mirror of
https://github.com/StanGirard/quivr.git
synced 2024-09-11 13:15:41 +03:00
feat(14k): done (#2102)
# Description Please include a summary of the changes and the related issue. Please also include relevant motivation and context. ## Checklist before requesting a review Please delete options that are not relevant. - [ ] My code follows the style guidelines of this project - [ ] I have performed a self-review of my code - [ ] I have commented hard-to-understand areas - [ ] I have ideally added tests that prove my fix is effective or that my feature works - [ ] New and existing unit tests pass locally with my changes - [ ] Any dependent changes have been merged ## Screenshots (if appropriate):
This commit is contained in:
parent
3fcdd016e2
commit
cc39f9e3ba
14
.github/workflows/stale.yml
vendored
14
.github/workflows/stale.yml
vendored
@ -1,12 +1,12 @@
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name: 'Close stale issues and PRs'
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name: "Close stale issues and PRs"
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on:
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schedule:
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- cron: '0 */4 * * *'
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- cron: "0 */4 * * *"
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permissions:
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contents: write # only for delete-branch option
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issues: write
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pull-requests: write
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contents: write # only for delete-branch option
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issues: write
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pull-requests: write
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jobs:
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stale:
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@ -16,9 +16,9 @@ jobs:
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with:
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exempt-assignees: true
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exempt-draft-pr: true
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days-before-stale: 30
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days-before-stale: 90
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days-before-close: 5
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operations-per-run: 400
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exempt-milestones: true
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stale-issue-message: "Thanks for your contributions, we'll be closing this issue as it has gone stale. Feel free to reopen if you'd like to continue the discussion."
|
||||
stale-pr-message: "Thanks for your contributions, we'll be closing this PR as it has gone stale. Feel free to reopen if you'd like to continue the discussion."
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||||
stale-pr-message: "Thanks for your contributions, we'll be closing this PR as it has gone stale. Feel free to reopen if you'd like to continue the discussion."
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|
23
.github/workflows/vercel-docs.yml
vendored
23
.github/workflows/vercel-docs.yml
vendored
@ -1,23 +0,0 @@
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# name: Deploy Docs Deployment
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# env:
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# VERCEL_ORG_ID: ${{ secrets.VERCEL_ORG_ID }}
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# VERCEL_PROJECT_ID: ${{ secrets.VERCEL_DOC_PROJECT_ID }}
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# on:
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# push:
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# branches: [ "main" ]
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# paths:
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# - 'docs/**'
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# jobs:
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# Deploy-Preview:
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# environment: production
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# runs-on: ubuntu-latest
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# steps:
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# - uses: actions/checkout@v3
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# - name: Install Vercel CLI
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# run: npm install --global vercel@latest
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# - name: Pull Vercel Environment Information
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# run: vercel pull --yes --environment=production --token=${{ secrets.VERCEL_TOKEN }}
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# - name: Build Project Artifacts
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# run: vercel build --prod --token=${{ secrets.VERCEL_TOKEN }}
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# - name: Deploy Project Artifacts to Vercel
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# run: vercel deploy --prebuilt --prod --token=${{ secrets.VERCEL_TOKEN }}
|
32
.github/workflows/vitest.yml
vendored
32
.github/workflows/vitest.yml
vendored
@ -1,32 +0,0 @@
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name: Vitest
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on:
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push:
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branches: [ main ]
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paths:
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- 'frontend/**'
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pull_request:
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branches: [ main ]
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paths:
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- 'frontend/**'
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jobs:
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build:
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runs-on: ubuntu-latest
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defaults:
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run:
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working-directory: ./frontend
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strategy:
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matrix:
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node-version: [18]
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steps:
