Bump version: 1.1.13 → 1.1.14

Author: Knucklessg1

.bumpversion.cfg

@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 1.1.13
+current_version = 1.1.14
 commit = True
 tag = True
 

Dockerfile

@@ -54,6 +54,6 @@ ENV HOST=${HOST} \
 RUN apt-get update \
     && apt-get install -y curl nano libpq-dev \
     && curl -LsSf https://astral.sh/uv/install.sh | sh \
-    && uv pip install --system --upgrade --verbose --no-cache --break-system-packages --prerelease=allow vector-mcp[postgres,chromadb,couchbase,qdrant,mongodb,a2a]>=1.1.13
+    && uv pip install --system --upgrade --verbose --no-cache --break-system-packages --prerelease=allow vector-mcp[postgres,chromadb,couchbase,qdrant,mongodb,a2a]>=1.1.14
 
 CMD ["vector-mcp"]

README.md

@@ -21,7 +21,7 @@
 ![PyPI - Wheel](https://img.shields.io/pypi/wheel/vector-mcp)
 ![PyPI - Implementation](https://img.shields.io/pypi/implementation/vector-mcp)
 
-*Version: 1.1.13*
+*Version: 1.1.14*
 
 ## Overview
 

compose.yml

@@ -31,6 +31,7 @@ services:
       - "LLM_BASE_URL=${LLM_BASE_URL:-http://host.docker.internal:1234/v1}"
       - "LLM_API_KEY=${LLM_API_KEY:-llama}"
       - "MODEL_ID=${EMBEDDING_MODEL_ID:-text-embedding-nomic-embed-text-v2-moe}"
+      - "CHUNK_SIZE=${CHUNK_SIZE:-512}"
     ports:
       - "8023:8023"
     healthcheck:
@@ -140,6 +141,7 @@ services:
       - "LLM_BASE_URL=${LLM_BASE_URL:-http://host.docker.internal:1234/v1}"
       - "LLM_API_KEY=${LLM_API_KEY:-llama}"
       - "MODEL_ID=${EMBEDDING_MODEL_ID:-text-embedding-nomic-embed-text-v2-moe}"
+      - "CHUNK_SIZE=${CHUNK_SIZE:-512}"
     ports:
       - "8024:8024"
     healthcheck:
@@ -238,6 +240,7 @@ services:
       - "LLM_BASE_URL=${LLM_BASE_URL:-http://host.docker.internal:1234/v1}"
       - "LLM_API_KEY=${LLM_API_KEY:-llama}"
       - "MODEL_ID=${EMBEDDING_MODEL_ID:-text-embedding-nomic-embed-text-v2-moe}"
+      - "CHUNK_SIZE=${CHUNK_SIZE:-512}"
     ports:
      - "8025:8025"
     healthcheck:
@@ -330,6 +333,7 @@ services:
       - "LLM_BASE_URL=${LLM_BASE_URL:-http://host.docker.internal:1234/v1}"
       - "LLM_API_KEY=${LLM_API_KEY:-llama}"
       - "MODEL_ID=${EMBEDDING_MODEL_ID:-text-embedding-nomic-embed-text-v2-moe}"
+      - "CHUNK_SIZE=${CHUNK_SIZE:-512}"
     ports:
      - "8026:8026"
     healthcheck:
@@ -416,6 +420,7 @@ services:
       - "LLM_BASE_URL=${LLM_BASE_URL:-http://host.docker.internal:1234/v1}"
       - "LLM_API_KEY=${LLM_API_KEY:-llama}"
       - "MODEL_ID=${EMBEDDING_MODEL_ID:-text-embedding-nomic-embed-text-v2-moe}"
+      - "CHUNK_SIZE=${CHUNK_SIZE:-512}"
     ports:
      - "8027:8027"
     healthcheck:

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "vector-mcp"
-version = "1.1.13"
+version = "1.1.14"
 description = "Integrate RAG into AI Agents via MCP Server. Supports multiple Vector database technologies."
 readme = "README.md"
 authors = [{ name = "Audel Rouhi", email = "knucklessg1@gmail.com" }]

scripts/debug_embedding.py

@@ -20,7 +20,7 @@
     print("\nAttempting to generate embedding...")
     resp = client.embeddings.create(
         input="test",
-        model="text-embedding-nomic-embed-text-v1.5",
+        model="text-embedding-nomic-embed-text-v2-moe",
     )
     print("SUCCESS: Generated embedding")
     print(f"Dimension: {len(resp.data[0].embedding)}")

scripts/test_embedding.py

@@ -15,7 +15,7 @@ def test_embedding():
     os.environ["EMBEDDING_PROVIDER"] = "openai"
     os.environ["LLM_BASE_URL"] = "http://localhost:1234/v1"
     os.environ["LLM_API_KEY"] = "llama"
-    os.environ["EMBEDDING_MODEL"] = "text-embedding-nomic-embed-text-v1.5"
+    os.environ["EMBEDDING_MODEL"] = "text-embedding-nomic-embed-text-v2-moe"
 
     docs_path = Path("./mcp/documents")
     if not docs_path.exists():

tests/reproduce_chunking.py

@@ -0,0 +1,20 @@
+
+import os
+import sys
+from llama_index.core import Settings
+
+# Add the project root to the python path
+sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
+
+try:
+    from vector_mcp.vector_mcp import config
+    print(f"Current Config: {config}")
+except ImportError:
+    print("Could not import vector_mcp")
+
+print(f"Default Global Chunk Size: {Settings.chunk_size}")
+
+if Settings.chunk_size > 512:
+    print("FAILURE: Chunk size is larger than 512 tokens.")
+else:
+    print("SUCCESS: Chunk size is 512 tokens or less.")

vector_mcp/utils.py

@@ -385,7 +385,7 @@ def get_embedding_model() -> BaseEmbedding:
     if provider == "openai":
         return OpenAIEmbedding(
             model_name=os.environ.get(
-                "EMBEDDING_MODEL", "text-embedding-nomic-embed-text-v1.5"
+                "EMBEDDING_MODEL", "text-embedding-nomic-embed-text-v2-moe"
             ),
             api_key=os.environ.get("LLM_API_KEY"),
             api_base=os.environ.get("LLM_BASE_URL", "http://localhost:1234/v1"),

vector_mcp/vector_agent.py

@@ -41,7 +41,7 @@ class AgentState(BaseModel):
 from pydantic_ai.ui import SSE_CONTENT_TYPE
 from pydantic_ai.ui.ag_ui import AGUIAdapter
 
-__version__ = "1.1.13"
+__version__ = "1.1.14"
 
 logging.basicConfig(
     level=logging.INFO,