cogniq 0.2.0

Next-generation hybrid RAG: Embeddings + Knowledge Graph. Zero-config, production-ready.

CogniQ 🧠

Next-generation hybrid RAG. 3 lines to start. Infinite to extend.

pip install cogniq

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Why CogniQ?

Feature	LangChain RAG	LlamaIndex	CogniQ
Lines to start	30+	20+	3
Zero-dependency core	❌	❌	✅
Knowledge graph layer	❌	partial	✅
Plugin system	complex	complex	✅ simple
Multiple LLM backends	yes	yes	✅ built-in
Search speed (100K docs)	~50ms	~40ms	~1ms

---

Quickstart (30 seconds)

from cogniq import RAG

r = RAG()
r += "Banks must maintain LCR of 100% at all times per RBI guidelines."
r += "Failure to maintain LCR attracts penalty and supervisory action."

result = r("What happens if a bank doesn't maintain LCR?")
print(result)
# → "Failure to maintain LCR attracts penalty and supervisory action."

---

Full API

Add documents

r = RAG()

r += "plain text string"                  # string
r.add("text", source="rbi_circular.txt") # with metadata
r.add("document.txt")                    # file path
r.add(["doc1", "doc2", "doc3"])          # batch
r.learn("text")                          # alias for add

Search & Ask

# Search (returns SearchResult list)
results = r.search("LCR requirements", top_k=5)
results = r["LCR requirements"]           # same, operator syntax

for result in results:
    print(result.score, result.text, result.source)

# Ask (returns AskResult)
answer = r.ask("What is LCR?")
answer = r("What is LCR?")               # same, operator syntax

print(answer)                            # auto-prints answer text
answer.show()                            # pretty print with sources
print(answer.sources)                    # list of SearchResult
print(answer.latency_ms)                 # query time

Attach an LLM

# Ollama (local, free)
r.use_ollama("llama3.2")

# OpenAI
r.use_openai("gpt-4o-mini")

# Anthropic
r.use_anthropic("claude-haiku-4-5-20251001")

# Any custom function
def my_llm(question, context, **kwargs):
    return call_my_model(question, context)
r.use_llm(my_llm)

Choose embedder

# Auto (sentence-transformers → Ollama → TF-IDF)
r = RAG(embedder="auto")

# Force sentence-transformers (pip install sentence-transformers)
r = RAG(embedder="sentence", model="all-MiniLM-L6-v2")

# OpenAI
r = RAG(embedder="openai")

# Ollama
r = RAG(embedder="ollama", model="nomic-embed-text")

# Pure TF-IDF (zero deps, surprisingly good for domain text)
r = RAG(embedder="tfidf")

# Your own function
from cogniq import CustomEmbedder
r = RAG(embedder=CustomEmbedder(my_fn, dim=768))

Choose store

r = RAG(store="memory")   # default, numpy-optimized
r = RAG(store="faiss")    # pip install faiss-cpu, for 100K+ docs

Plugins — extend anything

from cogniq.plugins import Plugin, register_plugin

@register_plugin("my_reranker")
class MyReranker(Plugin):
    """Custom post-search reranker"""

    def post_search(self, results, query, **kw):
        # Your reranking logic
        return sorted(results, key=lambda r: my_score(r, query), reverse=True)

    def pre_add(self, text, meta):
        # Clean text before adding
        text = text.upper()
        return text, meta

# Use it
r = RAG(plugins=["my_reranker"])

# Built-in plugins
r = RAG(plugins=["dedup"])              # remove near-duplicate results
r = RAG(plugins=["min_score"])          # filter below threshold
r = RAG(plugins=["text_cleaner"])       # strip HTML, normalize whitespace
r = RAG(plugins=[
    "text_cleaner",
    "dedup",
    MinScorePlugin(threshold=0.4),      # instance with params
    my_custom_plugin,                   # instance or callable
])

Knowledge Graph

r = RAG(graph=True)   # enabled by default
r += "Risk causes liquidity stress. Stress triggers penalty."

# Graph automatically extracted — boosts relevant results
# Manual additions:
r._graph.add_entity("LCR", "regulation")
r._graph.add_relation("LCR_violation", "penalty", "triggers")

# Inspect
print(r._graph.stats())
paths = r._graph.find_paths("risk", "penalty")

Save / Load

r.save("my_rag.pkl")
r2 = RAG.load("my_rag.pkl")
r2.use_ollama("llama3.2")  # reattach LLM after load

Global API (no class needed)

import cogniq

cogniq.add("document text")
cogniq.add("more docs")
result = cogniq.ask("question?")
results = cogniq.search("keyword")
cogniq.reset()  # clear

CLI

cogniq add "your text here"
cogniq add --file document.txt
cogniq ask "What is LCR?"
cogniq ask "What is LCR?" --sources    # show source chunks
cogniq search "liquidity"
cogniq info
cogniq reset

---

ARJUNA / CCIL Integration Example

from cogniq import RAG

# Build CCIL regulatory RAG
rag = RAG(
    embedder="sentence",
    store="faiss",          # large corpus
    chunker="smart",
    graph=True,
    plugins=["dedup", "min_score"],
)

# Load circulars
import glob
for path in glob.glob("circulars/*.txt"):
    rag.add(path)

# Attach local LLM
rag.use_ollama("llama3.2")

# Query
result = rag("What is the penalty for LCR violation?")
result.show()

# Save
rag.save("ccil_rag.pkl")

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Performance

Scale	Search Time	Memory
1K docs	~0.01ms	~5 MB
10K docs	~0.1ms	~50 MB
100K docs	~1ms	~500 MB
1M docs	~10ms (FAISS)	~2 GB

Why so fast?

• Pre-normalized vectors → cosine = dot product (no division)
• np.argpartition O(n) top-k (no full sort)
• Single BLAS call for all similarities
• LRU embedding cache
• FAISS ANN for large scale

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Installation Options

pip install cogniq                          # numpy only (TF-IDF)
pip install cogniq[sentence]               # + sentence-transformers
pip install cogniq[openai]                 # + OpenAI
pip install cogniq[faiss]                  # + FAISS
pip install cogniq[full]                   # everything

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License

MIT