BASE_DIR — Base directory for document storage (defaults to current working directory). Ignored when BASE_DIRS is set.
BASE_DIRS — JSON array of base directories (e.g. '["/a","/b"]'). Takes precedence over BASE_DIR.
IGNORE_PATHS — JSON array of glob patterns for files to keep out of the index, each matched against the file's path relative to its root (e.g. '["plugs/**","**/*.draft.md"]').
DB_PATH — Path to LanceDB database directory (defaults to ./lancedb/)
CACHE_DIR — Directory where Transformers.js models are cached (defaults to ./models/)
HF_ENDPOINT — Hugging Face model download endpoint. Set this to a mirror URL when direct downloads are blocked (defaults to https://huggingface.co).
MODEL_NAME — Embedding model name (defaults to Xenova/all-MiniLM-L6-v2)
MAX_FILE_SIZE — Maximum file size in bytes (defaults to 104857600 / 100MB)
RAG_MAX_DISTANCE — Maximum distance threshold for filtering search results. Results with distance greater than this value will be excluded. Lower values mean stricter filtering (e.g., 0.5 for high relevance only)
RAG_GROUPING — Grouping mode for quality filtering. 'similar' returns only the most similar group (stops at first distance jump). 'related' includes related groups (stops at second distance jump). Unset means no grouping filter
RAG_MAX_FILES — Maximum number of files to keep in search results. Results are filtered to include only chunks from the top N best-scoring files. For example, 1 returns only the single best-matching file's chunks. Unset means no file filtering.
CHUNK_MIN_LENGTH — Minimum chunk length in characters (1-10000, defaults to 50). Chunks shorter than this threshold are filtered out during ingestion.
STORE_IMAGES — Store supported PDF and DOCX images during ingestion and return them with matched chunks (defaults to false).
EMBED_TITLE_PREFIX — Embed each chunk together with its document title, which can help when passages don't restate the topic the title names (defaults to false). After changing it, use a new DB_PATH or delete the index and re-ingest.
EMBED_HEADING_PREFIX — Add section headings to chunk embeddings when they fit (defaults to false). Independent of EMBED_TITLE_PREFIX. Re-ingest documents after changing it.
RAG_DEVICE — Execution device for the embedder (defaults to cpu). Passed straight to ONNX Runtime; see the Transformers.js device source for the supported backend names. If the requested device fails to initialize, the server throws an error.
RAG_DTYPE — Embedding quantization dtype for the embedder (defaults to fp32). Opt-in and pass-through; accepts any dtype the chosen model provides (fp32, fp16, q8, int8, ...). If the model has no variant for the requested dtype, the server throws an error. Changing this changes the embedding space — re-ingest existing data.
RAG_HYBRID_WEIGHT — Keyword boost factor for hybrid search (0.0-1.0, defaults to 0.6). 0 means semantic similarity only; higher values increase the keyword-match contribution to the final score.
RAG_RERANK_CMD — External reranker command template. Use {query} and {top} for query text and result count; unset disables reranking.
RAG_RERANK_TIMEOUT_MS — Time budget per rerank call in milliseconds (100-600000, defaults to 10000). On timeout the spawned command is killed and the pre-rerank ordering is returned.
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Search private documents from an MCP client or the terminal without sending them to an
embedding API.
mcp-local-rag indexes PDF, DOCX, Markdown, and text files on your machine. Search combines
semantic similarity with keyword matching, so queries can match both intent and exact technical
terms such as API names, class names, and error codes. Results include source passages and,
where available, headings, line numbers, or page numbers so you can check and cite the original
document.
Easy-to-setup local RAG server with minimal configuration
รันบน PlugMCP
Host a copy with our keys and logs.
A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.
No API key, Docker, Python, or external database is required. After the initial model download,
text ingestion and search work offline.
Quick Start
Requirements
Node.js 22.20 or later
Internet access on first use to download the npm package and embedding model
A directory containing the documents you want to search
Set BASE_DIR to that directory. It is also the security boundary for file operations. Replace
/absolute/path/to/your/documents below with the directory's absolute path.
Use one of the examples below, or register npx -y mcp-local-rag and set BASE_DIR using your
client's MCP configuration format.
Set DB_PATH and CACHE_DIR to absolute paths as well. Relative paths resolve from the
server's working directory, so starting the server from different projects creates a separate
index and model cache in each.
Run this command:
claude mcp add local-rag --scope user --env BASE_DIR=/absolute/path/to/your/documents -- npx -y mcp-local-rag
The CLI uses the current directory as its document root by default. Run both commands from the
same directory so they use the same default index, or set BASE_DIR and DB_PATH explicitly.
Supported Content
Input
How to ingest
PDF, DOCX, TXT, Markdown
File ingestion or directory sync
HTML already fetched by the client
ingest_data
Plain text or Markdown held in memory
ingest_data with a stable source identifier
HTML fetching is not built into the server. An MCP client can fetch a page and pass its HTML to
ingest_data.