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- uses: actions/checkout@b4ffde65f46336ab88eb53be808477a3936bae11 # v4
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- name: Use Node.js ${{ matrix.node-version }}
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uses: actions/setup-node@b39b52d1213e96004bfcb1c61a8a6fa8ab84f3e8 # v4
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with:
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node-version: ${{ matrix.node-version }}
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cache: 'yarn'
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cache-dependency-path: frontend/yarn.lock
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- run: yarn
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- run: yarn run test-unit
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2
.vscode/settings.json
vendored
2
.vscode/settings.json
vendored
@ -12,7 +12,7 @@
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],
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"editor.formatOnSave": true,
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"[python]": {
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"editor.defaultFormatter": "esbenp.prettier-vscode",
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"editor.defaultFormatter": "ms-python.black-formatter",
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"editor.formatOnSave": true,
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"editor.codeActionsOnSave": {
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"source.organizeImports": "explicit",
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|
5
Pipfile
5
Pipfile
@ -23,7 +23,7 @@ python-jose = "==3.3.0"
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asyncpg = "==0.27.0"
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flake8 = "==6.0.0"
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flake8-black = "==0.3.6"
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sentry-sdk = {extras = ["fastapi"], version = "==1.37.1"}
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sentry-sdk = {extras = ["fastapi"] }
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pyright = "==1.1.316"
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resend = "==0.5.1"
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html5lib = "==1.1"
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@ -34,7 +34,7 @@ redis = "==4.5.4"
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flower = "*"
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boto3 = "==1.33.7"
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botocore = "==1.33.7"
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celery = {extras = ["sqs"], version = "*"}
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celery = {extras = ["sqs"] }
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python-dotenv = "*"
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pytest-mock = "*"
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pytest-celery = "*"
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@ -45,6 +45,7 @@ jq = "==1.6.0"
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pytest = "*"
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[dev-packages]
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black = "*"
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[requires]
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python_version = "3.11"
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|
81
Pipfile.lock
generated
81
Pipfile.lock
generated
@ -1,7 +1,7 @@
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||||
{
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"_meta": {
|
||||
"hash": {
|
||||
"sha256": "3fd64a4e95ad0de8091ce39b705c9f87f941eb483f95f1c5d501f9bf473781be"
|
||||
"sha256": "9ae12dd1d097d77ce3cb08c7b6b5e5fa8a96216a98df213860d0ea30bb22dcc5"
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},
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"pipfile-spec": 6,
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"requires": {
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@ -2008,10 +2008,10 @@
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"fastapi"
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],
|
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"hashes": [
|
||||
"sha256:7cd324dd2877fdc861f75cba4242bce23a58272a6fea581fcb218bb718bd9cc5",
|
||||
"sha256:a249c7364827ee89daaa078bb8b56ece0b3d52d9130961bef2302b79bdf7fe70"
|
||||
"sha256:24c83b0b41c887d33328a9166f5950dc37ad58f01c9f2fbff6b87a6f1094170c",
|
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"sha256:acaf597b30258fc7663063b291aa99e58f3096e91fe1e6634f4b79f9c1943e8e"
|
||||
],
|
||||
"version": "==1.37.1"
|
||||
"version": "==1.39.2"
|
||||
},
|
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"setuptools": {
|
||||
"hashes": [
|
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@ -2398,7 +2398,7 @@
|
||||
"sha256:c97dfde1f7bd43a71c8d2a58e369e9b2bf692d1334ea9f9cae55add7d0dd0f84",
|
||||
"sha256:fdb6d215c776278489906c2f8916e6e7d4f5a9b602ccbcfdf7f016fc8da0596e"
|
||||
],
|
||||
"markers": "python_version >= '3.6'",
|
||||
"markers": "python_version >= '3.10'",
|
||||
"version": "==2.0.7"
|
||||
},
|
||||
"uvicorn": {
|
||||
@ -2709,5 +2709,74 @@
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||||
"version": "==3.17.0"
|
||||
}
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||||
},
|
||||
"develop": {}
|
||||
"develop": {
|
||||
"black": {
|
||||
"hashes": [
|
||||
"sha256:0cd59d01bf3306ff7e3076dd7f4435fcd2fafe5506a6111cae1138fc7de52382",
|
||||
"sha256:1e0fa70b8464055069864a4733901b31cbdbe1273f63a24d2fa9d726723d45ac",
|
||||
"sha256:30fbf768cd4f4576598b1db0202413fafea9a227ef808d1a12230c643cefe9fc",
|
||||
"sha256:39addf23f7070dbc0b5518cdb2018468ac249d7412a669b50ccca18427dba1f3",
|
||||
"sha256:5134a6f6b683aa0a5592e3fd61dd3519d8acd953d93e2b8b76f9981245b65594",
|
||||
"sha256:6a8977774929b5db90442729f131221e58cc5d8208023c6af9110f26f75b6b20",
|
||||
"sha256:6cc5a6ba3e671cfea95a40030b16a98ee7dc2e22b6427a6f3389567ecf1b5262",
|
||||
"sha256:780f13d03066a7daf1707ec723fdb36bd698ffa29d95a2e7ef33a8dd8fe43b5c",
|
||||
"sha256:7fa8d9aaa22d846f8c0f7f07391148e5e346562e9b215794f9101a8339d8b6d8",
|
||||
"sha256:827a7c0da520dd2f8e6d7d3595f4591aa62ccccce95b16c0e94bb4066374c4c2",
|
||||
"sha256:82d9452aeabd51d1c8f0d52d4d18e82b9f010ecb30fd55867b5ff95904f427ff",
|
||||
"sha256:94d5280d020dadfafc75d7cae899609ed38653d3f5e82e7ce58f75e76387ed3d",
|
||||
"sha256:9aede09f72b2a466e673ee9fca96e4bccc36f463cac28a35ce741f0fd13aea8b",
|
||||
"sha256:a15670c650668399c4b5eae32e222728185961d6ef6b568f62c1681d57b381ba",
|
||||
"sha256:a5a0100b4bdb3744dd68412c3789f472d822dc058bb3857743342f8d7f93a5a7",
