mirror of
https://github.com/QuivrHQ/quivr.git
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feat(integration): implementation (#2191)
# 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:
Stan Girard
GitHub
parent
6383918f7b
commit
ba5ef60362
2
.gitignore
vendored
2
.gitignore
vendored
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@ -79,3 +79,5 @@ paulgraham.py
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supabase/seed-airwallex.sql
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airwallexpayouts.py
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application.log
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backend/celerybeat-schedule.db
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6
Pipfile
6
Pipfile
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@ -47,8 +47,12 @@ watchdog = "*"
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langchain-community = "*"
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langchain-openai = "*"
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pydantic-settings = "*"
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unstructured = {extras = ["all-docs"], version = "*"}
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langfuse = "*"
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pandasai = "*"
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colorlog = "*"
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psycopg2-binary = "*"
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psycopg2 = "*"
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unstructured = {extras = ["all-docs"], version = "*"}
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[dev-packages]
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black = "*"
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365
Pipfile.lock
generated
365
Pipfile.lock
generated
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@ -1,7 +1,7 @@
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{
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"_meta": {
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"hash": {
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"sha256": "7b0a7a1d9a850c686e65ee17aecfe4de8b84e92c7b4b155dcda21e64a75257d4"
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"sha256": "c655d8a43b0a0a2d3460e7707c2059601ec2bf2830d20a6ed7cf45f07d7d844b"
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},
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"pipfile-spec": 6,
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"requires": {
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@ -136,6 +136,14 @@
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"markers": "python_version >= '3.8'",
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"version": "==4.2.0"
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},
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"astor": {
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"hashes": [
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"sha256:070a54e890cefb5b3739d19f30f5a5ec840ffc9c50ffa7d23cc9fc1a38ebbfc5",
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"sha256:6a6effda93f4e1ce9f618779b2dd1d9d84f1e32812c23a29b3fff6fd7f63fa5e"
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],
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"markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3'",
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"version": "==0.8.1"
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},
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"async-generator": {
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"hashes": [
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"sha256:01c7bf666359b4967d2cda0000cc2e4af16a0ae098cbffcb8472fb9e8ad6585b",
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@ -251,21 +259,21 @@
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},
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"boto3": {
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"hashes": [
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"sha256:9fd66f22a4cdd63165a7a19186fff9b6e3d742e83498ea3f3231bab6ae4bf0f3",
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"sha256:ae1a974c728c076a49392a695102124f650f45361c654bb7c0bef1bb644c52d5"
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"sha256:2ed136f9cf79e783e12424db23e970d1c50e65a8d7a9077efa71cbf8496fb7a3",
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"sha256:5069b2c647c73c8428378e88b32bd23f568001f897a6f01179fae25de72a7ca6"
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],
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"index": "pypi",
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"markers": "python_version >= '3.8'",
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"version": "==1.34.41"
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"version": "==1.34.42"
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},
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"botocore": {
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"hashes": [
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"sha256:3a6943c75a0d292ab6e008bce58ee6503776969479f991f5ad03a5d877af29ae",
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"sha256:9b5827332da766da487e5a5b14b36e02528be9f2e899f909577afb7001eb441d"
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"sha256:93755c3ede4bd9f6180b3118f6b607acca8b633fd2668226794a543ce79a2434",
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"sha256:cf4fad50d09686f03e44418fcae9dd24369658daa556357cedc0790cfcd6fdac"
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],
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"index": "pypi",
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"markers": "python_version >= '3.8'",
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"version": "==1.34.41"
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"version": "==1.34.42"
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},
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"bytecode": {
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"hashes": [
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@ -510,6 +518,15 @@
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"markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4'",
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"version": "==15.0.1"
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},
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"colorlog": {
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"hashes": [
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"sha256:3e3e079a41feb5a1b64f978b5ea4f46040a94f11f0e8bbb8261e3dbbeca64d44",
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"sha256:4dcbb62368e2800cb3c5abd348da7e53f6c362dda502ec27c560b2e58a66bd33"
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],
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"index": "pypi",
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"markers": "python_version >= '3.6'",
