Upload folder using huggingface_hub

.gitignore

@@ -9,9 +9,6 @@ setAPIKEY.sh
 .AppleDouble
 .LSOverride
 
-# Historique conversasion with chatbot
-*.json
-
 # Icon must end with two \r
 Icon
 

README.md

@@ -1,5 +1,5 @@
 ---
-title: clara
+title: Clara
 app_file: app.py
 sdk: gradio
 sdk_version: 4.19.1

app.py

@@ -1,7 +1,9 @@
-
-
-# , get_pinecone_vectorstore, find_similar_vectors
-from climateqa.engine.vectorstore import build_vectores_stores, get_PDF_Names_from_GCP
+#from climateqa.engine.vectorstore import get_pinecone_vectorstore, 
+from climateqa.engine.vectorstore import build_vectores_stores
+from climateqa.engine.embeddings import get_embeddings_function
+from climateqa.engine.rag import make_rag_papers_chain
+from climateqa.engine.keywords import make_keywords_chain
+from climateqa.sample_questions import QUESTIONS
 from climateqa.engine.text_retriever import ClimateQARetriever
 from climateqa.engine.rag import make_rag_chain
 from climateqa.engine.llm import get_llm
@@ -10,9 +12,11 @@ from datetime import datetime
 import json
 import re
 import gradio as gr
+from climateqa.papers.openalex import OpenAlex
 from sentence_transformers import CrossEncoder
 
 reranker = CrossEncoder("mixedbread-ai/mxbai-rerank-xsmall-v1")
+oa = OpenAlex()
 
 # Load environment variables in local mode
 try:
@@ -22,9 +26,9 @@ except Exception as e:
     pass
 
 # Set up Gradio Theme
-theme = gr.themes.Soft(
-    primary_hue="yellow",
-    secondary_hue="orange",
+theme = gr.themes.Base(
+    primary_hue="blue",
+    secondary_hue="red",
     font=[gr.themes.GoogleFont("Poppins"), "ui-sans-serif",
           "system-ui", "sans-serif"],
 )
@@ -70,6 +74,9 @@ def serialize_docs(docs):
 
 
 # Create vectorstore and retriever
+embeddings_function = get_embeddings_function()
+
+#vectorstore = get_pinecone_vectorstore(embeddings_function)
 vectorstore = build_vectores_stores("./sources")
 llm = get_llm(provider="openai", max_tokens=1024, temperature=0.0)
 
@@ -160,7 +167,7 @@ async def chat(query, history):
         "answer": history[-1][1],
         "time": timestamp,
     }
-    #log_locally(log_file, logs)
+    log_locally(log_file, logs)
 
     yield history, docs_html, output_query, output_language, gallery, output_query, output_keywords
 
@@ -178,7 +185,7 @@ def make_html_source(source, i):
         <div class="card-content">
             <div>
                 <div style="float:right;width 10%;position:relative;top:0px">
-                    <a href='{meta['ax_url']}' target='_blank'><img style="width:20px" src='/file/assets/download.png' /></a>
+                    <a href='{meta['ax_url']}'><img style="width:20px" src='/file/assets/download.png' /></a>
                 </div>
                 <div>
                     <h2>Extrait {i}</h2>
@@ -188,9 +195,9 @@ def make_html_source(source, i):
             <p>{text_content}</p>
 
         </div>
-        <!-- <div class="card-footer">
+        <div class="card-footer">
             <span>{name}</span>
-        </div> -->
+        </div>
     </div>
     """
 
@@ -206,6 +213,79 @@ def log_locally(file, logs):
         f.write(logs_json)
 
