mirror of
https://github.com/imartinez/privateGPT.git
synced 2025-08-08 10:57:48 +00:00
Merge remote-tracking branch 'upstream/main'
This commit is contained in:
Steven Linn
commit
1e5b44368d
35
CHANGELOG.md
35
CHANGELOG.md
@ -1,5 +1,40 @@
|
||||
# Changelog
|
||||
|
||||
## [0.5.0](https://github.com/zylon-ai/private-gpt/compare/v0.4.0...v0.5.0) (2024-04-02)
|
||||
|
||||
|
||||
### Features
|
||||
|
||||
* **code:** improve concat of strings in ui ([#1785](https://github.com/zylon-ai/private-gpt/issues/1785)) ([bac818a](https://github.com/zylon-ai/private-gpt/commit/bac818add51b104cda925b8f1f7b51448e935ca1))
|
||||
* **docker:** set default Docker to use Ollama ([#1812](https://github.com/zylon-ai/private-gpt/issues/1812)) ([f83abff](https://github.com/zylon-ai/private-gpt/commit/f83abff8bc955a6952c92cc7bcb8985fcec93afa))
|
||||
* **docs:** Add guide Llama-CPP Linux AMD GPU support ([#1782](https://github.com/zylon-ai/private-gpt/issues/1782)) ([8a836e4](https://github.com/zylon-ai/private-gpt/commit/8a836e4651543f099c59e2bf497ab8c55a7cd2e5))
|
||||
* **docs:** Feature/upgrade docs ([#1741](https://github.com/zylon-ai/private-gpt/issues/1741)) ([5725181](https://github.com/zylon-ai/private-gpt/commit/572518143ac46532382db70bed6f73b5082302c1))
|
||||
* **docs:** upgrade fern ([#1596](https://github.com/zylon-ai/private-gpt/issues/1596)) ([84ad16a](https://github.com/zylon-ai/private-gpt/commit/84ad16af80191597a953248ce66e963180e8ddec))
|
||||
* **ingest:** Created a faster ingestion mode - pipeline ([#1750](https://github.com/zylon-ai/private-gpt/issues/1750)) ([134fc54](https://github.com/zylon-ai/private-gpt/commit/134fc54d7d636be91680dc531f5cbe2c5892ac56))
|
||||
* **llm - embed:** Add support for Azure OpenAI ([#1698](https://github.com/zylon-ai/private-gpt/issues/1698)) ([1efac6a](https://github.com/zylon-ai/private-gpt/commit/1efac6a3fe19e4d62325e2c2915cd84ea277f04f))
|
||||
* **llm:** adds serveral settings for llamacpp and ollama ([#1703](https://github.com/zylon-ai/private-gpt/issues/1703)) ([02dc83e](https://github.com/zylon-ai/private-gpt/commit/02dc83e8e9f7ada181ff813f25051bbdff7b7c6b))
|
||||
* **llm:** Ollama LLM-Embeddings decouple + longer keep_alive settings ([#1800](https://github.com/zylon-ai/private-gpt/issues/1800)) ([b3b0140](https://github.com/zylon-ai/private-gpt/commit/b3b0140e244e7a313bfaf4ef10eb0f7e4192710e))
|
||||
* **llm:** Ollama timeout setting ([#1773](https://github.com/zylon-ai/private-gpt/issues/1773)) ([6f6c785](https://github.com/zylon-ai/private-gpt/commit/6f6c785dac2bbad37d0b67fda215784298514d39))
|
||||
* **local:** tiktoken cache within repo for offline ([#1467](https://github.com/zylon-ai/private-gpt/issues/1467)) ([821bca3](https://github.com/zylon-ai/private-gpt/commit/821bca32e9ee7c909fd6488445ff6a04463bf91b))
|
||||
* **nodestore:** add Postgres for the doc and index store ([#1706](https://github.com/zylon-ai/private-gpt/issues/1706)) ([68b3a34](https://github.com/zylon-ai/private-gpt/commit/68b3a34b032a08ca073a687d2058f926032495b3))
|
||||
* **rag:** expose similarity_top_k and similarity_score to settings ([#1771](https://github.com/zylon-ai/private-gpt/issues/1771)) ([087cb0b](https://github.com/zylon-ai/private-gpt/commit/087cb0b7b74c3eb80f4f60b47b3a021c81272ae1))
|
||||
* **RAG:** Introduce SentenceTransformer Reranker ([#1810](https://github.com/zylon-ai/private-gpt/issues/1810)) ([83adc12](https://github.com/zylon-ai/private-gpt/commit/83adc12a8ef0fa0c13a0dec084fa596445fc9075))
|
||||
* **scripts:** Wipe qdrant and obtain db Stats command ([#1783](https://github.com/zylon-ai/private-gpt/issues/1783)) ([ea153fb](https://github.com/zylon-ai/private-gpt/commit/ea153fb92f1f61f64c0d04fff0048d4d00b6f8d0))
|
||||
* **ui:** Add Model Information to ChatInterface label ([f0b174c](https://github.com/zylon-ai/private-gpt/commit/f0b174c097c2d5e52deae8ef88de30a0d9013a38))
|
||||
* **ui:** add sources check to not repeat identical sources ([#1705](https://github.com/zylon-ai/private-gpt/issues/1705)) ([290b9fb](https://github.com/zylon-ai/private-gpt/commit/290b9fb084632216300e89bdadbfeb0380724b12))
|
||||
* **UI:** Faster startup and document listing ([#1763](https://github.com/zylon-ai/private-gpt/issues/1763)) ([348df78](https://github.com/zylon-ai/private-gpt/commit/348df781b51606b2f9810bcd46f850e54192fd16))
|
||||
* **ui:** maintain score order when curating sources ([#1643](https://github.com/zylon-ai/private-gpt/issues/1643)) ([410bf7a](https://github.com/zylon-ai/private-gpt/commit/410bf7a71f17e77c4aec723ab80c233b53765964))
|
||||
* unify settings for vector and nodestore connections to PostgreSQL ([#1730](https://github.com/zylon-ai/private-gpt/issues/1730)) ([63de7e4](https://github.com/zylon-ai/private-gpt/commit/63de7e4930ac90dd87620225112a22ffcbbb31ee))
|
||||
* wipe per storage type ([#1772](https://github.com/zylon-ai/private-gpt/issues/1772)) ([c2d6948](https://github.com/zylon-ai/private-gpt/commit/c2d694852b4696834962a42fde047b728722ad74))
|
||||
|
||||
|
||||
### Bug Fixes
|
||||
|
||||
* **docs:** Minor documentation amendment ([#1739](https://github.com/zylon-ai/private-gpt/issues/1739)) ([258d02d](https://github.com/zylon-ai/private-gpt/commit/258d02d87c5cb81d6c3a6f06aa69339b670dffa9))
|
||||
* Fixed docker-compose ([#1758](https://github.com/zylon-ai/private-gpt/issues/1758)) ([774e256](https://github.com/zylon-ai/private-gpt/commit/774e2560520dc31146561d09a2eb464c68593871))
|
||||
* **ingest:** update script label ([#1770](https://github.com/zylon-ai/private-gpt/issues/1770)) ([7d2de5c](https://github.com/zylon-ai/private-gpt/commit/7d2de5c96fd42e339b26269b3155791311ef1d08))
|
||||
* **settings:** set default tokenizer to avoid running make setup fail ([#1709](https://github.com/zylon-ai/private-gpt/issues/1709)) ([d17c34e](https://github.com/zylon-ai/private-gpt/commit/d17c34e81a84518086b93605b15032e2482377f7))
|
||||
|
||||
## [0.4.0](https://github.com/imartinez/privateGPT/compare/v0.3.0...v0.4.0) (2024-03-06)
|
||||
|
||||
|
||||
|
||||
@ -14,7 +14,7 @@ FROM base as dependencies
|
||||
WORKDIR /home/worker/app
|
||||
COPY pyproject.toml poetry.lock ./
|
||||
|
||||
RUN poetry install --extras "ui vector-stores-qdrant"
|
||||
