Home/Compare/rag_api vs ChatGLM-6B

Comparison

rag_api vs ChatGLM-6B

Verdict

Pick rag_api when license: rag_api is MIT, ChatGLM-6B is Apache-2.0; pick ChatGLM-6B when license: ChatGLM-6B is Apache-2.0, rag_api is MIT.

Markdown twin · rag_api alternatives · ChatGLM-6B alternatives

GraphCanon updated today

rag_api logo

rag_api

danny-avila/rag_api

863pushed Jun 18, 2026
vs
ChatGLM-6B logo

ChatGLM-6B

zai-org/ChatGLM-6B

41kpushed Jun 27, 2024

Trust & integrity

Signalrag_apiChatGLM-6B
Maintenance
Active (22d since push)
As of today · github_public_v1
Dormant (744d since push)
As of today · github_public_v1
Provenance
Not a fork · Personal account
As of today · github_public_v1
Not a fork · Organization account
As of today · github_public_v1
Security (OSV)
No lockfile
As of today · none
75 low (75 low)
As of today · osv@v1

Tagline

rag_api
ID-based RAG FastAPI: Integration with Langchain and PostgreSQL/pgvector
ChatGLM-6B
ChatGLM-6B: An Open Bilingual Dialogue Language Model | 开源双语对话语言模型

Stars

rag_api
863
ChatGLM-6B
41k

Forks

rag_api
376
ChatGLM-6B
5.1k

Open issues

rag_api
44
ChatGLM-6B
609

Language

rag_api
Python
ChatGLM-6B
Python

Adopt for

rag_api
Key Insights for Using rag_api as an ID-based RAG FastAPI Tool with Langchain and PostgreSQL/pgvector Integration
ChatGLM-6B
-

Persona

rag_api
-
ChatGLM-6B
-

Runtime

rag_api
-
ChatGLM-6B
-

License

rag_api
MIT
ChatGLM-6B
Apache-2.0

Last pushed

rag_api
Jun 18, 2026
ChatGLM-6B
Jun 27, 2024

Categories

rag_api
Vector Databases, Data & Retrieval
ChatGLM-6B
Vector Databases, LLM Frameworks, Data & Retrieval

Trust and health

Maintenance

rag_api
Active (82%)
ChatGLM-6B
Dormant (18%)

Days since push

rag_api
22d
ChatGLM-6B
744d

Open issues (now)

rag_api
44
ChatGLM-6B
609

Owner type

rag_api
User
ChatGLM-6B
Organization

Security scan

rag_api
No lockfile
ChatGLM-6B
75 low (75 low)

Full report

ChatGLM-6B
Trust report

Choose rag_api if…

  • License: rag_api is MIT, ChatGLM-6B is Apache-2.0.
  • Tags unique to rag_api: postgresql, psql, embeddings, fastapi.
  • rag_api ships Docker support for self-hosted deployment.
  • When you need rapid integration of REST API services for Retrieval-Augmented Generation (RAG) with robust vector storage.

When NOT to use rag_api

  • Avoid using if your project cannot leverage PostgreSQL/pgvector due to license or compatibility constraints.
  • Not recommended for scenarios where high-level orchestration of multiple APIs and services is necessary without a direct need for FastAPI's simplicity.

Choose ChatGLM-6B if…

  • License: ChatGLM-6B is Apache-2.0, rag_api is MIT.
  • Tags unique to ChatGLM-6B: python.
  • Also covers LLM Frameworks.

When NOT to use ChatGLM-6B

  • Last GitHub push was 745 days ago (dormant maintenance, Jun 27, 2024). Validate activity before betting a new project on ChatGLM-6B.
  • Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate.
  • LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
  • Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough.

Explore

Sources

Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.

GitHub stars on cards: rag_api 863 · ChatGLM-6B 41k (synced Jul 11, 2026).

Common questions

What is the difference between rag_api and ChatGLM-6B?
rag_api: ID-based RAG FastAPI: Integration with Langchain and PostgreSQL/pgvector. ChatGLM-6B: ChatGLM-6B: An Open Bilingual Dialogue Language Model | 开源双语对话语言模型. See the comparison table for live GitHub stats and shared categories.
When should I choose rag_api over ChatGLM-6B?
Choose rag_api over ChatGLM-6B when License: rag_api is MIT, ChatGLM-6B is Apache-2.0; Tags unique to rag_api: postgresql, psql, embeddings, fastapi; rag_api ships Docker support for self-hosted deployment; When you need rapid integration of REST API services for Retrieval-Augmented Generation (RAG) with robust vector storage.
When should I choose ChatGLM-6B over rag_api?
Choose ChatGLM-6B over rag_api when License: ChatGLM-6B is Apache-2.0, rag_api is MIT; Tags unique to ChatGLM-6B: python; Also covers LLM Frameworks.
When should I avoid rag_api?
Avoid using if your project cannot leverage PostgreSQL/pgvector due to license or compatibility constraints. Not recommended for scenarios where high-level orchestration of multiple APIs and services is necessary without a direct need for FastAPI's simplicity.
When should I avoid ChatGLM-6B?
Last GitHub push was 745 days ago (dormant maintenance, Jun 27, 2024). Validate activity before betting a new project on ChatGLM-6B. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough.
Is rag_api or ChatGLM-6B more popular on GitHub?
ChatGLM-6B has more GitHub stars (41,035 vs 863). Stars measure visibility, not whether either tool fits your constraints.
Are rag_api and ChatGLM-6B open source?
Yes - both are open-source projects on GitHub (rag_api: MIT, ChatGLM-6B: Apache-2.0).
Where can I find alternatives to rag_api or ChatGLM-6B?
GraphCanon lists graph-backed alternatives at rag_api alternatives and ChatGLM-6B alternatives (rag_api markdown twin, ChatGLM-6B markdown twin), ranked by typed relationship edges rather than popularity votes.
Is there a machine-readable version of this comparison?
Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
Which is better maintained, rag_api or ChatGLM-6B?
rag_api: Active. ChatGLM-6B: Dormant. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.
Where are the full trust reports for rag_api and ChatGLM-6B?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: rag_api trust report; ChatGLM-6B trust report.