diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 25f26c3..cb823c0 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -62,16 +62,34 @@ jobs: PIXELTABLE_HOME: ${{ runner.temp }}/pxt-release run: | uv venv /tmp/verify --python 3.12 - VIRTUAL_ENV=/tmp/verify uv pip install dist/*.whl - VIRTUAL_ENV=/tmp/verify uv pip list | grep -E "pixelmemory|pixeltable" - /tmp/verify/bin/python -c " + uv pip install --python /tmp/verify/bin/python dist/*.whl + uv pip check --python /tmp/verify/bin/python + uv pip list --python /tmp/verify/bin/python | grep -E "pixelmemory|pixeltable" + /tmp/verify/bin/python -I -c " + from pathlib import Path + import sys + import pixelmemory + assert Path(pixelmemory.__file__).resolve().is_relative_to(Path(sys.prefix).resolve()), pixelmemory.__file__ from pixelmemory import Memory from pixelmemory.context import Text m = Memory([Text(id='body', embed=False)], namespace='release_check') assert m.table.columns() == ['body'], m.table.columns() - print('built wheel installs and builds a memory') + m.add(m.Entry(body='persisted from the built wheel')) + m = Memory([Text(id='body', embed=False)], namespace='release_check') + assert m.table.collect()[0]['body'] == 'persisted from the built wheel' + print('built wheel installs, inserts and reopens a memory') " + - name: Test the installed wheel + env: + HF_HUB_DISABLE_PROGRESS_BARS: "1" + run: | + uv export --locked --only-group dev --no-emit-project --no-hashes -o /tmp/dev-requirements.txt + uv pip install --python /tmp/verify/bin/python -r /tmp/dev-requirements.txt + uv pip check --python /tmp/verify/bin/python + cd "$RUNNER_TEMP" + /tmp/verify/bin/python -I -m pytest "$GITHUB_WORKSPACE/tests" -c "$GITHUB_WORKSPACE/pyproject.toml" -v + - uses: actions/upload-artifact@v4 with: name: dist diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index b297352..430b7c3 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -10,7 +10,7 @@ on: pull_request: workflow_dispatch: schedule: - # Weekly canary. The install step below takes Pixeltable unpinned, so the + # Weekly canary. One matrix entry takes Pixeltable unpinned, so the # release that breaks pixelmemory turns this build red before a user finds it. - cron: "0 6 * * 1" @@ -24,7 +24,11 @@ jobs: strategy: fail-fast: false matrix: - python-version: ["3.11", "3.12"] + python-version: ["3.11", "3.12", "3.13"] + dependencies: [locked] + include: + - python-version: "3.12" + dependencies: latest steps: - uses: actions/checkout@v4 @@ -36,13 +40,18 @@ jobs: run: uv sync --locked --python ${{ matrix.python-version }} - name: Take Pixeltable unpinned + if: matrix.dependencies == 'latest' # Deliberately ignores the `<0.8` pin in pyproject.toml. This is the canary. # Every step after this uses --no-sync, or uv would restore the pinned version. - run: uv pip install --upgrade pixeltable + run: uv pip install --python .venv/bin/python --upgrade pixeltable - name: Record the Pixeltable version under test run: uv run --no-sync python -c "import pixeltable; print(pixeltable.__version__)" + - name: Check installed dependencies + if: matrix.dependencies == 'locked' + run: uv pip check --python .venv/bin/python + - name: Lint run: uv run --no-sync ruff check . diff --git a/README.md b/README.md index 6812d9d..d44e61a 100644 --- a/README.md +++ b/README.md @@ -47,7 +47,23 @@ Most AI applications today are stateless - they forget everything between sessio pip install pixelmemory ``` -*Note: Pixeltable is automatically installed as a dependency - no additional setup required.* +Python 3.11+ is required. Pixelmemory 0.2.0 uses Pixeltable 0.7.17+ (below +0.8) and sentence-transformers 5.4+. Both are installed automatically. The +default local embedding model downloads its weights on first use. + +Install the extras for the contexts you use: + +| Context | Additional setup | +| --- | --- | +| Text, Document | None for direct text search and token-based document chunks | +| Text with `use_chunking=True` | `pip install 'pixelmemory[sentence]'`, then `python -m spacy download en_core_web_sm` | +| Image | `pip install 'pixelmemory[openai]'` and `OPENAI_API_KEY`, or `pixelmemory[anthropic]` and `ANTHROPIC_API_KEY` | +| Audio, Video | `pip install 'pixelmemory[openai,sentence]'`, `OPENAI_API_KEY`, and `python -m spacy download en_core_web_sm` | + +Video uses OpenAI transcription even when frame captions use Anthropic. Add +the `anthropic` extra and key for that configuration. `use_clip=True` also +requires a compatible `torchvision` installation and downloads CLIP weights. +Document splitting with `separators="sentence"` needs the same sentence setup. ## Quick Start @@ -74,6 +90,10 @@ for r in results: print(r["score"], r["text"]) ``` +`search()` returns `context_id`, `text` and `score`. It searches text columns, +document chunks, transcripts and image/frame captions. Use `memory.views()` +and raw Pixeltable queries when you need source metadata or a CLIP index. + ## Architecture ```mermaid @@ -155,7 +175,7 @@ def chat_with_memory(session_id: str, message: str): All examples use the context-based API and are ready to run: -**Basic Examples** (no external dependencies): +**Basic Examples** (local embedding model; no provider key): - `examples/getting_started/01_basic_memory.py` - `examples/multimodal/text.py` @@ -168,6 +188,7 @@ All examples use the context-based API and are ready to run: - `examples/integrations/langchain_chat_history.py` (requires OpenAI API key) - `examples/integrations/crewai_agentic_rag.py` (requires `pip install crewai`) - `examples/fastapi/memory_service.py` (requires `pip install fastapi uvicorn`) +- `examples/mcp/memory.py` (requires `pip install 'mcp>=2.3'`; serves task tools over Streamable HTTP) ## Next Steps @@ -187,7 +208,7 @@ All examples use the context-based API and are ready to run: ## Migrating from 0.1.x -0.2.0 runs on Pixeltable 0.7.6. On 0.5.6+ the Document, Audio and Video +0.2.0 runs on Pixeltable 0.7.17+. On 0.5.6+ the Document, Audio and Video memories raised on construction, so most of 0.1.x did not work. Upgrade with `pip install -U pixelmemory`. @@ -216,13 +237,44 @@ results = (chunk_view.order_by(sim, asc=False).limit(3) results = memory.search(query, on="report", limit=3) ``` -`Memory(...)`, `memory.Entry`, `memory.add()`, the attribute passthrough and -every other `Context` field name are unchanged. +`Memory(...)`, `memory.Entry`, `memory.add()` and the attribute passthrough +keep the same API. Use `required=False` for a nullable context; its Entry field +can be omitted and defaults to `None`. Optional contexts can also be embedded. + +Embedding index names are scoped to each context so multiple embedded fields +work together. Existing legacy indexes are reused. For a raw Pixeltable query, +read the index name from `memory.search_targets` instead of assuming +`"similarity"`; `memory.search()` selects it for you. One behaviour change worth knowing: views and indexes now survive reconstruction. Building the same `Memory` twice used to drop and rebuild every view and index, re-running transcription and re-embedding everything. +Reopen a memory with the same context configuration. Reopening does not update +existing computed expressions, splitter settings or embedding models. Use +Pixeltable's explicit schema/index APIs for those changes, or a new namespace; +`if_exists="replace_force"` deletes the existing table and dependent views. + +## Development and verification + +```bash +uv sync --locked --python 3.12 +uv run ruff check . +uv run python -m pytest +uv build +``` + +Tests use an isolated catalog and remove provider credentials. They cover real +Pixeltable inserts, search, reconstruction, and multimodal iterators. Provider +responses are simulated in multimodal tests; text ranking also runs a real +local sentence-transformers model. Sentence tests use spaCy's local sentencizer +without downloading the English model. Live provider compatibility requires +separate credentialed testing. + +CI tests the locked dependencies on Python 3.11ā€“3.13 and an unpinned Pixeltable +canary on Python 3.12. The release workflow verifies the installed wheel from +outside the source tree before publishing. See [RELEASING.md](RELEASING.md). + ## When not to use pixelmemory If your memory schema is fixed when you write the code, you do not need a @@ -240,4 +292,4 @@ in the right dependency order. ## License -Apache 2.0 License \ No newline at end of file +Apache 2.0 License diff --git a/RELEASING.md b/RELEASING.md index 01a6828..45d6143 100644 --- a/RELEASING.md +++ b/RELEASING.md @@ -11,7 +11,16 @@ git tag vX.Y.Z && git push origin vX.Y.Z The tag push runs `.github/workflows/release.yml`, which builds the sdist and wheel, checks the tag matches `pyproject.toml`, installs the built wheel into a -clean environment and exercises it, then publishes to PyPI. +clean environment and exercises it, then publishes to PyPI. The smoke test +uses isolated Python import resolution (`-I`) and verifies that `pixelmemory` +is loaded from that environment. It inserts a row and reopens the memory, +then runs the full test suite against the installed wheel from outside the +checkout. Provider calls in the suite are simulated; publishing does not +establish live-provider compatibility. + +Before tagging, check that the test workflow passes for the intended commit +and run the release workflow with `dry_run` checked. The tag is the publish +action; building locally or opening a PR does not update PyPI. ## There is no PyPI token diff --git a/examples/fastapi/memory_service.py b/examples/fastapi/memory_service.py index 3aabe63..5a95e85 100644 --- a/examples/fastapi/memory_service.py +++ b/examples/fastapi/memory_service.py @@ -3,7 +3,7 @@ from typing import List, Dict, Any, Optional from pixelmemory import Memory from pixelmemory.context import Text, Image, Video, Audio, Document -import uvicorn +import pixeltable as pxt app = FastAPI( title="AI Memory Service", @@ -19,7 +19,8 @@ class ContextField(BaseModel): type: str # "text", "image", "video", "audio", "document" embed: Optional[bool] = True provider: Optional[str] = None # For image/video - model: Optional[str] = None # For image/video + model: Optional[str] = None # For image/video + class CreateMemoryRequest(BaseModel): namespace: str @@ -52,40 +53,45 @@ def get_memory(namespace: str, table_name: str) -> Memory: # Note: We can't recreate a Memory from scratch without the original context # In a real application, you'd store the context configuration alongside the memory raise HTTPException( - status_code=404, - detail=f"Memory '{cache_key}' not found in cache. Use create_memory first." + status_code=404, + detail=f"Memory '{cache_key}' not found in cache. Use create_memory first.", ) def context_from_fields(context_fields: List[ContextField]) -> List: """Converts context field definitions to context objects.""" context = [] - + for field in context_fields: if field.type == "text": context.append(Text(id=field.id, embed=field.embed)) elif field.type == "image": - context.append(Image( - id=field.id, - provider=field.provider or "openai", - model=field.model or "gpt-4o-mini" - )) + context.append( + Image( + id=field.id, + embed=field.embed, + provider=field.provider or "openai", + model=field.model or "gpt-4o-mini", + ) + ) elif field.type == "video": - context.append(Video( - id=field.id, - provider=field.provider or "openai", - model=field.model or "gpt-4o-mini" - )) + context.append( + Video( + id=field.id, + embed=field.embed, + provider=field.provider or "openai", + model=field.model or "gpt-4o-mini", + ) + ) elif field.type == "audio": - context.append(Audio(id=field.id)) + context.append(Audio(id=field.id, embed=field.embed)) elif field.type == "document": - context.append(Document(id=field.id)) + context.append(Document(id=field.id, embed=field.embed)) else: raise HTTPException( - status_code=400, - detail=f"Unsupported context type: {field.type}" + status_code=400, detail=f"Unsupported context type: {field.type}" ) - + return context @@ -106,16 +112,22 @@ def create_memory(req: CreateMemoryRequest): context=context, namespace=req.namespace, table_name=req.table_name, - if_exists=req.if_exists + if_exists=req.if_exists, ) - + # Cache the memory instance cache_key = f"{req.namespace}.