Merge branch 'feat/ragas-evaluation' of https://github.com/anouar-bm/LightRAG into feat/ragas-evaluation
This commit is contained in:
anouarbm
commit
debfa0ec96
6 changed files with 63 additions and 18 deletions
14
Dockerfile
14
Dockerfile
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@ -1,3 +1,5 @@
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# syntax=docker/dockerfile:1
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# Frontend build stage
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FROM oven/bun:1 AS frontend-builder
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@ -7,7 +9,8 @@ WORKDIR /app
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COPY lightrag_webui/ ./lightrag_webui/
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# Build frontend assets for inclusion in the API package
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RUN cd lightrag_webui \
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RUN --mount=type=cache,target=/root/.bun/install/cache \
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cd lightrag_webui \
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&& bun install --frozen-lockfile \
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&& bun run build
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@ -40,7 +43,8 @@ COPY setup.py .
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COPY uv.lock .
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# Install base, API, and offline extras without the project to improve caching
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RUN uv sync --frozen --no-dev --extra api --extra offline --no-install-project --no-editable
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RUN --mount=type=cache,target=/root/.local/share/uv \
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uv sync --frozen --no-dev --extra api --extra offline --no-install-project --no-editable
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# Copy project sources after dependency layer
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COPY lightrag/ ./lightrag/
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@ -49,7 +53,8 @@ COPY lightrag/ ./lightrag/
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COPY --from=frontend-builder /app/lightrag/api/webui ./lightrag/api/webui
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# Sync project in non-editable mode and ensure pip is available for runtime installs
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RUN uv sync --frozen --no-dev --extra api --extra offline --no-editable \
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RUN --mount=type=cache,target=/root/.local/share/uv \
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uv sync --frozen --no-dev --extra api --extra offline --no-editable \
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&& /app/.venv/bin/python -m ensurepip --upgrade
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# Prepare offline cache directory and pre-populate tiktoken data
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@ -81,7 +86,8 @@ ENV PATH=/app/.venv/bin:/root/.local/bin:$PATH
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# Install dependencies with uv sync (uses locked versions from uv.lock)
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# And ensure pip is available for runtime installs
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RUN uv sync --frozen --no-dev --extra api --extra offline --no-editable \
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RUN --mount=type=cache,target=/root/.local/share/uv \
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uv sync --frozen --no-dev --extra api --extra offline --no-editable \
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&& /app/.venv/bin/python -m ensurepip --upgrade
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# Create persistent data directories AFTER package installation
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@ -1,3 +1,5 @@
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# syntax=docker/dockerfile:1
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# Frontend build stage
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FROM oven/bun:1 AS frontend-builder
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@ -7,7 +9,8 @@ WORKDIR /app
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COPY lightrag_webui/ ./lightrag_webui/
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# Build frontend assets for inclusion in the API package
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RUN cd lightrag_webui \
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RUN --mount=type=cache,target=/root/.bun/install/cache \
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cd lightrag_webui \
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&& bun install --frozen-lockfile \
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&& bun run build
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@ -40,7 +43,8 @@ COPY setup.py .
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COPY uv.lock .
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# Install project dependencies (base + API extras) without the project to improve caching
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RUN uv sync --frozen --no-dev --extra api --no-install-project --no-editable
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RUN --mount=type=cache,target=/root/.local/share/uv \
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uv sync --frozen --no-dev --extra api --no-install-project --no-editable
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# Copy project sources after dependency layer
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COPY lightrag/ ./lightrag/
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@ -49,7 +53,8 @@ COPY lightrag/ ./lightrag/
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COPY --from=frontend-builder /app/lightrag/api/webui ./lightrag/api/webui
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# Sync project in non-editable mode and ensure pip is available for runtime installs
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RUN uv sync --frozen --no-dev --extra api --no-editable \
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RUN --mount=type=cache,target=/root/.local/share/uv \
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uv sync --frozen --no-dev --extra api --no-editable \
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&& /app/.venv/bin/python -m ensurepip --upgrade
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# Prepare tiktoken cache directory and pre-populate tokenizer data
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@ -81,7 +86,8 @@ ENV PATH=/app/.venv/bin:/root/.local/bin:$PATH
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# Sync dependencies inside the final image using uv
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# And ensure pip is available for runtime installs
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RUN uv sync --frozen --no-dev --extra api --no-editable \
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RUN --mount=type=cache,target=/root/.local/share/uv \
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uv sync --frozen --no-dev --extra api --no-editable \
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&& /app/.venv/bin/python -m ensurepip --upgrade
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# Create persistent data directories
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@ -59,10 +59,19 @@ LightRAG can be configured using environment variables in the `.env` file:
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Docker instructions work the same on all platforms with Docker Desktop installed.
