Merge pull request #611 from MervinPraison/develop

Update PraisonAI version to 2.2.30 across Dockerfiles and related files
2026-11-09 11:51:37 -10:00 · 2025-06-05 18:18:04 +01:00 · 2025-06-05 18:17:47 +01:00 · 2025-06-05 18:16:11 +01:00 · 2025-06-05 18:10:43 +01:00 · 2025-06-05 18:10:25 +01:00
16 changed files with 335 additions and 65 deletions
@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
    flask \
-    "praisonai>=2.2.29" \
+    "praisonai>=2.2.30" \
    "praisonai[api]" \
    gunicorn \
    markdown
@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
    praisonai_tools \
-    "praisonai>=2.2.29" \
+    "praisonai>=2.2.30" \
    "praisonai[chat]" \
    "embedchain[github,youtube]"

@@ -20,7 +20,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
    praisonai_tools \
-    "praisonai>=2.2.29" \
+    "praisonai>=2.2.30" \
    "praisonai[ui]" \
    "praisonai[chat]" \
    "praisonai[realtime]" \
@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
    praisonai_tools \
-    "praisonai>=2.2.29" \
+    "praisonai>=2.2.30" \
    "praisonai[ui]" \
    "praisonai[crewai]"

@@ -121,7 +121,7 @@ healthcheck:
 ## 📦 Package Versions

 All Docker images use consistent, up-to-date versions:
- PraisonAI: `>=2.2.29`
+- PraisonAI: `>=2.2.30`
 - PraisonAI Agents: `>=0.0.92`
 - Python: `3.11-slim`

@@ -218,7 +218,7 @@ docker-compose up -d
 ### Version Pinning
 To use specific versions, update the Dockerfile:
 ```dockerfile
-RUN pip install "praisonai==2.2.29" "praisonaiagents==0.0.92"
+RUN pip install "praisonai==2.2.30" "praisonaiagents==0.0.92"
 ```

 ## 🌐 Production Deployment
@@ -12,6 +12,7 @@ from .knowledge.knowledge import Knowledge
 from .knowledge.chunking import Chunking
 from .mcp.mcp import MCP
 from .session import Session
+from .memory.memory import Memory
 from .guardrails import GuardrailResult, LLMGuardrail
 from .main import (
    TaskOutput,
@@ -43,6 +44,7 @@ __all__ = [
    'ReflectionOutput',
    'AutoAgents',
    'Session',
+    'Memory',
    'display_interaction',
    'display_self_reflection',
    'display_instruction',
@@ -29,6 +29,12 @@ try:
 except ImportError:
    OPENAI_AVAILABLE = False

+try:
+    import litellm
+    LITELLM_AVAILABLE = True
+except ImportError:
+    LITELLM_AVAILABLE = False
+



@@ -340,14 +346,28 @@ class Memory:
            
        elif self.use_rag and hasattr(self, "chroma_col"):
            try:
-                from openai import OpenAI
-                client = OpenAI()
-                
-                response = client.embeddings.create(
-                    input=query,
-                    model="text-embedding-3-small"
-                )
-                query_embedding = response.data[0].embedding
+                if LITELLM_AVAILABLE:
+                    # Use LiteLLM for consistency with the rest of the codebase
+                    import litellm
+                    
+                    response = litellm.embedding(
+                        model="text-embedding-3-small",
+                        input=query
+                    )
+                    query_embedding = response.data[0]["embedding"]
+                elif OPENAI_AVAILABLE:
+                    # Fallback to OpenAI client
+                    from openai import OpenAI
+                    client = OpenAI()
+                    
+                    response = client.embeddings.create(
+                        input=query,
+                        model="text-embedding-3-small"
+                    )
+                    query_embedding = response.data[0].embedding
+                else:
+                    self._log_verbose("Neither litellm nor openai available for embeddings", logging.WARNING)
+                    return []
                
