From 953a5a4a2d8eb0e8350dfd3e3f903c5608eed4fe Mon Sep 17 00:00:00 2001
From: Parshva Daftari <89991302+parshvadaftari@users.noreply.github.com>
Date: Tue, 25 Mar 2025 00:34:21 +0530
Subject: [PATCH] Azure openai fixes (#2428)

---
 docs/components/llms/models/azure_openai.mdx |  3 +++
 docs/components/vectordbs/dbs/pinecone.mdx   |  2 +-
 mem0/llms/azure_openai.py                    | 16 ++++++++++++----
 tests/vector_stores/test_pinecone.py         |  2 +-
 4 files changed, 17 insertions(+), 6 deletions(-)

diff --git a/docs/components/llms/models/azure_openai.mdx b/docs/components/llms/models/azure_openai.mdx
index bacb8298..ede726b6 100644
--- a/docs/components/llms/models/azure_openai.mdx
+++ b/docs/components/llms/models/azure_openai.mdx
@@ -4,6 +4,9 @@ title: Azure OpenAI
 
 To use Azure OpenAI models, you have to set the `LLM_AZURE_OPENAI_API_KEY`, `LLM_AZURE_ENDPOINT`, `LLM_AZURE_DEPLOYMENT` and `LLM_AZURE_API_VERSION` environment variables. You can obtain the Azure API key from the [Azure](https://azure.microsoft.com/).
 
+> **Note**: The following are currently unsupported with reasoning models `Parallel tool calling`,`temperature`, `top_p`, `presence_penalty`, `frequency_penalty`, `logprobs`, `top_logprobs`, `logit_bias`, `max_tokens`
+
+
 ## Usage
 
 ```python
diff --git a/docs/components/vectordbs/dbs/pinecone.mdx b/docs/components/vectordbs/dbs/pinecone.mdx
index 8eeb625f..84673f18 100644
--- a/docs/components/vectordbs/dbs/pinecone.mdx
+++ b/docs/components/vectordbs/dbs/pinecone.mdx
@@ -1,6 +1,6 @@
 [Pinecone](https://www.pinecone.io/) is a fully managed vector database designed for machine learning applications, offering high performance vector search with low latency at scale. It's particularly well-suited for semantic search, recommendation systems, and other AI-powered applications.
 
-> **Note**: Before configuring Pinecone, you need to select an embedding model (e.g., OpenAI, Cohere, or custom models) and ensure the `embedding_model_dims` in your config matches your chosen model's dimensions. For example, OpenAI's text-embedding-ada-002 uses 1536 dimensions.
+> **Note**: Before configuring Pinecone, you need to select an embedding model (e.g., OpenAI, Cohere, or custom models) and ensure the `embedding_model_dims` in your config matches your chosen model's dimensions. For example, OpenAI's text-embedding-3-small uses 1536 dimensions.
 
 ### Usage
 
diff --git a/mem0/llms/azure_openai.py b/mem0/llms/azure_openai.py
index 3400b382..a7f1fdaf 100644
--- a/mem0/llms/azure_openai.py
+++ b/mem0/llms/azure_openai.py
@@ -80,13 +80,21 @@ class AzureOpenAILLM(LLMBase):
         Returns:
             str: The generated response.
         """
-        params = {
+
+        common_params = {
             "model": self.config.model,
             "messages": messages,
-            "temperature": self.config.temperature,
-            "max_tokens": self.config.max_tokens,
-            "top_p": self.config.top_p,
         }
+
+        if self.config.model in {"o3-mini", "o1-preview", "o1"}:
+            params = common_params
+        else:
+            params = {
+                **common_params,
+                "temperature": self.config.temperature,
+                "max_tokens": self.config.max_tokens,
+                "top_p": self.config.top_p,
+            }
         if response_format:
             params["response_format"] = response_format
         if tools:  # TODO: Remove tools if no issues found with new memory addition logic
diff --git a/tests/vector_stores/test_pinecone.py b/tests/vector_stores/test_pinecone.py
index c7e796d5..2ff5410d 100644
--- a/tests/vector_stores/test_pinecone.py
+++ b/tests/vector_stores/test_pinecone.py
@@ -67,7 +67,7 @@ def test_insert_vectors(pinecone_db):
 
 def test_search_vectors(pinecone_db):
     pinecone_db.index.query.return_value.matches = [{"id": "id1", "score": 0.9, "metadata": {"name": "vector1"}}]
-    results = pinecone_db.search([0.1] * 128, limit=1)
+    results = pinecone_db.search("test query",[0.1] * 128, limit=1)
     assert len(results) == 1
     assert results[0].id == "id1"
     assert results[0].score == 0.9