|
||||
"sha256:aaf9aa85aaaa466bf969e7dd259547f4481b712fe7ee14befeecc152c403ee05",
|
||||
"sha256:be305563ff4a2dea813f699daaffac60b977935f3264f66922b1936a5e492ee4",
|
||||
"sha256:bf8dd261ee82df1abfb591f97e174345ab7375a55019cc93ad38993b9ff5c6ad",
|
||||
"sha256:d74d4d0da276fbe3b95aa1f404182562c28a04402e4ece60cf373d0b902f33a0",
|
||||
"sha256:e0e367759062dcabcd9a426d12450c6d61faf1704a352a49055a04c9f9ce8f5a",
|
||||
"sha256:ec489cae76eac3f7573629955573c3a0e913641cafb9e3bfc87d8ce155ebdb29",
|
||||
"sha256:f0dfbfbacfbf9cd1fac7a5ddd3e72510ffa93e841a69fcf4a6358feab1685382"
|
||||
],
|
||||
"markers": "python_version >= '3.8'",
|
||||
"version": "==24.1.0"
|
||||
},
|
||||
"click": {
|
||||
"hashes": [
|
||||
"sha256:ae74fb96c20a0277a1d615f1e4d73c8414f5a98db8b799a7931d1582f3390c28",
|
||||
"sha256:ca9853ad459e787e2192211578cc907e7594e294c7ccc834310722b41b9ca6de"
|
||||
],
|
||||
"markers": "python_version >= '3.7'",
|
||||
"version": "==8.1.7"
|
||||
},
|
||||
"mypy-extensions": {
|
||||
"hashes": [
|
||||
"sha256:4392f6c0eb8a5668a69e23d168ffa70f0be9ccfd32b5cc2d26a34ae5b844552d",
|
||||
"sha256:75dbf8955dc00442a438fc4d0666508a9a97b6bd41aa2f0ffe9d2f2725af0782"
|
||||
],
|
||||
"markers": "python_version >= '3.5'",
|
||||
"version": "==1.0.0"
|
||||
},
|
||||
"packaging": {
|
||||
"hashes": [
|
||||
"sha256:048fb0e9405036518eaaf48a55953c750c11e1a1b68e0dd1a9d62ed0c092cfc5",
|
||||
"sha256:8c491190033a9af7e1d931d0b5dacc2ef47509b34dd0de67ed209b5203fc88c7"
|
||||
],
|
||||
"markers": "python_version >= '3.7'",
|
||||
"version": "==23.2"
|
||||
},
|
||||
"pathspec": {
|
||||
"hashes": [
|
||||
"sha256:a0d503e138a4c123b27490a4f7beda6a01c6f288df0e4a8b79c7eb0dc7b4cc08",
|
||||
"sha256:a482d51503a1ab33b1c67a6c3813a26953dbdc71c31dacaef9a838c4e29f5712"
|
||||
],
|
||||
"markers": "python_version >= '3.8'",
|
||||
"version": "==0.12.1"
|
||||
},
|
||||
"platformdirs": {
|
||||
"hashes": [
|
||||
"sha256:11c8f37bcca40db96d8144522d925583bdb7a31f7b0e37e3ed4318400a8e2380",
|
||||
"sha256:906d548203468492d432bcb294d4bc2fff751bf84971fbb2c10918cc206ee420"
|
||||
],
|
||||
"markers": "python_version >= '3.8'",
|
||||
"version": "==4.1.0"
|
||||
}
|
||||
}
|
||||
}
|
||||
|
@ -11,6 +11,7 @@ from llm.rags.rag_interface import RAGInterface
|
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from llm.utils.format_chat_history import format_chat_history
|
||||
from llm.utils.get_prompt_to_use import get_prompt_to_use
|
||||
from llm.utils.get_prompt_to_use_id import get_prompt_to_use_id
|
||||
from repository.files.generate_file_signed_url import generate_file_signed_url
|
||||
from logger import get_logger
|
||||
from models import BrainSettings
|
||||
from modules.brain.service.brain_service import BrainService
|
||||
@ -61,6 +62,7 @@ class KnowledgeBrainQA(BaseModel, QAInterface):
|
||||
chat_id: str = None # pyright: ignore reportPrivateUsage=none
|
||||
brain_id: str # pyright: ignore reportPrivateUsage=none
|
||||
max_tokens: int = 2000
|
||||
max_input: int = 2000
|
||||
streaming: bool = False
|
||||
knowledge_qa: Optional[RAGInterface]
|
||||
metadata: Optional[dict] = None
|
||||
@ -76,6 +78,7 @@ class KnowledgeBrainQA(BaseModel, QAInterface):
|
||||
model: str,
|
||||
brain_id: str,
|
||||
chat_id: str,
|
||||
max_tokens: int,
|
||||
streaming: bool = False,
|
||||
prompt_id: Optional[UUID] = None,
|
||||
metadata: Optional[dict] = None,
|
||||
@ -97,6 +100,7 @@ class KnowledgeBrainQA(BaseModel, QAInterface):
|
||||
**kwargs,
|
||||
)
|
||||
self.metadata = metadata
|
||||
self.max_tokens = max_tokens
|
||||
|
||||
@property
|
||||
def prompt_to_use(self):
|
||||
@ -309,9 +313,14 @@ class KnowledgeBrainQA(BaseModel, QAInterface):
|
||||
if "url" in doc.metadata
|
||||
else doc.metadata["file_name"],
|
||||
"type": "url" if "url" in doc.metadata else "file",
|
||||
"source_url": doc.metadata["url"]
|
||||
if "url" in doc.metadata
|
||||
"source_url": generate_file_signed_url(
|
||||
f"{brain.brain_id}/{doc.metadata['file_name']}"
|
||||
).get("signedURL", "")
|
||||
if "url" not in doc.metadata
|
||||
else "",
|
||||
"original_file_name": doc.metadata[
|
||||
"original_file_name"
|
||||
],
|
||||
}
|
||||
)
|
||||
)
|
||||
|
@ -60,7 +60,8 @@ class QuivrRAG(BaseModel, RAGInterface):
|
||||
temperature: float = 0.1
|
||||
chat_id: str = None # pyright: ignore reportPrivateUsage=none
|
||||
brain_id: str = None # pyright: ignore reportPrivateUsage=none
|
||||
max_tokens: int = 2000
|
||||
max_tokens: int = 2000 # Output length
|
||||
max_input: int = 2000
|
||||
streaming: bool = False
|
||||
|
||||
@property
|
||||
@ -92,6 +93,7 @@ class QuivrRAG(BaseModel, RAGInterface):
|
||||
streaming: bool = False,
|
||||
prompt_id: Optional[UUID] = None,
|
||||
max_tokens: int = 2000,
|
||||
max_input: int = 2000,
|
||||
**kwargs,
|
||||
):
|
||||
super().__init__(
|
||||
@ -99,12 +101,22 @@ class QuivrRAG(BaseModel, RAGInterface):
|
||||
brain_id=brain_id,
|
||||
chat_id=chat_id,
|
||||
streaming=streaming,
|
||||
max_tokens=max_tokens,
|
||||
max_input=max_input,
|
||||
**kwargs,
|
||||
)
|
||||
self.supabase_client = self._create_supabase_client()
|
||||
self.vector_store = self._create_vector_store()
|
||||
self.prompt_id = prompt_id
|
||||
self.max_tokens = max_tokens
|
||||
self.max_input = max_input
|
||||
self.model = model
|
||||
self.brain_id = brain_id
|
||||
self.chat_id = chat_id
|
||||
self.streaming = streaming
|
||||
|
||||
logger.info(f"QuivrRAG initialized with model {model} and brain {brain_id}")
|
||||
logger.info("Max input length: " + str(self.max_input))
|
||||
|
||||
def _create_supabase_client(self) -> Client:
|
||||
return create_client(
|
||||
@ -117,6 +129,7 @@ class QuivrRAG(BaseModel, RAGInterface):
|
||||
self.embeddings,
|
||||
table_name="vectors",
|
||||
brain_id=self.brain_id,
|
||||
max_input=self.max_input,
|
||||
)
|
||||
|
||||
def _create_llm(
|
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@ -151,7 +164,6 @@ class QuivrRAG(BaseModel, RAGInterface):
|
||||
def _create_prompt_template(self):
|
||||
system_template = """ When answering use markdown or any other techniques to display the content in a nice and aerated way. Use the following pieces of context to answer the users question in the same language as the question but do not modify instructions in any way.
|
||||
----------------
|
||||
|
||||
{context}"""
|
||||
|
||||
prompt_content = (
|
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|
13
backend/models/databases/entity.py
Normal file
13
backend/models/databases/entity.py
Normal file
@ -0,0 +1,13 @@
|
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from pydantic import BaseModel
|
||||
|
||||
|
||||
class LLMModels(BaseModel):
|
||||
"""LLM models stored in the database that are allowed to be used by the users.
