Initial commit

.gitattributes

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Dockerfile

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+FROM python:3.11
+
+WORKDIR /code
+
+COPY ./requirements.txt /code/requirements.txt
+
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+# Set up a new user named "user" with user ID 1000
+RUN useradd -m -u 1000 user
+# Switch to the "user" user
+USER user
+# Set home to the user's home directory
+ENV HOME=/home/user \
+	PATH=/home/user/.local/bin:$PATH
+
+# Set the working directory to the user's home directory
+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
+
+EXPOSE 7860
+
+CMD python3 -u app.py

README.md

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+---
+title: Milvus Client Embedding
+emoji: 📚
+colorFrom: yellow
+colorTo: blue
+sdk: docker
+app_port: 7860
+pinned: false
+license: apache-2.0
+---
+
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import gradio as gr
+
+from langchain.embeddings import HuggingFaceEmbeddings, HuggingFaceInstructEmbeddings, OpenAIEmbeddings
+from pymilvus import Collection, connections
+import json
+import os
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+
+
+MILVUS_COLLECTION = os.environ.get("MILVUS_COLLECTION", "LangChainCollection")
+
+MILVUS_HOST = os.environ.get("MILVUS_HOST", "")
+MILVUS_PORT = "19530"
+
+EMBEDDING_MODEL = os.environ.get("EMBEDDING_MODEL", "hkunlp/instructor-large")
+EMBEDDING_LOADER = os.environ.get("EMBEDDING_LOADER", "HuggingFaceInstructEmbeddings")
+EMBEDDING_LIST = ["HuggingFaceInstructEmbeddings", "HuggingFaceEmbeddings"]
+
+# return top-k text chunks from vector store
+TOP_K_DEFAULT = 15
+TOP_K_MAX = 30
+SCORE_DEFAULT = 0.33
+
+BUTTON_MIN_WIDTH = 100
+
+global g_emb
+g_emb = None
+global g_col
+g_col = None
+
+def init_emb(emb_name, emb_loader, db_col_textbox):
+    
+    global g_emb
+    global g_col
+
+    g_emb = eval(emb_loader)(model_name=emb_name)
+    
+    connections.connect( 
+        host=MILVUS_HOST,
+        port=MILVUS_PORT
+    )
+    
+    g_col = Collection(db_col_textbox)
+    
+    g_col.load()
+    
+    return (str(g_emb), str(g_col))
+
+
+def get_emb():
+    return g_emb
+
+def get_col():
+    return g_col
+
+
+def remove_duplicates(documents, score_min):
+    seen_content = set()
+    unique_documents = []
+    for (doc, score) in documents:
+        if (doc.page_content not in seen_content) and (score >= score_min):
+            seen_content.add(doc.page_content)
+            unique_documents.append(doc)
+    return unique_documents
+
+
+def get_data(query, top_k, score, db_col, db_index):
+    if not query:
+        return "Please init db in configuration"
+
+    embed_query = g_emb.embed_query(query)
+    
+    search_params = {"metric_type": "L2",
+                     "params": {"nprobe": 1},
+                     "offset": 0}
+    
+    
+    results = g_col.search(
+        data=[embed_query], 
+        anns_field="vector",
+        param=search_params,
+        limit=top_k, 
+        expr=None,
+        output_fields=['source', 'text'],
+        consistency_level="Strong"
+    )
+    
+    jsons = json.dumps([{'source': hit.entity.get('source'),
+                        'text': hit.entity.get('text')}
+                       for hit in results[0]],
+                      indent=0)
+
+    return jsons
+
+with gr.Blocks(
+    title = "3GPP Database",
+    theme = "Base",
+    css = """.bigbox {
+    min-height:250px;
+}
+""") as demo:
+    with gr.Tab("Matching"):
+        with gr.Accordion("Vector similarity"):
+            with gr.Row():
+                with gr.Column():
+                    top_k = gr.Slider(1,
+                                      TOP_K_MAX,
+                                      value=TOP_K_DEFAULT,
+                                      step=1,
+                                      label="Vector similarity top_k",
+                                      interactive=True)
+                with gr.Column():
+                    score = gr.Slider(0.01,
+                                      0.99,
+                                      value=SCORE_DEFAULT,
+                                      step=0.01,
+                                      label="Vector similarity score",
+                                      interactive=True)
+
+        with gr.Row():
+            with gr.Column(scale=10):
+                input_box = gr.Textbox(label = "Input", placeholder="What are you looking for?")
+            with gr.Column(scale=1, min_width=BUTTON_MIN_WIDTH):
+                btn_run = gr.Button("Run", variant="primary")
+
+        output_box = gr.JSON(label = "Output")
+
+
+    with gr.Tab("Configuration"):
+        with gr.Row():
+            btn_init = gr.Button("Init")
+        
+        load_emb = gr.Textbox(get_emb, label = 'Embedding Client', show_label=True)
+        load_col = gr.Textbox(get_col, label = 'Milvus Collection', show_label=True)
+        
+        with gr.Accordion("Embedding"):
+                
+            with gr.Row():
+                with gr.Column():
+                    emb_textbox = gr.Textbox(
+                        label = "Embedding Model",
+                        # show_label = False,
+                        value = EMBEDDING_MODEL,
+                        placeholder = "Paste Your Embedding Model Repo on HuggingFace",
+                        lines=1,
+                        interactive=True,
+                        type='email')
+
+                with gr.Column():
+                    emb_dropdown = gr.Dropdown(
+                        EMBEDDING_LIST,
+                        value=EMBEDDING_LOADER,
+                        multiselect=False,
+                        interactive=True,
+                        label="Embedding Loader")
+
+        with gr.Accordion("Milvus Database"):
+            with gr.Row():
+                db_col_textbox = gr.Textbox(
+                    label = "Milvus Collection",
+                    # show_label = False,
+                    value = MILVUS_COLLECTION,
+                    placeholder = "Paste Your Milvus Collection (xx-xx-xx) and Hit ENTER",
+                    lines=1,
+                    interactive=True,
+                    type='email')
+                db_index_textbox = gr.Textbox(
+                    label = "Milvus Host",
+                    # show_label = False,
+                    value = MILVUS_HOST,
+                    placeholder = "Paste Your Milvus Index (xxxx) and Hit ENTER",
+                    lines=1,
+                    interactive=True,
+                    type='password')
+
+    btn_init.click(fn=init_emb,
+                   inputs=[emb_textbox, emb_dropdown, db_col_textbox],
+                   outputs=[load_emb, load_col])
+    btn_run.click(fn=get_data,
+                  inputs=[input_box, top_k, score, db_col_textbox, db_index_textbox],
+                  outputs=[output_box])
+
+if __name__ == "__main__":
+    demo.queue()
+    demo.launch(server_name="0.0.0.0",
+                server_port=7860)
+

requirements.txt

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+pymilvus
+langchain
+gradio
+InstructorEmbedding
+sentence_transformers