RUN poetry install --extras "ui vector-stores-qdrant llms-ollama embeddings-ollama"
|
||||
|
||||
FROM base as app
|
||||
|
||||
|
||||
@ -33,7 +33,8 @@ ENV PORT=8080
|
||||
EXPOSE 8080
|
||||
|
||||
# Prepare a non-root user
|
||||
RUN adduser --system worker
|
||||
RUN adduser --group worker
|
||||
RUN adduser --system --ingroup worker worker
|
||||
WORKDIR /home/worker/app
|
||||
|
||||
RUN mkdir local_data; chown worker local_data
|
||||
|
||||
3
Makefile
3
Makefile
@ -51,6 +51,9 @@ api-docs:
|
||||
ingest:
|
||||
@poetry run python scripts/ingest_folder.py $(call args)
|
||||
|
||||
stats:
|
||||
poetry run python scripts/utils.py stats
|
||||
|
||||
wipe:
|
||||
poetry run python scripts/utils.py wipe
|
||||
|
||||
|
||||
@ -33,3 +33,18 @@ services:
|
||||
# ports:
|
||||
# - "8080:80"
|
||||
# restart: always
|
||||
private-gpt:
|
||||
build:
|
||||
dockerfile: Dockerfile.external
|
||||
volumes:
|
||||
- ./local_data/:/home/worker/app/local_data
|
||||
ports:
|
||||
- 8001:8080
|
||||
environment:
|
||||
PORT: 8080
|
||||
PGPT_PROFILES: docker
|
||||
PGPT_MODE: ollama
|
||||
ollama:
|
||||
image: ollama/ollama:latest
|
||||
volumes:
|
||||
- ./models:/root/.ollama
|
||||
|
||||
@ -64,6 +64,8 @@ navigation:
|
||||
contents:
|
||||
- page: LLM Backends
|
||||
path: ./docs/pages/manual/llms.mdx
|
||||
- page: Reranking
|
||||
path: ./docs/pages/manual/reranker.mdx
|
||||
- section: User Interface
|
||||
contents:
|
||||
- page: User interface (Gradio) Manual
|
||||
|
||||
@ -5,8 +5,8 @@ It is important that you review the Main Concepts before you start the installat
|
||||
* Clone PrivateGPT repository, and navigate to it:
|
||||
|
||||
```bash
|
||||
git clone https://github.com/imartinez/privateGPT
|
||||
cd privateGPT
|
||||
git clone https://github.com/zylon-ai/private-gpt
|
||||
cd private-gpt
|
||||
```
|
||||
|
||||
* Install Python `3.11` (*if you do not have it already*). Ideally through a python version manager like `pyenv`.
|
||||
@ -300,6 +300,40 @@ llama_new_context_with_model: total VRAM used: 4857.93 MB (model: 4095.05 MB, co
|
||||
AVX = 1 | AVX2 = 1 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 1 | NEON = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 0 | VSX = 0 |
|
||||
```
|
||||
|
||||
##### Llama-CPP Linux AMD GPU support
|
||||
|
||||
Linux GPU support is done through ROCm.
|
||||
Some tips:
|
||||
* Install ROCm from [quick-start install guide](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/tutorial/quick-start.html)
|
||||
* [Install PyTorch for ROCm](https://rocm.docs.amd.com/projects/radeon/en/latest/docs/install/install-pytorch.html)
|
||||
```bash
|
||||
wget https://repo.radeon.com/rocm/manylinux/rocm-rel-6.0/torch-2.1.1%2Brocm6.0-cp311-cp311-linux_x86_64.whl
|
||||
poetry run pip install --force-reinstall --no-cache-dir torch-2.1.1+rocm6.0-cp311-cp311-linux_x86_64.whl
|
||||
```
|
||||
* Install bitsandbytes for ROCm
|
||||
```bash
|
||||
PYTORCH_ROCM_ARCH=gfx900,gfx906,gfx908,gfx90a,gfx1030,gfx1100,gfx1101,gfx940,gfx941,gfx942
|
||||
BITSANDBYTES_VERSION=62353b0200b8557026c176e74ac48b84b953a854
|
||||
git clone https://github.com/arlo-phoenix/bitsandbytes-rocm-5.6
|
||||
cd bitsandbytes-rocm-5.6
|
||||
git checkout ${BITSANDBYTES_VERSION}
|
||||
make hip ROCM_TARGET=${PYTORCH_ROCM_ARCH} ROCM_HOME=/opt/rocm/
|
||||
pip install . --extra-index-url https://download.pytorch.org/whl/nightly
|
||||
```
|
||||
|
||||
After that running the following command in the repository will install llama.cpp with GPU support:
|
||||
```bash
|
||||
LLAMA_CPP_PYTHON_VERSION=0.2.56
|
||||
DAMDGPU_TARGETS=gfx900;gfx906;gfx908;gfx90a;gfx1030;gfx1100;gfx1101;gfx940;gfx941;gfx942
|
||||
CMAKE_ARGS="-DLLAMA_HIPBLAS=ON -DCMAKE_C_COMPILER=/opt/rocm/llvm/bin/clang -DCMAKE_CXX_COMPILER=/opt/rocm/llvm/bin/clang++ -DAMDGPU_TARGETS=${DAMDGPU_TARGETS}" poetry run pip install --force-reinstall --no-cache-dir llama-cpp-python==${LLAMA_CPP_PYTHON_VERSION}
|
||||
```
|
||||
|
||||
If your installation was correct, you should see a message similar to the following next time you start the server `BLAS = 1`.
|
||||
|
||||
```
|
||||
AVX = 1 | AVX_VNNI = 0 | AVX2 = 1 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 1 | NEON = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 |
|
||||
```
|
||||
|
||||
##### Llama-CPP Known issues and Troubleshooting
|
||||
|
||||
Execution of LLMs locally still has a lot of sharp edges, specially when running on non Linux platforms.
|
||||
|
||||
36
fern/docs/pages/manual/reranker.mdx
Normal file
36
fern/docs/pages/manual/reranker.mdx
Normal file
@ -0,0 +1,36 @@
|
||||
## Enhancing Response Quality with Reranking
|
||||
|
||||
PrivateGPT offers a reranking feature aimed at optimizing response generation by filtering out irrelevant documents, potentially leading to faster response times and enhanced relevance of answers generated by the LLM.
|
||||
|
||||
### Enabling Reranking
|
||||
|
||||
Document reranking can significantly improve the efficiency and quality of the responses by pre-selecting the most relevant documents before generating an answer. To leverage this feature, ensure that it is enabled in the RAG settings and consider adjusting the parameters to best fit your use case.
|
||||
|
||||
#### Additional Requirements
|
||||
|
||||
Before enabling reranking, you must install additional dependencies:
|
||||
|
||||
```bash
|
||||
poetry install --extras rerank-sentence-transformers
|
||||
```
|
||||
|
||||
This command installs dependencies for the cross-encoder reranker from sentence-transformers, which is currently the only supported method by PrivateGPT for document reranking.
|
||||
|
||||
#### Configuration
|
||||
|
||||
To enable and configure reranking, adjust the `rag` section within the `settings.yaml` file. Here are the key settings to consider:
|
||||
|
||||
- `similarity_top_k`: Determines the number of documents to initially retrieve and consider for reranking. This value should be larger than `top_n`.
|
||||
- `rerank`:
|
||||
- `enabled`: Set to `true` to activate the reranking feature.
|
||||
- `top_n`: Specifies the number of documents to use in the final answer generation process, chosen from the top-ranked documents provided by `similarity_top_k`.
