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[GH-ISSUE #3296] chat request timesout after 60s, and context lenght not detected automatically #28742

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opened 2026-04-25 03:19:24 -05:00 by GiteaMirror · 0 comments
GiteaMirror commented 2026-04-25 03:19:24 -05:00
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Originally created by @amida47 on GitHub (Jun 19, 2024).
Original GitHub issue: https://github.com/open-webui/open-webui/issues/3296

Bug Report

Description

Bug Summary:
on OpenWebui when I try long context, the chat request timeout after on 1 min

Steps to Reproduce:
create a GitHub codespace with 4-cores
install ollama through curl -fsSL https://ollama.com/install.sh | sh
run ollama serve
run ollama run phi3:14b-medium-128k-instruct-q4_0
run docker run -d --network=host -v open-webui:/app/backend/data -e OLLAMA_BASE_URL=http://127.0.0.1:11434 --name open-webui --restart always ghcr.io/open-webui/open-webui:main
go to openwebui
test connection to ollama by running simple prompt
upload long .tex document and ask to summarize
the request to /chat timesout after 60 s

Expected Behavior:
start streaming as normal

Actual Behavior:
the request to /chat timesout after 60 s

Environment

  • Open WebUI Version: latest as of 06/19/2024

  • Ollama (if applicable): ollama version is 0.1.44

  • Operating System: [e.g., Windows 10, macOS Big Sur, Ubuntu 20.04]

  • Browser (if applicable): [e.g., Chrome 100.0, Firefox 98.0]

Reproduction Details

Confirmation:

  • I have read and followed all the instructions provided in the README.md.
  • I am on the latest version of both Open WebUI and Ollama.
  • I have included the browser console logs.
  • I have included the Docker container logs.

Logs and Screenshots

https://github.com/open-webui/open-webui/assets/49212088/b0b98f79-cd2a-4f7a-b0a3-c750ee5d384a

another example, it started streaming but once the request time reaches 60s it failed
image
the logs for this screenshot , you can see the context lenght ollama used 2048 while phi model has 128k in context so openwebui didn't read this context