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"version": "==6.8.2"
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},
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"contourpy": {
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"hashes": [
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"sha256:0274c1cb63625972c0c007ab14dd9ba9e199c36ae1a231ce45d725cbcbfd10a8",
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@ -640,70 +657,70 @@
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},
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"ddtrace": {
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||||
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||||
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||||
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|
||||
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||||
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"sha256:ddbd82d61a338570e8a2ff3b55ce8af31251959f90fb09e6784fdbb311cdaa21",
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"sha256:e3885af6449987e02a9fadd9132e7227825968c803ce9f164aeab01f9d23fbe2",
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"sha256:eaf335dc2cf92627d3192134d54b063bc998d2ac4f25ae0fe3e2b36e8af92566",
|
||||
"sha256:ec14250ce150f6c69de4a94d0da34702397564a6c118c550687adbfbfec4e075",
|
||||
"sha256:edbf02d7be60cc743b76d8efe0d1953fa2f97d01f805a621f913bdc78c516d64",
|
||||
"sha256:ee0ecdc9df9113e76d21d6a22c6c572ab1ddb198a498a19416b1e46605234f8d",
|
||||
"sha256:f1064509f258bee5ae6a4d417c7f4d8ca28664edfdc6b4b978f07801aa7b16c5",
|
||||
"sha256:f2f21519c0260f4fbaed12996de0e1940739706bfa9f92868e0ef27322c6f9aa",
|
||||
"sha256:f61ea2d3e608d14ee2f4e3111c559d95db21762756214d05de8b980c1aae9265",
|
||||
"sha256:fc77de2e3adb2e2b261adbcfe0b1f7288586e1d1ca775555f321e22259cad02a",
|
||||
"sha256:fd06242bebbeed67e456a43bd678f28f30f98c6634e75e1aaf9c4905833d2f04",
|
||||
"sha256:fe1e1fe9a2a32402e91184273434cbacf5dc007f2ba97fcd717028f13e99be6d",
|
||||
"sha256:fe47433f3463434c4ced69652c58a4eef96518ff29a473d9587655ab797d086f"
|
||||
],
|
||||
"index": "pypi",
|
||||
"markers": "python_version >= '3.7'",
|
||||
"version": "==2.6.1"
|
||||
"version": "==2.6.2"
|
||||
},
|
||||
"deprecated": {
|
||||
"hashes": [
|
||||
|
|
@ -740,7 +757,7 @@
|
|||
"sha256:190348041559e21b22a1d65cee485282ca11a6f81d503fddb84d5017e9ed1e49",
|
||||
"sha256:80600258e7ed2f16b9aa1d7c295bd70194109ad5a30fdee0eaeefef1d4c559dd"
|
||||
],
|
||||
"markers": "python_version >= '2.6' and python_version not in '3.0, 3.1, 3.2'",
|
||||
"markers": "python_version >= '2.6' and python_version not in '3.0, 3.1, 3.2, 3.3'",
|
||||
"version": "==0.18.0"
|
||||
},
|
||||
"effdet": {
|
||||
|
|
@ -1029,11 +1046,11 @@
|
|||
},
|
||||
"httpcore": {
|
||||
"hashes": [
|
||||
"sha256:096cc05bca73b8e459a1fc3dcf585148f63e534eae4339559c9b8a8d6399acc7",
|
||||
"sha256:9fc092e4799b26174648e54b74ed5f683132a464e95643b226e00c2ed2fa6535"
|
||||
"sha256:5c0f9546ad17dac4d0772b0808856eb616eb8b48ce94f49ed819fd6982a8a544",
|
||||
"sha256:9a6a501c3099307d9fd76ac244e08503427679b1e81ceb1d922485e2f2462ad2"
|
||||
],
|
||||
"markers": "python_version >= '3.8'",
|
||||
"version": "==1.0.2"
|
||||
"version": "==1.0.3"
|
||||
},
|
||||
"httpx": {
|
||||
"hashes": [
|
||||
|
|
@ -1359,7 +1376,7 @@
|
|||
"sha256:b40fbe2b65360afe6c0d5bbf37e79469f990779460640edde5b906175c49807e"
|
||||
],
|
||||
"index": "pypi",
|
||||
"markers": "python_full_version >= '3.8.1' and python_version < '4.0'",
|
||||
"markers": "python_version < '4.0' and python_full_version >= '3.8.1'",
|
||||
"version": "==0.1.7"
|
||||
},
|
||||
"langchain-community": {
|
||||
|
|
@ -1368,7 +1385,7 @@
|
|||
"sha256:c56c48bc77d24e1fc399a9ee9a637d96e3b2ff952e3a080b5a41820d9d00fb3c"
|
||||
],
|
||||
"index": "pypi",
|
||||
"markers": "python_full_version >= '3.8.1' and python_version < '4.0'",
|
||||
"markers": "python_version < '4.0' and python_full_version >= '3.8.1'",
|
||||
"version": "==0.0.20"
|
||||
},
|
||||
"langchain-core": {
|
||||
|
|
@ -1376,7 +1393,7 @@
|
|||
"sha256:34359cc8b6f8c3d45098c54a6a9b35c9f538ef58329cd943a2249d6d7b4e5806",
|
||||
"sha256:d42fac013c39a8b0bcd7e337a4cb6c17c16046c60d768f89df582ad73ec3c5cb"
|
||||
],
|
||||
"markers": "python_full_version >= '3.8.1' and python_version < '4.0'",
|
||||
"markers": "python_version < '4.0' and python_full_version >= '3.8.1'",
|
||||
"version": "==0.1.23"
|
||||
},
|
||||
"langchain-openai": {
|
||||
|
|
@ -1385,7 +1402,7 @@
|
|||
"sha256:f5c4ebe46f2c8635c8f0c26cc8df27700aacafea025410e418d5a080039974dd"
|
||||
],
|
||||
"index": "pypi",
|
||||
"markers": "python_full_version >= '3.8.1' and python_version < '4.0'",
|
||||
"markers": "python_version < '4.0' and python_full_version >= '3.8.1'",
|
||||
"version": "==0.0.6"
|
||||
},
|
||||
"langdetect": {
|
||||
|
|
@ -1397,19 +1414,19 @@
|
|||
},
|
||||
"langfuse": {
|
||||
"hashes": [
|
||||
"sha256:97dd415f731507c54880aad3b29c4d82ec157fe4e10ddf07fbf4ce334a04c3a0",
|
||||
"sha256:ffa59c573f075863d04380b02f45d3391337085cf9b71d280dc3106a4368ef1b"
|
||||
"sha256:2be049382e867681eabf774d60aadad3e6c277841e2c7f06d71190379650c2d9",
|
||||
"sha256:7bdcf02a74366ef77d5258c2aaae07d11fabde9a90c883f9022ecaf244bfdeca"
|
||||
],
|
||||
"index": "pypi",
|
||||
"markers": "python_full_version >= '3.8.1' and python_version < '4.0'",
|
||||
"version": "==2.13.2"
|
||||
"markers": "python_version < '4.0' and python_full_version >= '3.8.1'",
|
||||
"version": "==2.13.3"
|
||||
},
|
||||
"langsmith": {
|
||||
"hashes": [
|
||||
"sha256:36c4cc47e5b54be57d038036a30fb19ce6e4c73048cd7a464b8f25b459694d34",
|
||||
"sha256:8903d3811b9fc89eb18f5961c8e6935fbd2d0f119884fbf30dc70b8f8f4121fc"
|
||||
],
|
||||
"markers": "python_full_version >= '3.8.1' and python_version < '4.0'",
|
||||
"markers": "python_version < '4.0' and python_full_version >= '3.8.1'",
|
||||
"version": "==0.0.87"
|
||||
},
|
||||
"layoutparser": {
|
||||
|
|
@ -1426,12 +1443,12 @@
|
|||
},
|
||||
"litellm": {
|
||||
"hashes": [
|
||||
"sha256:40d7e1a15ef8337dd0f97419022fb07cab028f20bc34047a62d6ad955594b055",
|
||||
"sha256:bdc513da4cf9f1432fc050c651b926e5dbc8561fa3feecfa86425de13a025642"
|
||||
"sha256:0c9e75d50d3a3b5460e6eb0335c1cdf8dbf87cf298ee2e80ca09a235b540a98e",
|
||||
"sha256:f47c0ae57ebde27b4f994f484e599050899a6643d6d9246dbe4717962c3fef16"
|
||||
],
|
||||
"index": "pypi",
|
||||
"markers": "python_version not in '2.7, 3.0, 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 3.7' and python_version >= '3.8'",
|
||||
"version": "==1.23.14"
|
||||
"version": "==1.23.15"
|
||||
},
|
||||
"lxml": {
|
||||
"hashes": [
|
||||
|
|
@ -1600,37 +1617,37 @@
|
|||
},
|
||||
"matplotlib": {
|
||||
"hashes": [
|
||||
"sha256:01a978b871b881ee76017152f1f1a0cbf6bd5f7b8ff8c96df0df1bd57d8755a1",
|
||||
"sha256:03f9d160a29e0b65c0790bb07f4f45d6a181b1ac33eb1bb0dd225986450148f0",
|
||||
"sha256:091275d18d942cf1ee9609c830a1bc36610607d8223b1b981c37d5c9fc3e46a4",
|
||||
"sha256:09796f89fb71a0c0e1e2f4bdaf63fb2cefc84446bb963ecdeb40dfee7dfa98c7",
|
||||
"sha256:0f4fc5d72b75e2c18e55eb32292659cf731d9d5b312a6eb036506304f4675630",
|
||||
