add azure search vectorstore link for presse

app.py

@@ -4,24 +4,27 @@ import logging
 import gradio as gr
 from langchain.prompts.chat import ChatPromptTemplate
 from huggingface_hub import hf_hub_download, whoami
-from app.source.backend.llm_utils import get_llm
-from app.source.backend.document_store import pickle_to_document_store
-from app.source.backend.get_prompts import get_qa_prompts
-from app.source.frontend.utils import (
+from spinoza_project.source.backend.llm_utils import get_llm, get_vectorstore
+from spinoza_project.source.backend.document_store import pickle_to_document_store
+from spinoza_project.source.backend.get_prompts import get_qa_prompts
+from spinoza_project.source.frontend.utils import (
     make_html_source,
     make_html_presse_source,
     init_env,
 )
-from app.source.backend.prompt_utils import to_chat_instruction, SpecialTokens
+from spinoza_project.source.backend.prompt_utils import (
+    to_chat_instruction,
+    SpecialTokens,
+)
 
 init_env()
 
-with open("./app/config.yaml") as f:
+with open("./spinoza_project/config.yaml") as f:
     config = yaml.full_load(f)
 
 prompts = {}
 for source in config["prompt_naming"]:
-    with open(f"./app/prompt_{source}.yaml") as f:
+    with open(f"./spinoza_project/prompt_{source}.yaml") as f:
         prompts[source] = yaml.full_load(f)
 
 ## Building LLM
@@ -40,8 +43,11 @@ qdrants = {
         )
     )
     for tab in config["prompt_naming"]
+    if tab != "Presse"
 }
 
+bdd_presse = get_vectorstore("presse")
+
 ## Load Prompts
 print("Loading Prompts")
 chat_qa_prompts, chat_reformulation_prompts, chat_summarize_memory_prompts = {}, {}, {}
@@ -51,6 +57,7 @@ for source, prompt in prompts.items():
     chat_reformulation_prompts[source] = chat_reformulation_prompt
     # chat_summarize_memory_prompts[source] = chat_summarize_memory_prompt
 
+
 with open("./assets/style.css", "r") as f:
     css = f.read()
 
@@ -277,18 +284,29 @@ def get_html_sources(buttons, cards):
 """
 
 
-def get_sources(outils, question, tab, qdrants=qdrants, config=config):
+def get_sources(
+    outils, question, tab, qdrants=qdrants, bdd_presse=bdd_presse, config=config
+):
     k = config["num_document_retrieved"]
     min_similarity = config["min_similarity"]
     if tab in outils:
-        sources = qdrants[
-            config["source_mapping"][tab]
-        ].similarity_search_with_relevance_scores(
-            config["query_preprompt"]
-            + question.replace("<p>", "").replace("</p>\n", ""),
-            k=k,
-            # filter=get_qdrant_filters(filters),
+        sources = (
+            (
+                bdd_presse.similarity_search_with_relevance_scores(
+                    question.replace("<p>", "").replace("</p>\n", ""),
+                    k=k,
+                )
+            )
+            if tab == "Presse"
+            else qdrants[
+                config["source_mapping"][tab]
+            ].similarity_search_with_relevance_scores(
+                config["query_preprompt"]
+                + question.replace("<p>", "").replace("</p>\n", ""),
+                k=k,
+            )
         )
+
         sources = [(doc, score) for doc, score in sources if score >= min_similarity]
 
         buttons_ids = list(range(len(sources)))
@@ -323,9 +341,11 @@ def get_sources(outils, question, tab, qdrants=qdrants, config=config):
         return "", ""
 
 
-def retrieve_sources(outils, *questions, qdrants=qdrants, config=config):
+def retrieve_sources(
+    outils, *questions, qdrants=qdrants, bdd_presse=bdd_presse, config=config
+):
     results = [
-        get_sources(outils, question, tab, qdrants, config)
+        get_sources(outils, question, tab, qdrants, bdd_presse, config)
         for question, tab in zip(questions, config["tabs"])
     ]
     formated_sources = [source[0] for source in results]

poetry.lock

@@ -223,6 +223,70 @@ files = [
 [package.dependencies]
 cryptography = "*"
 