                resp = self.chroma_col.query(
                    query_embeddings=[query_embedding],
@@ -464,19 +484,39 @@ class Memory:
        # Store in vector database if enabled
        if self.use_rag and hasattr(self, "chroma_col"):
            try:
-                from openai import OpenAI
-                client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))  # Ensure API key is correctly set
-                
-                logger.info("Getting embeddings from OpenAI...")
-                logger.debug(f"Embedding input text: {text}")  # Log the input text
-                
-                response = client.embeddings.create(
-                    input=text,
-                    model="text-embedding-3-small"
-                )
-                embedding = response.data[0].embedding
-                logger.info("Successfully got embeddings")
-                logger.debug(f"Received embedding of length: {len(embedding)}")  # Log embedding details
+                if LITELLM_AVAILABLE:
+                    # Use LiteLLM for consistency with the rest of the codebase
+                    import litellm
+                    
+                    logger.info("Getting embeddings from LiteLLM...")
+                    logger.debug(f"Embedding input text: {text}")
+                    
+                    response = litellm.embedding(
+                        model="text-embedding-3-small",
+                        input=text
+                    )
+                    embedding = response.data[0]["embedding"]
+                    logger.info("Successfully got embeddings from LiteLLM")
+                    logger.debug(f"Received embedding of length: {len(embedding)}")
+                    
+                elif OPENAI_AVAILABLE:
+                    # Fallback to OpenAI client
+                    from openai import OpenAI
+                    client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
+                    
+                    logger.info("Getting embeddings from OpenAI...")
+                    logger.debug(f"Embedding input text: {text}")
+                    
+                    response = client.embeddings.create(
+                        input=text,
+                        model="text-embedding-3-small"
+                    )
+                    embedding = response.data[0].embedding
+                    logger.info("Successfully got embeddings from OpenAI")
+                    logger.debug(f"Received embedding of length: {len(embedding)}")
+                else:
+                    logger.warning("Neither litellm nor openai available for embeddings")
+                    return
                
                # Sanitize metadata for ChromaDB
                sanitized_metadata = self._sanitize_metadata(metadata)
@@ -527,15 +567,28 @@ class Memory:

        elif self.use_rag and hasattr(self, "chroma_col"):
            try:
-                from openai import OpenAI
-                client = OpenAI()
-                
-                # Get query embedding
-                response = client.embeddings.create(
-                    input=query,
-                    model="text-embedding-3-small"  # Using consistent model
-                )
-                query_embedding = response.data[0].embedding
+                if LITELLM_AVAILABLE:
+                    # Use LiteLLM for consistency with the rest of the codebase
+                    import litellm
+                    
+                    response = litellm.embedding(
+                        model="text-embedding-3-small",
+                        input=query
+                    )
+                    query_embedding = response.data[0]["embedding"]
+                elif OPENAI_AVAILABLE:
+                    # Fallback to OpenAI client
+                    from openai import OpenAI
+                    client = OpenAI()
+                    
+                    response = client.embeddings.create(
+                        input=query,
+                        model="text-embedding-3-small"
+                    )
+                    query_embedding = response.data[0].embedding
+                else:
+                    self._log_verbose("Neither litellm nor openai available for embeddings", logging.WARNING)
+                    return []
                
                # Search ChromaDB with embedding
                resp = self.chroma_col.query(
@@ -910,21 +963,44 @@ class Memory:
        """