|
||||
|
||||
Example configuration snippet:
|
||||
|
||||
```yaml
|
||||
rag:
|
||||
similarity_top_k: 10 # Number of documents to retrieve and consider for reranking
|
||||
rerank:
|
||||
enabled: true
|
||||
top_n: 3 # Number of top-ranked documents to use for generating the answer
|
||||
```
|
||||
206
poetry.lock
generated
206
poetry.lock
generated
@ -1,4 +1,4 @@
|
||||
# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
|
||||
# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
|
||||
|
||||
[[package]]
|
||||
name = "aiofiles"
|
||||
@ -515,7 +515,7 @@ files = [
|
||||
name = "cffi"
|
||||
version = "1.16.0"
|
||||
description = "Foreign Function Interface for Python calling C code."
|
||||
optional = true
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "cffi-1.16.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:6b3d6606d369fc1da4fd8c357d026317fbb9c9b75d36dc16e90e84c26854b088"},
|
||||
@ -934,57 +934,42 @@ toml = ["tomli"]
|
||||
|
||||
[[package]]
|
||||
name = "cryptography"
|
||||
version = "42.0.5"
|
||||
version = "3.4.8"
|
||||
description = "cryptography is a package which provides cryptographic recipes and primitives to Python developers."
|
||||
optional = true
|
||||
python-versions = ">=3.7"
|
||||
optional = false
|
||||
python-versions = ">=3.6"
|
||||
files = [
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{file = "cryptography-42.0.5-cp37-abi3-macosx_10_12_universal2.whl", hash = "sha256:a30596bae9403a342c978fb47d9b0ee277699fa53bbafad14706af51fe543d16"},
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{file = "cryptography-42.0.5-cp37-abi3-macosx_10_12_x86_64.whl", hash = "sha256:b7ffe927ee6531c78f81aa17e684e2ff617daeba7f189f911065b2ea2d526dec"},
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{file = "cryptography-42.0.5-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:329906dcc7b20ff3cad13c069a78124ed8247adcac44b10bea1130e36caae0b4"},
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{file = "cryptography-42.0.5-cp37-abi3-manylinux_2_28_aarch64.whl", hash = "sha256:b03c2ae5d2f0fc05f9a2c0c997e1bc18c8229f392234e8a0194f202169ccd278"},
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{file = "cryptography-42.0.5-cp37-abi3-manylinux_2_28_x86_64.whl", hash = "sha256:f8837fe1d6ac4a8052a9a8ddab256bc006242696f03368a4009be7ee3075cdb7"},
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{file = "cryptography-42.0.5-cp37-abi3-musllinux_1_1_aarch64.whl", hash = "sha256:0270572b8bd2c833c3981724b8ee9747b3ec96f699a9665470018594301439ee"},
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{file = "cryptography-42.0.5-cp37-abi3-musllinux_1_1_x86_64.whl", hash = "sha256:b8cac287fafc4ad485b8a9b67d0ee80c66bf3574f655d3b97ef2e1082360faf1"},
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{file = "cryptography-42.0.5-cp37-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:16a48c23a62a2f4a285699dba2e4ff2d1cff3115b9df052cdd976a18856d8e3d"},
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{file = "cryptography-42.0.5-cp37-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:2bce03af1ce5a5567ab89bd90d11e7bbdff56b8af3acbbec1faded8f44cb06da"},
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||||
{file = "cryptography-42.0.5-cp37-abi3-win32.whl", hash = "sha256:b6cd2203306b63e41acdf39aa93b86fb566049aeb6dc489b70e34bcd07adca74"},
|
||||
{file = "cryptography-42.0.5-cp37-abi3-win_amd64.whl", hash = "sha256:98d8dc6d012b82287f2c3d26ce1d2dd130ec200c8679b6213b3c73c08b2b7940"},
|
||||
{file = "cryptography-42.0.5-cp39-abi3-macosx_10_12_universal2.whl", hash = "sha256:5e6275c09d2badf57aea3afa80d975444f4be8d3bc58f7f80d2a484c6f9485c8"},
|
||||
{file = "cryptography-42.0.5-cp39-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e4985a790f921508f36f81831817cbc03b102d643b5fcb81cd33df3fa291a1a1"},
|
||||
{file = "cryptography-42.0.5-cp39-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7cde5f38e614f55e28d831754e8a3bacf9ace5d1566235e39d91b35502d6936e"},
|
||||
{file = "cryptography-42.0.5-cp39-abi3-manylinux_2_28_aarch64.whl", hash = "sha256:7367d7b2eca6513681127ebad53b2582911d1736dc2ffc19f2c3ae49997496bc"},
|
||||
{file = "cryptography-42.0.5-cp39-abi3-manylinux_2_28_x86_64.whl", hash = "sha256:cd2030f6650c089aeb304cf093f3244d34745ce0cfcc39f20c6fbfe030102e2a"},
|
||||
{file = "cryptography-42.0.5-cp39-abi3-musllinux_1_1_aarch64.whl", hash = "sha256:a2913c5375154b6ef2e91c10b5720ea6e21007412f6437504ffea2109b5a33d7"},
|
||||
{file = "cryptography-42.0.5-cp39-abi3-musllinux_1_1_x86_64.whl", hash = "sha256:c41fb5e6a5fe9ebcd58ca3abfeb51dffb5d83d6775405305bfa8715b76521922"},
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||||
[package.dependencies]
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||||
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|
||||
cffi = ">=1.12"
|
||||
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||||
[package.extras]
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||||
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||||
nox = ["nox"]
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||||
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||||
sdist = ["build"]
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||||
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||||
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|
||||
ssh = ["bcrypt (>=3.1.5)"]
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||||
test = ["certifi", "pretend", "pytest (>=6.2.0)", "pytest-benchmark", "pytest-cov", "pytest-xdist"]
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||||
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||||
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|
||||
|
||||
[[package]]
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||||
name = "cycler"
|
||||
@ -1096,6 +1081,16 @@ idna = ["idna (>=2.1,<4.0)"]
|
||||
trio = ["trio (>=0.14,<0.23)"]
|
||||
wmi = ["wmi (>=1.5.1,<2.0.0)"]
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|
||||
[[package]]
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||||
name = "docx2txt"
|
||||
version = "0.8"
|
||||
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|
||||
optional = false
|
||||
python-versions = "*"
|
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[[package]]
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||||
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|
||||
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|
||||
@ -3938,7 +3933,7 @@ pyasn1 = ">=0.4.6,<0.6.0"
|
||||
name = "pycparser"
|
||||
version = "2.21"
|
||||
description = "C parser in Python"
|
||||
optional = true
|
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optional = false
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@ -4949,6 +4944,90 @@ tensorflow = ["safetensors[numpy]", "tensorflow (>=2.11.0)"]
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||||
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|
||||
torch = ["safetensors[numpy]", "torch (>=1.10)"]
|
||||
|
||||
[[package]]
|
||||
name = "scikit-learn"
|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
|
||||
[[package]]
|
||||
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|
||||
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|
||||
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|
||||
optional = true
|
||||
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|
||||
