Feat/add unstructured

api/delete.py

@@ -1,15 +1,19 @@
 from fastapi import APIRouter
 
 from models.delete import RequestPayload, ResponsePayload
+from service.embedding import get_encoder
 from service.vector_database import VectorService, get_vector_service
 
 router = APIRouter()
 
 
 @router.delete("/delete", response_model=ResponsePayload)
 async def delete(payload: RequestPayload):
+    encoder = get_encoder(encoder_type=payload.encoder)
     vector_service: VectorService = get_vector_service(
-        index_name=payload.index_name, credentials=payload.vector_database
+        index_name=payload.index_name,
+        credentials=payload.vector_database,
+        encoder=encoder,
     )
-    await vector_service.delete(file_url=payload.file_url)
-    return {"success": True}
+    data = await vector_service.delete(file_url=payload.file_url)
+    return ResponsePayload(success=True, data=data)

api/ingest.py

@@ -18,27 +18,20 @@ async def ingest(payload: RequestPayload) -> Dict:
         index_name=payload.index_name,
         vector_credentials=payload.vector_database,
     )
-    documents = await embedding_service.generate_documents()
-    chunks = await embedding_service.generate_chunks(documents=documents)
-
+    chunks = await embedding_service.generate_chunks()
     encoder = get_encoder(encoder_type=payload.encoder)
-
     summary_documents = await embedding_service.generate_summary_documents(
-        documents=documents
-    )
-    chunks, summary_chunks = await asyncio.gather(
-        embedding_service.generate_chunks(documents=documents),
-        embedding_service.generate_chunks(documents=summary_documents),
+        documents=chunks
     )
 
     await asyncio.gather(
-        embedding_service.generate_embeddings(
-            nodes=chunks, encoder=encoder, index_name=payload.index_name
+        embedding_service.generate_and_upsert_embeddings(
+            documents=chunks, encoder=encoder, index_name=payload.index_name
         ),
-        embedding_service.generate_embeddings(
-            nodes=summary_chunks,
+        embedding_service.generate_and_upsert_embeddings(
+            documents=summary_documents,
             encoder=encoder,
-            index_name=f"{payload.index_name}-{SUMMARY_SUFFIX}",
+            index_name=f"{payload.index_name}{SUMMARY_SUFFIX}",
         ),
     )
 

api/query.py

@@ -1,12 +1,18 @@
 from fastapi import APIRouter
 
-from models.query import RequestPayload, ResponsePayload
+from models.query import RequestPayload, ResponseData, ResponsePayload
 from service.router import query as _query
 
 router = APIRouter()
 
 
 @router.post("/query", response_model=ResponsePayload)
 async def query(payload: RequestPayload):
-    output = await _query(payload=payload)
-    return {"success": True, "data": output}
+    chunks = await _query(payload=payload)
+    response_data = [
+        ResponseData(
+            content=chunk.content, doc_url=chunk.doc_url, page_number=chunk.page_number
+        )
+        for chunk in chunks
+    ]
+    return {"success": True, "data": response_data}

dev/embedding.ipynb

@@ -17,7 +17,7 @@
     "\n",
     "file = File(\n",
     "    type=FileType.pdf,\n",
-    "    url=\"https://arxiv.org/pdf/2402.05131.pdf\"\n",
+    "    url=\"https://arxiv.org/pdf/2210.03629.pdf\"\n",
     ")\n",
     "vector_credentials = {\n",
     "        \"type\": \"pinecone\",\n",
@@ -40,7 +40,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "docs = await embedding_service.generate_documents()"
+    "elements = await embedding_service._download_and_extract_elements(file, strategy=\"auto\")\n"
    ]
   },
   {
@@ -49,7 +49,27 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "chunks = await embedding_service.generate_chunks(docs)"
+    "for element in elements:\n",
+    "    print(type(element))\n",
+    "    # print(f\"Text: {element.text}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "docs = await embedding_service.generate_chunks(strategy=\"auto\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "texts = [doc.content for doc in docs]"
    ]
   },
   {
@@ -62,13 +82,13 @@
     "\n",
     "concatenated_document = \"\"\n",
     "\n",
-    "for i, chunk in enumerate(chunks):\n",
+    "for i, chunk in enumerate(texts):\n",
     "    color = colors[i % len(colors)]\n",
-    "    colored_text = colored(chunk.text, color)\n",
+    "    colored_text = colored(chunk, color)\n",
     "    print(colored_text)\n",
-    "    concatenated_document += chunk.text + \" \"\n",
+    "    concatenated_document += chunk + \" \"\n",
     "\n",
-    "print(\"\\nConcatenated Document:\\n\", concatenated_document)"
+    "# print(\"\\nConcatenated Document:\\n\", concatenated_document)"
    ]
   },
   {