        try:
-            # Use LiteLLM for consistency with the rest of the codebase
-            import litellm
-            
-            # Convert model name if it's in litellm format
-            model_name = llm or "gpt-4o-mini"
-            
-            response = litellm.completion(
-                model=model_name,
-                messages=[{
-                    "role": "user", 
-                    "content": custom_prompt or default_prompt
-                }],
-                response_format={"type": "json_object"},
-                temperature=0.3
-            )
+            if LITELLM_AVAILABLE:
+                # Use LiteLLM for consistency with the rest of the codebase
+                import litellm
+                
+                # Convert model name if it's in litellm format
+                model_name = llm or "gpt-4o-mini"
+                
+                response = litellm.completion(
+                    model=model_name,
+                    messages=[{
+                        "role": "user", 
+                        "content": custom_prompt or default_prompt
+                    }],
+                    response_format={"type": "json_object"},
+                    temperature=0.3
+                )
+            elif OPENAI_AVAILABLE:
+                # Fallback to OpenAI client
+                from openai import OpenAI
+                client = OpenAI()
+                
+                response = client.chat.completions.create(
+                    model=llm or "gpt-4o-mini",
+                    messages=[{
+                        "role": "user", 
+                        "content": custom_prompt or default_prompt
+                    }],
+                    response_format={"type": "json_object"},
+                    temperature=0.3
+                )
+            else:
+                logger.error("Neither litellm nor openai available for quality calculation")
+                return {
+                    "completeness": 0.0,
+                    "relevance": 0.0,
+                    "clarity": 0.0,
+                    "accuracy": 0.0
+                }
            
            metrics = json.loads(response.choices[0].message.content)
            
@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"

 [project]
 name = "praisonaiagents"
-version = "0.0.101"
+version = "0.0.102"
 description = "Praison AI agents for completing complex tasks with Self Reflection Agents"
 requires-python = ">=3.10"
 authors = [
@@ -2398,7 +2398,7 @@ wheels = [

 [[package]]
 name = "praisonaiagents"
-version = "0.0.101"
+version = "0.0.102"
 source = { editable = "." }
 dependencies = [
    { name = "mcp" },
@@ -3,8 +3,8 @@ class Praisonai < Formula
  
    desc "AI tools for various AI applications"
    homepage "https://github.com/MervinPraison/PraisonAI"
-    url "https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.2.29.tar.gz"
-    sha256 `curl -sL https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.2.29.tar.gz | shasum -a 256`.split.first
+    url "https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.2.30.tar.gz"
+    sha256 `curl -sL https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.2.30.tar.gz | shasum -a 256`.split.first
    license "MIT"
  
    depends_on "[email protected]"
@@ -56,7 +56,7 @@ class CloudDeployer:
            file.write("FROM python:3.11-slim\n")
            file.write("WORKDIR /app\n")
            file.write("COPY . .\n")
-            file.write("RUN pip install flask praisonai==2.2.29 gunicorn markdown\n")
+            file.write("RUN pip install flask praisonai==2.2.30 gunicorn markdown\n")
            file.write("EXPOSE 8080\n")
            file.write('CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]\n')
            
@@ -1,6 +1,6 @@
 [project]
 name = "PraisonAI"
-version = "2.2.29"
+version = "2.2.30"
 description = "PraisonAI is an AI Agents Framework with Self Reflection. PraisonAI application combines PraisonAI Agents, AutoGen, and CrewAI into a low-code solution for building and managing multi-agent LLM systems, focusing on simplicity, customisation, and efficient human-agent collaboration."
 readme = "README.md"
 license = ""
@@ -12,7 +12,7 @@ dependencies = [
    "rich>=13.7",
    "markdown>=3.5",
    "pyparsing>=3.0.0",
-    "praisonaiagents>=0.0.101",
+    "praisonaiagents>=0.0.102",
    "python-dotenv>=0.19.0",
    "instructor>=1.3.3",
    "PyYAML>=6.0",
@@ -95,7 +95,7 @@ autogen = ["pyautogen>=0.2.19", "praisonai-tools>=0.0.15", "crewai"]