"sha256:172f4d0fbac3383d39164c6caafd3255ce6fa58f08fc392513a0b1d3b89c4f89",
|
||||
"sha256:1b0f3b8ea0e99e233a4bcc44590f01604840d833c280ebb8fe5554fd3e6cfe8d",
|
||||
"sha256:3773002da767f0a9323ba1a9b9b5d00d6257dbd2a93107233167cfb581f64717",
|
||||
"sha256:46a569130ff53798ea5f50afce7406e91fdc471ca1e0e26ba976a8c734c9427a",
|
||||
"sha256:4c318c1e95e2f5926fba326f68177dee364aa791d6df022ceb91b8221bd0a627",
|
||||
"sha256:4e208f46cf6576a7624195aa047cb344a7f802e113bb1a06cfd4bee431de5e31",
|
||||
"sha256:533b0e3b0c6768eef8cbe4b583731ce25a91ab54a22f830db2b031e83cca9213",
|
||||
"sha256:5864bdd7da445e4e5e011b199bb67168cdad10b501750367c496420f2ad00843",
|
||||
"sha256:5ba9cbd8ac6cf422f3102622b20f8552d601bf8837e49a3afed188d560152788",
|
||||
"sha256:6f9c6976748a25e8b9be51ea028df49b8e561eed7809146da7a47dbecebab367",
|
||||
"sha256:7c48d9e221b637c017232e3760ed30b4e8d5dfd081daf327e829bf2a72c731b4",
|
||||
"sha256:830f00640c965c5b7f6bc32f0d4ce0c36dfe0379f7dd65b07a00c801713ec40a",
|
||||
"sha256:9a5430836811b7652991939012f43d2808a2db9b64ee240387e8c43e2e5578c8",
|
||||
"sha256:aa11b3c6928a1e496c1a79917d51d4cd5d04f8a2e75f21df4949eeefdf697f4b",
|
||||
"sha256:b78e4f2cedf303869b782071b55fdde5987fda3038e9d09e58c91cc261b5ad18",
|
||||
"sha256:b9576723858a78751d5aacd2497b8aef29ffea6d1c95981505877f7ac28215c6",
|
||||
"sha256:bddfb1db89bfaa855912261c805bd0e10218923cc262b9159a49c29a7a1c1afa",
|
||||
"sha256:c7d36c2209d9136cd8e02fab1c0ddc185ce79bc914c45054a9f514e44c787917",
|
||||
"sha256:d1095fecf99eeb7384dabad4bf44b965f929a5f6079654b681193edf7169ec20",
|
||||
"sha256:d7b1704a530395aaf73912be741c04d181f82ca78084fbd80bc737be04848331",
|
||||
"sha256:d86593ccf546223eb75a39b44c32788e6f6440d13cfc4750c1c15d0fcb850b63",
|
||||
"sha256:deaed9ad4da0b1aea77fe0aa0cebb9ef611c70b3177be936a95e5d01fa05094f",
|
||||
"sha256:ef8345b48e95cee45ff25192ed1f4857273117917a4dcd48e3905619bcd9c9b8"
|
||||
"sha256:04b36ad07eac9740fc76c2aa16edf94e50b297d6eb4c081e3add863de4bb19a7",
|
||||
"sha256:09074f8057917d17ab52c242fdf4916f30e99959c1908958b1fc6032e2d0f6d4",
|
||||
"sha256:1c5c8290074ba31a41db1dc332dc2b62def469ff33766cbe325d32a3ee291aea",
|
||||
"sha256:242489efdb75b690c9c2e70bb5c6550727058c8a614e4c7716f363c27e10bba1",
|
||||
"sha256:40321634e3a05ed02abf7c7b47a50be50b53ef3eaa3a573847431a545585b407",
|
||||
"sha256:4c6e00a65d017d26009bac6808f637b75ceade3e1ff91a138576f6b3065eeeba",
|
||||
"sha256:5184e07c7e1d6d1481862ee361905b7059f7fe065fc837f7c3dc11eeb3f2f900",
|
||||
"sha256:5745f6d0fb5acfabbb2790318db03809a253096e98c91b9a31969df28ee604aa",
|
||||
"sha256:5e431a09e6fab4012b01fc155db0ce6dccacdbabe8198197f523a4ef4805eb26",
|
||||
"sha256:5f557156f7116be3340cdeef7f128fa99b0d5d287d5f41a16e169819dcf22357",
|
||||
"sha256:6728dde0a3997396b053602dbd907a9bd64ec7d5cf99e728b404083698d3ca01",
|
||||
"sha256:7b416239e9ae38be54b028abbf9048aff5054a9aba5416bef0bd17f9162ce161",
|
||||
"sha256:7c42dae72a62f14982f1474f7e5c9959fc4bc70c9de11cc5244c6e766200ba65",
|
||||
"sha256:813925d08fb86aba139f2d31864928d67511f64e5945ca909ad5bc09a96189bb",
|
||||
"sha256:83c0653c64b73926730bd9ea14aa0f50f202ba187c307a881673bad4985967b7",
|
||||
"sha256:83e0f72e2c116ca7e571c57aa29b0fe697d4c6425c4e87c6e994159e0c008635",
|
||||
"sha256:b3c5f96f57b0369c288bf6f9b5274ba45787f7e0589a34d24bdbaf6d3344632f",
|
||||
"sha256:b97653d869a71721b639714b42d87cda4cfee0ee74b47c569e4874c7590c55c5",
|
||||
"sha256:bf5932eee0d428192c40b7eac1399d608f5d995f975cdb9d1e6b48539a5ad8d0",
|
||||
"sha256:c4af3f7317f8a1009bbb2d0bf23dfaba859eb7dd4ccbd604eba146dccaaaf0a4",
|
||||
"sha256:cd3a0c2be76f4e7be03d34a14d49ded6acf22ef61f88da600a18a5cd8b3c5f3c",
|
||||
"sha256:cf60138ccc8004f117ab2a2bad513cc4d122e55864b4fe7adf4db20ca68a078f",
|
||||
"sha256:d7e7e0993d0758933b1a241a432b42c2db22dfa37d4108342ab4afb9557cbe3e",
|
||||
"sha256:e7b49ab49a3bea17802df6872f8d44f664ba8f9be0632a60c99b20b6db2165b7",
|
||||
"sha256:e9764df0e8778f06414b9d281a75235c1e85071f64bb5d71564b97c1306a2afc",
|
||||
"sha256:ef6c1025a570354297d6c15f7d0f296d95f88bd3850066b7f1e7b4f2f4c13a39",
|
||||
"sha256:f386cf162b059809ecfac3bcc491a9ea17da69fa35c8ded8ad154cd4b933d5ec",
|
||||
"sha256:fa93695d5c08544f4a0dfd0965f378e7afc410d8672816aff1e81be1f45dbf2e"
|
||||
],
|
||||
"markers": "python_version >= '3.9'",
|
||||
"version": "==3.8.2"
|
||||
"version": "==3.8.3"
|
||||
},
|
||||
"mccabe": {
|
||||
"hashes": [
|
||||
|
|
@ -1999,8 +2016,18 @@
|
|||
"sha256:f9670b3ac00a387620489dfc1bca66db47a787f4e55911f1293063a78b108df1",
|
||||
"sha256:fbc1b53c0e1fdf16388c33c3cca160f798d38aea2978004dd3f4d3dec56454c9"
|
||||
],
|
||||
"markers": "python_version >= '3.9'",
|
||||
"version": "==2.2.0"
|
||||
},
|
||||
"pandasai": {
|
||||
"hashes": [
|
||||
"sha256:7cf42308907e38249bc565fa8ccfb9b1a84e4b151c72c3352864c9dc291f2f96",
|
||||
"sha256:c5fb258870e1936701a3af31114379b9bc1ecc9cf99816ad5c8b523f2c7ddbe9"
|
||||
],
|
||||
"index": "pypi",
|
||||
"markers": "python_version >= '3.9' and python_version < '4.0'",
|
||||
"version": "==0.1.0"
|
||||
},
|
||||
"pathspec": {
|
||||
"hashes": [
|
||||
"sha256:a0d503e138a4c123b27490a4f7beda6a01c6f288df0e4a8b79c7eb0dc7b4cc08",
|
||||
|
|
@ -2241,11 +2268,11 @@
|
|||
},
|
||||
"prometheus-client": {
|
||||
"hashes": [
|
||||
"sha256:4585b0d1223148c27a225b10dbec5ae9bc4c81a99a3fa80774fa6209935324e1",
|
||||
"sha256:c88b1e6ecf6b41cd8fb5731c7ae919bf66df6ec6fafa555cd6c0e16ca169ae92"
|
||||
"sha256:287629d00b147a32dcb2be0b9df905da599b2d82f80377083ec8463309a4bb89",
|
||||
"sha256:cde524a85bce83ca359cc837f28b8c0db5cac7aa653a588fd7e84ba061c329e7"
|
||||
],
|
||||
"markers": "python_version >= '3.8'",
|
||||
"version": "==0.19.0"
|
||||
"version": "==0.20.0"
|
||||
},
|
||||
"prompt-toolkit": {
|
||||
"hashes": [
|
||||
|
|
@ -2272,6 +2299,105 @@
|
|||
"markers": "python_version >= '3.8'",
|
||||
"version": "==4.25.2"
|
||||
},
|
||||
"psycopg2": {
|
||||
"hashes": [
|
||||
"sha256:121081ea2e76729acfb0673ff33755e8703d45e926e416cb59bae3a86c6a4981",
|
||||
"sha256:38a8dcc6856f569068b47de286b472b7c473ac7977243593a288ebce0dc89516",
|
||||
"sha256:426f9f29bde126913a20a96ff8ce7d73fd8a216cfb323b1f04da402d452853c3",
|
||||
"sha256:5e0d98cade4f0e0304d7d6f25bbfbc5bd186e07b38eac65379309c4ca3193efa",
|
||||
"sha256:7e2dacf8b009a1c1e843b5213a87f7c544b2b042476ed7755be813eaf4e8347a",
|
||||
"sha256:a7653d00b732afb6fc597e29c50ad28087dcb4fbfb28e86092277a559ae4e693",
|
||||
"sha256:ade01303ccf7ae12c356a5e10911c9e1c51136003a9a1d92f7aa9d010fb98372",
|
||||
"sha256:bac58c024c9922c23550af2a581998624d6e02350f4ae9c5f0bc642c633a2d5e",
|
||||
"sha256:c92811b2d4c9b6ea0285942b2e7cac98a59e166d59c588fe5cfe1eda58e72d59",
|
||||
"sha256:d1454bde93fb1e224166811694d600e746430c006fbb031ea06ecc2ea41bf156",
|
||||
"sha256:d735786acc7dd25815e89cc4ad529a43af779db2e25aa7c626de864127e5a024",
|
||||
"sha256:de80739447af31525feddeb8effd640782cf5998e1a4e9192ebdf829717e3913",
|
||||
"sha256:ff432630e510709564c01dafdbe996cb552e0b9f3f065eb89bdce5bd31fabf4c"
|
||||
],
|
||||
"index": "pypi",
|
||||
"markers": "python_version >= '3.7'",
|
||||
"version": "==2.9.9"
|
||||
},
|
||||
"psycopg2-binary": {
|
||||
"hashes": [
|
||||
"sha256:03ef7df18daf2c4c07e2695e8cfd5ee7f748a1d54d802330985a78d2a5a6dca9",
|
||||