 
+def generate_keywords(query):
+    chain = make_keywords_chain(llm)
+    keywords = chain.invoke(query)
+    keywords = " AND ".join(keywords["keywords"])
+    return keywords
+
+
+papers_cols_widths = {
+    "doc": 50,
+    "id": 100,
+    "title": 300,
+    "doi": 100,
+    "publication_year": 100,
+    "abstract": 500,
+    "rerank_score": 100,
+    "is_oa": 50,
+}
+
+papers_cols = list(papers_cols_widths.keys())
+papers_cols_widths = list(papers_cols_widths.values())
+
+
+async def find_papers(query, keywords, after):
+
+    summary = ""
+
+    df_works = oa.search(keywords, after=after)
+    df_works = df_works.dropna(subset=["abstract"])
+    df_works = oa.rerank(query, df_works, reranker)
+    df_works = df_works.sort_values("rerank_score", ascending=False)
+    G = oa.make_network(df_works)
+
+    height = "750px"
+    network = oa.show_network(
+        G, color_by="rerank_score", notebook=False, height=height)
+    network_html = network.generate_html()
+
+    network_html = network_html.replace("'", "\"")
+    css_to_inject = "<style>#mynetwork { border: none !important; } .card { border: none !important; }</style>"
+    network_html = network_html + css_to_inject
+
+    network_html = f"""<iframe style="width: 100%; height: {height};margin:0 auto" name="result" allow="midi; geolocation; microphone; camera;
+    display-capture; encrypted-media;" sandbox="allow-modals allow-forms
+    allow-scripts allow-same-origin allow-popups
+    allow-top-navigation-by-user-activation allow-downloads" allowfullscreen=""
+    allowpaymentrequest="" frameborder="0" srcdoc='{network_html}'></iframe>"""
+
+    docs = df_works["content"].head(15).tolist()
+
+    df_works = df_works.reset_index(
+        drop=True).reset_index().rename(columns={"index": "doc"})
+    df_works["doc"] = df_works["doc"] + 1
+    df_works = df_works[papers_cols]
+
+    yield df_works, network_html, summary
+
+    chain = make_rag_papers_chain(llm)
+    result = chain.astream_log(
+        {"question": query, "docs": docs, "language": "English"})
+    path_answer = "/logs/StrOutputParser/streamed_output/-"
+
+    async for op in result:
+
+        op = op.ops[0]
+
+        if op['path'] == path_answer:  # reforulated question
+            new_token = op['value']  # str
+            summary += new_token
+        else:
+            continue
+        yield df_works, network_html, summary
+
+
 # --------------------------------------------------------------------
 # Gradio
 # --------------------------------------------------------------------
@@ -226,13 +306,8 @@ What would you like to know today?
 """
 
 
-with gr.Blocks(title="CLARA", css="style.css", theme=theme, elem_id="main-component", elem_classes="ax_background") as demo:
-
-    gr.HTML("""
-        <img style="width:100px" src="file/assets/axionable.svg"/>
-    """, elem_classes="logo-axio ")
+with gr.Blocks(title="CLARA", css="style.css", theme=theme, elem_id="main-component") as demo:
 
-    # TAB Clara
     with gr.Tab("CLARA"):
 
         with gr.Row(elem_id="chatbot-row"):
@@ -244,44 +319,57 @@ with gr.Blocks(title="CLARA", css="style.css", theme=theme, elem_id="main-compon
 
                 with gr.Row(elem_id="input-message"):
                     textbox = gr.Textbox(placeholder="Posez votre question", show_label=False,
-                                        scale=7, lines=1, interactive=True, elem_id="input-textbox")
-
+                                         scale=7, lines=1, interactive=True, elem_id="input-textbox")
 
             with gr.Column(scale=1, variant="panel", elem_id="right-panel"):
 
-                with gr.Column(scale=1, elem_id="tab-citations"):
-                
-                    gr.HTML("<p>Sources</p>")
+                with gr.Tabs() as tabs:
 
-                    sources_textbox = gr.HTML(
-                        show_label=False, elem_id="sources-textbox")
-                    docs_textbox = gr.State("")
+                    with gr.Tab("Sources", elem_id="tab-citations", id=1):
+                        sources_textbox = gr.HTML(
+                            show_label=False, elem_id="sources-textbox")
+                        docs_textbox = gr.State("")
 
-                # l'object tabs est necessaire actuellement
-                # J'ai l'impression qu'il est utiliser pour freezre les contenu des tabs
-                # pendant que l'ia gènère une reponse ..
-                with gr.Tabs() as tabs:
-                    None
+# ---------------------------------------------------------------------------------------
+# OTHER TABS
+# ---------------------------------------------------------------------------------------
 
+    with gr.Tab("Figures", elem_id="tab-images", elem_classes="max-height other-tabs"):
+        gallery_component = gr.Gallery()
+
+    with gr.Tab("Papers (beta)", elem_id="tab-papers", elem_classes="max-height other-tabs"):
 