 [tool.poetry]
 name = "PraisonAI"
-version = "2.2.29"
+version = "2.2.30"
 description = "PraisonAI is an AI Agents Framework with Self Reflection. PraisonAI application combines PraisonAI Agents, AutoGen, and CrewAI into a low-code solution for building and managing multi-agent LLM systems, focusing on simplicity, customisation, and efficient human-agent collaboration."
 authors = ["Mervin Praison"]
 license = ""
@@ -113,7 +113,7 @@ python = ">=3.10,<3.13"
 rich = ">=13.7"
 markdown = ">=3.5"
 pyparsing = ">=3.0.0"
-praisonaiagents = ">=0.0.101"
+praisonaiagents = ">=0.0.102"
 python-dotenv = ">=0.19.0"
 instructor = ">=1.3.3"
 PyYAML = ">=6.0"
@@ -0,0 +1,135 @@
+#!/usr/bin/env python3
+"""
+Comprehensive test script to verify all import scenarios work correctly
+Tests the original failing import from the GitHub issue
+"""
+
+import sys
+import os
+
+# Add the praisonai-agents source to Python path
+sys.path.insert(0, '/home/runner/work/PraisonAI/PraisonAI/src/praisonai-agents')
+
+def test_original_failing_import():
+    """Test the exact import that was failing in the GitHub issue"""
+    print("=== Testing Original Failing Import ===")
+    try:
+        from praisonaiagents.agents.agents import Agent, Task, PraisonAIAgents
+        print('✅ SUCCESS: from praisonaiagents.agents.agents import Agent, Task, PraisonAIAgents')
+        return True
+    except ImportError as e:
+        print(f'❌ ERROR: {e}')
+        import traceback
+        traceback.print_exc()
+        return False
+    except Exception as e:
+        print(f'❌ UNEXPECTED ERROR: {e}')
+        import traceback
+        traceback.print_exc()
+        return False
+
+def test_memory_direct_import():
+    """Test direct Memory import"""
+    print("\n=== Testing Direct Memory Import ===")
+    try:
+        from praisonaiagents.memory import Memory
+        print('✅ SUCCESS: from praisonaiagents.memory import Memory')
+        return True
+    except ImportError as e:
+        print(f'❌ ERROR: {e}')
+        import traceback
+        traceback.print_exc()
+        return False
+    except Exception as e:
+        print(f'❌ UNEXPECTED ERROR: {e}')
+        import traceback
+        traceback.print_exc()
+        return False
+
+def test_memory_from_package_root():
+    """Test Memory import from package root"""
+    print("\n=== Testing Memory Import from Package Root ===")
+    try:
+        from praisonaiagents import Memory
+        print('✅ SUCCESS: from praisonaiagents import Memory')
+        return True
+    except ImportError as e:
+        print(f'❌ ERROR: {e}')
+        import traceback
+        traceback.print_exc()
+        return False
+    except Exception as e:
+        print(f'❌ UNEXPECTED ERROR: {e}')
+        import traceback
+        traceback.print_exc()
+        return False
+
+def test_session_import():
+    """Test Session import which depends on Memory"""
+    print("\n=== Testing Session Import ===")
+    try:
+        from praisonaiagents.session import Session
+        print('✅ SUCCESS: from praisonaiagents.session import Session')
+        return True
+    except ImportError as e:
+        print(f'❌ ERROR: {e}')
+        import traceback
+        traceback.print_exc()
+        return False
+    except Exception as e:
+        print(f'❌ UNEXPECTED ERROR: {e}')
+        import traceback
+        traceback.print_exc()
+        return False
+
+def test_memory_instantiation():