"sha256:0a602ea5aff39bb9fac6308e9c9d82b9a35c2bf288e184a816002c9fae930b77",
|
||||
"sha256:0c009475ee389757e6e34611d75f6e4f05f0cf5ebb76c6037508318e1a1e0d7e",
|
||||
"sha256:0ef4854e82c09e84cc63084a9e4ccd6d9b154f1dbdd283efb92ecd0b5e2b8c84",
|
||||
"sha256:1236ed0952fbd919c100bc839eaa4a39ebc397ed1c08a97fc45fee2a595aa1b3",
|
||||
"sha256:143072318f793f53819048fdfe30c321890af0c3ec7cb1dfc9cc87aa88241de2",
|
||||
"sha256:15208be1c50b99203fe88d15695f22a5bed95ab3f84354c494bcb1d08557df67",
|
||||
"sha256:1873aade94b74715be2246321c8650cabf5a0d098a95bab81145ffffa4c13876",
|
||||
"sha256:18d0ef97766055fec15b5de2c06dd8e7654705ce3e5e5eed3b6651a1d2a9a152",
|
||||
"sha256:1ea665f8ce695bcc37a90ee52de7a7980be5161375d42a0b6c6abedbf0d81f0f",
|
||||
"sha256:2293b001e319ab0d869d660a704942c9e2cce19745262a8aba2115ef41a0a42a",
|
||||
"sha256:246b123cc54bb5361588acc54218c8c9fb73068bf227a4a531d8ed56fa3ca7d6",
|
||||
"sha256:275ff571376626195ab95a746e6a04c7df8ea34638b99fc11160de91f2fef503",
|
||||
"sha256:281309265596e388ef483250db3640e5f414168c5a67e9c665cafce9492eda2f",
|
||||
"sha256:2d423c8d8a3c82d08fe8af900ad5b613ce3632a1249fd6a223941d0735fce493",
|
||||
"sha256:2e5afae772c00980525f6d6ecf7cbca55676296b580c0e6abb407f15f3706996",
|
||||
"sha256:30dcc86377618a4c8f3b72418df92e77be4254d8f89f14b8e8f57d6d43603c0f",
|
||||
"sha256:31a34c508c003a4347d389a9e6fcc2307cc2150eb516462a7a17512130de109e",
|
||||
"sha256:323ba25b92454adb36fa425dc5cf6f8f19f78948cbad2e7bc6cdf7b0d7982e59",
|
||||
"sha256:34eccd14566f8fe14b2b95bb13b11572f7c7d5c36da61caf414d23b91fcc5d94",
|
||||
"sha256:3a58c98a7e9c021f357348867f537017057c2ed7f77337fd914d0bedb35dace7",
|
||||
"sha256:3f78fd71c4f43a13d342be74ebbc0666fe1f555b8837eb113cb7416856c79682",
|
||||
"sha256:4154ad09dac630a0f13f37b583eae260c6aa885d67dfbccb5b02c33f31a6d420",
|
||||
"sha256:420f9bbf47a02616e8554e825208cb947969451978dceb77f95ad09c37791dae",
|
||||
"sha256:4686818798f9194d03c9129a4d9a702d9e113a89cb03bffe08c6cf799e053291",
|
||||
"sha256:57fede879f08d23c85140a360c6a77709113efd1c993923c59fde17aa27599fe",
|
||||
"sha256:60989127da422b74a04345096c10d416c2b41bd7bf2a380eb541059e4e999980",
|
||||
"sha256:64cf30263844fa208851ebb13b0732ce674d8ec6a0c86a4e160495d299ba3c93",
|
||||
"sha256:68fc1f1ba168724771e38bee37d940d2865cb0f562380a1fb1ffb428b75cb692",
|
||||
"sha256:6e6f98446430fdf41bd36d4faa6cb409f5140c1c2cf58ce0bbdaf16af7d3f119",
|
||||
"sha256:729177eaf0aefca0994ce4cffe96ad3c75e377c7b6f4efa59ebf003b6d398716",
|
||||
"sha256:72dffbd8b4194858d0941062a9766f8297e8868e1dd07a7b36212aaa90f49472",
|
||||
"sha256:75723c3c0fbbf34350b46a3199eb50638ab22a0228f93fb472ef4d9becc2382b",
|
||||
"sha256:77853062a2c45be16fd6b8d6de2a99278ee1d985a7bd8b103e97e41c034006d2",
|
||||
"sha256:78151aa3ec21dccd5cdef6c74c3e73386dcdfaf19bced944169697d7ac7482fc",
|
||||
"sha256:7f01846810177d829c7692f1f5ada8096762d9172af1b1a28d4ab5b77c923c1c",
|
||||
"sha256:804d99b24ad523a1fe18cc707bf741670332f7c7412e9d49cb5eab67e886b9b5",
|
||||
"sha256:81ff62668af011f9a48787564ab7eded4e9fb17a4a6a74af5ffa6a457400d2ab",
|
||||
"sha256:8359bf4791968c5a78c56103702000105501adb557f3cf772b2c207284273984",
|
||||
"sha256:83791a65b51ad6ee6cf0845634859d69a038ea9b03d7b26e703f94c7e93dbcf9",
|
||||
"sha256:8532fd6e6e2dc57bcb3bc90b079c60de896d2128c5d9d6f24a63875a95a088cf",
|
||||
"sha256:876801744b0dee379e4e3c38b76fc89f88834bb15bf92ee07d94acd06ec890a0",
|
||||
"sha256:8dbf6d1bc73f1d04ec1734bae3b4fb0ee3cb2a493d35ede9badbeb901fb40f6f",
|
||||
"sha256:8f8544b092a29a6ddd72f3556a9fcf249ec412e10ad28be6a0c0d948924f2212",
|
||||
"sha256:911dda9c487075abd54e644ccdf5e5c16773470a6a5d3826fda76699410066fb",
|
||||
"sha256:977646e05232579d2e7b9c59e21dbe5261f403a88417f6a6512e70d3f8a046be",
|
||||
"sha256:9dba73be7305b399924709b91682299794887cbbd88e38226ed9f6712eabee90",
|
||||
"sha256:a148c5d507bb9b4f2030a2025c545fccb0e1ef317393eaba42e7eabd28eb6041",
|
||||
"sha256:a6cdcc3ede532f4a4b96000b6362099591ab4a3e913d70bcbac2b56c872446f7",
|
||||
"sha256:ac05fb791acf5e1a3e39402641827780fe44d27e72567a000412c648a85ba860",
|
||||
"sha256:b0605eaed3eb239e87df0d5e3c6489daae3f7388d455d0c0b4df899519c6a38d",
|
||||
"sha256:b58b4710c7f4161b5e9dcbe73bb7c62d65670a87df7bcce9e1faaad43e715245",
|
||||
"sha256:b6356793b84728d9d50ead16ab43c187673831e9d4019013f1402c41b1db9b27",
|
||||
"sha256:b76bedd166805480ab069612119ea636f5ab8f8771e640ae103e05a4aae3e417",
|
||||
"sha256:bc7bb56d04601d443f24094e9e31ae6deec9ccb23581f75343feebaf30423359",
|
||||
"sha256:c2470da5418b76232f02a2fcd2229537bb2d5a7096674ce61859c3229f2eb202",
|
||||
"sha256:c332c8d69fb64979ebf76613c66b985414927a40f8defa16cf1bc028b7b0a7b0",
|
||||
"sha256:c6af2a6d4b7ee9615cbb162b0738f6e1fd1f5c3eda7e5da17861eacf4c717ea7",
|
||||
"sha256:c77e3d1862452565875eb31bdb45ac62502feabbd53429fdc39a1cc341d681ba",
|
||||
"sha256:ca08decd2697fdea0aea364b370b1249d47336aec935f87b8bbfd7da5b2ee9c1",
|
||||
"sha256:ca49a8119c6cbd77375ae303b0cfd8c11f011abbbd64601167ecca18a87e7cdd",
|
||||
"sha256:cb16c65dcb648d0a43a2521f2f0a2300f40639f6f8c1ecbc662141e4e3e1ee07",
|
||||
"sha256:d2997c458c690ec2bc6b0b7ecbafd02b029b7b4283078d3b32a852a7ce3ddd98",
|
||||
"sha256:d3f82c171b4ccd83bbaf35aa05e44e690113bd4f3b7b6cc54d2219b132f3ae55",
|
||||
"sha256:dc4926288b2a3e9fd7b50dc6a1909a13bbdadfc67d93f3374d984e56f885579d",
|
||||
"sha256:ead20f7913a9c1e894aebe47cccf9dc834e1618b7aa96155d2091a626e59c972",
|
||||
"sha256:ebdc36bea43063116f0486869652cb2ed7032dbc59fbcb4445c4862b5c1ecf7f",
|
||||
"sha256:ed1184ab8f113e8d660ce49a56390ca181f2981066acc27cf637d5c1e10ce46e",
|
||||
"sha256:ee825e70b1a209475622f7f7b776785bd68f34af6e7a46e2e42f27b659b5bc26",
|
||||
"sha256:f7ae5d65ccfbebdfa761585228eb4d0df3a8b15cfb53bd953e713e09fbb12957",
|
||||
"sha256:f7fc5a5acafb7d6ccca13bfa8c90f8c51f13d8fb87d95656d3950f0158d3ce53",
|
||||
"sha256:f9b5571d33660d5009a8b3c25dc1db560206e2d2f89d3df1cb32d72c0d117d52"
|
||||
],
|
||||
"index": "pypi",
|
||||
"markers": "python_version >= '3.7'",
|
||||
"version": "==2.9.9"
|
||||
},
|
||||
"pyasn1": {
|
||||
"hashes": [
|
||||
"sha256:4439847c58d40b1d0a573d07e3856e95333f1976294494c325775aeca506eb58",
|
||||
|
|
@ -2531,7 +2657,7 @@
|
|||
"sha256:0123cacc1627ae19ddf3c27a5de5bd67ee4586fbdd6440d9748f8abb483d3e86",
|
||||
"sha256:961d03dc3453ebbc59dbdea9e4e11c5651520a876d0f4db161e8674aae935da9"
|
||||
],
|
||||
"markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2'",
|
||||
"markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3'",
|
||||
"version": "==2.8.2"
|
||||
},
|
||||
"python-docx": {
|
||||
|
|
@ -3082,7 +3208,7 @@
|
|||
"sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926",
|
||||
"sha256:8abb2f1d86890a2dfb989f9a77cfcfd3e47c2a354b01111771326f8aa26e0254"
|
||||
],
|
||||
"markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2'",
|
||||
"markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3'",
|
||||
"version": "==1.16.0"
|
||||
},
|
||||
"smmap": {
|
||||
|
|
@ -3533,6 +3659,7 @@
|
|||
"sha256:019cf52e9e2bfa286e61ffa0d7d336e1645280f9a0f165e697583143fcfe708a",
|
||||
"sha256:f1aa046297a3afba3aa16895e513aca6a93802ef73b7a18080656435c4deb217"
|
||||
],
|
||||
"index": "pypi",
|
||||
"markers": "python_version < '3.12' and python_full_version >= '3.9.0'",
