diff --git a/labs/config/redis_client.py b/labs/config/redis_client.py
index efa8f2e..16f9e68 100644
--- a/labs/config/redis_client.py
+++ b/labs/config/redis_client.py
@@ -10,11 +10,13 @@ class RedisVariable(Enum):
     BRANCH_NAME = "branch_name"
     CONTEXT = "context"
     EMBEDDINGS = "embeddings"
+    EMBEDDINGS_TOKENS = "embeddings_tokens"
     FILES_MODIFIED = "files_modified"
     ISSUE_BODY = "issue_body"
     ISSUE_NUMBER = "issue_number"
     ISSUE_TITLE = "issue_title"
     LLM_RESPONSE = "llm_response"
+    LLM_TOKENS = "llm_tokens"
     ORIGINAL_BRANCH_NAME = "original_branch_name"
     PRE_COMMIT_ERROR = "pre_commit_error"
     PROJECT = "project"
diff --git a/labs/core/admin.py b/labs/core/admin.py
index 6fbed17..aa228cb 100644
--- a/labs/core/admin.py
+++ b/labs/core/admin.py
@@ -1,12 +1,10 @@
 from django.contrib import admin
 from django.urls import reverse
 
-from .forms import ProjectForm, EmbeddingModelFormSet, LLMModelFormSet
+from .forms import EmbeddingModelFormSet, LLMModelFormSet, ProjectForm
 from .mixins import JSONFormatterMixin
-from .models import (
-    EmbeddingModel, LLMModel, Project, Prompt, 
-    Variable, VectorizerModel, WorkflowResult
-)
+from .models import EmbeddingModel, LLMModel, Project, Prompt, Variable, VectorizerModel, WorkflowResult
+
 
 @admin.register(EmbeddingModel)
 class EmbeddingModelAdmin(admin.ModelAdmin):
@@ -165,8 +163,10 @@ class WorkflowResultAdmin(admin.ModelAdmin, JSONFormatterMixin):
         "embed_model",
         "prompt_model",
         "pretty_embeddings",
+        "embeddings_tokens",
         "pretty_context",
         "pretty_llm_response",
+        "llm_tokens",
         "pretty_modified_files",
         "pretty_pre_commit_error",
     )
diff --git a/labs/core/migrations/0010_workflowresult_embeddings_tokens.py b/labs/core/migrations/0010_workflowresult_embeddings_tokens.py
new file mode 100644
index 0000000..4a32927
--- /dev/null
+++ b/labs/core/migrations/0010_workflowresult_embeddings_tokens.py
@@ -0,0 +1,17 @@
+# Generated by Django 5.1.5 on 2025-03-11 11:10
+
+from django.db import migrations, models
+
+
+class Migration(migrations.Migration):
+    dependencies = [
+        ("core", "0009_embeddingmodel_llmmodel_delete_model_and_more"),
+    ]
+
+    operations = [
+        migrations.AddField(
+            model_name="workflowresult",
+            name="embeddings_tokens",
+            field=models.IntegerField(default=None, null=True),
+        ),
+    ]
diff --git a/labs/core/migrations/0011_workflowresult_prompt_tokens.py b/labs/core/migrations/0011_workflowresult_prompt_tokens.py
new file mode 100644
index 0000000..dcab41f
--- /dev/null
+++ b/labs/core/migrations/0011_workflowresult_prompt_tokens.py
@@ -0,0 +1,17 @@
+# Generated by Django 5.1.5 on 2025-03-11 11:42
+
+from django.db import migrations, models
+
+
+class Migration(migrations.Migration):
+    dependencies = [
+        ("core", "0010_workflowresult_embeddings_tokens"),
+    ]
+
+    operations = [
+        migrations.AddField(
+            model_name="workflowresult",
+            name="prompt_tokens",
+            field=models.IntegerField(default=None, null=True),
+        ),
+    ]
diff --git a/labs/core/migrations/0012_remove_workflowresult_prompt_tokens_and_more.py b/labs/core/migrations/0012_remove_workflowresult_prompt_tokens_and_more.py
new file mode 100644
index 0000000..4640c91
--- /dev/null
+++ b/labs/core/migrations/0012_remove_workflowresult_prompt_tokens_and_more.py
@@ -0,0 +1,21 @@
+# Generated by Django 5.1.7 on 2025-03-12 11:24
+
+from django.db import migrations, models
+
+
+class Migration(migrations.Migration):
+    dependencies = [
+        ("core", "0011_workflowresult_prompt_tokens"),
+    ]
+
+    operations = [
+        migrations.RemoveField(
+            model_name="workflowresult",
+            name="prompt_tokens",
+        ),
+        migrations.AddField(
+            model_name="workflowresult",
+            name="llm_tokens",
+            field=models.IntegerField(default=None, null=True),
+        ),
+    ]
diff --git a/labs/core/models.py b/labs/core/models.py
index c77eb71..414fa71 100644
--- a/labs/core/models.py
+++ b/labs/core/models.py
@@ -26,10 +26,11 @@
 }
 
 vectorizer_model_class = {
-    "CHUNK_VECTORIZER": ChunkVectorizer, 
+    "CHUNK_VECTORIZER": ChunkVectorizer,
     "PYTHON_VECTORIZER": PythonVectorizer,
 }
 
+
 class ProviderEnum(Enum):
     NO_PROVIDER = "No provider"
     OPENAI = "OpenAI"
@@ -86,14 +87,14 @@ def _default_vectorizer_value_validation(self):
 
     def __str__(self) -> str:
         return self.name
-    
+
     class Meta:
         unique_together = ("provider", "name")
 
 
 class EmbeddingModel(models.Model):
     provider = models.CharField(choices=ProviderEnum.choices())
-    name = models.CharField(max_length=255,help_text="Ensure this Embedding exists and is downloaded.")
+    name = models.CharField(max_length=255, help_text="Ensure this Embedding exists and is downloaded.")
     active = models.BooleanField(default=True, help_text="Only one Embedding can be active.")
 