+    """Test that Memory can be instantiated without errors"""
+    print("\n=== Testing Memory Instantiation ===")
+    try:
+        from praisonaiagents.memory import Memory
+        
+        # Test with minimal config (no external dependencies)
+        config = {"provider": "none"}
+        memory = Memory(config=config)
+        print('✅ SUCCESS: Memory instance created with provider="none"')
+        
+        # Test basic methods don't fail immediately
+        memory.store_short_term("test content", metadata={"test": True})
+        results = memory.search_short_term("test", limit=1)
+        print('✅ SUCCESS: Basic memory operations work')
+        
+        return True
+    except Exception as e:
+        print(f'❌ ERROR: {e}')
+        import traceback
+        traceback.print_exc()
+        return False
+
+def run_all_tests():
+    """Run all tests and report results"""
+    print("🔍 Running comprehensive import tests...")
+    
+    tests = [
+        test_original_failing_import,
+        test_memory_direct_import, 
+        test_memory_from_package_root,
+        test_session_import,
+        test_memory_instantiation
+    ]
+    
+    results = []
+    for test in tests:
+        results.append(test())
+    
+    print(f"\n📊 Test Results: {sum(results)}/{len(results)} tests passed")
+    
+    if all(results):
+        print("🎉 ALL TESTS PASSED! The Memory import issue has been resolved.")
+    else:
+        print("❌ Some tests failed. The issue may not be fully resolved.")
+    
+    return all(results)
+
+if __name__ == "__main__":
+    success = run_all_tests()
+    sys.exit(0 if success else 1)
@@ -0,0 +1,29 @@
+#!/usr/bin/env python3
+"""
+Test script to verify Memory import works correctly
+"""
+
+import sys
+import os
+
+# Add the praisonai-agents source to Python path
+sys.path.insert(0, '/home/runner/work/PraisonAI/PraisonAI/src/praisonai-agents')
+
+try:
+    from praisonaiagents.memory import Memory
+    print('SUCCESS: Memory import works correctly')
+    print('Memory class found:', Memory)
+    
+    # Try to create a minimal instance to ensure it doesn't fail immediately
+    config = {"provider": "none"}  # Use the simplest provider
+    memory = Memory(config=config)
+    print('SUCCESS: Memory instance created successfully')
+    
+except ImportError as e:
+    print('ERROR:', e)
+    import traceback
+    traceback.print_exc()
+except Exception as e:
+    print('UNEXPECTED ERROR:', e)
+    import traceback
+    traceback.print_exc()
@@ -0,0 +1,28 @@
+#!/usr/bin/env python3
+"""
+Test script to verify session.py import works correctly (which imports Memory)
+"""
+
+import sys
+import os
+
+# Add the praisonai-agents source to Python path
+sys.path.insert(0, '/home/runner/work/PraisonAI/PraisonAI/src/praisonai-agents')
+
+try:
+    from praisonaiagents.session import Session
+    print('SUCCESS: Session import works correctly')
+    print('Session class found:', Session)
+    
+    # This was the failing import chain
+    from praisonaiagents.agents.agents import Agent, Task, PraisonAIAgents
+    print('SUCCESS: Agent, Task, PraisonAIAgents import works correctly')
+    
+except ImportError as e:
+    print('ERROR:', e)
+    import traceback
+    traceback.print_exc()
+except Exception as e:
+    print('UNEXPECTED ERROR:', e)
+    import traceback
+    traceback.print_exc()
@@ -3931,7 +3931,7 @@ wheels = [