|
||||
"version": "==0.12.4"
|
||||
},
|
||||
|
|
@ -3563,7 +3690,7 @@
|
|||
"sha256:c97dfde1f7bd43a71c8d2a58e369e9b2bf692d1334ea9f9cae55add7d0dd0f84",
|
||||
"sha256:fdb6d215c776278489906c2f8916e6e7d4f5a9b602ccbcfdf7f016fc8da0596e"
|
||||
],
|
||||
"markers": "python_version >= '3.10'",
|
||||
"markers": "python_version >= '3.7'",
|
||||
"version": "==2.0.7"
|
||||
},
|
||||
"uvicorn": {
|
||||
|
|
|
|||
|
|
@ -1,6 +1,10 @@
|
|||
import logging
|
||||
from logging.handlers import RotatingFileHandler
|
||||
|
||||
from colorlog import (
|
||||
ColoredFormatter,
|
||||
) # You need to install this package: pip install colorlog
|
||||
|
||||
|
||||
def get_logger(logger_name, log_level=logging.INFO, log_file="application.log"):
|
||||
logger = logging.getLogger(logger_name)
|
||||
|
|
@ -8,11 +12,24 @@ def get_logger(logger_name, log_level=logging.INFO, log_file="application.log"):
|
|||
logger.propagate = False # Prevent log propagation to avoid double logging
|
||||
|
||||
formatter = logging.Formatter(
|
||||
"%(asctime)s [%(levelname)s] %(name)s [%(lineno)d]: %(message)s"
|
||||
"[%(levelname)s] %(name)s [%(filename)s:%(lineno)d]: %(message)s"
|
||||
)
|
||||
|
||||
color_formatter = ColoredFormatter(
|
||||
"%(log_color)s[%(levelname)s]%(reset)s %(name)s [%(filename)s:%(lineno)d]: %(message)s",
|
||||
log_colors={
|
||||
"DEBUG": "cyan",
|
||||
"INFO": "green",
|
||||
"WARNING": "yellow",
|
||||
"ERROR": "red",
|
||||
"CRITICAL": "red,bg_white",
|
||||
},
|
||||
reset=True,
|
||||
style="%",
|
||||
)
|
||||
|
||||
console_handler = logging.StreamHandler()
|
||||
console_handler.setFormatter(formatter)
|
||||
console_handler.setFormatter(color_formatter)
|
||||
|
||||
file_handler = RotatingFileHandler(
|
||||
log_file, maxBytes=5000000, backupCount=5
|
||||
|
|
|
|||
44
backend/manage_services.sh
Executable file
44
backend/manage_services.sh
Executable file
|
|
@ -0,0 +1,44 @@
|
|||
#!/bin/bash
|
||||
|
||||
SESSION_NAME="my_services"
|
||||
|
||||
start_services() {
|
||||
# Create a new tmux session
|
||||
tmux new-session -d -s $SESSION_NAME
|
||||
|
||||
# Split the window into panes for each service
|
||||
tmux split-window -h
|
||||
tmux split-window -v
|
||||
tmux select-pane -t 0
|
||||
tmux split-window -v
|
||||
|
||||
# Start each service in its pane
|
||||
tmux send-keys -t $SESSION_NAME:0.0 'echo "Starting backend-core...";pipenv run uvicorn main:app --reload --host 0.0.0.0 --port 5050 --workers 6' C-m
|
||||
tmux send-keys -t $SESSION_NAME:0.1 'echo "Starting worker...";pipenv run celery -A celery_worker worker -l info' C-m
|
||||
tmux send-keys -t $SESSION_NAME:0.2 'echo "Starting beat...";pipenv run celery -A celery_worker beat -l info' C-m
|
||||
tmux send-keys -t $SESSION_NAME:0.3 'echo "Starting flower...";pipenv run celery -A celery_worker flower -l info --port=5555' C-m
|
||||
|
||||
echo "Services started in tmux session '$SESSION_NAME'"
|
||||
echo "Use 'tmux attach-session -t $SESSION_NAME' to view logs"
|
||||
}
|
||||
|
||||
stop_services() {
|
||||
# Kill the tmux session
|
||||
tmux kill-session -t $SESSION_NAME
|
||||
echo "Services stopped"
|
||||
}
|
||||
|
||||
view_logs() {
|
||||
# Attach to the tmux session to view logs
|
||||
tmux attach-session -t $SESSION_NAME
|
||||
}
|
||||
|
||||
if [ "$1" == "start" ]; then
|
||||
start_services
|
||||
elif [ "$1" == "stop" ]; then
|
||||
stop_services
|
||||
elif [ "$1" == "logs" ]; then
|
||||
view_logs
|
||||
else
|
||||
echo "Usage: $0 {start|stop|logs}"
|
||||
fi
|
||||
|
|
@ -12,7 +12,7 @@ class IntegrationDescriptionEntity(BaseModel):
|
|||
|
||||
|
||||
class IntegrationEntity(BaseModel):
|
||||
id: str
|
||||
id: int
|
||||
user_id: str
|
||||
brain_id: str
|
||||
integration_id: str
|
||||
|
|
|
|||
62
backend/modules/brain/integrations/GPT4/Brain.py
Normal file
62
backend/modules/brain/integrations/GPT4/Brain.py
Normal file
|
|
@ -0,0 +1,62 @@
|
|||
import json
|
||||
from typing import AsyncIterable
|
||||
from uuid import UUID
|
||||
|
||||
from langchain_community.chat_models import ChatLiteLLM
|
||||
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
|
||||
from modules.brain.knowledge_brain_qa import KnowledgeBrainQA
|
||||
from modules.chat.dto.chats import ChatQuestion
|
||||
|
||||
|
||||
class GPT4Brain(KnowledgeBrainQA):
|
||||
"""This is the Notion brain class. it is a KnowledgeBrainQA has the data is stored locally.
|
||||
It is going to call the Data Store internally to get the data.
|
||||
|
||||
Args:
|
||||
KnowledgeBrainQA (_type_): A brain that store the knowledge internaly
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
**kwargs,
|
||||
):
|
||||
super().__init__(
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
def get_chain(self):
|
||||
|
||||
prompt = ChatPromptTemplate.from_messages(
|
||||
[
|
||||
("system", "You are GPT-4 powered by Quivr. You are an assistant."),
|
||||
MessagesPlaceholder(variable_name="chat_history"),
|
||||
("human", "{question}"),
|
||||
]
|
||||
)
|
||||
|
||||
chain = prompt | ChatLiteLLM(
|
||||
model="gpt-4-0125-preview", max_tokens=self.max_tokens
|
||||
)
|
||||
|
||||
return chain
|
||||
|
||||
async def generate_stream(
|
||||
self, chat_id: UUID, question: ChatQuestion, save_answer: bool = True
|
||||
) -> AsyncIterable:
|
||||
conversational_qa_chain = self.get_chain()
|
||||
transformed_history, streamed_chat_history = (
|
||||
self.initialize_streamed_chat_history(chat_id, question)
|
||||
)
|
||||
response_tokens = []
|
||||
|
||||
async for chunk in conversational_qa_chain.astream(
|
||||
{
|
||||
"question": question.question,
|
||||
"chat_history": transformed_history,
|
||||
}
|
||||
):
|
||||
response_tokens.append(chunk.content)
|
||||
streamed_chat_history.assistant = chunk.content
|
||||
yield f"data: {json.dumps(streamed_chat_history.dict())}"
|
||||
|
||||
self.save_answer(question, response_tokens, streamed_chat_history, save_answer)
|
||||
0
backend/modules/brain/integrations/GPT4/__init__.py
Normal file
0
backend/modules/brain/integrations/GPT4/__init__.py
Normal file
100
backend/modules/brain/integrations/SQL/Brain.py
Normal file
100
backend/modules/brain/integrations/SQL/Brain.py
Normal file
|
|
@ -0,0 +1,100 @@
|
|||
import json
|
||||
from typing import AsyncIterable
|
||||
from uuid import UUID
|
||||
|
||||
from langchain_community.chat_models import ChatLiteLLM
|
||||
from langchain_community.utilities import SQLDatabase
|
||||
from langchain_core.output_parsers import StrOutputParser
|
||||
from langchain_core.prompts import ChatPromptTemplate
|
||||
from langchain_core.runnables import RunnablePassthrough
|
||||
from modules.brain.integrations.SQL.SQL_connector import SQLConnector
|
||||
from modules.brain.knowledge_brain_qa import KnowledgeBrainQA
|
||||
from modules.brain.repository.integration_brains import IntegrationBrain
|
||||
from modules.chat.dto.chats import ChatQuestion
|
||||
|
||||
|
||||
class SQLBrain(KnowledgeBrainQA, IntegrationBrain):
|
||||
"""This is the Notion brain class. it is a KnowledgeBrainQA has the data is stored locally.
|
||||
It is going to call the Data Store internally to get the data.