     @classmethod
@@ -133,10 +134,7 @@ class LLMModel(models.Model):
     name = models.CharField(max_length=255, help_text="Ensure this LLM exists and is downloaded.")
     active = models.BooleanField(default=True, help_text="Only one LLM can be active.")
     max_output_tokens = models.IntegerField(
-        null=True, 
-        blank=True, 
-        default=None,  
-        help_text="Leave blank for auto-detection, set only if required."
+        null=True, blank=True, default=None, help_text="Leave blank for auto-detection, set only if required."
     )
 
     @classmethod
@@ -208,7 +206,7 @@ def get_active_vectorizer(project_id: int) -> Vectorizer:
         vector_model = VectorizerModel.objects.filter(project__id=project_id).first()
         if not vector_model:
             raise ValueError("No vectorizer configured for this project.")
-        
+
         try:
             vec_class = vectorizer_model_class[vector_model.vectorizer_type]
         except KeyError:
@@ -230,8 +228,16 @@ class WorkflowResult(models.Model):
     embed_model = models.CharField(max_length=255, null=True)
     prompt_model = models.CharField(max_length=255, null=True)
     embeddings = models.TextField(null=True)
+    embeddings_tokens = models.IntegerField(
+        null=True,
+        default=None,
+    )
     context = models.TextField(null=True)
     llm_response = models.TextField(null=True)
+    llm_tokens = models.IntegerField(
+        null=True,
+        default=None,
+    )
     modified_files = models.TextField(null=True)
     pre_commit_error = models.TextField(null=True)
     created_at = models.DateTimeField(auto_now_add=True)
diff --git a/labs/embeddings/embedder.py b/labs/embeddings/embedder.py
index 10e22b4..c52cdce 100644
--- a/labs/embeddings/embedder.py
+++ b/labs/embeddings/embedder.py
@@ -13,7 +13,8 @@
 @dataclass
 class Embeddings:
     model: str
-    embeddings: Union[List[Dict[str, Any]], List[List[int]]]
+    tokens: Optional[int]
+    embeddings: List[List[float]]
     model_config: Optional[Dict[str, Any]] = None
 
 
@@ -26,20 +27,16 @@ def embed(self, prompt, *args, **kwargs) -> Embeddings:
 
     def retrieve_files_path(
         self,
-        query: str,
+        embedded_prompt: Embeddings,
         project_id: int,
         similarity_threshold: float = settings.EMBEDDINGS_SIMILARITY_THRESHOLD,
         max_results: int = settings.EMBEDDINGS_MAX_RESULTS,
     ) -> List[str]:
-        query = query.replace("\n", "")
-        embedded_query = self.embed(prompt=query).embeddings
-        if not embedded_query:
-            raise ValueError(f"No embeddings found with the given {query=} with {similarity_threshold=}")
-
         files_path = (
             Embedding.objects.filter(project__id=project_id)
-            .values("file_path")  # the combination of values and annotate, is the Django way of making a group by
-            .annotate(distance=Min(CosineDistance("embedding", embedded_query[0])))
+            # the combination of values and annotate, is the Django way of making a group by
+            .values("file_path")
+            .annotate(distance=Min(CosineDistance("embedding", embedded_prompt.embeddings[0])))
             .order_by("distance")
             .values_list("file_path", flat=True)
         )[:max_results]
diff --git a/labs/embeddings/gemini.py b/labs/embeddings/gemini.py
index 953e925..4a68203 100644
--- a/labs/embeddings/gemini.py
+++ b/labs/embeddings/gemini.py
@@ -1,32 +1,37 @@
 import os
-import google.generativeai as genai
+
 from embeddings.embedder import Embeddings
+from google import genai
+
 
 class GeminiEmbedder:
     def __init__(self, model):
         self._model_name = model.name
         api_key = os.environ.get("GEMINI_API_KEY")
-        genai.configure(api_key=api_key)
+        self._client = genai.Client(api_key=api_key)
 
     def embed(self, prompt: str, *args, **kwargs) -> Embeddings:
         try:
-            result = genai.embed_content(
+            result = self._client.models.embed_content(
                 model=self._model_name,
-                content=prompt,
-                *args, 
+                contents=[prompt],
+                *args,
                 **kwargs,
             )
 