+[[package]]
+name = "azure-common"
+version = "1.1.28"
+description = "Microsoft Azure Client Library for Python (Common)"
+optional = false
+python-versions = "*"
+files = [
+    {file = "azure-common-1.1.28.zip", hash = "sha256:4ac0cd3214e36b6a1b6a442686722a5d8cc449603aa833f3f0f40bda836704a3"},
+    {file = "azure_common-1.1.28-py2.py3-none-any.whl", hash = "sha256:5c12d3dcf4ec20599ca6b0d3e09e86e146353d443e7fcc050c9a19c1f9df20ad"},
+]
+
+[[package]]
+name = "azure-core"
+version = "1.30.2"
+description = "Microsoft Azure Core Library for Python"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "azure-core-1.30.2.tar.gz", hash = "sha256:a14dc210efcd608821aa472d9fb8e8d035d29b68993819147bc290a8ac224472"},
+    {file = "azure_core-1.30.2-py3-none-any.whl", hash = "sha256:cf019c1ca832e96274ae85abd3d9f752397194d9fea3b41487290562ac8abe4a"},
+]
+
+[package.dependencies]
+requests = ">=2.21.0"
+six = ">=1.11.0"
+typing-extensions = ">=4.6.0"
+
+[package.extras]
+aio = ["aiohttp (>=3.0)"]
+
+[[package]]
+name = "azure-identity"
+version = "1.17.1"
+description = "Microsoft Azure Identity Library for Python"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "azure-identity-1.17.1.tar.gz", hash = "sha256:32ecc67cc73f4bd0595e4f64b1ca65cd05186f4fe6f98ed2ae9f1aa32646efea"},
+    {file = "azure_identity-1.17.1-py3-none-any.whl", hash = "sha256:db8d59c183b680e763722bfe8ebc45930e6c57df510620985939f7f3191e0382"},
+]
+
+[package.dependencies]
+azure-core = ">=1.23.0"
+cryptography = ">=2.5"
+msal = ">=1.24.0"
+msal-extensions = ">=0.3.0"
+typing-extensions = ">=4.0.0"
+
+[[package]]
+name = "azure-search-documents"
+version = "11.4.0"
+description = "Microsoft Azure Cognitive Search Client Library for Python"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "azure-search-documents-11.4.0.tar.gz", hash = "sha256:599f269f106fb51e646ff426a218c21811575598e6a769b23fa4a0127c0f57e0"},
+    {file = "azure_search_documents-11.4.0-py3-none-any.whl", hash = "sha256:e435266dc992a3450dc475309c9475f89a4bb0e9dac838140e609d9f1c7608ac"},
+]
+
+[package.dependencies]
+azure-common = ">=1.1,<2.0"
+azure-core = ">=1.28.0,<2.0.0"
+isodate = ">=0.6.0"
+
 [[package]]
 name = "certifi"
 version = "2024.6.2"
@@ -1419,6 +1483,20 @@ files = [
     {file = "intel_openmp-2021.4.0-py2.py3-none-win_amd64.whl", hash = "sha256:eef4c8bcc8acefd7f5cd3b9384dbf73d59e2c99fc56545712ded913f43c4a94f"},
 ]
 
+[[package]]
+name = "isodate"
+version = "0.6.1"
+description = "An ISO 8601 date/time/duration parser and formatter"
+optional = false
+python-versions = "*"
+files = [
+    {file = "isodate-0.6.1-py2.py3-none-any.whl", hash = "sha256:0751eece944162659049d35f4f549ed815792b38793f07cf73381c1c87cbed96"},
+    {file = "isodate-0.6.1.tar.gz", hash = "sha256:48c5881de7e8b0a0d648cb024c8062dc84e7b840ed81e864c7614fd3c127bde9"},
+]
+
+[package.dependencies]
+six = "*"
+
 [[package]]
 name = "itsdangerous"
 version = "2.2.0"
@@ -1998,6 +2076,25 @@ requests = ">=2.0.0,<3"
 [package.extras]
 broker = ["pymsalruntime (>=0.13.2,<0.17)"]
 