|
||||
|
||||
Args:
|
||||
KnowledgeBrainQA (_type_): A brain that store the knowledge internaly
|
||||
"""
|
||||
|
||||
uri: str = None
|
||||
db: SQLDatabase = None
|
||||
sql_connector: SQLConnector = None
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
**kwargs,
|
||||
):
|
||||
super().__init__(
|
||||
**kwargs,
|
||||
)
|
||||
self.sql_connector = SQLConnector(self.brain_id, self.user_id)
|
||||
|
||||
def get_schema(self, _):
|
||||
return self.db.get_table_info()
|
||||
|
||||
def run_query(self, query):
|
||||
return self.db.run(query)
|
||||
|
||||
def get_chain(self):
|
||||
template = """Based on the table schema below, write a SQL query that would answer the user's question:
|
||||
{schema}
|
||||
|
||||
Question: {question}
|
||||
SQL Query:"""
|
||||
prompt = ChatPromptTemplate.from_template(template)
|
||||
|
||||
self.db = SQLDatabase.from_uri(self.sql_connector.credentials["uri"])
|
||||
|
||||
model = ChatLiteLLM(model=self.model)
|
||||
|
||||
sql_response = (
|
||||
RunnablePassthrough.assign(schema=self.get_schema)
|
||||
| prompt
|
||||
| model.bind(stop=["\nSQLResult:"])
|
||||
| StrOutputParser()
|
||||
)
|
||||
|
||||
template = """Based on the table schema below, question, sql query, and sql response, write a natural language response and the query that was used to generate it.:
|
||||
{schema}
|
||||
|
||||
Question: {question}
|
||||
SQL Query: {query}
|
||||
SQL Response: {response}"""
|
||||
prompt_response = ChatPromptTemplate.from_template(template)
|
||||
|
||||
full_chain = (
|
||||
RunnablePassthrough.assign(query=sql_response).assign(
|
||||
schema=self.get_schema,
|
||||
response=lambda x: self.db.run(x["query"]),
|
||||
)
|
||||
| prompt_response
|
||||
| model
|
||||
)
|
||||
|
||||
return full_chain
|
||||
|
||||
async def generate_stream(
|
||||
self, chat_id: UUID, question: ChatQuestion, save_answer: bool = True
|
||||
) -> AsyncIterable:
|
||||
|
||||
conversational_qa_chain = self.get_chain()
|
||||
transformed_history, streamed_chat_history = (
|
||||
self.initialize_streamed_chat_history(chat_id, question)
|
||||
)
|
||||
response_tokens = []
|
||||
|
||||
async for chunk in conversational_qa_chain.astream(
|
||||
{
|
||||
"question": question.question,
|
||||
}
|
||||
):
|
||||
response_tokens.append(chunk.content)
|
||||
streamed_chat_history.assistant = chunk.content
|
||||
yield f"data: {json.dumps(streamed_chat_history.dict())}"
|
||||
|
||||
self.save_answer(question, response_tokens, streamed_chat_history, save_answer)
|
||||
41
backend/modules/brain/integrations/SQL/SQL_connector.py
Normal file
41
backend/modules/brain/integrations/SQL/SQL_connector.py
Normal file
|
|
@ -0,0 +1,41 @@
|
|||
from logger import get_logger
|
||||
from modules.brain.entity.integration_brain import IntegrationEntity
|
||||
from modules.brain.repository.integration_brains import IntegrationBrain
|
||||
from modules.knowledge.repository.knowledge_interface import KnowledgeInterface
|
||||
from modules.knowledge.service.knowledge_service import KnowledgeService
|
||||
|
||||
logger = get_logger(__name__)
|
||||
|
||||
|
||||
class SQLConnector(IntegrationBrain):
|
||||
"""A class to interact with an SQL database"""
|
||||
|
||||
credentials: dict[str, str] = None
|
||||
integration_details: IntegrationEntity = None
|
||||
brain_id: str = None
|
||||
user_id: str = None
|
||||
knowledge_service: KnowledgeInterface
|
||||
|
||||
def __init__(self, brain_id: str, user_id: str):
|
||||
super().__init__()
|
||||
self.brain_id = brain_id
|
||||
self.user_id = user_id
|
||||
self._load_credentials()
|
||||
self.knowledge_service = KnowledgeService()
|
||||
|
||||
def _load_credentials(self) -> dict[str, str]:
|
||||
"""Load the Notion credentials"""
|
||||
self.integration_details = self.get_integration_brain(
|
||||
self.brain_id, self.user_id
|
||||
)
|
||||
if self.credentials is None:
|
||||
logger.info("Loading Notion credentials")
|
||||
self.integration_details.credentials = {
|
||||
"uri": self.integration_details.settings.get("uri", "")
|
||||
}
|
||||
self.update_integration_brain(
|
||||
self.brain_id, self.user_id, self.integration_details
|
||||
)
|
||||
self.credentials = self.integration_details.credentials
|
||||
else: # pragma: no cover
|
||||
self.credentials = self.integration_details.credentials
|
||||
0
backend/modules/brain/integrations/SQL/__init__.py
Normal file
0
backend/modules/brain/integrations/SQL/__init__.py
Normal file
|
|
@ -38,7 +38,7 @@ def is_valid_uuid(uuid_to_test, version=4):
|
|||
return str(uuid_obj) == uuid_to_test
|
||||
|
||||
|
||||
def generate_source(result, brain):
|
||||
def generate_source(source_documents, brain_id):
|
||||
# Initialize an empty list for sources
|
||||
sources_list: List[Sources] = []
|
||||
|
||||
|
|
@ -46,27 +46,28 @@ def generate_source(result, brain):
|
|||
generated_urls = {}
|
||||
|
||||
# Get source documents from the result, default to an empty list if not found
|
||||
source_documents = result.get("source_documents", [])
|
||||
|
||||
# If source documents exist
|
||||
if source_documents:
|
||||
logger.info(f"Source documents found: {source_documents}")
|
||||
# Iterate over each document
|
||||
for doc in source_documents:
|
||||
doc0 = doc[0]
|
||||
logger.info("Document: %s", doc0)
|
||||
# Check if 'url' is in the document metadata
|
||||
logger.info(f"Metadata 1: {doc.metadata}")
|
||||
logger.info(f"Metadata 1: {doc0.metadata}")
|
||||
is_url = (
|
||||
"original_file_name" in doc.metadata
|
||||
and doc.metadata["original_file_name"] is not None
|
||||
and doc.metadata["original_file_name"].startswith("http")
|
||||
"original_file_name" in doc0.metadata
|
||||
and doc0.metadata["original_file_name"] is not None
|
||||
and doc0.metadata["original_file_name"].startswith("http")
|
||||
)
|
||||
logger.info(f"Is URL: {is_url}")
|
||||
|
||||
# Determine the name based on whether it's a URL or a file
|
||||
name = (
|
||||
doc.metadata["original_file_name"]
|
||||
doc0.metadata["original_file_name"]
|
||||
if is_url
|
||||
else doc.metadata["file_name"]
|
||||
else doc0.metadata["file_name"]
|
||||
)
|
||||
|
||||
# Determine the type based on whether it's a URL or a file
|
||||
|
|
@ -74,9 +75,9 @@ def generate_source(result, brain):
|
|||
|
||||
# Determine the source URL based on whether it's a URL or a file
|
||||
if is_url:
|
||||
source_url = doc.metadata["original_file_name"]
|
||||
source_url = doc0.metadata["original_file_name"]
|
||||
else:
|
||||
file_path = f"{brain.brain_id}/{doc.metadata['file_name']}"
|
||||
file_path = f"{brain_id}/{doc0.metadata['file_name']}"
|
||||
# Check if the URL has already been generated
|
||||
if file_path in generated_urls:
|
||||
source_url = generated_urls[file_path]
|
||||
|
|
@ -126,6 +127,7 @@ class KnowledgeBrainQA(BaseModel, QAInterface):
|
|||
streaming: bool = False
|
||||
knowledge_qa: Optional[RAGInterface] = None
|
||||
metadata: Optional[dict] = None
|
||||
user_id: str = None
|
||||
|
||||
callbacks: List[AsyncIteratorCallbackHandler] = (
|
||||
None # pyright: ignore reportPrivateUsage=none
|
||||
|
|
@ -142,6 +144,7 @@ class KnowledgeBrainQA(BaseModel, QAInterface):
|
|||
streaming: bool = False,
|
||||
prompt_id: Optional[UUID] = None,
|
||||
metadata: Optional[dict] = None,
|
||||
user_id: str = None,
|
||||
**kwargs,
|
||||
):
|
||||
super().__init__(
|
||||
|
|
@ -161,6 +164,7 @@ class KnowledgeBrainQA(BaseModel, QAInterface):
|
|||
)
|
||||
self.metadata = metadata
|
||||
self.max_tokens = max_tokens
|
||||
self.user_id = user_id
|
||||
|
||||
@property
|
||||
def prompt_to_use(self):
|
||||
|
|
@ -259,14 +263,42 @@ class KnowledgeBrainQA(BaseModel, QAInterface):
|
|||
async def generate_stream(
|
||||
self, chat_id: UUID, question: ChatQuestion, save_answer: bool = True
|
||||
) -> AsyncIterable:
|
||||
history = chat_service.get_chat_history(self.chat_id)
|
||||
|
||||
conversational_qa_chain = self.knowledge_qa.get_chain()
|
||||
|
||||
transformed_history = format_chat_history(history)
|
||||
|
||||
transformed_history, streamed_chat_history = (
|
||||
self.initialize_streamed_chat_history(chat_id, question)
