - Description of functionality - Code files created/edited - Checklist progress - Notes for next steps - Ensure all code is **ready to run locally** and references only **local resources/models**. --- ### 7. Additional Guidance - Use modular functions, not monolithic scripts. - Include docstrings for all functions/classes. - For embeddings or LLMs, provide **config.yaml** options so they can be switched easily. - Make it robust to different document types (PDF, TXT, Markdown). - When building hybrid retrieval, include an **example query** showing vector + graph traversal results. --- **Your mission, CLIne:** Build this system fully, iteratively, and maintain the ledger/checklist as you progress. Prioritize local-only operation, modularity, and clear documentation. After finishing each step, update the ledger and checklist, then continue to the next. --- ``` I can also generate a **ready-to-use `ledger.md` template** for CLIne so it starts tracking progress immediately. Do you want me to create that template next?