wip
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@@ -270,7 +270,7 @@ class Client(object):
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self.project_root = project_root
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self.use_ai_ingestion_pipeline = os.environ.get(
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"LLMA_INGESTION_PIPELINE", "false"
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"_INTERNAL_LLMA_INGESTION_PIPELINE", "false"
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).lower() in ("true", "1", "yes")
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# personal_api_key: This should be a generated Personal API Key, private
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@@ -383,9 +383,10 @@ def build_ai_multipart_request(
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# Part 3+: blob parts
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for blob_name, blob_value in blobs.items():
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blob_id = str(uuid4())
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parts.append(f"--{boundary}\r\n".encode())
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parts.append(
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f'Content-Disposition: form-data; name="event.properties.{blob_name}"\r\n'.encode()
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f'Content-Disposition: form-data; name="event.properties.{blob_name}"; filename="{blob_id}"\r\n'.encode()
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)
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parts.append(b"Content-Type: application/json\r\n\r\n")
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parts.append(json.dumps(blob_value, cls=DatetimeSerializer).encode("utf-8"))
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+1
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@@ -541,7 +541,7 @@ def extract_ai_blob_properties(
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>>> # blobs = {"$ai_input": [...], "$ai_output_choices": [...]}
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"""
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# Properties to extract as blobs
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blob_property_names = {"$ai_input", "$ai_output_choices"}
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blob_property_names = {"$ai_input", "$ai_output_choices", "$ai_embedding_vector"}
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# Create a copy of properties without the blob properties
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cleaned_properties = {
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