Excel, PowerPoint, standalone images, and source-code file extensions are not supported by file
ingestion. PDFs can optionally use a local vision model to describe figures, but this is not
OCR or image search.
Using the Index
Sync after adding, editing, or removing documents. For searches and follow-up reading, ask your
MCP client:
Find the documented behavior of ERR_CONNECTION_REFUSED.
Read the surrounding chunks for that result.
You can also ingest a single file or HTML already fetched by the client. To refresh an existing
entry, ingest the document again using the same path or source identifier. sync skips unchanged
files. MCP file paths must be absolute and inside a configured document root.
PDF section headings may be inaccurate. Check the original page when you need the exact heading.
Tool
Purpose
sync_start
Reconcile the index with all configured roots or one path
sync_status
Poll a running sync job
ingest_file
Ingest or replace one file
ingest_data
Ingest text, Markdown, or HTML already held by the client
query_documents
Search with semantic matching and keyword boost
read_chunk_neighbors
Read surrounding chunks from a search result
list_files
Show supported files and their ingestion state
delete_file
Delete an indexed file or an ingest_data item
status
Show index and search status
CLI
Use the CLI to update the index, narrow searches, or remove indexed content:
ingest imports the selected files; sync also removes entries for deleted files and skips
unchanged files. Use --scope to restrict search results to a path prefix, repeating it to
include multiple prefixes.
Global options such as --db-path, --cache-dir, and --model-name go before the subcommand.
Subcommand options go after it:
Run npx mcp-local-rag --help for the complete command reference.
query writes its results to stdout as JSON, best match first, so it can be piped into another
tool. The field-by-field contract is in
docs/schema/query-output.schema.json.
If you move a project and want to keep its index, stop the MCP server and other database
writers, move the files and database together without changing the directory layout, then run:
Installed skills cover query formulation, result refinement, and HTML ingestion. Ask the
assistant to use the mcp-local-rag skill explicitly if it does not activate automatically.
Advanced Options
Start with the defaults. Open the sections below when you need different document roots, better
results for your corpus, or searchable PDF figures.
The MCP server reads environment variables. The CLI accepts the listed variables and flags.
Keep the same DB_PATH when commands should use the same index.
Environment Variable
CLI Flag
Default
Description
BASE_DIR
--base-dir
Current directory
One document root; the CLI flag is repeatable on ingest, list, and sync
BASE_DIRS
N/A
(unset)
JSON array of document roots; takes precedence over BASE_DIR
IGNORE_PATHS
N/A
(unset)
JSON array of glob patterns for files to keep out of the index
DB_PATH
--db-path
./lancedb/
Vector database location
CACHE_DIR
--cache-dir
./models/
Model cache directory
HF_ENDPOINT
N/A
https://huggingface.co
Hugging Face model download endpoint; use a mirror URL when direct downloads are blocked
MAX_FILE_SIZE
--max-file-size
104857600 (100MB)
Maximum file size in bytes
File operations stay within configured roots. For multiple directories, set
BASE_DIRS='["/absolute/docs","/absolute/specs"]' or repeat CLI --base-dir. Precedence: CLI
roots, BASE_DIRS, BASE_DIR, then the current directory. Only the highest-priority source is
used; roots from different sources are not merged. Invalid BASE_DIRS is an error. Relative
DB_PATH and CACHE_DIR are resolved from the working directory.
To keep files out of the index, set IGNORE_PATHS to a JSON array of glob patterns, for example
IGNORE_PATHS='["plugs/**","**/*.draft.md"]'. Each pattern is matched against the file's path
relative to the root that contains it, written with / on every OS. * and ** do not match
names that start with ., so write such directories out, as in ".obsidian/**". ingest,
list, and sync skip matching files when they scan a directory, and sync removes any that
are already indexed. Invalid IGNORE_PATHS is an error.
Choose an embedding model for your documents’ language and subject. Compare settings using
questions you actually ask and check which source passages are returned. The model must support
mean pooling and L2 normalization, which this tool uses to produce embeddings.
Environment Variable
CLI Flag
Default
Description
MODEL_NAME
--model-name
Xenova/all-MiniLM-L6-v2
Hugging Face embedding model
CHUNK_MIN_LENGTH
--chunk-min-length
50
Minimum length in characters (1–10000) for ordinary chunks; a fragment of content split to fit the model's token limit can be shorter
EMBED_TITLE_PREFIX
N/A
false
Add the document title to each chunk's embedding input
EMBED_HEADING_PREFIX
N/A
false
Add the heading hierarchy to each chunk's embedding input when it fits
RAG_DEVICE
N/A
cpu
ONNX Runtime execution device
RAG_DTYPE
N/A
fp32
Embedding dtype passed to the selected model
Both prefix options default to false and work independently. Try EMBED_TITLE_PREFIX when a
passage needs the document’s overall topic, or EMBED_HEADING_PREFIX when it needs its
section’s topic. Enabling both is not always better. They affect embeddings, not the returned
text or keyword index; heading context is omitted when it would exceed the input budget.