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|
||||
{file = "scipy-1.12.0-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:8bee4993817e204d761dba10dbab0774ba5a8612e57e81319ea04d84945375ba"},
|
||||
{file = "scipy-1.12.0-cp311-cp311-win_amd64.whl", hash = "sha256:a24024d45ce9a675c1fb8494e8e5244efea1c7a09c60beb1eeb80373d0fecc70"},
|
||||
{file = "scipy-1.12.0-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:e7e76cc48638228212c747ada851ef355c2bb5e7f939e10952bc504c11f4e372"},
|
||||
{file = "scipy-1.12.0-cp312-cp312-macosx_12_0_arm64.whl", hash = "sha256:f7ce148dffcd64ade37b2df9315541f9adad6efcaa86866ee7dd5db0c8f041c3"},
|
||||
{file = "scipy-1.12.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9c39f92041f490422924dfdb782527a4abddf4707616e07b021de33467f917bc"},
|
||||
{file = "scipy-1.12.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a7ebda398f86e56178c2fa94cad15bf457a218a54a35c2a7b4490b9f9cb2676c"},
|
||||
{file = "scipy-1.12.0-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:95e5c750d55cf518c398a8240571b0e0782c2d5a703250872f36eaf737751338"},
|
||||
{file = "scipy-1.12.0-cp312-cp312-win_amd64.whl", hash = "sha256:e646d8571804a304e1da01040d21577685ce8e2db08ac58e543eaca063453e1c"},
|
||||
{file = "scipy-1.12.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:913d6e7956c3a671de3b05ccb66b11bc293f56bfdef040583a7221d9e22a2e35"},
|
||||
{file = "scipy-1.12.0-cp39-cp39-macosx_12_0_arm64.whl", hash = "sha256:bba1b0c7256ad75401c73e4b3cf09d1f176e9bd4248f0d3112170fb2ec4db067"},
|
||||
{file = "scipy-1.12.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:730badef9b827b368f351eacae2e82da414e13cf8bd5051b4bdfd720271a5371"},
|
||||
{file = "scipy-1.12.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6546dc2c11a9df6926afcbdd8a3edec28566e4e785b915e849348c6dd9f3f490"},
|
||||
{file = "scipy-1.12.0-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:196ebad3a4882081f62a5bf4aeb7326aa34b110e533aab23e4374fcccb0890dc"},
|
||||
{file = "scipy-1.12.0-cp39-cp39-win_amd64.whl", hash = "sha256:b360f1b6b2f742781299514e99ff560d1fe9bd1bff2712894b52abe528d1fd1e"},
|
||||
{file = "scipy-1.12.0.tar.gz", hash = "sha256:4bf5abab8a36d20193c698b0f1fc282c1d083c94723902c447e5d2f1780936a3"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
numpy = ">=1.22.4,<1.29.0"
|
||||
|
||||
[package.extras]
|
||||
dev = ["click", "cython-lint (>=0.12.2)", "doit (>=0.36.0)", "mypy", "pycodestyle", "pydevtool", "rich-click", "ruff", "types-psutil", "typing_extensions"]
|
||||
doc = ["jupytext", "matplotlib (>2)", "myst-nb", "numpydoc", "pooch", "pydata-sphinx-theme (==0.9.0)", "sphinx (!=4.1.0)", "sphinx-design (>=0.2.0)"]
|
||||
test = ["asv", "gmpy2", "hypothesis", "mpmath", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
|
||||
|
||||
[[package]]
|
||||
name = "semantic-version"
|
||||
version = "2.10.0"
|
||||
@ -4964,6 +5043,27 @@ files = [
|
||||
dev = ["Django (>=1.11)", "check-manifest", "colorama (<=0.4.1)", "coverage", "flake8", "nose2", "readme-renderer (<25.0)", "tox", "wheel", "zest.releaser[recommended]"]
|
||||
doc = ["Sphinx", "sphinx-rtd-theme"]
|
||||
|
||||
[[package]]
|
||||
name = "sentence-transformers"
|
||||
version = "2.6.1"
|
||||
description = "Multilingual text embeddings"
|
||||
optional = true
|
||||
python-versions = ">=3.8.0"
|
||||
files = [
|
||||
{file = "sentence-transformers-2.6.1.tar.gz", hash = "sha256:633ad6b70e390ea335de8689652a5d6c21a323b79ed19519c2f392451088487f"},
|
||||
{file = "sentence_transformers-2.6.1-py3-none-any.whl", hash = "sha256:a887e17696b513f99a709ce1f37fd547f53857aebe863785ede546c303b09ea0"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
huggingface-hub = ">=0.15.1"
|
||||
numpy = "*"
|
||||
Pillow = "*"
|
||||
scikit-learn = "*"
|
||||
scipy = "*"
|
||||
torch = ">=1.11.0"
|
||||
tqdm = "*"
|
||||
transformers = ">=4.32.0,<5.0.0"
|
||||
|
||||
[[package]]
|
||||
name = "setuptools"
|
||||
version = "69.0.2"
|
||||
@ -5156,6 +5256,17 @@ files = [
|
||||
[package.extras]
|
||||
doc = ["reno", "sphinx", "tornado (>=4.5)"]
|
||||
|
||||
[[package]]
|
||||
name = "threadpoolctl"
|
||||
version = "3.4.0"
|
||||
description = "threadpoolctl"
|
||||
optional = true
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "threadpoolctl-3.4.0-py3-none-any.whl", hash = "sha256:8f4c689a65b23e5ed825c8436a92b818aac005e0f3715f6a1664d7c7ee29d262"},
|
||||
{file = "threadpoolctl-3.4.0.tar.gz", hash = "sha256:f11b491a03661d6dd7ef692dd422ab34185d982466c49c8f98c8f716b5c93196"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "tiktoken"
|
||||
version = "0.5.2"
|
||||
@ -6206,6 +6317,7 @@ llms-ollama = ["llama-index-llms-ollama"]
|
||||
llms-openai = ["llama-index-llms-openai"]
|
||||
llms-openai-like = ["llama-index-llms-openai-like"]
|
||||
llms-sagemaker = ["boto3"]
|
||||
rerank-sentence-transformers = ["sentence-transformers", "torch"]
|
||||
storage-nodestore-postgres = ["asyncpg", "llama-index-storage-docstore-postgres", "llama-index-storage-index-store-postgres", "psycopg2-binary"]
|
||||
ui = ["gradio"]
|
||||
vector-stores-chroma = ["llama-index-vector-stores-chroma"]
|
||||
@ -6215,4 +6327,4 @@ vector-stores-qdrant = ["llama-index-vector-stores-qdrant"]
|
||||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = ">=3.11,<3.12"
|
||||
content-hash = "3d5f21e5e41ea66d655891a6d9b01bcdd8348b275e27a54e90b65ac9d5719981"
|
||||
content-hash = "992c2486ee05e66eab29026e4275dd5509074b38a31ead9db2271e6f94f6da08"
|
||||
|
||||
@ -70,7 +70,7 @@ class EmbeddingComponent:
|
||||
ollama_settings = settings.ollama
|
||||
self.embedding_model = OllamaEmbedding(
|
||||
model_name=ollama_settings.embedding_model,
|
||||
base_url=ollama_settings.api_base,
|
||||
base_url=ollama_settings.embedding_api_base,
|
||||
)
|
||||
case "azopenai":
|
||||
try:
|
||||
|
||||
@ -218,7 +218,7 @@ class SagemakerLLM(CustomLLM):
|
||||
|
||||
response_body = resp["Body"]
|
||||
response_str = response_body.read().decode("utf-8")
|
||||
response_dict = eval(response_str)
|
||||
response_dict = json.loads(response_str)
|
||||
|
||||
return CompletionResponse(
|
||||
text=response_dict[0]["generated_text"][len(prompt) :], raw=resp
|
||||
@ -243,12 +243,19 @@ class SagemakerLLM(CustomLLM):
|
||||
event_stream = resp["Body"]
|
||||
start_json = b"{"
|
||||
stop_token = "<|endoftext|>"
|
||||
first_token = True
|
||||
|
||||
for line in LineIterator(event_stream):
|
||||
if line != b"" and start_json in line:
|
||||
data = json.loads(line[line.find(start_json) :].decode("utf-8"))
|
||||
if data["token"]["text"] != stop_token:
|
||||
special = data["token"]["special"]
|
||||
stop = data["token"]["text"] == stop_token
|
||||
if not special and not stop:
|
||||
delta = data["token"]["text"]
|
||||
# trim the leading space for the first token if present
|
||||
if first_token:
|
||||
delta = delta.lstrip()
|
||||
first_token = False
|
||||
text += delta
|
||||
yield CompletionResponse(delta=delta, text=text, raw=data)
|
||||
|
||||
|
||||
@ -1,4 +1,6 @@
|
||||
import logging
|
||||
from collections.abc import Callable
|
||||
from typing import Any
|
||||
|
||||
from injector import inject, singleton
|
||||
from llama_index.core.llms import LLM, MockLLM
|
||||
@ -20,13 +22,24 @@ class LLMComponent:
|
||||
@inject
|
||||
def __init__(self, settings: Settings) -> None:
|
||||
llm_mode = settings.llm.mode
|
||||
if settings.llm.tokenizer:
|
||||
if settings.llm.tokenizer and settings.llm.mode != "mock":
