Fix query to align with Qdrant mixin usage (#115)

* fix: query in text_embedding_base to work with both Iterable and str as users might supply both

* Fix Qdrant query to align with future usage

* * refactor(text_embedding_base.py): change query parameter type from str to Union[str, Iterable[str]] in query_embed method

* Update return type of query_embed method

* Update return type in TextEmbeddingBase

Author: NirantK

docs/examples/Retrieval_with_FastEmbed.ipynb

@@ -21,7 +21,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -37,7 +37,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -58,7 +58,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -105,7 +105,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -138,7 +138,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -148,7 +148,7 @@
       "Rank 1: Maharana Pratap was a Rajput warrior king from Mewar\n",
       "Rank 2: Maharana Pratap is considered a symbol of Rajput resistance against foreign rule\n",
       "Rank 3: His legacy is celebrated in Rajasthan through festivals and monuments\n",
-      "Rank 4: His capital was Chittorgarh, which he lost to the Mughals\n",
+      "Rank 4: He had 11 wives and 17 sons, including Amar Singh I who succeeded him as ruler of Mewar\n",
       "Rank 5: He fought against the Mughal Empire led by Akbar\n"
      ]
     }
@@ -166,16 +166,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Rank 1: He died in 1597 at the age of 57\n",
-      "Rank 2: His life has been depicted in various films, TV shows, and books\n",
-      "Rank 3: Maharana Pratap was a Rajput warrior king from Mewar\n",
+      "Rank 1: Maharana Pratap was a Rajput warrior king from Mewar\n",
+      "Rank 2: Maharana Pratap is considered a symbol of Rajput resistance against foreign rule\n",
+      "Rank 3: His legacy is celebrated in Rajasthan through festivals and monuments\n",
       "Rank 4: He had 11 wives and 17 sons, including Amar Singh I who succeeded him as ruler of Mewar\n",
       "Rank 5: He fought against the Mughal Empire led by Akbar\n"
      ]
@@ -213,7 +213,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.17"
+   "version": "3.11.5"
   },
   "orig_nbformat": 4
  },

docs/examples/Usage_With_Qdrant.ipynb

@@ -102,19 +102,26 @@
    "execution_count": 4,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 77.7M/77.7M [00:05<00:00, 14.6MiB/s]\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
-       "['6e8fcf7e0ecc407b9b6bb011d169f629',\n",
-       " 'c9d26e7e0ea741b2b1082d097796b28b',\n",
-       " 'cf05747e7eb34d2490b1df1f8be94049',\n",
-       " '208c197266d547a880dfb65e46738b19',\n",
-       " '27bd985c5d6f49d68fc2cf73dac74199',\n",
-       " 'c5e929c8837f4370818c97f63996f8ef',\n",
-       " 'c12213c6cdac470aa2471f2d30dc4041',\n",
-       " '974e64a7d8624f6e9824fa7b9c94f99d',\n",
-       " '0129fae193c740eba092512d8e53ab4a',\n",
-       " '492cad6e741e4aeebb196bd818a97d17']"
+       "['4fa8b10c78da4b18ba0830ba8a57367a',\n",
+       " '2eae04b515ee4e9185a9a0e6be812bba',\n",
+       " 'c6039f88486f47f1835ae3b069c5823c',\n",
+       " 'c2c8c51e305144d1917b373125fb4d95',\n",
+       " '79fd23b9ec0648cdab38d1947c6b933e',\n",
+       " '036aa200d8c3492b8a438e4f825f5e7f',\n",
+       " 'c35c77f3ea37460a9a13723fb77b7367',\n",
+       " '6ebccbca571b40d0ab6e83e5e0f2f562',\n",
+       " '38048c2ccc1d4962a4f8f1bd89c8357a',\n",
+       " 'c6b09308360140c7b4f106af3658a31e']"
       ]
      },
      "execution_count": 4,
@@ -187,12 +194,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[QueryResponse(id='42', embedding=None, metadata={'document': 'Qdrant has Langchain integrations', 'source': 'Langchain-docs'}, document='Qdrant has Langchain integrations', score=0.8496814051311954), QueryResponse(id='2', embedding=None, metadata={'document': 'Qdrant also has Llama Index integrations', 'source': 'Linkedin-docs'}, document='Qdrant also has Llama Index integrations', score=0.8478494193031256)]\n"
+      "[QueryResponse(id=42, embedding=None, metadata={'document': 'Qdrant has Langchain integrations', 'source': 'Langchain-docs'}, document='Qdrant has Langchain integrations', score=0.8276550115796268), QueryResponse(id=2, embedding=None, metadata={'document': 'Qdrant also has Llama Index integrations', 'source': 'Linkedin-docs'}, document='Qdrant also has Llama Index integrations', score=0.8265536935180283)]\n"
      ]
     }
    ],
    "source": [
-    "search_result = client.query(collection_name=\"demo_collection\", query_text=[\"This is a query document\"])\n",
+    "search_result = client.query(collection_name=\"demo_collection\", query_text=\"This is a query document\")\n",
     "print(search_result)"
    ]
   },
@@ -226,7 +233,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.17"
+   "version": "3.11.5"
   },
   "orig_nbformat": 4
  },

fastembed/text/text_embedding_base.py

@@ -1,4 +1,4 @@
-from typing import Optional, Union, Iterable, List, Dict, Any
+from typing import Any, Dict, Iterable, List, Optional, Union
 
 import numpy as np
 
@@ -39,17 +39,19 @@ def passage_embed(self, texts: Iterable[str], **kwargs) -> Iterable[np.ndarray]:
         # This is model-specific, so that different models can have specialized implementations
         yield from self.embed(texts, **kwargs)
 
-    def query_embed(self, query: str, **kwargs) -> np.ndarray:
+    def query_embed(self, query: Union[str, Iterable[str]], **kwargs) -> Iterable[np.ndarray]:
         """
-        Embeds a query
+        Embeds queries
 
         Args:
-            query (str): The query to search for.
+            query (Union[str, Iterable[str]]): The query to embed, or an iterable e.g. list of queries.
 
         Returns:
-            np.ndarray: The embeddings.
+            Iterable[np.ndarray]: The embeddings.
         """
 
         # This is model-specific, so that different models can have specialized implementations
-        query_embedding = list(self.embed([query], **kwargs))[0]
-        return query_embedding
+        if isinstance(query, str):
+            yield from self.embed([query], **kwargs)
+        if isinstance(query, Iterable):
+            yield from self.embed(query, **kwargs)