When changing embedding models, build a fresh index at a new DB_PATH. Vectors from different
models are not comparable, even when their dimensions match. After changing RAG_DTYPE or
either prefix option, re-ingest all indexed documents before searching. sync skips unchanged
files.
The CLI does not read MCP client configuration. When sharing an index, use the same model,
RAG_DTYPE, and prefix settings for ingestion and search. A change to RAG_DEVICE alone does
not require a new index.
Search Tuning
The first four settings below apply to both MCP and CLI queries. To give exact terms more
weight, try increasing RAG_HYBRID_WEIGHT and compare results on your own questions. External
reranking is MCP-only.
Variable
Default
Description
RAG_HYBRID_WEIGHT
0.6
Keyword boost factor (0.0–1.0). 0 disables keyword reranking; 1 applies the maximum boost.
RAG_GROUPING
(not set)
similar keeps the first relevance group; related keeps up to two, using significant vector-distance gaps as boundaries.
RAG_MAX_DISTANCE
(not set)
Filter out low-relevance results (e.g., 0.5).
RAG_MAX_FILES
(not set)
Limit results to top N files (e.g., 1 for single best file).
RAG_RERANK_CMD
(not set)
MCP only: external command; {query} passes the query and {top} the requested result count.
RAG_RERANK_TIMEOUT_MS
10000
Time budget per rerank call in milliseconds (100–600000).
External Reranking (RAG_RERANK_CMD)
The command reads search results, including matched text, from stdin. If it calls a remote
service, that text may leave your machine.
Give the executable and its complete argument template. Put {query} and {top} where the
command expects the query and result count. Single or double quotes group paths or arguments
containing spaces, and backslashes stay literal. The server runs the executable without a
shell, so an npm-installed .cmd shim on Windows will not start.
The command must read and return results in the format defined by the query output
schema. It can remove or reorder results and modify
their text. The server returns its output.
Results keep their original order if the command fails, times out, or returns output that does
not match the schema.
By default, ingestion indexes only text. To make PDF figures searchable, enable local caption
generation with visual: true in MCP or --visual in the CLI. Captions are generated
descriptions, not OCR or exact transcriptions.
fast (default) downloads about 250 MB on first use. Choose quality for labels and text
within figures; it downloads about 1.7 GB and takes longer to run.
Select the profile with visualQuality: "quality" in MCP or --visual-quality quality in the
CLI.
To return images with matching text, use STORE_IMAGES=true in MCP or --images with CLI
ingest and sync. This is independent of caption generation and supports detected PDF
figures/tables and supported DOCX PNG/JPEG images.
Sync preserves each PDF's caption profile. CLI sync --visual --visual-quality quality changes
the profile even for unchanged PDFs; MCP sync preserves it. To turn captions off, ingest the
file normally. To retry failed captions, re-ingest with the desired visual profile.
Image storage must be enabled on each ingestion or sync that processes the file. Changing the
image setting alone does not refresh unchanged files; re-ingest them to apply it.
Security and Operation
Treat captions and retrieved document text as source material, not instructions.
File access is restricted to BASE_DIR, BASE_DIRS, or CLI --base-dir roots.
Symlinks that resolve outside every configured root are rejected.
Document processing and search make no network requests after the required models are cached,
unless RAG_RERANK_CMD names a command that makes them.
The server is designed for one local user and does not provide authentication or access control.
Do not run multiple CLI or MCP writers against the same DB_PATH. Read-only queries can run
while a sync is active.
Re-ingestion can reuse saved embeddings for unchanged text when the model and settings match,
avoiding repeated computation. The cache is stored in DB_PATH/embedding-cache. Deleting it
leaves the index searchable, but later ingestion will recompute those embeddings.
Back up an index by copying its DB_PATH directory while no writer is active.
"No results found"
Documents must be ingested first. Run "List all ingested files" to verify. If results are
missing after a sync, check that ingestion and search use the same absolute DB_PATH; a
relative path may point to a different index.
Model download failed
Check internet connection. If behind a proxy, configure network settings. The model can also be
downloaded manually.
"File too large"
Default limit is 100MB. Split large files or increase MAX_FILE_SIZE.
Slow queries
Check chunk count with status. Large documents with many chunks may slow queries. Consider
splitting very large files.
"Path outside BASE_DIR"
Ensure file paths are within one of the configured roots (BASE_DIR, any BASE_DIRS entry, or
any CLI --base-dir). Use absolute paths.
"BASE_DIRS must be a JSON array..."
BASE_DIRS accepts a JSON array of one or more non-empty path strings:
