diff --git a/reports/benchmarks/2024-01-29_12-07-12/README.md b/reports/benchmarks/2024-01-29_12-07-12/README.md
new file mode 100644
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@@ -0,0 +1,18 @@
+# RAGStack - Benchmarks Report - 2024-01-29 12:07:09
+
+| Test Case | p50 | p90 | p95 | p99 |
+|---|---|---|---|---|
+| embeddings_batch100_chunk256-openai_ada002 | 3.06 | 3.06 | 3.06 | 3.06 |
+| embeddings_batch100_chunk512-openai_ada002 | 3.2 | 3.2 | 3.2 | 3.2 |
+| embeddings_batch50_chunk256-openai_ada002 | 3.55 | 3.55 | 3.55 | 3.55 |
+| embeddings_batch50_chunk512-openai_ada002 | 3.55 | 3.55 | 3.55 | 3.55 |
+| embeddings_batch10_chunk256-openai_ada002 | 5.4 | 5.4 | 5.4 | 5.4 |
+| embeddings_batch50_chunk512-nvidia_nvolveqa40k | 5.52 | 5.52 | 5.52 | 5.52 |
+| embeddings_batch100_chunk256-nvidia_nvolveqa40k | 6.2 | 6.2 | 6.2 | 6.2 |
+| embeddings_batch100_chunk512-nvidia_nvolveqa40k | 6.21 | 6.21 | 6.21 | 6.21 |
+| embeddings_batch10_chunk512-openai_ada002 | 6.92 | 6.92 | 6.92 | 6.92 |
+| embeddings_batch50_chunk256-nvidia_nvolveqa40k | 7.2 | 7.2 | 7.2 | 7.2 |
+| embeddings_batch10_chunk512-nvidia_nvolveqa40k | 9.1 | 9.1 | 9.1 | 9.1 |
+| embeddings_batch10_chunk256-nvidia_nvolveqa40k | 9.12 | 9.12 | 9.12 | 9.12 |
+
+<img src='plot.svg' />
\ No newline at end of file
diff --git a/reports/benchmarks/2024-01-29_12-07-12/benchmarks.log b/reports/benchmarks/2024-01-29_12-07-12/benchmarks.log
new file mode 100644
index 000000000..b783f490c
--- /dev/null
+++ b/reports/benchmarks/2024-01-29_12-07-12/benchmarks.log
@@ -0,0 +1,76 @@
+INFO:root:Starting test case
+INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
+INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
+INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
+INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
+INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
+INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
+INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
+INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
+INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
+INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
+INFO:root:Starting test case
+INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
+INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
+INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
+INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
+INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
+INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
+INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
+INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
+INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
+INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
+INFO:root:Starting test case
+INFO:root:Starting test case
+INFO:root:Starting test case
+INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
+INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
+INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
+INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
+INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
+INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
+INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
+INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
+INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
+INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
+INFO:root:Starting test case
+INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
+INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
+INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
+INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
+INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
+INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
+INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
+INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
+INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
+INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
+INFO:root:Starting test case
+INFO:root:Starting test case
+INFO:root:Starting test case
+INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
+INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
+INFO:root:Starting test case
+INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
+INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
+INFO:root:Starting test case
+INFO:root:Starting test case
+INFO:root:Starting test case
+INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
+INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
+INFO:root:Starting test case
+INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
+INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
+INFO:root:Starting test case
+INFO:root:Starting test case
+INFO:root:Starting test case
+INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
+INFO:root:Starting test case
+INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
+INFO:root:Starting test case
+INFO:root:Starting test case
+INFO:root:Starting test case
+INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
+INFO:root:Starting test case
+INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
+INFO:root:Starting test case
+INFO:root:Starting test case
diff --git a/reports/benchmarks/2024-01-29_12-07-12/embeddings_batch100_chunk256-nvidia_nvolveqa40k.json b/reports/benchmarks/2024-01-29_12-07-12/embeddings_batch100_chunk256-nvidia_nvolveqa40k.json
new file mode 100644
index 000000000..1875cd7db
--- /dev/null
+++ b/reports/benchmarks/2024-01-29_12-07-12/embeddings_batch100_chunk256-nvidia_nvolveqa40k.json
@@ -0,0 +1 @@
+{"benchmarks":[{"runs":[{"values":[6.202193088000001],"warmups":[[1,8.033775862000027]]}]}],"metadata":{"aslr":"Full randomization","boot_time":"2024-01-29 12:01:40","command":"/home/runner/.cache/pypoetry/virtualenvs/ragstack-e2e-tests-KLvcJhlJ-py3.11/bin/python /home/runner/work/ragstack-ai/ragstack-ai/ragstack-e2e-tests/benchmarks/testcases.py /home/runner/work/ragstack-ai/ragstack-ai/ragstack-e2e-tests/benchmarks/reports/benchmarks.log embeddings_batch100_chunk256 nvidia_nvolveqa40k","command_max_rss":83632128,"cpu_config":"idle:none","cpu_count":4,"cpu_freq":"0=2935 MHz; 1=2877 MHz; 2=3242 MHz; 3=3297 MHz","cpu_model_name":"AMD EPYC 7763 64-Core Processor","date":"2024-01-29 12:06:47.762126","duration":14.306412134000027,"hostname":"fv-az525-874","load_avg_1min":0.48,"loops":1,"name":"command","perf_version":"2.6.2","platform":"Linux-6.2.0-1018-azure-x86_64-with-glibc2.35","runnable_threads":1,"unit":"second","uptime":307.76432180404663},"version":"1.0"}
diff --git a/reports/benchmarks/2024-01-29_12-07-12/embeddings_batch100_chunk256-openai_ada002.json b/reports/benchmarks/2024-01-29_12-07-12/embeddings_batch100_chunk256-openai_ada002.json
new file mode 100644
index 000000000..707bdd21d
--- /dev/null
+++ b/reports/benchmarks/2024-01-29_12-07-12/embeddings_batch100_chunk256-openai_ada002.json
@@ -0,0 +1 @@
+{"benchmarks":[{"runs":[{"values":[3.0632637260000024],"warmups":[[1,3.056866897999953]]}]}],"metadata":{"aslr":"Full randomization","boot_time":"2024-01-29 12:01:40","command":"/home/runner/.cache/pypoetry/virtualenvs/ragstack-e2e-tests-KLvcJhlJ-py3.11/bin/python /home/runner/work/ragstack-ai/ragstack-ai/ragstack-e2e-tests/benchmarks/testcases.py /home/runner/work/ragstack-ai/ragstack-ai/ragstack-e2e-tests/benchmarks/reports/benchmarks.log embeddings_batch100_chunk256 openai_ada002","command_max_rss":151453696,"cpu_config":"idle:none","cpu_count":4,"cpu_freq":"0=3243 MHz; 1=3199 MHz; 2=3332 MHz; 3=3019 MHz","cpu_model_name":"AMD EPYC 7763 64-Core Processor","date":"2024-01-29 12:06:33.327916","duration":6.1933870070000125,"hostname":"fv-az525-874","load_avg_1min":0.62,"loops":1,"name":"command","perf_version":"2.6.2","platform":"Linux-6.2.0-1018-azure-x86_64-with-glibc2.35","runnable_threads":1,"unit":"second","uptime":293.33025908470154},"version":"1.0"}
diff --git a/reports/benchmarks/2024-01-29_12-07-12/embeddings_batch100_chunk512-nvidia_nvolveqa40k.json b/reports/benchmarks/2024-01-29_12-07-12/embeddings_batch100_chunk512-nvidia_nvolveqa40k.json
new file mode 100644
index 000000000..17e4a7c2e
--- /dev/null
+++ b/reports/benchmarks/2024-01-29_12-07-12/embeddings_batch100_chunk512-nvidia_nvolveqa40k.json
@@ -0,0 +1 @@
+{"benchmarks":[{"runs":[{"values":[6.214858456000002],"warmups":[[1,6.478936356999952]]}]}],"metadata":{"aslr":"Full randomization","boot_time":"2024-01-29 12:01:40","command":"/home/runner/.cache/pypoetry/virtualenvs/ragstack-e2e-tests-KLvcJhlJ-py3.11/bin/python /home/runner/work/ragstack-ai/ragstack-ai/ragstack-e2e-tests/benchmarks/testcases.py /home/runner/work/ragstack-ai/ragstack-ai/ragstack-e2e-tests/benchmarks/reports/benchmarks.log embeddings_batch100_chunk512 nvidia_nvolveqa40k","command_max_rss":83763200,"cpu_config":"idle:none","cpu_count":4,"cpu_freq":"0=3244 MHz; 1=3255 MHz; 2=2814 MHz; 3=2966 MHz","cpu_model_name":"AMD EPYC 7763 64-Core Processor","date":"2024-01-29 12:07:07.330620","duration":12.766234963999977,"hostname":"fv-az525-874","load_avg_1min":0.7,"loops":1,"name":"command","perf_version":"2.6.2","platform":"Linux-6.2.0-1018-azure-x86_64-with-glibc2.35","runnable_threads":1,"unit":"second","uptime":327.3327782154083},"version":"1.0"}
diff --git a/reports/benchmarks/2024-01-29_12-07-12/embeddings_batch100_chunk512-openai_ada002.json b/reports/benchmarks/2024-01-29_12-07-12/embeddings_batch100_chunk512-openai_ada002.json
new file mode 100644
index 000000000..4a56c8dda
--- /dev/null
+++ b/reports/benchmarks/2024-01-29_12-07-12/embeddings_batch100_chunk512-openai_ada002.json
@@ -0,0 +1 @@
+{"benchmarks":[{"runs":[{"values":[3.2019995630000153],"warmups":[[1,3.2139059389999716]]}]}],"metadata":{"aslr":"Full randomization","boot_time":"2024-01-29 12:01:40","command":"/home/runner/.cache/pypoetry/virtualenvs/ragstack-e2e-tests-KLvcJhlJ-py3.11/bin/python /home/runner/work/ragstack-ai/ragstack-ai/ragstack-e2e-tests/benchmarks/testcases.py /home/runner/work/ragstack-ai/ragstack-ai/ragstack-e2e-tests/benchmarks/reports/benchmarks.log embeddings_batch100_chunk512 openai_ada002","command_max_rss":153448448,"cpu_config":"idle:none","cpu_count":4,"cpu_freq":"0=3242 MHz; 1=3213 MHz; 2=2789 MHz; 3=2698 MHz","cpu_model_name":"AMD EPYC 7763 64-Core Processor","date":"2024-01-29 12:06:54.438789","duration":6.488751931999957,"hostname":"fv-az525-874","load_avg_1min":0.52,"loops":1,"name":"command","perf_version":"2.6.2","platform":"Linux-6.2.0-1018-azure-x86_64-with-glibc2.35","runnable_threads":1,"unit":"second","uptime":314.44088101387024},"version":"1.0"}
diff --git a/reports/benchmarks/2024-01-29_12-07-12/embeddings_batch10_chunk256-nvidia_nvolveqa40k.json b/reports/benchmarks/2024-01-29_12-07-12/embeddings_batch10_chunk256-nvidia_nvolveqa40k.json
new file mode 100644
index 000000000..cf1c118b4
--- /dev/null
+++ b/reports/benchmarks/2024-01-29_12-07-12/embeddings_batch10_chunk256-nvidia_nvolveqa40k.json
@@ -0,0 +1 @@
+{"benchmarks":[{"runs":[{"values":[9.117978915999998],"warmups":[[1,9.415244596999997]]}]}],"metadata":{"aslr":"Full randomization","boot_time":"2024-01-29 12:01:40","command":"/home/runner/.cache/pypoetry/virtualenvs/ragstack-e2e-tests-KLvcJhlJ-py3.11/bin/python /home/runner/work/ragstack-ai/ragstack-ai/ragstack-e2e-tests/benchmarks/testcases.py /home/runner/work/ragstack-ai/ragstack-ai/ragstack-e2e-tests/benchmarks/reports/benchmarks.log embeddings_batch10_chunk256 nvidia_nvolveqa40k","command_max_rss":81498112,"cpu_config":"idle:none","cpu_count":4,"cpu_freq":"0=2880 MHz; 1=3058 MHz; 2=2889 MHz; 3=3059 MHz","cpu_model_name":"AMD EPYC 7763 64-Core Processor","date":"2024-01-29 12:05:18.664709","duration":18.610249519000007,"hostname":"fv-az525-874","load_avg_1min":0.56,"loops":1,"name":"command","perf_version":"2.6.2","platform":"Linux-6.2.0-1018-azure-x86_64-with-glibc2.35","runnable_threads":1,"unit":"second","uptime":218.66720867156982},"version":"1.0"}
diff --git a/reports/benchmarks/2024-01-29_12-07-12/embeddings_batch10_chunk256-openai_ada002.json b/reports/benchmarks/2024-01-29_12-07-12/embeddings_batch10_chunk256-openai_ada002.json
new file mode 100644
index 000000000..2f9f09c28
--- /dev/null
+++ b/reports/benchmarks/2024-01-29_12-07-12/embeddings_batch10_chunk256-openai_ada002.json
@@ -0,0 +1 @@
+{"benchmarks":[{"runs":[{"values":[5.398796991000012],"warmups":[[1,7.291147414999983]]}]}],"metadata":{"aslr":"Full randomization","boot_time":"2024-01-29 12:01:40","command":"/home/runner/.cache/pypoetry/virtualenvs/ragstack-e2e-tests-KLvcJhlJ-py3.11/bin/python /home/runner/work/ragstack-ai/ragstack-ai/ragstack-e2e-tests/benchmarks/testcases.py /home/runner/work/ragstack-ai/ragstack-ai/ragstack-e2e-tests/benchmarks/reports/benchmarks.log embeddings_batch10_chunk256 openai_ada002","command_max_rss":152076288,"cpu_config":"idle:none","cpu_count":4,"cpu_freq":"0=2868 MHz; 1=2929 MHz; 2=3242 MHz; 3=3333 MHz","cpu_model_name":"AMD EPYC 7763 64-Core Processor","date":"2024-01-29 12:04:59.923632","duration":12.76397141199999,"hostname":"fv-az525-874","load_avg_1min":0.67,"loops":1,"name":"command","perf_version":"2.6.2","platform":"Linux-6.2.0-1018-azure-x86_64-with-glibc2.35","runnable_threads":1,"unit":"second","uptime":199.92592096328735},"version":"1.0"}
diff --git a/reports/benchmarks/2024-01-29_12-07-12/embeddings_batch10_chunk512-nvidia_nvolveqa40k.json b/reports/benchmarks/2024-01-29_12-07-12/embeddings_batch10_chunk512-nvidia_nvolveqa40k.json
new file mode 100644
index 000000000..d6236cc52
--- /dev/null
+++ b/reports/benchmarks/2024-01-29_12-07-12/embeddings_batch10_chunk512-nvidia_nvolveqa40k.json
@@ -0,0 +1 @@
+{"benchmarks":[{"runs":[{"values":[9.098559965000021],"warmups":[[1,7.789102850000006]]}]}],"metadata":{"aslr":"Full randomization","boot_time":"2024-01-29 12:01:40","command":"/home/runner/.cache/pypoetry/virtualenvs/ragstack-e2e-tests-KLvcJhlJ-py3.11/bin/python /home/runner/work/ragstack-ai/ragstack-ai/ragstack-e2e-tests/benchmarks/testcases.py /home/runner/work/ragstack-ai/ragstack-ai/ragstack-e2e-tests/benchmarks/reports/benchmarks.log embeddings_batch10_chunk512 nvidia_nvolveqa40k","command_max_rss":81600512,"cpu_config":"idle:none","cpu_count":4,"cpu_freq":"0=3078 MHz; 1=3311 MHz; 2=3236 MHz; 3=2893 MHz","cpu_model_name":"AMD EPYC 7763 64-Core Processor","date":"2024-01-29 12:05:48.432511","duration":16.958478074999988,"hostname":"fv-az525-874","load_avg_1min":0.4,"loops":1,"name":"command","perf_version":"2.6.2","platform":"Linux-6.2.0-1018-azure-x86_64-with-glibc2.35","runnable_threads":1,"unit":"second","uptime":248.43471765518188},"version":"1.0"}
diff --git a/reports/benchmarks/2024-01-29_12-07-12/embeddings_batch10_chunk512-openai_ada002.json b/reports/benchmarks/2024-01-29_12-07-12/embeddings_batch10_chunk512-openai_ada002.json
new file mode 100644
index 000000000..3db50dad2
--- /dev/null
+++ b/reports/benchmarks/2024-01-29_12-07-12/embeddings_batch10_chunk512-openai_ada002.json
@@ -0,0 +1 @@
+{"benchmarks":[{"runs":[{"values":[6.916698567999987],"warmups":[[1,5.499823508999981]]}]}],"metadata":{"aslr":"Full randomization","boot_time":"2024-01-29 12:01:40","command":"/home/runner/.cache/pypoetry/virtualenvs/ragstack-e2e-tests-KLvcJhlJ-py3.11/bin/python /home/runner/work/ragstack-ai/ragstack-ai/ragstack-e2e-tests/benchmarks/testcases.py /home/runner/work/ragstack-ai/ragstack-ai/ragstack-e2e-tests/benchmarks/reports/benchmarks.log embeddings_batch10_chunk512 openai_ada002","command_max_rss":149278720,"cpu_config":"idle:none","cpu_count":4,"cpu_freq":"0=3251 MHz; 1=3244 MHz; 2=2972 MHz; 3=2653 MHz","cpu_model_name":"AMD EPYC 7763 64-Core Processor","date":"2024-01-29 12:05:31.347007","duration":12.489086231000016,"hostname":"fv-az525-874","load_avg_1min":0.51,"loops":1,"name":"command","perf_version":"2.6.2","platform":"Linux-6.2.0-1018-azure-x86_64-with-glibc2.35","runnable_threads":1,"unit":"second","uptime":231.34922337532043},"version":"1.0"}
diff --git a/reports/benchmarks/2024-01-29_12-07-12/embeddings_batch50_chunk256-nvidia_nvolveqa40k.json b/reports/benchmarks/2024-01-29_12-07-12/embeddings_batch50_chunk256-nvidia_nvolveqa40k.json
new file mode 100644
index 000000000..dd95bd5d3
--- /dev/null
+++ b/reports/benchmarks/2024-01-29_12-07-12/embeddings_batch50_chunk256-nvidia_nvolveqa40k.json
@@ -0,0 +1 @@
+{"benchmarks":[{"runs":[{"values":[7.2019749669999555],"warmups":[[1,5.734251422999989]]}]}],"metadata":{"aslr":"Full randomization","boot_time":"2024-01-29 12:01:40","command":"/home/runner/.cache/pypoetry/virtualenvs/ragstack-e2e-tests-KLvcJhlJ-py3.11/bin/python /home/runner/work/ragstack-ai/ragstack-ai/ragstack-e2e-tests/benchmarks/testcases.py /home/runner/work/ragstack-ai/ragstack-ai/ragstack-e2e-tests/benchmarks/reports/benchmarks.log embeddings_batch50_chunk256 nvidia_nvolveqa40k","command_max_rss":83742720,"cpu_config":"idle:none","cpu_count":4,"cpu_freq":"0=2888 MHz; 1=3234 MHz; 2=3238 MHz; 3=2878 MHz","cpu_model_name":"AMD EPYC 7763 64-Core Processor","date":"2024-01-29 12:06:08.749461","duration":13.007608946000005,"hostname":"fv-az525-874","load_avg_1min":0.35,"loops":1,"name":"command","perf_version":"2.6.2","platform":"Linux-6.2.0-1018-azure-x86_64-with-glibc2.35","runnable_threads":1,"unit":"second","uptime":268.7517671585083},"version":"1.0"}
diff --git a/reports/benchmarks/2024-01-29_12-07-12/embeddings_batch50_chunk256-openai_ada002.json b/reports/benchmarks/2024-01-29_12-07-12/embeddings_batch50_chunk256-openai_ada002.json
new file mode 100644
index 000000000..e8ac50b01
--- /dev/null
+++ b/reports/benchmarks/2024-01-29_12-07-12/embeddings_batch50_chunk256-openai_ada002.json
@@ -0,0 +1 @@
+{"benchmarks":[{"runs":[{"values":[3.547764126000004],"warmups":[[1,3.3797298540000043]]}]}],"metadata":{"aslr":"Full randomization","boot_time":"2024-01-29 12:01:40","command":"/home/runner/.cache/pypoetry/virtualenvs/ragstack-e2e-tests-KLvcJhlJ-py3.11/bin/python /home/runner/work/ragstack-ai/ragstack-ai/ragstack-e2e-tests/benchmarks/testcases.py /home/runner/work/ragstack-ai/ragstack-ai/ragstack-e2e-tests/benchmarks/reports/benchmarks.log embeddings_batch50_chunk256 openai_ada002","command_max_rss":149700608,"cpu_config":"idle:none","cpu_count":4,"cpu_freq":"0=3239 MHz; 1=2971 MHz; 2=2606 MHz; 3=2960 MHz","cpu_model_name":"AMD EPYC 7763 64-Core Processor","date":"2024-01-29 12:05:55.617879","duration":6.999468656000005,"hostname":"fv-az525-874","load_avg_1min":0.37,"loops":1,"name":"command","perf_version":"2.6.2","platform":"Linux-6.2.0-1018-azure-x86_64-with-glibc2.35","runnable_threads":1,"unit":"second","uptime":255.6200726032257},"version":"1.0"}
diff --git a/reports/benchmarks/2024-01-29_12-07-12/embeddings_batch50_chunk512-nvidia_nvolveqa40k.json b/reports/benchmarks/2024-01-29_12-07-12/embeddings_batch50_chunk512-nvidia_nvolveqa40k.json
new file mode 100644
index 000000000..e18bf97fb
--- /dev/null
+++ b/reports/benchmarks/2024-01-29_12-07-12/embeddings_batch50_chunk512-nvidia_nvolveqa40k.json
@@ -0,0 +1 @@