+[[package]]
+name = "msal-extensions"
+version = "1.1.0"
+description = "Microsoft Authentication Library extensions (MSAL EX) provides a persistence API that can save your data on disk, encrypted on Windows, macOS and Linux. Concurrent data access will be coordinated by a file lock mechanism."
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "msal-extensions-1.1.0.tar.gz", hash = "sha256:6ab357867062db7b253d0bd2df6d411c7891a0ee7308d54d1e4317c1d1c54252"},
+    {file = "msal_extensions-1.1.0-py3-none-any.whl", hash = "sha256:01be9711b4c0b1a151450068eeb2c4f0997df3bba085ac299de3a66f585e382f"},
+]
+
+[package.dependencies]
+msal = ">=0.4.1,<2.0.0"
+packaging = "*"
+portalocker = [
+    {version = ">=1.0,<3", markers = "platform_system != \"Windows\""},
+    {version = ">=1.6,<3", markers = "platform_system == \"Windows\""},
+]
+
 [[package]]
 name = "multidict"
 version = "6.0.5"
@@ -4859,4 +4956,4 @@ multidict = ">=4.0"
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.10"
-content-hash = "4b2da1198ef4ee6995118810fcfe20d43f21d1eac07b71d7b71a2bab98b633e0"
+content-hash = "adbf6715ae5e4d0cd93b90c6754be4996023e0ad1296f700b738887a06123a73"

pyproject.toml

@@ -1,10 +1,10 @@
 [tool.poetry]
-name = "spinoza-project"
+name = "spinoza_project"
 version = "0.1.0"
 description = ""
 authors = ["Miguel Omenaca Muro <[email protected]>"]
 readme = "README.md"
-package-mode = false
+package-mode = true
 
 [tool.poetry.dependencies]
 python = "^3.10"
@@ -18,6 +18,8 @@ loadenv = "^0.1.1"
 datasets = "^2.20.0"
 langchain-community = "^0.2.5"
 transformers = "4.39.0"
+azure-search-documents = "^11.4.0"
+azure-identity = "^1.17.1"
 
 
 [build-system]