 [[package]]
 name = "praisonai"
-version = "2.2.29"
+version = "2.2.30"
 source = { editable = "." }
 dependencies = [
    { name = "instructor" },
@@ -4073,7 +4073,7 @@ requires-dist = [
    { name = "plotly", marker = "extra == 'realtime'", specifier = ">=5.24.0" },
    { name = "praisonai-tools", marker = "extra == 'autogen'", specifier = ">=0.0.15" },
    { name = "praisonai-tools", marker = "extra == 'crewai'", specifier = ">=0.0.15" },
-    { name = "praisonaiagents", specifier = ">=0.0.101" },
+    { name = "praisonaiagents", specifier = ">=0.0.102" },
    { name = "pyautogen", marker = "extra == 'autogen'", specifier = ">=0.2.19" },
    { name = "pydantic", marker = "extra == 'chat'", specifier = "<=2.10.1" },
    { name = "pydantic", marker = "extra == 'code'", specifier = "<=2.10.1" },
@@ -4130,7 +4130,7 @@ wheels = [

 [[package]]
 name = "praisonaiagents"
-version = "0.0.101"
+version = "0.0.102"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
    { name = "mcp" },
@@ -4138,9 +4138,9 @@ dependencies = [
    { name = "pydantic" },
    { name = "rich" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/6a/20/5b34f6a42752a0033034483ef116d419e5b0c51a5bcb92aa285312a62466/praisonaiagents-0.0.101.tar.gz", hash = "sha256:55d2a79726aac286a93815768a6cfa04c821f430370f276b18e5a2aab698aa2b", size = 145011 }
+sdist = { url = "https://files.pythonhosted.org/packages/19/e5/6f89d32b022f86561a9ffd5ceba5e8bcd29a531396c19a591ac12efdbf3b/praisonaiagents-0.0.102.tar.gz", hash = "sha256:c51ef93663a4d46c09c956b34a291fbb2bfefc07f546add587c441f8d801113a", size = 145079 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/89/57/3f17cc757f6c12125a6a6e3518d8221ba74bae10d5512362134db6dda8b5/praisonaiagents-0.0.101-py3-none-any.whl", hash = "sha256:4c6174b50e6084cf375b1eef196dfc4f48c72b82d1edf043660d48754558e14c", size = 164959 },
+    { url = "https://files.pythonhosted.org/packages/0e/e4/2ddc2b3a38e118454ee6c047e08b2f0baea3fcf45fe85cffa19221e79968/praisonaiagents-0.0.102-py3-none-any.whl", hash = "sha256:52dfe9d15a0a0c6d44194e45d91fe513e5633164f3ff6426c41068c302c5ff7a", size = 165203 },
 ]

 [[package]]
Author	SHA1	Message	Date
Mervin Praison	dc5b10fba8	Merge pull request #611 from MervinPraison/develop Update PraisonAI version to 2.2.30 across Dockerfiles and related files	2025-06-05 18:18:04 +01:00
MervinPraison	f0172e8339	Update PraisonAI version to 2.2.30 across Dockerfiles and related files - Incremented the version of PraisonAI from 2.2.29 to 2.2.30 in Dockerfiles (Dockerfile, Dockerfile.chat, Dockerfile.dev, Dockerfile.ui). - Updated the version in the README.md and pyproject.toml files to reflect the new version. - Adjusted the deploy.py script to install the updated version of PraisonAI. - Ensured consistency across all relevant files for seamless integration.	2025-06-05 18:17:47 +01:00
Mervin Praison	5c498a12de	Merge pull request #610 from MervinPraison/claude/issue-605-20250605_165941 fix: resolve Memory import error with litellm compatibility	2025-06-05 18:16:11 +01:00
Mervin Praison	88410881e1	Rename test_session_import.py to src/praisonai/tests/unit/test_session_import.py	2025-06-05 18:10:43 +01:00
Mervin Praison	791d59f96e	Rename test_memory_import.py to src/praisonai/tests/unit/test_memory_import.py	2025-06-05 18:10:25 +01:00
Mervin Praison	cdbe1303ad	Rename test_comprehensive_import.py to src/praisonai/tests/unit/test_comprehensive_import.py	2025-06-05 18:10:10 +01:00
claude[bot]andMervinPraison	b63f931bea	feat: add Memory to main package exports and comprehensive tests - Export Memory class from main praisonaiagents package - Add Memory to __all__ list for proper package API - Create comprehensive test suite to verify all import scenarios - Tests original failing import from GitHub issue - Tests direct Memory imports and instantiation Co-authored-by: MervinPraison <[email protected]>	2025-06-05 17:02:47 +00:00
claude[bot]andMervinPraison	ff25b96f91	fix: resolve Memory import error by implementing litellm compatibility - Add LITELLM_AVAILABLE flag for proper dependency detection - Implement litellm → openai fallback pattern for all embedding operations - Update search_short_term, store_long_term, search_long_term embedding calls - Update calculate_quality_metrics to use litellm → openai fallback - Add graceful degradation when neither litellm nor openai is available - Fixes ImportError: cannot import name 'Memory' from 'praisonaiagents.memory' Co-authored-by: MervinPraison <[email protected]>	2025-06-05 17:02:03 +00:00