+{"benchmarks":[{"runs":[{"values":[5.517936516999953],"warmups":[[1,5.370841561999953]]}]}],"metadata":{"aslr":"Full randomization","boot_time":"2024-01-29 12:01:40","command":"/home/runner/.cache/pypoetry/virtualenvs/ragstack-e2e-tests-KLvcJhlJ-py3.11/bin/python /home/runner/work/ragstack-ai/ragstack-ai/ragstack-e2e-tests/benchmarks/testcases.py /home/runner/work/ragstack-ai/ragstack-ai/ragstack-e2e-tests/benchmarks/reports/benchmarks.log embeddings_batch50_chunk512 nvidia_nvolveqa40k","command_max_rss":83763200,"cpu_config":"idle:none","cpu_count":4,"cpu_freq":"0=3273 MHz; 1=3243 MHz; 2=2908 MHz; 3=3210 MHz","cpu_model_name":"AMD EPYC 7763 64-Core Processor","date":"2024-01-29 12:06:26.939454","duration":10.95967594199999,"hostname":"fv-az525-874","load_avg_1min":0.59,"loops":1,"name":"command","perf_version":"2.6.2","platform":"Linux-6.2.0-1018-azure-x86_64-with-glibc2.35","runnable_threads":1,"unit":"second","uptime":286.94176840782166},"version":"1.0"}
diff --git a/reports/benchmarks/2024-01-29_12-07-12/embeddings_batch50_chunk512-openai_ada002.json b/reports/benchmarks/2024-01-29_12-07-12/embeddings_batch50_chunk512-openai_ada002.json
new file mode 100644
index 000000000..07b8f6c69
--- /dev/null
+++ b/reports/benchmarks/2024-01-29_12-07-12/embeddings_batch50_chunk512-openai_ada002.json
@@ -0,0 +1 @@
+{"benchmarks":[{"runs":[{"values":[3.5479530439999962],"warmups":[[1,3.2966138630000046]]}]}],"metadata":{"aslr":"Full randomization","boot_time":"2024-01-29 12:01:40","command":"/home/runner/.cache/pypoetry/virtualenvs/ragstack-e2e-tests-KLvcJhlJ-py3.11/bin/python /home/runner/work/ragstack-ai/ragstack-ai/ragstack-e2e-tests/benchmarks/testcases.py /home/runner/work/ragstack-ai/ragstack-ai/ragstack-e2e-tests/benchmarks/reports/benchmarks.log embeddings_batch50_chunk512 openai_ada002","command_max_rss":149684224,"cpu_config":"idle:none","cpu_count":4,"cpu_freq":"0=2690 MHz; 1=3236 MHz; 2=2912 MHz; 3=3237 MHz","cpu_model_name":"AMD EPYC 7763 64-Core Processor","date":"2024-01-29 12:06:15.853579","duration":6.916700273000004,"hostname":"fv-az525-874","load_avg_1min":0.4,"loops":1,"name":"command","perf_version":"2.6.2","platform":"Linux-6.2.0-1018-azure-x86_64-with-glibc2.35","runnable_threads":1,"unit":"second","uptime":275.8559684753418},"version":"1.0"}
diff --git a/reports/benchmarks/2024-01-29_12-07-12/index.html b/reports/benchmarks/2024-01-29_12-07-12/index.html
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index 000000000..88703cb1e
--- /dev/null
+++ b/reports/benchmarks/2024-01-29_12-07-12/index.html
@@ -0,0 +1,24 @@
+
+        <html>
+            <head>
+                <meta charset="UTF-8">
+                <meta name="viewport" content="width=device-width, initial-scale=1.0">
+                <script src="https://cdn.tailwindcss.com"></script>
+            </head>
+            <body>
+            <header class="bg-gray-100 py-6 px-4 text-center">
+                <h1 class="text-3xl font-bold text-gray-800">RAGStack - Benchmarks Report - 2024-01-29 12:07:09</h1>
+            </header>
+             <div class="container mx-auto p-4">
+                <div class="grid grid-cols-1 lg:grid-cols-2 gap-4">
+                  <div>
+                    <h2 class="text-xl font-semibold mb-2">Table</h2>