-    # TAB A propos
-    with gr.Tab("À propos", elem_classes="max-height other-tabs"):
         with gr.Row():
             with gr.Column(scale=1):
-                gr.Markdown(
-                    ("CLARA (Climate LLM for Adaptation & Risks Answers) by [Axionable](https://www.axionable.com/)"
-                    "– Fork de [ClimateQ&A](https://huggingface.co/spaces/Ekimetrics/climate-question-answering/tree/main)"), elem_classes="a-propos")
+                query_papers = gr.Textbox(
+                    placeholder="Question", show_label=False, lines=1, interactive=True, elem_id="query-papers")
+                keywords_papers = gr.Textbox(
+                    placeholder="Keywords", show_label=False, lines=1, interactive=True, elem_id="keywords-papers")
+                after = gr.Slider(minimum=1950, maximum=2023, step=1, value=1960,
+                                  label="Publication date", show_label=True, interactive=True, elem_id="date-papers")
+                search_papers = gr.Button(
+                    "Search", elem_id="search-papers", interactive=True)
 
+            with gr.Column(scale=7):
 
-#    # TAB Configuration
-#    with gr.Tab("Configuration"):
-#
-#        with gr.Row(elem_id="config-row"):
-#            with gr.Column(scale=1):
-#
-#                for pdfName in get_PDF_Names_from_GCP():
-#                        gr.Markdown( pdfName, elem_classes="a-propos")
+                with gr.Tab("Summary", elem_id="papers-summary-tab"):
+                    papers_summary = gr.Markdown(
+                        visible=True, elem_id="papers-summary")
 
+                with gr.Tab("Relevant papers", elem_id="papers-results-tab"):
+                    papers_dataframe = gr.Dataframe(
+                        visible=True, elem_id="papers-table", headers=papers_cols)
+
+                with gr.Tab("Citations network", elem_id="papers-network-tab"):
+                    citations_network = gr.HTML(
+                        visible=True, elem_id="papers-citations-network")
+
+    with gr.Tab("À propos", elem_classes="max-height other-tabs"):
+        with gr.Row():
+            with gr.Column(scale=1):
+                gr.Markdown(
+                    "CLARA (Climate LLM for Adaptation & Risks Answers) by [Axionable](https://www.axionable.com/)"
+                    "– Fork de [ClimateQ&A](https://huggingface.co/spaces/Ekimetrics/climate-question-answering/tree/main)")
 
     def start_chat(query, history):
         history = history + [(query, None)]
@@ -298,8 +386,21 @@ with gr.Blocks(title="CLARA", css="style.css", theme=theme, elem_id="main-compon
      )
 
 
+
+    def change_sample_questions(key):
+        index = list(QUESTIONS.keys()).index(key)
+        visible_bools = [False] * len(samples)
+        visible_bools[index] = True
+        return [gr.update(visible=visible_bools[i]) for i in range(len(samples))]
+
+ #   dropdown_samples.change(change_sample_questions,dropdown_samples,samples)
+
+    query_papers.submit(generate_keywords, [query_papers], [keywords_papers])
+    search_papers.click(find_papers, [query_papers, keywords_papers, after], [
+                        papers_dataframe, citations_network, papers_summary])
+
     demo.queue()
 
 demo.launch(allowed_paths=["assets/download.png",
-            "assets/logo4.png",
-            "assets/axionable.svg"],favicon_path="assets/logo4.png")
+            "assets/logo4.png"], 
+            favicon_path="assets/logo4.png")

climateqa/engine/embeddings.py

@@ -8,12 +8,8 @@ def get_embeddings_function(version = "v1.2"):
 
         # https://huggingface.co/BAAI/bge-base-en-v1.5
         # Best embedding model at a reasonable size at the moment (2023-11-22)
-        # model_name = "BAAI/bge-base-en-v1.5"
-
-        # https://huggingface.co/BAAI/bge-m3
-        # A better one from 2024-04
-        model_name = "BAAI/bge-m3"
-
+    
+        model_name = "BAAI/bge-base-en-v1.5"
         encode_kwargs = {'normalize_embeddings': True} # set True to compute cosine similarity
         print("Loading embeddings model: ", model_name)
         embeddings_function = HuggingFaceBgeEmbeddings(

climateqa/engine/text_retriever.py

@@ -45,4 +45,3 @@ class ClimateQARetriever(BaseRetriever):
             doc.metadata["page_number"] = 1
             results.append(doc)
         return results
-

climateqa/engine/vectorstore.py

@@ -1,94 +1,58 @@
+# Pinecone
+# More info at https://docs.pinecone.io/docs/langchain
+# And https://python.langchain.com/docs/integrations/vectorstores/pinecone
+# import os
+# from pinecone import Pinecone
+# from langchain_community.vectorstores import Pinecone as PineconeVectorstore
 