|
||||
# Try to download the tokenizer. If it fails, the LLM will still work
|
||||
# using the default one, which is less accurate.
|
||||
try:
|
||||
set_global_tokenizer(
|
||||
AutoTokenizer.from_pretrained(
|
||||
pretrained_model_name_or_path=settings.llm.tokenizer,
|
||||
cache_dir=str(models_cache_path),
|
||||
token=settings.huggingface.access_token,
|
||||
)
|
||||
)
|
||||
except Exception as e:
|
||||
logger.warning(
|
||||
"Failed to download tokenizer %s. Falling back to "
|
||||
"default tokenizer.",
|
||||
settings.llm.tokenizer,
|
||||
e,
|
||||
)
|
||||
|
||||
logger.info("Initializing the LLM in mode=%s", llm_mode)
|
||||
match settings.llm.mode:
|
||||
@ -38,7 +51,7 @@ class LLMComponent:
|
||||
"Local dependencies not found, install with `poetry install --extras llms-llama-cpp`"
|
||||
) from e
|
||||
|
||||
prompt_style = get_prompt_style(settings.llamacpp.prompt_style)
|
||||
prompt_style = get_prompt_style(settings.llm.prompt_style)
|
||||
settings_kwargs = {
|
||||
"tfs_z": settings.llamacpp.tfs_z, # ollama and llama-cpp
|
||||
"top_k": settings.llamacpp.top_k, # ollama and llama-cpp
|
||||
@ -96,15 +109,20 @@ class LLMComponent:
|
||||
raise ImportError(
|
||||
"OpenAILike dependencies not found, install with `poetry install --extras llms-openai-like`"
|
||||
) from e
|
||||
|
||||
prompt_style = get_prompt_style(settings.llm.prompt_style)
|
||||
openai_settings = settings.openai
|
||||
self.llm = OpenAILike(
|
||||
api_base=openai_settings.api_base,
|
||||
api_key=openai_settings.api_key,
|
||||
model=openai_settings.model,
|
||||
is_chat_model=True,
|
||||
max_tokens=None,
|
||||
max_tokens=settings.llm.max_new_tokens,
|
||||
api_version="",
|
||||
temperature=settings.llm.temperature,
|
||||
context_window=settings.llm.context_window,
|
||||
max_new_tokens=settings.llm.max_new_tokens,
|
||||
messages_to_prompt=prompt_style.messages_to_prompt,
|
||||
completion_to_prompt=prompt_style.completion_to_prompt,
|
||||
)
|
||||
case "ollama":
|
||||
try:
|
||||
@ -133,6 +151,24 @@ class LLMComponent:
|
||||
additional_kwargs=settings_kwargs,
|
||||
request_timeout=ollama_settings.request_timeout,
|
||||
)
|
||||
|
||||
if (
|
||||
ollama_settings.keep_alive
|
||||
!= ollama_settings.model_fields["keep_alive"].default
|
||||
):
|
||||
# Modify Ollama methods to use the "keep_alive" field.
|
||||
def add_keep_alive(func: Callable[..., Any]) -> Callable[..., Any]:
|
||||
def wrapper(*args: Any, **kwargs: Any) -> Any:
|
||||
kwargs["keep_alive"] = ollama_settings.keep_alive
|
||||
return func(*args, **kwargs)
|
||||
|
||||
return wrapper
|
||||
|
||||
Ollama.chat = add_keep_alive(Ollama.chat)
|
||||
Ollama.stream_chat = add_keep_alive(Ollama.stream_chat)
|
||||
Ollama.complete = add_keep_alive(Ollama.complete)
|
||||
Ollama.stream_complete = add_keep_alive(Ollama.stream_complete)
|
||||
|
||||
case "azopenai":
|
||||
try:
|
||||
from llama_index.llms.azure_openai import ( # type: ignore
|
||||
|
||||
@ -9,6 +9,7 @@ from llama_index.core.indices import VectorStoreIndex
|
||||
from llama_index.core.indices.postprocessor import MetadataReplacementPostProcessor
|
||||
from llama_index.core.llms import ChatMessage, MessageRole
|
||||
from llama_index.core.postprocessor import (
|
||||
SentenceTransformerRerank,
|
||||
SimilarityPostprocessor,
|
||||
)
|
||||
from llama_index.core.storage import StorageContext
|
||||
@ -113,16 +114,24 @@ class ChatService:
|
||||
context_filter=context_filter,
|
||||
similarity_top_k=self.settings.rag.similarity_top_k,
|
||||
)
|
||||
return ContextChatEngine.from_defaults(
|
||||
system_prompt=system_prompt,
|
||||
retriever=vector_index_retriever,
|
||||
llm=self.llm_component.llm, # Takes no effect at the moment
|
||||
node_postprocessors=[
|
||||
node_postprocessors = [
|
||||
MetadataReplacementPostProcessor(target_metadata_key="window"),
|
||||
SimilarityPostprocessor(
|
||||
similarity_cutoff=settings.rag.similarity_value
|
||||
),
|
||||
],
|
||||
]
|
||||
|
||||
if settings.rag.rerank.enabled:
|
||||
rerank_postprocessor = SentenceTransformerRerank(
|
||||
model=settings.rag.rerank.model, top_n=settings.rag.rerank.top_n
|
||||
)
|
||||
node_postprocessors.append(rerank_postprocessor)
|
||||
|
||||
return ContextChatEngine.from_defaults(
|
||||
system_prompt=system_prompt,
|
||||
retriever=vector_index_retriever,
|
||||
llm=self.llm_component.llm, # Takes no effect at the moment
|
||||
node_postprocessors=node_postprocessors,
|
||||
)
|
||||
else:
|
||||
return SimpleChatEngine.from_defaults(
|
||||
|
||||
@ -104,6 +104,17 @@ class LLMSettings(BaseModel):
|
||||
0.1,
|
||||
description="The temperature of the model. Increasing the temperature will make the model answer more creatively. A value of 0.1 would be more factual.",
|
||||
)
|
||||
prompt_style: Literal["default", "llama2", "tag", "mistral", "chatml"] = Field(
|
||||
"llama2",
|
||||
description=(
|
||||
"The prompt style to use for the chat engine. "
|
||||
"If `default` - use the default prompt style from the llama_index. It should look like `role: message`.\n"
|
||||
"If `llama2` - use the llama2 prompt style from the llama_index. Based on `<s>`, `[INST]` and `<<SYS>>`.\n"
|
||||
"If `tag` - use the `tag` prompt style. It should look like `<|role|>: message`. \n"
|
||||
"If `mistral` - use the `mistral prompt style. It shoudl look like <s>[INST] {System Prompt} [/INST]</s>[INST] { UserInstructions } [/INST]"
|
||||
"`llama2` is the historic behaviour. `default` might work better with your custom models."