{req.table_name}" memory_cache[cache_key] = memory - + return { "message": f"Memory '{req.namespace}.{req.table_name}' created successfully." } + except HTTPException: + raise + except (TypeError, ValueError, pxt.Error) as e: + raise HTTPException( + status_code=400, detail=f"Failed to create memory: {e}" + ) from e except Exception as e: raise HTTPException(status_code=500, detail=f"Failed to create memory: {e}") @@ -132,9 +144,11 @@ def add_items(namespace: str, table_name: str, req: AddItemsRequest): for item in req.items: entry = mem.Entry(**item) entries.append(entry) - + mem.add(*entries) return {"message": f"Successfully added {len(req.items)} items."} + except (TypeError, ValueError, pxt.Error) as e: + raise HTTPException(status_code=400, detail=f"Failed to add items: {e}") from e except Exception as e: raise HTTPException(status_code=500, detail=f"Failed to add items: {e}") @@ -146,7 +160,7 @@ def search_items( query: Optional[str] = None, search_column: Optional[str] = None, filter_expression: Optional[str] = Query(None, alias="filter"), - limit: int = 10, + limit: int = Query(10, ge=1), select: Optional[str] = None, ): """ @@ -157,30 +171,27 @@ def search_items( try: q = mem - # Apply semantic search if query and search_column are provided - if query and search_column: - try: - similarity = getattr(mem, search_column).similarity(string=query) - q = q.order_by(similarity, asc=False) - except AttributeError: - raise HTTPException( - status_code=400, - detail=f"Search column '{search_column}' not found or not searchable.", - ) - # Apply filter expression if filter_expression: # Note: Direct filter expression parsing would require more complex implementation # For now, we skip this feature in the context-based API raise HTTPException( status_code=400, - detail="Filter expressions not yet supported in context-based API" + detail="Filter expressions not yet supported in context-based API", ) + if query: + if select: + raise HTTPException( + status_code=400, + detail="Semantic search returns text, score and context_id; select is not supported.", + ) + return mem.search(query, on=search_column, limit=limit) + # Apply select columns if select: select_cols = [col.strip() for col in select.split(",")] - q = q.select(*select_cols) + q = q.select(*(getattr(mem, col) for col in select_cols)) # Apply limit results = q.limit(limit).collect() @@ -188,6 +199,19 @@ def search_items( # Convert results to a list of dicts for JSON response return [{k: v for k, v in row.items()} for row in results] + except HTTPException: + raise + except ( + AttributeError, + KeyError, + RuntimeError, + TypeError, + ValueError, + pxt.Error, + ) as e: + raise HTTPException( + status_code=400, detail=f"Failed to search items: {e}" + ) from e except Exception as e: raise HTTPException(status_code=500, detail=f"Failed to search items: {e}") @@ -195,35 +219,16 @@ def search_items( @app.get("/memories", response_model=List[str]) def list_memories(namespace: Optional[str] = None): """ - Lists all available memory tables, optionally filtered by namespace. + Lists memories initialized in this process, optionally filtered by namespace. + + These are the memories get_memory() can retrieve. Pixeltable stores its + catalog in PostgreSQL, not in per-namespace directories on disk. """ - try: - # This is a simplified way to list tables. - # In a real-world scenario, you might have a more robust way of tracking tables. - # Here, we list directories in the .pixeltable path. - from pathlib import Path - import os - - pxt_dir = Path(os.environ.get("PIXELTABLE_HOME", Path.home() / ".pixeltable")) - if not pxt_dir.exists(): - return [] - - tables = [] - if namespace: - ns_path = pxt_dir / namespace - if ns_path.exists(): - tables = [ - f"{namespace}.{d.name}" for d in ns_path.iterdir() if d.is_dir() - ] - else: - for ns_dir in pxt_dir.iterdir(): - if ns_dir.is_dir(): - for table_dir in ns_dir.iterdir(): - if table_dir.is_dir(): - tables.append(f"{ns_dir.name}.{table_dir.name}") - return tables - except Exception as e: - raise HTTPException(status_code=500, detail=f"Failed to list memories: {e}") + return sorted( + key + for key, memory in memory_cache.items() + if namespace is None or memory.namespace == namespace + ) @app.get("/memories/{namespace}/{table_name}", response_model=MemoryInfoResponse) @@ -234,21 +239,21 @@ def get_memory_info(namespace: str, table_name: str): mem = get_memory(namespace, table_name) try: metadata = mem.table.get_metadata() - + # Extract context information from the memory instance context_info = [] for ctx in mem.context: ctx_dict = { "id": ctx.id, "type": ctx.__class__.__name__.lower(), - "embed": ctx.embed + "embed": ctx.embed, } - if hasattr(ctx, 'provider'): + if hasattr(ctx, "provider"): ctx_dict["provider"] = ctx.provider - if hasattr(ctx, 'model'): + if hasattr(ctx, "model"): ctx_dict["model"] = ctx.model context_info.append(ctx_dict) - + return { "namespace": namespace, "table_name": table_name, @@ -266,7 +271,7 @@ def delete_memory(namespace: str, table_name: str): """ mem = get_memory(namespace, table_name) try: - mem.drop() + pxt.drop_table(mem.table, force=True) # Remove from cache if it exists cache_key = f"{namespace}.{table_name}" if cache_key in memory_cache: @@ -277,4 +282,6 @@ def delete_memory(namespace: str, table_name: str): if __name__ == "__main__": + import uvicorn + uvicorn.run(app, host="0.0.0.0", port=8000) diff --git a/examples/integrations/crewai_agentic_rag.py b/examples/integrations/crewai_agentic_rag.py index 53fc652..6ca48fb 100644 --- a/examples/integrations/crewai_agentic_rag.py +++ b/examples/integrations/crewai_agentic_rag.py @@ -13,23 +13,39 @@ context = [ Text(id="doc_id", embed=False), - Document(id="content", chunk_params=DocumentSplitterParams(separators="token_limit", limit=400, overlap=40)), + Document( + id="content", + chunk_params=DocumentSplitterParams( + separators="token_limit", limit=400, overlap=40 + ), + ), ] website_knowledge = Memory( context=context, namespace="crewai_rag_example", table_name="web_pages", - if_exists="replace_force" + if_exists="ignore", ) @tool def website_knowledge_tool(search_query: str, limit: int = 5) -> str: """Search for relevant information in the website knowledge.""" - results = website_knowledge.search(search_query, on="content", limit=limit) + target = next( + t for t in website_knowledge.search_targets if t.context_id == "content" + ) + chunks = target.table + score = chunks.text.similarity(string=search_query, idx=target.index_name) + results = ( + chunks.order_by(score, asc=False) + .limit(limit) + .select(chunks.text, chunks.doc_id, score=score) + .collect() + ) context = "\n\n".join( - f"Content: {r['text']}\nSimilarity: {r['score']:.4f}" for r in results + f"Source: {r['doc_id']}\nContent: {r['text']}\nSimilarity: {r['score']:.4f}" + for r in results ) return context @@ -62,8 +78,9 @@ def website_knowledge_tool(search_query: str, limit: int = 5) -> str: ) # Add knowledge to memory -entries = [website_knowledge.Entry(doc_id=url, content=url) for url in urls] -website_knowledge.add(*entries) +if website_knowledge.count() == 0: + entries = [website_knowledge.Entry(doc_id=url, content=url) for url in urls] + website_knowledge.add(*entries) # Kickoff crew question = "What is reward hacking and what are some examples? Provide sources." diff --git a/examples/integrations/langchain_chat_history.py b/examples/integrations/langchain_chat_history.py index 0b3b5bb..ac5b763 100644 --- a/examples/integrations/langchain_chat_history.py +++ b/examples/integrations/langchain_chat_history.py @@ -1,4 +1,6 @@ # pip install langchain-openai langchain pixelmemory +import json + from pixelmemory import Memory from langchain.chat_models import init_chat_model from langchain_core.messages import ( @@ -17,19 +19,11 @@ Text(id="messages", embed=False), ] -mem = Memory( - context=context, - namespace="langchain_memory", - table_name="chat_history", - if_exists="replace_force" -) - -# 2. Initialize the LangChain model -model = init_chat_model("gpt-4o-mini", model_provider="openai") - # 3. Create a simple chat function with persistent memory def chat( + mem: Memory, + model, session_id: str, user_input: str, system_prompt: str = "You are a helpful assistant.", @@ -44,7 +38,7 @@ def chat( .collect() ) messages = ( - messages_from_dict(history[0]["messages"]) + messages_from_dict(json.loads(history[0]["messages"])) if history else [SystemMessage(system_prompt)] ) @@ -55,23 +49,30 @@ def chat( messages.append(ai_response) # Save the updated history back to PixelMemory - entry = mem.Entry(session_id=session_id, messages=str(messages_to_dict(messages))) - mem.add(entry) + serialized = json.dumps(messages_to_dict(messages)) + if history: + mem.update({"messages": serialized}, where=mem.session_id == session_id) + else: + mem.add(mem.Entry(session_id=session_id, messages=serialized)) return ai_response -# 4. Demonstrate the conversation -SESSION_ID = "user_123" -print("--- Conversation Turn 1 ---") -response1 = chat(SESSION_ID, "Hi! I'm Bob and I live in Paris.") -print(f"AI: {response1.content}") +if __name__ == "__main__": + mem = Memory( + context=context, namespace="langchain_memory", table_name="chat_history" + ) + model = init_chat_model("gpt-4o-mini", model_provider="openai") + + SESSION_ID = "user_123" + print("--- Conversation Turn 1 ---") + response1 = chat(mem, model, SESSION_ID, "Hi! I'm Bob and I live in Paris.") + print(f"AI: {response1.content}") -print("\n--- Conversation Turn 2 (Memory in action) ---") -response2 = chat(SESSION_ID, "What's my name and where do I live?") -print(f"AI: {response2.content}") + print("\n--- Conversation Turn 2 (Memory in action) ---") + response2 = chat(mem, model, SESSION_ID, "What's my name and where do I live?") + print(f"AI: {response2.content}") -# Verify that a new session is independent -print("\n--- New Conversation (Session user_456) ---") -response3 = chat("user_456", "What's my name?") -print(f"AI: {response3.content}") + print("\n--- New Conversation (Session user_456) ---") + response3 = chat(mem, model, "user_456", "What's my name?") + print(f"AI: {response3.content}") diff --git a/examples/integrations/langchain_financial_rag_agent.py b/examples/integrations/langchain_financial_rag_agent.py index 5ced18a..f16cedb 100644 --- a/examples/integrations/langchain_financial_rag_agent.py +++ b/examples/integrations/langchain_financial_rag_agent.py @@ -1,7 +1,13 @@ -# uv pip install langchain-openai pixelmemory httpx "unstructured[pdf]" +# uv pip install langchain langchain-openai pixelmemory from pixelmemory import Memory from langchain.chat_models import init_chat_model -from langchain_core.messages import HumanMessage, SystemMessage, AIMessage +from langchain_core.messages import ( + HumanMessage, + SystemMessage, + messages_from_dict, + messages_to_dict, +) +import json import uuid # --- 1. Setup: Create two PixelMemory instances --- @@ -18,54 +24,25 @@ Text(id="year", embed=False), ] -kb_mem = Memory( - context=kb_context, - namespace="financial_kb", - table_name="reports", - if_exists="replace_force" -) - # Conversation History Memory chat_context = [ Text(id="session_id", embed=False), Text(id="messages", embed=False), ] -chat_mem = Memory( - context=chat_context, - namespace="financial_chat", - table_name="history", - if_exists="replace_force" -) - # --- 2. Populate the Knowledge Base --- # We'll use a sample Zacks Nvidia financial report and a fictional AMD report. REPORT_URL_NVDA = "https://raw.githubusercontent.com/pixeltable/pixeltable/main/docs/resources/rag-demo/Zacks-Nvidia-Report.pdf" -entries = [ - kb_mem.Entry( - doc_id="zacks-nvidia-report", - report=REPORT_URL_NVDA, - company="Nvidia", - report_type="Equity Research", - year="2024" - ), - kb_mem.Entry( - doc_id="fictional-amd-report", - report=REPORT_URL_NVDA, # Using same PDF for demo purposes - company="AMD", - report_type="Earnings Call Transcript", - year="2023" - ) -] -kb_mem.add(*entries) -print("šŸ“š Knowledge base populated with financial reports.") - -# --- 3. The RAG Agent Logic --- -llm = init_chat_model("gpt-4o-mini", model_provider="openai") - -def financial_chat(session_id: str, user_text: str, filters: dict = None) -> str: +def financial_chat( + kb_mem: Memory, + chat_mem: Memory, + llm, + session_id: str, + user_text: str, + filters: dict | None = None, +) -> str: """ Handles a turn in a financial RAG conversation, with optional metadata filtering. """ @@ -79,13 +56,7 @@ def financial_chat(session_id: str, user_text: str, filters: dict = None) -> str ) ] if history_data: - messages.extend( - [ - AIMessage(m["content"]) - for m in history_data[0]["messages"] - if m["type"] == "ai" - ] - ) + messages = messages_from_dict(json.loads(history_data[0]["messages"])) # b. Augment with RAG: Search the knowledge base print(f"šŸ’¬ User says: '{user_text}'") @@ -96,7 +67,8 @@ def financial_chat(session_id: str, user_text: str, filters: dict = None) -> str chunk_view = kb_mem.views("report")["report"] # Calculate similarity across all chunks - sim = chunk_view.text.similarity(string=user_text, idx="similarity") + target = next(t for t in kb_mem.search_targets if t.context_id == "report") + sim = chunk_view.text.similarity(string=user_text, idx=target.index_name) # Start with the base view query = chunk_view @@ -136,37 +108,50 @@ def financial_chat(session_id: str, user_text: str, filters: dict = None) -> str # e. Save updated history updated_messages = messages + [ai_response] - entry = chat_mem.Entry( - session_id=session_id, - messages=str([m.dict() for m in updated_messages]) - ) - chat_mem.add(entry) + serialized = json.dumps(messages_to_dict(updated_messages)) + if history_data: + chat_mem.update( + {"messages": serialized}, where=chat_mem.session_id == session_id + ) + else: + chat_mem.add(chat_mem.Entry(session_id=session_id, messages=serialized)) print("šŸ’¾ Saved conversation history.") return ai_response.content -# --- 4. Run a Sample Support Conversation --- -SESSION_ID = f"financial_session_{uuid.uuid4()}" - -# Turn 1: User asks a general question (searches all documents) -response1 = financial_chat( - SESSION_ID, "What is the general outlook for the semiconductor industry?" -) -print(f"\nšŸ¤– AI Response: {response1}") - -# Turn 2: User asks a specific question about Nvidia, filtering by company -response2 = financial_chat( - SESSION_ID, - "What are the key growth drivers for them?", - filters={"company": "Nvidia"}, -) -print(f"\nšŸ¤– AI Response: {response2}") - -# Turn 3: User asks about a different type of report from 2023 -response3 = financial_chat( - SESSION_ID, - "What was discussed in the earnings call?", - filters={"report_type": "Earnings Call Transcript", "year": 2023}, -) -print(f"\nšŸ¤– AI Response: {response3}") +if __name__ == "__main__": + kb_mem = Memory(context=kb_context, namespace="financial_kb", table_name="reports") + chat_mem = Memory( + context=chat_context, namespace="financial_chat", table_name="history" + ) + # Only populate an empty store; rerunning the example retains the knowledge base. + if kb_mem.count() == 0: + kb_mem.add( + kb_mem.Entry( + doc_id="zacks-nvidia-report", + report=REPORT_URL_NVDA, + company="Nvidia", + report_type="Equity Research", + year="2024", + ), + kb_mem.Entry( + doc_id="fictional-amd-report", + report=REPORT_URL_NVDA, + company="AMD", + report_type="Earnings Call Transcript", + year="2023", + ), + ) + llm = init_chat_model("gpt-4o-mini", model_provider="openai") + SESSION_ID = f"financial_session_{uuid.uuid4()}" + for question, filters in [ + ("What is the general outlook for the semiconductor industry?", None), + ("What are the key growth drivers for them?", {"company": "Nvidia"}), + ( + "What was discussed in the earnings call?", + {"report_type": "Earnings Call Transcript", "year": "2023"}, + ), + ]: + response = financial_chat(kb_mem, chat_mem, llm, SESSION_ID, question, filters) + print(f"\nšŸ¤– AI Response: {response}") diff --git a/examples/integrations/langchain_multimodal_memory.py b/examples/integrations/langchain_multimodal_memory.py index 8ea325a..1f807c5 100644 --- a/examples/integrations/langchain_multimodal_memory.py +++ b/examples/integrations/langchain_multimodal_memory.py @@ -1,4 +1,4 @@ -# uv pip install langchain-openai pixelmemory httpx +# uv pip install langchain langchain-openai pixelmemory from pixelmemory import Memory from langchain.chat_models import init_chat_model from langchain_core.messages import HumanMessage @@ -15,12 +15,12 @@ context=context, namespace="langchain_multimodal", table_name="image_store", - if_exists="replace_force" + if_exists="ignore", ) # 2. Store an image URL in PixelMemory IMAGE_ID = "boardwalk_image" -IMAGE_URL = "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg" +IMAGE_URL = "https://raw.githubusercontent.com/pixeltable/pixeltable/release/docs/resources/images/000000000030.jpg" entry = mem.Entry(image_id=IMAGE_ID, image_url=IMAGE_URL) mem.add(entry) @@ -37,7 +37,7 @@ message = HumanMessage( content=[ - {"type": "text", "text": "Describe the weather in this image:"}, + {"type": "text", "text": "Describe the scene in this image:"}, {"type": "image_url", "image_url": {"url": retrieved_url}}, ] ) diff --git a/examples/mcp/memory.py b/examples/mcp/memory.py index a06b30b..f559f0d 100644 --- a/examples/mcp/memory.py +++ b/examples/mcp/memory.py @@ -1,18 +1,13 @@ -import contextlib +# pip install pixelmemory 'mcp>=2.3' import logging -from collections.abc import AsyncIterator from datetime import datetime +from functools import lru_cache import json import uuid from typing import Optional -import uvicorn -from mcp.server.fastmcp import FastMCP -from mcp.server.streamable_http_manager import StreamableHTTPSessionManager +from mcp.server.mcpserver import MCPServer from pixelmemory import Memory -from starlette.applications import Starlette -from starlette.routing import Mount -from starlette.types import Receive, Scope, Send # --- Memory Context --- from pixelmemory.context import Text @@ -25,15 +20,18 @@ Text(id="updated_at", embed=False), ] + # --- Memory Initialization --- -task_mem = Memory( - context=context, - namespace="agentic_developer_memory", - table_name="tasks", - if_exists="ignore" -) +@lru_cache(maxsize=1) +def get_task_memory() -> Memory: + return Memory( + context=context, + namespace="agentic_developer_memory", + table_name="tasks", + ) -mcp = FastMCP() + +mcp = MCPServer("Pixelmemory tasks") # --- Agentic Tool for Task Management --- @@ -45,6 +43,7 @@ def manage_tasks( status: Optional[str] = None, ) -> str: """Manages a persistent to-do list for the development project.""" + task_mem = get_task_memory() now = datetime.now() if action == "add": @@ -56,7 +55,7 @@ def manage_tasks( description=task_description, status="pending", created_at=str(now), - updated_at=str(now) + updated_at=str(now), ) task_mem.add(entry) return f"Task added with ID: {new_task_id}" @@ -87,17 +86,10 @@ def manage_tasks( if not original_task: return f"Error: Task with ID '{task_id}' not found." - task_mem.delete(task_mem.task_id == task_id) - - original = original_task[0] - updated_entry = task_mem.Entry( - task_id=original["task_id"], - description=original["description"], - status=status, - created_at=str(original["created_at"]), - updated_at=str(now) + task_mem.update( + {"status": status, "updated_at": str(now)}, + where=task_mem.task_id == task_id, ) - task_mem.add(updated_entry) return f"Task '{task_id}' updated to status '{status}'." @@ -109,17 +101,10 @@ def manage_tasks( if not original_task: return f"Error: Task with ID '{task_id}' not found." - task_mem.delete(task_mem.task_id == task_id) - - original = original_task[0] - completed_entry = task_mem.Entry( - task_id=original["task_id"], - description=original["description"], - status="completed", - created_at=str(original["created_at"]), - updated_at=str(now) + task_mem.update( + {"status": "completed", "updated_at": str(now)}, + where=task_mem.task_id == task_id, ) - task_mem.add(completed_entry) return f"Task '{task_id}' marked as completed." @@ -129,31 +114,4 @@ def manage_tasks( if __name__ == "__main__": logging.basicConfig(level=logging.INFO) - logger = logging.getLogger(__name__) - - session_manager = StreamableHTTPSessionManager(app=mcp) - - async def handle_streamable_http( - scope: Scope, receive: Receive, send: Send - ) -> None: - await session_manager.handle_request(scope, receive, send) - - @contextlib.asynccontextmanager - async def lifespan(app: Starlette) -> AsyncIterator[None]: - """Context manager for managing session manager lifecycle.""" - async with session_manager.run(): - logger.info("Application started with StreamableHTTP session manager!") - try: - yield - finally: - logger.info("Application shutting down...") - - starlette_app = Starlette( - debug=True, - routes=[ - Mount("/", app=handle_streamable_http), - ], - lifespan=lifespan, - ) - - uvicorn.run(starlette_app, host="127.0.0.1", port=8000) + mcp.run(transport="streamable-http", host="127.0.0.1", port=8000) diff --git a/examples/multimodal/images.py b/examples/multimodal/images.py index 63b5f09..c6c2705 100644 --- a/examples/multimodal/images.py +++ b/examples/multimodal/images.py @@ -10,24 +10,21 @@ ] memory = Memory( - context=context, - namespace="image_memory_example", - table_name="image_files" + context=context, namespace="image_memory_example", table_name="image_files" ) image_url = "https://raw.githubusercontent.com/pixeltable/pixeltable/release/docs/resources/images/000000000030.jpg" entry = memory.Entry( - memory_id=str(uuid.uuid4()), - image=image_url, - inserted_at=str(datetime.now()) + memory_id=str(uuid.uuid4()), image=image_url, inserted_at=str(datetime.now()) ) memory.add(entry) query = "A person on a skateboard" # This wants the image itself alongside the caption, which search() does not # return, so query the table directly. -sim = memory.image_description.similarity(string=query, idx="similarity") +target = next(t for t in memory.search_targets if t.context_id == "image") +sim = memory.image_description.similarity(string=query, idx=target.index_name) results = ( memory.order_by(sim, asc=False) .limit(3) diff --git a/examples/multimodal/text.py b/examples/multimodal/text.py index 3db31fd..d4525d1 100644 --- a/examples/multimodal/text.py +++ b/examples/multimodal/text.py @@ -10,9 +10,7 @@ ] memory = Memory( - context=context, - namespace="text_memory_example", - table_name="text_files" + context=context, namespace="text_memory_example", table_name="text_files" ) text_data = ( @@ -22,9 +20,7 @@ "You can build complex AI workflows with just a few lines of Python code." ) entry = memory.Entry( - memory_id=str(uuid.uuid4()), - text_content=text_data, - inserted_at=str(datetime.now()) + memory_id=str(uuid.uuid4()), text_content=text_data, inserted_at=str(datetime.now()) ) memory.add(entry) @@ -37,5 +33,4 @@ for res in results: print(f"Similarity: {res['score']:.4f}") print(f"Text: {res['text'][:100]}...") - print(f"ID: {res['memory_id']}") print("---") diff --git a/examples/multimodal/video.py b/examples/multimodal/video.py index b285220..9caefdd 100644 --- a/examples/multimodal/video.py +++ b/examples/multimodal/video.py @@ -5,22 +5,18 @@ context = [ Text(id="memory_id", embed=False), - Video(id="video", provider="openai", model="gpt-4o-mini", embed_model="text-embedding-3-small"), + Video(id="video", provider="openai", model="gpt-4o-mini"), Text(id="inserted_at", embed=False), ] memory = Memory( - context=context, - namespace="video_memory_example", - table_name="video_files" + context=context, namespace="video_memory_example", table_name="video_files" ) video_url = "https://github.com/pixeltable/pixeltable/raw/release/docs/resources/audio-transcription-demo/Lex-Fridman-Podcast-430-Excerpt-0.mp4" entry = memory.Entry( - memory_id=str(uuid.uuid4()), - video=video_url, - inserted_at=str(datetime.now()) + memory_id=str(uuid.uuid4()), video=video_url, inserted_at=str(datetime.now()) ) memory.add(entry) diff --git a/pixelmemory/indexing.py b/pixelmemory/indexing.py index 791ae56..4d90216 100644 --- a/pixelmemory/indexing.py +++ b/pixelmemory/indexing.py @@ -21,6 +21,43 @@ ) +def _add_embedding_index( + table: pxt.Table, + column: str, + embedding: pxt.Function, + index_name: str, + legacy_name: Optional[str] = None, +) -> str: + index_name = index_name.casefold() + indexes = table.get_metadata()["indexes"] + legacy = indexes.get(legacy_name) + if ( + index_name not in indexes + and legacy is not None + and legacy["index_type"] == "embedding" + and legacy["columns"] == [column.casefold()] + ): + index_name = legacy_name + existing = indexes.get(index_name) + if existing is not None and ( + existing["index_type"] != "embedding" + or existing["columns"] != [column.casefold()] + ): + raise ValueError( + f"Index {index_name!r} already belongs to another column. " + "Use a different index_name for each context." + ) + table.add_embedding_index( + column=column, idx_name=index_name, embedding=embedding, if_exists="ignore" + ) + return index_name + + +def _register_target(memory_instance: Memory, target: SearchTarget) -> None: + if target not in memory_instance.resources.search_targets: + memory_instance.resources.search_targets.append(target) + + def setup_column_indexing( memory_instance: Memory, col_name: str, @@ -28,7 +65,7 @@ def setup_column_indexing( col_settings: Optional[Any] = None, ) -> None: embed_model = memory_instance._get_embed_model(col_settings.embed_model) - index_name = col_settings.index_name or "similarity" + index_name = col_settings.index_name or f"{col_name}_similarity" if col_type == pxt.Image: setup_image_indexing( @@ -80,30 +117,33 @@ def setup_vision_indexing( col_settings.provider, img_col_name, vision_func, vision_args, target_obj ) - target_obj.add_embedding_index( - column=description_col_name, - idx_name=index_name, - embedding=embed_model, - if_exists="ignore", + index_name = _add_embedding_index( + target_obj, + description_col_name, + embed_model, + index_name, + "similarity" if col_settings.index_name is None else None, ) if memory_instance is not None: - memory_instance.resources.search_targets.append( + _register_target( + memory_instance, SearchTarget( context_id=context_id, table=target_obj, column=description_col_name, index_name=index_name, - ) + ), ) if col_settings.use_clip: from pixeltable.functions.huggingface import clip - target_obj.add_embedding_index( - column=img_col_name, - idx_name=f"{index_name}_clip", - embedding=clip.using(model_id=col_settings.clip_model), - if_exists="ignore", + _add_embedding_index( + target_obj, + img_col_name, + clip.using(model_id=col_settings.clip_model), + f"{index_name}_clip", + "similarity_clip" if col_settings.index_name is None else None, ) @@ -119,7 +159,7 @@ def setup_document_indexing( chunk_view_name = f"{memory_instance.table_name}_{col_name}_chunks" chunk_view_path = f"{memory_instance.namespace}.{chunk_view_name}" - document_source = getattr(memory_instance.table, col_name) + document_source = memory_instance.table[col_name] chunk_view = pxt.create_view( chunk_view_path, @@ -132,24 +172,28 @@ def setup_document_indexing( if chunk_view is None: chunk_view = pxt.get_table(chunk_view_path) - memory_instance.resources.chunk_views.append( - ChunkView(name=col_name, table=chunk_view) - ) + resource = ChunkView(name=col_name, table=chunk_view) + if resource not in memory_instance.resources.chunk_views: + memory_instance.resources.chunk_views.append(resource) - chunk_view.add_embedding_index( - column="text", idx_name=index_name, embedding=embed_model, if_exists="ignore" + index_name = _add_embedding_index( + chunk_view, + "text", + embed_model, + index_name, + "similarity" if col_settings.index_name is None else None, ) - memory_instance.resources.search_targets.append( + _register_target( + memory_instance, SearchTarget( context_id=col_name, table=chunk_view, column="text", index_name=index_name, - ) + ), ) - def setup_image_indexing( memory_instance: Memory, col_name: str, @@ -190,7 +234,7 @@ def setup_audio_indexing( audio_chunk_view_path = f"{memory_instance.namespace}.{audio_chunk_view_name}" audio_source = ( - audio_col if audio_col is not None else getattr(memory_instance.table, col_name) + audio_col if audio_col is not None else memory_instance.table[col_name] ) audio_chunk_view = pxt.create_view( @@ -219,7 +263,7 @@ def setup_audio_indexing( sentence_view_name = f"{memory_instance.table_name}_{col_name}_sentence_chunks" sentence_view_path = f"{memory_instance.namespace}.{sentence_view_name}" - transcription_text_col = getattr(audio_chunk_view, transcription_col_name).text + transcription_text_col = audio_chunk_view[transcription_col_name].text sentence_chunk_view = pxt.create_view( sentence_view_path, @@ -230,24 +274,28 @@ def setup_audio_indexing( if sentence_chunk_view is None: sentence_chunk_view = pxt.get_table(sentence_view_path) - memory_instance.resources.chunk_views.append( - ChunkView(name=col_name, table=sentence_chunk_view) - ) + resource = ChunkView(name=col_name, table=sentence_chunk_view) + if resource not in memory_instance.resources.chunk_views: + memory_instance.resources.chunk_views.append(resource) - sentence_chunk_view.add_embedding_index( - column="text", idx_name=index_name, embedding=embed_model, if_exists="ignore" + index_name = _add_embedding_index( + sentence_chunk_view, + "text", + embed_model, + index_name, + "similarity" if col_settings.index_name is None else None, ) - memory_instance.resources.search_targets.append( + _register_target( + memory_instance, SearchTarget( context_id=col_name, table=sentence_chunk_view, column="text", index_name=index_name, - ) + ), ) - def setup_video_indexing( memory_instance: Memory, col_name: str, @@ -259,12 +307,13 @@ def setup_video_indexing( audio_col_name = f"{col_name}_audio" memory_instance.table.add_computed_column( - **{audio_col_name: extract_audio(getattr(memory_instance.table, col_name))}, + **{audio_col_name: extract_audio(memory_instance.table[col_name])}, if_exists="ignore", ) - audio_col = getattr(memory_instance.table, audio_col_name) + audio_col = memory_instance.table[audio_col_name] audio_col_settings = Audio( id=col_name, + index_name=col_settings.index_name, chunk_params=col_settings.audio_chunk_params, transcription_model=col_settings.transcription_model, transcription_kwargs=col_settings.transcription_kwargs, @@ -285,19 +334,20 @@ def setup_video_indexing( frame_view_path, memory_instance.table, iterator=frame_iterator( - video=getattr(memory_instance.table, col_name), + video=memory_instance.table[col_name], **dataclasses.asdict(col_settings.frame_params), ), if_exists="ignore", ) if frame_view is None: frame_view = pxt.get_table(frame_view_path) - memory_instance.resources.frame_views.append( - FrameView(name=col_name, table=frame_view) - ) + resource = FrameView(name=col_name, table=frame_view) + if resource not in memory_instance.resources.frame_views: + memory_instance.resources.frame_views.append(resource) image_col_settings = Image( id="frame", + index_name=col_settings.index_name, provider=col_settings.provider, model=col_settings.model, prompt=col_settings.prompt, @@ -330,7 +380,7 @@ def setup_string_indexing( chunk_view_name = f"{memory_instance.table_name}_{col_name}_chunks" chunk_view_path = f"{memory_instance.namespace}.