-from google.cloud import storage
-#storage_client = storage.Client()
-storage_client = storage.Client.create_anonymous_client()
-bucket_name = "docs-axio-clara"
+# # LOAD ENVIRONMENT VARIABLES
+# try:
+#     from dotenv import load_dotenv
+#     load_dotenv()
+# except:
+#     pass
 
-from langchain_pinecone import PineconeVectorStore
 
-from langchain_community.document_loaders import TextLoader
-from langchain_text_splitters import CharacterTextSplitter
-from climateqa.engine.embeddings import get_embeddings_function
-embeddings_function = get_embeddings_function()
+# def get_pinecone_vectorstore(embeddings,text_key = "content"):
 
+#     pc = Pinecone(api_key=os.getenv("PINECONE_API_KEY"))
+#     index = pc.Index(os.getenv("PINECONE_API_INDEX"))
 
-
-index_name = "my-index"
-namespace = "my-namespace"
+#     vectorstore = PineconeVectorstore(
+#         index, embeddings, text_key,
+#     )
+#     return vectorstore
 
 
+from langchain_community.vectorstores import Annoy
+from langchain_community.document_loaders import TextLoader
+from langchain_text_splitters import CharacterTextSplitter
+from climateqa.engine.embeddings import get_embeddings_function
+embeddings_function = get_embeddings_function()
 import os
 import pdfplumber
 
-def get_PDF_Names_from_GCP():
-
-    listName = []
-    # Récupération des fichier depuis GCP storage
-    blobs = storage_client.list_blobs(bucket_name, prefix='sources/')
-    for blob in blobs:
-        listName.append(blob.name)
-    return listName
-
-def get_PDF_from_GCP(folder_path, pdf_folder="./PDF"):
-
-    # Récupération des fichier depuis GCP storage
-    blobs = storage_client.list_blobs(bucket_name, prefix='sources/')
-    for blob in blobs:
-
-        print( "\n"+blob.name+":")
-        print( " <- Téléchargement Depuis GCP")
-        blob.download_to_filename(pdf_folder+"/"+blob.name)
-
-        # Extraction des textes dpuis les fichiers PDF
-        print(" >>> Extraction PDF")
-        for pdf_file in os.listdir(pdf_folder):
-            if pdf_file.startswith("."):
-                continue
-            print(" >  "+pdf_folder+"/"+pdf_file)
-            pdf_total_pages = 0
-            with pdfplumber.open(pdf_folder+"/"+pdf_file) as pdf:
-                pdf_total_pages = len(pdf.pages)
-            
-            # Fuite mémoire pour les gros fichiers
-            # Reouvrir le fichier à chaque N page semble rélgler le problème
-            N_page = 300
-            page_number = 0
-            while page_number < pdf_total_pages:
-
-                print(" -- ouverture du fichier pour "+str(N_page)+ " pages --" )
-                with pdfplumber.open(pdf_folder+"/"+pdf_file) as pdf:
-
-                    npage = 0
-                    while (npage < N_page and page_number < pdf_total_pages) :
-
-                        print(" >>> "+str(page_number+1))
-                        f = open(folder_path+"/"+pdf_file+"..:page:.."+str(page_number+1), "w")
-                        for char_pdf in pdf.pages[page_number].chars:
-                            f.write(char_pdf["text"])
-                        f.close()
-
-                        npage = npage + 1
-                        page_number = page_number + 1
-
-
-        print(" X removing: " + blob.name )
-        os.remove(pdf_folder+"/"+blob.name)
-
-
 def build_vectores_stores(folder_path, pdf_folder="./PDF", vectors_path = "./vectors"):
 
-    vectorstore = PineconeVectorStore(
-    index_name=index_name,
-    embedding=embeddings_function,
-    #namespace=namespace
-    )
-
-    return vectorstore
+    if os.path.isfile(vectors_path+"/index.annoy"):
+        return Annoy.load_local(vectors_path, embeddings_function,allow_dangerous_deserialization=True)
 