-            emb = result.get("embedding")
-            if isinstance(emb, list) and all(isinstance(e, list) for e in emb):
-                flat_vectors = emb
-            else:
-                flat_vectors = [emb]
-    
+            assert result.embeddings
+
+            embeddings = []
+
+            for content_embedding in result.embeddings:
+                if content_embedding.values:
+                    embeddings.append(content_embedding.values)
+
             return Embeddings(
                 model=self._model_name,
-                model_config=result.get("model_config", {}),
-                embeddings=flat_vectors
+                model_config=result.model_config,
+                embeddings=embeddings,
+                tokens=None,
             )
 
         except Exception as e:
diff --git a/labs/embeddings/ollama.py b/labs/embeddings/ollama.py
index 1c693f6..ab57553 100644
--- a/labs/embeddings/ollama.py
+++ b/labs/embeddings/ollama.py
@@ -10,4 +10,4 @@ def __init__(self, model):
 
     def embed(self, prompt, *args, **kwargs) -> Embeddings:
         result = self._client.embed(self._model_name, prompt, *args, **kwargs)
-        return Embeddings(model=result["model"], embeddings=result["embeddings"])
+        return Embeddings(model=result["model"], embeddings=result["embeddings"], tokens=None)
diff --git a/labs/embeddings/openai.py b/labs/embeddings/openai.py
index 981eac4..b7fcb2c 100644
--- a/labs/embeddings/openai.py
+++ b/labs/embeddings/openai.py
@@ -1,4 +1,5 @@
 import os
+
 import openai
 from embeddings.embedder import Embeddings
 from litellm import embedding
@@ -11,8 +12,13 @@ def __init__(self, model):
 
     def embed(self, prompt, *args, **kwargs) -> Embeddings:
         result = embedding(model=self._model_name, input=prompt, *args, **kwargs)
+
+        assert result.model is not None
+        assert result.usage is not None
+
         return Embeddings(
             model=result.model,
             model_config=result.model_config,
             embeddings=[data["embedding"] for data in result.data],
+            tokens=result.usage.total_tokens,
         )
diff --git a/labs/llm/anthropic.py b/labs/llm/anthropic.py
index 57e2821..1ba2a09 100644
--- a/labs/llm/anthropic.py
+++ b/labs/llm/anthropic.py
@@ -20,7 +20,7 @@ def completion_without_proxy(
         messages: List[Dict[str, str]],
         *args,
         **kwargs,
-    ) -> Tuple[str, Dict[str, Any]]:
+    ) -> Tuple[str, Dict[str, Any], int]:
         system_prompt = "\n".join([message["content"] for message in messages if message["role"] == "system"])
         user_messages = [message for message in messages if message["role"] == "user"]
 
@@ -32,9 +32,11 @@ def completion_without_proxy(
                 max_tokens=self._model_max_output_tokens,
             )
 
+            tokens = response.usage.input_tokens + response.usage.output_tokens
+
             response_steps = self.response_to_steps(response)
 
-            return self._model_name, {"choices": [{"message": {"content": response_steps}}]}
+            return self._model_name, {"choices": [{"message": {"content": response_steps}}]}, tokens
         except Exception as e:
             raise RuntimeError(f"Anthropic API call failed: {e}") from e
 
diff --git a/labs/llm/gemini.py b/labs/llm/gemini.py
index 2bc1899..836d0d4 100644
--- a/labs/llm/gemini.py
+++ b/labs/llm/gemini.py
@@ -1,33 +1,38 @@
-import os
 import json
-from typing import List, Dict, Tuple, Any
+import logging
+import os
+from typing import Any, Dict, List, Tuple, cast
+
+from google import genai
+from google.genai import types
 
-import google.generativeai as genai
+logger = logging.getLogger(__name__)
 
 
 class GeminiRequester:
     def __init__(self, model):
         self._model_name = model.name
         api_key = os.environ.get("GEMINI_API_KEY")
-        genai.configure(api_key=api_key)
-        self.generative_model = genai.GenerativeModel(self._model_name)
-        self.generation_config = genai.GenerationConfig(response_mime_type="application/json")
+        self._client = genai.Client(api_key=api_key)
 
     def completion_without_proxy(
         self,
         messages: List[Dict[str, str]],
         *args,
         **kwargs,
-    ) -> Tuple[str, Dict[str, Any]]:
+    ) -> Tuple[str, Dict[str, Any], int]:
         try:
-            gemini_response = self.generative_model.generate_content(
+            gemini_response = self._client.models.generate_content(
+                model=self._model_name,
                 contents=json.dumps(messages),
-                generation_config=self.generation_config,
+                config=types.GenerateContentConfig(response_mime_type="application/json"),
                 *args,
                 **kwargs,
             )
-            return self._model_name, {
-                "choices": [{"message": {"content": gemini_response.text}}]
-            }
+
+            assert gemini_response.usage_metadata
+            tokens = cast(int, gemini_response.usage_metadata.total_token_count)
+
+            return self._model_name, {"choices": [{"message": {"content": gemini_response.text}}]}, tokens
         except Exception as e:
             raise RuntimeError(f"Gemini API call failed: {e}") from e
diff --git a/labs/llm/openai.py b/labs/llm/openai.py
index 420f101..2638928 100644
--- a/labs/llm/openai.py
+++ b/labs/llm/openai.py
@@ -1,8 +1,9 @@
 import os
-from typing import Tuple, Dict, Any
+from typing import Any, Dict, Tuple
 
 import openai
 from litellm import completion
+from litellm.types.utils import ModelResponse
 
 
 class OpenAIRequester:
@@ -10,11 +11,15 @@ def __init__(self, model):
         self._model_name = model.name
         openai.api_key = os.environ.get("OPENAI_API_KEY")
 
