Spaces:
Sleeping
Sleeping
created vector store in Supabase
Browse files- README.md +28 -0
- metadata.jsonl +0 -0
- pdm.lock +374 -17
- pyproject.toml +1 -1
- upload_metadata.py +96 -0
README.md
CHANGED
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@@ -14,3 +14,31 @@ hf_oauth_expiration_minutes: 480
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---
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---
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Script to setting up tables and vector search
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"CREATE EXTENSION IF NOT EXISTS vector;"
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"""
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CREATE TABLE IF NOT EXISTS documents2 (
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id BIGINT PRIMARY KEY,
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content TEXT,
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metadata JSONB,
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embedding VECTOR(1536)
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);
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"""
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create or replace function match_documents_2(
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query_embedding vector(1536),
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match_count int default 5
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)
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returns table (
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id bigint,
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content text,
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metadata jsonb,
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embedding vector
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)
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language sql
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as $$
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select id, content, metadata, embedding
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from documents2
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order by embedding <#> query_embedding
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limit match_count;
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$$;
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metadata.jsonl
ADDED
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The diff for this file is too large to render.
See raw diff
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pdm.lock
CHANGED
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@@ -5,7 +5,7 @@
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groups = ["default"]
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strategy = ["inherit_metadata"]
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lock_version = "4.5.0"
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content_hash = "sha256:
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[[metadata.targets]]
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requires_python = "==3.12.*"
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@@ -220,7 +220,7 @@ version = "0.4.6"
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requires_python = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7"
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summary = "Cross-platform colored terminal text."
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groups = ["default"]
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-
marker = "platform_system == \"Windows\""
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files = [
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{file = "colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6"},
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{file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"},
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{file = "datasets-3.6.0.tar.gz", hash = "sha256:1b2bf43b19776e2787e181cfd329cb0ca1a358ea014780c3581e0f276375e041"},
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[[package]]
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name = "dill"
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version = "0.3.8"
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@@ -424,6 +437,23 @@ files = [
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{file = "fsspec-2025.3.0.tar.gz", hash = "sha256:a935fd1ea872591f2b5148907d103488fc523295e6c64b835cfad8c3eca44972"},
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[[package]]
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name = "gradio"
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version = "5.29.1"
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@@ -516,6 +546,25 @@ files = [