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
class VectorstoreSettings(BaseModel):
|
||||
@ -117,18 +128,6 @@ class NodeStoreSettings(BaseModel):
|
||||
class LlamaCPPSettings(BaseModel):
|
||||
llm_hf_repo_id: str
|
||||
llm_hf_model_file: str
|
||||
prompt_style: Literal["default", "llama2", "tag", "mistral", "chatml"] = Field(
|
||||
"llama2",
|
||||
description=(
|
||||
"The prompt style to use for the chat engine. "
|
||||
"If `default` - use the default prompt style from the llama_index. It should look like `role: message`.\n"
|
||||
"If `llama2` - use the llama2 prompt style from the llama_index. Based on `<s>`, `[INST]` and `<<SYS>>`.\n"
|
||||
"If `tag` - use the `tag` prompt style. It should look like `<|role|>: message`. \n"
|
||||
"If `mistral` - use the `mistral prompt style. It shoudl look like <s>[INST] {System Prompt} [/INST]</s>[INST] { UserInstructions } [/INST]"
|
||||
"`llama2` is the historic behaviour. `default` might work better with your custom models."
|
||||
),
|
||||
)
|
||||
|
||||
tfs_z: float = Field(
|
||||
1.0,
|
||||
description="Tail free sampling is used to reduce the impact of less probable tokens from the output. A higher value (e.g., 2.0) will reduce the impact more, while a value of 1.0 disables this setting.",
|
||||
@ -151,6 +150,10 @@ class HuggingFaceSettings(BaseModel):
|
||||
embedding_hf_model_name: str = Field(
|
||||
description="Name of the HuggingFace model to use for embeddings"
|
||||
)
|
||||
access_token: str = Field(
|
||||
None,
|
||||
description="Huggingface access token, required to download some models",
|
||||
)
|
||||
|
||||
|
||||
class EmbeddingSettings(BaseModel):
|
||||
@ -209,6 +212,10 @@ class OllamaSettings(BaseModel):
|
||||
"http://localhost:11434",
|
||||
description="Base URL of Ollama API. Example: 'https://localhost:11434'.",
|
||||
)
|
||||
embedding_api_base: str = Field(
|
||||
"http://localhost:11434",
|
||||
description="Base URL of Ollama embedding API. Example: 'https://localhost:11434'.",
|
||||
)
|
||||
llm_model: str = Field(
|
||||
None,
|
||||
description="Model to use. Example: 'llama2-uncensored'.",
|
||||
@ -217,6 +224,10 @@ class OllamaSettings(BaseModel):
|
||||
None,
|
||||
description="Model to use. Example: 'nomic-embed-text'.",
|
||||
)
|
||||
keep_alive: str = Field(
|
||||
"5m",
|
||||
description="Time the model will stay loaded in memory after a request. examples: 5m, 5h, '-1' ",
|
||||
)
|
||||
tfs_z: float = Field(
|
||||
1.0,
|
||||
description="Tail free sampling is used to reduce the impact of less probable tokens from the output. A higher value (e.g., 2.0) will reduce the impact more, while a value of 1.0 disables this setting.",
|
||||
@ -284,15 +295,31 @@ class UISettings(BaseModel):
|
||||
)
|
||||
|
||||
|
||||
class RerankSettings(BaseModel):
|
||||
enabled: bool = Field(
|
||||
False,
|
||||
description="This value controls whether a reranker should be included in the RAG pipeline.",
|
||||
)
|
||||
model: str = Field(
|
||||
"cross-encoder/ms-marco-MiniLM-L-2-v2",
|
||||
description="Rerank model to use. Limited to SentenceTransformer cross-encoder models.",
|
||||
)
|
||||
top_n: int = Field(
|
||||
2,
|
||||
description="This value controls the number of documents returned by the RAG pipeline.",
|
||||
)
|
||||
|
||||
|
||||
class RagSettings(BaseModel):
|
||||
similarity_top_k: int = Field(
|
||||
2,
|
||||
description="This value controls the number of documents returned by the RAG pipeline",
|
||||
description="This value controls the number of documents returned by the RAG pipeline or considered for reranking if enabled.",
|
||||
)
|
||||
similarity_value: float = Field(
|
||||
None,
|
||||
description="If set, any documents retrieved from the RAG must meet a certain match score. Acceptable values are between 0 and 1.",
|
||||
)
|
||||
rerank: RerankSettings
|
||||
|
||||
|
||||
class PostgresSettings(BaseModel):
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
"""This file should be imported only and only if you want to run the UI locally."""
|
||||
"""This file should be imported if and only if you want to run the UI locally."""
|
||||
|
||||
import itertools
|
||||
import logging
|
||||
@ -103,12 +103,12 @@ class PrivateGptUi:
|
||||
sources_text = "\n\n\n"
|
||||
used_files = set()
|
||||
for index, source in enumerate(cur_sources, start=1):
|
||||
if (source.file + "-" + source.page) not in used_files:
|
||||
if f"{source.file}-{source.page}" not in used_files:
|
||||
sources_text = (
|
||||
sources_text
|
||||
+ f"{index}. {source.file} (page {source.page}) \n\n"
|
||||
)
|
||||
used_files.add(source.file + "-" + source.page)
|
||||
used_files.add(f"{source.file}-{source.page}")
|
||||
full_response += sources_text
|
||||
yield full_response
|
||||
|
||||
@ -418,11 +418,54 @@ class PrivateGptUi:
|
||||
inputs=system_prompt_input,
|
||||
)
|
||||
|
||||
def get_model_label() -> str | None:
|
||||
"""Get model label from llm mode setting YAML.
|
||||
|
||||
Raises:
|
||||
ValueError: If an invalid 'llm_mode' is encountered.
|
||||
|
||||
Returns:
|
||||
str: The corresponding model label.
|
||||
"""
|
||||
# Get model label from llm mode setting YAML
|
||||
# Labels: local, openai, openailike, sagemaker, mock, ollama
|
||||
config_settings = settings()
|
||||
if config_settings is None:
|
||||
raise ValueError("Settings are not configured.")