{chunk_view_name}" - text_source = getattr(memory_instance.table, col_name) + text_source = memory_instance.table[col_name] chunk_view = pxt.create_view( chunk_view_path, @@ -343,54 +393,60 @@ def setup_string_indexing( if chunk_view is None: chunk_view = pxt.get_table(chunk_view_path) - memory_instance.resources.chunk_views.append( - ChunkView(name=col_name, table=chunk_view) - ) + resource = ChunkView(name=col_name, table=chunk_view) + if resource not in memory_instance.resources.chunk_views: + memory_instance.resources.chunk_views.append(resource) - chunk_view.add_embedding_index( - column="text", - idx_name=index_name, - embedding=embed_model, - if_exists="ignore", + chunk_index_name = _add_embedding_index( + chunk_view, + "text", + embed_model, + index_name, + "similarity" if col_settings.index_name is None else None, ) - memory_instance.resources.search_targets.append( + _register_target( + memory_instance, SearchTarget( context_id=col_name, table=chunk_view, column="text", - index_name=index_name, - ) + index_name=chunk_index_name, + ), ) - memory_instance.table.add_embedding_index( - column=col_name, - idx_name=f"{index_name}_direct", - embedding=embed_model, - if_exists="ignore", + direct_index_name = _add_embedding_index( + memory_instance.table, + col_name, + embed_model, + f"{index_name}_direct", + "similarity_direct" if col_settings.index_name is None else None, ) - memory_instance.resources.search_targets.append( + _register_target( + memory_instance, SearchTarget( - context_id=f'{col_name}_direct', + context_id=f"{col_name}_direct", table=memory_instance.table, column=col_name, - index_name=f"{index_name}_direct", - ) + index_name=direct_index_name, + ), ) else: - memory_instance.table.add_embedding_index( - column=col_name, - idx_name=index_name, - embedding=embed_model, - if_exists="ignore", + index_name = _add_embedding_index( + memory_instance.table, + col_name, + embed_model, + index_name, + "similarity" if col_settings.index_name is None else None, ) - memory_instance.resources.search_targets.append( + _register_target( + memory_instance, SearchTarget( context_id=col_name, table=memory_instance.table, column=col_name, index_name=index_name, - ) + ), ) diff --git a/pixelmemory/memory.py b/pixelmemory/memory.py index 75f265b..5c1fb6c 100644 --- a/pixelmemory/memory.py +++ b/pixelmemory/memory.py @@ -1,12 +1,17 @@ from typing import Dict, Any, Literal, List, Union, Optional, TYPE_CHECKING -from dataclasses import dataclass, make_dataclass, asdict +from dataclasses import dataclass, field, make_dataclass, asdict +import keyword +import math +from numbers import Real import pixeltable as pxt +from pixeltable.catalog import Column +from pixeltable.type_system import ColumnType from .config import ( ChunkView, FrameView, SearchTarget, ) -from .context import Context +from .context import Audio, Context, Image, Video if TYPE_CHECKING: from dataclasses import dataclass as _dataclass_base @@ -31,18 +36,77 @@ def __init__( if_exists: Literal["ignore", "error", "replace_force"] = "ignore", **kwargs, ): + context_ids = set() + for col in context: + if not isinstance(col, Context) or not hasattr(col, "_pxt_type"): + raise TypeError("Use a Text, Image, Audio, Video, or Document context.") + if ( + not isinstance(col.id, str) + or not col.id.isidentifier() + or keyword.iskeyword(col.id) + ): + raise ValueError(f"Invalid context id: {col.id!r}") + Column.validate_name(col.id) + if col.id.casefold() in context_ids: + raise ValueError(f"Duplicate context id: {col.id!r}") + context_ids.add(col.id.casefold()) + base_column_names = set(context_ids) + for col in context: + suffixes = () + if col.embed and isinstance(col, Image): + suffixes = ("_response", "_description") + elif col.embed and isinstance(col, Video): + suffixes = ("_audio",) + for suffix in suffixes: + generated_name = f"{col.id}{suffix}" + if generated_name.casefold() in context_ids: + raise ValueError( + f"Context id {generated_name!r} conflicts with a generated column " + f"for {col.id!r}. Use a different context id." + ) + base_column_names.add(generated_name.casefold()) + for col in context: + if not col.embed: + continue + generated_view_columns = [] + if isinstance(col, (Audio, Video)): + generated_view_columns.append(f"{col.id}_transcription") + if isinstance(col, Video): + generated_view_columns.extend(("frame_response", "frame_description")) + for generated_name in generated_view_columns: + if generated_name.casefold() in base_column_names: + raise ValueError( + f"Column {generated_name!r} conflicts with a generated view column " + f"for {col.id!r}. Use a different context id." + ) + self.namespace = namespace self.table_name = table_name self.context = context self.if_exists = if_exists - self.schema: Dict[str, pxt.ColumnType] = { + self.schema: Dict[str, Any] = { col.id: (col._pxt_type if col.required else col._pxt_type | None) for col in self.context } self.columns_to_embed: Dict[str, Context] = { col.id: col for col in self.context if col.embed } + entry_fields = [] + has_required_after = False + for col in reversed(self.context): + if col.required: + entry_fields.append((col.id, Any)) + has_required_after = True + else: + entry_fields.append( + (col.id, Any, field(default=None, kw_only=has_required_after)) + ) + self.Entry = make_dataclass( + "MemoryEntry", + list(reversed(entry_fields)), + bases=(_dataclass_base,), + ) table_path = f"{self.namespace}.{self.table_name}" pxt.create_dir(self.namespace, if_exists="ignore", parents=True) @@ -50,6 +114,23 @@ def __init__( self.table: pxt.Table = pxt.create_table( table_path, schema=self.schema, if_exists=self.if_exists, **kwargs ) + stored_columns = { + name: metadata + for name, metadata in self.table.get_metadata()["columns"].items() + if not metadata["is_computed"] + } + requested_columns = { + name.casefold(): spec for name, spec in self.schema.items() + } + if set(stored_columns) != set(requested_columns) or any( + self.table[name].col_type != ColumnType.normalize_type(spec) + for name, spec in requested_columns.items() + if name in stored_columns + ): + raise ValueError( + f"The existing table {table_path!r} has a different context schema. " + "Reopen it with the original contexts or use a different table name." + ) self.resources = MemoryResources( main_table=self.table, chunk_views=[], frame_views=[], search_targets=[] @@ -58,12 +139,6 @@ def __init__( if self.columns_to_embed: self.setup_indexing() - self.Entry = make_dataclass( - "MemoryEntry", - [(col.id, Any) for col in self.context], - bases=(_dataclass_base,), - ) - def _get_embed_model( self, override_model: Optional[Union[str, pxt.Function]] = None ) -> pxt.Function: @@ -77,14 +152,18 @@ def _get_embed_model( def setup_indexing(self, columns_to_index: Optional[List[str]] = None) -> None: from .indexing import setup_column_indexing - columns_to_index = columns_to_index or list(self.columns_to_embed.keys()) + if columns_to_index is None: + columns_to_index = list(self.columns_to_embed) + invalid = [ + name for name in columns_to_index if name not in self.columns_to_embed + ] + if invalid: + raise ValueError(f"Contexts are unknown or have embed=False: {invalid}") if not columns_to_index: return for col_name in columns_to_index: - if col_name not in self.schema: - continue - col_type = self.schema[col_name] - col_settings = self.columns_to_embed.get(col_name) + col_settings = self.columns_to_embed[col_name] + col_type = col_settings._pxt_type setup_column_indexing(self, col_name, col_type, col_settings) def add(self, *rows: "Memory.Entry") -> None: @@ -137,9 +216,7 @@ def views(self, context_id: Optional[str] = None) -> Dict[str, pxt.Table]: if context_id is None: return out if context_id not in out: - raise KeyError( - f"No view for {context_id!r}. Available: {sorted(out)}" - ) + raise KeyError(f"No view for {context_id!r}. Available: {sorted(out)}") return {context_id: out[context_id]} def search( @@ -166,30 +243,53 @@ def search( write by hand. `idx` is always passed, because one column can carry two indexes and a bare `.similarity()` on it is ambiguous. """ + if not isinstance(query, str): + raise TypeError("query must be a string") + if not isinstance(limit, int) or isinstance(limit, bool): + raise TypeError("limit must be a non-negative integer") + if limit < 0: + raise ValueError("limit must be a non-negative integer") + if min_score is not None and ( + not isinstance(min_score, Real) + or isinstance(min_score, bool) + or not math.isfinite(min_score) + ): + raise ValueError("min_score must be a finite number") + targets = self.resources.search_targets if on is not None: targets = [t for t in targets if t.context_id == on] if not targets: - available = sorted({t.context_id for t in self.resources.search_targets}) + available = sorted( + {t.context_id for t in self.resources.search_targets} + ) raise KeyError(f"No search target {on!r}. Available: {available}") if not targets: raise RuntimeError( "This memory has no embedding index, so there is nothing to " "search. Pass embed=True on at least one Context." ) + if limit == 0: + return [] rows: List[dict] = [] for t in targets: - col = getattr(t.table, t.column) + col = t.table[t.column] sim = col.similarity(string=query, idx=t.index_name) + # Missing or failed embeddings have NULL scores. Exclude them before + # applying the per-target limit, so they cannot hide valid matches. + predicate = sim != None # noqa: E711 + if min_score is not None: + predicate = predicate & (sim >= min_score) results = ( - t.table.order_by(sim, asc=False) + t.table.where(predicate) + .order_by(sim, asc=False) .limit(limit) .select(text=col, score=sim) .collect() ) for r in results: - if min_score is not None and r["score"] < min_score: + if r["score"] is None: continue rows.append( {"context_id": t.context_id, "text": r["text"], "score": r["score"]} diff --git a/pixelmemory/vision.py b/pixelmemory/vision.py index afead60..7fee728 100644 --- a/pixelmemory/vision.py +++ b/pixelmemory/vision.py @@ -5,6 +5,32 @@ DEFAULT_MAX_TOKENS = 1024 +@pxt.udf +def _image_messages(encoded: str, prompt: str, provider: str) -> list[dict[str, Any]]: + """Keep the whole request null when the optional source image is absent. + + Pixeltable skips this function for a null `encoded` argument, then skips the + provider because its non-nullable `messages` argument is null too. + """ + if provider == "openai": + image = { + "type": "image_url", + "image_url": {"url": f"data:image/png;base64,{encoded}"}, + } + elif provider == "anthropic": + image = { + "type": "image", + "source": { + "type": "base64", + "media_type": "image/png", + "data": encoded, + }, + } + else: + raise ValueError(f"Unsupported vision provider: {provider}") + return [{"role": "user", "content": [{"type": "text", "text": prompt}, image]}] + + def get_vision_function(provider: str): if provider == "openai": try: @@ -42,18 +68,7 @@ def prepare_vision_args( if provider == "openai": return { - "messages": [ - { - "role": "user", - "content": [ - {"type": "text", "text": prompt}, - { - "type": "image_url", - "image_url": {"url": "data:image/png;base64," + encoded}, - }, - ], - } - ], + "messages": _image_messages(encoded, prompt, provider), "model": model, "model_kwargs": model_kwargs or None, } @@ -61,22 +76,7 @@ def prepare_vision_args( elif provider == "anthropic": max_tokens = model_kwargs.pop("max_tokens", DEFAULT_MAX_TOKENS) return { - "messages": [ - { - "role": "user", - "content": [ - {"type": "text", "text": prompt}, - { - "type": "image", - "source": { - "type": "base64", - "media_type": "image/png", - "data": encoded, - }, - }, - ], - } - ], + "messages": _image_messages(encoded, prompt, provider), "model": model, "max_tokens": max_tokens, "model_kwargs": model_kwargs or None, @@ -105,9 +105,10 @@ def create_vision_computed_column( raise ValueError(f"Unsupported vision provider: {provider}") # Both providers return Json, so the extracted field is Json too. An embedding - # index needs a String column, hence the cast. + # index needs a String column. Keep it nullable for skipped or empty responses. target_obj.add_computed_column( - **{description_col_name: description.astype(pxt.String)}, if_exists="ignore" + **{description_col_name: description.astype(pxt.String | None)}, + if_exists="ignore", ) return description_col_name diff --git a/pyproject.toml b/pyproject.toml index a73c064..fc991bc 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -30,16 +30,21 @@ classifiers = [ "Topic :: Software Development :: Libraries :: Python Modules", ] dependencies = [ - "pixeltable>=0.7.6,<0.8", - "sentence-transformers>=4.1.0", + "pixeltable>=0.7.17,<0.8", + "sentence-transformers>=5.4", # DocumentSplitterParams defaults to separators="token_limit", which needs tiktoken. "tiktoken>=0.7", ] +[project.optional-dependencies] +openai = ["openai>=3.0"] +anthropic = ["anthropic>=0.40"] +sentence = ["spacy>=3.8"] + [project.urls] homepage = "https://pixeltable.com/" repository = "https://github.com/pixeltable/pixelmemory" -documentation = "https://docs.pixeltable.com/docs/libraries/pixelmemory" +documentation = "https://github.com/pixeltable/pixelmemory#readme" [dependency-groups] dev = [ @@ -47,8 +52,12 @@ dev = [ "ruff>=0.12.2,<0.15", # Provider SDKs are optional at runtime (pixelmemory raises a clear ImportError # if you use an Image context without them), but CI must cover the vision paths. - "openai>=1.0", + "openai>=3.0", "anthropic>=0.40", + "spacy>=3.8", + "fastapi>=0.120", + "langchain>=1.0", + "mcp>=2.3", ] [tool.ruff] diff --git a/tests/conftest.py b/tests/conftest.py index bec8553..d6b4802 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -13,10 +13,13 @@ """ import os +from pathlib import Path import tempfile _CATALOG = tempfile.mkdtemp(prefix="pixelmemory-tests-") os.environ["PIXELTABLE_HOME"] = _CATALOG +os.environ["PIXELTABLE_PGDATA"] = str(Path(_CATALOG) / "pgdata") +os.environ.pop("PIXELTABLE_DB_CONNECT_STR", None) os.environ.pop("OPENAI_API_KEY", None) os.environ.pop("ANTHROPIC_API_KEY", None) @@ -28,6 +31,22 @@ EMBED_MODEL = "sentence-transformers/all-MiniLM-L6-v2" +@pytest.fixture(scope="session", autouse=True) +def test_catalog_server(): + """Stop the throwaway server after the suite, including after test failures.""" + import pixeltable_pgserver + + # Register the server before Pixeltable opens it. Its normal persistent + # cleanup mode leaves a PostgreSQL process behind for every test run. + server = pixeltable_pgserver.get_server( + Path(_CATALOG) / "pgdata", cleanup_mode="stop" + ) + try: + yield + finally: + server.cleanup() + + @pytest.fixture(scope="session") def embed_model() -> str: return EMBED_MODEL diff --git a/tests/test_construction.py b/tests/test_construction.py index bb21067..56b6a22 100644 --- a/tests/test_construction.py +++ b/tests/test_construction.py @@ -123,12 +123,12 @@ def test_openai_vision_uses_chat_completions(embed_model, namespace) -> None: columns = memory.table.get_metadata()["columns"] call = columns["img_response"]["computed_with"] assert call.startswith("chat_completions(") - assert "'type': 'image_url'" in call + assert "_image_messages(" in call assert "b64_encode(img, 'png')" in call assert "model_kwargs={'temperature': 0.2}" in call assert ( columns["img_description"]["computed_with"] - == "img_response.choices[0].message.content.astype(String)" + == "img_response.choices[0].message.content.astype(String | None)" ) @@ -150,7 +150,7 @@ def test_anthropic_vision_passes_max_tokens(embed_model, namespace) -> None: call = memory.table.get_metadata()["columns"]["img_response"]["computed_with"] assert call.startswith("messages(") assert "max_tokens=1024" in call - assert "'data': b64_encode(img, 'png')" in call + assert "_image_messages(b64_encode(img, 'png')" in call assert "model_kwargs={'temperature': 0.2}" in call diff --git a/tests/test_examples.py b/tests/test_examples.py new file mode 100644 index 0000000..2d683ad --- /dev/null +++ b/tests/test_examples.py @@ -0,0 +1,237 @@ +"""Exercise example entry points without contacting a model provider.""" + +import importlib.util +import json +from pathlib import Path +from types import SimpleNamespace +from dataclasses import replace + +import numpy as np +import pixeltable as pxt +import pytest +from fastapi.testclient import TestClient +from langchain_core.messages import AIMessage, messages_from_dict + +from pixelmemory import Memory +from pixelmemory.context import Text + + +def load_example(relative_path): + path = Path(__file__).resolve().parent.parent / "examples" / relative_path + spec = importlib.util.spec_from_file_location(path.stem, path) + module = importlib.util.module_from_spec(spec) + spec.loader.exec_module(module) + return module + + +class ChatModel: + def __init__(self): + self.calls = [] + + def invoke(self, messages): + self.calls.append(list(messages)) + return AIMessage(content=f"answer {len(self.calls)}") + + +def test_chat_history_survives_turns_and_reconstruction(namespace): + example = load_example("integrations/langchain_chat_history.py") + memory = Memory(example.context, namespace=namespace) + model = ChatModel() + for text in ("first question", "second question", "third question"): + example.chat(memory, model, "session_a", text) + + assert [m.content for m in model.calls[2]][1:] == [ + "first question", + "answer 1", + "second question", + "answer 2", + "third question", + ] + assert memory.count() == 1 + history = messages_from_dict(json.loads(memory.collect()[0]["messages"])) + assert len(history) == 7 + + reopened = Memory(example.context, namespace=namespace) + example.chat(reopened, model, "session_a", "after reopening") + assert len(model.calls[-1]) == 8 + example.chat(reopened, model, "session_b", "separate session") + assert len(model.calls[-1]) == 2 + assert reopened.count() == 2 + + +@pxt.udf +def example_embedding(text: str) -> pxt.Array[(3,), pxt.Float]: + """A local embedding keeps example tests independent of downloaded weights.""" + return np.array([1.0, float("Nvidia" in text), 1.0]) + + +def test_financial_chat_preserves_history_and_filters_metadata(namespace): + example = load_example("integrations/langchain_financial_rag_agent.py") + memory = Memory(example.chat_context, namespace=namespace) + chunks = pxt.create_table( + f"{namespace}.report_chunks", {"text": pxt.String, "company": pxt.String} + ) + chunks.add_embedding_index( + "text", embedding=example_embedding, idx_name="report_idx" + ) + chunks.insert( + [ + {"text": "Nvidia report excerpt", "company": "Nvidia"}, + {"text": "AMD report excerpt", "company": "AMD"}, + ] + ) + # Use a real indexed table for retrieval, without fetching a remote PDF. + kb_memory = SimpleNamespace( + views=lambda name: {name: chunks}, + search_targets=[SimpleNamespace(context_id="report", index_name="report_idx")], + ) + model = ChatModel() + for text in ("first question", "second question", "third question"): + example.financial_chat( + kb_memory, memory, model, "session_a", text, {"company": "Nvidia"} + ) + + third_turn = model.calls[-1] + assert len(third_turn) == 6 + assert [m.type for m in third_turn] == [ + "system", + "human", + "ai", + "human", + "ai", + "human", + ] + assert "Nvidia report excerpt" in third_turn[-1].content[0]["text"] + assert "AMD report excerpt" not in third_turn[-1].content[0]["text"] + assert memory.count() == 1 + assert len(messages_from_dict(json.loads(memory.collect()[0]["messages"]))) == 7 + + +def test_fastapi_memory_lifecycle_and_client_errors(namespace): + example = load_example("fastapi/memory_service.py") + with TestClient(example.app) as client: + payload = { + "namespace": namespace, + "table_name": "entries", + "context": [{"id": "body", "type": "text", "embed": False}], + } + response = client.post("/memories", json=payload) + assert response.status_code == 201, response.text + assert client.get("/memories").json() == [f"{namespace}.entries"] + assert client.get("/memories", params={"namespace": "other"}).json() == [] + + path = f"/memories/{namespace}/entries" + assert ( + client.post( + f"{path}/items", json={"items": [{"body": "hello"}]} + ).status_code + == 201 + ) + assert client.get(f"{path}/items", params={"select": "body"}).json() == [ + {"body": "hello"} + ] + assert client.get(path).status_code == 200 + for params in ( + {"filter": "body = hello"}, + {"select": "missing"}, + {"query": "hello"}, + {"query": "hello", "search_column": "missing"}, + ): + response = client.get(f"{path}/items", params=params) + assert response.status_code == 400, response.text + assert client.get(f"{path}/items", params={"limit": 0}).status_code == 422 + assert ( + client.post( + f"{path}/items", json={"items": [{"unknown": "value"}]} + ).status_code + == 400 + ) + + invalid = {**payload, "context": [{"id": "body", "type": "unknown"}]} + assert client.post("/memories", json=invalid).status_code == 400 + assert client.delete(path).status_code == 200 + assert client.get("/memories").json() == [] + assert client.get(path).status_code == 404 + assert not pxt.list_tables(namespace) + + +def test_fastapi_semantic_search_uses_registered_targets(namespace): + example = load_example("fastapi/memory_service.py") + memory = Memory( + [Text(id="body", embed_model=example_embedding)], + namespace=namespace, + ) + memory.add(memory.Entry(body="Nvidia report")) + example.memory_cache[f"{namespace}.memory"] = memory + with TestClient(example.app) as client: + response = client.get( + f"/memories/{namespace}/memory/items", + params={"query": "Nvidia", "search_column": "body"}, + ) + assert response.status_code == 200, response.text + assert response.json()[0]["text"] == "Nvidia report" + assert response.json()[0]["context_id"] == "body" + + +@pytest.mark.parametrize("kind", ["text", "image", "video", "audio", "document"]) +def test_fastapi_honors_embed_false_for_every_context(kind): + example = load_example("fastapi/memory_service.py") + context = example.context_from_fields( + [example.ContextField(id="content", type=kind, embed=False)] + ) + assert context[0].embed is False + + +def test_mcp_http_initialization_and_task_lifecycle(namespace, monkeypatch): + example = load_example("mcp/memory.py") + # Importing or connecting to the server must not initialize a catalog or + # fetch embeddings; the memory is first needed when a tool runs. + assert example.get_task_memory.cache_info().currsize == 0 + app = example.mcp.streamable_http_app(stateless_http=True, json_response=True) + with TestClient(app, base_url="http://127.0.0.1:8000") as client: + client.headers["Accept"] = "application/json, text/event-stream" + + def rpc(method, params): + response = client.post( + "/mcp", + json={"jsonrpc": "2.0", "id": 1, "method": method, "params": params}, + ) + assert response.status_code == 200, response.text + assert "error" not in response.json(), response.text + return response.json()["result"] + + initialized = rpc( + "initialize", + { + "protocolVersion": "2025-11-25", + "capabilities": {}, + "clientInfo": {"name": "pixelmemory-test", "version": "0.1"}, + }, + ) + assert initialized["serverInfo"]["name"] == "Pixelmemory tasks" + assert example.get_task_memory.cache_info().currsize == 0 + + memory = Memory( + [replace(ctx, embed=False) for ctx in example.context], namespace=namespace + ) + monkeypatch.setattr(example, "get_task_memory", lambda: memory) + assert rpc("tools/list", {})["tools"][0]["name"] == "manage_tasks" + + def call(**arguments): + result = rpc("tools/call", {"name": "manage_tasks", "arguments": arguments}) + assert not result.get("isError"), result + return result["content"][0]["text"] + + assert call(action="list") == "No