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{file = "groovy-0.1.2.tar.gz", hash = "sha256:25c1dc09b3f9d7e292458aa762c6beb96ea037071bf5e917fc81fb78d2231083"},
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[[package]]
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name = "h11"
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version = "0.16.0"
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{file = "h11-0.16.0.tar.gz", hash = "sha256:4e35b956cf45792e4caa5885e69fba00bdbc6ffafbfa020300e549b208ee5ff1"},
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[[package]]
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name = "httpcore"
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version = "1.0.9"
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{file = "httpx_sse-0.4.0-py3-none-any.whl", hash = "sha256:f329af6eae57eaa2bdfd962b42524764af68075ea87370a2de920af5341e318f"},
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[[package]]
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name = "huggingface-hub"
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version = "0.31.2"
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{file = "huggingface_hub-0.31.2.tar.gz", hash = "sha256:7053561376ed7f6ffdaecf09cc54d70dc784ac6315fa4bb9b93e19662b029675"},
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[[package]]
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name = "idna"
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version = "3.10"
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{file = "idna-3.10.tar.gz", hash = "sha256:12f65c9b470abda6dc35cf8e63cc574b1c52b11df2c86030af0ac09b01b13ea9"},
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[[package]]
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name = "jinja2"
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version = "3.1.6"
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{file = "mdurl-0.1.2.tar.gz", hash = "sha256:bb413d29f5eea38f31dd4754dd7377d4465116fb207585f97bf925588687c1ba"},
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[[package]]
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name = "mpmath"
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version = "1.3.0"
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{file = "pillow-11.2.1.tar.gz", hash = "sha256:a64dd61998416367b7ef979b73d3a85853ba9bec4c2925f74e588879a58716b6"},
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[[package]]
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name = "primp"
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version = "0.15.0"
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{file = "propcache-0.3.1.tar.gz", hash = "sha256:40d980c33765359098837527e18eddefc9a24cea5b45e078a7f3bb5b032c6ecf"},
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[[package]]
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name = "pyarrow"
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version = "20.0.0"
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{file = "pygments-2.19.1.tar.gz", hash = "sha256:61c16d2a8576dc0649d9f39e089b5f02bcd27fba10d8fb4dcc28173f7a45151f"},
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[[package]]
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name = "pymupdf"
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version = "1.25.5"
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{file = "pymupdf-1.25.5.tar.gz", hash = "sha256:5f96311cacd13254c905f6654a004a0a2025b71cabc04fda667f5472f72c15a0"},
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[[package]]
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name = "python-dateutil"
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version = "2.9.0.post0"
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{file = "rank_bm25-0.2.2.tar.gz", hash = "sha256:096ccef76f8188563419aaf384a02f0ea459503fdf77901378d4fd9d87e5e51d"},
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[[package]]
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name = "regex"
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version = "2024.11.6"
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requires_python = ">=3.9"
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summary = "Easily download, build, install, upgrade, and uninstall Python packages"
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groups = ["default"]
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-
marker = "platform_system == \"Linux\" and platform_machine == \"x86_64\" or python_version >= \"3.12\""
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files = [
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{file = "setuptools-80.7.1-py3-none-any.whl", hash = "sha256:ca5cc1069b85dc23070a6628e6bcecb3292acac802399c7f8edc0100619f9009"},