|
||||
|
||||
# Get llm_mode from settings
|
||||
llm_mode = config_settings.llm.mode
|
||||
|
||||
# Mapping of 'llm_mode' to corresponding model labels
|
||||
model_mapping = {
|
||||
"llamacpp": config_settings.llamacpp.llm_hf_model_file,
|
||||
"openai": config_settings.openai.model,
|
||||
"openailike": config_settings.openai.model,
|
||||
"sagemaker": config_settings.sagemaker.llm_endpoint_name,
|
||||
"mock": llm_mode,
|
||||
"ollama": config_settings.ollama.llm_model,
|
||||
}
|
||||
|
||||
if llm_mode not in model_mapping:
|
||||
print(f"Invalid 'llm mode': {llm_mode}")
|
||||
return None
|
||||
|
||||
return model_mapping[llm_mode]
|
||||
|
||||
with gr.Column(scale=7, elem_id="col"):
|
||||
# Determine the model label based on the value of PGPT_PROFILES
|
||||
model_label = get_model_label()
|
||||
if model_label is not None:
|
||||
label_text = (
|
||||
f"LLM: {settings().llm.mode} | Model: {model_label}"
|
||||
)
|
||||
else:
|
||||
label_text = f"LLM: {settings().llm.mode}"
|
||||
|
||||
_ = gr.ChatInterface(
|
||||
self._chat,
|
||||
chatbot=gr.Chatbot(
|
||||
label=f"LLM: {settings().llm.mode}",
|
||||
label=label_text,
|
||||
show_copy_button=True,
|
||||
elem_id="chatbot",
|
||||
render=False,
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
[tool.poetry]
|
||||
name = "private-gpt"
|
||||
version = "0.4.0"
|
||||
version = "0.5.0"
|
||||
description = "Private GPT"
|
||||
authors = ["Zylon <hi@zylon.ai>"]
|
||||
|
||||
@ -13,6 +13,8 @@ injector = "^0.21.0"
|
||||
pyyaml = "^6.0.1"
|
||||
watchdog = "^4.0.0"
|
||||
transformers = "^4.38.2"
|
||||
docx2txt = "^0.8"
|
||||
cryptography = "^3.1"
|
||||
# LlamaIndex core libs
|
||||
llama-index-core = "^0.10.14"
|
||||
llama-index-readers-file = "^0.1.6"
|
||||
@ -37,6 +39,11 @@ asyncpg = {version="^0.29.0", optional = true}
|
||||
|
||||
# Optional Sagemaker dependency
|
||||
boto3 = {version ="^1.34.51", optional = true}
|
||||
|
||||
# Optional Reranker dependencies
|
||||
torch = {version ="^2.1.2", optional = true}
|
||||
sentence-transformers = {version ="^2.6.1", optional = true}
|
||||
|
||||
# Optional UI
|
||||
gradio = {version ="^4.19.2", optional = true}
|
||||
|
||||
@ -57,6 +64,7 @@ vector-stores-qdrant = ["llama-index-vector-stores-qdrant"]
|
||||
vector-stores-chroma = ["llama-index-vector-stores-chroma"]
|
||||
vector-stores-postgres = ["llama-index-vector-stores-postgres"]
|
||||
storage-nodestore-postgres = ["llama-index-storage-docstore-postgres","llama-index-storage-index-store-postgres","psycopg2-binary","asyncpg"]
|
||||
rerank-sentence-transformers = ["torch", "sentence-transformers"]
|
||||
|
||||
[tool.poetry.group.dev.dependencies]
|
||||
black = "^22"
|
||||
|
||||
185
scripts/utils.py
185
scripts/utils.py
@ -1,26 +1,12 @@
|
||||
import argparse
|
||||
import os
|
||||
import shutil
|
||||
from typing import Any, ClassVar
|
||||
|
||||
from private_gpt.paths import local_data_path
|
||||
from private_gpt.settings.settings import settings
|
||||
|
||||
|
||||
def wipe() -> None:
|
||||
WIPE_MAP = {
|
||||
"simple": wipe_simple, # node store
|
||||
"chroma": wipe_chroma, # vector store
|
||||
"postgres": wipe_postgres, # node, index and vector store
|
||||
}
|
||||
for dbtype in ("nodestore", "vectorstore"):
|
||||
database = getattr(settings(), dbtype).database
|
||||
func = WIPE_MAP.get(database)
|
||||
if func:
|
||||
func(dbtype)
|
||||
else:
|
||||
print(f"Unable to wipe database '{database}' for '{dbtype}'")
|
||||
|
||||
|
||||
def wipe_file(file: str) -> None:
|
||||
if os.path.isfile(file):
|
||||
os.remove(file)
|
||||
@ -50,8 +36,62 @@ def wipe_tree(path: str) -> None:
|
||||
continue
|
||||
|
||||
|
||||
def wipe_simple(dbtype: str) -> None:
|
||||
assert dbtype == "nodestore"
|
||||
class Postgres:
|
||||
tables: ClassVar[dict[str, list[str]]] = {
|
||||
"nodestore": ["data_docstore", "data_indexstore"],
|
||||
"vectorstore": ["data_embeddings"],
|
||||
}
|
||||
|
||||
def __init__(self) -> None:
|
||||
try:
|
||||
import psycopg2
|
||||
except ModuleNotFoundError:
|
||||
raise ModuleNotFoundError("Postgres dependencies not found") from None
|
||||
|
||||
connection = settings().postgres.model_dump(exclude_none=True)
|
||||
self.schema = connection.pop("schema_name")
|
||||
self.conn = psycopg2.connect(**connection)
|
||||
|
||||
def wipe(self, storetype: str) -> None:
|
||||
cur = self.conn.cursor()
|
||||
try:
|
||||
for table in self.tables[storetype]:
|
||||
sql = f"DROP TABLE IF EXISTS {self.schema}.{table}"
|
||||
cur.execute(sql)
|
||||
print(f"Table {self.schema}.{table} dropped.")
|
||||
self.conn.commit()
|
||||
finally:
|
||||
cur.close()
|
||||
|
||||
def stats(self, store_type: str) -> None:
|
||||
template = "SELECT '{table}', COUNT(*), pg_size_pretty(pg_total_relation_size('{table}')) FROM {table}"
|
||||
sql = " UNION ALL ".join(
|
||||
template.format(table=tbl) for tbl in self.tables[store_type]
|
||||
)
|
||||
|
||||
cur = self.conn.cursor()
|
||||
try:
|
||||
print(f"Storage for Postgres {store_type}.")
|
||||
print("{:<15} | {:>15} | {:>9}".format("Table", "Rows", "Size"))
|
||||
print("-" * 45) # Print a line separator
|
||||
|
||||
cur.execute(sql)
|
||||
for row in cur.fetchall():
|
||||
formatted_row_count = f"{row[1]:,}"
|
||||
print(f"{row[0]:<15} | {formatted_row_count:>15} | {row[2]:>9}")
|
||||
|
||||
print()
|
||||
finally:
|
||||
cur.close()
|
||||
|
||||
def __del__(self):
|
||||
if hasattr(self, "conn") and self.conn:
|
||||
self.conn.close()
|
||||
|
||||
|
||||
class Simple:
|
||||
def wipe(self, store_type: str) -> None:
|
||||
assert store_type == "nodestore"
|
||||
from llama_index.core.storage.docstore.types import (
|
||||
DEFAULT_PERSIST_FNAME as DOCSTORE,
|
||||
)
|
||||
@ -63,49 +103,82 @@ def wipe_simple(dbtype: str) -> None:
|
||||
wipe_file(str((local_data_path / store).absolute()))
|
||||
|
||||
|
||||
def wipe_postgres(dbtype: str) -> None:
|
||||
try:
|
||||
import psycopg2
|
||||
except ImportError as e:
|
||||
raise ImportError("Postgres dependencies not found") from e
|
||||
|
||||
cur = conn = None
|
||||
try:
|
||||
tables = {
|
||||
"nodestore": ["data_docstore", "data_indexstore"],
|
||||
"vectorstore": ["data_embeddings"],
|
||||
}[dbtype]
|
||||
connection = settings().postgres.model_dump(exclude_none=True)
|
||||
schema = connection.pop("schema_name")
|
||||
conn = psycopg2.connect(**connection)
|
||||
cur = conn.cursor()
|
||||
for table in tables:
|
||||
sql = f"DROP TABLE IF EXISTS {schema}.{table}"
|
||||
cur.execute(sql)
|
||||
print(f"Table {schema}.{table} dropped.")
|
||||
conn.commit()
|
||||
except psycopg2.Error as e:
|
||||
print("Error:", e)
|
||||
finally:
|
||||
if cur:
|
||||
cur.close()
|
||||
if conn:
|
||||
conn.close()
|
||||
|
||||
|
||||
def wipe_chroma(dbtype: str):
|
||||
assert dbtype == "vectorstore"
|
||||
class Chroma:
|
||||
def wipe(self, store_type: str) -> None:
|
||||
assert store_type == "vectorstore"
|
||||
wipe_tree(str((local_data_path / "chroma_db").absolute()))
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
commands = {
|
||||
"wipe": wipe,
|
||||
class Qdrant:
|
||||
COLLECTION = (
|
||||
"make_this_parameterizable_per_api_call" # ?! see vector_store_component.py
|
||||
)
|
||||
|
||||
def __init__(self) -> None:
|
||||
try:
|
||||
from qdrant_client import QdrantClient # type: ignore
|
||||
except ImportError:
|
||||
raise ImportError("Qdrant dependencies not found") from None
|
||||
self.client = QdrantClient(**settings().qdrant.model_dump(exclude_none=True))
|
||||
|
||||
def wipe(self, store_type: str) -> None:
|
||||
assert store_type == "vectorstore"
|
||||
try:
|
||||
self.client.delete_collection(self.COLLECTION)
|
||||
print("Collection dropped successfully.")
