From 5287193a2deda1c686d52e1bd3a05b2d4aecaed9 Mon Sep 17 00:00:00 2001
From: "estevez.sebastian@gmail.com" <estevezsebastian@gmail.com>
Date: Thu, 10 Jul 2025 23:26:28 -0400
Subject: [PATCH] embeddings

---
 pyproject.toml |   1 +
 src/app.py     | 198 +++++++++++++++++++++++-------------
 uv.lock        | 268 +++++++++++++++++++++++++++++++++++++++++++++++++
 3 files changed, 396 insertions(+), 71 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index acc4f1d1..13ae7705 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -5,6 +5,7 @@ description = "Add your description here"
 readme = "README.md"
 requires-python = ">=3.13"
 dependencies = [
+    "agentd>=0.1.7",
     "aiofiles>=24.1.0",
     "docling>=2.41.0",
     "opensearch-py[async]>=3.0.0",
diff --git a/src/app.py b/src/app.py
index 02c8b599..3277083d 100644
--- a/src/app.py
+++ b/src/app.py
@@ -1,9 +1,10 @@
 # app.py
 
 import os
+from collections import defaultdict
+
 os.environ['USE_CPU_ONLY'] = 'true'
 
-import json
 import hashlib
 import tempfile
 import asyncio
@@ -17,10 +18,11 @@ import aiofiles
 from opensearchpy import AsyncOpenSearch
 from opensearchpy._async.http_aiohttp import AIOHttpConnection
 from docling.document_converter import DocumentConverter
-
+from agentd.patch import patch_openai_with_mcp
+from openai import OpenAI
 
 # Initialize Docling converter
-converter = DocumentConverter()  # basic converter; tweak via PipelineOptions if you need OCR, etc. :contentReference[oaicite:0]{index=0}
+converter = DocumentConverter()  # basic converter; tweak via PipelineOptions if you need OCR, etc.
 
 # Initialize Async OpenSearch (adjust hosts/auth as needed)
 es = AsyncOpenSearch(
@@ -35,19 +37,50 @@ es = AsyncOpenSearch(
 )
 
 INDEX_NAME = "documents"
-
+VECTOR_DIM = 1536  # e.g. text-embedding-3-small output size
+EMBED_MODEL = "text-embedding-3-small"
 index_body = {
-    "settings": {"number_of_shards":1, "number_of_replicas":1},
+    "settings": {
+        "index": {"knn": True},
+        "number_of_shards": 1,
+        "number_of_replicas": 1
+    },
     "mappings": {
         "properties": {
+            "id":        { "type": "keyword" },
             "origin": {
                 "properties": {
-                    "binary_hash": {"type":"keyword"}
+                    "binary_hash": { "type": "keyword" }
+                }
+            },
+            "filename":  { "type": "keyword" },
+            "mimetype":  { "type": "keyword" },
+            "chunks": {
+                "type": "nested",
+                "properties": {
+                    "page":  { "type": "integer" },
+                    "text":  { "type": "text" },
+                    "chunk_embedding": {
+                        "type": "knn_vector",
+                        "dimension": VECTOR_DIM,
+                        "method": {
+                            "name":       "disk_ann",
+                            "engine":     "jvector",
+                            "space_type": "l2",
+                            "parameters": {
+                                "ef_construction": 100,
+                                "m":               16
+                            }
+                        }
+                    }
                 }
             }
         }
     }
 }
+
+client = patch_openai_with_mcp(OpenAI())  # Get the patched client back
+
 async def init_index():
     if not await es.indices.exists(index=INDEX_NAME):
         await es.indices.create(index=INDEX_NAME, body=index_body)
@@ -55,51 +88,93 @@ async def init_index():
     else:
         print(f"Index '{INDEX_NAME}' already exists, skipping creation.")
 