time=2024-06-19T13:53:42.769Z level=INFO source=memory.go:133 msg="offload to gpu" layers.requested=-1 layers.real=1 memory.available="751.5 MiB" memory.required.full="8.1 GiB" memory.required.partial="642.4 MiB" memory.required.kv="400.0 MiB" memory.weights.total="7.3 GiB" memory.weights.repeating="7.1 GiB" memory.weights.nonrepeating="128.4 MiB" memory.graph.full="266.7 MiB" memory.graph.partial="266.7 MiB"
time=2024-06-19T13:53:42.770Z level=INFO source=server.go:341 msg="starting llama server" cmd="/tmp/ollama3165410411/runners/cpu_avx2/ollama_llama_server --model /home/codespace/.ollama/models/blobs/sha256-b62bc11c25b7e38174045d9ada511e94d81466656dad2bf90a805d027a04fb25 --ctx-size 2048 --batch-size 512 --embedding --log-disable --parallel 1 --port 35253"
time=2024-06-19T13:53:42.770Z level=INFO source=sched.go:338 msg="loaded runners" count=1
time=2024-06-19T13:53:42.770Z level=INFO source=server.go:529 msg="waiting for llama runner to start responding"
time=2024-06-19T13:53:42.770Z level=INFO source=server.go:567 msg="waiting for server to become available" status="llm server error"
INFO [main] build info | build=1 commit="5921b8f" tid="129792223676288" timestamp=1718805222
INFO [main] system info | n_threads=2 n_threads_batch=-1 system_info="AVX = 1 | AVX_VNNI = 0 | AVX2 = 1 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | AVX512_BF16 = 0 | FMA = 1 | NEON = 0 | SVE = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 0 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 1 | " tid="129792223676288" timestamp=1718805222 total_threads=4
INFO [main] HTTP server listening | hostname="127.0.0.1" n_threads_http="3" port="35253" tid="129792223676288" timestamp=1718805222
llama_model_loader: loaded meta data with 27 key-value pairs and 245 tensors from /home/codespace/.ollama/models/blobs/sha256-b62bc11c25b7e38174045d9ada511e94d81466656dad2bf90a805d027a04fb25 (version GGUF V3 (latest))
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: - kv   0:                       general.architecture str              = phi3
llama_model_loader: - kv   1:                               general.name str              = Phi3
llama_model_loader: - kv   2:                        phi3.context_length u32              = 131072
llama_model_loader: - kv   3:  phi3.rope.scaling.original_context_length u32              = 4096
llama_model_loader: - kv   4:                      phi3.embedding_length u32              = 5120
llama_model_loader: - kv   5:                   phi3.feed_forward_length u32              = 17920
llama_model_loader: - kv   6:                           phi3.block_count u32              = 40
llama_model_loader: - kv   7:                  phi3.attention.head_count u32              = 40
llama_model_loader: - kv   8:               phi3.attention.head_count_kv u32              = 10
llama_model_loader: - kv   9:      phi3.attention.layer_norm_rms_epsilon f32              = 0.000010
llama_model_loader: - kv  10:                  phi3.rope.dimension_count u32              = 128
llama_model_loader: - kv  11:                        phi3.rope.freq_base f32              = 10000.000000
llama_model_loader: - kv  12:                          general.file_type u32              = 2
llama_model_loader: - kv  13:              phi3.rope.scaling.attn_factor f32              = 1.190238
llama_model_loader: - kv  14:                       tokenizer.ggml.model str              = llama
llama_model_loader: - kv  15:                         tokenizer.ggml.pre str              = default
llama_model_loader: - kv  16:                      tokenizer.ggml.tokens arr[str,32064]   = ["<unk>", "<s>", "</s>", "<0x00>", "<...
llama_model_loader: - kv  17:                      tokenizer.ggml.scores arr[f32,32064]   = [-1000.000000, -1000.000000, -1000.00...
llama_model_loader: - kv  18:                  tokenizer.ggml.token_type arr[i32,32064]   = [3, 3, 4, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...
llama_model_loader: - kv  19:                tokenizer.ggml.bos_token_id u32              = 1
llama_model_loader: - kv  20:                tokenizer.ggml.eos_token_id u32              = 32000
llama_model_loader: - kv  21:            tokenizer.ggml.unknown_token_id u32              = 0