-    print(" Vectorisation ...")
+    # Extract text from PDF files
+    print("Extraction PDF ...")
+    for pdf_file in os.listdir(pdf_folder):
+        if pdf_file.startswith("."):
+            continue
+        print(" >  "+pdf_folder+"/"+pdf_file)
+        with pdfplumber.open(pdf_folder+"/"+pdf_file) as pdf:
+            for pdf_page in pdf.pages:
+                f = open(folder_path+"/"+pdf_file+" page "+str(pdf_page.page_number), "w")
+                # f.write(pdf_file+" page "+str(pdf_page.page_number))
+                for char_pdf in pdf_page.chars:
+                    f.write(char_pdf["text"])
+                f.close()
 
     docs = []
-    #vector_store_from_docs = ()  # Créer un nouvel objet Annoy ou utiliser celui déjà initialisé selon votre code existant
+    vector_store_from_docs = ()  # Créer un nouvel objet Annoy ou utiliser celui déjà initialisé selon votre code existant
     for filename in os.listdir(folder_path):
         if filename.startswith("."):
             continue
@@ -99,17 +63,12 @@ def build_vectores_stores(folder_path, pdf_folder="./PDF", vectors_path = "./vec
 
             for doc in documents:
                 if (doc.metadata):
-                    doc.metadata["ax_page"] = doc.metadata['source'].split("..:page:..")[-1]
-                    doc.metadata["ax_name"] = doc.metadata['source'].split("..:page:..")[0].split("/")[-1]
+                    doc.metadata["ax_page"] = doc.metadata['source'].split(" ")[-1]
+                    doc.metadata["ax_name"] = doc.metadata['source'].split(" ")[0].split("/")[-1]
                     doc.metadata["ax_url"] = "https://storage.googleapis.com/docs-axio-clara/sources/"+doc.metadata["ax_name"]
 
             text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
             docs += text_splitter.split_documents(documents)
-    #vector_store_from_docs = Annoy.from_documents(docs, embeddings_function)
-    vectorstore = PineconeVectorStore.from_documents(docs, embeddings_function, index_name=index_name)
-    #vector_store_from_docs.save_local(vectors_path)
-    return vectorstore
-
-
-    print("MISSING VECTORS")
-    exit(0)
+    vector_store_from_docs = Annoy.from_documents(docs, embeddings_function)
+    vector_store_from_docs.save_local(vectors_path)
+    return vector_store_from_docs

logs/1715672103.255797.json

@@ -0,0 +1 @@
+{"user_id": "245d9442-2651-4578-8f63-4ed4145c0a40", "prompt": "quels risques physiques en 2024 ?", "query": "quels risques physiques en 2024 ?", "question": "What are the projected physical risks in 2024 related to climate change?", "sources": ["Custom"], "docs": [], "answer": "Je suis d\u00e9sol\u00e9, mais les extraits fournis ne contiennent pas d'informations sur les risques physiques projet\u00e9s en 2024 li\u00e9s au changement climatique. Je n'ai pas suffisamment d'informations pour r\u00e9pondre \u00e0 cette question.", "time": "1715672103.255797"}

logs/1715673060.457813.json

@@ -0,0 +1 @@
+{"user_id": "c936b5ac-affe-4df7-9f9c-6f78ae83525e", "prompt": "quels risques physiques ?", "query": "quels risques physiques ?", "question": "What are the physical risks associated with climate change?", "sources": ["Custom"], "docs": [], "answer": "Je suis d\u00e9sol\u00e9, mais les extraits fournis ne contiennent pas d'informations sur les risques physiques associ\u00e9s au changement climatique. Je n'ai pas assez d'informations pour r\u00e9pondre \u00e0 cette question.", "time": "1715673060.457813"}

logs/1715673178.788617.json

@@ -0,0 +1 @@
+{"user_id": "80fd8b5f-4cf2-430c-8c14-09b4acc7436c", "prompt": "quels risques physiques ?", "query": "quels risques physiques ?", "question": "What are the physical risks associated with climate change?", "sources": ["Custom"], "docs": [], "answer": "Je suis d\u00e9sol\u00e9e, mais les extraits fournis ne contiennent pas d'informations sur les risques physiques associ\u00e9s au changement climatique. Je n'ai pas suffisamment d'informations pour r\u00e9pondre \u00e0 cette question.", "time": "1715673178.788617"}