-    def completion_without_proxy(self, messages, *args, **kwargs) -> Tuple[str, Dict[str, Any]]:
-        return self._model_name, completion(
+    def completion_without_proxy(self, messages, *args, **kwargs) -> Tuple[str, Dict[str, Any], int]:
+        response: ModelResponse = completion(
             model=self._model_name,
             messages=messages,
             response_format={"type": "json_object"},
             *args,
             **kwargs,
         )
+
+        tokens: int = response["usage"]["total_tokens"]
+
+        return self._model_name, response, tokens
diff --git a/labs/llm/requester.py b/labs/llm/requester.py
index e8a50f1..570fa3e 100644
--- a/labs/llm/requester.py
+++ b/labs/llm/requester.py
@@ -1,9 +1,11 @@
-from typing import Dict, List
+from typing import Any, Dict, List, Tuple
 
 
 class Requester:
     def __init__(self, requester, *args, **kwargs):
         self.requester = requester(*args, **kwargs)
 
-    def completion_without_proxy(self, messages: List[Dict[str, str]], *args, **kwargs):
+    def completion_without_proxy(
+        self, messages: List[Dict[str, str]], *args, **kwargs
+    ) -> Tuple[str, Dict[str, Any], int]:
         return self.requester.completion_without_proxy(messages, *args, **kwargs)
diff --git a/labs/tasks/llm.py b/labs/tasks/llm.py
index ff1efab..b8d1836 100644
--- a/labs/tasks/llm.py
+++ b/labs/tasks/llm.py
@@ -4,7 +4,7 @@
 
 from config.celery import app
 from config.redis_client import RedisVariable, redis_client
-from core.models import LLMModel, EmbeddingModel, Project, VectorizerModel
+from core.models import EmbeddingModel, LLMModel, Project, VectorizerModel
 from django.conf import settings
 from embeddings.embedder import Embedder
 from embeddings.vectorizers.vectorizer import Vectorizer
@@ -24,9 +24,13 @@ def get_llm_response(prompt):
     is_invalid, reason = True, None
 
     llm_response = None
+    tokens = 0
     while is_invalid and retries < max_retries:
         try:
             llm_response = requester.completion_without_proxy(prompt)
+
+            tokens += llm_response[2]
+
             logger.debug(f"LLM response: {llm_response}")
 
             is_invalid, reason = run_response_checks(llm_response)
@@ -38,9 +42,9 @@ def get_llm_response(prompt):
         if is_invalid:
             retries += 1
             llm_response = None
-            logger.info(f"Redoing LLM response request doe to error (retries: {retries} of {max_retries}): {reason}")
+            logger.info(f"Redoing LLM response request due to error (retries: {retries} of {max_retries}): {reason}")
 
-    return True, llm_response
+    return True, llm_response, tokens
 
 
 @app.task
@@ -69,10 +73,20 @@ def find_embeddings_task(
     max_results=settings.EMBEDDINGS_MAX_RESULTS,
 ):
     project_id = redis_client.get(RedisVariable.PROJECT, prefix=prefix, default=project_id)
+    prompt = redis_client.get(RedisVariable.ISSUE_BODY, prefix=prefix, default=issue_body)
 
     embedder_class, *embeder_args = EmbeddingModel.get_active_model()
-    files_path = Embedder(embedder_class, *embeder_args).retrieve_files_path(
-        redis_client.get(RedisVariable.ISSUE_BODY, prefix=prefix, default=issue_body),
+
+    embedder = Embedder(embedder_class, *embeder_args)
+
+    prompt = str(prompt).replace("\n", "")
+    embedded_prompt = embedder.embed(prompt=prompt)
+
+    if not embedded_prompt:
+        raise ValueError(f"No embeddings found with the given {prompt=} with {similarity_threshold=}")
+
+    files_path = embedder.retrieve_files_path(
+        embedded_prompt,
         project_id,
         similarity_threshold,
         max_results,
@@ -80,6 +94,10 @@ def find_embeddings_task(
 
     if prefix:
         redis_client.set(RedisVariable.EMBEDDINGS, prefix=prefix, value=json.dumps(files_path))
+
+        if embedded_prompt.tokens:
+            redis_client.set(RedisVariable.EMBEDDINGS_TOKENS, prefix=prefix, value=json.dumps(embedded_prompt.tokens))
+
         return prefix
     return files_path
 
@@ -111,13 +129,21 @@ def get_llm_response_task(prefix="", context=None):
         context = {}
 
     context = json.loads(redis_client.get(RedisVariable.CONTEXT, prefix=prefix, default=context))
+
     llm_response = get_llm_response(context)
 