+                    <table class="w-full divide-y divide-gray-200 border border-collapse"><thead class="bg-gray-100 text-xs font-semibold uppercase text-center text-gray-800"><tr><th class='px-6 py-3'>Test Case</th><th class='px-6 py-3'>p50</th><th class='px-6 py-3'>p90</th><th class='px-6 py-3'>p95</th><th class='px-6 py-3'>p99</th></tr></thead><tbody class="bg-white divide-y divide-gray-200 text-center text-sm"><tr class="hover:bg-gray-100"><td class='px-6 py-4'>embeddings_batch100_chunk256-openai_ada002</td><td class='px-6 py-4'>3.06</td><td class='px-6 py-4'>3.06</td><td class='px-6 py-4'>3.06</td><td class='px-6 py-4'>3.06</td></tr><tr class="hover:bg-gray-100"><td class='px-6 py-4'>embeddings_batch100_chunk512-openai_ada002</td><td class='px-6 py-4'>3.2</td><td class='px-6 py-4'>3.2</td><td class='px-6 py-4'>3.2</td><td class='px-6 py-4'>3.2</td></tr><tr class="hover:bg-gray-100"><td class='px-6 py-4'>embeddings_batch50_chunk256-openai_ada002</td><td class='px-6 py-4'>3.55</td><td class='px-6 py-4'>3.55</td><td class='px-6 py-4'>3.55</td><td class='px-6 py-4'>3.55</td></tr><tr class="hover:bg-gray-100"><td class='px-6 py-4'>embeddings_batch50_chunk512-openai_ada002</td><td class='px-6 py-4'>3.55</td><td class='px-6 py-4'>3.55</td><td class='px-6 py-4'>3.55</td><td class='px-6 py-4'>3.55</td></tr><tr class="hover:bg-gray-100"><td class='px-6 py-4'>embeddings_batch10_chunk256-openai_ada002</td><td class='px-6 py-4'>5.4</td><td class='px-6 py-4'>5.4</td><td class='px-6 py-4'>5.4</td><td class='px-6 py-4'>5.4</td></tr><tr class="hover:bg-gray-100"><td class='px-6 py-4'>embeddings_batch50_chunk512-nvidia_nvolveqa40k</td><td class='px-6 py-4'>5.52</td><td class='px-6 py-4'>5.52</td><td class='px-6 py-4'>5.52</td><td class='px-6 py-4'>5.52</td></tr><tr class="hover:bg-gray-100"><td class='px-6 py-4'>embeddings_batch100_chunk256-nvidia_nvolveqa40k</td><td class='px-6 py-4'>6.2</td><td class='px-6 py-4'>6.2</td><td class='px-6 py-4'>6.2</td><td class='px-6 py-4'>6.2</td></tr><tr class="hover:bg-gray-100"><td class='px-6 py-4'>embeddings_batch100_chunk512-nvidia_nvolveqa40k</td><td class='px-6 py-4'>6.21</td><td class='px-6 py-4'>6.21</td><td class='px-6 py-4'>6.21</td><td class='px-6 py-4'>6.21</td></tr><tr class="hover:bg-gray-100"><td class='px-6 py-4'>embeddings_batch10_chunk512-openai_ada002</td><td class='px-6 py-4'>6.92</td><td class='px-6 py-4'>6.92</td><td class='px-6 py-4'>6.92</td><td class='px-6 py-4'>6.92</td></tr><tr class="hover:bg-gray-100"><td class='px-6 py-4'>embeddings_batch50_chunk256-nvidia_nvolveqa40k</td><td class='px-6 py-4'>7.2</td><td class='px-6 py-4'>7.2</td><td class='px-6 py-4'>7.2</td><td class='px-6 py-4'>7.2</td></tr><tr class="hover:bg-gray-100"><td class='px-6 py-4'>embeddings_batch10_chunk512-nvidia_nvolveqa40k</td><td class='px-6 py-4'>9.1</td><td class='px-6 py-4'>9.1</td><td class='px-6 py-4'>9.1</td><td class='px-6 py-4'>9.1</td></tr><tr class="hover:bg-gray-100"><td class='px-6 py-4'>embeddings_batch10_chunk256-nvidia_nvolveqa40k</td><td class='px-6 py-4'>9.12</td><td class='px-6 py-4'>9.12</td><td class='px-6 py-4'>9.12</td><td class='px-6 py-4'>9.12</td></tr></tbody></table>
+                  </div>
+                  <div>
+                    <h2 class="text-xl font-semibold mb-2">Plot</h2>
+                    <img 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