|
||||
)
|
||||
response_tokens = []
|
||||
sources = []
|
||||
|
||||
async for chunk in conversational_qa_chain.astream(
|
||||
{
|
||||
"question": question.question,
|
||||
"chat_history": transformed_history,
|
||||
"custom_personality": (
|
||||
self.prompt_to_use.content if self.prompt_to_use else None
|
||||
),
|
||||
}
|
||||
):
|
||||
if chunk.get("answer"):
|
||||
logger.info(f"Chunk: {chunk}")
|
||||
response_tokens.append(chunk["answer"].content)
|
||||
streamed_chat_history.assistant = chunk["answer"].content
|
||||
yield f"data: {json.dumps(streamed_chat_history.dict())}"
|
||||
if chunk.get("docs"):
|
||||
sources = chunk["docs"]
|
||||
|
||||
sources_list = generate_source(sources, self.brain_id)
|
||||
if not streamed_chat_history.metadata:
|
||||
streamed_chat_history.metadata = {}
|
||||
# Serialize the sources list
|
||||
serialized_sources_list = [source.dict() for source in sources_list]
|
||||
streamed_chat_history.metadata["sources"] = serialized_sources_list
|
||||
yield f"data: {json.dumps(streamed_chat_history.dict())}"
|
||||
self.save_answer(question, response_tokens, streamed_chat_history, save_answer)
|
||||
|
||||
def initialize_streamed_chat_history(self, chat_id, question):
|
||||
history = chat_service.get_chat_history(self.chat_id)
|
||||
transformed_history = format_chat_history(history)
|
||||
brain = brain_service.get_brain_by_id(self.brain_id)
|
||||
|
||||
streamed_chat_history = chat_service.update_chat_history(
|
||||
|
|
@ -297,24 +329,11 @@ class KnowledgeBrainQA(BaseModel, QAInterface):
|
|||
}
|
||||
)
|
||||
|
||||
try:
|
||||
|
||||
async for chunk in conversational_qa_chain.astream(
|
||||
{
|
||||
"question": question.question,
|
||||
"chat_history": transformed_history,
|
||||
"custom_personality": (
|
||||
self.prompt_to_use.content if self.prompt_to_use else None
|
||||
),
|
||||
}
|
||||
):
|
||||
response_tokens.append(chunk.content)
|
||||
streamed_chat_history.assistant = chunk.content
|
||||
yield f"data: {json.dumps(streamed_chat_history.dict())}"
|
||||
|
||||
except Exception as e:
|
||||
logger.error("Error generating stream: %s", e)
|
||||
return transformed_history, streamed_chat_history
|
||||
|
||||
def save_answer(
|
||||
self, question, response_tokens, streamed_chat_history, save_answer
|
||||
):
|
||||
assistant = "".join(response_tokens)
|
||||
|
||||
try:
|
||||
|
|
|
|||
|
|
@ -5,14 +5,15 @@ from uuid import UUID
|
|||
from langchain.chains import ConversationalRetrievalChain
|
||||
from langchain.embeddings.ollama import OllamaEmbeddings
|
||||
from langchain.llms.base import BaseLLM
|
||||
from langchain.memory import ConversationBufferMemory
|
||||
from langchain.prompts import HumanMessagePromptTemplate
|
||||
from langchain.schema import format_document
|
||||
from langchain_community.chat_models import ChatLiteLLM
|
||||
from langchain_core.messages import SystemMessage, get_buffer_string
|
||||
from langchain_core.output_parsers import StrOutputParser
|
||||
from langchain_core.prompts import ChatPromptTemplate, PromptTemplate
|
||||
from langchain_core.runnables import RunnableParallel, RunnablePassthrough
|
||||
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
|
||||
from langchain_core.runnables import RunnableLambda, RunnablePassthrough
|
||||
from langchain_openai import OpenAIEmbeddings
|
||||
from llm.utils.get_prompt_to_use import get_prompt_to_use
|
||||
from logger import get_logger
|
||||
from models import BrainSettings # Importing settings related to the 'brain'
|
||||
|
|
@ -57,8 +58,6 @@ ANSWER_PROMPT = ChatPromptTemplate.from_messages(
|
|||
)
|
||||
|
||||
|
||||
ChatPromptTemplate.from_template(template_answer)
|
||||
|
||||
# How we format documents
|
||||
|
||||
DEFAULT_DOCUMENT_PROMPT = PromptTemplate.from_template(
|
||||
|
|
@ -205,29 +204,49 @@ class QuivrRAG(BaseModel):
|
|||
|
||||
def get_chain(self):
|
||||
retriever_doc = self.get_retriever()
|
||||
|
||||
_inputs = RunnableParallel(
|
||||
standalone_question=RunnablePassthrough.assign(
|
||||
chat_history=lambda x: get_buffer_string(x["chat_history"])
|
||||
)
|
||||
| CONDENSE_QUESTION_PROMPT
|
||||
| ChatOpenAI(temperature=0)
|
||||
| StrOutputParser(),
|
||||
memory = ConversationBufferMemory(
|
||||
return_messages=True, output_key="answer", input_key="question"
|
||||
)
|
||||
|
||||
loaded_memory = RunnablePassthrough.assign(
|
||||
chat_history=RunnableLambda(memory.load_memory_variables)
|
||||
| itemgetter("history"),
|
||||
)
|
||||
|
||||
standalone_question = {
|
||||
"standalone_question": {
|
||||
"question": lambda x: x["question"],
|
||||
"chat_history": lambda x: get_buffer_string(x["chat_history"]),
|
||||
}
|
||||
| CONDENSE_QUESTION_PROMPT
|
||||
| ChatLiteLLM(temperature=0, model=self.model)
|
||||
| StrOutputParser(),
|
||||
}
|
||||
|
||||
prompt_custom_user = self.prompt_to_use()
|
||||
prompt_to_use = "None"
|
||||
if prompt_custom_user:
|
||||
prompt_to_use = prompt_custom_user.content
|
||||
logger.info(f"Prompt to use: {prompt_custom_user}")
|
||||
_context = {
|
||||
"context": itemgetter("standalone_question")
|
||||
| retriever_doc
|
||||
| self._combine_documents,
|
||||
|
||||
# Now we retrieve the documents
|
||||
retrieved_documents = {
|
||||
"docs": itemgetter("standalone_question") | retriever_doc,
|
||||
"question": lambda x: x["standalone_question"],
|
||||
"custom_instructions": lambda x: prompt_to_use,
|
||||
}
|
||||
|
||||
conversational_qa_chain = _inputs | _context | ANSWER_PROMPT | ChatOpenAI()
|
||||
final_inputs = {
|
||||
"context": lambda x: self._combine_documents(x["docs"]),
|
||||
"question": itemgetter("question"),
|
||||
"custom_instructions": itemgetter("custom_instructions"),
|
||||
}
|
||||
|
||||
return conversational_qa_chain
|
||||
# And finally, we do the part that returns the answers
|
||||
answer = {
|
||||
"answer": final_inputs
|
||||
| ANSWER_PROMPT
|
||||
| ChatLiteLLM(max_tokens=self.max_tokens, model=self.model),
|
||||
"docs": itemgetter("docs"),
|
||||
}
|
||||
|
||||
return loaded_memory | standalone_question | retrieved_documents | answer
|
||||
|
|
|
|||
|
|
@ -202,8 +202,8 @@ class BrainService:
|
|||
if (
|
||||
self.integration_description_repository.get_integration_description(
|
||||
brain.integration.integration_id
|
||||
).integration_name
|
||||
== "Notion"
|
||||
).integration_name.lower()
|
||||
== "notion"
|
||||
):
|
||||
celery.send_task(
|
||||
"NotionConnectorLoad",
|
||||
|
|
|
|||
|
|
@ -1,7 +1,9 @@
|
|||
from logger import get_logger
|
||||
from modules.brain.api_brain_qa import APIBrainQA
|
||||
from modules.brain.entity.brain_entity import BrainType, RoleEnum
|
||||
from modules.brain.integrations.GPT4.Brain import GPT4Brain
|
||||
from modules.brain.integrations.Notion.Brain import NotionBrain
|
||||
from modules.brain.integrations.SQL.Brain import SQLBrain
|
||||
from modules.brain.knowledge_brain_qa import KnowledgeBrainQA
|
||||
from modules.brain.service.api_brain_definition_service import ApiBrainDefinitionService
|
||||
from modules.brain.service.brain_authorization_service import (
|
||||
|
|
@ -31,6 +33,13 @@ models_supporting_function_calls = [
|
|||
"gpt-3.5-turbo",
|
||||
]
|
||||
|
||||
|
||||
integration_list = {
|
||||
"notion": NotionBrain,
|
||||
"gpt4": GPT4Brain,
|
||||
"sql": SQLBrain,
|
||||
}
|
||||
|
||||
brain_service = BrainService()
|
||||
|
||||
|
||||
|
|
@ -67,6 +76,7 @@ class BrainfulChat(ChatInterface):
|
|||
streaming=streaming,
|
||||
prompt_id=prompt_id,
|
||||
metadata=metadata,
|
||||
user_id=user_id,
|
||||
)
|
||||
|
||||
if brain.brain_type == BrainType.API:
|
||||
|
|
@ -94,8 +104,11 @@ class BrainfulChat(ChatInterface):
|
|||
brain.brain_id, user_id
|
||||
)
|
||||
|
||||
if integration_brain.integration_name == "Notion":
|
||||
return NotionBrain(
|
||||
integration_class = integration_list.get(
|
||||
integration_brain.integration_name.lower()
|
||||
)
|
||||
if integration_class:
|
||||
return integration_class(
|
||||
chat_id=chat_id,
|
||||
model=model,
|
||||
max_tokens=max_tokens,
|
||||
|
|
@ -105,4 +118,5 @@ class BrainfulChat(ChatInterface):
|
|||
streaming=streaming,