     if prefix:
+        _, response, tokens = llm_response
         redis_client.set(
             RedisVariable.LLM_RESPONSE,
             prefix=prefix,
-            value=llm_response[1][1]["choices"][0]["message"]["content"],
+            value=response[1]["choices"][0]["message"]["content"],
         )
+        if tokens:
+            redis_client.set(
+                RedisVariable.LLM_TOKENS,
+                prefix=prefix,
+                value=tokens,
+            )
         return prefix
     return llm_response
diff --git a/labs/tasks/logging.py b/labs/tasks/logging.py
index ec0f5c7..ca960d9 100644
--- a/labs/tasks/logging.py
+++ b/labs/tasks/logging.py
@@ -9,8 +9,10 @@ def save_workflow_result_task(prefix):
     _, llm_model_name = LLMModel.get_active_model()
     project_id = redis_client.get(RedisVariable.PROJECT, prefix)
     embeddings = redis_client.get(RedisVariable.EMBEDDINGS, prefix)
+    embeddings_tokens = redis_client.get(RedisVariable.EMBEDDINGS_TOKENS, prefix)
     context = redis_client.get(RedisVariable.CONTEXT, prefix)
     llm_response = redis_client.get(RedisVariable.LLM_RESPONSE, prefix)
+    llm_tokens = redis_client.get(RedisVariable.LLM_TOKENS, prefix)
     modified_files = redis_client.get(RedisVariable.FILES_MODIFIED, prefix)
     pre_commit_error = redis_client.get(RedisVariable.PRE_COMMIT_ERROR, prefix)
 
@@ -20,8 +22,10 @@ def save_workflow_result_task(prefix):
         embed_model=embedding_model_name,
         prompt_model=llm_model_name,
         embeddings=embeddings,
+        embeddings_tokens=embeddings_tokens,
         context=context,
         llm_response=llm_response,
+        llm_tokens=llm_tokens,
         modified_files=modified_files,
         pre_commit_error=pre_commit_error,
     )
diff --git a/labs/tests/database/test_embeddings.py b/labs/tests/database/test_embeddings.py
index cc71eb9..5773c79 100644
--- a/labs/tests/database/test_embeddings.py
+++ b/labs/tests/database/test_embeddings.py
@@ -2,11 +2,11 @@
 from typing import Union
 
 import pytest
+from core.factories import EmbeddingModelFactory
 from core.models import Project
 from embeddings.embedder import Embedder, Embeddings
 from embeddings.models import Embedding
 from embeddings.openai import OpenAIEmbedder
-from core.factories import EmbeddingModelFactory
 from tests.constants import MULTIPLE_EMBEDDINGS, OPENAI_EMBEDDING_MODEL_NAME, SINGLE_EMBEDDING
 
 
@@ -55,12 +55,13 @@ def test_reembed_code(create_test_project):
             random.sample(range(1, 5000), k=1536),
             random.sample(range(1, 5000), k=1536),
         ],
+        tokens=0,
     )
 
     embedding_model = EmbeddingModelFactory(
         provider="OPENAI",
         name=OPENAI_EMBEDDING_MODEL_NAME,
-        active=True,  
+        active=True,
     )
 
     Embedder(OpenAIEmbedder, model=embedding_model).reembed_code(
diff --git a/poetry.lock b/poetry.lock
index 35518c8..7472779 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -2,98 +2,103 @@
 
 [[package]]
 name = "aiohappyeyeballs"
-version = "2.4.4"
+version = "2.5.0"
 description = "Happy Eyeballs for asyncio"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
-    {file = "aiohappyeyeballs-2.4.4-py3-none-any.whl", hash = "sha256:a980909d50efcd44795c4afeca523296716d50cd756ddca6af8c65b996e27de8"},
-    {file = "aiohappyeyeballs-2.4.4.tar.gz", hash = "sha256:5fdd7d87889c63183afc18ce9271f9b0a7d32c2303e394468dd45d514a757745"},
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 name = "decorator"
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 description = "Decorators for Humans"
 optional = false
-python-versions = ">=3.5"
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 files = [
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 [[package]]
@@ -875,13 +881,13 @@ files = [
 
 [[package]]
 name = "django"
-version = "5.1.5"
+version = "5.1.7"
 description = "A high-level Python web framework that encourages rapid development and clean, pragmatic design."
 optional = false
 python-versions = ">=3.10"
 files = [
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 ]
 
 [package.dependencies]
@@ -947,18 +953,17 @@ doc = ["Sphinx", "sphinx-rtd-theme", "sphinxcontrib-spelling"]
 
 [[package]]
 name = "faker"
-version = "35.2.0"
+version = "37.0.0"
 description = "Faker is a Python package that generates fake data for you."
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
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 ]
 
 [package.dependencies]
-python-dateutil = ">=2.4"
-typing-extensions = "*"
+tzdata = "*"
 
 [[package]]
 name = "fastjsonschema"
@@ -1104,13 +1109,13 @@ files = [
 
 [[package]]
 name = "fsspec"
-version = "2025.2.0"
+version = "2025.3.0"
 description = "File-system specification"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.extras]
@@ -1173,73 +1178,6 @@ gitdb = ">=4.0.1,<5"
 doc = ["sphinx (>=7.1.2,<7.2)", "sphinx-autodoc-typehints", "sphinx_rtd_theme"]
 test = ["coverage[toml]", "ddt (>=1.1.1,!=1.4.3)", "mock", "mypy", "pre-commit", "pytest (>=7.3.1)", "pytest-cov", "pytest-instafail", "pytest-mock", "pytest-sugar", "typing-extensions"]
 
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-
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-grpcio-gcp = ["grpcio-gcp (>=0.2.2,<1.0.dev0)"]
-
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-
 [[package]]
 name = "google-auth"
 version = "2.38.0"
@@ -1265,59 +1203,24 @@ reauth = ["pyu2f (>=0.1.5)"]
 requests = ["requests (>=2.20.0,<3.0.0.dev0)"]
 