logs/1715675920.752972.json

@@ -0,0 +1 @@
+{"user_id": "d06c2c8a-ab31-4dcd-8d5c-69a4a678221b", "prompt": "quels sont les risques physiques en 2024 ?", "query": "quels sont les risques physiques en 2024 ?", "question": "What are the projected physical risks in 2024?", "sources": ["Custom"], "docs": [], "answer": "Je suis d\u00e9sol\u00e9e, mais les documents fournis ne contiennent pas d'informations sur les risques physiques projet\u00e9s en 2024. Je n'ai pas assez d'informations pour r\u00e9pondre \u00e0 cette question.", "time": "1715675920.752972"}

requirements.txt

@@ -1,5 +1,5 @@
-google-cloud-storage==2.16.0
 gradio==4.19.1
+gunicorn==22.0.0
 python-dotenv==1.0.0
 langchain==0.1.10
 langchain_openai==0.0.6
@@ -11,5 +11,4 @@ pyalex==0.13
 networkx==3.2.1
 pyvis==0.3.2
 annoy==1.17.3
-langchain_pinecone
 pdfplumber

style.css

@@ -3,78 +3,6 @@
     --user-image: url('https://ih1.redbubble.net/image.4776899543.6215/st,small,507x507-pad,600x600,f8f8f8.jpg');
   } */
 
-.logo-axio {
-    float: right;
-    position: absolute;
-    right: 0px;
-}
-
-
-/* couleur text */
-p {
-    color: black !important;
-}
-li {
-    color: black !important;
-}
-
-button.selected {
-    border-radius: 20px !important;
-}
-button:hover {
-    color: #ffc000 !important;
-}
-
-
-/* fond panels/blocks */
-.panel {
-    background-color: #eeeeee !important;
-    border: 0px;
-}
-.block {
-    background-color: #eeeeee !important;
-}
-
-/* fond bot */
-.bot {
-    background-color: #eeeeee !important;
-}
-
-/* avatar en debut de reponse */
-.avatar-container {
-    align-self: baseline !important;
-    margin-top: 35px;
-}
-
-
-
-/* fond user */
-.user {
-    background-color: #d2d2d2 !important;
-}
-textarea {
-    background-color: #d2d2d2 !important;
-    color: black !important;
-}
-
-
-/* fond app */
-gradio-app {
-    background-color: #ffffff !important;
-}
-.gradio-container {
-    background-color: #ffffff !important;
-    max-width: 100% !important;
-    width: 100% !important;
-}
-
-
-.a-propos {
-    margin: 20px !important;
-}
-
-
-
 .telecharger {
     border: 1px solid;
     padding: 5px;
@@ -115,7 +43,7 @@ body.dark .warning-box * {
 
 
 body.dark .tip-box * {
-    color:rgb(216, 216, 216) !important;
+    color:black !important;
 }
 
 

test

@@ -19,7 +19,8 @@ ENV HOME=/home/user \
 	GRADIO_NUM_PORTS=1 \
 	GRADIO_SERVER_NAME=0.0.0.0 \
 	GRADIO_THEME=huggingface \
-	SYSTEM=spaces
+	SYSTEM=spaces  \
+	PORT=7860
  
 # Set the working directory to the user's home directory
 WORKDIR $HOME/app
@@ -27,6 +28,8 @@ WORKDIR $HOME/app
 # Copy the current directory contents into the container at $HOME/app setting the owner to the user
 COPY --chown=user . $HOME/app
 
-CMD ["python","setup.py"]
+#CMD ["python","setup.py"]
 
-CMD ["python", "app.py"]
+#CMD ["python", "app.py"]
+
+CMD gunicorn -b 0.0.0.0:$PORT app:demo

vectors/index.annoy

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:0b2b8ad228674a015ce9f4c7a4969e0267400140d450773f86802f50ac27ec45
+oid sha256:b94e9d486dbe3a9e2397672bda1d1c17198cca42a53afaa16ef8ecfcebd22fc9
 size 2238984

vectors/index.pkl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:5db10ad74c42238caac4b738e24f14f80b9688353f721397c3db34eb642ca3e2
+oid sha256:4eb3d63539603642200f07f8fac2e290e94104fbbe4f4471dc663eff850263f6
 size 3223915