llama_model_loader: - kv  22:            tokenizer.ggml.padding_token_id u32              = 32000
llama_model_loader: - kv  23:               tokenizer.ggml.add_bos_token bool             = false
llama_model_loader: - kv  24:               tokenizer.ggml.add_eos_token bool             = false
llama_model_loader: - kv  25:                    tokenizer.chat_template str              = {% for message in messages %}{% if (m...
llama_model_loader: - kv  26:               general.quantization_version u32              = 2
llama_model_loader: - type  f32:   83 tensors
llama_model_loader: - type q4_0:  161 tensors
llama_model_loader: - type q6_K:    1 tensors
llm_load_vocab: special tokens cache size = 323
llm_load_vocab: token to piece cache size = 0.3372 MB
llm_load_print_meta: format           = GGUF V3 (latest)
llm_load_print_meta: arch             = phi3
llm_load_print_meta: vocab type       = SPM
llm_load_print_meta: n_vocab          = 32064
llm_load_print_meta: n_merges         = 0
llm_load_print_meta: n_ctx_train      = 131072
llm_load_print_meta: n_embd           = 5120
llm_load_print_meta: n_head           = 40
llm_load_print_meta: n_head_kv        = 10
llm_load_print_meta: n_layer          = 40
llm_load_print_meta: n_rot            = 128
llm_load_print_meta: n_embd_head_k    = 128
llm_load_print_meta: n_embd_head_v    = 128
llm_load_print_meta: n_gqa            = 4
llm_load_print_meta: n_embd_k_gqa     = 1280
llm_load_print_meta: n_embd_v_gqa     = 1280
llm_load_print_meta: f_norm_eps       = 0.0e+00
llm_load_print_meta: f_norm_rms_eps   = 1.0e-05
llm_load_print_meta: f_clamp_kqv      = 0.0e+00
llm_load_print_meta: f_max_alibi_bias = 0.0e+00
llm_load_print_meta: f_logit_scale    = 0.0e+00
llm_load_print_meta: n_ff             = 17920
llm_load_print_meta: n_expert         = 0
llm_load_print_meta: n_expert_used    = 0
llm_load_print_meta: causal attn      = 1
llm_load_print_meta: pooling type     = 0
llm_load_print_meta: rope type        = 2
llm_load_print_meta: rope scaling     = linear
llm_load_print_meta: freq_base_train  = 10000.0
llm_load_print_meta: freq_scale_train = 1
llm_load_print_meta: n_yarn_orig_ctx  = 4096
llm_load_print_meta: rope_finetuned   = unknown
llm_load_print_meta: ssm_d_conv       = 0
llm_load_print_meta: ssm_d_inner      = 0
llm_load_print_meta: ssm_d_state      = 0
llm_load_print_meta: ssm_dt_rank      = 0
llm_load_print_meta: model type       = 14B
llm_load_print_meta: model ftype      = Q4_0
llm_load_print_meta: model params     = 13.96 B
llm_load_print_meta: model size       = 7.35 GiB (4.53 BPW) 
llm_load_print_meta: general.name     = Phi3
llm_load_print_meta: BOS token        = 1 '<s>'
llm_load_print_meta: EOS token        = 32000 '<|endoftext|>'
llm_load_print_meta: UNK token        = 0 '<unk>'
llm_load_print_meta: PAD token        = 32000 '<|endoftext|>'
llm_load_print_meta: LF token         = 13 '<0x0A>'
llm_load_print_meta: EOT token        = 32007 '<|end|>'
llm_load_tensors: ggml ctx size =    0.14 MiB
time=2024-06-19T13:53:43.221Z level=INFO source=server.go:567 msg="waiting for server to become available" status="llm server not responding"
llm_load_tensors:        CPU buffer size =  7530.58 MiB
llama_new_context_with_model: n_ctx      = 2048
llama_new_context_with_model: n_batch    = 512
llama_new_context_with_model: n_ubatch   = 512
llama_new_context_with_model: flash_attn = 0
llama_new_context_with_model: freq_base  = 10000.0
llama_new_context_with_model: freq_scale = 1
llama_kv_cache_init:        CPU KV buffer size =   400.00 MiB
llama_new_context_with_model: KV self size  =  400.00 MiB, K (f16):  200.00 MiB, V (f16):  200.00 MiB
llama_new_context_with_model:        CPU  output buffer size =     0.14 MiB
llama_new_context_with_model:        CPU compute buffer size =   209.01 MiB
llama_new_context_with_model: graph nodes  = 1606
llama_new_context_with_model: graph splits = 1
time=2024-06-19T13:53:43.473Z level=INFO source=server.go:567 msg="waiting for server to become available" status="llm server loading model"
INFO [main] model loaded | tid="129792223676288" timestamp=1718805224
time=2024-06-19T13:53:44.225Z level=INFO source=server.go:572 msg="llama runner started in 1.46 seconds"
[GIN] 2024/06/19 - 13:54:42 | 200 | 59.796477624s |       127.0.0.1 | POST     "/api/chat"