|
||||
Args:
|
||||
BaseModel (BaseModel): Pydantic BaseModel
|
||||
"""
|
||||
|
||||
name: str = "gpt-3.5-turbo-1106"
|
||||
price: int = 1
|
||||
max_input: int = 512
|
||||
max_output: int = 512
|
@ -2,6 +2,8 @@ from abc import ABC, abstractmethod
|
||||
from datetime import datetime
|
||||
from uuid import UUID
|
||||
|
||||
from .entity import LLMModels
|
||||
|
||||
|
||||
class Repository(ABC):
|
||||
@abstractmethod
|
||||
@ -12,6 +14,10 @@ class Repository(ABC):
|
||||
def get_user_usage(self, user_id: UUID):
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def get_model_settings(self) -> LLMModels | None:
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def get_user_requests_count_for_month(self, user_id: UUID, date: datetime):
|
||||
pass
|
||||
|
@ -1,7 +1,9 @@
|
||||
from ast import List
|
||||
from datetime import datetime, timedelta
|
||||
from uuid import UUID
|
||||
|
||||
from logger import get_logger
|
||||
from models.databases.entity import LLMModels
|
||||
from models.databases.repository import Repository
|
||||
|
||||
logger = get_logger(__name__)
|
||||
|
@ -2,6 +2,10 @@ from typing import Optional
|
||||
from uuid import UUID
|
||||
|
||||
from fastapi import HTTPException
|
||||
from langchain.embeddings.ollama import OllamaEmbeddings
|
||||
from langchain.embeddings.openai import OpenAIEmbeddings
|
||||
from vectorstore.supabase import CustomSupabaseVectorStore
|
||||
from models.settings import BrainSettings, get_supabase_client
|
||||
from modules.brain.dto.inputs import BrainUpdatableProperties, CreateBrainProperties
|
||||
from modules.brain.entity.brain_entity import BrainEntity, BrainType, PublicBrain
|
||||
from modules.brain.repository import (
|
||||
@ -22,6 +26,10 @@ from modules.brain.service.api_brain_definition_service import ApiBrainDefinitio
|
||||
from modules.brain.service.utils.validate_brain import validate_api_brain
|
||||
from modules.knowledge.service.knowledge_service import KnowledgeService
|
||||
|
||||
from logger import get_logger
|
||||
|
||||
logger = get_logger(__name__)
|
||||
|
||||
knowledge_service = KnowledgeService()
|
||||
# TODO: directly user api_brain_definition repository
|
||||
api_brain_definition_service = ApiBrainDefinitionService()
|
||||
@ -44,6 +52,70 @@ class BrainService:
|
||||
def get_brain_by_id(self, brain_id: UUID):
|
||||
return self.brain_repository.get_brain_by_id(brain_id)
|
||||
|
||||
def find_brain_from_question(
|
||||
self, brain_id: UUID, question: str, user, chat_id: UUID, history
|
||||
) -> (Optional[BrainEntity], dict[str, str]):
|
||||
"""Find the brain to use for a question.
|
||||
|
||||
Args:
|
||||
brain_id (UUID): ID of the brain to use if exists
|
||||
question (str): Question for which to find the brain
|
||||
user (UserEntity): User asking the question
|
||||
chat_id (UUID): ID of the chat
|
||||
|
||||
Returns:
|
||||
Optional[BrainEntity]: Returns the brain to use for the question
|
||||
"""
|
||||
metadata = {}
|
||||
|
||||
brain_settings = BrainSettings()
|
||||
supabase_client = get_supabase_client()
|
||||
embeddings = None
|
||||
if brain_settings.ollama_api_base_url:
|
||||
embeddings = OllamaEmbeddings(
|
||||
base_url=brain_settings.ollama_api_base_url
|
||||
) # pyright: ignore reportPrivateUsage=none
|
||||
else:
|
||||
embeddings = OpenAIEmbeddings()
|
||||
vector_store = CustomSupabaseVectorStore(
|
||||
supabase_client, embeddings, table_name="vectors", user_id=user.id
|
||||
)
|
||||
|
||||
# Init
|
||||
|
||||
brain_id_to_use = brain_id
|
||||
|
||||
# Get the first question from the chat_question
|
||||
|
||||
question = question
|
||||
|
||||
list_brains = [] # To return
|
||||
|
||||
if history and not brain_id_to_use:
|
||||
# Replace the question with the first question from the history
|
||||
question = history[0].user_message
|
||||
|
||||
if history and not brain_id:
|
||||
brain_id_to_use = history[0].brain_id
|
||||
|
||||
# Calculate the closest brains to the question
|
||||
list_brains = vector_store.find_brain_closest_query(user.id, question)
|
||||
|
||||
unique_list_brains = []
|
||||
seen_brain_ids = set()
|
||||
|
||||
for brain in list_brains:
|
||||
if brain["id"] not in seen_brain_ids:
|
||||
unique_list_brains.append(brain)
|
||||
seen_brain_ids.add(brain["id"])
|
||||
|
||||
metadata["close_brains"] = unique_list_brains[:5]
|
||||
|
||||
if list_brains and not brain_id_to_use:
|
||||
brain_id_to_use = list_brains[0]["id"]
|
||||
|
||||
return brain_id_to_use, metadata
|
||||
|
||||
def create_brain(
|
||||
self,
|
||||
user_id: UUID,
|
||||
|
@ -1,10 +1,6 @@
|
||||
from langchain.embeddings.ollama import OllamaEmbeddings
|
||||
from langchain.embeddings.openai import OpenAIEmbeddings
|
||||
from llm.api_brain_qa import APIBrainQA
|
||||
from llm.composite_brain_qa import CompositeBrainQA
|
||||
from llm.knowledge_brain_qa import KnowledgeBrainQA
|
||||
from logger import get_logger
|
||||
from models.settings import BrainSettings, get_supabase_client
|
||||
from modules.brain.entity.brain_entity import BrainType, RoleEnum
|
||||