-# Index will be initialized when the app starts
 
+def extract_relevant(doc_dict: dict) -> dict:
+    """
+    Given the full export_to_dict() result:
+      - Grabs origin metadata (hash, filename, mimetype)
+      - Finds every text fragment in `texts`, groups them by page_no
+      - Concatenates each page’s fragments into one string chunk
+    Returns a slimmed dict ready for indexing.
+    """
+    origin = doc_dict.get("origin", {})
+    texts = doc_dict.get("texts", [])
 
-# ——————————————
-# CORE PROCESSING LOGIC
-# ——————————————
+    # Group all text fragments by page number
+    page_texts = defaultdict(list)
+    for txt in texts:
+        # Each txt['prov'][0]['page_no'] tells you which page it came from
+        prov = txt.get("prov", [])
+        page_no = prov[0].get("page_no") if prov else None
+        if page_no is not None:
+            page_texts[page_no].append(txt.get("text", "").strip())
+
+    # Build an ordered list of {page, text}
+    chunks = []
+    for page in sorted(page_texts):
+        joined = "\n".join(page_texts[page])
+        chunks.append({
+            "page": page,
+            "text": joined
+        })
+
+    return {
+        "id": origin.get("binary_hash"),
+        "filename": origin.get("filename"),
+        "mimetype": origin.get("mimetype"),
+        "chunks": chunks
+    }
+
+async def process_file_common(file_path: str, file_hash: str = None):
+    """
+    Common processing logic for both upload and upload_path.
+    1. Optionally compute SHA256 hash if not provided.
+    2. Convert with docling and extract relevant content.
+    3. Add embeddings.
+    4. Index into OpenSearch.
+    """
+    if file_hash is None:
+        sha256 = hashlib.sha256()
+        async with aiofiles.open(file_path, "rb") as f:
+            while True:
+                chunk = await f.read(1 << 20)
+                if not chunk:
+                    break
+                sha256.update(chunk)
+        file_hash = sha256.hexdigest()
+
+    #exists = await es.exists(index=INDEX_NAME, id=file_hash)
+    #if exists:
+    #    return {"status": "unchanged", "id": file_hash}
+
+    # convert and extract
+    result = converter.convert(file_path)
+    full_doc = result.document.export_to_dict()
+    slim_doc = extract_relevant(full_doc)
+
+    texts = [c["text"] for c in slim_doc["chunks"]]
+    resp = client.embeddings.create(model=EMBED_MODEL, input=texts)
+    embeddings = [d.embedding for d in resp.data]
+
+    # attach embeddings
+    for chunk, vect in zip(slim_doc["chunks"], embeddings):
+        chunk["chunk_embedding"] = vect
+
+    await es.index(index=INDEX_NAME, id=file_hash, body=slim_doc)
+    return {"status": "indexed", "id": file_hash}
 
 async def process_file_on_disk(path: str):
     """
-    1. Compute SHA256 hash by streaming the file in chunks.
-    2. If OpenSearch already has a doc with that ID, skip.
-    3. Otherwise, run Docling.convert(path) → JSON → index into OpenSearch.
+    Process a file already on disk.
     """
-    # 1) compute hash
-    sha256 = hashlib.sha256()
-    async with aiofiles.open(path, "rb") as f:
-        while True:
-            chunk = await f.read(1 << 20)  # 1 MiB
-            if not chunk:
-                break
-            sha256.update(chunk)
-    file_hash = sha256.hexdigest()
-
-    # 2) check in OpenSearch
-    exists = await es.exists(index=INDEX_NAME, id=file_hash)
-    if exists:
-        return {"path": path, "status": "unchanged", "id": file_hash}
-
-    # 3) parse + index
-    result = converter.convert(path)
-    doc_dict = result.document.export_to_dict()
-    await es.index(index=INDEX_NAME, id=file_hash, body=doc_dict)
-
-    return {"path": path, "status": "indexed", "id": file_hash}
-
+    result = await process_file_common(path)
+    result["path"] = path
+    return result
 
 async def upload(request: Request):
-    """
-    POST /upload
-    Form-data with a `file` field. Streams to disk + processes it.
-    """
     form = await request.form()
-    upload_file = form["file"]  # starlette.datastructures.UploadFile
+    upload_file = form["file"]
 