tasks found." + call(action="add", task_description="Keep my task") + task = memory.collect()[0] + task_id = task["task_id"] + call(action="update", task_id=task_id, status="in_progress") + call(action="complete", task_id=task_id) + assert memory.count() == 1 + updated = memory.collect()[0] + assert updated["status"] == "completed" + assert updated["description"] == task["description"] + assert updated["created_at"] == task["created_at"] + assert json.loads(call(action="list"))[0]["status"] == "completed" + assert "not found" in call(action="complete", task_id="missing") diff --git a/tests/test_memory_regressions.py b/tests/test_memory_regressions.py new file mode 100644 index 0000000..ae26db4 --- /dev/null +++ b/tests/test_memory_regressions.py @@ -0,0 +1,286 @@ +"""Exercise persistence and search with a deterministic, offline embedding UDF.""" + +import os +import subprocess +import sys + +import numpy as np +import pixeltable as pxt +import pytest + +from pixelmemory import Memory +from pixelmemory.context import Audio, Document, Image, Text, Video + + +@pxt.udf +def regression_embedding(text: str) -> pxt.Array[(3,), pxt.Float]: + if text == "embedding failure": + raise ValueError("intentional embedding failure") + return np.array([1.0, float("cat" in text), float("dog" in text)], dtype=np.float32) + + +def test_multiple_embedded_columns_are_searchable(namespace): + memory = Memory( + [ + Text(id="title", embed_model=regression_embedding), + Text(id="body", embed_model=regression_embedding), + ], + namespace=namespace, + ) + memory.add(memory.Entry(title="cat title", body="dog body")) + assert {hit["context_id"] for hit in memory.search("cat")} == {"title", "body"} + assert len(memory.table.get_metadata()["indexes"]) == 2 + + +def test_multiple_chunked_columns_have_distinct_direct_indexes(namespace): + memory = Memory( + [ + Text(id="title", use_chunking=True, embed_model=regression_embedding), + Text(id="body", use_chunking=True, embed_model=regression_embedding), + ], + namespace=namespace, + ) + indexes = memory.table.get_metadata()["indexes"] + assert {tuple(index["columns"]) for index in indexes.values()} == { + ("title",), + ("body",), + } + assert len(memory.search_targets) == 4 + + +@pytest.mark.parametrize("kind", [Text, Image, Document]) +def test_optional_contexts_are_indexed(kind, namespace): + memory = Memory( + [kind(id="optional", required=False, embed_model=regression_embedding)], + namespace=namespace, + ) + assert [target.context_id for target in memory.search_targets] == ["optional"] + + +def test_optional_entry_and_null_scores(namespace): + memory = Memory( + [ + Text(id="note", required=False, embed_model=regression_embedding), + Text(id="body", embed=False), + ], + namespace=namespace, + ) + memory.add(memory.Entry("missing"), memory.Entry("present", note="cat")) + hits = memory.search("cat", limit=1) + assert len(hits) == 1 + assert hits[0]["text"] == "cat" + assert memory.search("cat", min_score=0.0) == hits + + +def test_trailing_optional_fields_keep_positional_arguments(namespace): + memory = Memory( + [Text(id="body", embed=False), Text(id="note", required=False, embed=False)], + namespace=namespace, + ) + assert memory.Entry("cat").note is None + assert memory.Entry("cat", "dog").note == "dog" + + +def test_failed_embeddings_do_not_hide_valid_matches(namespace): + memory = Memory( + [Text(id="body", embed_model=regression_embedding)], + namespace=namespace, + on_error="ignore", + ) + memory.table.insert( + [{"body": "embedding failure"}, {"body": "cat"}], on_error="ignore" + ) + assert [hit["text"] for hit in memory.search("cat", limit=1)] == ["cat"] + + +def test_legacy_indexes_are_reused_without_reembedding(namespace): + pxt.create_dir(namespace) + table = pxt.create_table(f"{namespace}.memory", {"body": pxt.String}) + table.add_embedding_index( + "body", embedding=regression_embedding, idx_name="similarity" + ) + table.insert(body="cat") + version = table.get_metadata()["version"] + memory = Memory( + [Text(id="body", embed_model=regression_embedding)], namespace=namespace + ) + assert table.get_metadata()["version"] == version + assert memory.search_targets[0].index_name == "similarity" + assert memory.search("cat")[0]["text"] == "cat" + + +def test_legacy_collision_is_repaired_without_replacing_existing_index(namespace): + pxt.create_dir(namespace) + table = pxt.create_table( + f"{namespace}.memory", {"title": pxt.String, "body": pxt.String} + ) + table.add_embedding_index( + "title", embedding=regression_embedding, idx_name="similarity" + ) + memory = Memory( + [ + Text(id="title", embed_model=regression_embedding), + Text(id="body", embed_model=regression_embedding), + ], + namespace=namespace, + ) + assert memory.search_targets[0].index_name == "similarity" + assert memory.search_targets[1].index_name == "body_similarity" + assert table.get_metadata()["indexes"]["similarity"]["columns"] == ["title"] + + +def test_explicit_index_name_collision_is_rejected(namespace): + with pytest.raises(ValueError, match="already belongs to another column"): + Memory( + [ + Text(id="title", index_name="shared", embed_model=regression_embedding), + Text(id="body", index_name="shared", embed_model=regression_embedding), + ], + namespace=namespace, + ) + + +def test_index_name_collisions_are_case_insensitive(namespace): + with pytest.raises(ValueError, match="already belongs to another column"): + Memory( + [ + Text(id="title", index_name="Shared", embed_model=regression_embedding), + Text(id="body", index_name="shared", embed_model=regression_embedding), + ], + namespace=namespace, + ) + + +@pytest.mark.parametrize( + "context, conflicting_id", + [ + (Image(id="photo"), "photo_response"), + (Image(id="photo"), "photo_description"), + (Video(id="clip"), "clip_audio"), + ], + ids=["response", "description", "audio"], +) +def test_generated_column_collisions_fail_before_mutation( + context, conflicting_id, namespace +): + with pytest.raises(ValueError, match="conflicts with a generated column"): + Memory([context, Text(id=conflicting_id, embed=False)], namespace=namespace) + assert namespace not in pxt.list_dirs() + + +@pytest.mark.parametrize( + "contexts", + [ + [Text(id="frame_response", embed=False), Video(id="clip")], + [Text(id="frame_description", embed=False), Video(id="clip")], + [Image(id="frame"), Video(id="clip")], + [Video(id="clip"), Image(id="frame")], + [Text(id="aud_transcription", embed=False), Audio(id="aud")], + [Text(id="clip_transcription", embed=False), Video(id="clip")], + ], + ids=[ + "frame_response", + "frame_description", + "image_video", + "video_image", + "audio", + "video", + ], +) +def test_generated_view_collisions_fail_before_mutation(contexts, namespace): + with pytest.raises(ValueError, match="conflicts with a generated view column"): + Memory(contexts, namespace=namespace) + assert namespace not in pxt.list_dirs() + + +def test_disabled_video_pipeline_does_not_reserve_view_columns(namespace): + memory = Memory( + [Text(id="frame_description", embed=False), Video(id="clip", embed=False)], + namespace=namespace, + ) + assert memory.table.columns() == ["frame_description", "clip"] + + +def test_repeated_setup_is_idempotent_and_empty_selection_is_noop(namespace): + memory = Memory( + [Text(id="body", embed_model=regression_embedding)], namespace=namespace + ) + memory.add(memory.Entry("cat")) + version = memory.table.get_metadata()["version"] + memory.setup_indexing([]) + memory.setup_indexing() + assert len(memory.search_targets) == 1 + assert memory.table.get_metadata()["version"] == version + assert len(memory.search("cat")) == 1 + + +@pytest.mark.parametrize("name", ["missing", "plain"]) +def test_setup_rejects_unknown_or_unembedded_columns(name, namespace): + memory = Memory([Text(id="plain", embed=False)], namespace=namespace) + with pytest.raises(ValueError, match="unknown or have embed=False"): + memory.setup_indexing([name]) + + +@pytest.mark.parametrize("ids", [["body", "body"], ["body", "BODY"]]) +def test_duplicate_context_ids_fail_before_catalog_mutation(ids, namespace): + with pytest.raises(ValueError, match="Duplicate context id"): + Memory([Text(id=name, embed=False) for name in ids], namespace=namespace) + assert namespace not in pxt.list_dirs() + + +@pytest.mark.parametrize("context_id", ["count", "_rowid"]) +def test_reserved_context_ids_fail_before_catalog_mutation(context_id, namespace): + with pytest.raises(pxt.Error, match="reserved name|Invalid column name"): + Memory([Text(id=context_id, embed=False)], namespace=namespace) + assert namespace not in pxt.list_dirs() + + +def test_mixed_case_context_reopens(namespace): + contexts = [Text(id="Body", embed=False)] + memory = Memory(contexts, namespace=namespace) + memory.add(memory.Entry(Body="cat")) + reopened = Memory(contexts, namespace=namespace) + assert reopened.count() == 1 + + +@pytest.mark.parametrize( + "changed", + [ + [Text(id="renamed", embed=False)], + [Text(id="body", embed=False, required=False)], + [Image(id="body", embed=False)], + ], +) +def test_reopen_rejects_changed_schema_without_losing_rows(changed, namespace): + memory = Memory([Text(id="body", embed=False)], namespace=namespace) + memory.add(memory.Entry("cat")) + with pytest.raises(ValueError, match="different context schema"): + Memory(changed, namespace=namespace) + assert memory.count() == 1 + + +def test_crud_and_reopen_from_another_process(namespace): + memory = Memory([Text(id="body", embed=False)], namespace=namespace) + memory.add(memory.Entry("cat"), memory.Entry("dog")) + memory.where(memory.table.body == "cat").update({"body": "kitten"}) + memory.where(memory.table.body == "dog").delete() + script = ( + "from pixelmemory import Memory; from pixelmemory.context import Text; " + f"m = Memory([Text(id='body', embed=False)], namespace={namespace!r}); " + "assert [row['body'] for row in m.collect()] == ['kitten']" + ) + subprocess.run([sys.executable, "-c", script], check=True, env=os.environ.copy()) + + +@pytest.mark.parametrize("limit", [-1, 1.5, True], ids=["negative", "fraction", "bool"]) +def test_invalid_search_limit_is_rejected(limit, namespace): + memory = Memory([Text(id="body", embed=False)], namespace=namespace) + with pytest.raises((TypeError, ValueError), match="limit"): + memory.search("cat", limit=limit) + + +def test_zero_limit_returns_no_results(namespace): + memory = Memory( + [Text(id="body", embed_model=regression_embedding)], namespace=namespace + ) + assert memory.search("cat", limit=0) == [] diff --git a/tests/test_multimodal.py b/tests/test_multimodal.py new file mode 100644 index 0000000..712a329 --- /dev/null +++ b/tests/test_multimodal.py @@ -0,0 +1,341 @@ +"""Execute the real multimodal pipelines without provider calls or model downloads. + +Only provider clients and the spaCy model loader are replaced. Pixeltable still +executes its own media iterators, provider UDFs, response conversion, indexes, +dependent views, and similarity queries. +""" + +import base64 +from fractions import Fraction +import json +from pathlib import Path +from types import SimpleNamespace +import wave + +import av +import numpy as np +import pixeltable as pxt +from PIL import Image as PILImage +import pytest + +from pixelmemory import Memory +from pixelmemory.config import ( + AudioSplitterParams, + DocumentSplitterParams, + FrameIteratorParams, +) +from pixelmemory.context import Audio, Document, Image, Text, Video + + +@pxt.udf +def local_embedding(text: str) -> pxt.Array[(3,), np.float32]: + """A deterministic index that needs no downloaded embedding model.""" + return np.array( + [1.0, float("cat" in text.lower()), float("dog" in text.lower())], + dtype=np.float32, + ) + + +@pytest.fixture +def sentence_model(monkeypatch): + import spacy + import pixeltable.functions.string as string_functions + + model = spacy.blank("en") + model.add_pipe("sentencizer") + monkeypatch.setattr(string_functions, "get_spacy_model", lambda name: model) + + +@pytest.fixture +def provider_requests(monkeypatch): + import pixeltable.functions.anthropic as anthropic_functions + import pixeltable.functions.openai as openai_functions + + requests = [] + + async def transcribe(**kwargs): + assert Path(kwargs["file"]).stat().st_size > 0 + requests.append({"kind": "transcription", **kwargs}) + return SimpleNamespace( + model_dump=lambda **_: {"text": "A cat walks. A dog sleeps."