support data: in URL for memory. Add ootb support for pdfs

llama_toolchain/agentic_system/client.py

@@ -83,13 +83,12 @@ class AgenticSystemClient(AgenticSystem):
                     if line.startswith("data:"):
                         data = line[len("data: ") :]
                         try:
-                            if "error" in data:
+                            jdata = json.loads(data)
+                            if "error" in jdata:
                                 cprint(data, "red")
                                 continue
 
-                            yield AgenticSystemTurnResponseStreamChunk(
+                            yield AgenticSystemTurnResponseStreamChunk(**jdata)
-                                **json.loads(data)
-                            )
                         except Exception as e:
                             print(data)
                             print(f"Error with parsing or validation: {e}")

llama_toolchain/memory/api/api.py

@@ -8,7 +8,9 @@
 #
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
-
+import base64
+import mimetypes
+import os
 from typing import List, Optional, Protocol
 
 from llama_models.schema_utils import json_schema_type, webmethod
@@ -23,10 +25,25 @@ from llama_models.llama3.api.datatypes import *  # noqa: F403
 class MemoryBankDocument(BaseModel):
     document_id: str
     content: InterleavedTextMedia | URL
-    mime_type: str
+    mime_type: str | None = None
     metadata: Dict[str, Any] = Field(default_factory=dict)
 
 
+def data_url_from_file(file_path: str) -> URL:
+    if not os.path.exists(file_path):
+        raise FileNotFoundError(f"File not found: {file_path}")
+
+    with open(file_path, "rb") as file:
+        file_content = file.read()
+
+    base64_content = base64.b64encode(file_content).decode("utf-8")
+    mime_type, _ = mimetypes.guess_type(file_path)
+
+    data_url = f"data:{mime_type};base64,{base64_content}"
+
+    return URL(uri=data_url)
+
+
 @json_schema_type
 class MemoryBankType(Enum):
     vector = "vector"

llama_toolchain/memory/client.py

@@ -5,11 +5,14 @@
 # the root directory of this source tree.
 
 import asyncio
+import json
+from pathlib import Path
 
 from typing import Any, Dict, List, Optional
 
 import fire
 import httpx
+from termcolor import cprint
 
 from llama_toolchain.core.datatypes import RemoteProviderConfig
 
@@ -120,7 +123,7 @@ async def run_main(host: str, port: int, stream: bool):
             overlap_size_in_tokens=64,
         ),
     )
-    print(bank)
+    cprint(json.dumps(bank.dict(), indent=4), "green")
 
     retrieved_bank = await client.get_memory_bank(bank.bank_id)
     assert retrieved_bank is not None
@@ -145,6 +148,16 @@ async def run_main(host: str, port: int, stream: bool):
         for i, url in enumerate(urls)
     ]
 
+    this_dir = os.path.dirname(__file__)
+    files = [Path(this_dir).parent.parent / "CONTRIBUTING.md"]
+    documents += [
+        MemoryBankDocument(
+            document_id=f"num-{i}",
+            content=data_url_from_file(path),
+        )
+        for i, path in enumerate(files)
+    ]
+
     # insert some documents
     await client.insert_documents(
         bank_id=bank.bank_id,

llama_toolchain/memory/common/vector_store.py

@@ -3,21 +3,23 @@
 #
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
-
+import io
+import re
 from abc import ABC, abstractmethod
 from dataclasses import dataclass
 from typing import Any, Dict, List, Optional
 
+import chardet
 import httpx
 import numpy as np
 from numpy.typing import NDArray
+from pypdf import PdfReader
 
 from llama_models.llama3.api.datatypes import *  # noqa: F403
 from llama_models.llama3.api.tokenizer import Tokenizer
 
 from llama_toolchain.memory.api import *  # noqa: F403
 
-
 ALL_MINILM_L6_V2_DIMENSION = 384
 
 EMBEDDING_MODEL = None
@@ -36,11 +38,48 @@ def get_embedding_model() -> "SentenceTransformer":
     return EMBEDDING_MODEL
 
 
+def content_from_data(data_url: str) -> str:
+    match = re.match(r"data:([^;,]+)(?:;charset=([^;,]+))?(?:;base64)?,(.+)", data_url)
+    if not match:
+        raise ValueError("Invalid Data URL format")
+
+    mime_type, charset, data = match.groups()
+
+    if ";base64," in data_url:
+        data = base64.b64decode(data)
+    else:
+        data = data.encode("utf-8")
+
+    mime_category = mime_type.split("/")[0]
+
+    if mime_category == "text":
+        # For text-based files (including CSV, MD)
+        if charset:
+            return data.decode(charset)
+        else:
+            # Try to detect encoding if charset is not specified
+            detected = chardet.detect(data)
+            return data.decode(detected["encoding"])
+
+    elif mime_type == "application/pdf":
+        # For PDF and DOC/DOCX files, we can't reliably convert to string)
+        pdf_bytes = io.BytesIO(data)
+        pdf_reader = PdfReader(pdf_bytes)
+        return "\n".join([page.extract_text() for page in pdf_reader.pages])
+
+    else:
+        cprint("Could not extract content from data_url properly.", color="red")
+        return ""
+
+
 async def content_from_doc(doc: MemoryBankDocument) -> str:
     if isinstance(doc.content, URL):
-        async with httpx.AsyncClient() as client:
+        if doc.content.uri.startswith("data:"):
-            r = await client.get(doc.content.uri)
+            return content_from_data(doc.content.uri)
-            return r.text
+        else:
+            async with httpx.AsyncClient() as client:
+                r = await client.get(doc.content.uri)
+                return r.text
 
     return interleaved_text_media_as_str(doc.content)
 

llama_toolchain/memory/providers.py

@@ -10,6 +10,8 @@ from llama_toolchain.core.datatypes import *  # noqa: F403
 
 EMBEDDING_DEPS = [
     "blobfile",
+    "chardet",
+    "PdfReader",
     "sentence-transformers",
 ]