-    # stream into a temp file while hashing
     sha256 = hashlib.sha256()
     tmp = tempfile.NamedTemporaryFile(delete=False)
     try:
@@ -112,61 +187,42 @@ async def upload(request: Request):
         tmp.flush()
 
         file_hash = sha256.hexdigest()
-        # if you prefer the Datastax pattern for naming IDs, see:
-        # https://github.com/datastax/astra-assistants-api/blob/main/impl/utils.py#L229 :contentReference[oaicite:1]{index=1}
+        #exists = await es.exists(index=INDEX_NAME, id=file_hash)
+        #if exists:
+        #    return JSONResponse({"status": "unchanged", "id": file_hash})
 
-        # check + index
-        exists = await es.exists(index=INDEX_NAME, id=file_hash)
-        if exists:
-            return JSONResponse({"status": "unchanged", "id": file_hash})
-
-        result = converter.convert(tmp.name)
-        doc_dict = result.document.export_to_dict()
-        await es.index(index=INDEX_NAME, id=file_hash, body=doc_dict)
-
-        return JSONResponse({"status": "indexed", "id": file_hash})
+        result = await process_file_common(tmp.name, file_hash)
+        return JSONResponse(result)
 
     finally:
         tmp.close()
         os.remove(tmp.name)
 
-
 async def upload_path(request: Request):
-    """
-    POST /upload_path
-    JSON body: { "path": "/absolute/path/to/dir" }
-    Recursively processes every file found there in parallel.
-    """
     payload = await request.json()
     base_dir = payload.get("path")
     if not base_dir or not os.path.isdir(base_dir):
         return JSONResponse({"error": "Invalid path"}, status_code=400)
 
-    tasks = []
-    for root, _, files in os.walk(base_dir):
-        for fn in files:
-            full = os.path.join(root, fn)
-            tasks.append(process_file_on_disk(full))
+    tasks = [process_file_on_disk(os.path.join(root, fn))
+             for root, _, files in os.walk(base_dir)
+             for fn in files]
 
     results = await asyncio.gather(*tasks)
     return JSONResponse({"results": results})
 
-
 app = Starlette(debug=True, routes=[
     Route("/upload",      upload,       methods=["POST"]),
     Route("/upload_path", upload_path,  methods=["POST"]),
 ])
 
-
 if __name__ == "__main__":
     import uvicorn
-
-    # Initialize index before starting server
     asyncio.run(init_index())
-    
     uvicorn.run(
-        "app:app",        # "module:variable"
+        "app:app",
         host="0.0.0.0",
         port=8000,
-        reload=True,      # dev only
+        reload=True,
     )
+
diff --git a/uv.lock b/uv.lock
index a231e92d..48f99088 100644
--- a/uv.lock
+++ b/uv.lock
@@ -7,6 +7,22 @@ resolution-markers = [
     "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')",
 ]
 
+[[package]]
+name = "agentd"
+version = "0.1.7"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "litellm" },
+    { name = "mcp-subscribe" },
+    { name = "openai" },
+    { name = "openai-agents" },
+    { name = "pyyaml" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/1c/f6/6fc7c171e0c874e816ec063bf3cbabda4f05c6f4f1f15b65955255951027/agentd-0.1.7.tar.gz", hash = "sha256:cdcbf56dfdc0ca56067f354d890841bd71ee52210c12e468e711554007a6724c", size = 114216, upload-time = "2025-06-05T04:50:52.693Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/ab/c7/e786759ebd86afba150ccc1e8189e8515c760445f04374e2c4d72701c2aa/agentd-0.1.7-py3-none-any.whl", hash = "sha256:39332b8cf57dfc14b48628dfeb588afb6dbe05405945389fbe2925f3358b7c65", size = 13446, upload-time = "2025-06-05T04:50:51.046Z" },
+]
+
 [[package]]
 name = "aiofiles"
 version = "24.1.0"
@@ -176,6 +192,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/50/3d/9373ad9c56321fdab5b41197068e1d8c25883b3fea29dd361f9b55116869/dill-0.4.0-py3-none-any.whl", hash = "sha256:44f54bf6412c2c8464c14e8243eb163690a9800dbe2c367330883b19c7561049", size = 119668, upload-time = "2025-04-16T00:41:47.671Z" },
 ]
 