dev/walkthrough.ipynb

@@ -46,7 +46,40 @@
     "        }\n",
     "    },\n",
     "    \"index_name\": PINECONE_INDEX,\n",
-    "    \"encoder\": \"openai\",\n",
+    "    \"encoder\": \"cohere\",\n",
+    "}\n",
+    "\n",
+    "response = requests.post(url, json=payload)\n",
+    "\n",
+    "print(response.json())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Ingest a file\n",
+    "url = f\"{API_URL}/api/v1/ingest\"\n",
+    "\n",
+    "payload = {\n",
+    "    \"files\": [\n",
+    "        {\n",
+    "            \"type\": \"PDF\",\n",
+    "            \"url\": \"https://arxiv.org/pdf/2402.05131.pdf\"\n",
+    "        }\n",
+    "    ],\n",
+    "    \"vector_database\": {\n",
+    "        \"type\": \"weaviate\",\n",
+    "        \"config\": {\n",
+    "            \"api_key\": \"9eXH8oNR0uqN3GvvzAgaUD11ltPnGqZG2RFQ\",\n",
+    "            \"host\": \"https://superagent-ragas-1575sjfq.weaviate.network\"\n",
+    "        }\n",
+    "    },\n",
+    "    \"index_name\": \"homanp11\",\n",
+    "    \"encoder\": \"cohere\",\n",
+    "    \"webhook_url\": \"https://webhook.site/0e217d1c-49f1-424a-9992-497db09f7793\"\n",
     "}\n",
     "\n",
     "response = requests.post(url, json=payload)\n",
@@ -64,7 +97,7 @@
     "query_url = f\"{API_URL}/api/v1/query\"\n",
     "\n",
     "query_payload = {\n",
-    "    \"input\": \"What is the best chunk strategy?\",\n",
+    "    \"input\": \"What are the chunking strategies?\",\n",
     "    \"vector_database\": {\n",
     "        \"type\": \"pinecone\",\n",
     "        \"config\": {\n",
@@ -73,12 +106,59 @@
     "        }\n",
     "    },\n",
     "    \"index_name\": PINECONE_INDEX,\n",
-    "    \"encoder\": \"openai\",\n",
+    "    \"encoder\": \"cohere\",\n",
+    "}\n",
+    "\n",
+    "query_response = requests.post(query_url, json=query_payload)\n",
+    "\n",
+    "print(query_response.json())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Query the index\n",
+    "query_url = f\"{API_URL}/api/v1/query\"\n",
+    "\n",
+    "query_payload = {\n",
+    "    \"input\": \"What are the chunking strategies?\",\n",
+    "    \"vector_database\": {\n",
+    "        \"type\": \"weaviate\",\n",
+    "        \"config\": {\n",
+    "            \"api_key\": \"9eXH8oNR0uqN3GvvzAgaUD11ltPnGqZG2RFQ\",\n",
+    "            \"host\": \"https://superagent-ragas-1575sjfq.weaviate.network\"\n",
+    "        }\n",
+    "    },\n",
+    "    \"index_name\": \"homanp11\",\n",
+    "    \"encoder\": \"cohere\",\n",
     "}\n",
     "\n",
     "query_response = requests.post(query_url, json=query_payload)\n",
     "\n",
-    "print(query_response.json())\n"
+    "print(query_response.json())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = query_response.json().get('data', [])\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = query_response.json().get('data', [])\n",
+    "data"
    ]
   },
   {
@@ -91,7 +171,7 @@
     "query_url = f\"{API_URL}/api/v1/delete\"\n",
     "\n",
     "delete_payload = {\n",
-    "    \"file_url\": \"https://arxiv.org/pdf/2402.05131.pdf\",\n",
+    "    \"file_url\": \"https://arxiv.org/pdf/2210.03629.pdf\",\n",
     "    \"vector_database\": {\n",
     "        \"type\": \"pinecone\",\n",
     "        \"config\": {\n",
@@ -100,6 +180,34 @@
     "        }\n",
     "    },\n",
     "    \"index_name\": PINECONE_INDEX,\n",
+    "    \"encoder\": \"cohere\",\n",
+    "}\n",
+    "\n",
+    "delete_response = requests.delete(query_url, json=delete_payload)\n",
+    "\n",
+    "print(delete_response.json())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Delete the index\n",
+    "query_url = f\"{API_URL}/api/v1/delete\"\n",
+    "\n",
+    "delete_payload = {\n",
+    "    \"file_url\": \"https://arxiv.org/pdf/2402.05131.pdf\",\n",
+    "    \"vector_database\": {\n",
+    "        \"type\": \"weaviate\",\n",
+    "        \"config\": {\n",
+    "            \"api_key\": \"9eXH8oNR0uqN3GvvzAgaUD11ltPnGqZG2RFQ\",\n",
+    "            \"host\": \"https://superagent-ragas-1575sjfq.weaviate.network\"\n",
+    "        }\n",
+    "    },\n",
+    "    \"index_name\": \"homanp11\",\n",
+    "    \"encoder\": \"cohere\"\n",
     "}\n",
     "\n",
     "delete_response = requests.delete(query_url, json=delete_payload)\n",

encoders/__init__.py

@@ -1,7 +1,6 @@
 from encoders.base import BaseEncoder
 from encoders.bm25 import BM25Encoder
 from encoders.cohere import CohereEncoder
-from encoders.fastembed import FastEmbedEncoder
 from encoders.huggingface import HuggingFaceEncoder
 from encoders.openai import OpenAIEncoder
 
@@ -10,6 +9,5 @@
     "CohereEncoder",
     "OpenAIEncoder",
     "BM25Encoder",
-    "FastEmbedEncoder",
     "HuggingFaceEncoder",
 ]

encoders/cohere.py

@@ -9,6 +9,7 @@
 class CohereEncoder(BaseEncoder):
     client: Optional[cohere.Client] = None
     type: str = "cohere"
+    dimension: int = 1024  # https://docs.cohere.com/reference/embed
 
     def __init__(
         self,

models/delete.py

@@ -1,13 +1,20 @@
 from pydantic import BaseModel
 
+from models.ingest import EncoderEnum
 from models.vector_database import VectorDatabase
 
 
 class RequestPayload(BaseModel):
     index_name: str
     file_url: str
     vector_database: VectorDatabase
+    encoder: EncoderEnum
+
+
+class DeleteResponse(BaseModel):
+    num_of_deleted_chunks: int
 
 
 class ResponsePayload(BaseModel):
     success: bool
+    data: DeleteResponse