Files
crewAI/lib/crewai/tests/cassettes/utilities/TestSummarizeDirectGemini.test_summarize_direct_gemini.yaml
Lorenze Jay 2ed0c2c043
Some checks failed
CodeQL Advanced / Analyze (actions) (push) Has been cancelled
CodeQL Advanced / Analyze (python) (push) Has been cancelled
Notify Downstream / notify-downstream (push) Has been cancelled
Check Documentation Broken Links / Check broken links (push) Has been cancelled
imp compaction (#4399)
* imp compaction

* fix lint

* cassette gen

* cassette gen

* improve assert

* adding azure

* fix global docstring
2026-02-11 15:52:03 -08:00

104 lines
5.4 KiB
YAML

interactions:
- request:
body: '{"contents": [{"parts": [{"text": "Analyze the following conversation and
create a structured summary that preserves all information needed to continue
the task seamlessly.\n\n<conversation>\n[USER]: Research the latest developments
in large language models. Focus on architecture improvements and training techniques.\n\n[ASSISTANT]:
I''ll research the latest developments in large language models. Based on my
knowledge, recent advances include:\n1. Mixture of Experts (MoE) architectures\n2.
Improved attention mechanisms like Flash Attention\n3. Better training data
curation techniques\n4. Constitutional AI and RLHF improvements\n\n[USER]: Can
you go deeper on the MoE architectures? What are the key papers?\n\n[ASSISTANT]:
Key papers on Mixture of Experts:\n- Switch Transformers (Google, 2021) - simplified
MoE routing\n- GShard - scaling to 600B parameters\n- Mixtral (Mistral AI) -
open-source MoE model\nThe main advantage is computational efficiency: only
a subset of experts is activated per token.\n</conversation>\n\nCreate a summary
with these sections:\n1. **Task Overview**: What is the agent trying to accomplish?\n2.
**Current State**: What has been completed so far? What step is the agent on?\n3.
**Important Discoveries**: Key facts, data, tool results, or findings that must
not be lost.\n4. **Next Steps**: What should the agent do next based on the
conversation?\n5. **Context to Preserve**: Any specific values, names, URLs,
code snippets, or details referenced in the conversation.\n\nWrap your entire
summary in <summary> tags.\n\n<summary>\n[Your structured summary here]\n</summary>"}],
"role": "user"}], "systemInstruction": {"parts": [{"text": "You are a precise
assistant that creates structured summaries of agent conversations. You preserve
critical context needed for seamless task continuation."}], "role": "user"},
"generationConfig": {"temperature": 0.0}}'
headers:
User-Agent:
- X-USER-AGENT-XXX
accept:
- '*/*'
accept-encoding:
- ACCEPT-ENCODING-XXX
connection:
- keep-alive
content-length:
- '1895'
content-type:
- application/json
host:
- generativelanguage.googleapis.com
x-goog-api-client:
- google-genai-sdk/1.49.0 gl-python/3.13.3
x-goog-api-key:
- X-GOOG-API-KEY-XXX
method: POST
uri: https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:generateContent
response:
body:
string: "{\n \"candidates\": [\n {\n \"content\": {\n \"parts\":
[\n {\n \"text\": \"```xml\\n\\u003csummary\\u003e\\n**Task
Overview**: Research the latest developments in large language models, focusing
on architecture improvements and training techniques.\\n\\n**Current State**:
The agent has identified several key areas of advancement in LLMs: Mixture
of Experts (MoE) architectures, improved attention mechanisms (Flash Attention),
better training data curation, and Constitutional AI/RLHF improvements. The
user has requested a deeper dive into MoE architectures. The agent has provided
an initial overview of MoE architectures and listed some key papers.\\n\\n**Important
Discoveries**:\\n* Key MoE papers: Switch Transformers (Google, 2021), GShard,
Mixtral (Mistral AI).\\n* MoE advantage: Computational efficiency through
selective activation of experts.\\n\\n**Next Steps**: Continue researching
MoE architectures based on the user's request for more detail. The agent should
elaborate further on the listed papers and potentially find more recent or
relevant publications.\\n\\n**Context to Preserve**:\\n* Focus areas: Architecture
improvements and training techniques for LLMs.\\n* Specific architectures:
Mixture of Experts (MoE), Flash Attention.\\n* Training techniques: Data
curation, Constitutional AI, RLHF.\\n* Specific papers: Switch Transformers
(Google, 2021), GShard, Mixtral (Mistral AI).\\n\\u003c/summary\\u003e\\n```\"\n
\ }\n ],\n \"role\": \"model\"\n },\n \"finishReason\":
\"STOP\",\n \"avgLogprobs\": -0.14186729703630721\n }\n ],\n \"usageMetadata\":
{\n \"promptTokenCount\": 373,\n \"candidatesTokenCount\": 280,\n \"totalTokenCount\":
653,\n \"promptTokensDetails\": [\n {\n \"modality\": \"TEXT\",\n
\ \"tokenCount\": 373\n }\n ],\n \"candidatesTokensDetails\":
[\n {\n \"modality\": \"TEXT\",\n \"tokenCount\": 280\n
\ }\n ]\n },\n \"modelVersion\": \"gemini-2.0-flash\",\n \"responseId\":
\"GEGKabP3OcGH-8YPzZCj2Ao\"\n}\n"
headers:
Alt-Svc:
- h3=":443"; ma=2592000,h3-29=":443"; ma=2592000
Content-Type:
- application/json; charset=UTF-8
Date:
- Mon, 09 Feb 2026 20:18:35 GMT
Server:
- scaffolding on HTTPServer2
Server-Timing:
- gfet4t7; dur=2310
Transfer-Encoding:
- chunked
Vary:
- Origin
- X-Origin
- Referer
X-Content-Type-Options:
- X-CONTENT-TYPE-XXX
X-Frame-Options:
- X-FRAME-OPTIONS-XXX
X-XSS-Protection:
- '0'
status:
code: 200
message: OK
version: 1