## Installation Method

install ollama through `curl -fsSL https://ollama.com/install.sh | sh`
run `ollama serve`
run ` ollama run phi3:14b-medium-128k-instruct-q4_0`
run `docker run -d --network=host -v open-webui:/app/backend/data -e OLLAMA_BASE_URL=http://127.0.0.1:11434 --name open-webui --restart always ghcr.io/open-webui/open-webui:main`
Originally created by @amida47 on GitHub (Jun 19, 2024). Original GitHub issue: https://github.com/open-webui/open-webui/issues/3296 # Bug Report ## Description **Bug Summary:** on OpenWebui when I try long context, the chat request timeout after on 1 min **Steps to Reproduce:** create a GitHub codespace with 4-cores install ollama through `curl -fsSL https://ollama.com/install.sh | sh` run `ollama serve` run ` ollama run phi3:14b-medium-128k-instruct-q4_0` run `docker run -d --network=host -v open-webui:/app/backend/data -e OLLAMA_BASE_URL=http://127.0.0.1:11434 --name open-webui --restart always ghcr.io/open-webui/open-webui:main` go to openwebui test connection to ollama by running simple prompt upload long .tex document and ask to summarize the request to /chat timesout after 60 s **Expected Behavior:** start streaming as normal **Actual Behavior:** the request to /chat timesout after 60 s ## Environment - **Open WebUI Version:** latest as of 06/19/2024 - **Ollama (if applicable):** ollama version is 0.1.44 - **Operating System:** [e.g., Windows 10, macOS Big Sur, Ubuntu 20.04] - **Browser (if applicable):** [e.g., Chrome 100.0, Firefox 98.0] ## Reproduction Details **Confirmation:** - [x] I have read and followed all the instructions provided in the README.md. - [x] I am on the latest version of both Open WebUI and Ollama. - [x] I have included the browser console logs. - [x] I have included the Docker container logs. ## Logs and Screenshots https://github.com/open-webui/open-webui/assets/49212088/b0b98f79-cd2a-4f7a-b0a3-c750ee5d384a another example, it started streaming but once the request time reaches 60s it failed ![image](https://github.com/open-webui/open-webui/assets/49212088/2ca17017-23e2-4a15-9a9d-1b313a9fbde9) the logs for this screenshot , you can see the context lenght ollama used 2048 while phi model has 128k in context so openwebui didn't read this context ```console time=2024-06-19T13:53:42.769Z level=INFO source=memory.go:133 msg="offload to gpu" layers.requested=-1 layers.real=1 memory.available="751.5 MiB" memory.required.full="8.1 GiB" memory.required.partial="642.4 MiB" memory.required.kv="400.0 MiB" memory.weights.total="7.3 GiB" memory.weights.repeating="7.1 GiB" memory.weights.nonrepeating="128.4 MiB" memory.graph.full="266.7 MiB" memory.graph.partial="266.7 MiB" time=2024-06-19T13:53:42.770Z level=INFO source=server.go:341 msg="starting llama server" cmd="/tmp/ollama3165410411/runners/cpu_avx2/ollama_llama_server --model /home/codespace/.ollama/models/blobs/sha256-b62bc11c25b7e38174045d9ada511e94d81466656dad2bf90a805d027a04fb25 --ctx-size 2048 --batch-size 512 --embedding --log-disable --parallel 1 --port 35253" time=2024-06-19T13:53:42.770Z level=INFO source=sched.go:338 msg="loaded runners" count=1 time=2024-06-19T13:53:42.770Z level=INFO source=server.go:529 msg="waiting for llama runner to start responding" time=2024-06-19T13:53:42.770Z level=INFO source=server.go:567 msg="waiting for server to become available" status="llm server error" INFO [main] build info | build=1 commit="5921b8f" tid="129792223676288" timestamp=1718805222 INFO [main] system info | n_threads=2 n_threads_batch=-1 system_info="AVX = 1 | AVX_VNNI = 0 | AVX2 = 1 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | AVX512_BF16 = 0 | FMA = 1 | NEON = 0 | SVE = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 0 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 1 | " tid="129792223676288" timestamp=1718805222 total_threads=4 INFO [main] HTTP server listening | hostname="127.0.0.1" n_threads_http="3" port="35253" tid="129792223676288" timestamp=1718805222 llama_model_loader: loaded meta data with 27 key-value pairs and 245 tensors from /home/codespace/.ollama/models/blobs/sha256-b62bc11c25b7e38174045d9ada511e94d81466656dad2bf90a805d027a04fb25 (version GGUF V3 (latest)) llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output. llama_model_loader: - kv 0: general.architecture str = phi3 llama_model_loader: - kv 1: general.name str = Phi3 llama_model_loader: - kv 2: phi3.context_length u32 = 131072 llama_model_loader: - kv 3: phi3.rope.scaling.original_context_length u32 = 4096 llama_model_loader: - kv 4: phi3.embedding_length u32 = 5120 llama_model_loader: - kv 5: phi3.feed_forward_length u32 = 17920 llama_model_loader: - kv 6: phi3.block_count u32 = 40 llama_model_loader: - kv 7: phi3.attention.head_count u32 = 40 llama_model_loader: - kv 8: phi3.attention.head_count_kv u32 = 10 llama_model_loader: - kv 9: phi3.attention.layer_norm_rms_epsilon f32 = 0.000010 llama_model_loader: - kv 10: phi3.rope.dimension_count u32 = 128 llama_model_loader: - kv 11: phi3.rope.freq_base f32 = 10000.000000 llama_model_loader: - kv 12: general.file_type u32 = 2 llama_model_loader: - kv 13: phi3.rope.scaling.attn_factor f32 = 1.190238 llama_model_loader: - kv 14: tokenizer.ggml.model str = llama llama_model_loader: - kv 15: tokenizer.ggml.pre str = default llama_model_loader: - kv 16: tokenizer.ggml.tokens arr[str,32064] = ["<unk>", "<s>", "</s>", "<0x00>", "<... llama_model_loader: - kv 17: tokenizer.ggml.scores arr[f32,32064] = [-1000.000000, -1000.000000, -1000.00... llama_model_loader: - kv 18: tokenizer.ggml.token_type arr[i32,32064] = [3, 3, 4, 6, 6, 6, 6, 6, 6, 6, 6, 6, ... llama_model_loader: - kv 19: tokenizer.ggml.bos_token_id u32 = 1 llama_model_loader: - kv 20: tokenizer.ggml.eos_token_id u32 = 32000 llama_model_loader: - kv 21: tokenizer.ggml.unknown_token_id u32 = 0 llama_model_loader: - kv 22: tokenizer.ggml.padding_token_id u32 = 32000 llama_model_loader: - kv 23: tokenizer.ggml.add_bos_token bool = false llama_model_loader: - kv 24: tokenizer.ggml.add_eos_token bool = false llama_model_loader: - kv 25: tokenizer.chat_template str = {% for message in messages %}{% if (m... llama_model_loader: - kv 26: general.quantization_version u32 = 2 llama_model_loader: - type f32: 83 tensors llama_model_loader: - type q4_0: 161 tensors llama_model_loader: - type q6_K: 1 tensors llm_load_vocab: special tokens cache size = 323 llm_load_vocab: token to piece cache size = 0.3372 MB llm_load_print_meta: format = GGUF V3 (latest) llm_load_print_meta: arch = phi3 llm_load_print_meta: vocab type = SPM llm_load_print_meta: n_vocab = 32064 llm_load_print_meta: n_merges = 0 llm_load_print_meta: n_ctx_train = 131072 llm_load_print_meta: n_embd = 5120 llm_load_print_meta: n_head = 40 llm_load_print_meta: n_head_kv = 10 llm_load_print_meta: n_layer = 40 llm_load_print_meta: n_rot = 128 llm_load_print_meta: n_embd_head_k = 128 llm_load_print_meta: n_embd_head_v = 128 llm_load_print_meta: n_gqa = 4 llm_load_print_meta: n_embd_k_gqa = 1280 llm_load_print_meta: n_embd_v_gqa = 1280 llm_load_print_meta: f_norm_eps = 0.0e+00 llm_load_print_meta: f_norm_rms_eps = 1.0e-05 llm_load_print_meta: f_clamp_kqv = 0.0e+00 llm_load_print_meta: f_max_alibi_bias = 0.0e+00 llm_load_print_meta: f_logit_scale = 0.0e+00 llm_load_print_meta: n_ff = 17920 llm_load_print_meta: n_expert = 0 llm_load_print_meta: n_expert_used = 0 llm_load_print_meta: causal attn = 1 llm_load_print_meta: pooling type = 0 llm_load_print_meta: rope type = 2 llm_load_print_meta: rope scaling = linear llm_load_print_meta: freq_base_train = 10000.0 llm_load_print_meta: freq_scale_train = 1 llm_load_print_meta: n_yarn_orig_ctx = 4096 llm_load_print_meta: rope_finetuned = unknown llm_load_print_meta: ssm_d_conv = 0 llm_load_print_meta: ssm_d_inner = 0 llm_load_print_meta: ssm_d_state = 0 llm_load_print_meta: ssm_dt_rank = 0 llm_load_print_meta: model type = 14B llm_load_print_meta: model ftype = Q4_0 llm_load_print_meta: model params = 13.96 B llm_load_print_meta: model size = 7.35 GiB (4.53 BPW) llm_load_print_meta: general.name = Phi3 llm_load_print_meta: BOS token = 1 '<s>' llm_load_print_meta: EOS token = 32000 '<|endoftext|>' llm_load_print_meta: UNK token = 0 '<unk>' llm_load_print_meta: PAD token = 32000 '<|endoftext|>' llm_load_print_meta: LF token = 13 '<0x0A>' llm_load_print_meta: EOT token = 32007 '<|end|>' llm_load_tensors: ggml ctx size = 0.14 MiB time=2024-06-19T13:53:43.221Z level=INFO source=server.go:567 msg="waiting for server to become available" status="llm server not responding" llm_load_tensors: CPU buffer size = 7530.58 MiB llama_new_context_with_model: n_ctx = 2048 llama_new_context_with_model: n_batch = 512 llama_new_context_with_model: n_ubatch = 512 llama_new_context_with_model: flash_attn = 0 llama_new_context_with_model: freq_base = 10000.0 llama_new_context_with_model: freq_scale = 1 llama_kv_cache_init: CPU KV buffer size = 400.00 MiB llama_new_context_with_model: KV self size = 400.00 MiB, K (f16): 200.00 MiB, V (f16): 200.00 MiB llama_new_context_with_model: CPU output buffer size = 0.14 MiB llama_new_context_with_model: CPU compute buffer size = 209.01 MiB llama_new_context_with_model: graph nodes = 1606 llama_new_context_with_model: graph splits = 1 time=2024-06-19T13:53:43.473Z level=INFO source=server.go:567 msg="waiting for server to become available" status="llm server loading model" INFO [main] model loaded | tid="129792223676288" timestamp=1718805224 time=2024-06-19T13:53:44.225Z level=INFO source=server.go:572 msg="llama runner started in 1.46 seconds" [GIN] 2024/06/19 - 13:54:42 | 200 | 59.796477624s | 127.0.0.1 | POST "/api/chat" ## Installation Method install ollama through `curl -fsSL https://ollama.com/install.sh | sh` run `ollama serve` run ` ollama run phi3:14b-medium-128k-instruct-q4_0` run `docker run -d --network=host -v open-webui:/app/backend/data -e OLLAMA_BASE_URL=http://127.0.0.1:11434 --name open-webui --restart always ghcr.io/open-webui/open-webui:main`
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Reference: github-starred/open-webui#28742
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