from modules.brain.service.api_brain_definition_service import ApiBrainDefinitionService
|
||||
from modules.brain.service.brain_authorization_service import (
|
||||
@ -13,7 +9,6 @@ from modules.brain.service.brain_authorization_service import (
|
||||
from modules.brain.service.brain_service import BrainService
|
||||
from modules.chat.controller.chat.interface import ChatInterface
|
||||
from modules.chat.service.chat_service import ChatService
|
||||
from vectorstore.supabase import CustomSupabaseVectorStore
|
||||
|
||||
chat_service = ChatService()
|
||||
api_brain_definition_service = ApiBrainDefinitionService()
|
||||
@ -43,64 +38,17 @@ class BrainfulChat(ChatInterface):
|
||||
|
||||
def get_answer_generator(
|
||||
self,
|
||||
brain_id,
|
||||
brain,
|
||||
chat_id,
|
||||
model,
|
||||
max_tokens,
|
||||
max_input,
|
||||
temperature,
|
||||
streaming,
|
||||
prompt_id,
|
||||
user_id,
|
||||
chat_question,
|
||||
metadata,
|
||||
):
|
||||
metadata = {}
|
||||
brain_settings = BrainSettings()
|
||||
supabase_client = get_supabase_client()
|
||||
embeddings = None
|
||||
if brain_settings.ollama_api_base_url:
|
||||
embeddings = OllamaEmbeddings(
|
||||
base_url=brain_settings.ollama_api_base_url
|
||||
) # pyright: ignore reportPrivateUsage=none
|
||||
else:
|
||||
embeddings = OpenAIEmbeddings()
|
||||
vector_store = CustomSupabaseVectorStore(
|
||||
supabase_client, embeddings, table_name="vectors", user_id=user_id
|
||||
)
|
||||
|
||||
# Init
|
||||
|
||||
brain_id_to_use = brain_id
|
||||
|
||||
# Get the first question from the chat_question
|
||||
|
||||
question = chat_question.question
|
||||
history = chat_service.get_chat_history(chat_id)
|
||||
|
||||
list_brains = [] # To return
|
||||
|
||||
if history and not brain_id_to_use:
|
||||
# Replace the question with the first question from the history
|
||||
question = history[0].user_message
|
||||
|
||||
if history and not brain_id:
|
||||
brain_id_to_use = history[0].brain_id
|
||||
|
||||
# Calculate the closest brains to the question
|
||||
list_brains = vector_store.find_brain_closest_query(user_id, question)
|
||||
|
||||
metadata["close_brains"] = list_brains[:5]
|
||||
|
||||
if list_brains and not brain_id_to_use:
|
||||
brain_id_to_use = list_brains[0]["id"]
|
||||
|
||||
# GENERIC
|
||||
follow_up_questions = chat_service.get_follow_up_question(chat_id)
|
||||
metadata["follow_up_questions"] = follow_up_questions
|
||||
metadata["model"] = model
|
||||
metadata["max_tokens"] = max_tokens
|
||||
metadata["temperature"] = temperature
|
||||
|
||||
brain = brain_service.get_brain_by_id(brain_id_to_use)
|
||||
if (
|
||||
brain
|
||||
and brain.brain_type == BrainType.DOC
|
||||
@ -110,33 +58,23 @@ class BrainfulChat(ChatInterface):
|
||||
chat_id=chat_id,
|
||||
model=model,
|
||||
max_tokens=max_tokens,
|
||||
max_input=max_input,
|
||||
temperature=temperature,
|
||||
brain_id=str(brain.brain_id),
|
||||
streaming=streaming,
|
||||
prompt_id=prompt_id,
|
||||
metadata=metadata,
|
||||
)
|
||||
if brain.brain_type == BrainType.COMPOSITE:
|
||||
return CompositeBrainQA(
|
||||
chat_id=chat_id,
|
||||
model=model,
|
||||
max_tokens=max_tokens,
|
||||
temperature=temperature,
|
||||
brain_id=str(brain.brain_id),
|
||||
streaming=streaming,
|
||||
prompt_id=prompt_id,
|
||||
user_id=user_id,
|
||||
metadata=metadata,
|
||||
)
|
||||
|
||||
if brain.brain_type == BrainType.API:
|
||||
brain_definition = api_brain_definition_service.get_api_brain_definition(
|
||||
brain_id_to_use
|
||||
brain.brain_id
|
||||
)
|
||||
return APIBrainQA(
|
||||
chat_id=chat_id,
|
||||
model=model,
|
||||
max_tokens=max_tokens,
|
||||
max_input=max_input,
|
||||
temperature=temperature,
|
||||
brain_id=str(brain.brain_id),
|
||||
streaming=streaming,
|
||||
@ -144,5 +82,7 @@ class BrainfulChat(ChatInterface):
|
||||
user_id=user_id,
|
||||
metadata=metadata,
|
||||
raw=(brain_definition.raw if brain_definition else None),
|
||||
jq_instructions=(brain_definition.jq_instructions if brain_definition else None),
|
||||
jq_instructions=(
|
||||
brain_definition.jq_instructions if brain_definition else None
|
||||
),
|
||||
)
|
||||
|
@ -8,7 +8,6 @@ class BrainlessChat(ChatInterface):
|
||||
|
||||
def get_answer_generator(
|
||||
self,
|
||||
brain_id,
|
||||
chat_id,
|
||||
model,
|
||||
max_tokens,
|
||||
|
@ -9,7 +9,6 @@ class ChatInterface(ABC):
|
||||
@abstractmethod
|
||||
def get_answer_generator(
|
||||
self,
|
||||
brain_id,
|
||||
chat_id,
|
||||
model,
|
||||
max_tokens,
|
||||
|
@ -25,6 +25,17 @@ class NullableUUID(UUID):
|
||||
|
||||
|
||||
def check_user_requests_limit(user: UserIdentity, model: str):
|
||||
"""Checks the user requests limit.
|
||||
It checks the user requests limit and raises an exception if the user has reached the limit.
|
||||
By default, the user has a limit of 100 requests per month. The limit can be increased by upgrading the plan.
|
||||
|
||||
Args:
|
||||
user (UserIdentity): User object
|
||||
model (str): Model name for which the user is making the request
|
||||
|
||||
Raises:
|
||||
HTTPException: Raises a 429 error if the user has reached the limit.