+[[package]]
+name = "distro"
+version = "1.9.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/fc/f8/98eea607f65de6527f8a2e8885fc8015d3e6f5775df186e443e0964a11c3/distro-1.9.0.tar.gz", hash = "sha256:2fa77c6fd8940f116ee1d6b94a2f90b13b5ea8d019b98bc8bafdcabcdd9bdbed", size = 60722, upload-time = "2023-12-24T09:54:32.31Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/12/b3/231ffd4ab1fc9d679809f356cebee130ac7daa00d6d6f3206dd4fd137e9e/distro-1.9.0-py3-none-any.whl", hash = "sha256:7bffd925d65168f85027d8da9af6bddab658135b840670a223589bc0c8ef02b2", size = 20277, upload-time = "2023-12-24T09:54:30.421Z" },
+]
+
 [[package]]
 name = "docling"
 version = "2.41.0"
@@ -397,6 +422,7 @@ name = "gendb"
 version = "0.1.0"
 source = { virtual = "." }
 dependencies = [
+    { name = "agentd" },
     { name = "aiofiles" },
     { name = "docling" },
     { name = "opensearch-py", extra = ["async"] },
@@ -408,6 +434,7 @@ dependencies = [
 
 [package.metadata]
 requires-dist = [
+    { name = "agentd", specifier = ">=0.1.7" },
     { name = "aiofiles", specifier = ">=24.1.0" },
     { name = "docling", specifier = ">=2.41.0" },
     { name = "opensearch-py", extras = ["async"], specifier = ">=3.0.0" },
@@ -417,6 +444,18 @@ requires-dist = [
     { name = "uvicorn", specifier = ">=0.35.0" },
 ]
 
+[[package]]
+name = "griffe"
+version = "1.7.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "colorama" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/a9/3e/5aa9a61f7c3c47b0b52a1d930302992229d191bf4bc76447b324b731510a/griffe-1.7.3.tar.gz", hash = "sha256:52ee893c6a3a968b639ace8015bec9d36594961e156e23315c8e8e51401fa50b", size = 395137, upload-time = "2025-04-23T11:29:09.147Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/58/c6/5c20af38c2a57c15d87f7f38bee77d63c1d2a3689f74fefaf35915dd12b2/griffe-1.7.3-py3-none-any.whl", hash = "sha256:c6b3ee30c2f0f17f30bcdef5068d6ab7a2a4f1b8bf1a3e74b56fffd21e1c5f75", size = 129303, upload-time = "2025-04-23T11:29:07.145Z" },
+]
+
 [[package]]
 name = "h11"
 version = "0.16.0"
@@ -441,6 +480,43 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/f0/55/ef77a85ee443ae05a9e9cba1c9f0dd9241eb42da2aeba1dc50f51154c81a/hf_xet-1.1.5-cp37-abi3-win_amd64.whl", hash = "sha256:73e167d9807d166596b4b2f0b585c6d5bd84a26dea32843665a8b58f6edba245", size = 2738931, upload-time = "2025-06-20T21:48:39.482Z" },
 ]
 