requirements.txt

@@ -99,6 +99,18 @@ attrs==23.2.0 ; python_version >= "3.10" and python_version < "4.0" \
 authlib==1.3.1 ; python_version >= "3.10" and python_version < "4.0" \
     --hash=sha256:7ae843f03c06c5c0debd63c9db91f9fda64fa62a42a77419fa15fbb7e7a58917 \
     --hash=sha256:d35800b973099bbadc49b42b256ecb80041ad56b7fe1216a362c7943c088f377
+azure-common==1.1.28 ; python_version >= "3.10" and python_version < "4.0" \
+    --hash=sha256:4ac0cd3214e36b6a1b6a442686722a5d8cc449603aa833f3f0f40bda836704a3 \
+    --hash=sha256:5c12d3dcf4ec20599ca6b0d3e09e86e146353d443e7fcc050c9a19c1f9df20ad
+azure-core==1.30.2 ; python_version >= "3.10" and python_version < "4.0" \
+    --hash=sha256:a14dc210efcd608821aa472d9fb8e8d035d29b68993819147bc290a8ac224472 \
+    --hash=sha256:cf019c1ca832e96274ae85abd3d9f752397194d9fea3b41487290562ac8abe4a
+azure-identity==1.17.1 ; python_version >= "3.10" and python_version < "4.0" \
+    --hash=sha256:32ecc67cc73f4bd0595e4f64b1ca65cd05186f4fe6f98ed2ae9f1aa32646efea \
+    --hash=sha256:db8d59c183b680e763722bfe8ebc45930e6c57df510620985939f7f3191e0382
+azure-search-documents==11.4.0 ; python_version >= "3.10" and python_version < "4.0" \
+    --hash=sha256:599f269f106fb51e646ff426a218c21811575598e6a769b23fa4a0127c0f57e0 \
+    --hash=sha256:e435266dc992a3450dc475309c9475f89a4bb0e9dac838140e609d9f1c7608ac
 certifi==2024.6.2 ; python_version >= "3.10" and python_version < "4.0" \
     --hash=sha256:3cd43f1c6fa7dedc5899d69d3ad0398fd018ad1a17fba83ddaf78aa46c747516 \
     --hash=sha256:ddc6c8ce995e6987e7faf5e3f1b02b302836a0e5d98ece18392cb1a36c72ad56
@@ -724,6 +736,9 @@ intel-openmp==2021.4.0 ; python_version >= "3.10" and python_version < "4.0" and
     --hash=sha256:6e863d8fd3d7e8ef389d52cf97a50fe2afe1a19247e8c0d168ce021546f96fc9 \
     --hash=sha256:e2240ab8d01472fed04f3544a878cda5da16c26232b7ea1b59132dbfb48b186e \
     --hash=sha256:eef4c8bcc8acefd7f5cd3b9384dbf73d59e2c99fc56545712ded913f43c4a94f
+isodate==0.6.1 ; python_version >= "3.10" and python_version < "4.0" \
+    --hash=sha256:0751eece944162659049d35f4f549ed815792b38793f07cf73381c1c87cbed96 \
+    --hash=sha256:48c5881de7e8b0a0d648cb024c8062dc84e7b840ed81e864c7614fd3c127bde9
 itsdangerous==2.2.0 ; python_version >= "3.10" and python_version < "4.0" \
     --hash=sha256:c6242fc49e35958c8b15141343aa660db5fc54d4f13a1db01a3f5891b98700ef \
     --hash=sha256:e0050c0b7da1eea53ffaf149c0cfbb5c6e2e2b69c4bef22c81fa6eb73e5f6173
@@ -980,6 +995,9 @@ mkl==2021.4.0 ; python_version >= "3.10" and python_version < "4.0" and platform
 mpmath==1.3.0 ; python_version >= "3.10" and python_version < "4.0" \
     --hash=sha256:7a28eb2a9774d00c7bc92411c19a89209d5da7c4c9a9e227be8330a23a25b91f \
     --hash=sha256:a0b2b9fe80bbcd81a6647ff13108738cfb482d481d826cc0e02f5b35e5c88d2c
+msal-extensions==1.1.0 ; python_version >= "3.10" and python_version < "4.0" \
+    --hash=sha256:01be9711b4c0b1a151450068eeb2c4f0997df3bba085ac299de3a66f585e382f \
+    --hash=sha256:6ab357867062db7b253d0bd2df6d411c7891a0ee7308d54d1e4317c1d1c54252
 msal==1.28.1 ; python_version >= "3.10" and python_version < "4.0" \
     --hash=sha256:563c2d70de77a2ca9786aab84cb4e133a38a6897e6676774edc23d610bfc9e7b \
     --hash=sha256:d72bbfe2d5c2f2555f4bc6205be4450ddfd12976610dd9a16a9ab0f05c68b64d

spinoza_project/config.yaml

@@ -0,0 +1,44 @@
+demo_name: Spinoza Q&A
+tabs:
+  GIEC et IPBES: "*Outil dédié aux rapports du GIEC et de l'IPBES.*"
+  Textes Juridiques: "*Outil dédié aux codes Français modifiés par la loi climat (21/73).*"
+  Documents Stratégiques: "*Outil dédié aux données centrées sur le plan politique (SNBC).*"
+  ADEME:
+    "*Outil dédié aux données issues de l'ADEME et nous avons sélectionnés notamment différentes catégories de rapports:*\n
+    * *Les guides mis à disposition de la population*\n
+    * *Les rapport d'expériences sur des nouvelles technologies*\n
+    * *Des études et recherches sur des impacts locaux*\n
+    * *Des documents institutionnels (analyses demandées par la France & rapports d'activité)*\n
+    * *Les plans de transition sectoriels pour les secteurs industriels les plus émetteurs : (verre, papier, ciment, acier, aluminium, chimie, sucre)*"
+  Presse: "*Outil dédié aux données fournies par Aday concernant la presse.*"
+
+logo_rsf: 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"
+
+source_mapping:
+  GIEC et IPBES: "Science"
+  Textes Juridiques: "Loi"
+  Documents Stratégiques: "Politique"
+  ADEME: "ADEME"
+  Presse: "Presse"
+
+prompt_naming:
+  Science: "Science"
+  Loi: "Loi"
+  Politique: "Politique"
+  ADEME: "ADEME"
+  Presse: "Presse"
+
+database_index_path: "./app/data/database_tab_placeholder.pickle"
+query_preprompt: "query: "
+passage_preprompt: "passage: "
+embedding_model: "intfloat/multilingual-e5-base"
+num_document_retrieved: 5
+min_similarity: 0.05
+
+## Chat API
+user_token: "user"
+assistant_token: "assistant"
+system_token: "system"
+stop_token: "" ## useless in chat mode