 [[package]]
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-description = "Google Authentication Library: httplib2 transport"
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-
-[[package]]
-name = "google-generativeai"
-version = "0.8.4"
-description = "Google Generative AI High level API client library and tools."
+name = "google-genai"
+version = "1.5.0"
+description = "GenAI Python SDK"
 optional = false
 python-versions = ">=3.9"
 files = [
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 ]
 
 [package.dependencies]
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-
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-
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 [[package]]
 name = "greenlet"
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 docs = ["Sphinx", "furo"]
 test = ["objgraph", "psutil"]
 
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-
-[package.extras]
-protobuf = ["grpcio-tools (>=1.70.0)"]
-
-[[package]]
-name = "grpcio-status"
-version = "1.70.0"
-description = "Status proto mapping for gRPC"
-optional = false
-python-versions = ">=3.8"
-files = [
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-[package.dependencies]
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-grpcio = ">=1.70.0"
-protobuf = ">=5.26.1,<6.0dev"
-
 [[package]]
 name = "h11"
 version = "0.14.0"
@@ -1520,29 +1340,15 @@ http2 = ["h2 (>=3,<5)"]
 socks = ["socksio (==1.*)"]
 trio = ["trio (>=0.22.0,<1.0)"]
 
-[[package]]
-name = "httplib2"
-version = "0.22.0"
-description = "A comprehensive HTTP client library."
-optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
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-
-[package.dependencies]
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-
 [[package]]
 name = "httpx"
-version = "0.27.2"
+version = "0.28.1"
 description = "The next generation HTTP client."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -1550,7 +1356,6 @@ anyio = "*"
 certifi = "*"
 httpcore = "==1.*"
 idna = "*"
-sniffio = "*"
 
 [package.extras]
 brotli = ["brotli", "brotlicffi"]
@@ -1572,13 +1377,13 @@ files = [
 
 [[package]]
 name = "huggingface-hub"
-version = "0.28.1"
+version = "0.29.3"
 description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
 optional = false
 python-versions = ">=3.8.0"
 files = [
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 ]
 
 [package.dependencies]
@@ -1606,13 +1411,13 @@ typing = ["types-PyYAML", "types-requests", "types-simplejson", "types-toml", "t
 
 [[package]]
 name = "identify"
-version = "2.6.6"
+version = "2.6.9"
 description = "File identification library for Python"
 optional = false
 python-versions = ">=3.9"
 files = [
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 ]
 
 [package.extras]
@@ -1701,13 +1506,13 @@ test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio
 
 [[package]]
 name = "ipython"
-version = "8.32.0"
+version = "8.34.0"
 description = "IPython: Productive Interactive Computing"
 optional = false
 python-versions = ">=3.10"
 files = [
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 ]
 
 [package.dependencies]
@@ -1792,13 +1597,13 @@ testing = ["Django", "attrs", "colorama", "docopt", "pytest (<9.0.0)"]
 
 [[package]]
 name = "jinja2"
-version = "3.1.5"
+version = "3.1.6"
 description = "A very fast and expressive template engine."
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
@@ -1809,87 +1614,87 @@ i18n = ["Babel (>=2.7)"]
 
 [[package]]
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+version = "0.9.0"
 description = "Fast iterable JSON parser."
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 python-versions = ">=3.8"
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 ]
 
 [[package]]
@@ -2269,52 +2074,60 @@ zookeeper = ["kazoo (>=2.8.0)"]
 
 [[package]]
 name = "langchain"
-version = "0.3.17"
+version = "0.3.20"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = "<4.0,>=3.9"
 files = [
-    {file = "langchain-0.3.17-py3-none-any.whl", hash = "sha256:4d6d3cf454cc261a5017fd1fa5014cffcc7aeaccd0ec0530fc10c5f71e6e97a0"},
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+    {file = "langchain-0.3.20.tar.gz", hash = "sha256:edcc3241703e1f6557ef5a5c35cd56f9ccc25ff12e38b4829c66d94971737a93"},
 ]
 
 [package.dependencies]
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-langchain-core = ">=0.3.33,<0.4.0"
-langchain-text-splitters = ">=0.3.3,<0.4.0"
+langchain-core = ">=0.3.41,<1.0.0"
+langchain-text-splitters = ">=0.3.6,<1.0.0"
 langsmith = ">=0.1.17,<0.4"
-numpy = [
-    {version = ">=1.22.4,<2", markers = "python_version < \"3.12\""},
-    {version = ">=1.26.2,<3", markers = "python_version >= \"3.12\""},
-]
 pydantic = ">=2.7.4,<3.0.0"
 PyYAML = ">=5.3"
 requests = ">=2,<3"
 SQLAlchemy = ">=1.4,<3"
-tenacity = ">=8.1.0,<8.4.0 || >8.4.0,<10"
+
+[package.extras]
+anthropic = ["langchain-anthropic"]
+aws = ["langchain-aws"]
+cohere = ["langchain-cohere"]
+community = ["langchain-community"]
+deepseek = ["langchain-deepseek"]
+fireworks = ["langchain-fireworks"]
+google-genai = ["langchain-google-genai"]
+google-vertexai = ["langchain-google-vertexai"]
+groq = ["langchain-groq"]
+huggingface = ["langchain-huggingface"]
+mistralai = ["langchain-mistralai"]
+ollama = ["langchain-ollama"]
+openai = ["langchain-openai"]
+together = ["langchain-together"]
+xai = ["langchain-xai"]
 