} + ) + + async def openai_caption(**kwargs): + image_url = kwargs["messages"][0]["content"][1]["image_url"]["url"] + prefix, encoded = image_url.split(",", 1) + assert prefix == "data:image/png;base64" + assert base64.b64decode(encoded).startswith(b"\x89PNG") + requests.append({"kind": "openai", **kwargs}) + return SimpleNamespace( + headers={}, + text=json.dumps( + {"choices": [{"message": {"content": "A cat on a red mat."}}]} + ), + ) + + async def anthropic_caption(**kwargs): + source = kwargs["messages"][0]["content"][1]["source"] + assert source["media_type"] == "image/png" + assert base64.b64decode(source["data"]).startswith(b"\x89PNG") + requests.append({"kind": "anthropic", **kwargs}) + + async def text(): + return json.dumps( + {"content": [{"type": "text", "text": "A cat on a red mat."}]} + ) + + return SimpleNamespace(headers={}, text=text) + + openai_client = SimpleNamespace( + audio=SimpleNamespace(transcriptions=SimpleNamespace(create=transcribe)), + chat=SimpleNamespace( + completions=SimpleNamespace( + with_raw_response=SimpleNamespace(create=openai_caption) + ) + ), + ) + anthropic_client = SimpleNamespace( + messages=SimpleNamespace( + with_raw_response=SimpleNamespace(create=anthropic_caption) + ) + ) + monkeypatch.setattr(openai_functions, "_openai_client", lambda: openai_client) + monkeypatch.setattr( + anthropic_functions, "_anthropic_client", lambda: anthropic_client + ) + return requests + + +@pytest.fixture +def audio_file(tmp_path): + path = tmp_path / "sample.wav" + with wave.open(str(path), "wb") as output: + output.setnchannels(1) + output.setsampwidth(2) + output.setframerate(16000) + output.writeframes(np.zeros(48000, dtype=np.int16).tobytes()) + return str(path) + + +def _video_file(path: Path, *, with_audio: bool) -> str: + with av.open(str(path), "w") as output: + video = output.add_stream("mpeg4", rate=2) + video.width = 32 + video.height = 32 + video.pix_fmt = "yuv420p" + audio = output.add_stream("aac", rate=16000) if with_audio else None + for i in range(6): + frame = av.VideoFrame.from_ndarray( + np.full((32, 32, 3), i * 30, dtype=np.uint8), format="rgb24" + ) + for packet in video.encode(frame): + output.mux(packet) + for packet in video.encode(): + output.mux(packet) + if audio is not None: + frame = av.AudioFrame.from_ndarray( + np.zeros((1, 48000), dtype=np.float32), format="fltp", layout="mono" + ) + frame.sample_rate = 16000 + frame.pts = 0 + frame.time_base = Fraction(1, 16000) + for packet in audio.encode(frame): + output.mux(packet) + for packet in audio.encode(): + output.mux(packet) + return str(path) + + +def test_chunked_text_insert_search_and_delete(namespace, sentence_model): + memory = Memory( + [Text(id="body", use_chunking=True, embed_model=local_embedding)], + namespace=namespace, + ) + memory.add(memory.Entry(body="A cat walks. A dog sleeps.")) + chunks = memory.views()["body"] + assert chunks.select(chunks.text).collect()["text"] == [ + "A cat walks.", + "A dog sleeps.", + ] + hits = memory.search("cat", on="body", limit=1) + assert hits[0]["text"] == "A cat walks." + memory.table.delete() + assert chunks.count() == 0 + assert memory.search("cat") == [] + + +@pytest.mark.parametrize("extension", ["txt", "html", "pdf"]) +def test_document_insert_search(namespace, tmp_path, extension): + path = tmp_path / f"sample.{extension}" + if extension == "pdf": + # A tiny PDF fixture avoids adding a PDF authoring dependency to tests. + stream = b"BT /F1 12 Tf 72 720 Td (A cat walks. A dog sleeps.) Tj ET" + objects = [ + b"<< /Type /Catalog /Pages 2 0 R >>", + b"<< /Type /Pages /Kids [3 0 R] /Count 1 >>", + b"<< /Type /Page /Parent 2 0 R /MediaBox [0 0 612 792] " + b"/Resources << /Font << /F1 4 0 R >> >> /Contents 5 0 R >>", + b"<< /Type /Font /Subtype /Type1 /BaseFont /Helvetica >>", + f"<< /Length {len(stream)} >>\nstream\n".encode() + stream + b"\nendstream", + ] + pdf = b"%PDF-1.4\n" + offsets = [] + for number, obj in enumerate(objects, 1): + offsets.append(len(pdf)) + pdf += f"{number} 0 obj\n".encode() + obj + b"\nendobj\n" + xref = len(pdf) + pdf += b"xref\n0 6\n0000000000 65535 f \n" + pdf += b"".join(f"{offset:010} 00000 n \n".encode() for offset in offsets) + pdf += ( + f"trailer\n<< /Size 6 /Root 1 0 R >>\nstartxref\n{xref}\n%%EOF\n".encode() + ) + path.write_bytes(pdf) + elif extension == "html": + path.write_text( + "

A cat walks.

A dog sleeps.

" + ) + else: + path.write_text("A cat walks. A dog sleeps.") + memory = Memory( + [ + Document( + id="report", + embed_model=local_embedding, + chunk_params=DocumentSplitterParams(separators="char_limit", limit=15), + ) + ], + namespace=namespace, + ) + memory.add(memory.Entry(report=str(path))) + hits = memory.search("cat", on="report", limit=10) + assert hits and any("cat" in hit["text"] for hit in hits) + assert all("Navigation" not in hit["text"] for hit in hits) + + +def test_default_document_token_chunking(namespace, tmp_path): + path = tmp_path / "sample.txt" + path.write_text("A cat walks. A dog sleeps. " * 200) + memory = Memory( + [Document(id="report", embed_model=local_embedding)], namespace=namespace + ) + memory.add(memory.Entry(report=str(path))) + chunks = memory.views()["report"] + assert chunks.count() > 1 + assert memory.search("cat", on="report") + + +@pytest.mark.parametrize( + "kind,kwargs", + [ + pytest.param(Image, {}, id="image"), + pytest.param( + Image, + {"provider": "anthropic", "model": "claude-sonnet-4-5"}, + id="anthropic", + ), + pytest.param(Audio, {}, id="audio"), + pytest.param(Video, {}, id="video"), + pytest.param(Document, {}, id="document"), + ], +) +def test_nullable_media_skips_providers_and_keeps_targets( + namespace, kind, kwargs, sentence_model, provider_requests +): + memory = Memory( + [kind(id="source", required=False, embed_model=local_embedding, **kwargs)], + namespace=namespace, + ) + assert memory.search_targets + memory.add(memory.Entry()) + assert memory.table.count() == 1 + assert provider_requests == [] + assert memory.search("cat") == [] + assert all(view.count() == 0 for view in memory.views().values()) + + +@pytest.mark.parametrize("provider", ["openai", "anthropic"]) +def test_image_caption_insert_and_reopen(namespace, provider, provider_requests): + context = [ + Image( + id="photo", + embed_model=local_embedding, + provider=provider, + model="gpt-4o-mini" if provider == "openai" else "claude-sonnet-4-5", + llm_kwargs={"temperature": 0.2}, + ) + ] + memory = Memory(context, namespace=namespace) + memory.add(memory.Entry(photo=PILImage.new("RGB", (16, 16), "red"))) + assert memory.search("cat")[0]["text"] == "A cat on a red mat." + assert provider_requests[0]["temperature"] == 0.2 + if provider == "anthropic": + assert provider_requests[0]["max_tokens"] == 1024 + version = memory.table.get_metadata()["version"] + reopened = Memory(context, namespace=namespace) + assert reopened.table.get_metadata()["version"] == version + assert reopened.search("cat") == memory.search("cat") + assert len(provider_requests) == 1 + + +def test_audio_insert_transcribes_segments_and_searches_sentences( + namespace, audio_file, sentence_model, provider_requests +): + memory = Memory( + [ + Audio( + id="recording", + embed_model=local_embedding, + chunk_params=AudioSplitterParams(duration=1.0), + ) + ], + namespace=namespace, + ) + memory.add(memory.Entry(recording=audio_file)) + assert len(provider_requests) >= 2 + assert all(request["kind"] == "transcription" for request in provider_requests) + hits = memory.search("cat", on="recording") + assert hits and all(hit["text"] == "A cat walks." for hit in hits[:2]) + memory.table.delete() + assert memory.views()["recording"].count() == 0 + + +@pytest.mark.parametrize("with_audio", [False, True]) +def test_video_insert_searches_frames_and_optional_audio( + namespace, tmp_path, with_audio, sentence_model, provider_requests +): + path = _video_file(tmp_path / "sample.mp4", with_audio=with_audio) + memory = Memory( + [ + Video( + id="clip", + embed_model=local_embedding, + frame_params=FrameIteratorParams(num_frames=2), + ) + ], + namespace=namespace, + ) + memory.add(memory.Entry(clip=path)) + assert memory.views()["clip_frames"].count() == 2 + assert memory.search("cat", on="clip_frames")[0]["text"] == "A cat on a red mat." + transcripts = memory.search("cat", on="clip") + assert bool(transcripts) == with_audio + assert sum(r["kind"] == "transcription" for r in provider_requests) == int( + with_audio + ) + memory.table.delete() + assert all(view.count() == 0 for view in memory.views().values()) + + +def test_provider_failure_aborts_insert(namespace, monkeypatch): + import pixeltable.functions.openai as openai_functions + + def unavailable(): + raise RuntimeError("caption service unavailable") + + monkeypatch.setattr(openai_functions, "_openai_client", unavailable) + memory = Memory( + [Image(id="photo", embed_model=local_embedding)], namespace=namespace + ) + with pytest.raises(pxt.Error, match="caption service unavailable"): + memory.add(memory.Entry(photo=PILImage.new("RGB", (16, 16), "red"))) + assert memory.table.count() == 0 diff --git a/tests/test_search.py b/tests/test_search.py index e1910c8..27258e5 100644 --- a/tests/test_search.py +++ b/tests/test_search.py @@ -20,7 +20,7 @@ def test_search_targets_registered(namespace): targets = m.search_targets assert [t.context_id for t in targets] == ["body"] # idx_name is recorded, not inferred: one column can carry two indexes. - assert targets[0].index_name == "similarity" + assert targets[0].index_name == "body_similarity" assert targets[0].column == "body" diff --git a/uv.lock b/uv.lock index a2f06b3..87940e8 100644 --- a/uv.lock +++ b/uv.lock @@ -250,6 +250,83 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/88/c6/92fcd42f1ba33e1184263f25bfabf3d27c383410470f169e4b8163bf9c17/beautifulsoup4-4.15.0-py3-none-any.whl", hash = "sha256:d6f88de62e1d4e38ecb1077eb9724cd0eff29d2a08ca16a401e9b9e93f117cf9", size = 109924, upload-time = 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