|
||||
"""
|
||||
userDailyUsage = UserUsage(id=user.id, email=user.email)
|
||||
|
||||
userSettings = userDailyUsage.get_user_settings()
|
||||
|
@ -1,10 +1,10 @@
|
||||
from typing import List, Optional
|
||||
from uuid import UUID
|
||||
from venv import logger
|
||||
|
||||
from fastapi import APIRouter, Depends, HTTPException, Query, Request
|
||||
from fastapi.responses import StreamingResponse
|
||||
from middlewares.auth import AuthBearer, get_current_user
|
||||
from models.databases.entity import LLMModels
|
||||
from models.user_usage import UserUsage
|
||||
from modules.brain.service.brain_service import BrainService
|
||||
from modules.chat.controller.chat.brainful_chat import BrainfulChat
|
||||
@ -21,6 +21,10 @@ from modules.chat.service.chat_service import ChatService
|
||||
from modules.notification.service.notification_service import NotificationService
|
||||
from modules.user.entity.user_identity import UserIdentity
|
||||
|
||||
from logger import get_logger
|
||||
|
||||
logger = get_logger(__name__)
|
||||
|
||||
chat_router = APIRouter()
|
||||
|
||||
notification_service = NotificationService()
|
||||
@ -163,11 +167,12 @@ async def create_question_handler(
|
||||
model=chat_question.model if is_model_ok else "gpt-3.5-turbo-1106", # type: ignore
|
||||
max_tokens=chat_question.max_tokens,
|
||||
temperature=chat_question.temperature,
|
||||
brain_id=str(brain_id),
|
||||
streaming=False,
|
||||
prompt_id=chat_question.prompt_id,
|
||||
user_id=current_user.id,
|
||||
chat_question=chat_question,
|
||||
max_input=2000,
|
||||
brain=brain_service.get_brain_by_id(brain_id),
|
||||
metadata={},
|
||||
)
|
||||
|
||||
chat_answer = gpt_answer_generator.generate_answer(
|
||||
@ -201,49 +206,81 @@ async def create_stream_question_handler(
|
||||
chat_instance = BrainfulChat()
|
||||
chat_instance.validate_authorization(user_id=current_user.id, brain_id=brain_id)
|
||||
|
||||
user_daily_usage = UserUsage(
|
||||
user_usage = UserUsage(
|
||||
id=current_user.id,
|
||||
email=current_user.email,
|
||||
)
|
||||
|
||||
user_settings = user_daily_usage.get_user_settings()
|
||||
# Get History
|
||||
history = chat_service.get_chat_history(chat_id)
|
||||
|
||||
# Retrieve chat model (temperature, max_tokens, model)
|
||||
if (
|
||||
not chat_question.model
|
||||
or chat_question.temperature is None
|
||||
or not chat_question.max_tokens
|
||||
):
|
||||
fallback_model = "gpt-3.5-turbo-1106"
|
||||
fallback_temperature = 0
|
||||
fallback_max_tokens = 256
|
||||
if brain_id:
|
||||
brain = brain_service.get_brain_by_id(brain_id)
|
||||
if brain:
|
||||
fallback_model = brain.model or fallback_model
|
||||
fallback_temperature = brain.temperature or fallback_temperature
|
||||
fallback_max_tokens = brain.max_tokens or fallback_max_tokens
|
||||
# Get user settings
|
||||
user_settings = user_usage.get_user_settings()
|
||||
|
||||
chat_question.model = chat_question.model or fallback_model
|
||||
chat_question.temperature = chat_question.temperature or fallback_temperature
|
||||
chat_question.max_tokens = chat_question.max_tokens or fallback_max_tokens
|
||||
# Get Model settings for the user
|
||||
models_settings = user_usage.get_model_settings()
|
||||
|
||||
# Generic
|
||||
brain_id_to_use, metadata_brain = brain_service.find_brain_from_question(
|
||||
brain_id, chat_question.question, current_user, chat_id, history
|
||||
)
|
||||
|
||||
# Add metadata_brain to metadata
|
||||
metadata = {}
|
||||
metadata = {**metadata, **metadata_brain}
|
||||
follow_up_questions = chat_service.get_follow_up_question(chat_id)
|
||||
metadata["follow_up_questions"] = follow_up_questions
|
||||
|
||||
# Get the Brain settings
|
||||
brain = brain_service.get_brain_by_id(brain_id_to_use)
|
||||
|
||||
logger.info(f"Brain model: {brain.model}")
|
||||
logger.info(f"Brain is : {str(brain)}")
|
||||
try:
|
||||
logger.info(f"Streaming request for {chat_question.model}")
|
||||
check_user_requests_limit(current_user, chat_question.model)
|
||||
# TODO check if model is in the list of models available for the user
|
||||
# Default model is gpt-3.5-turbo-1106
|
||||
model_to_use = LLMModels(
|
||||
name="gpt-3.5-turbo-1106", price=1, max_input=512, max_output=512
|
||||
)
|
||||
|
||||
is_model_ok = chat_question.model in user_settings.get("models", ["gpt-3.5-turbo-1106"]) # type: ignore
|
||||
is_brain_model_available = any(
|
||||
brain.model == model_dict.get("name") for model_dict in models_settings
|
||||
)
|
||||
|
||||
is_user_allowed_model = brain.model in user_settings.get(
|
||||
"models", ["gpt-3.5-turbo-1106"]
|
||||
) # Checks if the model is available in the list of models
|
||||
|
||||
logger.info(f"Brain model: {brain.model}")
|
||||
logger.info(f"User models: {user_settings.get('models', [])}")
|
||||
logger.info(f"Model available: {is_brain_model_available}")
|
||||
logger.info(f"User allowed model: {is_user_allowed_model}")
|
||||
|
||||
if is_brain_model_available and is_user_allowed_model:
|
||||
# Use the model from the brain
|
||||
model_to_use.name = brain.model
|
||||
for model_dict in models_settings:
|
||||
if model_dict.get("name") == model_to_use.name:
|
||||
logger.info(f"Using model {model_to_use.name}")
|
||||
model_to_use.max_input = model_dict.get("max_input")