|
||||
except Exception as e:
|
||||
print("Error dropping collection:", e)
|
||||
|
||||
def stats(self, store_type: str) -> None:
|
||||
print(f"Storage for Qdrant {store_type}.")
|
||||
try:
|
||||
collection_data = self.client.get_collection(self.COLLECTION)
|
||||
if collection_data:
|
||||
# Collection Info
|
||||
# https://qdrant.tech/documentation/concepts/collections/
|
||||
print(f"\tPoints: {collection_data.points_count:,}")
|
||||
print(f"\tVectors: {collection_data.vectors_count:,}")
|
||||
print(f"\tIndex Vectors: {collection_data.indexed_vectors_count:,}")
|
||||
return
|
||||
except ValueError:
|
||||
pass
|
||||
print("\t- Qdrant collection not found or empty")
|
||||
|
||||
|
||||
class Command:
|
||||
DB_HANDLERS: ClassVar[dict[str, Any]] = {
|
||||
"simple": Simple, # node store
|
||||
"chroma": Chroma, # vector store
|
||||
"postgres": Postgres, # node, index and vector store
|
||||
"qdrant": Qdrant, # vector store
|
||||
}
|
||||
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument(
|
||||
"mode", help="select a mode to run", choices=list(commands.keys())
|
||||
def for_each_store(self, cmd: str):
|
||||
for store_type in ("nodestore", "vectorstore"):
|
||||
database = getattr(settings(), store_type).database
|
||||
handler_class = self.DB_HANDLERS.get(database)
|
||||
if handler_class is None:
|
||||
print(f"No handler found for database '{database}'")
|
||||
continue
|
||||
handler_instance = handler_class() # Instantiate the class
|
||||
# If the DB can handle this cmd dispatch it.
|
||||
if hasattr(handler_instance, cmd) and callable(
|
||||
func := getattr(handler_instance, cmd)
|
||||
):
|
||||
func(store_type)
|
||||
else:
|
||||
print(
|
||||
f"Unable to execute command '{cmd}' on '{store_type}' in database '{database}'"
|
||||
)
|
||||
|
||||
def execute(self, cmd: str) -> None:
|
||||
if cmd in ("wipe", "stats"):
|
||||
self.for_each_store(cmd)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("mode", help="select a mode to run", choices=["wipe", "stats"])
|
||||
args = parser.parse_args()
|
||||
commands[args.mode.lower()]()
|
||||
|
||||
Command().execute(args.mode.lower())
|
||||
|
||||
@ -19,6 +19,18 @@ sagemaker:
|
||||
llm_endpoint_name: ${PGPT_SAGEMAKER_LLM_ENDPOINT_NAME:}
|
||||
embedding_endpoint_name: ${PGPT_SAGEMAKER_EMBEDDING_ENDPOINT_NAME:}
|
||||
|
||||
ollama:
|
||||
llm_model: ${PGPT_OLLAMA_LLM_MODEL:mistral}
|
||||
embedding_model: ${PGPT_OLLAMA_EMBEDDING_MODEL:nomic-embed-text}
|
||||
api_base: ${PGPT_OLLAMA_API_BASE:http://ollama:11434}
|
||||
embedding_api_base: ${PGPT_OLLAMA_EMBEDDING_API_BASE:http://ollama:11434}
|
||||
tfs_z: ${PGPT_OLLAMA_TFS_Z:1.0}
|
||||
top_k: ${PGPT_OLLAMA_TOP_K:40}
|
||||
top_p: ${PGPT_OLLAMA_TOP_P:0.9}
|
||||
repeat_last_n: ${PGPT_OLLAMA_REPEAT_LAST_N:64}
|
||||
repeat_penalty: ${PGPT_OLLAMA_REPEAT_PENALTY:1.2}
|
||||
request_timeout: ${PGPT_OLLAMA_REQUEST_TIMEOUT:600.0}
|
||||
|
||||
ui:
|
||||
enabled: true
|
||||
path: /
|
||||
|
||||
@ -8,9 +8,9 @@ llm:
|
||||
max_new_tokens: 512
|
||||
context_window: 3900
|
||||
tokenizer: mistralai/Mistral-7B-Instruct-v0.2
|
||||
prompt_style: "mistral"
|
||||
|
||||
llamacpp:
|
||||
prompt_style: "mistral"
|
||||
llm_hf_repo_id: TheBloke/Mistral-7B-Instruct-v0.2-GGUF
|
||||
llm_hf_model_file: mistral-7b-instruct-v0.2.Q4_K_M.gguf
|
||||
|
||||
|
||||
@ -14,6 +14,8 @@ ollama:
|
||||
llm_model: mistral
|
||||
embedding_model: nomic-embed-text
|
||||
api_base: http://localhost:11434
|
||||
embedding_api_base: http://localhost:11434 # change if your embedding model runs on another ollama
|
||||
keep_alive: 5m
|
||||
tfs_z: 1.0 # Tail free sampling is used to reduce the impact of less probable tokens from the output. A higher value (e.g., 2.0) will reduce the impact more, while a value of 1.0 disables this setting.
|
||||
top_k: 40 # Reduces the probability of generating nonsense. A higher value (e.g. 100) will give more diverse answers, while a lower value (e.g. 10) will be more conservative. (Default: 40)
|
||||
top_p: 0.9 # Works together with top-k. A higher value (e.g., 0.95) will lead to more diverse text, while a lower value (e.g., 0.5) will generate more focused and conservative text. (Default: 0.9)
|
||||
|
||||
@ -36,6 +36,7 @@ ui:
|
||||
|
||||
llm:
|
||||
mode: llamacpp
|
||||
prompt_style: "mistral"
|
||||
# Should be matching the selected model
|
||||
max_new_tokens: 512
|
||||
context_window: 3900
|
||||
@ -47,9 +48,12 @@ rag:
|
||||
#This value controls how many "top" documents the RAG returns to use in the context.
|
||||
#similarity_value: 0.45
|
||||
#This value is disabled by default. If you enable this settings, the RAG will only use articles that meet a certain percentage score.
|
||||
rerank:
|
||||
enabled: false
|
||||
model: cross-encoder/ms-marco-MiniLM-L-2-v2
|
||||
top_n: 1
|
||||
|
||||
llamacpp:
|
||||
prompt_style: "mistral"
|
||||
llm_hf_repo_id: TheBloke/Mistral-7B-Instruct-v0.2-GGUF
|
||||
llm_hf_model_file: mistral-7b-instruct-v0.2.Q4_K_M.gguf
|
||||
tfs_z: 1.0 # Tail free sampling is used to reduce the impact of less probable tokens from the output. A higher value (e.g., 2.0) will reduce the impact more, while a value of 1.0 disables this setting
|
||||
@ -65,6 +69,7 @@ embedding:
|
||||
|
||||
huggingface:
|
||||
embedding_hf_model_name: BAAI/bge-small-en-v1.5
|
||||
access_token: ${HUGGINGFACE_TOKEN:}
|
||||
|
||||
vectorstore:
|
||||
database: postgres
|
||||
@ -95,6 +100,8 @@ ollama:
|
||||
llm_model: llama2
|
||||
embedding_model: nomic-embed-text
|
||||
api_base: http://localhost:11434
|
||||
embedding_api_base: http://localhost:11434 # change if your embedding model runs on another ollama
|
||||
keep_alive: 5m
|
||||
request_timeout: 120.0
|
||||
|
||||
azopenai:
|
||||
|
||||
@ -1 +1 @@
|
||||
0.4.0
|
||||
0.5.0
|
||||
|
||||
Loading…
Reference in New Issue
Block a user