+[[package]]
+name = "httpcore"
+version = "1.0.9"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "certifi" },
+    { name = "h11" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/06/94/82699a10bca87a5556c9c59b5963f2d039dbd239f25bc2a63907a05a14cb/httpcore-1.0.9.tar.gz", hash = "sha256:6e34463af53fd2ab5d807f399a9b45ea31c3dfa2276f15a2c3f00afff6e176e8", size = 85484, upload-time = "2025-04-24T22:06:22.219Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/7e/f5/f66802a942d491edb555dd61e3a9961140fd64c90bce1eafd741609d334d/httpcore-1.0.9-py3-none-any.whl", hash = "sha256:2d400746a40668fc9dec9810239072b40b4484b640a8c38fd654a024c7a1bf55", size = 78784, upload-time = "2025-04-24T22:06:20.566Z" },
+]
+
+[[package]]
+name = "httpx"
+version = "0.28.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "anyio" },
+    { name = "certifi" },
+    { name = "httpcore" },
+    { name = "idna" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/b1/df/48c586a5fe32a0f01324ee087459e112ebb7224f646c0b5023f5e79e9956/httpx-0.28.1.tar.gz", hash = "sha256:75e98c5f16b0f35b567856f597f06ff2270a374470a5c2392242528e3e3e42fc", size = 141406, upload-time = "2024-12-06T15:37:23.222Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/2a/39/e50c7c3a983047577ee07d2a9e53faf5a69493943ec3f6a384bdc792deb2/httpx-0.28.1-py3-none-any.whl", hash = "sha256:d909fcccc110f8c7faf814ca82a9a4d816bc5a6dbfea25d6591d6985b8ba59ad", size = 73517, upload-time = "2024-12-06T15:37:21.509Z" },
+]
+
+[[package]]
+name = "httpx-sse"
+version = "0.4.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/6e/fa/66bd985dd0b7c109a3bcb89272ee0bfb7e2b4d06309ad7b38ff866734b2a/httpx_sse-0.4.1.tar.gz", hash = "sha256:8f44d34414bc7b21bf3602713005c5df4917884f76072479b21f68befa4ea26e", size = 12998, upload-time = "2025-06-24T13:21:05.71Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/25/0a/6269e3473b09aed2dab8aa1a600c70f31f00ae1349bee30658f7e358a159/httpx_sse-0.4.1-py3-none-any.whl", hash = "sha256:cba42174344c3a5b06f255ce65b350880f962d99ead85e776f23c6618a377a37", size = 8054, upload-time = "2025-06-24T13:21:04.772Z" },
+]
+
 [[package]]
 name = "huggingface-hub"
 version = "0.33.2"
@@ -482,6 +558,18 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/cb/bd/b394387b598ed84d8d0fa90611a90bee0adc2021820ad5729f7ced74a8e2/imageio-2.37.0-py3-none-any.whl", hash = "sha256:11efa15b87bc7871b61590326b2d635439acc321cf7f8ce996f812543ce10eed", size = 315796, upload-time = "2025-01-20T02:42:34.931Z" },
 ]
 
+[[package]]
+name = "importlib-metadata"
+version = "8.7.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "zipp" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/76/66/650a33bd90f786193e4de4b3ad86ea60b53c89b669a5c7be931fac31cdb0/importlib_metadata-8.7.0.tar.gz", hash = "sha256:d13b81ad223b890aa16c5471f2ac3056cf76c5f10f82d6f9292f0b415f389000", size = 56641, upload-time = "2025-04-27T15:29:01.736Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/20/b0/36bd937216ec521246249be3bf9855081de4c5e06a0c9b4219dbeda50373/importlib_metadata-8.7.0-py3-none-any.whl", hash = "sha256:e5dd1551894c77868a30651cef00984d50e1002d06942a7101d34870c5f02afd", size = 27656, upload-time = "2025-04-27T15:29:00.214Z" },
+]
+
 [[package]]
 name = "jinja2"
 version = "3.1.6"
@@ -494,6 +582,42 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/62/a1/3d680cbfd5f4b8f15abc1d571870c5fc3e594bb582bc3b64ea099db13e56/jinja2-3.1.6-py3-none-any.whl", hash = "sha256:85ece4451f492d0c13c5dd7c13a64681a86afae63a5f347908daf103ce6d2f67", size = 134899, upload-time = "2025-03-05T20:05:00.369Z" },
 ]
 
+[[package]]
+name = "jiter"
+version = "0.10.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/ee/9d/ae7ddb4b8ab3fb1b51faf4deb36cb48a4fbbd7cb36bad6a5fca4741306f7/jiter-0.10.0.tar.gz", hash = "sha256:07a7142c38aacc85194391108dc91b5b57093c978a9932bd86a36862759d9500", size = 162759, upload-time = "2025-05-18T19:04:59.73Z" }
+wheels = [
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