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{file = "setuptools-80.7.1.tar.gz", hash = "sha256:f6ffc5f0142b1bd8d0ca94ee91b30c0ca862ffd50826da1ea85258a06fd94552"},
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{file = "starlette-0.46.2.tar.gz", hash = "sha256:7f7361f34eed179294600af672f565727419830b54b7b084efe44bb82d2fccd5"},
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[[package]]
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name = "sympy"
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version = "1.14.0"
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@@ -2199,6 +2536,26 @@ files = [
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{file = "tzdata-2025.2.tar.gz", hash = "sha256:b60a638fcc0daffadf82fe0f57e53d06bdec2f36c4df66280ae79bce6bd6f2b9"},
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[[package]]
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name = "urllib3"
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version = "2.4.0"
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@@ -2229,24 +2586,24 @@ files = [
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[[package]]
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name = "websockets"
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-
version = "
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requires_python = ">=3.9"
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summary = "An implementation of the WebSocket Protocol (RFC 6455 & 7692)"
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groups = ["default"]
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files = [
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| 2237 |
-
{file = "websockets-
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-
{file = "websockets-
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-
{file = "websockets-
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{file = "websockets-
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{file = "websockets-
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{file = "websockets-
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{file = "websockets-
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{file = "websockets-
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{file = "websockets-
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[[package]]
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groups = ["default"]
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strategy = ["inherit_metadata"]
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lock_version = "4.5.0"
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| 8 |
+
content_hash = "sha256:0c6e1c83ed4ec2b1751cc0917ce598d614bf977d7f7b4568110a8a1b16d40739"
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| 10 |
[[metadata.targets]]
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| 11 |
requires_python = "==3.12.*"
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|
| 220 |
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{file = "websockets-14.2-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:0a6f3efd47ffd0d12080594f434faf1cd2549b31e54870b8470b28cc1d3817d9"},
|
| 2601 |
+
{file = "websockets-14.2-cp312-cp312-musllinux_1_2_i686.whl", hash = "sha256:065ce275e7c4ffb42cb738dd6b20726ac26ac9ad0a2a48e33ca632351a737267"},
|
| 2602 |
+
{file = "websockets-14.2-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:e9d0e53530ba7b8b5e389c02282f9d2aa47581514bd6049d3a7cffe1385cf5fe"},
|
| 2603 |
+
{file = "websockets-14.2-cp312-cp312-win32.whl", hash = "sha256:20e6dd0984d7ca3037afcb4494e48c74ffb51e8013cac71cf607fffe11df7205"},
|
| 2604 |
+
{file = "websockets-14.2-cp312-cp312-win_amd64.whl", hash = "sha256:44bba1a956c2c9d268bdcdf234d5e5ff4c9b6dc3e300545cbe99af59dda9dcce"},
|
| 2605 |
+
{file = "websockets-14.2-py3-none-any.whl", hash = "sha256:7a6ceec4ea84469f15cf15807a747e9efe57e369c384fa86e022b3bea679b79b"},
|
| 2606 |
+
{file = "websockets-14.2.tar.gz", hash = "sha256:5059ed9c54945efb321f097084b4c7e52c246f2c869815876a69d1efc4ad6eb5"},
|
| 2607 |
]
|
| 2608 |
|
| 2609 |
[[package]]
|
pyproject.toml
CHANGED
|
@@ -5,7 +5,7 @@ description = "Default template for PDM package"
|
|
| 5 |
authors = [
|
| 6 |
{name = "Supphawit_Golf", email = "supphawit.n@gmail.com"},
|
| 7 |
]
|
| 8 |
-
dependencies = ["langchain>=0.3.25", "huggingface-hub>=0.31.2", "pandas>=2.2.3", "langgraph>=0.4.5", "langchain-openai>=0.3.17", "dotenv>=0.9.9", "datasets>=3.6.0", "langchain-community>=0.3.24", "rank-bm25>=0.2.2", "langchain-huggingface>=0.2.0", "duckduckgo-search>=8.0.2", "requests>=2.32.3", "gradio>=5.29.1", "arxiv>=2.2.0", "pymupdf>=1.25.5"]
|
| 9 |
requires-python = "==3.12.*"
|
| 10 |
readme = "README.md"
|
| 11 |
license = {text = "MIT"}
|
|
|
|
| 5 |
authors = [
|
| 6 |
{name = "Supphawit_Golf", email = "supphawit.n@gmail.com"},
|
| 7 |
]
|
| 8 |
+