 [[package]]
 name = "langchain-community"
-version = "0.3.16"
+version = "0.3.19"
 description = "Community contributed LangChain integrations."
 optional = false
 python-versions = "<4.0,>=3.9"
 files = [
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 ]
 
 [package.dependencies]
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 dataclasses-json = ">=0.5.7,<0.7"
-httpx-sse = ">=0.4.0,<0.5.0"
-langchain = ">=0.3.16,<0.4.0"
-langchain-core = ">=0.3.32,<0.4.0"
+httpx-sse = ">=0.4.0,<1.0.0"
+langchain = ">=0.3.20,<1.0.0"
+langchain-core = ">=0.3.41,<1.0.0"
 langsmith = ">=0.1.125,<0.4"
-numpy = [
-    {version = ">=1.22.4,<2", markers = "python_version < \"3.12\""},
-    {version = ">=1.26.2,<3", markers = "python_version >= \"3.12\""},
-]
+numpy = ">=1.26.2,<3"
 pydantic-settings = ">=2.4.0,<3.0.0"
 PyYAML = ">=5.3"
 requests = ">=2,<3"
@@ -2323,13 +2136,13 @@ tenacity = ">=8.1.0,<8.4.0 || >8.4.0,<10"
 
 [[package]]
 name = "langchain-core"
-version = "0.3.33"
+version = "0.3.43"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = "<4.0,>=3.9"
 files = [
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+    {file = "langchain_core-0.3.43-py3-none-any.whl", hash = "sha256:caa6bc1f4c6ab71d3c2e400f8b62e1cd6dc5ac2c37e03f12f3e2c60befd5b273"},
+    {file = "langchain_core-0.3.43.tar.gz", hash = "sha256:bec60f4f5665b536434ff747b8f23375a812e82cfa529f519b54cc1e7a94a875"},
 ]
 
 [package.dependencies]
@@ -2346,17 +2159,17 @@ typing-extensions = ">=4.7"
 
 [[package]]
 name = "langchain-text-splitters"
-version = "0.3.5"
+version = "0.3.6"
 description = "LangChain text splitting utilities"
 optional = false
 python-versions = "<4.0,>=3.9"
 files = [
-    {file = "langchain_text_splitters-0.3.5-py3-none-any.whl", hash = "sha256:8c9b059827438c5fa8f327b4df857e307828a5ec815163c9b5c9569a3e82c8ee"},
-    {file = "langchain_text_splitters-0.3.5.tar.gz", hash = "sha256:11cb7ca3694e5bdd342bc16d3875b7f7381651d4a53cbb91d34f22412ae16443"},
+    {file = "langchain_text_splitters-0.3.6-py3-none-any.whl", hash = "sha256:e5d7b850f6c14259ea930be4a964a65fa95d9df7e1dbdd8bad8416db72292f4e"},
+    {file = "langchain_text_splitters-0.3.6.tar.gz", hash = "sha256:c537972f4b7c07451df431353a538019ad9dadff7a1073ea363946cea97e1bee"},
 ]
 
 [package.dependencies]
-langchain-core = ">=0.3.29,<0.4.0"
+langchain-core = ">=0.3.34,<1.0.0"
 
 [[package]]
 name = "langdetect"
@@ -2374,18 +2187,19 @@ six = "*"
 
 [[package]]
 name = "langsmith"
-version = "0.3.5"
+version = "0.3.13"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = false
 python-versions = "<4.0,>=3.9"
 files = [
-    {file = "langsmith-0.3.5-py3-none-any.whl", hash = "sha256:29da924d2e3662dd56f96d179ebc06662b66dd0b2317362ccebe0de1b78750e7"},
-    {file = "langsmith-0.3.5.tar.gz", hash = "sha256:d891a205f70ab0b2c26311db6c52486ffc9fc1124238b999619445f6ae900725"},
+    {file = "langsmith-0.3.13-py3-none-any.whl", hash = "sha256:73aaf52bbc293b9415fff4f6dad68df40658081eb26c9cb2c7bd1ff57cedd695"},
+    {file = "langsmith-0.3.13.tar.gz", hash = "sha256:14014058cff408772acb93344e03cb64174837292d5f1ae09b2c8c1d8df45e92"},
 ]
 
 [package.dependencies]
 httpx = ">=0.23.0,<1"
 orjson = {version = ">=3.9.14,<4.0.0", markers = "platform_python_implementation != \"PyPy\""}
+packaging = ">=23.2"
 pydantic = [
     {version = ">=1,<3", markers = "python_full_version < \"3.12.4\""},
     {version = ">=2.7.4,<3.0.0", markers = "python_full_version >= \"3.12.4\""},
@@ -2400,19 +2214,19 @@ pytest = ["pytest (>=7.0.0)", "rich (>=13.9.4,<14.0.0)"]
 
 [[package]]
 name = "litellm"
-version = "1.60.2"
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 [[package]]
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 windows-terminal = ["colorama (>=0.4.6)"]
 