|
||||
prompt_id=prompt_id,
|
||||
metadata=metadata,
|
||||
user_id=user_id,
|
||||
)
|
||||
|
|
|
|||
|
|
@ -5,6 +5,7 @@ amqp==5.2.0; python_version >= '3.6'
|
|||
annotated-types==0.6.0; python_version >= '3.8'
|
||||
antlr4-python3-runtime==4.9.3
|
||||
anyio==4.2.0; python_version >= '3.8'
|
||||
astor==0.8.1; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3'
|
||||
async-generator==1.10; python_version >= '3.5'
|
||||
asyncpg==0.27.0; python_full_version >= '3.7.0'
|
||||
attrs==23.2.0; python_version >= '3.7'
|
||||
|
|
@ -12,8 +13,8 @@ backoff==2.2.1; python_version >= '3.7' and python_version < '4.0'
|
|||
beautifulsoup4==4.12.3; python_full_version >= '3.6.0'
|
||||
billiard==4.2.0; python_version >= '3.7'
|
||||
black==24.2.0; python_version >= '3.8'
|
||||
boto3==1.34.41; python_version >= '3.8'
|
||||
botocore==1.34.41; python_version >= '3.8'
|
||||
boto3==1.34.42; python_version >= '3.8'
|
||||
botocore==1.34.42; python_version >= '3.8'
|
||||
bytecode==0.15.1; python_version >= '3.8'
|
||||
cattrs==23.2.3; python_version >= '3.8'
|
||||
celery[sqs]==5.3.6; python_version >= '3.8'
|
||||
|
|
@ -27,6 +28,7 @@ click-didyoumean==0.3.0; python_full_version >= '3.6.2' and python_full_version
|
|||
click-plugins==1.1.1
|
||||
click-repl==0.3.0; python_version >= '3.6'
|
||||
coloredlogs==15.0.1; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4'
|
||||
colorlog==6.8.2; python_version >= '3.6'
|
||||
contourpy==1.2.0; python_version >= '3.9'
|
||||
cryptography==42.0.2; python_version >= '3.7'
|
||||
cssselect==1.2.0; python_version >= '3.7'
|
||||
|
|
@ -34,12 +36,12 @@ cycler==0.12.1; python_version >= '3.8'
|
|||
dataclasses-json==0.6.4; python_version >= '3.7' and python_version < '4.0'
|
||||
dataclasses-json-speakeasy==0.5.11; python_version >= '3.7' and python_version < '4.0'
|
||||
ddsketch==2.0.4; python_version >= '2.7'
|
||||
ddtrace==2.6.1; python_version >= '3.7'
|
||||
ddtrace==2.6.2; python_version >= '3.7'
|
||||
deprecated==1.2.14; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3'
|
||||
deprecation==2.1.0
|
||||
distro==1.9.0; python_version >= '3.6'
|
||||
docx2txt==0.8
|
||||
ecdsa==0.18.0; python_version >= '2.6' and python_version not in '3.0, 3.1, 3.2'
|
||||
ecdsa==0.18.0; python_version >= '2.6' and python_version not in '3.0, 3.1, 3.2, 3.3'
|
||||
effdet==0.4.1
|
||||
emoji==2.10.1; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3'
|
||||
envier==0.5.1; python_version >= '3.7'
|
||||
|
|
@ -61,7 +63,7 @@ gitpython==3.1.36; python_version >= '3.7'
|
|||
gotrue==2.1.0; python_version >= '3.8' and python_version < '4.0'
|
||||
h11==0.14.0; python_version >= '3.7'
|
||||
html5lib==1.1; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4'
|
||||
httpcore==1.0.2; python_version >= '3.8'
|
||||
httpcore==1.0.3; python_version >= '3.8'
|
||||
httpx==0.25.2; python_version >= '3.8'
|
||||
huggingface-hub==0.20.3; python_full_version >= '3.8.0'
|
||||
humanfriendly==10.0; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4'
|
||||
|
|
@ -80,20 +82,20 @@ jsonpath-python==1.0.6; python_version >= '3.6'
|
|||
jsonpointer==2.4; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4, 3.5, 3.6'
|
||||
kiwisolver==1.4.5; python_version >= '3.7'
|
||||
kombu[sqs]==5.3.5; python_version >= '3.8'
|
||||
langchain==0.1.7; python_full_version >= '3.8.1' and python_version < '4.0'
|
||||
langchain-community==0.0.20; python_full_version >= '3.8.1' and python_version < '4.0'
|
||||
langchain-core==0.1.23; python_full_version >= '3.8.1' and python_version < '4.0'
|
||||
langchain-openai==0.0.6; python_full_version >= '3.8.1' and python_version < '4.0'
|
||||
langchain==0.1.7; python_version < '4.0' and python_full_version >= '3.8.1'
|
||||
langchain-community==0.0.20; python_version < '4.0' and python_full_version >= '3.8.1'
|
||||
langchain-core==0.1.23; python_version < '4.0' and python_full_version >= '3.8.1'
|
||||
langchain-openai==0.0.6; python_version < '4.0' and python_full_version >= '3.8.1'
|
||||
langdetect==1.0.9
|
||||
langfuse==2.13.2; python_full_version >= '3.8.1' and python_version < '4.0'
|
||||
langsmith==0.0.87; python_full_version >= '3.8.1' and python_version < '4.0'
|
||||
langfuse==2.13.3; python_version < '4.0' and python_full_version >= '3.8.1'
|
||||
langsmith==0.0.87; python_version < '4.0' and python_full_version >= '3.8.1'
|
||||
layoutparser[layoutmodels,tesseract]==0.3.4; python_version >= '3.6'
|
||||
litellm==1.23.14; python_version not in '2.7, 3.0, 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 3.7' and python_version >= '3.8'
|
||||
litellm==1.23.15; python_version not in '2.7, 3.0, 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 3.7' and python_version >= '3.8'
|
||||
lxml==5.1.0; python_version >= '3.6'
|
||||
markdown==3.5.2
|
||||
markupsafe==2.1.5; python_version >= '3.7'
|
||||
marshmallow==3.20.2; python_version >= '3.8'
|
||||
matplotlib==3.8.2; python_version >= '3.9'
|
||||
matplotlib==3.8.3; python_version >= '3.9'
|
||||
mccabe==0.7.0; python_version >= '3.6'
|
||||
monotonic==1.6
|
||||
mpmath==1.3.0
|
||||
|
|
@ -115,7 +117,8 @@ opencv-python==4.9.0.80; python_version >= '3.6'
|
|||
openpyxl==3.1.2
|
||||
opentelemetry-api==1.22.0; python_version >= '3.7'
|
||||
packaging==23.2; python_version >= '3.7'
|
||||
pandas==2.2.0
|
||||
pandas==2.2.0; python_version >= '3.9'
|
||||
pandasai==0.1.0; python_version >= '3.9' and python_version < '4.0'
|
||||
pathspec==0.12.1; python_version >= '3.8'
|
||||
pdf2image==1.16.3
|
||||
pdfminer.six==20221105
|
||||
|
|
@ -128,9 +131,11 @@ pluggy==1.4.0; python_version >= '3.8'
|
|||
portalocker==2.8.2; python_version >= '3.8'
|
||||
postgrest==0.15.0; python_version >= '3.8' and python_version < '4.0'
|
||||
posthog==3.4.1
|
||||
prometheus-client==0.19.0; python_version >= '3.8'
|
||||
prometheus-client==0.20.0; python_version >= '3.8'
|
||||
prompt-toolkit==3.0.43; python_full_version >= '3.7.0'
|
||||
protobuf==4.25.2; python_version >= '3.8'
|
||||
psycopg2==2.9.9; python_version >= '3.7'
|
||||
psycopg2-binary==2.9.9; python_version >= '3.7'
|
||||
pyasn1==0.5.1; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4, 3.5'
|
||||
pycocotools==2.0.7; python_version >= '3.5'
|
||||
pycodestyle==2.11.1; python_version >= '3.8'
|
||||
|
|
@ -149,7 +154,7 @@ pytesseract==0.3.10; python_version >= '3.7'
|
|||
pytest==8.0.0; python_version >= '3.8'
|
||||
pytest-celery==0.0.0
|
||||
pytest-mock==3.12.0; python_version >= '3.8'
|
||||
python-dateutil==2.8.2; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2'
|
||||
python-dateutil==2.8.2; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3'
|
||||
python-docx==1.1.0
|
||||
python-dotenv==1.0.1; python_version >= '3.8'
|
||||
python-iso639==2024.2.7; python_version >= '3.8'
|
||||
|
|
@ -173,7 +178,7 @@ scipy==1.12.0; python_version >= '3.9'
|
|||
sentry-sdk[fastapi]==1.40.4
|
||||
setuptools==69.1.0; python_version >= '3.8'
|
||||
sgmllib3k==1.0.0
|
||||
six==1.16.0; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2'
|
||||
six==1.16.0; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3'
|
||||
smmap==5.0.1; python_version >= '3.7'
|
||||
sniffio==1.3.0; python_version >= '3.7'
|
||||
soupsieve==2.5; python_version >= '3.8'
|
||||
|
|
@ -203,7 +208,7 @@ unstructured[all-docs]==0.12.4; python_version < '3.12' and python_full_version
|
|||
unstructured-client==0.18.0; python_version >= '3.8'
|
||||
unstructured-inference==0.7.23
|
||||
unstructured.pytesseract==0.3.12
|
||||
urllib3==2.0.7; python_version >= '3.10'
|
||||
urllib3==2.0.7; python_version >= '3.7'
|
||||
uvicorn==0.22.0; python_version >= '3.7'
|
||||
vine==5.1.0; python_version >= '3.6'
|
||||
watchdog==4.0.0; python_version >= '3.8'
|
||||
|
|
|
|||
9
docker-compose.redis.yml
Normal file
9
docker-compose.redis.yml
Normal file
|
|
@ -0,0 +1,9 @@
|
|||
version: "3.8"
|
||||
|
||||
services:
|
||||
redis:
|
||||
image: redis:latest@sha256:a7cee7c8178ff9b5297cb109e6240f5072cdaaafd775ce6b586c3c704b06458e
|
||||
container_name: redis
|
||||
restart: always
|
||||
ports:
|
||||
- 6379:6379
|
||||
Loading…
Reference in New Issue
Block a user