spinoza_project/source/backend/llm_utils.py

@@ -0,0 +1,83 @@
+from langchain_openai import AzureChatOpenAI
+from msal import ConfidentialClientApplication
+from langchain_openai import AzureOpenAIEmbeddings
+from langchain.vectorstores.azuresearch import AzureSearch
+import os
+
+
+class LLM:
+    def __init__(self, llm):
+        self.llm = llm
+        self.callbacks = []
+
+    def stream(self, prompt, prompt_arguments):
+        self.llm.streaming = True
+        streamed_content = self.llm.stream(prompt.format_messages(**prompt_arguments))
+        output = ""
+        for op in streamed_content:
+            output += op.content
+            yield output
+
+    def get_prediction(self, prompt, prompt_arguments):
+        self.llm.callbacks = self.callbacks
+        return self.llm.predict_messages(
+            prompt.format_messages(**prompt_arguments)
+        ).content
+
+    async def get_aprediction(self, prompt, prompt_arguments):
+        self.llm.callbacks = self.callbacks
+        prediction = await self.llm.apredict_messages(
+            prompt.format_messages(**prompt_arguments)
+        )
+        return prediction
+
+    async def get_apredictions(self, prompts, prompts_arguments):
+        self.llm.callbacks = self.callbacks
+        predictions = []
+        for prompt_, prompt_args_ in zip(prompts.keys(), prompts_arguments):
+            prediction = await self.llm.apredict_messages(
+                prompts[prompt_].format_messages(**prompt_args_)
+            )
+            predictions.append(prediction.content)
+        return predictions
+
+
+def get_token() -> str | None:
+    app = ConfidentialClientApplication(
+        client_id=os.getenv("CLIENT_ID"),
+        client_credential=os.getenv("CLIENT_SECRET"),
+        authority=f"https://login.microsoftonline.com/{os.getenv('TENANT_ID')}",
+    )
+    result = app.acquire_token_for_client(scopes=[os.getenv("SCOPE")])
+    if result is not None:
+        return result["access_token"]
+
+
+def get_llm():
+    os.environ["OPENAI_API_KEY"] = get_token()
+    os.environ["AZURE_OPENAI_ENDPOINT"] = (
+        f"{os.getenv('OPENAI_API_ENDPOINT')}{os.getenv('DEPLOYMENT_ID')}/chat/completions?api-version={os.getenv('OPENAI_API_VERSION')}"
+    )
+
+    return LLM(AzureChatOpenAI())
+
+
+def get_vectorstore(index_name, model="text-embedding-ada-002"):
+    os.environ["AZURE_OPENAI_ENDPOINT"] = (
+        f"{os.getenv('OPENAI_API_ENDPOINT')}{os.getenv('DEPLOYMENT_EMB_ID')}/embeddings?api-version={os.getenv('OPENAI_API_VERSION')}"
+    )
+    os.environ["AZURE_OPENAI_API_KEY"] = get_token()
+
+    aoai_embeddings = AzureOpenAIEmbeddings(
+        azure_deployment=model,
+        openai_api_version=os.getenv("OPENAI_API_VERSION"),
+    )
+
+    vector_store: AzureSearch = AzureSearch(
+        azure_search_endpoint=os.getenv("VECTOR_STORE_ADDRESS"),
+        azure_search_key=os.getenv("VECTOR_STORE_PASSWORD"),
+        index_name=index_name,
+        embedding_function=aoai_embeddings.embed_query,
+    )
+
+    return vector_store