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-
 [[package]]
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 python-versions = ">=3.8"
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 [package.dependencies]
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 [[package]]
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-version = "4.9.0"
+version = "4.10.0"
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 optional = false
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 [package.dependencies]
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 [[package]]
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 ]
 
 [package.dependencies]
@@ -5043,18 +4769,18 @@ test = ["argcomplete (>=3.0.3)", "mypy (>=1.7.0)", "pre-commit", "pytest (>=7.0,
 
 [[package]]
 name = "transformers"
-version = "4.48.2"
+version = "4.49.0"
 description = "State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow"
 optional = false
 python-versions = ">=3.9.0"
 files = [
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+    {file = "transformers-4.49.0-py3-none-any.whl", hash = "sha256:6b4fded1c5fee04d384b1014495b4235a2b53c87503d7d592423c06128cbbe03"},
+    {file = "transformers-4.49.0.tar.gz", hash = "sha256:7e40e640b5b8dc3f48743f5f5adbdce3660c82baafbd3afdfc04143cdbd2089e"},
 ]
 
 [package.dependencies]
 filelock = "*"
-huggingface-hub = ">=0.24.0,<1.0"
+huggingface-hub = ">=0.26.0,<1.0"
 numpy = ">=1.17"
 packaging = ">=20.0"
 pyyaml = ">=5.1"
@@ -5067,13 +4793,13 @@ tqdm = ">=4.27"
 [package.extras]
 accelerate = ["accelerate (>=0.26.0)"]
 agents = ["Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.26.0)", "datasets (!=2.5.0)", "diffusers", "opencv-python", "sentencepiece (>=0.1.91,!=0.1.92)", "torch (>=2.0)"]
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 codecarbon = ["codecarbon (>=2.8.1)"]
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 deepspeed-testing = ["GitPython (<3.1.19)", "accelerate (>=0.26.0)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "deepspeed (>=0.9.3)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "nltk (<=3.8.1)", "optuna", "parameterized", "protobuf", "psutil", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-asyncio", "pytest-rich", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.5.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "timeout-decorator"]
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@@ -5106,8 +4832,8 @@ tokenizers = ["tokenizers (>=0.21,<0.22)"]
 torch = ["accelerate (>=0.26.0)", "torch (>=2.0)"]
 torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
 torch-vision = ["Pillow (>=10.0.1,<=15.0)", "torchvision"]
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-video = ["av (==9.2.0)"]
+torchhub = ["filelock", "huggingface-hub (>=0.26.0,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.21,<0.22)", "torch (>=2.0)", "tqdm (>=4.27)"]
+video = ["av"]
 vision = ["Pillow (>=10.0.1,<=15.0)"]
 
 [[package]]
@@ -5172,17 +4898,6 @@ files = [
 [package.extras]
 dev = ["flake8", "flake8-annotations", "flake8-bandit", "flake8-bugbear", "flake8-commas", "flake8-comprehensions", "flake8-continuation", "flake8-datetimez", "flake8-docstrings", "flake8-import-order", "flake8-literal", "flake8-modern-annotations", "flake8-noqa", "flake8-pyproject", "flake8-requirements", "flake8-typechecking-import", "flake8-use-fstring", "mypy", "pep8-naming", "types-PyYAML"]
 
-[[package]]
-name = "uritemplate"
-version = "4.1.1"
-description = "Implementation of RFC 6570 URI Templates"
-optional = false
-python-versions = ">=3.6"
-files = [
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-
 [[package]]
 name = "urllib3"
 version = "1.26.20"
@@ -5270,13 +4985,13 @@ files = [
 
 [[package]]
 name = "virtualenv"
-version = "20.29.1"
+version = "20.29.3"
 description = "Virtual Python Environment builder"
 optional = false
 python-versions = ">=3.8"
 files = [
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 [package.dependencies]
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 optional = ["python-socks", "wsaccel"]
 test = ["websockets"]
 
+[[package]]
+name = "websockets"
+version = "14.2"
+description = "An implementation of the WebSocket Protocol (RFC 6455 & 7692)"
+optional = false
+python-versions = ">=3.9"
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+]
+
 [[package]]
 name = "widgetsnbextension"
 version = "4.0.13"
@@ -5666,4 +5459,4 @@ cffi = ["cffi (>=1.11)"]
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.11.7"
-content-hash = "4a01c9cbe3825315b390bf2e37f84534b134a62efc709e92f5d54f9753fc2d2e"
+content-hash = "738bd542627f3db8a2421caa1e7b9cb340af91c41a86827cb5bc3defef018e32"
diff --git a/pyproject.toml b/pyproject.toml
index 67adbb0..b10e21f 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -32,13 +32,13 @@ langchain-community = "^0.3.0"
 celery = "^5.4.0"
 celery-redbeat = "^2.2.0"
 redis = "^5.1.0"
-ollama = "^0.3.3"
+ollama = "^0.4.7"
 uvicorn = "^0.32.0"
 django = "^5.1.2"
 django-ninja = "^1.3.0"
 factory-boy = "^3.3.1"
-google-generativeai = "^0.8.4"
 anthropic = "^0.46.0"
+google-genai = "^1.5.0"
 
 [tool.poetry.group.dev.dependencies]
 ruff = "^0.6.2"