dependencies = ["langchain>=0.3.25", "huggingface-hub>=0.31.2", "pandas>=2.2.3", "langgraph>=0.4.5", "langchain-openai>=0.3.17", "dotenv>=0.9.9", "datasets>=3.6.0", "langchain-community>=0.3.24", "rank-bm25>=0.2.2", "langchain-huggingface>=0.2.0", "duckduckgo-search>=8.0.2", "requests>=2.32.3", "gradio>=5.29.1", "arxiv>=2.2.0", "pymupdf>=1.25.5", "pymilvus>=2.5.9", "supabase>=2.15.1", "psycopg2-binary>=2.9.10"]
|
| 9 |
requires-python = "==3.12.*"
|
| 10 |
readme = "README.md"
|
| 11 |
license = {text = "MIT"}
|
upload_metadata.py
ADDED
|
@@ -0,0 +1,96 @@
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|
|
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|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import json
|
| 2 |
+
import os
|
| 3 |
+
from dotenv import load_dotenv
|
| 4 |
+
load_dotenv()
|
| 5 |
+
with open('metadata.jsonl', 'r') as f:
|
| 6 |
+
json_list = list(f)
|
| 7 |
+
|
| 8 |
+
json_QA = []
|
| 9 |
+
for json_str in json_list:
|
| 10 |
+
json_data = json.loads(json_str)
|
| 11 |
+
json_QA.append(json_data)
|
| 12 |
+
|
| 13 |
+
#test access to the metadata
|
| 14 |
+
# import random
|
| 15 |
+
# random_samples = random.sample(json_QA, 1)
|
| 16 |
+
# for sample in random_samples:
|
| 17 |
+
# print("=" * 50)
|
| 18 |
+
# print(f"Task ID: {sample['task_id']}")
|
| 19 |
+
# print(f"Question: {sample['Question']}")
|
| 20 |
+
# print(f"Level: {sample['Level']}")
|
| 21 |
+
# print(f"Final Answer: {sample['Final answer']}")
|
| 22 |
+
# print(f"Annotator Metadata: ")
|
| 23 |
+
# print(f" βββ Steps: ")
|
| 24 |
+
# for step in sample['Annotator Metadata']['Steps'].split('\n'):
|
| 25 |
+
# print(f" β βββ {step}")
|
| 26 |
+
# print(f" βββ Number of steps: {sample['Annotator Metadata']['Number of steps']}")
|
| 27 |
+
# print(f" βββ How long did this take?: {sample['Annotator Metadata']['How long did this take?']}")
|
| 28 |
+
# print(f" βββ Tools:")
|
| 29 |
+
# for tool in sample['Annotator Metadata']['Tools'].split('\n'):
|
| 30 |
+
# print(f" β βββ {tool}")
|
| 31 |
+
# print(f" βββ Number of tools: {sample['Annotator Metadata']['Number of tools']}")
|
| 32 |
+
# print("=" * 50)
|
| 33 |
+
#initialize the supabase client
|
| 34 |
+
import os
|
| 35 |
+
from dotenv import load_dotenv
|
| 36 |
+
from langchain_huggingface import HuggingFaceEmbeddings
|
| 37 |
+
from langchain_community.vectorstores import SupabaseVectorStore
|
| 38 |
+
from supabase.client import Client, create_client
|
| 39 |
+
from langchain.embeddings import OpenAIEmbeddings
|
| 40 |
+
|
| 41 |
+
load_dotenv()
|
| 42 |
+
|
| 43 |
+
supabase_url = os.environ.get("SUPABASE_URL")
|
| 44 |
+
supabase_key = os.environ.get("SUPABASE_KEY")
|
| 45 |
+
supabase: Client = create_client(supabase_url, supabase_key)
|
| 46 |
+
|
| 47 |
+
#setup embedding model
|
| 48 |
+
embeddings = OpenAIEmbeddings(
|
| 49 |
+
model="text-embedding-3-small",api_key=os.environ.get("OPENAI_KEY"))
|
| 50 |
+
|
| 51 |
+
def get_embedding(text: str) -> list[float]:
|
| 52 |
+
"""Get the embedding for a given text using OpenAI's API."""
|
| 53 |
+
response = embeddings.embed_query(text)
|
| 54 |
+
return response
|
| 55 |
+
|
| 56 |
+
# #insert data into database
|
| 57 |
+
# from langchain.schema import Document
|
| 58 |
+
# docs = []
|
| 59 |
+
# cnt = 0
|
| 60 |
+
# for sample in json_QA:
|
| 61 |
+
# content = f"Question : {sample['Question']}\n\nFinal answer : {sample['Final answer']}"
|
| 62 |
+
# doc = {
|
| 63 |
+
# "id" : cnt,
|
| 64 |
+
# "content" : content,
|
| 65 |
+
# "metadata" : {
|
| 66 |
+
# "source" : sample['task_id']
|
| 67 |
+
# },
|
| 68 |
+
# "embedding" : get_embedding(content),
|
| 69 |
+
# }
|
| 70 |
+
# docs.append(doc)
|
| 71 |
+
# cnt += 1
|
| 72 |
+
# print(f'total number of documents: {cnt+1}')
|
| 73 |
+
# # upload the documents to the vector database
|
| 74 |
+
# try:
|
| 75 |
+
# response = (
|
| 76 |
+
# supabase.table("documents_agent")
|
| 77 |
+
# .insert(docs)
|
| 78 |
+
# .execute()
|
| 79 |
+
# )
|
| 80 |
+
# except Exception as exception:
|
| 81 |
+
# print("Error inserting data into Supabase:", exception)
|
| 82 |
+
|
| 83 |
+
#Check data in table and setup vectorstore
|
| 84 |
+
# add items to vector database
|
| 85 |
+
vector_store = SupabaseVectorStore(
|
| 86 |
+
client=supabase,
|
| 87 |
+
embedding= embeddings,
|
| 88 |
+
table_name="documents_agent",
|
| 89 |
+
query_name="match_documents",
|
| 90 |
+
)
|
| 91 |
+
retriever = vector_store.as_retriever()
|
| 92 |
+
|
| 93 |
+
query = "On June 6, 2023, an article by Carolyn Collins Petersen was published in Universe Today. This article mentions a team that produced a paper about their observations, linked at the bottom of the article. Find this paper. Under what NASA award number was the work performed by R. G. Arendt supported by?"
|
| 94 |
+
# matched_docs = vector_store.similarity_search(query, k=2)
|
| 95 |
+
retrived_docs = retriever.invoke(query)
|
| 96 |
+
print(retrived_docs[0])
|