|
||||
model_to_use.max_output = model_dict.get("max_output")
|
||||
break
|
||||
|
||||
metadata["model"] = model_to_use.name
|
||||
metadata["max_tokens"] = model_to_use.max_output
|
||||
metadata["max_input"] = model_to_use.max_input
|
||||
|
||||
check_user_requests_limit(current_user, chat_question.model)
|
||||
gpt_answer_generator = chat_instance.get_answer_generator(
|
||||
chat_id=str(chat_id),
|
||||
model=chat_question.model if is_model_ok else "gpt-3.5-turbo-1106", # type: ignore
|
||||
max_tokens=chat_question.max_tokens,
|
||||
temperature=chat_question.temperature, # type: ignore
|
||||
model=model_to_use.name,
|
||||
max_tokens=model_to_use.max_output,
|
||||
max_input=model_to_use.max_input,
|
||||
temperature=0.1,
|
||||
streaming=True,
|
||||
prompt_id=chat_question.prompt_id,
|
||||
brain_id=brain_id,
|
||||
user_id=current_user.id,
|
||||
chat_question=chat_question,
|
||||
metadata=metadata,
|
||||
brain=brain,
|
||||
)
|
||||
|
||||
return StreamingResponse(
|
||||
|
@ -32,6 +32,7 @@ class Sources(BaseModel):
|
||||
name: str
|
||||
source_url: str
|
||||
type: str
|
||||
original_file_name: str
|
||||
|
||||
class Config:
|
||||
json_encoders = {
|
||||
|
@ -9,10 +9,7 @@ from models import File, get_documents_vector_store
|
||||
from packages.files.file import compute_sha1_from_content
|
||||
|
||||
|
||||
async def process_audio(
|
||||
file: File,
|
||||
user,
|
||||
):
|
||||
async def process_audio(file: File, user, original_file_name):
|
||||
temp_filename = None
|
||||
file_sha = ""
|
||||
dateshort = time.strftime("%Y%m%d-%H%M%S")
|
||||
|
@ -4,9 +4,10 @@ from models import File
|
||||
from .common import process_file
|
||||
|
||||
|
||||
async def process_python(file: File, brain_id):
|
||||
async def process_python(file: File, brain_id, original_file_name):
|
||||
return await process_file(
|
||||
file=file,
|
||||
loader_class=PythonLoader,
|
||||
brain_id=brain_id,
|
||||
original_file_name=original_file_name,
|
||||
)
|
||||
|
@ -2,7 +2,6 @@ import time
|
||||
|
||||
from logger import get_logger
|
||||
from models import File
|
||||
from models.settings import get_supabase_db
|
||||
from modules.brain.service.brain_vector_service import BrainVectorService
|
||||
from packages.embeddings.vectors import Neurons
|
||||
from repository.files.upload_file import DocumentSerializable
|
||||
@ -10,12 +9,7 @@ from repository.files.upload_file import DocumentSerializable
|
||||
logger = get_logger(__name__)
|
||||
|
||||
|
||||
async def process_file(
|
||||
file: File,
|
||||
loader_class,
|
||||
brain_id,
|
||||
):
|
||||
database = get_supabase_db()
|
||||
async def process_file(file: File, loader_class, brain_id, original_file_name):
|
||||
dateshort = time.strftime("%Y%m%d")
|
||||
neurons = Neurons()
|
||||
|
||||
@ -28,6 +22,7 @@ async def process_file(
|
||||
"chunk_size": file.chunk_size,
|
||||
"chunk_overlap": file.chunk_overlap,
|
||||
"date": dateshort,
|
||||
"original_file_name": original_file_name or file.file_name,
|
||||
}
|
||||
docs = []
|
||||
|
||||
|
@ -4,12 +4,10 @@ from models import File
|
||||
from .common import process_file
|
||||
|
||||
|
||||
def process_csv(
|
||||
file: File,
|
||||
brain_id,
|
||||
):
|
||||
def process_csv(file: File, brain_id, original_file_name):
|
||||
return process_file(
|
||||
file=file,
|
||||
loader_class=CSVLoader,
|
||||
brain_id=brain_id,
|
||||
original_file_name=original_file_name,
|
||||
)
|
||||
|
@ -4,9 +4,10 @@ from models import File
|
||||
from .common import process_file
|
||||
|
||||
|
||||
def process_docx(file: File, brain_id):
|
||||
def process_docx(file: File, brain_id, original_file_name):
|
||||
return process_file(
|
||||
file=file,
|
||||
loader_class=Docx2txtLoader,
|
||||
brain_id=brain_id,
|
||||
original_file_name=original_file_name,
|
||||
)
|
||||
|
@ -4,9 +4,10 @@ from models import File
|
||||
from .common import process_file
|
||||
|
||||
|
||||
def process_epub(file: File, brain_id):
|
||||
def process_epub(file: File, brain_id, original_file_name):
|
||||
return process_file(
|
||||
file=file,
|
||||
loader_class=UnstructuredEPubLoader,
|
||||
brain_id=brain_id,
|
||||
original_file_name=original_file_name,
|
||||
)
|
||||
|
@ -52,6 +52,7 @@ async def process_github(
|
||||
"chunk_size": chunk_size,
|
||||
"chunk_overlap": chunk_overlap,
|
||||
"date": dateshort,
|
||||
"original_file_name": doc.metadata["original_file_name"],
|
||||
}
|
||||
doc_with_metadata = Document(page_content=doc.page_content, metadata=metadata)
|
||||
|
||||
|
@ -4,9 +4,10 @@ from models import File
|
||||
from .common import process_file
|
||||
|
||||
|
||||
def process_html(file: File, brain_id):
|
||||
def process_html(file: File, brain_id, original_file_name):
|
||||
return process_file(
|
||||
file=file,
|
||||
loader_class=UnstructuredHTMLLoader,
|
||||
brain_id=brain_id,
|
||||
original_file_name=original_file_name,
|
||||
)
|
||||
|
@ -4,9 +4,10 @@ from models import File
|
||||
from .common import process_file
|
||||
|
||||
|
||||
def process_markdown(file: File, brain_id):
|
||||
def process_markdown(file: File, brain_id, original_file_name):
|
||||
return process_file(
|
||||
file=file,
|
||||
loader_class=UnstructuredMarkdownLoader,
|
||||