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### Build Optimization
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The Dockerfile uses BuildKit cache mounts to significantly improve build performance:
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- **Automatic cache management**: BuildKit is automatically enabled via `# syntax=docker/dockerfile:1` directive
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- **Faster rebuilds**: Only downloads changed dependencies when `uv.lock` or `bun.lock` files are modified
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- **Efficient package caching**: UV and Bun package downloads are cached across builds
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- **No manual configuration needed**: Works out of the box in Docker Compose and GitHub Actions
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### Start LightRAG server:
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```bash
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docker-compose up -d
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docker compose up -d
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```
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LightRAG Server uses the following paths for data storage:
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@ -77,9 +86,9 @@ data/
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To update the Docker container:
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```bash
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docker-compose pull
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docker-compose down
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docker-compose up
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docker compose pull
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docker compose down
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docker compose up
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```
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### Offline deployment
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@ -91,10 +100,15 @@ Software packages requiring `transformers`, `torch`, or `cuda` will is not prein
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### For local development and testing
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```bash
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# Build and run with docker-compose
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# Build and run with Docker Compose (BuildKit automatically enabled)
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docker compose up --build
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# Or explicitly enable BuildKit if needed
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DOCKER_BUILDKIT=1 docker compose up --build
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```
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**Note**: BuildKit is automatically enabled by the `# syntax=docker/dockerfile:1` directive in the Dockerfile, ensuring optimal caching performance.
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### For production release
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**multi-architecture build and push**:
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10
env.example
10
env.example
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@ -166,10 +166,11 @@ MAX_PARALLEL_INSERT=2
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### Num of chunks send to Embedding in single request
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# EMBEDDING_BATCH_NUM=10
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###########################################################
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###########################################################################
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### LLM Configuration
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### LLM_BINDING type: openai, ollama, lollms, azure_openai, aws_bedrock
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###########################################################
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### LLM_BINDING_HOST: host only for Ollama, endpoint for other LLM service
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###########################################################################
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### LLM request timeout setting for all llm (0 means no timeout for Ollma)
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# LLM_TIMEOUT=180
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@ -224,10 +225,11 @@ OLLAMA_LLM_NUM_CTX=32768
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### Bedrock Specific Parameters
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# BEDROCK_LLM_TEMPERATURE=1.0
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####################################################################################
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#######################################################################################
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### Embedding Configuration (Should not be changed after the first file processed)
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### EMBEDDING_BINDING: ollama, openai, azure_openai, jina, lollms, aws_bedrock
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####################################################################################
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### EMBEDDING_BINDING_HOST: host only for Ollama, endpoint for other Embedding service
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#######################################################################################
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# EMBEDDING_TIMEOUT=30
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EMBEDDING_BINDING=ollama
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EMBEDDING_MODEL=bge-m3:latest
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@ -11,7 +11,6 @@ if not pm.is_installed("openai"):
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pm.install("openai")
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from openai import (
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AsyncOpenAI,
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APIConnectionError,
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RateLimitError,
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APITimeoutError,
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@ -27,6 +26,19 @@ from lightrag.utils import (
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safe_unicode_decode,
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logger,
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)
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# Try to import Langfuse for LLM observability (optional)
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# Falls back to standard OpenAI client if not available
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try:
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from langfuse.openai import AsyncOpenAI
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LANGFUSE_ENABLED = True
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logger.info("Langfuse observability enabled for OpenAI client")
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except ImportError:
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from openai import AsyncOpenAI
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LANGFUSE_ENABLED = False
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logger.debug("Langfuse not available, using standard OpenAI client")
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from lightrag.types import GPTKeywordExtractionFormat
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from lightrag.api import __api_version__
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"pytest-asyncio>=1.2.0",
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]
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observability = [
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# LLM observability and tracing dependencies
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"langfuse>=3.8.1",
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]
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[project.scripts]
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lightrag-server = "lightrag.api.lightrag_server:main"
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lightrag-gunicorn = "lightrag.api.run_with_gunicorn:main"
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