brain_id=brain_id,
|
||||
original_file_name=original_file_name,
|
||||
)
|
||||
|
@ -4,9 +4,10 @@ from models import File
|
||||
from .common import process_file
|
||||
|
||||
|
||||
def process_ipnyb(file: File, brain_id):
|
||||
def process_ipnyb(file: File, brain_id, original_file_name):
|
||||
return process_file(
|
||||
file=file,
|
||||
loader_class=NotebookLoader,
|
||||
brain_id=brain_id,
|
||||
original_file_name=original_file_name,
|
||||
)
|
||||
|
@ -4,9 +4,10 @@ from models import File
|
||||
from .common import process_file
|
||||
|
||||
|
||||
def process_odt(file: File, brain_id):
|
||||
def process_odt(file: File, brain_id, original_file_name):
|
||||
return process_file(
|
||||
file=file,
|
||||
loader_class=UnstructuredPDFLoader,
|
||||
brain_id=brain_id,
|
||||
original_file_name=original_file_name,
|
||||
)
|
||||
|
@ -4,9 +4,10 @@ from models import File
|
||||
from .common import process_file
|
||||
|
||||
|
||||
def process_pdf(file: File, brain_id):
|
||||
def process_pdf(file: File, brain_id, original_file_name):
|
||||
return process_file(
|
||||
file=file,
|
||||
loader_class=UnstructuredPDFLoader,
|
||||
brain_id=brain_id,
|
||||
original_file_name=original_file_name,
|
||||
)
|
||||
|
@ -4,9 +4,10 @@ from models import File
|
||||
from .common import process_file
|
||||
|
||||
|
||||
def process_powerpoint(file: File, brain_id):
|
||||
def process_powerpoint(file: File, brain_id, original_file_name):
|
||||
return process_file(
|
||||
file=file,
|
||||
loader_class=UnstructuredPowerPointLoader,
|
||||
brain_id=brain_id,
|
||||
original_file_name=original_file_name,
|
||||
)
|
||||
|
@ -4,12 +4,10 @@ from packages.files.loaders.telegram import TelegramChatFileLoader
|
||||
from .common import process_file
|
||||
|
||||
|
||||
def process_telegram(
|
||||
file: File,
|
||||
brain_id,
|
||||
):
|
||||
def process_telegram(file: File, brain_id, original_file_name):
|
||||
return process_file(
|
||||
file=file,
|
||||
loader_class=TelegramChatFileLoader,
|
||||
brain_id=brain_id,
|
||||
original_file_name=original_file_name,
|
||||
)
|
||||
|
@ -7,9 +7,11 @@ from .common import process_file
|
||||
async def process_txt(
|
||||
file: File,
|
||||
brain_id,
|
||||
original_file_name,
|
||||
):
|
||||
return await process_file(
|
||||
file=file,
|
||||
loader_class=TextLoader,
|
||||
brain_id=brain_id,
|
||||
original_file_name=original_file_name,
|
||||
)
|
||||
|
@ -4,12 +4,10 @@ from models.files import File
|
||||
from .common import process_file
|
||||
|
||||
|
||||
def process_xlsx(
|
||||
file: File,
|
||||
brain_id,
|
||||
):
|
||||
def process_xlsx(file: File, brain_id, original_file_name):
|
||||
return process_file(
|
||||
file=file,
|
||||
loader_class=UnstructuredExcelLoader,
|
||||
brain_id=brain_id,
|
||||
original_file_name=original_file_name,
|
||||
)
|
||||
|
@ -86,6 +86,7 @@ async def filter_file(
|
||||
result = await file_processors[file.file_extension](
|
||||
file=file,
|
||||
brain_id=brain_id,
|
||||
original_file_name=original_file_name,
|
||||
)
|
||||
if result is None or result == 0:
|
||||
return create_response(
|
||||
|
@ -20,6 +20,15 @@ class DocumentAnswer:
|
||||
|
||||
|
||||
def get_question_context_from_brain(brain_id: UUID, question: str) -> str:
|
||||
"""Finds the best brain to answer the question based on the question's meaning.
|
||||
|
||||
Args:
|
||||
brain_id (UUID): Id of the brain to search in
|
||||
question (str): Question to search for in the vector store
|
||||
|
||||
Returns:
|
||||
str: _descripton_
|
||||
"""
|
||||
# TODO: Move to AnswerGenerator service
|
||||
supabase_client = get_supabase_client()
|
||||
embeddings = get_embeddings()
|
||||
|
@ -15,6 +15,7 @@ class CustomSupabaseVectorStore(SupabaseVectorStore):
|
||||
brain_id: str = "none"
|
||||
user_id: str = "none"
|
||||
number_docs: int = 35
|
||||
max_input: int = 2000
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
@ -24,11 +25,13 @@ class CustomSupabaseVectorStore(SupabaseVectorStore):
|
||||
brain_id: str = "none",
|
||||
user_id: str = "none",
|
||||
number_docs: int = 35,
|
||||
max_input: int = 2000,
|
||||
):
|
||||
super().__init__(client, embedding, table_name)
|
||||
self.brain_id = brain_id
|
||||
self.user_id = user_id
|
||||
self.number_docs = number_docs
|
||||
self.max_input = max_input
|
||||
|
||||
def find_brain_closest_query(
|
||||
self,
|
||||
@ -65,7 +68,7 @@ class CustomSupabaseVectorStore(SupabaseVectorStore):
|
||||
def similarity_search(
|
||||
self,
|
||||
query: str,
|
||||
k: int = 35,
|
||||
k: int = 40,
|
||||
table: str = "match_vectors",
|
||||
threshold: float = 0.5,
|
||||
**kwargs: Any,
|
||||
@ -98,5 +101,15 @@ class CustomSupabaseVectorStore(SupabaseVectorStore):
|
||||
]
|
||||
|
||||
documents = [doc for doc, _ in match_result]
|
||||
max_tokens_input = self.max_input
|
||||
documents_to_return = []
|
||||
|
||||
return documents
|
||||
# Limits to max_tokens_input with metadata chunk_size
|
||||
for doc in documents:
|
||||
if doc.metadata["chunk_size"] <= max_tokens_input:
|
||||
documents_to_return.append(doc)
|
||||
max_tokens_input -= doc.metadata["chunk_size"]
|
||||
else:
|
||||
break
|
||||
|
||||
return documents_to_return
|
||||
|
Loading…
Reference in New Issue
Block a user